A Usage Template for the R Package msDiaLogue

Load R package

library(msDiaLogue)

Preprocessing

Usage

preprocessing(
  fileName,            # name of Spectronaut file
  dataSet = NULL,      # name of dataset if already loaded into R
  filterNaN = TRUE,    # Should NaN values be removed?
  filterUnique = 2,    # Minimum number of unique peptides
  replaceBlank = TRUE, # Replace blank protein names with Accession num.
  saveRm = TRUE        # Should excluded proteins be saved to a file?
)

Details & Examples

The function preprocessing() takes a .csv file of summarized protein abundances, exported from Spectronaut. The most important columns that need to be included in this file are: R.Condition, R.Replicate, PG.ProteinAccessions, PG.ProteinNames, PG.NrOfStrippedSequencesIdentified, and PG.Quantity. This function will reformat the data and provide functionality for some initial filtering (based on the number of unique peptides). The steps below describe the functions that happen in the Preprocessing code.

1. Loads the raw data

If the raw data is in a .csv file Toy_Spectronaut_Data.csv, specify the fileName to read the raw data file into R.
If the raw data is stored as an .RData file Toy_Spectronaut_Data.RData, first load the data file directly, then specify the dataSet in the function.

2. Filters out identified proteins that exhibit “NaN” quantitative values

NaN, which stands for ‘Not a Number,’ can be found in the PG.Quantity column for proteins that were identified by MS and MS/MS evidence in the raw data, but all peptides from that protein lack an associated integrated peak area or intensity. This usually occurs in low abundance peptides that exhibit intensities close to the limit of detection resulting in poor signal-to-noise (S/N) and/or when there is interference from other co-eluting peptide ions with very similar or identical m/z values that lead to difficulty in parsing out individual intensity profiles.

3. Applies a unique peptides per protein filter

General practice in the proteomics field is to filter out proteins which were identified on the basis of a single peptide. Because approximately 1% of all identified peptides are false positive matches, it’s more likely that 1 peptide was incorrectly identified and that protein ID is incorrect than that, for example, 5 peptides from the same protein were all incorrectly identified and that protein ID is incorrect. We recommend focusing on proteins with 2 or more peptide identifications, as these will be higher confidence. If you have a protein of interest with only 1 peptide identified, contact PMF faculty and we can help you evaluate the evidence from the raw data to determine believability.

4. Adds accession numbers to identified proteins without informative names

Spectronaut reports contain 4 different columns of identifying information:

PG.Genes, which is the gene name (e.g. CDK1).
PG.ProteinAccessions, which is the UniProt identifier number for a unique entry in the online database (e.g. P06493).
PG.ProteinDescriptions, which is the protein name as provided on UniProt (e.g. cyclin-dependent kinase 1).
PG.ProteinNames, which is a concatenation of an identifier and the species (e.g. CDK1_HUMAN).

Every entry in UniProt will have an accession number, but may not have all of the other identifiers, due to incomplete annotation. Because Uniprot includes entries for fragments of proteins and some proteins entries are redundant, a peptide can match to multiple entries for the same protein, which generates multiple possible identifiers in Spectronaut. Further, the ProteinNames entry in Spectronaut can switch formats: the preference is accession number and species, but can also be gene name and species instead.

This option tells msDiaLogue to substitute the accession number for an identifier if it tries to pull an identifier from a column with no information.

Note: Not all proteins can be identified unambiguously. In many cases, the identified peptides can be found in multiple protein sequences, which yields a protein group or protein cluster rather than a single protein identification. When this happens, the accession numbers for all potential matches are concatenated into one string, separated by periods. When you see long strings of multiple identifiers later in your data processing, this is why. Spectronaut sorts these alphanumerically, so you should not assume that the first protein in the list is most likely to be correct (other search algorithms such as MaxQuant, which is used in PMF for most Scaffold-based results, do rank protein cluster IDs by likelihood of correctness).

5. Saves a document to your working directory with all filtered out data, if desired

If saveRm = TRUE, the data removed in step 2 (preprocess_Filtered_Out_NaN.csv) and step 3 (preprocess_Filtered_Out_Unique.csv) will be saved in the current working directory.

As part of the preprocessing(), a histogram of $log_2$ -transformed protein abundances is provided. This is a helpful way to confirm that the data have been read in correctly, and there are no issues with the numerical values of the protein abundances. Ideally, this histogram will appear fairly symmetrical (bell-shaped) without too much skew towards smaller or larger values.

## if the raw data is in a .csv file
fileName <- "../tests/testData/Toy_Spectronaut_Data.csv"
dataSet <- preprocessing(fileName,
                         filterNaN = TRUE, filterUnique = 2,
                         replaceBlank = TRUE, saveRm = TRUE)

Note: preprocessing() does not perform a transformation on your data. You still need to use the function transform().

## if the raw data is in an .Rdata file
load("../tests/testData/Toy_Spectronaut_Data.RData")
dataSet <- preprocessing(dataSet = Toy_Spectronaut_Data,
                         filterNaN = TRUE, filterUnique = 2,
                         replaceBlank = TRUE, saveRm = TRUE)
#> Warning: Removed 62 rows containing non-finite outside the scale range
#> (`stat_bin()`).

#> Summary of Full Data Signals (Raw):
#>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max. 
#>     20.93    263.87    669.79   6897.92   1963.53 117803.49

#> Levels of Condition: 100pmol 200pmol 50pmol 
#> Levels of Replicate: 1 2 3 4

R.Condition	R.Replicate	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	TMC5B_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	KRT16_MOUSE	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TCPR2_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	PIP_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN	A0A7I2PK40_HUMAN	NBDY_HUMAN	H0Y5R1_HUMAN
100pmol	1	1547.983	3168.32568	2819.7874	318.54376	495.5136	456.3309	213.21727	237.1306	111209.7	10737.953	15097.67	1799.391	630.1937	1311.8127	1279.6390	280.6318	299.51523	1154.5566	16461.2012	179.3190	516.1104	1234.587	27599.42	13798.590	23840.03	614.0895	990.5613	440.0417	132.31737	150.6033	3578.014	26872.50	109.55331	211.6450	1292.5234	1963.5321	189.79155	1106.1482	981.11432	180.6320	199.14555	209.7806	NA	NA	NA
100pmol	2	1680.730	4576.37158	1061.9502	404.25836	556.8611	501.0473	184.89574	314.0320	111659.9	10655.384	15840.28	NA	575.0490	1114.2773	1294.9751	271.8160	248.04329	1032.0381	1460.7496	213.1137	492.3771	1186.433	27221.59	13880.411	23963.31	640.2153	1077.4829	364.5241	128.78983	128.2592	3412.794	26742.22	155.37483	348.6104	1066.3511	1509.1512	153.90802	1303.6520	388.65823	122.7458	751.19849	247.3832	1420.1351	NA	NA
100pmol	3	1414.811	4675.13281	2177.8496	275.09167	559.3206	NA	111.24314	501.2060	105982.9	10663.714	15022.21	NA	613.3968	1224.3837	946.0795	309.7599	270.67770	1808.1924	21555.3555	200.7485	342.1992	1227.435	26587.62	13723.719	22957.35	551.6828	1176.7791	319.0364	NA	118.5104	3499.113	26124.20	91.82145	319.1320	1003.3372	1342.4712	143.12419	1352.7024	430.13318	144.6799	171.13177	221.9161	1889.0665	835.6825	NA
100pmol	4	1620.490	3828.19971	2062.8384	385.05573	558.0967	422.0465	84.27336	334.6389	104442.6	10843.115	15160.49	NA	886.5406	1148.7343	1091.7800	NA	229.40149	901.5703	22937.2500	240.7981	418.1846	1190.952	26168.72	13944.603	22311.30	438.5425	1162.6656	351.5390	NA	137.8860	3481.821	25910.39	88.26187	217.7478	489.8084	1721.8601	99.95578	990.6649	393.55930	134.5238	145.17339	216.3736	1610.2407	950.3087	913.3416
200pmol	1	1512.770	4232.05078	2004.8613	338.27777	156.3478	364.5416	146.80331	NA	109245.3	19524.863	21577.97	2212.190	491.7787	1246.4460	1080.4132	270.1487	252.09808	1454.3271	21113.4512	223.8396	313.7860	1176.982	48693.35	24344.188	41234.67	364.7307	1203.0853	385.5154	65.40555	151.0895	3553.484	26261.47	81.22160	185.4865	939.8899	2149.7632	131.13179	381.0588	429.62201	239.4998	145.04378	424.7914	2337.8496	NA	837.8737
200pmol	2	1480.490	3496.84155	2177.9534	NA	550.4083	NA	135.78349	295.8571	113357.5	20072.297	22968.96	NA	669.7894	1068.2001	NA	285.4891	259.50000	1049.7526	25760.0527	190.3054	452.8294	1220.266	49866.29	24742.227	42899.43	633.5656	1234.5601	414.1271	NA	135.8605	3686.869	27638.89	69.56509	250.4035	1020.4291	725.6615	116.20615	877.0164	438.22589	133.4297	160.92671	155.0986	NA	1053.8444	1000.5491
200pmol	3	1555.834	356.43225	2280.6846	379.62103	564.2863	496.0772	103.30424	473.9141	114321.8	20787.127	20720.13	1451.198	586.7260	1378.0652	1194.8448	291.6754	184.18954	1123.7469	NA	174.5702	432.1681	1216.306	50704.73	24803.633	42904.95	446.4135	1082.7312	357.6343	NA	129.0676	3530.710	27101.22	62.08423	136.7023	1171.5715	1675.6870	109.60301	938.3956	568.89239	315.7039	146.75146	198.4779	1397.9890	837.2197	694.5791
200pmol	4	1529.628	350.70822	2223.3093	410.82349	292.9041	522.1325	95.18819	318.4948	116439.8	19924.240	22153.40	NA	539.0703	923.3237	1115.3848	322.9086	97.65465	957.0436	NA	164.7767	NA	1183.197	53744.70	26381.047	43279.84	527.1628	1121.3438	342.5055	NA	121.3068	3751.769	27545.24	70.39470	199.2453	996.0696	1696.6189	125.31519	611.6407	506.49115	204.4332	161.96100	376.5362	895.9138	NA	NA
50pmol	1	1480.210	561.38837	189.9275	264.24271	308.9420	NA	599.90497	192.3859	117803.5	6758.298	12183.81	NA	594.8999	899.5010	1163.1122	291.4431	176.21545	620.2048	14107.1250	152.5492	292.2440	1186.543	16408.28	7169.955	14728.67	2984.7190	1029.7336	288.4770	891.24725	129.7482	3547.950	25668.78	846.95880	146.3040	NA	461.3821	86.84789	373.6308	49.93938	236.2902	20.92994	142.3466	NA	NA	NA
50pmol	2	1486.144	NA	1462.2559	325.74991	351.2331	NA	254.75084	308.6775	110086.7	6721.135	12521.78	NA	582.8912	531.7106	1119.5256	287.1180	103.58258	849.2368	24912.3613	140.6493	362.3117	1260.574	16444.63	7797.536	14736.71	857.5026	NA	361.4482	179.10303	166.8891	3530.004	26351.25	207.83086	165.6463	265.2173	1184.9562	93.91448	768.2026	489.40918	146.9422	88.41573	101.6087	NA	NA	NA
50pmol	3	1468.554	42.51457	1364.9075	83.99377	296.5147	396.0038	257.78970	279.2477	105640.2	6172.877	11926.22	1373.660	569.8922	NA	1067.0791	294.0919	88.48861	738.7719	666.5015	NA	NA	1175.953	16618.11	7432.793	14160.20	916.4893	992.5451	319.6350	128.63672	120.6974	3458.023	26017.54	203.64948	132.5755	291.4759	932.9668	93.50905	547.0935	263.86734	313.0341	111.88376	85.4563	NA	NA	NA
50pmol	4	1497.531	927.07886	1435.5588	275.60831	242.4643	425.7305	197.71338	382.4084	110446.0	6028.398	12021.50	NA	NA	593.1353	1302.1250	339.3387	30.13688	873.1840	15711.3106	142.4270	291.5121	1150.711	16282.51	7543.633	14758.73	886.7808	1138.6193	NA	152.56187	NA	3575.316	25969.99	190.47060	220.1901	676.8246	996.8993	31.57284	523.4712	450.08408	164.1874	143.96025	135.2896	NA	NA	NA

Transformation

Usage

transform(dataSet,        # a preprocessed dataset
          method = "log", # method of transformation
          logFold = 2,    # base value for log transformation
          root = 2)       # degree of the root for a root transformation

Details & Examples

Raw mass spectrometry intensity measurements are often unsuitable for direct statistical modeling because the shape of the data is usually not symmetrical and the variance is not consistent across the range of intensities. Most proteomic workflows will convert these raw values with a log $_2$ transformation, which both reshapes the data into a more symmetrical distribution, making it easier to interpret mean-based fold changes, and also stabilizes the variance across the intensity range (i.e. reduces heteroscedasticity).

dataTran <- transform(dataSet, logFold = 2)

R.Condition	R.Replicate	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	TMC5B_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	KRT16_MOUSE	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TCPR2_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	PIP_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN	A0A7I2PK40_HUMAN	NBDY_HUMAN	H0Y5R1_HUMAN
100pmol	1	10.59617	11.629505	11.461371	8.315348	8.952781	8.833937	7.736180	7.889538	16.76292	13.39043	13.88204	10.81329	9.299651	10.357346	10.321521	8.132535	8.226486	10.173123	14.006782	7.486384	9.011536	10.26981	14.75235	13.75223	14.54110	9.262305	9.952103	8.781496	7.047859	7.234610	11.80494	14.71384	6.775489	7.725502	10.335975	10.939236	7.568272	10.111329	9.938277	7.496910	7.637679	7.712738	NA	NA	NA
100pmol	2	10.71487	12.159989	10.052500	8.659134	9.121174	8.968803	7.530568	8.294768	16.76875	13.37929	13.95131	NA	9.167541	10.121893	10.338709	8.086487	7.954448	10.011280	10.512493	7.735480	8.943620	10.21241	14.73246	13.76076	14.54854	9.322413	10.073449	8.509870	7.008875	7.002919	11.73674	14.70683	7.279609	8.445472	10.058467	10.559522	7.265925	10.348343	8.602358	6.939530	9.553050	7.950604	10.471813	NA	NA
100pmol	3	10.46639	12.190792	11.088689	8.103769	9.127531	NA	6.797573	8.969260	16.69347	13.38042	13.87481	NA	9.260677	10.257840	9.885818	8.275007	8.080432	10.820332	14.395759	7.649245	8.418693	10.26143	14.69847	13.74438	14.48667	9.107695	10.200628	8.317577	NA	6.888870	11.77277	14.67310	6.520759	8.318009	9.970591	10.390675	7.161124	10.401629	8.748640	7.176720	7.418964	7.793871	10.883458	9.706811	NA
100pmol	4	10.66221	11.902450	11.010415	8.588923	9.124371	8.721258	6.397005	8.386462	16.67235	13.40449	13.88803	NA	9.792043	10.165829	10.092467	NA	7.841731	9.816296	14.485405	7.911680	8.707996	10.21790	14.67556	13.76742	14.44549	8.776573	10.183221	8.457541	NA	7.107332	11.76563	14.66124	6.463718	7.766514	8.936074	10.749752	6.643218	9.952253	8.620437	7.071718	7.181633	7.757381	10.653061	9.892252	9.835011
200pmol	1	10.56298	12.047141	10.969287	8.402065	7.288615	8.509940	7.197741	NA	16.73721	14.25302	14.39727	11.11126	8.941866	10.283605	10.077367	8.077610	7.977841	10.506136	14.365875	7.806321	8.293637	10.20088	15.57144	14.57129	15.33157	8.510688	10.232523	8.590645	6.031341	7.239260	11.79502	14.68066	6.343792	7.535170	9.876348	11.069962	7.034874	8.573870	8.746924	7.903880	7.180345	8.730611	11.190966	NA	9.710589
200pmol	2	10.53186	11.771837	11.088757	NA	9.104358	NA	7.085164	8.208757	16.79052	14.29292	14.48740	NA	9.387564	10.060966	NA	8.157292	8.019591	10.035834	14.652848	7.572173	8.822824	10.25298	15.60578	14.59469	15.38867	9.307350	10.269781	8.693930	NA	7.085982	11.84818	14.75441	6.120292	7.968111	9.994960	9.503153	6.860543	9.776460	8.775531	7.059936	7.330260	7.277041	NA	10.041446	9.966576
200pmol	3	10.60347	8.477484	11.155251	8.568416	9.140283	8.954421	6.690756	8.888482	16.80274	14.34340	14.33875	10.50303	9.196543	10.428428	10.222608	8.188220	7.525047	10.134101	NA	7.447663	8.755449	10.24829	15.62983	14.59826	15.38886	8.802237	10.080459	8.482341	NA	7.011984	11.78574	14.72607	5.956155	7.094894	10.194229	10.710537	6.776144	9.874052	9.152012	8.302428	7.197231	7.632834	10.449137	9.709462	9.439995
200pmol	4	10.57897	8.454127	11.118493	8.682375	8.194285	9.028272	6.572711	8.315126	16.82923	14.28224	14.43524	NA	9.074329	9.850693	10.123326	8.334982	6.609617	9.902441	NA	7.364369	NA	10.20847	15.71383	14.68721	15.40141	9.042105	10.131013	8.419983	NA	6.922516	11.87336	14.74952	6.137395	7.638402	9.960103	10.728447	6.969417	9.256541	8.984393	7.675486	7.339503	8.556645	9.807216	NA	NA
50pmol	1	10.53159	9.132855	7.569305	8.045720	8.271192	NA	9.228590	7.587860	16.84602	12.72244	13.57268	NA	9.216503	9.812981	10.183775	8.187071	7.461197	9.276601	13.784136	7.253131	8.191030	10.21255	14.00214	12.80775	13.84634	11.543379	10.008055	8.172313	9.799682	7.019571	11.79277	14.64773	9.726148	7.192825	NA	8.849818	6.440419	8.545470	5.642106	7.884416	4.387496	7.153265	NA	NA	NA
50pmol	2	10.53736	NA	10.513980	8.347621	8.456285	NA	7.992943	8.269956	16.74828	12.71449	13.61215	NA	9.187083	9.054498	10.128672	8.165500	6.694638	9.730023	14.604574	7.135959	8.501088	10.29986	14.00533	12.92880	13.84713	9.743997	NA	8.497645	7.484646	7.382746	11.78545	14.68558	7.699266	7.371963	8.051031	10.210618	6.553276	9.585343	8.934897	7.199104	6.466231	6.666879	NA	NA	NA
50pmol	3	10.52018	5.409885	10.414587	6.392210	8.211960	8.629371	8.010051	8.125402	16.68880	12.59173	13.54185	10.42381	9.154545	NA	10.059451	8.200124	6.467420	9.528985	9.380464	NA	NA	10.19961	14.02047	12.85969	13.78955	9.839974	9.954989	8.320282	7.007159	6.915251	11.75573	14.66720	7.669944	7.050670	8.187233	9.865682	6.547034	9.095644	8.043669	8.290176	6.805857	6.417115	NA	NA	NA
50pmol	4	10.54837	9.856548	10.487397	8.106476	7.921629	8.733797	7.627267	8.578971	16.75298	12.55756	13.55333	NA	NA	9.212217	10.346652	8.406582	4.913458	9.770142	13.939516	7.154078	8.187412	10.16831	13.99104	12.88104	13.84928	9.792434	10.153070	NA	7.253251	NA	11.80386	14.66456	7.573424	7.782606	9.402638	9.961304	4.980612	9.031966	8.814051	7.359200	7.169527	7.079907	NA	NA	NA

Filtering

Usage

filterOutIn(
  dataSet,           # dataset of values
  listName = c(),    # character vector of proteins
  regexName = c(),   # character vector for use within a regex
  removeList = TRUE, # should named proteins be removed?
  saveRm = TRUE      # should removed proteins be saved?
)

Details & Examples

In some cases, a researcher may wish to filter out a specific protein or proteins from the dataset. The most common instance of this would be proteins identified from the common contaminants database, where we don’t want something like BSA to be matched to a human protein because the search algorithm didn’t have the correct option available, but we don’t actually care about BSA itself and want to leave it out of our visualization. Other examples may be filtering out entries from the decoy database (specific to a Scaffold file only, will not be present in a Spectronaut file), or a mixed-species experiment where the researcher wants to evaluate data from only one species at a time. This step allows you to set aside specific proteins from downstream analysis, using either an exact match identifier (the listName = argument), or text-containing identifiers (the regexName = argument).

Note: If both listName and regexName are defined, the proteins to be selected or removed is the union of the two terms.
Keep in mind: Removal of any proteins, including common contaminants, will affect any global calculations performed after this step (such as normalization). This should not be done without a clear understanding of how this will affect your results.

Case 1. Remove proteins specified by the user in this step and keep everything else.

In the example below, the specific protein with the identifier “ALBU_BOVIN” will be removed, as will anything entries with an identifier that contains the characters “HUMAN”. If removeList = TRUE, this function will remove what you’ve specified and keep the rest.

filterOutIn(dataTran, listName = "ALBU_BOVIN", regexName = "HUMAN",
            removeList = TRUE, saveRm = TRUE)

R.Condition	R.Replicate	CYC_BOVIN	TRFE_BOVIN	KRT16_MOUSE	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI
100pmol	1	13.39043	13.88204	10.81329	14.75235	13.75223	14.54110
100pmol	2	13.37929	13.95131	NA	14.73246	13.76076	14.54854
100pmol	3	13.38042	13.87481	NA	14.69847	13.74438	14.48667
100pmol	4	13.40449	13.88803	NA	14.67556	13.76742	14.44549
200pmol	1	14.25302	14.39727	11.11126	15.57144	14.57129	15.33157
200pmol	2	14.29292	14.48740	NA	15.60578	14.59469	15.38867
200pmol	3	14.34340	14.33875	10.50303	15.62983	14.59826	15.38886
200pmol	4	14.28224	14.43524	NA	15.71383	14.68721	15.40141
50pmol	1	12.72244	13.57268	NA	14.00214	12.80775	13.84634
50pmol	2	12.71449	13.61215	NA	14.00533	12.92880	13.84713
50pmol	3	12.59173	13.54185	10.42381	14.02047	12.85969	13.78955
50pmol	4	12.55756	13.55333	NA	13.99104	12.88104	13.84928

If you want to exclude two sets of proteins and no specific ones (e.g. contaminants and decoys, but not specifically albumin), you can drop the listName designator entirely, and set the regexName to include a combination, like this:

filterOutIn(dataTran, regexName = c("DECOY", "CON__"),
            removeList = TRUE, saveRm = TRUE)

R.Condition	R.Replicate	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	TMC5B_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	KRT16_MOUSE	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TCPR2_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	PIP_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN	A0A7I2PK40_HUMAN	NBDY_HUMAN	H0Y5R1_HUMAN
100pmol	1	10.59617	11.629505	11.461371	8.315348	8.952781	8.833937	7.736180	7.889538	16.76292	13.39043	13.88204	10.81329	9.299651	10.357346	10.321521	8.132535	8.226486	10.173123	14.006782	7.486384	9.011536	10.26981	14.75235	13.75223	14.54110	9.262305	9.952103	8.781496	7.047859	7.234610	11.80494	14.71384	6.775489	7.725502	10.335975	10.939236	7.568272	10.111329	9.938277	7.496910	7.637679	7.712738	NA	NA	NA
100pmol	2	10.71487	12.159989	10.052500	8.659134	9.121174	8.968803	7.530568	8.294768	16.76875	13.37929	13.95131	NA	9.167541	10.121893	10.338709	8.086487	7.954448	10.011280	10.512493	7.735480	8.943620	10.21241	14.73246	13.76076	14.54854	9.322413	10.073449	8.509870	7.008875	7.002919	11.73674	14.70683	7.279609	8.445472	10.058467	10.559522	7.265925	10.348343	8.602358	6.939530	9.553050	7.950604	10.471813	NA	NA
100pmol	3	10.46639	12.190792	11.088689	8.103769	9.127531	NA	6.797573	8.969260	16.69347	13.38042	13.87481	NA	9.260677	10.257840	9.885818	8.275007	8.080432	10.820332	14.395759	7.649245	8.418693	10.26143	14.69847	13.74438	14.48667	9.107695	10.200628	8.317577	NA	6.888870	11.77277	14.67310	6.520759	8.318009	9.970591	10.390675	7.161124	10.401629	8.748640	7.176720	7.418964	7.793871	10.883458	9.706811	NA
100pmol	4	10.66221	11.902450	11.010415	8.588923	9.124371	8.721258	6.397005	8.386462	16.67235	13.40449	13.88803	NA	9.792043	10.165829	10.092467	NA	7.841731	9.816296	14.485405	7.911680	8.707996	10.21790	14.67556	13.76742	14.44549	8.776573	10.183221	8.457541	NA	7.107332	11.76563	14.66124	6.463718	7.766514	8.936074	10.749752	6.643218	9.952253	8.620437	7.071718	7.181633	7.757381	10.653061	9.892252	9.835011
200pmol	1	10.56298	12.047141	10.969287	8.402065	7.288615	8.509940	7.197741	NA	16.73721	14.25302	14.39727	11.11126	8.941866	10.283605	10.077367	8.077610	7.977841	10.506136	14.365875	7.806321	8.293637	10.20088	15.57144	14.57129	15.33157	8.510688	10.232523	8.590645	6.031341	7.239260	11.79502	14.68066	6.343792	7.535170	9.876348	11.069962	7.034874	8.573870	8.746924	7.903880	7.180345	8.730611	11.190966	NA	9.710589
200pmol	2	10.53186	11.771837	11.088757	NA	9.104358	NA	7.085164	8.208757	16.79052	14.29292	14.48740	NA	9.387564	10.060966	NA	8.157292	8.019591	10.035834	14.652848	7.572173	8.822824	10.25298	15.60578	14.59469	15.38867	9.307350	10.269781	8.693930	NA	7.085982	11.84818	14.75441	6.120292	7.968111	9.994960	9.503153	6.860543	9.776460	8.775531	7.059936	7.330260	7.277041	NA	10.041446	9.966576
200pmol	3	10.60347	8.477484	11.155251	8.568416	9.140283	8.954421	6.690756	8.888482	16.80274	14.34340	14.33875	10.50303	9.196543	10.428428	10.222608	8.188220	7.525047	10.134101	NA	7.447663	8.755449	10.24829	15.62983	14.59826	15.38886	8.802237	10.080459	8.482341	NA	7.011984	11.78574	14.72607	5.956155	7.094894	10.194229	10.710537	6.776144	9.874052	9.152012	8.302428	7.197231	7.632834	10.449137	9.709462	9.439995
200pmol	4	10.57897	8.454127	11.118493	8.682375	8.194285	9.028272	6.572711	8.315126	16.82923	14.28224	14.43524	NA	9.074329	9.850693	10.123326	8.334982	6.609617	9.902441	NA	7.364369	NA	10.20847	15.71383	14.68721	15.40141	9.042105	10.131013	8.419983	NA	6.922516	11.87336	14.74952	6.137395	7.638402	9.960103	10.728447	6.969417	9.256541	8.984393	7.675486	7.339503	8.556645	9.807216	NA	NA
50pmol	1	10.53159	9.132855	7.569305	8.045720	8.271192	NA	9.228590	7.587860	16.84602	12.72244	13.57268	NA	9.216503	9.812981	10.183775	8.187071	7.461197	9.276601	13.784136	7.253131	8.191030	10.21255	14.00214	12.80775	13.84634	11.543379	10.008055	8.172313	9.799682	7.019571	11.79277	14.64773	9.726148	7.192825	NA	8.849818	6.440419	8.545470	5.642106	7.884416	4.387496	7.153265	NA	NA	NA
50pmol	2	10.53736	NA	10.513980	8.347621	8.456285	NA	7.992943	8.269956	16.74828	12.71449	13.61215	NA	9.187083	9.054498	10.128672	8.165500	6.694638	9.730023	14.604574	7.135959	8.501088	10.29986	14.00533	12.92880	13.84713	9.743997	NA	8.497645	7.484646	7.382746	11.78545	14.68558	7.699266	7.371963	8.051031	10.210618	6.553276	9.585343	8.934897	7.199104	6.466231	6.666879	NA	NA	NA
50pmol	3	10.52018	5.409885	10.414587	6.392210	8.211960	8.629371	8.010051	8.125402	16.68880	12.59173	13.54185	10.42381	9.154545	NA	10.059451	8.200124	6.467420	9.528985	9.380464	NA	NA	10.19961	14.02047	12.85969	13.78955	9.839974	9.954989	8.320282	7.007159	6.915251	11.75573	14.66720	7.669944	7.050670	8.187233	9.865682	6.547034	9.095644	8.043669	8.290176	6.805857	6.417115	NA	NA	NA
50pmol	4	10.54837	9.856548	10.487397	8.106476	7.921629	8.733797	7.627267	8.578971	16.75298	12.55756	13.55333	NA	NA	9.212217	10.346652	8.406582	4.913458	9.770142	13.939516	7.154078	8.187412	10.16831	13.99104	12.88104	13.84928	9.792434	10.153070	NA	7.253251	NA	11.80386	14.66456	7.573424	7.782606	9.402638	9.961304	4.980612	9.031966	8.814051	7.359200	7.169527	7.079907	NA	NA	NA

Keep in mind that if you only type “CON”, many protein names have CON somewhere in a text string, and those will be selected too. This is why the contaminants database uses two underscores to set off the identifier tag (CON__), so you can distinguish between contaminants and proteins with names like “condensin” or “ubiquitin-conjugating” or “domain-containing”.

If saveRm = TRUE, the filtered-out data (“ALBU_BOVIN” + “*HUMAN”) will be saved as a .csv file named filtered_out_data.csv in the current working directory, and you can inspect this list to see what was removed.

Case 2. Keep the proteins specified by the user in this step and remove everything else.

If we set removeList to FALSE, running this code will remove everything you didn’t specify and keep only things that matched your search terms.

filterOutIn(dataTran, listName = "ALBU_BOVIN", regexName = "HUMAN",
            removeList = FALSE)

R.Condition	R.Replicate	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	TMC5B_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TCPR2_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	PIP_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN	A0A7I2PK40_HUMAN	NBDY_HUMAN	H0Y5R1_HUMAN
100pmol	1	10.59617	11.629505	11.461371	8.315348	8.952781	8.833937	7.736180	7.889538	16.76292	9.299651	10.357346	10.321521	8.132535	8.226486	10.173123	14.006782	7.486384	9.011536	10.26981	9.262305	9.952103	8.781496	7.047859	7.234610	11.80494	14.71384	6.775489	7.725502	10.335975	10.939236	7.568272	10.111329	9.938277	7.496910	7.637679	7.712738	NA	NA	NA
100pmol	2	10.71487	12.159989	10.052500	8.659134	9.121174	8.968803	7.530568	8.294768	16.76875	9.167541	10.121893	10.338709	8.086487	7.954448	10.011280	10.512493	7.735480	8.943620	10.21241	9.322413	10.073449	8.509870	7.008875	7.002919	11.73674	14.70683	7.279609	8.445472	10.058467	10.559522	7.265925	10.348343	8.602358	6.939530	9.553050	7.950604	10.471813	NA	NA
100pmol	3	10.46639	12.190792	11.088689	8.103769	9.127531	NA	6.797573	8.969260	16.69347	9.260677	10.257840	9.885818	8.275007	8.080432	10.820332	14.395759	7.649245	8.418693	10.26143	9.107695	10.200628	8.317577	NA	6.888870	11.77277	14.67310	6.520759	8.318009	9.970591	10.390675	7.161124	10.401629	8.748640	7.176720	7.418964	7.793871	10.883458	9.706811	NA
100pmol	4	10.66221	11.902450	11.010415	8.588923	9.124371	8.721258	6.397005	8.386462	16.67235	9.792043	10.165829	10.092467	NA	7.841731	9.816296	14.485405	7.911680	8.707996	10.21790	8.776573	10.183221	8.457541	NA	7.107332	11.76563	14.66124	6.463718	7.766514	8.936074	10.749752	6.643218	9.952253	8.620437	7.071718	7.181633	7.757381	10.653061	9.892252	9.835011
200pmol	1	10.56298	12.047141	10.969287	8.402065	7.288615	8.509940	7.197741	NA	16.73721	8.941866	10.283605	10.077367	8.077610	7.977841	10.506136	14.365875	7.806321	8.293637	10.20088	8.510688	10.232523	8.590645	6.031341	7.239260	11.79502	14.68066	6.343792	7.535170	9.876348	11.069962	7.034874	8.573870	8.746924	7.903880	7.180345	8.730611	11.190966	NA	9.710589
200pmol	2	10.53186	11.771837	11.088757	NA	9.104358	NA	7.085164	8.208757	16.79052	9.387564	10.060966	NA	8.157292	8.019591	10.035834	14.652848	7.572173	8.822824	10.25298	9.307350	10.269781	8.693930	NA	7.085982	11.84818	14.75441	6.120292	7.968111	9.994960	9.503153	6.860543	9.776460	8.775531	7.059936	7.330260	7.277041	NA	10.041446	9.966576
200pmol	3	10.60347	8.477484	11.155251	8.568416	9.140283	8.954421	6.690756	8.888482	16.80274	9.196543	10.428428	10.222608	8.188220	7.525047	10.134101	NA	7.447663	8.755449	10.24829	8.802237	10.080459	8.482341	NA	7.011984	11.78574	14.72607	5.956155	7.094894	10.194229	10.710537	6.776144	9.874052	9.152012	8.302428	7.197231	7.632834	10.449137	9.709462	9.439995
200pmol	4	10.57897	8.454127	11.118493	8.682375	8.194285	9.028272	6.572711	8.315126	16.82923	9.074329	9.850693	10.123326	8.334982	6.609617	9.902441	NA	7.364369	NA	10.20847	9.042105	10.131013	8.419983	NA	6.922516	11.87336	14.74952	6.137395	7.638402	9.960103	10.728447	6.969417	9.256541	8.984393	7.675486	7.339503	8.556645	9.807216	NA	NA
50pmol	1	10.53159	9.132855	7.569305	8.045720	8.271192	NA	9.228590	7.587860	16.84602	9.216503	9.812981	10.183775	8.187071	7.461197	9.276601	13.784136	7.253131	8.191030	10.21255	11.543379	10.008055	8.172313	9.799682	7.019571	11.79277	14.64773	9.726148	7.192825	NA	8.849818	6.440419	8.545470	5.642106	7.884416	4.387496	7.153265	NA	NA	NA
50pmol	2	10.53736	NA	10.513980	8.347621	8.456285	NA	7.992943	8.269956	16.74828	9.187083	9.054498	10.128672	8.165500	6.694638	9.730023	14.604574	7.135959	8.501088	10.29986	9.743997	NA	8.497645	7.484646	7.382746	11.78545	14.68558	7.699266	7.371963	8.051031	10.210618	6.553276	9.585343	8.934897	7.199104	6.466231	6.666879	NA	NA	NA
50pmol	3	10.52018	5.409885	10.414587	6.392210	8.211960	8.629371	8.010051	8.125402	16.68880	9.154545	NA	10.059451	8.200124	6.467420	9.528985	9.380464	NA	NA	10.19961	9.839974	9.954989	8.320282	7.007159	6.915251	11.75573	14.66720	7.669944	7.050670	8.187233	9.865682	6.547034	9.095644	8.043669	8.290176	6.805857	6.417115	NA	NA	NA
50pmol	4	10.54837	9.856548	10.487397	8.106476	7.921629	8.733797	7.627267	8.578971	16.75298	NA	9.212217	10.346652	8.406582	4.913458	9.770142	13.939516	7.154078	8.187412	10.16831	9.792434	10.153070	NA	7.253251	NA	11.80386	14.66456	7.573424	7.782606	9.402638	9.961304	4.980612	9.031966	8.814051	7.359200	7.169527	7.079907	NA	NA	NA

Extension

Besides protein names, the function filterProtein() provides a similar function to filter proteins by additional protein information.

For Spectronaut: “PG.Genes”, “PG.ProteinAccessions”, “PG.ProteinDescriptions”, and “PG.ProteinNames”.
For Scaffold: “ProteinDescriptions”, “AccessionNumber”, and “AlternateID”.

filterProtein(dataTran, proteinInformation = "preprocess_protein_information.csv",
              text = c("Putative zinc finger protein 840", "Bovine serum albumin"),
              by = "PG.ProteinDescriptions",
              removeList = FALSE)

where proteinInformation is the file name for protein information, automatically generated by preprocessing(). In this case, the proteins whose "PG.ProteinDescriptions" match with “Putative zinc finger protein 840” or “Bovine serum albumin” will be kept. Note that the search value text is used for exact equality search.

R.Condition	R.Replicate	ZN840_HUMAN	ALBU_BOVIN
100pmol	1	8.315348	16.76292
100pmol	2	8.659134	16.76875
100pmol	3	8.103769	16.69347
100pmol	4	8.588923	16.67235
200pmol	1	8.402065	16.73721
200pmol	2	NA	16.79052
200pmol	3	8.568416	16.80274
200pmol	4	8.682375	16.82923
50pmol	1	8.045720	16.84602
50pmol	2	8.347621	16.74828
50pmol	3	6.392210	16.68880
50pmol	4	8.106476	16.75298

Normalization

Usage

normalize(dataSet,                 # dataset of experimental values
          applyto = "sample",      # specify the target of normalization
          normalizeType = "quant", # what type of normalization to apply
          plot = TRUE)             # should a plot of normalized values be produced?

Details & Examples

Normalization is designed to address systematic biases in the data. Biases can arise from inadvertent sample grouping during generation or preparation, from variations in instrument performance during acquisition, analysis of different peptide amounts across experiments, or other reasons. These factors can artificially mask or enhance actual biological changes.

Many normalization methods have been developed for large datasets, each with its own strengths and weaknesses. The following factors should be considered when choosing a normalization method:

Experiment-Specific Normalization:
Most experiments run with UConn PMF are normalized by injection amount at the time of analysis to facilitate comparison. “Amount” is measured by UV absorbance at 280 nm, a standard method for generic protein quantification.
Assumption of Non-Changing Species:
Most biological experiments implicitly assume that the majority of measured species in an experiment will not change across conditions. This assumption is more robust the more measurements your experiment has (e.g. several thousand proteins). It may not be true at all for small datasets (tens of proteins).

If you are analyzing a batch of samples with very different complexities (e.g. a set of IPs where the control samples have tens of proteins and the experimental samples have hundreds of proteins), you should not normalize all of these together, but break them up into subsets of similar complexity.

By default, normalization is performed across samples, adjusting protein expression levels within each sample relative to the other samples. So far, this package provides eight normalization methods for use:

“auto”: Auto scaling (mean centering and then dividing by the standard deviation of each variable) (Jackson 1991).
“level”: Level scaling (mean centering and then dividing by the mean of each variable).
“mean”: Mean centering.
“median”: Median centering.
“pareto”: Pareto scaling (mean centering and then dividing by the square root of the standard deviation of each variable).
“quant”: Quantile normalization (Bolstad et al. 2003).
“range”: Range scaling (mean centering and then dividing by the range of each variable).
“vast”: Variable stability (VAST) scaling (Keun et al. 2003).

Quantile normalization is generally recommended by UConn SCS.

dataNorm <- normalize(dataTran, normalizeType = "quant")
#> Warning: Removed 55 rows containing non-finite outside the scale range
#> (`stat_boxplot()`).

#> Warning: Removed 55 rows containing non-finite outside the scale range
#> (`stat_boxplot()`).

The message “Warning: Removed 55 rows containing non-finite values” indicates the presence of 55 NA (Not Available) values in the data. These NA values arise when a protein was not identified in a particular sample or condition and are automatically excluded when generating the boxplot but retained in the actual dataset.

R.Condition	R.Replicate	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	TMC5B_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	KRT16_MOUSE	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TCPR2_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	PIP_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN	A0A7I2PK40_HUMAN	NBDY_HUMAN	H0Y5R1_HUMAN
100pmol	1	10.37045	11.406514	10.956950	8.392426	8.710518	8.610420	7.829510	8.023133	16.75777	12.96499	13.97388	10.51096	9.136271	10.231965	10.048461	8.179306	8.279169	9.874410	14.201118	7.001503	8.832972	9.978488	15.16303	13.62766	14.44005	8.964155	9.574185	8.517979	6.420716	6.764393	12.07953	14.76033	6.004586	7.670711	10.129049	10.681337	7.242036	9.727210	9.376507	7.109682	7.393910	7.530379	NA	NA	NA
100pmol	2	11.40651	12.964987	9.727210	8.517979	8.832972	8.710518	7.242036	8.023133	16.75777	13.62766	14.20112	NA	8.964155	10.048461	10.231965	7.829510	7.670711	9.574185	10.681337	7.393910	8.610420	10.129049	15.16303	13.97388	14.44005	9.136271	9.978488	8.279169	6.764393	6.420716	12.07953	14.76033	7.109682	8.179306	9.874410	10.956950	7.001503	10.370449	8.392426	6.004586	9.376507	7.530379	10.510962	NA	NA
100pmol	3	10.32522	11.893804	10.851852	7.868171	8.887142	NA	6.429596	8.646475	16.75777	12.81909	13.94448	NA	9.027184	9.940284	9.504539	8.074082	7.698334	10.467264	14.178809	7.413486	8.433160	10.022816	15.15272	13.55168	14.42169	8.758911	9.812352	8.213284	NA	6.777937	11.29130	14.74334	6.004586	8.311138	9.649565	10.097660	7.009435	10.195272	8.550204	7.121143	7.262869	7.555404	10.625304	9.244669	NA
100pmol	4	10.51096	12.079525	10.956950	8.279169	8.964155	8.610420	6.004586	8.023133	16.75777	12.96499	13.97388	NA	9.136271	10.048461	9.978488	NA	7.670711	9.376507	14.440054	7.829510	8.517979	10.231965	15.16303	13.62766	14.20112	8.710518	10.129049	8.179306	NA	7.109682	11.40651	14.76033	6.420716	7.530379	8.832972	10.681337	6.764393	9.874410	8.392426	7.001503	7.242036	7.393910	10.370449	9.727210	9.574185
200pmol	1	10.27762	12.256403	10.413259	8.356482	7.375266	8.476493	7.098768	NA	16.75777	13.10393	14.00189	10.74088	9.255807	10.077232	9.798890	8.142561	7.974610	10.164570	13.700017	7.644405	8.253204	9.927121	15.17286	14.22236	14.77650	8.577167	10.010298	8.782531	6.004586	7.222195	11.51625	14.45755	6.412260	7.506546	9.641587	10.556023	6.751494	8.669939	9.040857	7.792691	6.993948	8.905805	11.057043	NA	9.504539
200pmol	2	10.53171	11.078642	10.729201	NA	8.732804	NA	7.026553	8.267212	16.75777	12.49496	13.38772	NA	9.012072	10.120238	NA	8.149770	7.964140	9.954832	14.130668	7.608925	8.620541	10.229206	15.13045	13.88102	14.70670	8.866513	10.377729	8.386314	NA	7.145874	11.59517	14.38206	6.004586	7.766206	9.827232	9.232358	6.448756	9.504539	8.520402	6.807164	7.455730	7.307825	NA	10.036651	9.658384
200pmol	3	11.05704	8.142561	12.256403	8.356482	8.905805	8.782531	6.412260	8.669939	16.75777	14.00189	13.70002	10.74088	9.255807	10.413259	10.164570	7.792691	7.506546	10.010298	NA	7.375266	8.476493	10.277619	15.17286	14.22236	14.77650	8.577167	9.927121	8.253204	NA	6.993948	13.10393	14.45755	6.004586	7.098768	10.077232	11.516245	6.751494	9.798890	9.040857	7.974610	7.222195	7.644405	10.556023	9.641587	9.504539
200pmol	4	10.72920	8.520402	11.595175	8.732804	7.964140	9.012072	6.448756	8.149770	16.75777	13.38772	13.88102	NA	9.504539	9.954832	10.229206	8.267212	6.807164	10.036651	NA	7.455730	NA	10.531713	15.13045	14.13067	14.70670	9.232358	10.377729	8.386314	NA	7.026553	12.49496	14.38206	6.004586	7.608925	10.120238	11.078642	7.145874	9.658384	8.866513	7.766206	7.307825	8.620541	9.827232	NA	NA
50pmol	1	10.72920	9.232358	7.766206	8.267212	8.732804	NA	9.658384	7.964140	16.75777	12.49496	13.88102	NA	9.504539	10.120238	10.377729	8.520402	7.608925	9.827232	14.130668	7.455730	8.620541	10.531713	14.70670	13.38772	14.38206	11.078642	10.229206	8.386314	10.036651	7.026553	11.59517	15.13045	9.954832	7.307825	NA	9.012072	6.807164	8.866513	6.448756	8.149770	6.004586	7.145874	NA	NA	NA
50pmol	2	10.96831	NA	10.662903	8.659793	8.785723	NA	8.190682	8.555305	16.75777	12.30540	13.84672	NA	9.753718	9.581714	10.189464	8.429646	7.035806	10.008606	14.686886	7.159242	9.099265	10.482590	14.36063	13.25790	14.10465	10.086066	NA	8.926299	7.806291	7.637117	11.47571	15.11842	8.017625	7.480137	8.298269	10.329078	6.459113	9.911682	9.362666	7.332126	6.004586	6.822962	NA	NA	NA
50pmol	3	11.59517	6.004586	10.729201	6.448756	8.732804	9.232358	8.149770	8.386314	16.75777	13.38772	14.13067	11.07864	9.658384	NA	10.377729	8.620541	7.026553	9.954832	9.827232	NA	NA	10.531713	14.70670	13.88102	14.38206	10.036651	10.229206	9.012072	7.608925	7.455730	12.49496	15.13045	7.964140	7.766206	8.520402	10.120238	7.145874	9.504539	8.267212	8.866513	7.307825	6.807164	NA	NA	NA
50pmol	4	10.96831	10.008606	10.662903	8.298269	8.190682	8.785723	7.806291	8.659793	16.75777	12.30540	13.84672	NA	NA	9.362666	10.482590	8.555305	6.004586	9.753718	14.360635	7.035806	8.429646	10.329078	14.68689	13.25790	14.10465	9.911682	10.189464	NA	7.332126	NA	11.47571	15.11842	7.637117	8.017625	9.581714	10.086066	6.459113	9.099265	8.926299	7.480137	7.159242	6.822962	NA	NA	NA

Imputation

Usage

dataMissing(
  dataSet,                # dataset of experimental values
  sort_miss = FALSE,      # should columns be ordered by missingness?
  plot = FALSE,           # should missingness be plotted?
  show_pct_legend = TRUE, # should % missing be displayed on plot?
  show_labels = TRUE,     # should column headings be displayed on plot?
  show_pct_col = TRUE     # should % missing by column be displayed on plot?
)

impute.min_local(dataSet,                  # dataset of experimental values
                 reportImputing = FALSE,   # should a record of imputed values be kept?
                 reqPercentPresent = 0.51) # what percent of replicates should be experimentally observed for the others to be imputed?

impute.min_global(dataSet,                # dataset of experimental values
                  reportImputing = FALSE) # should a record of imputed values be kept?

impute.knn_seq(dataSet,                # dataset of experimental values
               reportImputing = FALSE, # should a record of imputed values be kept?
               k = 10)                 # number of neighbors for imputation

Details & Examples

The two primary MS/MS acquisition types implemented in large scale MS-based proteomics have unique advantages and disadvantages. Traditional Data-Dependent Acquisition (DDA) methods favor specificity in MS/MS sampling over comprehensive proteome coverage. Small peptide isolation windows (<3 m/z) result in MS/MS spectra that contain fragmentation data from ideally only one peptide. This specificity promotes clear peptide identifications but comes at the expense of added scan time. In DDA experiments, the number of peptides that can be selected for MS/MS is limited by instrument scan speeds and is therefore prioritized by highest peptide abundance. Low abundance peptides are sampled less frequently for MS/MS and this can result in variable peptide coverage and many missing protein data across large sample datasets.

Data-Independent Acquisition (DIA) methods promote comprehensive peptide coverage over specificity by sampling many peptides for MS/MS simultaneously. Sequential and large mass isolation windows (4-50 m/z) are used to isolate large numbers of peptides at once for concurrent MS/MS. This produces complicated fragmentation spectra, but these spectra contain data on every observable peptide. A major disadvantage with this type of acquisition is that DIA MS/MS spectra are incredibly complex and difficult to deconvolve. Powerful and relatively new software programs like Spectronaut are capable of successfully parsing out which fragment ions came from each co-fragmented peptide using custom libraries, machine learning algorithms, and precisely determined retention times or measured ion mobility data. Because all observable ions are sampled for MS/MS, DIA reduces missingness substantially compared to DDA, though not entirely.

Function dataMissing() is designed to summarize the missingness for each protein, where plot = TRUE indicates plotting the missingness, and show_labels = TRUE means that the protein names are displayed in the printed plot. Note that the visual representation is not generated by default, and the plot generation time varies with project size.

dataMissing <- dataMissing(dataNorm, plot = TRUE, show_labels = TRUE)

The percentage in the protein labels represents the proportion of missing data in the samples for that protein. For instance, the label “ZN840_HUMAN (8%)” indicates that, within all observations for the protein “ZN840_HUMAN”, 8% of the data is missing. Additionally, the percentage in the legend represents the proportion of missing data in the whole dataset. In this case, 10.2% of the data in dataNorm is missing.

Regardless of plot generation, the function dataMissing() always returns a table providing the following information:

count_miss: The count of missing values for each protein.
pct_miss_col: The percentage of missing values for each protein.
pct_miss_tot: The percentage of missing values for each protein relative to the total missing values in the entire dataset.

	A0A7P0T808_HUMAN	ZN840_HUMAN	TMC5B_HUMAN	C9JNU9_HUMAN	KRT16_MOUSE	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	TCPR2_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	KPCB_HUMAN	LIPL_HUMAN	PIP_HUMAN	CO6_HUMAN	DALD3_HUMAN	A0A7I2PK40_HUMAN	NBDY_HUMAN	H0Y5R1_HUMAN
count_miss	1.000000	1.000000	4.000000	1.000000	8.00000	1.000000	1.000000	1.000000	1.000000	2.000000	1.000000	2.000000	1.000000	1.000000	5.000000	1.000000	1.000000	6.00000	8.00000	8.00000
pct_miss_col	8.333333	8.333333	33.333333	8.333333	66.66667	8.333333	8.333333	8.333333	8.333333	16.666667	8.333333	16.666667	8.333333	8.333333	41.666667	8.333333	8.333333	50.00000	66.66667	66.66667
pct_miss_tot	1.818182	1.818182	7.272727	1.818182	14.54545	1.818182	1.818182	1.818182	1.818182	3.636364	1.818182	3.636364	1.818182	1.818182	9.090909	1.818182	1.818182	10.90909	14.54545	14.54545

For example, in the case of the protein “ZN840_HUMAN,” there are 1 NA values in the samples, representing 8.33% of the missing data for “ZN840_HUMAN” within that sample and 1.82% of the total missing data in the entire dataset.

Various imputation methods have been developed to address the missing-value issue and assign a reasonable guess of quantitative value to proteins with missing values. So far, this package provides 10 imputation methods for use:

impute.min_local(): Replaces missing values with the lowest measured value for that protein in that condition.
impute.min_global(): Replaces missing values with the lowest measured value from any protein found within the entire dataset.
impute.knn(): Replaces missing values using the k-nearest neighbors algorithm (Troyanskaya et al. 2001).
impute.knn_seq(): Replaces missing values using the sequential k-nearest neighbors algorithm (Kim, Kim, and Yi 2004).
impute.knn_trunc(): Replaces missing values using the truncated k-nearest neighbors algorithm (Shah et al. 2017).
impute.nuc_norm(): Replaces missing values using the nuclear-norm regularization (Hastie et al. 2015).
impute.mice_cart(): Replaces missing values using the classification and regression trees (Breiman et al. 1984; Doove, van Buuren, and Dusseldorp 2014; van Buuren 2018).
impute.mice_norm(): Replaces missing values using the Bayesian linear regression (Rubin 1987; Schafer 1997; van Buuren and Groothuis-Oudshoorn 2011).
impute.pca_bayes(): Replaces missing values using the Bayesian principal components analysis (Oba et al. 2003).
impute.pca_prob(): Replaces missing values using the probabilistic principal components analysis (Stacklies et al. 2007).

Additional methods will be added later.

For example, to impute the NA value of dataNorm using impute.min_local(), set the required percentage of values that must be present in a given protein by condition combination for values to be imputed to 51%.

Note: There is no rule in the field of proteomics for filtering based on percentage of missingness, similar to there being no rule for the number of replicates required to draw a conclusion. However, reproducible observations make conclusions more credible. Setting the reqPercentPresent to 0.51 requires that any protein be observed in a majority of the replicates by condition in order to be considered. For 3 replicates, this would require 2 measurements to allow imputation of the 3rd value. If only 1 measurement is seen, the other values will remain NA, and will be filtered out in a subsequent step.

dataImput <- impute.min_local(dataNorm, reportImputing = FALSE,
                              reqPercentPresent = 0.51)

R.Condition	R.Replicate	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	TMC5B_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	KRT16_MOUSE	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TCPR2_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	PIP_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN	A0A7I2PK40_HUMAN	NBDY_HUMAN	H0Y5R1_HUMAN
100pmol	1	10.37045	11.406514	10.956950	8.392426	8.710518	8.610420	7.829510	8.023133	16.75777	12.96499	13.97388	10.51096	9.136271	10.231965	10.048461	8.179306	8.279169	9.874410	14.201118	7.001503	8.832972	9.978488	15.16303	13.62766	14.44005	8.964155	9.574185	8.517979	6.420716	6.764393	12.07953	14.76033	6.004586	7.670711	10.129049	10.681337	7.242036	9.727210	9.376507	7.109682	7.393910	7.530379	10.370449	NA	NA
100pmol	2	11.40651	12.964987	9.727210	8.517979	8.832972	8.710518	7.242036	8.023133	16.75777	13.62766	14.20112	NA	8.964155	10.048461	10.231965	7.829510	7.670711	9.574185	10.681337	7.393910	8.610420	10.129049	15.16303	13.97388	14.44005	9.136271	9.978488	8.279169	6.764393	6.420716	12.07953	14.76033	7.109682	8.179306	9.874410	10.956950	7.001503	10.370449	8.392426	6.004586	9.376507	7.530379	10.510962	NA	NA
100pmol	3	10.32522	11.893804	10.851852	7.868171	8.887142	8.610420	6.429596	8.646475	16.75777	12.81909	13.94448	NA	9.027184	9.940284	9.504539	8.074082	7.698334	10.467264	14.178809	7.413486	8.433160	10.022816	15.15272	13.55168	14.42169	8.758911	9.812352	8.213284	NA	6.777937	11.29130	14.74334	6.004586	8.311138	9.649565	10.097660	7.009435	10.195272	8.550204	7.121143	7.262869	7.555404	10.625304	9.244669	NA
100pmol	4	10.51096	12.079525	10.956950	8.279169	8.964155	8.610420	6.004586	8.023133	16.75777	12.96499	13.97388	NA	9.136271	10.048461	9.978488	7.829510	7.670711	9.376507	14.440054	7.829510	8.517979	10.231965	15.16303	13.62766	14.20112	8.710518	10.129049	8.179306	NA	7.109682	11.40651	14.76033	6.420716	7.530379	8.832972	10.681337	6.764393	9.874410	8.392426	7.001503	7.242036	7.393910	10.370449	9.727210	9.574185
200pmol	1	10.27762	12.256403	10.413259	8.356482	7.375266	8.476493	7.098768	8.149770	16.75777	13.10393	14.00189	10.74088	9.255807	10.077232	9.798890	8.142561	7.974610	10.164570	13.700017	7.644405	8.253204	9.927121	15.17286	14.22236	14.77650	8.577167	10.010298	8.782531	6.004586	7.222195	11.51625	14.45755	6.412260	7.506546	9.641587	10.556023	6.751494	8.669939	9.040857	7.792691	6.993948	8.905805	11.057043	NA	9.504539
200pmol	2	10.53171	11.078642	10.729201	8.356482	8.732804	8.476493	7.026553	8.267212	16.75777	12.49496	13.38772	NA	9.012072	10.120238	9.798890	8.149770	7.964140	9.954832	14.130668	7.608925	8.620541	10.229206	15.13045	13.88102	14.70670	8.866513	10.377729	8.386314	NA	7.145874	11.59517	14.38206	6.004586	7.766206	9.827232	9.232358	6.448756	9.504539	8.520402	6.807164	7.455730	7.307825	9.827232	10.036651	9.658384
200pmol	3	11.05704	8.142561	12.256403	8.356482	8.905805	8.782531	6.412260	8.669939	16.75777	14.00189	13.70002	10.74088	9.255807	10.413259	10.164570	7.792691	7.506546	10.010298	NA	7.375266	8.476493	10.277619	15.17286	14.22236	14.77650	8.577167	9.927121	8.253204	NA	6.993948	13.10393	14.45755	6.004586	7.098768	10.077232	11.516245	6.751494	9.798890	9.040857	7.974610	7.222195	7.644405	10.556023	9.641587	9.504539
200pmol	4	10.72920	8.520402	11.595175	8.732804	7.964140	9.012072	6.448756	8.149770	16.75777	13.38772	13.88102	NA	9.504539	9.954832	10.229206	8.267212	6.807164	10.036651	NA	7.455730	8.253204	10.531713	15.13045	14.13067	14.70670	9.232358	10.377729	8.386314	NA	7.026553	12.49496	14.38206	6.004586	7.608925	10.120238	11.078642	7.145874	9.658384	8.866513	7.766206	7.307825	8.620541	9.827232	NA	9.504539
50pmol	1	10.72920	9.232358	7.766206	8.267212	8.732804	NA	9.658384	7.964140	16.75777	12.49496	13.88102	NA	9.504539	10.120238	10.377729	8.520402	7.608925	9.827232	14.130668	7.455730	8.620541	10.531713	14.70670	13.38772	14.38206	11.078642	10.229206	8.386314	10.036651	7.026553	11.59517	15.13045	9.954832	7.307825	8.298269	9.012072	6.807164	8.866513	6.448756	8.149770	6.004586	7.145874	NA	NA	NA
50pmol	2	10.96831	6.004586	10.662903	8.659793	8.785723	NA	8.190682	8.555305	16.75777	12.30540	13.84672	NA	9.753718	9.581714	10.189464	8.429646	7.035806	10.008606	14.686886	7.159242	9.099265	10.482590	14.36063	13.25790	14.10465	10.086066	10.189464	8.926299	7.806291	7.637117	11.47571	15.11842	8.017625	7.480137	8.298269	10.329078	6.459113	9.911682	9.362666	7.332126	6.004586	6.822962	NA	NA	NA
50pmol	3	11.59517	6.004586	10.729201	6.448756	8.732804	9.232358	8.149770	8.386314	16.75777	13.38772	14.13067	11.07864	9.658384	9.362666	10.377729	8.620541	7.026553	9.954832	9.827232	7.035806	8.429646	10.531713	14.70670	13.88102	14.38206	10.036651	10.229206	9.012072	7.608925	7.455730	12.49496	15.13045	7.964140	7.766206	8.520402	10.120238	7.145874	9.504539	8.267212	8.866513	7.307825	6.807164	NA	NA	NA
50pmol	4	10.96831	10.008606	10.662903	8.298269	8.190682	8.785723	7.806291	8.659793	16.75777	12.30540	13.84672	NA	9.504539	9.362666	10.482590	8.555305	6.004586	9.753718	14.360635	7.035806	8.429646	10.329078	14.68689	13.25790	14.10465	9.911682	10.189464	8.386314	7.332126	7.026553	11.47571	15.11842	7.637117	8.017625	9.581714	10.086066	6.459113	9.099265	8.926299	7.480137	7.159242	6.822962	NA	NA	NA

If reportImputing = TRUE, the returned result structure will be altered to a list, adding a shadow data frame with imputed data labels, where 1 indicates the corresponding entries have been imputed, and 0 indicates otherwise.

After the above imputation, any entries that did not pass the percent present threshold will still have NA values and will need to be filtered out.

dataImput <- filterNA(dataImput, saveRm = TRUE)

where saveRm = TRUE indicates that the filtered data will be saved as a .csv file named filtered_NA_data.csv in the current working directory.

The dataImput is as follows:

R.Condition	R.Replicate	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
100pmol	1	10.37045	11.406514	10.956950	8.392426	8.710518	7.829510	8.023133	16.75777	12.96499	13.97388	9.136271	10.231965	10.048461	8.179306	8.279169	9.874410	7.001503	8.832972	9.978488	15.16303	13.62766	14.44005	8.964155	9.574185	8.517979	6.764393	12.07953	14.76033	6.004586	7.670711	10.129049	10.681337	7.242036	9.727210	9.376507	7.109682	7.393910	7.530379
100pmol	2	11.40651	12.964987	9.727210	8.517979	8.832972	7.242036	8.023133	16.75777	13.62766	14.20112	8.964155	10.048461	10.231965	7.829510	7.670711	9.574185	7.393910	8.610420	10.129049	15.16303	13.97388	14.44005	9.136271	9.978488	8.279169	6.420716	12.07953	14.76033	7.109682	8.179306	9.874410	10.956950	7.001503	10.370449	8.392426	6.004586	9.376507	7.530379
100pmol	3	10.32522	11.893804	10.851852	7.868171	8.887142	6.429596	8.646475	16.75777	12.81909	13.94448	9.027184	9.940284	9.504539	8.074082	7.698334	10.467264	7.413486	8.433160	10.022816	15.15272	13.55168	14.42169	8.758911	9.812352	8.213284	6.777937	11.29130	14.74334	6.004586	8.311138	9.649565	10.097660	7.009435	10.195272	8.550204	7.121143	7.262869	7.555404
100pmol	4	10.51096	12.079525	10.956950	8.279169	8.964155	6.004586	8.023133	16.75777	12.96499	13.97388	9.136271	10.048461	9.978488	7.829510	7.670711	9.376507	7.829510	8.517979	10.231965	15.16303	13.62766	14.20112	8.710518	10.129049	8.179306	7.109682	11.40651	14.76033	6.420716	7.530379	8.832972	10.681337	6.764393	9.874410	8.392426	7.001503	7.242036	7.393910
200pmol	1	10.27762	12.256403	10.413259	8.356482	7.375266	7.098768	8.149770	16.75777	13.10393	14.00189	9.255807	10.077232	9.798890	8.142561	7.974610	10.164570	7.644405	8.253204	9.927121	15.17286	14.22236	14.77650	8.577167	10.010298	8.782531	7.222195	11.51625	14.45755	6.412260	7.506546	9.641587	10.556023	6.751494	8.669939	9.040857	7.792691	6.993948	8.905805
200pmol	2	10.53171	11.078642	10.729201	8.356482	8.732804	7.026553	8.267212	16.75777	12.49496	13.38772	9.012072	10.120238	9.798890	8.149770	7.964140	9.954832	7.608925	8.620541	10.229206	15.13045	13.88102	14.70670	8.866513	10.377729	8.386314	7.145874	11.59517	14.38206	6.004586	7.766206	9.827232	9.232358	6.448756	9.504539	8.520402	6.807164	7.455730	7.307825
200pmol	3	11.05704	8.142561	12.256403	8.356482	8.905805	6.412260	8.669939	16.75777	14.00189	13.70002	9.255807	10.413259	10.164570	7.792691	7.506546	10.010298	7.375266	8.476493	10.277619	15.17286	14.22236	14.77650	8.577167	9.927121	8.253204	6.993948	13.10393	14.45755	6.004586	7.098768	10.077232	11.516245	6.751494	9.798890	9.040857	7.974610	7.222195	7.644405
200pmol	4	10.72920	8.520402	11.595175	8.732804	7.964140	6.448756	8.149770	16.75777	13.38772	13.88102	9.504539	9.954832	10.229206	8.267212	6.807164	10.036651	7.455730	8.253204	10.531713	15.13045	14.13067	14.70670	9.232358	10.377729	8.386314	7.026553	12.49496	14.38206	6.004586	7.608925	10.120238	11.078642	7.145874	9.658384	8.866513	7.766206	7.307825	8.620541
50pmol	1	10.72920	9.232358	7.766206	8.267212	8.732804	9.658384	7.964140	16.75777	12.49496	13.88102	9.504539	10.120238	10.377729	8.520402	7.608925	9.827232	7.455730	8.620541	10.531713	14.70670	13.38772	14.38206	11.078642	10.229206	8.386314	7.026553	11.59517	15.13045	9.954832	7.307825	8.298269	9.012072	6.807164	8.866513	6.448756	8.149770	6.004586	7.145874
50pmol	2	10.96831	6.004586	10.662903	8.659793	8.785723	8.190682	8.555305	16.75777	12.30540	13.84672	9.753718	9.581714	10.189464	8.429646	7.035806	10.008606	7.159242	9.099265	10.482590	14.36063	13.25790	14.10465	10.086066	10.189464	8.926299	7.637117	11.47571	15.11842	8.017625	7.480137	8.298269	10.329078	6.459113	9.911682	9.362666	7.332126	6.004586	6.822962
50pmol	3	11.59517	6.004586	10.729201	6.448756	8.732804	8.149770	8.386314	16.75777	13.38772	14.13067	9.658384	9.362666	10.377729	8.620541	7.026553	9.954832	7.035806	8.429646	10.531713	14.70670	13.88102	14.38206	10.036651	10.229206	9.012072	7.455730	12.49496	15.13045	7.964140	7.766206	8.520402	10.120238	7.145874	9.504539	8.267212	8.866513	7.307825	6.807164
50pmol	4	10.96831	10.008606	10.662903	8.298269	8.190682	7.806291	8.659793	16.75777	12.30540	13.84672	9.504539	9.362666	10.482590	8.555305	6.004586	9.753718	7.035806	8.429646	10.329078	14.68689	13.25790	14.10465	9.911682	10.189464	8.386314	7.026553	11.47571	15.11842	7.637117	8.017625	9.581714	10.086066	6.459113	9.099265	8.926299	7.480137	7.159242	6.822962

Summarization

Usage

summarize(dataSet,         # dataset of experimental values
          saveSumm = TRUE) # save a table of summary statistics?

Details & Examples

This summarization provides a table of values for each protein in the final dataset that include the final processed abundances and fold changes in each condition, and that protein’s statistical relation to the global dataset in terms of its mean, median, standard deviation, and other parameters.

dataSumm <- summarize(dataImput, saveSumm = TRUE)

Condition	Stat	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
100pmol	n	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.00000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000
100pmol	mean	10.6532858	12.0862074	10.6232407	8.2644365	8.8486965	6.8764321	8.1789684	16.75777	13.0941802	14.0233390	9.0659703	10.0672927	9.9408634	7.9781022	7.8297313	9.8230915	7.4096023	8.5986327	10.0905795	15.1604531	13.6952175	14.3757288	8.8924636	9.8735187	8.2974346	6.7681823	11.7142153	14.7560796	6.3848929	7.9228836	9.6214990	10.6043210	7.0043419	10.0418355	8.6778910	6.8092287	7.8188307	7.5025180
100pmol	sd	0.5083417	0.6509735	0.5994044	0.2816079	0.1066916	0.8168644	0.3116711	0.00000	0.3622393	0.1193272	0.0851570	0.1210474	0.3098987	0.1768750	0.2999079	0.4757395	0.3381959	0.1721820	0.1134697	0.0051583	0.1891971	0.1167288	0.1962332	0.2378070	0.1527627	0.2813447	0.4244383	0.0084914	0.5214944	0.3804140	0.5609915	0.3619004	0.1950282	0.2935700	0.4716454	0.5391294	1.0406244	0.0733600
100pmol	median	10.4407055	11.9866645	10.9044011	8.3357977	8.8600566	6.8358159	8.0231329	16.75777	12.9649865	13.9738814	9.0817277	10.0484610	10.0134747	7.9517960	7.6845225	9.7242975	7.4036982	8.5641998	10.0759323	15.1630323	13.6276559	14.4308715	8.8615327	9.8954204	8.2462263	6.7711653	11.7430198	14.7603253	6.2126514	7.9250089	9.7619876	10.6813371	7.0054690	10.0348410	8.4713152	7.0555925	7.3283898	7.5303791
100pmol	trimmed	10.6532858	12.0862074	10.6232407	8.2644365	8.8486965	6.8764321	8.1789684	16.75777	13.0941802	14.0233390	9.0659703	10.0672927	9.9408634	7.9781022	7.8297313	9.8230915	7.4096023	8.5986327	10.0905795	15.1604531	13.6952175	14.3757288	8.8924636	9.8735187	8.2974346	6.7681823	11.7142153	14.7560796	6.3848929	7.9228836	9.6214990	10.6043210	7.0043419	10.0418355	8.6778910	6.8092287	7.8188307	7.5025180
100pmol	mad	0.1376914	0.4989033	0.0779088	0.1770303	0.0972461	0.9173216	0.0000000	0.00000	0.1081516	0.0217987	0.0808661	0.0801917	0.1879022	0.1813007	0.0204763	0.3690956	0.3054035	0.1314032	0.1116108	0.0000000	0.0563232	0.0136146	0.1880212	0.2347672	0.0740280	0.2559629	0.4989033	0.0000000	0.3084771	0.4747480	0.3554415	0.2043118	0.1783076	0.3469740	0.1169605	0.0886895	0.1125840	0.0185508
100pmol	min	10.3252183	11.4065141	9.7272105	7.8681709	8.7105178	6.0045864	8.0231329	16.75777	12.8190919	13.9444754	8.9641549	9.9402840	9.5045393	7.8295103	7.6707114	9.3765069	7.0015026	8.4331596	9.9784883	15.1527157	13.5516770	14.2011179	8.7105178	9.5741849	8.1793065	6.4207163	11.2912962	14.7433424	6.0045864	7.5303791	8.8329716	10.0976598	6.7643932	9.7272105	8.3924265	6.0045864	7.2420363	7.3939101
100pmol	max	11.4065141	12.9649865	10.9569499	8.5179795	8.9641549	7.8295103	8.6464751	16.75777	13.6276559	14.2011179	9.1362711	10.2319649	10.2319649	8.1793065	8.2791689	10.4672641	7.8295103	8.8329716	10.2319649	15.1630323	13.9738814	14.4400544	9.1362711	10.1290492	8.5179795	7.1096825	12.0795254	14.7603253	7.1096825	8.3111376	10.1290492	10.9569499	7.2420363	10.3704495	9.3765069	7.1211431	9.3765069	7.5554037
100pmol	range	1.0812958	1.5584724	1.2297394	0.6498087	0.2536371	1.8249239	0.6233422	0.00000	0.8085640	0.2566425	0.1721162	0.2916809	0.7274256	0.3497962	0.6084576	1.0907573	0.8280077	0.3998121	0.2534766	0.0103166	0.4222044	0.2389365	0.4257533	0.5548643	0.3386730	0.6889662	0.7882292	0.0169829	1.1050961	0.7807586	1.2960775	0.8592901	0.4776431	0.6432390	0.9840804	1.1165567	2.1344706	0.1614936
100pmol	skew	0.6975575	0.3239948	-0.7349674	-0.4906058	-0.2153643	0.0746172	0.7500000	NaN	0.6663690	0.7189584	-0.1695960	0.3387430	-0.4796405	0.1114972	0.7458050	0.3783514	0.0392202	0.3827303	0.1991275	-0.7500000	0.6668586	-0.7378644	0.2135796	-0.1711657	0.5944647	-0.0238336	-0.0237842	-0.7500000	0.4773224	-0.0050802	-0.4861771	-0.4499095	-0.0129955	0.0322210	0.6966507	-0.7281063	0.7407580	-0.6901803
100pmol	kurtosis	-1.7260359	-1.8707238	-1.6982833	-1.8448761	-1.9494341	-2.1798795	-1.6875000	NaN	-1.7327385	-1.7058522	-2.2461322	-1.8404530	-1.8192490	-2.3106519	-1.6904097	-1.9467364	-1.8757984	-1.9169430	-2.1242147	-1.6875000	-1.7325055	-1.6961436	-2.1614305	-2.0285590	-1.8014680	-1.8757000	-2.4101207	-1.6875000	-1.9257747	-2.3455313	-1.8463172	-1.8132208	-1.8755701	-2.2325877	-1.7280983	-1.7033814	-1.6940017	-1.7210573
100pmol	se	0.2541708	0.3254868	0.2997022	0.1408039	0.0533458	0.4084322	0.1558356	0.00000	0.1811196	0.0596636	0.0425785	0.0605237	0.1549494	0.0884375	0.1499539	0.2378698	0.1690980	0.0860910	0.0567349	0.0025791	0.0945985	0.0583644	0.0981166	0.1189035	0.0763814	0.1406723	0.2122191	0.0042457	0.2607472	0.1902070	0.2804957	0.1809502	0.0975141	0.1467850	0.2358227	0.2695647	0.5203122	0.0366800
200pmol	n	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.00000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000
200pmol	mean	10.6488941	9.9995022	11.2485095	8.4505628	8.2445037	6.7465842	8.3091726	16.75777	13.2471248	13.7426617	9.2570565	10.1413903	9.9978891	8.0880583	7.5631150	10.0415877	7.5210813	8.4008606	10.2414149	15.1516556	14.1141044	14.7415973	8.8133014	10.1732192	8.4520910	7.0971425	12.1775775	14.4198014	6.1065048	7.4951113	9.9165721	10.5958169	6.7744045	9.4079382	8.8671576	7.5851679	7.2449244	8.1196440
200pmol	sd	0.3289432	1.9911564	0.8373275	0.1881608	0.7094445	0.3664652	0.2468009	0.00000	0.6260180	0.2671713	0.2010541	0.1943193	0.2312942	0.2050339	0.5491845	0.0887966	0.1270932	0.1803556	0.2480010	0.0244819	0.1612898	0.0403015	0.3108902	0.2385763	0.2290557	0.1059008	0.7605739	0.0435834	0.2038367	0.2849943	0.2242693	0.9901032	0.2858241	0.5064716	0.2453451	0.5268763	0.1931332	0.7646020
200pmol	median	10.6304571	9.7995222	11.1621879	8.3564824	8.3484718	6.7376547	8.2084906	16.75777	13.2458280	13.7905186	9.2558074	10.0987350	9.9817300	8.1461655	7.7353428	10.0234743	7.5323272	8.3648486	10.2534127	15.1516556	14.1765162	14.7415973	8.7218404	10.1940134	8.3863145	7.0862137	12.0450654	14.4198014	6.0045864	7.5577355	9.9522318	10.8173322	6.7514941	9.5814615	8.9536853	7.7794486	7.2650103	8.1324727
200pmol	trimmed	10.6488941	9.9995022	11.2485095	8.4505628	8.2445037	6.7465842	8.3091726	16.75777	13.2471248	13.7426617	9.2570565	10.1413903	9.9978891	8.0880583	7.5631150	10.0415877	7.5210813	8.4008606	10.2414149	15.1516556	14.1141044	14.7415973	8.8133014	10.1732192	8.4520910	7.0971425	12.1775775	14.4198014	6.1065048	7.4951113	9.9165721	10.5958169	6.7744045	9.4079382	8.8671576	7.5851679	7.2449244	8.1196440
200pmol	mad	0.3347575	2.1765166	0.8761545	0.0000000	0.6980566	0.4553758	0.0870596	0.00000	0.6618078	0.2237764	0.1806811	0.1226153	0.2710782	0.0924032	0.3469760	0.0606518	0.1398648	0.1655239	0.2242486	0.0314340	0.0679752	0.0517460	0.2144922	0.2723765	0.0986746	0.1126230	0.7255181	0.0559598	0.0000000	0.1924863	0.2172056	0.7118132	0.2244197	0.2182024	0.1292411	0.1544898	0.1731190	0.9350762
200pmol	min	10.2776193	8.1425613	10.4132587	8.3564824	7.3752661	6.4122599	8.1497697	16.75777	12.4949559	13.3877224	9.0120719	9.9548324	9.7988903	7.7926907	6.8071641	9.9548324	7.3752661	8.2532043	9.9271211	15.1304537	13.8810204	14.7066952	8.5771674	9.9271211	8.2532043	6.9939476	11.5162454	14.3820570	6.0045864	7.0987676	9.6415866	9.2323577	6.4487561	8.6699394	8.5204024	6.8071641	6.9939476	7.3078254
200pmol	max	11.0570429	12.2564034	12.2564034	8.7328039	8.9058051	7.0987676	8.6699394	16.75777	14.0018871	14.0018871	9.5045393	10.4132587	10.2292060	8.2672115	7.9746103	10.1645698	7.6444045	8.6205408	10.5317133	15.1728576	14.2223649	14.7764995	9.2323577	10.3777288	8.7825308	7.2221951	13.1039337	14.4575457	6.4122599	7.7662064	10.1202381	11.5162454	7.1458739	9.7988903	9.0408573	7.9746103	7.4557295	8.9058051
200pmol	range	0.7794236	4.1138421	1.8431447	0.3763215	1.5305390	0.6865077	0.5201698	0.00000	1.5069312	0.6141647	0.4924674	0.4584263	0.4303157	0.4745208	1.1674462	0.2097374	0.2691384	0.3673366	0.6045922	0.0424039	0.3413445	0.0698043	0.6551903	0.4506077	0.5293266	0.2282475	1.5876883	0.0754887	0.4076734	0.6674389	0.4786515	2.2838877	0.6971178	1.1289508	0.5204548	1.1674462	0.4617819	1.5979797
200pmol	skew	0.1104304	0.0985966	0.1459698	0.7500000	-0.1912656	0.0093515	0.6449274	NaN	0.0043414	-0.3241272	0.0139784	0.4541046	0.0251965	-0.5691072	-0.4497921	0.4200330	-0.1005912	0.1958428	-0.1071276	0.0000000	-0.6030234	0.0000000	0.3944221	-0.0390722	0.5750774	0.1306423	0.1932636	0.0000000	0.7500000	-0.4413140	-0.1975122	-0.4230438	0.1788057	-0.6265175	-0.5283584	-0.6785550	-0.2152922	-0.0172273
200pmol	kurtosis	-1.9967978	-2.3005634	-2.1833090	-1.6875000	-2.1856515	-2.4193545	-1.7713239	NaN	-1.9494170	-1.9815481	-1.8749421	-1.8267257	-2.4085003	-1.7742906	-1.9525773	-1.8484890	-2.2706637	-2.2173611	-1.8856612	-2.4375000	-1.8081396	-2.4375000	-2.0080976	-2.3926118	-1.7717059	-2.2279404	-2.2134019	-2.4375000	-1.6875000	-1.8538031	-2.1927634	-1.9000912	-1.8654864	-1.7689149	-1.8768601	-1.7272841	-1.9293663	-2.3195181
200pmol	se	0.1644716	0.9955782	0.4186638	0.0940804	0.3547223	0.1832326	0.1234004	0.00000	0.3130090	0.1335856	0.1005271	0.0971597	0.1156471	0.1025170	0.2745923	0.0443983	0.0635466	0.0901778	0.1240005	0.0122409	0.0806449	0.0201508	0.1554451	0.1192882	0.1145278	0.0529504	0.3802869	0.0217917	0.1019184	0.1424971	0.1121346	0.4950516	0.1429120	0.2532358	0.1226726	0.2634382	0.0965666	0.3823010
50pmol	n	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.00000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000	4.0000000
50pmol	mean	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.2782600	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.8997407
50pmol	sd	0.3708294	2.1115541	1.4597325	0.9959061	0.2809902	0.8229885	0.3063097	0.00000	0.5173426	0.1372130	0.1226801	0.3575152	0.1220664	0.0795662	0.6676726	0.1165080	0.1981263	0.3160745	0.0959664	0.1699854	0.2963109	0.1601637	0.5386107	0.0229454	0.3383377	0.3091468	0.4929381	0.0069476	1.0544361	0.3132572	0.6136998	0.5930033	0.3291763	0.4604039	1.2832479	0.7029085	0.7121212	0.1642577
50pmol	median	10.9683118	7.6184720	10.6629030	8.2827405	8.7328039	8.1702259	8.4708099	16.75777	12.4001796	13.8638704	9.5814615	9.4721898	10.3777288	8.5378538	7.0311799	9.8910321	7.0975242	8.5250934	10.5071517	14.6967905	13.3228123	14.2433512	10.0613581	10.2093348	8.6563070	7.2411414	11.5354444	15.1244369	7.9908823	7.6231715	8.4093360	10.1031519	6.6331386	9.3019021	8.5967555	7.8149531	6.5819142	6.8229624
50pmol	trimmed	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.2782600	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.8997407
50pmol	mad	0.1772529	2.3927468	0.0491467	0.2910204	0.0392287	0.2849493	0.2027294	0.00000	0.1405153	0.0254266	0.1140448	0.1623808	0.0777336	0.0742326	0.4317120	0.1344530	0.0915028	0.1415103	0.0364151	0.0146847	0.0962357	0.2056452	0.1292707	0.0294612	0.4002908	0.3181482	0.0885564	0.0089205	0.2820706	0.3397979	0.1646671	0.1801448	0.2580102	0.4729686	0.8120600	0.6061193	0.8559462	0.0117113
50pmol	min	10.7292010	6.0045864	7.7662064	6.4487561	8.1906821	7.8062907	7.9641396	16.75777	12.3054034	13.8467203	9.5045393	9.3626655	10.1894635	8.4296461	6.0045864	9.7537182	7.0358064	8.4296461	10.3290776	14.3606346	13.2579022	14.1046454	9.9116818	10.1894635	8.3863145	7.0265534	11.4757140	15.1184202	7.6371168	7.3078254	8.2982695	9.0120719	6.4591131	8.8665134	6.4487561	7.3321259	6.0045864	6.8071641
50pmol	max	11.5951749	10.0086064	10.7292010	8.6597927	8.7857227	9.6583837	8.6597927	16.75777	13.3877224	14.1306676	9.7537182	10.1202381	10.4825900	8.6205408	7.6089249	10.0086064	7.4557295	9.0992648	10.5317133	14.7066952	13.8810204	14.3820570	11.0786419	10.2292060	9.0120719	7.6371168	12.4949559	15.1304537	9.9548324	8.0176249	9.5817142	10.3290776	7.1458739	9.9116818	9.3626655	8.8665134	7.3078254	7.1458739
50pmol	range	0.8659739	4.0040200	2.9629945	2.2110366	0.5950406	1.8520929	0.6956531	0.00000	1.0823190	0.2839473	0.2491788	0.7575726	0.2931265	0.1908947	1.6043385	0.2548883	0.4199231	0.6696188	0.2026357	0.3460606	0.6231181	0.2774116	1.1669601	0.0397425	0.6257574	0.6105634	1.0192419	0.0120335	2.3177155	0.7097995	1.2834446	1.3170057	0.6867608	1.0451685	2.9139094	1.5343875	1.3032390	0.3387098
50pmol	skew	0.5345800	0.0433923	-0.7489636	-0.6758677	-0.7319474	0.6482699	-0.4719094	NaN	0.6863654	0.7197869	0.1859854	0.5810642	-0.3693859	-0.1673018	-0.3638798	-0.0569700	0.5757638	0.5855057	-0.6293395	-0.7433054	0.6602470	0.0000000	0.7076234	0.0000000	0.0207596	0.1085464	0.7215694	0.0000000	0.6913135	0.1018067	0.6878480	-0.6741805	0.3120345	0.1554845	-0.4939928	0.2926118	0.0140868	0.7453289
50pmol	kurtosis	-1.7865070	-2.3876770	-1.6881769	-1.7283357	-1.6985019	-1.7416477	-1.8858332	NaN	-1.7364638	-1.7097999	-2.2281384	-1.8279658	-1.8337864	-1.9347058	-1.8350942	-2.2311257	-1.8328016	-1.8239282	-1.7848527	-1.6921905	-1.7582380	-2.4375000	-1.7131106	-2.4375000	-2.4135931	-2.3139585	-1.7084245	-2.4375000	-1.7208848	-2.1366725	-1.7352502	-1.7295690	-2.0930099	-2.1113622	-1.8670313	-2.0889369	-2.4212634	-1.6904792
50pmol	se	0.1854147	1.0557771	0.7298663	0.4979530	0.1404951	0.4114942	0.1531548	0.00000	0.2586713	0.0686065	0.0613401	0.1787576	0.0610332	0.0397831	0.3338363	0.0582540	0.0990632	0.1580372	0.0479832	0.0849927	0.1481555	0.0800818	0.2693054	0.0114727	0.1691689	0.1545734	0.2464691	0.0034738	0.5272181	0.1566286	0.3068499	0.2965017	0.1645881	0.2302020	0.6416239	0.3514543	0.3560606	0.0821289

The column “Stat” in the generated result includes the following statistics:

n: Number.
mean: Mean.
sd: Standard deviation.
median: Median.
trimmed: Trimmed mean with a trim of 0.1.
mad: Median absolute deviation (from the median).
min: Minimum.
max: Maximum.
range: The difference between the maximum and minimum value.
skew: Skewness.
kurtosis: Kurtosis.
se: Standard error.

Analysis

Usage

analyze.t(dataSet,                # dataset of experimental values
          ref = NULL,             # which level of condition to use as reference
          adjust.method = "none", # what method of p-value adjustment to use
          paired = FALSE,         # are the data paired?
          pool.sd = FALSE)        # used pooled standard deviation?

analyze.mod_t(dataSet,                # dataset of experimental values
              ref = NULL,             # which level of condition to use as reference
              adjust.method = "none") # what method of p-value adjustment to use

analyze.wilcox(dataSet,                # dataset of experimental values
               ref = NULL,             # which level of condition to use as reference
               adjust.method = "none", # what method of p-value adjustment to use
               paired = FALSE)         # are the data paired?

analyze.ma(dataSet,    # dataset of experimental values
           ref = NULL) # which level of condition to use as reference

analyze.pca(dataSet,       # dataset of experimental values
            center = TRUE, # should data be centered before PCA?
            scale = TRUE)  # should data be scaled before PCA?

Details & Examples

The functions in the analyze module calculate the results that can be used in subsequent visualizations.

Note: The following analyses compare all other conditions against the reference condition, which is specified by the argument ref, for multiple comparisons. If ref is not provided, it will be automatically generated by all the combinations of two conditions, based on the level attributes of the condition.
For example, suppose there are three conditions in the data: “A”, “B”, and “C”. If you specify ref = "A", then the result includes two comparisons: “B-A” and “C-A”. If ref = NULL, there will be three comparisons: “A-B”, “A-C”, and “B-C”.

ref <- "50pmol"

Student’s t-test

The Student’s t-test is used to compare the means between two conditions for each protein, reporting both the difference in means between the conditions and the p-value of the test.

The argument adjust.method is used to specify the testing correction procedure to be applied to p-values. This adjustment is very common in DNA or RNA-Seq analyses, where datasets are very large and where researchers are most interested in controlling the Type I error rate when conducting multiple comparisons.

However, for mass spectrometry-based proteomics results, the dataset sizes are smaller than in sequencing analyses, and testing corrections can be too harsh of a threshold to apply. Most often, applying any testing correction to proteomics data results in there being zero significant changes. This does not mean that nothing is meaningfully changing in your dataset. It does mean that these corrections are usually not a useful tool for finding biologically-relevant changes in your dataset.

Also keep in mind that reducing Type I error typically comes at the cost of increasing Type II error, and vice versa. There is no way to eliminate all error; each researcher must decide whether they are more comfortable with having more false positives or more false negatives in the dataset, and choose their analysis strategies accordingly.

UConn PMF recommends not applying testing corrections to your proteomics dataset, but if you would like to explore the effects of doing so, several methods are provided below:

“BH” or its alias “fdr”: Benjamini and Hochberg (1995).
“BY”: Benjamini and Yekutieli (2001).
“bonferroni”: Bonferroni (1936).
“hochberg”: Hochberg (1988).
“holm”: Holm (1979).
“hommel”: Hommel (1988).

Each method offers its own balance between statistical power and error control. The default value "none" indicates that no correction is applied.

anlys_t <- analyze.t(dataImput, ref = ref, adjust.method = "none")
#> Data are essentially constant.
#> Data are essentially constant.

Note: In the Student’s t-test, a warning message might appear, stating “Data are essentially constant,” which means that the data contain proteins with the same value in all samples. In this case, the p-value of t-test returns NaN.

#> $`100pmol-50pmol`

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
100pmol mean	10.6532858	12.0862074	10.6232407	8.2644365	8.8486965	6.8764321	8.1789684	16.75777	13.0941802	14.0233390	9.0659703	10.0672927	9.9408634	7.9781022	7.8297313	9.8230915	7.4096023	8.5986327	10.0905795	15.1604531	13.6952175	14.3757288	8.892464	9.8735187	8.2974346	6.7681823	11.7142153	14.7560796	6.3848929	7.9228836	9.6214990	10.6043210	7.0043419	10.0418355	8.6778910	6.8092287	7.8188307	7.5025180
50pmol mean	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.278260	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.8997407
difference	-0.4119641	4.2736732	0.6679373	0.3459290	0.2381933	-1.5748494	-0.2124196	0.00000	0.4708090	0.0970569	-0.5393248	0.4604719	-0.4160144	-0.5533715	0.9107635	-0.0630057	0.2379563	-0.0461417	-0.3781941	0.5452255	0.2490808	0.1323776	-1.385796	-0.3358160	-0.3803155	-0.5183059	-0.0461744	-0.3683573	-2.0085355	0.2799353	0.9468351	0.7174576	0.2865258	0.6963357	0.4266578	-1.1479077	1.1997707	0.6027773
p-value	0.2425281	0.0223332	0.4450987	0.5455559	0.1909446	0.0348158	0.3685157	NaN	0.1922051	0.3275609	0.0005957	0.0767642	0.0683551	0.0041165	0.0651420	0.8119403	0.2805476	0.8086313	0.0024306	0.0076435	0.2145975	0.2343297	0.009692	0.0658082	0.1068935	0.0481610	0.8918516	0.0000000	0.0232926	0.3007562	0.0633640	0.0942018	0.1959323	0.0503672	0.5680738	0.0436514	0.1120976	0.0022576

#> $`200pmol-50pmol`

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
200pmol mean	10.6488941	9.9995022	11.2485095	8.4505628	8.2445037	6.7465842	8.3091726	16.75777	13.2471248	13.7426617	9.2570565	10.1413903	9.9978891	8.0880583	7.5631150	10.0415877	7.5210813	8.4008606	10.2414149	15.1516556	14.1141044	14.7415973	8.8133014	10.1732192	8.4520910	7.0971425	12.1775775	14.4198014	6.1065048	7.4951113	9.9165721	10.5958169	6.7744045	9.4079382	8.8671576	7.5851679	7.2449244	8.1196440
50pmol mean	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.2782600	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.8997407
difference	-0.4163558	2.1869680	1.2932062	0.5320553	-0.3659995	-1.7046973	-0.0822154	0.00000	0.6237535	-0.1836205	-0.3482386	0.5345694	-0.3589887	-0.4434153	0.6441472	0.1554905	0.3494352	-0.2439139	-0.2273586	0.5364280	0.6679676	0.4982461	-1.4649585	-0.0361156	-0.2256591	-0.1893458	0.4171878	-0.7046356	-2.2869237	-0.1478371	1.2419082	0.7089535	0.0565885	0.0624383	0.6159244	-0.3719685	0.6258644	1.2199033
p-value	0.1446884	0.1826838	0.1875421	0.3662078	0.3927857	0.0181442	0.6911235	NaN	0.1771520	0.2818360	0.0319217	0.0503531	0.0448461	0.0166531	0.1885601	0.0812056	0.0303572	0.2404181	0.1646796	0.0073303	0.0124946	0.0064381	0.0058656	0.7824826	0.3171743	0.3158950	0.3983925	0.0000458	0.0205236	0.5114326	0.0210858	0.2748735	0.8040085	0.8612941	0.4109857	0.4319860	0.1765836	0.0465999

#> $total

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
100pmol mean	10.6532858	12.0862074	10.6232407	8.2644365	8.8486965	6.8764321	8.1789684	16.75777	13.0941802	14.0233390	9.0659703	10.0672927	9.9408634	7.9781022	7.8297313	9.8230915	7.4096023	8.5986327	10.0905795	15.1604531	13.6952175	14.3757288	8.8924636	9.8735187	8.2974346	6.7681823	11.7142153	14.7560796	6.3848929	7.9228836	9.6214990	10.6043210	7.0043419	10.0418355	8.6778910	6.8092287	7.8188307	7.5025180
200pmol mean	10.6488941	9.9995022	11.2485095	8.4505628	8.2445037	6.7465842	8.3091726	16.75777	13.2471248	13.7426617	9.2570565	10.1413903	9.9978891	8.0880583	7.5631150	10.0415877	7.5210813	8.4008606	10.2414149	15.1516556	14.1141044	14.7415973	8.8133014	10.1732192	8.4520910	7.0971425	12.1775775	14.4198014	6.1065048	7.4951113	9.9165721	10.5958169	6.7744045	9.4079382	8.8671576	7.5851679	7.2449244	8.1196440
50pmol mean	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.2782600	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.8997407
100pmol-50pmol: difference	-0.4119641	4.2736732	0.6679373	0.3459290	0.2381933	-1.5748494	-0.2124196	0.00000	0.4708090	0.0970569	-0.5393248	0.4604719	-0.4160144	-0.5533715	0.9107635	-0.0630057	0.2379563	-0.0461417	-0.3781941	0.5452255	0.2490808	0.1323776	-1.3857964	-0.3358160	-0.3803155	-0.5183059	-0.0461744	-0.3683573	-2.0085355	0.2799353	0.9468351	0.7174576	0.2865258	0.6963357	0.4266578	-1.1479077	1.1997707	0.6027773
100pmol-50pmol: p-value	0.2425281	0.0223332	0.4450987	0.5455559	0.1909446	0.0348158	0.3685157	NaN	0.1922051	0.3275609	0.0005957	0.0767642	0.0683551	0.0041165	0.0651420	0.8119403	0.2805476	0.8086313	0.0024306	0.0076435	0.2145975	0.2343297	0.0096920	0.0658082	0.1068935	0.0481610	0.8918516	0.0000000	0.0232926	0.3007562	0.0633640	0.0942018	0.1959323	0.0503672	0.5680738	0.0436514	0.1120976	0.0022576
200pmol-50pmol: difference	-0.4163558	2.1869680	1.2932062	0.5320553	-0.3659995	-1.7046973	-0.0822154	0.00000	0.6237535	-0.1836205	-0.3482386	0.5345694	-0.3589887	-0.4434153	0.6441472	0.1554905	0.3494352	-0.2439139	-0.2273586	0.5364280	0.6679676	0.4982461	-1.4649585	-0.0361156	-0.2256591	-0.1893458	0.4171878	-0.7046356	-2.2869237	-0.1478371	1.2419082	0.7089535	0.0565885	0.0624383	0.6159244	-0.3719685	0.6258644	1.2199033
200pmol-50pmol: p-value	0.1446884	0.1826838	0.1875421	0.3662078	0.3927857	0.0181442	0.6911235	NaN	0.1771520	0.2818360	0.0319217	0.0503531	0.0448461	0.0166531	0.1885601	0.0812056	0.0303572	0.2404181	0.1646796	0.0073303	0.0124946	0.0064381	0.0058656	0.7824826	0.3171743	0.3158950	0.3983925	0.0000458	0.0205236	0.5114326	0.0210858	0.2748735	0.8040085	0.8612941	0.4109857	0.4319860	0.1765836	0.0465999

Empirical Bayes moderated t-test

The main distinction between the Student’s and empirical Bayes moderated t-tests (Smyth 2004) lies in how variance is computed. While the Student’s t-test calculates variance based on the data available for each protein individually (which will be limited by the number of replicates included for each condition), the moderated t-test utilizes information from all replicates of every protein in the current dataset to calculate variance.

anlys_modt <- analyze.mod_t(dataImput, ref = ref, adjust.method = "none")
#> Warning: Zero sample variances detected, have been offset away from zero

Note: In the moderated t-test, a warning message might occur stating, “Zero sample variances detected, have been offset away from zero.” This warning corresponds to examples of proteins that exhibited identical quant values, either pre- or post-imputation, and therefore no variance is present across conditions for those proteins. This does not impede downstream analysis; it merely serves to alert users to its occurrence.

#> $`100pmol-50pmol`

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
100pmol mean	10.6532858	12.0862074	10.6232407	8.2644365	8.8486965	6.8764321	8.1789684	16.75777	13.094180	14.0233390	9.0659703	10.0672927	9.9408634	7.9781022	7.8297313	9.8230915	7.4096023	8.5986327	10.0905795	15.1604531	13.6952175	14.3757288	8.8924636	9.8735187	8.2974346	6.7681823	11.7142153	14.7560796	6.3848929	7.9228836	9.6214990	10.6043210	7.0043419	10.0418355	8.6778910	6.8092287	7.818831	7.5025180
50pmol mean	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.623371	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.2782600	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.619060	6.8997407
difference	-0.4119641	4.2736732	0.6679373	0.3459290	0.2381933	-1.5748494	-0.2124196	0.00000	0.470809	0.0970569	-0.5393248	0.4604719	-0.4160144	-0.5533715	0.9107635	-0.0630057	0.2379563	-0.0461417	-0.3781941	0.5452255	0.2490808	0.1323776	-1.3857964	-0.3358160	-0.3803155	-0.5183059	-0.0461744	-0.3683573	-2.0085355	0.2799353	0.9468351	0.7174576	0.2865258	0.6963357	0.4266578	-1.1479077	1.199771	0.6027773
p-value	0.1692657	0.0041928	0.3594271	0.4188776	0.4476151	0.0077729	0.3082409	1.00000	0.205110	0.4719321	0.0003809	0.0210540	0.0273503	0.0007011	0.0290176	0.7547940	0.1763304	0.7787863	0.0090768	0.0000260	0.1367383	0.1511826	0.0002994	0.0325244	0.0523968	0.0126619	0.9082025	0.0000018	0.0015443	0.2394099	0.0184304	0.1590979	0.1596566	0.0381075	0.4486484	0.0171955	0.036571	0.0782396

#> $`200pmol-50pmol`

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
200pmol mean	10.6488941	9.9995022	11.2485095	8.4505628	8.2445037	6.7465842	8.3091726	16.75777	13.2471248	13.7426617	9.2570565	10.1413903	9.9978891	8.0880583	7.5631150	10.0415877	7.5210813	8.4008606	10.2414149	15.1516556	14.1141044	14.7415973	8.813301	10.1732192	8.4520910	7.0971425	12.1775775	14.4198014	6.1065048	7.4951113	9.9165721	10.5958169	6.7744045	9.4079382	8.8671576	7.5851679	7.2449244	8.1196440
50pmol mean	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.278260	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.8997407
difference	-0.4163558	2.1869680	1.2932062	0.5320553	-0.3659995	-1.7046973	-0.0822154	0.00000	0.6237535	-0.1836205	-0.3482386	0.5345694	-0.3589887	-0.4434153	0.6441472	0.1554905	0.3494352	-0.2439139	-0.2273586	0.5364280	0.6679676	0.4982461	-1.464959	-0.0361156	-0.2256591	-0.1893458	0.4171878	-0.7046356	-2.2869237	-0.1478371	1.2419082	0.7089535	0.0565885	0.0624383	0.6159244	-0.3719685	0.6258644	1.2199033
p-value	0.1651853	0.0881485	0.0926706	0.2239144	0.2525044	0.0049216	0.6864892	1.00000	0.1028334	0.1877393	0.0069376	0.0099405	0.0500060	0.0031304	0.1016953	0.4466320	0.0584428	0.1582493	0.0809141	0.0000298	0.0014873	0.0001655	0.000195	0.7948930	0.2207008	0.2936324	0.3105040	0.0000000	0.0006214	0.5236720	0.0042098	0.1636145	0.7701943	0.8346277	0.2815464	0.3767021	0.2366530	0.0026681

#> $total

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
100pmol mean	10.6532858	12.0862074	10.6232407	8.2644365	8.8486965	6.8764321	8.1789684	16.75777	13.0941802	14.0233390	9.0659703	10.0672927	9.9408634	7.9781022	7.8297313	9.8230915	7.4096023	8.5986327	10.0905795	15.1604531	13.6952175	14.3757288	8.8924636	9.8735187	8.2974346	6.7681823	11.7142153	14.7560796	6.3848929	7.9228836	9.6214990	10.6043210	7.0043419	10.0418355	8.6778910	6.8092287	7.8188307	7.5025180
200pmol mean	10.6488941	9.9995022	11.2485095	8.4505628	8.2445037	6.7465842	8.3091726	16.75777	13.2471248	13.7426617	9.2570565	10.1413903	9.9978891	8.0880583	7.5631150	10.0415877	7.5210813	8.4008606	10.2414149	15.1516556	14.1141044	14.7415973	8.8133014	10.1732192	8.4520910	7.0971425	12.1775775	14.4198014	6.1065048	7.4951113	9.9165721	10.5958169	6.7744045	9.4079382	8.8671576	7.5851679	7.2449244	8.1196440
50pmol mean	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.2782600	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.8997407
100pmol-50pmol: difference	-0.4119641	4.2736732	0.6679373	0.3459290	0.2381933	-1.5748494	-0.2124196	0.00000	0.4708090	0.0970569	-0.5393248	0.4604719	-0.4160144	-0.5533715	0.9107635	-0.0630057	0.2379563	-0.0461417	-0.3781941	0.5452255	0.2490808	0.1323776	-1.3857964	-0.3358160	-0.3803155	-0.5183059	-0.0461744	-0.3683573	-2.0085355	0.2799353	0.9468351	0.7174576	0.2865258	0.6963357	0.4266578	-1.1479077	1.1997707	0.6027773
100pmol-50pmol: p-value	0.1692657	0.0041928	0.3594271	0.4188776	0.4476151	0.0077729	0.3082409	1.00000	0.2051100	0.4719321	0.0003809	0.0210540	0.0273503	0.0007011	0.0290176	0.7547940	0.1763304	0.7787863	0.0090768	0.0000260	0.1367383	0.1511826	0.0002994	0.0325244	0.0523968	0.0126619	0.9082025	0.0000018	0.0015443	0.2394099	0.0184304	0.1590979	0.1596566	0.0381075	0.4486484	0.0171955	0.0365710	0.0782396
200pmol-50pmol: difference	-0.4163558	2.1869680	1.2932062	0.5320553	-0.3659995	-1.7046973	-0.0822154	0.00000	0.6237535	-0.1836205	-0.3482386	0.5345694	-0.3589887	-0.4434153	0.6441472	0.1554905	0.3494352	-0.2439139	-0.2273586	0.5364280	0.6679676	0.4982461	-1.4649585	-0.0361156	-0.2256591	-0.1893458	0.4171878	-0.7046356	-2.2869237	-0.1478371	1.2419082	0.7089535	0.0565885	0.0624383	0.6159244	-0.3719685	0.6258644	1.2199033
200pmol-50pmol: p-value	0.1651853	0.0881485	0.0926706	0.2239144	0.2525044	0.0049216	0.6864892	1.00000	0.1028334	0.1877393	0.0069376	0.0099405	0.0500060	0.0031304	0.1016953	0.4466320	0.0584428	0.1582493	0.0809141	0.0000298	0.0014873	0.0001655	0.0001950	0.7948930	0.2207008	0.2936324	0.3105040	0.0000000	0.0006214	0.5236720	0.0042098	0.1636145	0.7701943	0.8346277	0.2815464	0.3767021	0.2366530	0.0026681

Wilcoxon test

The Wilcoxon test is a non-parametric alternative to the two-sample t-test. If paired = TRUE, a Wilcoxon signed-rank test is performed to test the null hypothesis that the distribution of the difference between the two conditions for the protein is symmetric about zero. If paired = FALSE, a Wilcoxon rank-sum test (also known as Mann-Whitney test) is performed to test the null hypothesis that the distribution of the two conditions for the protein are the same.

anlys_wilcox <- analyze.wilcox(dataImput, ref = ref, adjust.method = "none")

Note: In the Wilcoxon test, the warning message “cannot compute exact p-value with ties.” may be displayed. This warning means that some values with tied rankings and the sample size is lower than 50, which prevents the exact p-value from being calculated. In such cases, a normal approximation is used. If all samples for a protein have the same value, the corresponding p-value returns NaN.

#> $`100pmol-50pmol`

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
100pmol mean	10.6532858	12.086207	10.6232407	8.2644365	8.8486965	6.8764321	8.1789684	16.75777	13.0941802	14.0233390	9.0659703	10.0672927	9.9408634	7.9781022	7.8297313	9.8230915	7.4096023	8.5986327	10.0905795	15.1604531	13.6952175	14.3757288	8.8924636	9.8735187	8.2974346	6.7681823	11.7142153	14.7560796	6.384893	7.9228836	9.6214990	10.6043210	7.0043419	10.0418355	8.6778910	6.8092287	7.8188307	7.5025180
50pmol mean	11.0652499	7.812534	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.2782600	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.393428	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.8997407
difference	-0.4119641	4.273673	0.6679373	0.3459290	0.2381933	-1.5748494	-0.2124196	0.00000	0.4708090	0.0970569	-0.5393248	0.4604719	-0.4160144	-0.5533715	0.9107635	-0.0630057	0.2379563	-0.0461417	-0.3781941	0.5452255	0.2490808	0.1323776	-1.3857964	-0.3358160	-0.3803155	-0.5183059	-0.0461744	-0.3683573	-2.008535	0.2799353	0.9468351	0.7174576	0.2865258	0.6963357	0.4266578	-1.1479077	1.1997707	0.6027773
p-value	0.1912670	0.029401	0.1885823	0.8857143	0.1912670	0.0571429	0.6572552	NaN	0.1885823	0.1885823	0.0284295	0.1885823	0.0590719	0.0294010	0.0294010	0.6857143	0.6631172	0.8845494	0.0294010	0.0255801	0.1885823	0.1059111	0.0285714	0.0284295	0.1102102	0.1102102	0.6611967	0.0246533	0.029401	0.3428571	0.0590719	0.1102102	0.3094241	0.1142857	0.6631172	0.0285714	0.1102102	0.0284295

#> $`200pmol-50pmol`

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
200pmol mean	10.6488941	9.9995022	11.2485095	8.4505628	8.2445037	6.7465842	8.3091726	16.75777	13.2471248	13.7426617	9.2570565	10.1413903	9.9978891	8.0880583	7.5631150	10.0415877	7.5210813	8.4008606	10.2414149	15.1516556	14.1141044	14.7415973	8.813301	10.1732192	8.4520910	7.0971425	12.1775775	14.4198014	6.1065048	7.4951113	9.916572	10.5958169	6.7744045	9.4079382	8.8671576	7.5851679	7.2449244	8.119644
50pmol mean	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.278260	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.674664	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.899741
difference	-0.4163558	2.1869680	1.2932062	0.5320553	-0.3659995	-1.7046973	-0.0822154	0.00000	0.6237535	-0.1836205	-0.3482386	0.5345694	-0.3589887	-0.4434153	0.6441472	0.1554905	0.3494352	-0.2439139	-0.2273586	0.5364280	0.6679676	0.4982461	-1.464959	-0.0361156	-0.2256591	-0.1893458	0.4171878	-0.7046356	-2.2869237	-0.1478371	1.241908	0.7089535	0.0565885	0.0624383	0.6159244	-0.3719685	0.6258644	1.219903
p-value	0.2425256	0.3094241	0.2425256	0.1831502	0.6572552	0.0285714	0.8845494	NaN	0.1858767	0.5589857	0.0530079	0.1440506	0.0575470	0.0285714	0.3428571	0.0814291	0.0795941	0.3778216	0.1831502	0.0274686	0.0396087	0.0265187	0.029401	1.0000000	0.2817179	0.4595974	0.3035251	0.0265187	0.0265187	0.7715034	0.029401	0.2000000	1.0000000	1.0000000	0.6631172	0.6857143	0.2425256	0.029401

#> $total

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
100pmol mean	10.6532858	12.0862074	10.6232407	8.2644365	8.8486965	6.8764321	8.1789684	16.75777	13.0941802	14.0233390	9.0659703	10.0672927	9.9408634	7.9781022	7.8297313	9.8230915	7.4096023	8.5986327	10.0905795	15.1604531	13.6952175	14.3757288	8.8924636	9.8735187	8.2974346	6.7681823	11.7142153	14.7560796	6.3848929	7.9228836	9.6214990	10.6043210	7.0043419	10.0418355	8.6778910	6.8092287	7.8188307	7.5025180
200pmol mean	10.6488941	9.9995022	11.2485095	8.4505628	8.2445037	6.7465842	8.3091726	16.75777	13.2471248	13.7426617	9.2570565	10.1413903	9.9978891	8.0880583	7.5631150	10.0415877	7.5210813	8.4008606	10.2414149	15.1516556	14.1141044	14.7415973	8.8133014	10.1732192	8.4520910	7.0971425	12.1775775	14.4198014	6.1065048	7.4951113	9.9165721	10.5958169	6.7744045	9.4079382	8.8671576	7.5851679	7.2449244	8.1196440
50pmol mean	11.0652499	7.8125342	9.9553033	7.9185075	8.6105032	8.4512816	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.2782600	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.3934284	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.8997407
100pmol-50pmol: difference	-0.4119641	4.2736732	0.6679373	0.3459290	0.2381933	-1.5748494	-0.2124196	0.00000	0.4708090	0.0970569	-0.5393248	0.4604719	-0.4160144	-0.5533715	0.9107635	-0.0630057	0.2379563	-0.0461417	-0.3781941	0.5452255	0.2490808	0.1323776	-1.3857964	-0.3358160	-0.3803155	-0.5183059	-0.0461744	-0.3683573	-2.0085355	0.2799353	0.9468351	0.7174576	0.2865258	0.6963357	0.4266578	-1.1479077	1.1997707	0.6027773
100pmol-50pmol: p-value	0.1912670	0.0294010	0.1885823	0.8857143	0.1912670	0.0571429	0.6572552	NaN	0.1885823	0.1885823	0.0284295	0.1885823	0.0590719	0.0294010	0.0294010	0.6857143	0.6631172	0.8845494	0.0294010	0.0255801	0.1885823	0.1059111	0.0285714	0.0284295	0.1102102	0.1102102	0.6611967	0.0246533	0.0294010	0.3428571	0.0590719	0.1102102	0.3094241	0.1142857	0.6631172	0.0285714	0.1102102	0.0284295
200pmol-50pmol: difference	-0.4163558	2.1869680	1.2932062	0.5320553	-0.3659995	-1.7046973	-0.0822154	0.00000	0.6237535	-0.1836205	-0.3482386	0.5345694	-0.3589887	-0.4434153	0.6441472	0.1554905	0.3494352	-0.2439139	-0.2273586	0.5364280	0.6679676	0.4982461	-1.4649585	-0.0361156	-0.2256591	-0.1893458	0.4171878	-0.7046356	-2.2869237	-0.1478371	1.2419082	0.7089535	0.0565885	0.0624383	0.6159244	-0.3719685	0.6258644	1.2199033
200pmol-50pmol: p-value	0.2425256	0.3094241	0.2425256	0.1831502	0.6572552	0.0285714	0.8845494	NaN	0.1858767	0.5589857	0.0530079	0.1440506	0.0575470	0.0285714	0.3428571	0.0814291	0.0795941	0.3778216	0.1831502	0.0274686	0.0396087	0.0265187	0.0294010	1.0000000	0.2817179	0.4595974	0.3035251	0.0265187	0.0265187	0.7715034	0.0294010	0.2000000	1.0000000	1.0000000	0.6631172	0.6857143	0.2425256	0.0294010

MA

The result of method = "MA" is to generate the data for an MA plot, which plots the average fold change between two conditions (y-axis) against the average abundance of that protein (x-axis). This is helpful for evaluating whether a fold-change difference is being enhanced by low overall intensities (e.g. a change from 200 to 400 is the same fold-change as from 20,000 to 40,000, but the latter is a more robust measurement and less susceptible to noise interference).

anlys_MA <- analyze.ma(dataImput, ref = ref)

#> $`100pmol-50pmol`

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
100pmol mean	10.6532858	12.086207	10.6232407	8.264437	8.8486965	6.876432	8.1789684	16.75777	13.094180	14.0233390	9.0659703	10.0672927	9.9408634	7.9781022	7.8297313	9.8230915	7.4096023	8.5986327	10.0905795	15.1604531	13.6952175	14.3757288	8.892464	9.873519	8.2974346	6.7681823	11.7142153	14.7560796	6.384893	7.9228836	9.6214990	10.6043210	7.0043419	10.0418355	8.6778910	6.809229	7.818831	7.5025180
50pmol mean	11.0652499	7.812534	9.9553033	7.918507	8.6105032	8.451282	8.3913880	16.75777	12.623371	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.6152277	13.4461368	14.2433512	10.278260	10.209335	8.6777501	7.2864883	11.7603897	15.1244369	8.393428	7.6429483	8.6746639	9.8868633	6.7178161	9.3454998	8.2512331	7.957136	6.619060	6.8997407
A	10.8592679	9.949371	10.2892720	8.091472	8.7295998	7.663857	8.2851782	16.75777	12.858776	13.9748106	9.3356327	9.8370568	10.1488706	8.2547879	7.3743495	9.8545943	7.2906242	8.6217036	10.2796765	14.8878404	13.5706772	14.3095400	9.585362	10.041427	8.4875924	7.0273353	11.7373025	14.9402583	7.389161	7.7829160	9.1480815	10.2455921	6.8610790	9.6936677	8.4645621	7.383183	7.218945	7.2011293
M	-0.4119641	4.273673	0.6679373	0.345929	0.2381933	-1.574849	-0.2124196	0.00000	0.470809	0.0970569	-0.5393248	0.4604719	-0.4160144	-0.5533715	0.9107635	-0.0630057	0.2379563	-0.0461417	-0.3781941	0.5452255	0.2490808	0.1323776	-1.385796	-0.335816	-0.3803155	-0.5183059	-0.0461744	-0.3683573	-2.008535	0.2799353	0.9468351	0.7174576	0.2865258	0.6963357	0.4266578	-1.147908	1.199771	0.6027773

#> $`200pmol-50pmol`

	NUD4B_HUMAN	A0A7P0T808_HUMAN	A0A8I5KU53_HUMAN	ZN840_HUMAN	CC85C_HUMAN	C9JEV0_HUMAN	C9JNU9_HUMAN	ALBU_BOVIN	CYC_BOVIN	TRFE_BOVIN	F8W0H2_HUMAN	H0Y7V7_HUMAN	H0YD14_HUMAN	H3BUF6_HUMAN	H7C1W4_HUMAN	H7C3M7_HUMAN	TLR3_HUMAN	LRIG2_HUMAN	RAB3D_HUMAN	ADH1_YEAST	LYSC_CHICK	BGAL_ECOLI	CYTA_HUMAN	KPCB_HUMAN	LIPL_HUMAN	CO6_HUMAN	BGAL_HUMAN	SYTC_HUMAN	CASPE_HUMAN	DCAF6_HUMAN	DALD3_HUMAN	HGNAT_HUMAN	RFFL_HUMAN	RN185_HUMAN	ZN462_HUMAN	ALKB7_HUMAN	POLK_HUMAN	ACAD8_HUMAN
200pmol mean	10.6488941	9.999502	11.248510	8.4505628	8.2445037	6.746584	8.3091726	16.75777	13.2471248	13.7426617	9.2570565	10.1413903	9.9978891	8.0880583	7.5631150	10.0415877	7.5210813	8.4008606	10.2414149	15.151656	14.1141044	14.7415973	8.813301	10.1732192	8.4520910	7.0971425	12.1775775	14.4198014	6.106505	7.4951113	9.916572	10.5958169	6.7744045	9.4079382	8.8671576	7.5851679	7.2449244	8.119644
50pmol mean	11.0652499	7.812534	9.955303	7.9185075	8.6105032	8.451282	8.3913880	16.75777	12.6233713	13.9262822	9.6052951	9.6068208	10.3568778	8.5314736	6.9189678	9.8860972	7.1716461	8.6447744	10.4687736	14.615228	13.4461368	14.2433512	10.278260	10.2093348	8.6777501	7.2864883	11.7603897	15.1244369	8.393428	7.6429483	8.674664	9.8868633	6.7178161	9.3454998	8.2512331	7.9571364	6.6190600	6.899741
A	10.8570720	8.906018	10.601906	8.1845351	8.4275034	7.598933	8.3502803	16.75777	12.9352480	13.8344719	9.4311758	9.8741055	10.1773834	8.3097660	7.2410414	9.9638424	7.3463637	8.5228175	10.3550942	14.883442	13.7801206	14.4924743	9.545781	10.1912770	8.5649206	7.1918154	11.9689836	14.7721191	7.249967	7.5690298	9.295618	10.2413401	6.7461103	9.3767190	8.5591954	7.7711521	6.9319922	7.509692
M	-0.4163558	2.186968	1.293206	0.5320553	-0.3659995	-1.704697	-0.0822154	0.00000	0.6237535	-0.1836205	-0.3482386	0.5345694	-0.3589887	-0.4434153	0.6441472	0.1554905	0.3494352	-0.2439139	-0.2273586	0.536428	0.6679676	0.4982461	-1.464959	-0.0361156	-0.2256591	-0.1893458	0.4171878	-0.7046356	-2.286924	-0.1478371	1.241908	0.7089535	0.0565885	0.0624383	0.6159244	-0.3719685	0.6258644	1.219903

PCA

Principal component analysis (PCA) is a powerful technique used in data analysis to simplify and reduce the dimensionality of large datasets. It transforms original variables into uncorrelated components that capture the maximum variance. By selecting a subset of these components, PCA projects the data points onto these key directions, enabling visualization and analysis in a lower-dimensional space. This aids in identifying patterns and relationships within complex datasets.

For PCA, the arguments center and scale are used to center the data to zero mean and scale to unit variance, with default setting at TRUE.

Note: Data scaling is done to ensure that the scale differences between different features do not affect the results of PCA. If not scaled, features with larger scales will dominate the computation of principal components (PCs).
Note: The most common error message for the PCA is “Cannot rescale a constant/zero column to unit variance.” This clearly occurs when columns representing proteins contain only zeros or have constant values. Typically, there are two ways to address this error: one is to remove these proteins, and the other is to set scale = FALSE.

In the case of dataImput, one protein, namely “ALBU_BOVIN”, has constant values, leading to the error message. We choose to remove this protein in the PCA.

names(dataImput)[sapply(dataImput, function(col) length(unique(col)) == 1)]
#> [1] "ALBU_BOVIN"
dataPCA <- dataImput[, colnames(dataImput) != "ALBU_BOVIN"]
anlys_PCA <- analyze.pca(dataPCA, center = TRUE, scale = TRUE)

#> Standard deviations (1, .., p=12):

#>  [1] 3.581885e+00 2.285701e+00 2.092323e+00 1.907073e+00 1.649030e+00
#>  [6] 1.513033e+00 1.352933e+00 1.210993e+00 1.042803e+00 9.208106e-01
#> [11] 8.307195e-01 2.172933e-14

#> Rotation (n x k) = (37 x 12):

	PC1	PC2	PC3	PC4	PC5	PC6	PC7	PC8	PC9	PC10	PC11	PC12
NUD4B_HUMAN	0.1188769	-0.1772058	0.3013469	0.1166326	-0.1171230	-0.1140154	0.0852809	-0.0052137	0.2575418	-0.0874986	0.2653179	-0.0429097
A0A7P0T808_HUMAN	-0.1923637	0.2673578	-0.0130253	0.0704243	0.1390564	-0.1433468	-0.0613349	-0.0192361	-0.0110135	-0.1744425	0.0072387	-0.1217506
A0A8I5KU53_HUMAN	-0.1289231	-0.2613306	0.0670152	-0.3011696	-0.0801726	0.0031264	0.0131632	-0.0692313	-0.0814145	-0.0598182	-0.2562817	0.1205890
ZN840_HUMAN	-0.0946885	0.1276385	-0.1185999	-0.0799084	-0.2528080	-0.0920193	-0.4545132	0.1824215	-0.0575034	0.2849940	0.1804638	0.1408170
CC85C_HUMAN	0.0279395	0.1453424	0.2659663	-0.0335283	-0.2857038	0.2540437	0.3190762	-0.0065456	-0.0636165	0.0124279	-0.0867440	0.1434867
C9JEV0_HUMAN	0.2195074	0.0888077	-0.0259365	0.1759781	0.0826490	0.1627861	-0.1151784	0.2401487	0.1698661	-0.0474144	0.0727410	0.1155364
C9JNU9_HUMAN	0.0506167	-0.1477131	0.0540367	-0.3813773	0.0086479	-0.0297355	0.2233856	0.1898863	-0.1766131	-0.1663948	0.3947327	-0.2027905
CYC_BOVIN	-0.1529865	-0.2605458	0.1716647	0.2067954	-0.0597879	0.0664754	0.0508447	-0.0598310	-0.0361334	-0.0545473	0.1969247	0.0150975
TRFE_BOVIN	0.0166056	-0.0168069	0.2563064	0.2120388	0.3158137	0.0055484	-0.1816376	-0.3163176	-0.1781309	0.0867631	0.1887143	0.1720558
F8W0H2_HUMAN	0.2324368	-0.2037737	-0.0520720	-0.0262713	-0.0057226	0.0016452	-0.1483219	-0.0509623	-0.1060480	0.1500850	0.0055537	-0.2274161
H0Y7V7_HUMAN	-0.1988603	0.0693049	-0.1140835	0.1625452	-0.2104495	0.2552643	-0.0405976	0.1477780	-0.0798505	-0.0086801	0.0640366	0.1890137
H0YD14_HUMAN	0.1759816	-0.1299604	0.1545766	0.1813077	-0.1487485	-0.1570639	-0.2451852	0.1617803	-0.0081617	-0.1506331	-0.0924136	-0.3278828
H3BUF6_HUMAN	0.2391701	-0.0562345	-0.0842242	-0.0083829	0.2109445	-0.0478691	-0.0051651	0.1629583	0.0829339	0.0852264	-0.2389092	-0.2424864
H7C1W4_HUMAN	-0.1571658	0.1583107	-0.0869895	0.1324719	0.0195078	0.4070862	0.0582437	-0.1056046	0.2157584	-0.0357477	-0.0274390	-0.1195775
H7C3M7_HUMAN	-0.0268758	-0.1273659	-0.1694855	-0.1822225	0.2810524	0.1921604	0.2311994	0.2416035	-0.0435478	0.3395591	0.3337349	0.0905416
TLR3_HUMAN	-0.1357144	0.1117065	-0.2380256	0.1189789	-0.1890502	-0.1435597	0.1431369	-0.3576237	-0.0887085	0.0761335	0.1526797	-0.2646218
LRIG2_HUMAN	0.0995903	0.1751785	0.1182372	-0.1571006	-0.1962309	0.3532671	-0.2061901	0.0078915	0.2643580	0.1422136	0.0010136	-0.1265372
RAB3D_HUMAN	0.2023449	-0.1505532	0.0064984	0.0888776	-0.2543353	-0.1184410	0.1012599	0.0188862	-0.0559570	0.3159153	-0.1320459	0.1380167
ADH1_YEAST	-0.2668869	0.0081800	-0.0189212	0.1051980	0.0259162	-0.0238403	0.0829892	0.0157430	-0.0507490	-0.0663319	-0.1758915	0.2204639
LYSC_CHICK	-0.1849233	-0.2564786	-0.0640937	0.1743153	-0.0146319	-0.0206389	0.0603589	-0.0655267	0.2384278	-0.0317316	0.1313226	0.0535367
BGAL_ECOLI	-0.1785988	-0.1870954	-0.2022390	0.1424334	-0.0116692	0.0681519	0.1151700	0.1944421	0.2250153	0.0457059	0.1041234	-0.0961512
CYTA_HUMAN	0.2559654	0.0494146	0.0060060	0.1543365	-0.0138220	-0.0088571	-0.0246560	0.1585144	-0.0276568	0.1505360	0.0409124	0.0159581
KPCB_HUMAN	0.1253089	-0.0808968	-0.1725579	0.0478656	-0.2166705	-0.3758153	0.1601399	-0.1157743	0.2303199	0.2291294	-0.0922683	0.3094787
LIPL_HUMAN	0.1525946	-0.1743527	-0.0549491	-0.0480936	0.2202094	0.1896217	-0.1268498	-0.2373904	0.4209711	0.0006371	0.0513403	0.1108841
CO6_HUMAN	0.1695935	-0.1593740	-0.2012358	-0.1658137	-0.0579254	0.0942043	0.0302497	-0.3395673	0.1237697	-0.0386718	-0.0644920	-0.0069248
BGAL_HUMAN	-0.0549373	-0.3398065	0.1489976	0.1531309	-0.1653884	0.1216312	0.0174027	0.2036603	0.0402230	-0.0405328	-0.0633152	0.0677152
SYTC_HUMAN	0.2388616	0.1058242	0.1738849	0.0106153	0.0994714	0.0475180	-0.0305461	-0.0496722	-0.1463871	-0.1240736	0.0653873	0.4412066
CASPE_HUMAN	0.2394326	0.0905518	-0.0019253	0.2112729	-0.0194369	0.0094043	-0.0243169	0.0389020	-0.0373315	-0.0237263	0.2765286	0.0479283
DCAF6_HUMAN	-0.0381553	0.1591755	0.2198207	-0.1190328	0.3275744	-0.2690164	0.1868800	0.0709852	0.1486062	0.1974166	0.0071470	0.0560832
DALD3_HUMAN	-0.2266936	-0.0794761	0.0331701	-0.0648178	0.0502899	-0.1128253	-0.1004736	0.3813898	0.0760953	-0.0233357	-0.1372768	0.0957882
HGNAT_HUMAN	-0.1494193	-0.2325232	0.1921014	-0.0404533	-0.0723579	0.0148836	-0.2920131	-0.1432496	-0.2087894	0.0202744	0.1469799	-0.0267353
RFFL_HUMAN	-0.0806236	-0.1075798	0.1876089	0.2489770	0.2560138	0.1844149	0.0279090	0.0055965	-0.1577143	0.3487386	-0.3588672	-0.1701846
RN185_HUMAN	-0.0969748	0.0449635	0.3444740	-0.1393251	-0.1458840	0.0858452	0.1314834	-0.1094733	0.0092404	0.4236179	0.0600972	-0.1669904
ZN462_HUMAN	-0.1012649	-0.1599062	0.0717821	-0.3698936	0.0658706	0.0399805	-0.2972022	-0.0645871	0.1494307	-0.0475510	-0.0870126	0.1292038
ALKB7_HUMAN	0.1400932	-0.2796786	-0.1450878	0.1117422	0.1001606	0.1285143	0.1473570	0.0045354	-0.2482360	-0.1677388	-0.0951268	0.0526540
POLK_HUMAN	-0.1657505	0.0162727	0.2882023	0.1061381	0.0512940	-0.2214101	0.0338060	0.0168708	0.3273576	-0.0500830	0.0379469	-0.0494715
ACAD8_HUMAN	-0.1876209	-0.1271427	-0.2161112	0.1170599	0.1377752	-0.0681053	-0.1983427	-0.0601201	-0.0648194	0.2567213	0.1180605	0.0174867

Visualization

There is a wide variety of plotting options in msDiaLogue. What plots are possible depend on what data you have and what analyses have been run. See below for more details of individual plot types.

List of some of the plot options:

Boxplots
Heatmaps
MA plots
UpSet plots
Venn diagrams
Volcano plots

Usage

visualize.boxplot(dataSet) # dataset of experimental values

visualize.heatmap(dataSet,              # dataset of experimental values
                  pkg = "pheatmap",     # package option for heatmap plotting
                  cluster_cols = TRUE,  # cluster heatmap by columns?
                  cluster_rows = FALSE, # cluster heatmap by rows?
                  show_colnames = TRUE, # display heatmap columns?
                  show_rownames = TRUE) # display heatmap rows?

visualize.ma(dataSet,     # output from the function analyze.ma()
             M.thres = 1) # threshold by fold-change

visualize.rank(dataSet,   # dataset of experimental values
               listName,  # a character vector of proteins to highlight
               regexName, # a character vector specifying proteins for regular expression pattern matching to highlight
               facet,     # grouping variables for faceting
               color)     # the color used to highlight proteins

visualize.test(dataSet) # output from the function analyze.mod_t(), analyze.t(), or analyze.wilcox()

visualize.upset(dataSet) # dataset of experimental values

visualize.venn(dataSet,                                                   # dataset of experimental values
               show_percentage = TRUE,                                    # should % be shown in Venn diagram?
               fill_color = c("blue", "yellow", "green", "red"),          # colors to use for Venn diagram
               saveVenn = TRUE,                                           # should the Venn diagram be exported?
               proteinInformation = "preprocess_protein_information.csv") # name of file containing protein information

visualize.volcano(dataSet,        # output from the function analyze.mod_t(), analyze.t(), or analyze.wilcox()
                  P.thres = 0.05, # significance level for volcano plot graphing
                  F.thres = 1).   # fold-change threshold for volcano plot graphing

visualize.scree(dataSet,                 # output from the function analyze.pca()
                type = c("bar", "line"), # type of image to produce
                bar.color = "gray",      # color of the bar outline in the bar plot
                bar.fill = "gray",       # fill color of the bars in the bar plot
                line.color = "black",    # color of the line and point in the line plot
                label = TRUE,            # should labels be added to plot?
                ncp = 10)                # number of components

visualize.ind(dataSet,              # output from the function analyze.pca()
              label = TRUE,         # should labels be added to plot?
              ellipse = TRUE,       # should ellipses be added?
              ellipse.level = 0.95) # level for ellipses

visualize.var(dataSet,      # output from the function analyze.pca()
              label = TRUE) # should labels be added to plot?

visualize.biplot(dataSet,              # output from the function analyze.pca()
                 label = "all",        # what to label in the plot
                 ellipse = TRUE,       # should ellipses be added?
                 ellipse.level = 0.95) # level for ellipses

Details & Examples

This section provides a variety of options for getting a global view of your data, making comparisons, and highlighting trends.

Note: Data visualization is most effective when illustrating a point or answering a question you have about your data, not as a means to find a point/question.

heatmap

The package offers two options for plotting the heatmap.

Option 1 utilizes the source package pheatmap, capable of plotting the dendrogram simultaneously. It is the default choice for heatmaps in this package.

visualize.heatmap(dataImput, pkg = "pheatmap",
                  cluster_cols = TRUE, cluster_rows = TRUE,
                  show_colnames = TRUE, show_rownames = TRUE)

When protein names are excessively long, it is recommended to set show_rownames = FALSE to view the full heatmap.

Option 2 use the source package ggplot2 to generate a ggplot object but does not include the dendrogram.

visualize.heatmap(dataImput, pkg = "ggplot2")

In a heatmap, similar colors within a row indicate relatively consistent values, suggesting similar protein expression levels across different samples.

rank abundance plot

The rank abundance plot shows proteins ranked by abundance to visualize distribution patterns and highlight specific proteins across conditions or replicates.

visualize.rank(dataImput, listName = "POLK_HUMAN",
               facet = c("Replicate", "Condition"))

MA

An MA plot visualizes the differences between measurements taken in two samples, by transforming the data onto M (log ratio or fold change) and A (mean average) scales. The MA plot puts the variable M on the y-axis and A on the x-axis and gives a quick overview of the distribution of the data. Most proteins are expected to show little variation, and so the majority of the points will be concentrated around the M = 0 line (no difference between group means). Typically, points falling above $abs(1)$ are highlighted.

Note: As the MA plot does not include any measure of statistical significance, it cannot directly identify which proteins are statistically differentially expressed.

An MA plot, short for “M vs. A plot,” which uses two axes:

M axis (vertical): Represents the fold change, usually on the logarithm base 2 scale, or the ratio of the expression levels, between two conditions. It is calculated as: $M = log_2(X/Y) = log_2 X - log_2 Y$
A axis (horizontal): Represents the average intensity of the two conditions, calculated as: $A = \frac{1}{2}log_2(XY) = \frac{1}{2}\left[log_2(X)+log_2(Y)\right]$

Most proteins are expected to exhibit little variation, leading to the majority of points concentrating around the line M = 0 (indicating no difference between group means).

visualize.ma(anlys_MA$`100pmol-50pmol`, M.thres = 1)
#> Warning: Removed 32 rows containing missing values or values outside the scale range
#> (`geom_text_repel()`).

where M.thres = 1 means the M thresholds are set to -1 and 1. The scatters are split into three parts: up regulation (M > 1), no regulation (-1 $\leq$ M $\leq$ 1), and down regulation (M < -1). Additionally, the warning message “Removed 32 rows containing missing values” indicates that there are 32 proteins with no regulation.

If the input dataSet is the whole list anlys_MA, msDiaLogue will produce individual subplots corresponding to each comparison.

visualize.ma(anlys_MA, M.thres = 1)
#> Warning: Removed 63 rows containing missing values or values outside the scale range
#> (`geom_text_repel()`).

boxplot

visualize.boxplot(dataNorm)
#> Warning: Removed 55 rows containing non-finite outside the scale range
#> (`stat_boxplot()`).

PCA_scree

One way to help identify how many PCs to retain is to explore a scree plot. The scree plot shows the percentage of variance explained by each PC.

visualize.scree(anlys_PCA, type = c("bar", "line"), label = TRUE, ncp = 10)

where label = TRUE adds information labels at the top of bars/points, and ncp = 10 sets the number of dimension to be displayed.

PCA_ind

The primary PCA plot of individual data visually represents the distribution of individual observations in a reduced-dimensional space, typically defined by the PCs. The x and y axes of the PCA plot represent the PCs. Each axis corresponds to a linear combination of the original variables. Individual data points on the PCA plot represent observations (e.g., samples) from the original dataset. Points that are close to the origin (0, 0), are close to the “average” across all protein abundances. If sufficient samples are present, the plot will also produce a 95% confidence ellipse, as well as a centroid (mean for each group provided), for each groups (condition) provided.

visualize.ind(anlys_PCA, label = TRUE, ellipse = TRUE, ellipse.level = 0.95)

PCA_var

This plot will be more useful if your analyses are based on a relatively small number of proteins. It represents the association, or loading of each protein on the first two PCs. Longer arrows represents stronger associations.

Note: Proteins that are weakly associated with PC1 or PC2 may still be highly correlated with other PCs not being plotted. Consult the scree plot (and other available methods) to determine the appropriate number of PCs to investigate.

visualize.var(anlys_PCA, label = TRUE)

PCA_biplot

The PCA biplot includes individual and variable plots. Again, with a large number of proteins, this plot can be unwieldy.

visualize.biplot(anlys_PCA, ellipse = TRUE, ellipse.level = 0.95, label = "all")

test

The function visualize().test can be applied to any test output. It generates two useful plots: a histogram of fold changes across the analyzed proteins and a histogram of p-values. The majority of proteins are expected to show very small change between conditions, so the fold change histogram will have a peak at around zero. For the p-values, most p-values are expected to be non-significant (above 0.05). Depending on the strength of the treatment effect, there may be a peak of p-values near 0.

visualize.test(anlys_modt$`100pmol-50pmol`)

If the input dataSet is the whole list anlys_modt, msDiaLogue will produce individual subplots corresponding to each comparison.

visualize.test(anlys_modt)

upset

The UpSet plot is a visual representation that helps display the overlap and intersection of sets or categories in a dataset. It is particularly useful for illustrating the presence or absence of elements in combinations of sets.

dataSort <- sortcondition(dataSet)
visualize.upset(dataSort)

This plot reveals that 42 proteins are shared by 50pmol, 100pmol, and 200pmol, while only 3 proteins are shared by 100 pmol and 200pmol, but not with 50pmol.

Venn

The Venn plot is another graphical representation of the relationships between sets. Each circle represents a set, and the overlapping regions show the elements that are shared between sets.

visualize.venn(dataSort, show_percentage = TRUE,
               fill_color = c("blue", "yellow", "green", "red"),
               saveVenn = TRUE)

where saveVenn = TRUE refers to the data containing logical columns representing sets in Venn plot information will be saved as a .csv file named Venn_information.csv in the current working directory.

In the example above, 50pmol, 100pmol, and 200pmol groups share 42 proteins. Notably, 3 proteins are exclusively found in the 100pmol and 200pmol groups.

volcano

A volcano plot is a graphical representation commonly used in proteomics and genomics to visualize differential expression analysis results. It is particularly useful for identifying significant changes in extensive data. It displays two important pieces of information about differences between conditions in a dataset:

Statistical significance (vertical): Represents the negative log10 of the p-value.
Fold change (horizontal): Represents the fold change.

visualize.volcano(anlys_modt$`100pmol-50pmol`, P.thres = 0.05, F.thres = 1)
#> Warning: Removed 32 rows containing missing values or values outside the scale range
#> (`geom_text_repel()`).

If the input dataSet is the whole list anlys_modt, msDiaLogue will produce individual subplots corresponding to each comparison.

visualize.volcano(anlys_modt, P.thres = 0.05, F.thres = 1)
#> Warning: Removed 65 rows containing missing values or values outside the scale range
#> (`geom_text_repel()`).

Other useful functions

The function pullProteinPath() allows you to see the quantitative values associated with specific proteins at each step of processing, using either the exact match argument listname =, or the text match argument regexName =, or both.

This can be useful for questions such as,

“Which of the values for my favorite protein were actually measured, vs. imputed?”
“Why didn’t my favorite protein make it to the final list? At what step was it filtered out?”.

It can also be used to check whether the fold-change observed for a specific protein is an artifact from one of the processing steps.

Check <- pullProteinPath(
  listName = c("LYSC_CHICK", "BGAL_ECOLI"),
  regexName = c("BOVIN"),
  by = "PG.ProteinNames",
  dataSetList = list(Initial = dataSet,
                     Transformed = dataTran,
                     Normalized = dataNorm,
                     Imputed = dataImput))

PG.ProteinNames	PG.Genes	PG.ProteinAccessions	PG.ProteinDescriptions	R.Condition	R.Replicate	Initial	Transformed	Normalized	Imputed
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	100pmol	1	111209.703	16.76292	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	100pmol	2	111659.883	16.76875	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	100pmol	3	105982.914	16.69347	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	100pmol	4	104442.562	16.67235	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	200pmol	1	109245.289	16.73721	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	200pmol	2	113357.508	16.79052	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	200pmol	3	114321.836	16.80274	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	200pmol	4	116439.820	16.82923	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	50pmol	1	117803.492	16.84602	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	50pmol	2	110086.680	16.74828	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	50pmol	3	105640.203	16.68880	16.75777	16.75777
ALBU_BOVIN	ALB	CON__P02769	Bovine serum albumin	50pmol	4	110446.000	16.75298	16.75777	16.75777
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	100pmol	1	23840.031	14.54110	14.44005	14.44005
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	100pmol	2	23963.307	14.54854	14.44005	14.44005
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	100pmol	3	22957.350	14.48667	14.42169	14.42169
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	100pmol	4	22311.297	14.44549	14.20112	14.20112
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	200pmol	1	41234.672	15.33157	14.77650	14.77650
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	200pmol	2	42899.434	15.38867	14.70670	14.70670
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	200pmol	3	42904.945	15.38886	14.77650	14.77650
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	200pmol	4	43279.844	15.40141	14.70670	14.70670
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	50pmol	1	14728.673	13.84634	14.38206	14.38206
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	50pmol	2	14736.710	13.84713	14.10465	14.10465
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	50pmol	3	14160.203	13.78955	14.38206	14.38206
BGAL_ECOLI	lacZ	P00722	Beta-galactosidase	50pmol	4	14758.731	13.84928	14.10465	14.10465
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	100pmol	1	10737.953	13.39043	12.96499	12.96499
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	100pmol	2	10655.384	13.37929	13.62766	13.62766
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	100pmol	3	10663.714	13.38042	12.81909	12.81909
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	100pmol	4	10843.115	13.40449	12.96499	12.96499
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	200pmol	1	19524.863	14.25302	13.10393	13.10393
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	200pmol	2	20072.297	14.29292	12.49496	12.49496
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	200pmol	3	20787.127	14.34340	14.00189	14.00189
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	200pmol	4	19924.240	14.28224	13.38772	13.38772
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	50pmol	1	6758.298	12.72244	12.49496	12.49496
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	50pmol	2	6721.135	12.71449	12.30540	12.30540
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	50pmol	3	6172.877	12.59173	13.38772	13.38772
CYC_BOVIN	CYCS	CON__P62894	Cytochrome c	50pmol	4	6028.398	12.55756	12.30540	12.30540
LYSC_CHICK	LYZ	P00698	Lysozyme C	100pmol	1	13798.590	13.75223	13.62766	13.62766
LYSC_CHICK	LYZ	P00698	Lysozyme C	100pmol	2	13880.411	13.76076	13.97388	13.97388
LYSC_CHICK	LYZ	P00698	Lysozyme C	100pmol	3	13723.719	13.74438	13.55168	13.55168
LYSC_CHICK	LYZ	P00698	Lysozyme C	100pmol	4	13944.603	13.76742	13.62766	13.62766
LYSC_CHICK	LYZ	P00698	Lysozyme C	200pmol	1	24344.188	14.57129	14.22236	14.22236
LYSC_CHICK	LYZ	P00698	Lysozyme C	200pmol	2	24742.227	14.59469	13.88102	13.88102
LYSC_CHICK	LYZ	P00698	Lysozyme C	200pmol	3	24803.633	14.59826	14.22236	14.22236
LYSC_CHICK	LYZ	P00698	Lysozyme C	200pmol	4	26381.047	14.68721	14.13067	14.13067
LYSC_CHICK	LYZ	P00698	Lysozyme C	50pmol	1	7169.955	12.80775	13.38772	13.38772
LYSC_CHICK	LYZ	P00698	Lysozyme C	50pmol	2	7797.536	12.92880	13.25790	13.25790
LYSC_CHICK	LYZ	P00698	Lysozyme C	50pmol	3	7432.793	12.85969	13.88102	13.88102
LYSC_CHICK	LYZ	P00698	Lysozyme C	50pmol	4	7543.633	12.88104	13.25790	13.25790
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	100pmol	1	15097.670	13.88204	13.97388	13.97388
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	100pmol	2	15840.281	13.95131	14.20112	14.20112
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	100pmol	3	15022.215	13.87481	13.94448	13.94448
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	100pmol	4	15160.493	13.88803	13.97388	13.97388
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	200pmol	1	21577.973	14.39727	14.00189	14.00189
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	200pmol	2	22968.959	14.48740	13.38772	13.38772
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	200pmol	3	20720.127	14.33875	13.70002	13.70002
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	200pmol	4	22153.398	14.43524	13.88102	13.88102
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	50pmol	1	12183.812	13.57268	13.88102	13.88102
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	50pmol	2	12521.783	13.61215	13.84672	13.84672
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	50pmol	3	11926.220	13.54185	14.13067	14.13067
TRFE_BOVIN	TF	CON__Q0IIK2	Serotransferrin (UP merge to Q29443)	50pmol	4	12021.495	13.55333	13.84672	13.84672

Reference

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2025-04-20

Load R package

Preprocessing

Usage

Details & Examples

Transformation

Usage

Details & Examples

Filtering

Usage

Details & Examples

Normalization

Usage

Details & Examples

Imputation

Usage

Details & Examples

Summarization

Usage

Details & Examples

Analysis

Usage

Details & Examples

Student’s t-test

Empirical Bayes moderated t-test

Wilcoxon test

MA

PCA

Visualization

Usage

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heatmap

rank abundance plot

MA

boxplot

PCA_scree

PCA_ind

PCA_var

PCA_biplot

test

upset

Venn

volcano

Other useful functions

Reference