Instructions for Exporting Data from Scaffold for Use in msDiaLogue Analysis

1. Setting Quantitative Value in Scaffold

The quantitative value must be set to an intensity measurement and not a count measurement. Inside Scaffold 5, on the Samples tab, the Display Options dropdown menu at the top left of the window must be set to “Quantitative Value” and the quantitative value must be defined (in Experiment –> Quantitative Analysis –> Other Settings, Quantitative Method dropdown). PMF recommends the Average Precursor Intensity value.

Note: Any value may be selected here, if you prefer a different quantification schema, with the exception of Total Spectra or Weighted Spectra. Do not use these.

2. Turning Off Normalization in Scaffold

Normalization must be turned off. In (Experiment –> Quantitative Analysis –> Other Settings), make sure the “Use Normalization” box is unchecked. You will have the option to normalize by various methods in msDiaLogue, but you should not stack normalizations between programs.

Note: Non-quantifiable abundance values are reported in Scaffold as 0 or 1, and msDiaLogue preprocessing knows to treat these as NA. If normalization is applied in Scaffold, non-quantifiable abundances are transformed into fractional values which will not be converted properly in preprocessing and your data will no longer yield sensible analyses.

3. Minimum Requirements for Experiment Conditions

The experiment must contain a minimum of 2 conditions, and each condition must have a minimum of 3 replicates. More conditions are fine, more replicates are fine, and conditions do not need to have the same number of replicates. Having fewer than 3 replicates for any condition, or having only 1 condition, will throw an error in msDiaLogue and you will not be able to process your data.

Note: You can’t sensibly estimate sample variance from 2 replicates, so statistical tests really should not be run on fewer than 3 replicates in general, whether in msDiaLogue or other programs.

4. Sample Naming Format Requirements

The samples must be named in the following format: YYYYMMDD_initials_condition-replicate# (e.g. 20240101_JL_ctrl-1). Your files may already be named this way; please check in Scaffold in the “Samples” tab, for the heading above any quantitative value column. The sample name will appear vertically. If the name is not formatted as above, you can change it by going into the “Load Data” tab, selecting the tab of each sample individually, right-clicking the tab, choosing “Edit BioSample”, typing the correct name format into the “Sample Name” box, and clicking “Apply”.

Note: Scaffold 5 will send you back to the “Samples” tab each time “Apply” is clicked, so this step is a little tedious to do in Scaffold. You can also fix this in Excel after exporting, before you import into R, which will likely be easier.

5. Filtering Dataset for Protein Identification

We strongly recommend that you filter the dataset to hide proteins that only had 1 peptide identified. In the “Samples” tab, at the top-most menu bar, in the “Min # Peptides” dropdown, set this to 2.

Note: This is not required, and if you choose to evaluate the dataset with 1-peptide identifications included, msDiaLogue will not stop you. However, the Scaffold report does not provide information about how many peptides/protein were identified, so unlike with a Spectronaut-based report, you cannot filter based on this information once in msDiaLogue.

6. Collapsing Protein Clusters in Samples Tab

All protein clusters must be collapsed. In the “Samples” tab, in the very first column (header “#”), right click any of the numbered entries here, select “Clusters”, and select “Collapse All”. This converts any gray-shaded entries, where multiple proteins are inferred from the peptide evidence, from an expanded view with a line for each possible protein match in that cluster to a collapsed view with only the first entry appearing in the table. In msDiaLogue, this first entry and its accession number is the only one that will be represented in your data from this cluster.

You’re now ready to export the data. From the “Samples” tab in Scaffold, right-click anywhere in the main data table, choose “Export” (bottom of menu), and “Export to Excel”.

Save with a descriptive filename that will make sense to someone else in the future and choose the location you’ll be using as your working directory in R.

The report can be saved as .xls or .csv.

You can now use the preprocessing_scaffold script available on this page and pick up at the transformation step in the msDiaLogue script as provided in the main Usage Template page.

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

Note: Decoys and contaminants can be removed either in the Excel report before Preprocessing, or can be removed in msDiaLogue with the filter step, if the accession number has the “CON__” or “DECOY__” prefix. (This prefix is not applied with search algorithms like MSFragger, so alternative filters would need to be developed in that case.)

library(msDiaLogue)

## if the raw data is in a .xls file
dataSet <- preprocessing_scaffold(fileName = "../tests/testData/Toy_Scaffold_Data.xls")

## if the raw data is in an .Rdata file
load("../tests/testData/Toy_Scaffold_Data.RData")
dataSet <- preprocessing_scaffold(dataSet = Toy_Scaffold_Data)
#> Warning: Removed 4194 rows containing non-finite outside the scale range
#> (`stat_bin()`).

#> Summary of Full Data Signals:
#>      Min.   1st Qu.    Median      Mean   3rd Qu.      Max.      NA's 
#> 7.300e+05 9.640e+06 2.250e+07 5.546e+07 5.630e+07 3.480e+09      4194 
#> 
#> Levels of Condition: 6 ctrl DM SIC 
#> Levels of Replicate: 1 2 3