Can't assign 4 names to a 0 column data.table MSStats errror

[Kimmiecc19]

Description of the bug

I get the following error when running quant.ms using an sdrf.tsv file:

Error in setnames(x, value) : Can't assign 4 names to a 0 column data.table Calls: OpenMStoMSstatsFormat ... colnames<- -> names<- -> names<-.data.table -> setnames Execution halted

Command error: Loading required package: MSstats

Attaching package: ‘MSstats’

The following object is masked from ‘package:grDevices’:

  savePlot

Loading required package: tibble Loading required package: data.table INFO [2023-09-01 12:20:06] Raw data from OpenMS imported successfully. INFO [2023-09-01 12:20:06] Raw data from OpenMS cleaned successfully. INFO [2023-09-01 12:20:06] Using annotation extracted from quantification data. INFO [2023-09-01 12:20:06] Run labels were standardized to remove symbols such as '.' or '%'. INFO [2023-09-01 12:20:06] ** The following options are used:

• Features will be defined by the columns: PeptideSequence, PrecursorCharge, FragmentIon, ProductCharge
• Shared peptides will be removed.
• Proteins with a single feature will be removed.
• Features with less than 3 measurements across runs will be removed. INFO [2023-09-01 12:20:06] Features with all missing measurements across runs are removed. INFO [2023-09-01 12:20:06] Shared peptides are removed. INFO [2023-09-01 12:20:07] Multiple measurements in a feature and a run are summarized by summaryforMultipleRows: max INFO [2023-09-01 12:20:07] Features with one or two measurements across runs are removed. INFO [2023-09-01 12:20:07] Proteins with a single feature are removed. INFO [2023-09-01 12:20:07] Run annotation merged with quantification data. INFO [2023-09-01 12:20:07] Multiple fractionations exist: 36 fractionations per MS replicate. INFO [2023-09-01 12:20:27] Features with one or two measurements across runs are removed. INFO [2023-09-01 12:20:27] Fractionation handled. Error in setnames(x, value) : Can't assign 4 names to a 0 column data.table Calls: OpenMStoMSstatsFormat ... colnames<- -> names<- -> names<-.data.table -> setnames Execution halted

Work dir: /home/kcoetzer/nf/nf-core-quantms-1.1.1/workflow/work/76/86b9b6fe2a2b16cf1173bf95dc7275

Tip: you can try to figure out what's wrong by changing to the process work dir and showing the script file named .command.sh

Execution cancelled -- Finishing pending tasks before exit

Command used and terminal output

NXF_VER=22.10.1 nextflow run main.nf --outdir /home/kcoetzer/nf/nf-core-quantms-1.1.1/ -profile singularity -params-file /home/kcoetzer/nfparams_quantms.json -resume

Relevant files

PXD010154.sdrf_openms_design_msstats_in.csv PXD010154.sdrf.csv nfparams.txt

System information

Hardware: local server Container: singularity Version of nf-core/quantms: 1.1.1

[YoujiaMa]

@Kimmiecc19 i also meet the same error. u can try set tow or more Condition. MSstats seem to compare different type of data.

[jpfeuffer]

Hmm yes true, maybe it's because of the fact that you only have one condition in your design.

MSstats is mostly used to find significant proteins between conditions.

We should check this during validation, warn and maybe disable msstats automatically then. Or only use the normalization or imputation functions in msstats that do not need multiple fractions in such a case.

[Kimmiecc19]

Hmm... I did try and add two conditions and it still didn't work for some the same reason. I even tried to just keep the sdrf file exactly the same with all samples and conditions and I still got the same error.

Also, is there an issue with using OpenMS files as input? It wouldn't allow me to and said I need to use sdrf instead. I, however, can't recall the exact error right now.

[Kimmiecc19]

It would be great if the pipeline would still run and output results with just one condition but I know that's ultimately not the purpose of the pipeline. Is there a minimum number of each condition that I need? For example three of each? Available data is always so limited.

[simone-pignotti]

I am also running into the same error with quantms v1.2.0. Indeed, checking this during validation and adding an informative error message would be great.