2.1 Official BioConductor releases (recommended)

artMS is available on BioConductor.

install.packages("BiocManager")
BiocManager::install("artMS")

Why Bioconductor? Here you can find a nice summary of good reasons.

2.2 Development version from this repo

(Warning: not stable, but it has the latest)

Assuming that you have an R (>= 3.5) version running on your system, follow these steps:

install.packages("devtools")
library(devtools)
install_github("biodavidjm/artMS")

• Check that it is up and running by checking, for example, the documentation of the QC function artmsQualityControlEvidenceBasic:

library(artMS)
?artmsQualityControlEvidenceBasic

Once installed, we suggest you to do a quick test by running the quality control functions using the “evidence” (artms_data_ph_evidence) and “keys” (artms_data_ph_keys) files included in artMS as test datasets.

library(artMS)

## 

## 

suppressWarnings(
  artmsQualityControlEvidenceBasic(evidence_file = artms_data_ph_evidence,
                                 keys_file = artms_data_ph_keys, 
                                 prot_exp =  "PH", 
                                 plotINTDIST = FALSE,
                                 plotREPRO = TRUE,
                                 plotCORMAT = FALSE,
                                 plotINTMISC = FALSE,
                                 plotPTMSTATS = FALSE,
                                 printPDF = FALSE,
                                 verbose = FALSE))

(To learn more about these testing datasets, check the documentation by running ?artms_data_ph_keys or ?artms_data_ph_evidence on the R console)

Once the QC is done, go to the folder "/path/to/your/working/directory/" and check out all the generated QC (pdf) files.

3.1 evidence.txt

The output of the quantitative proteomics software package MaxQuant. It combines all the information about the identified peptides.

3.2 keys.txt

Tab delimited file generated by the user. It summarizes the experimental design of the evidence file. When using artMS, the keys.txt file will be merged with the evidence.txt by the “RawFile” column. Each RawFile corresponds to a unique individual experimental technical replicate / biological replicate / Condition / Run.

For any basic label-free proteomics experiment, the keys file must contain the following columns:

• RawFile: The name of the RAW-file for which the mass spectral data was derived from.
• IsotopeLabelType: 'L' for label free experiments ('H' will be used for SILAC experiments, see below)
• Condition:
  • Use only numbers (0 - 9) and the letters (A - Z, both uppercase and lowercase) for the Conditions’ names. The only special character allowed is underscore (_).
  • A condition name CANNOT start with a number.
• BioReplicate: biological replicate number. Use as prefix the corresponding Condition name, and add as suffix a dash (-) and the number of biological replicate. For example, if condition H1N1_06H has too biological replicates, name them as H1N1_06H-1 and H1N1_06H-2
• Run: a number for all the MS runs. It will be specially useful when having technical replicates.

Example of keys file: check the data object artms_data_ph_keys

Tip: it is recommended to use Microsoft Excel (OpenOffice Cal / or similar) to generate the keys file. Do not forget to choose the format = Tab Delimited Text (.txt) when saving the file (use save as option)

3.3 contrast.txt

The comparisons between conditions that the user wants to quantify. For example, to quantify changes in protein abundance between wild type WT_A549 relative to two additional experimental conditions with drugs WT_DRUG_A and WT_DRUG_B, but also changes in protein abundance between DRUG_A and DRUG_B, the contrast file would look like this:

WT_DRUG_A-WT_A549
WT_DRUG_B-WT_A549
WT_DRUG_A-WT_DRUG_B

Requirements:

• The two conditions to be compared must be separated by a dash symbol (-), and only one dash symbol is allowed, i.e., only one comparison per line.

As a result of the quantification, the condition on the left will take the positive log2FC sign -if the protein is more abundant in condition WT_DRUG_A, and the condition on the right the negative log2FC -if a protein is more abundant in condition WT_A549.

3.4 The configuration file (.yaml)

The configuration file (in yaml format) contains the configuration details for the quantification performed by artMS using MSstats.

To generate a sample configuration file, go to the project folder (setwd(/path/to/your/working/folder/)) and execute:

artmsWriteConfigYamlFile(config_file_name = "config.yaml", verbose = FALSE)

Open the config.yaml file with your favorite editor (RStudio works very well as well). Although it might look complex, the default options work very well.

The configuration (yaml) file contains the following sections:

files :
  evidence : /path/to/the/evidence.txt
  keys : /path/to/the/keys.txt
  contrasts : /path/to/the/contrast.txt
  output : /path/to/the/results_folder/ph-results.txt

qc:
  basic: 1 # 1 = yes; 0 = no
  extended: 1 # 1 = yes; 0 = no

data:
  enabled : 1 # 1 = yes; 0 = no
  fractions: 
    enabled : 0 # 1 for protein fractionation
  silac: 
    enabled : 0 # 1 for SILAC experiments
  filters: 
    enabled : 1
    contaminants : 1
    protein_groups : remove # remove, keep
    modifications : AB # PH, UB, AB, APMS
  sample_plots : 1 # correlation plots

msstats :
  enabled : 1
  msstats_input : 
  profilePlots : none 
  normalization_method : equalizeMedians 
  normalization_reference :  
  summaryMethod : TMP 
  censoredInt : NA  
  cutoffCensored : minFeature  
  MBimpute : 1 
  feature_subset: all

  enabled : 1 # if 0, won't process anything on this section
  annotate :  
    enabled: 1 # if 1, will generate a `-results-annotated.txt` file that 
    including Gene and Protein.Name
    species : HUMAN
  plots:
    volcano: 1
    heatmap: 1
    LFC : -1.5 1.5 # Range of minimal log2fc
    FDR : 0.05
    heatmap_cluster_cols : 0
    heatmap_display : log2FC # log2FC or pvalue

3.5 Special case: Protein fractionation

To handle protein fractionation experiments, two options need to be activated

1. The keys’ file must contain an additional column named “FractionKey” with the information about fractions. For example:

2. Enable fractions in the configuration file as follow:

fractions: 
  enabled : 1 # 1 for protein fractions, 0 otherwise

3.6 Special case: SILAC

One of the most widely used techniques that enable relative protein quantification is stable isotope labeling by amino acids in cell culture (SILAC). The keys file can capture the typical SILAC experiment. The following example shows a SILAC experiment with two conditions, two biological replicates, and two technical replicates:

It is also required to activate the silac option in the yaml file as follows:

silac: 
  enabled : 1 # 1 for SILAC experiments

4.1 Basic QC (evidence.txt-based)

The basic quality control analysis takes as input both the evidence.txt and keys.txt.

# But it is recommended to get the full pdf package of QC plots by running:
# artmsQualityControlEvidenceBasic(evidence_file = artms_data_ph_evidence,
#                                 keys_file = artms_data_ph_keys,
#                                 prot_exp = "PH")
# But for illustration purposes printing only INTDIST plot:
artmsQualityControlEvidenceBasic(evidence_file = artms_data_ph_evidence,
                                 keys_file = artms_data_ph_keys,
                                 prot_exp = "PH",
                                 plotINTDIST = TRUE,
                                 plotREPRO = FALSE,
                                 plotCORMAT = FALSE,
                                 plotINTMISC = FALSE,
                                 plotPTMSTATS = FALSE,
                                 printPDF = FALSE,
                                 verbose = FALSE)

Running artmsQualityControlEvidenceBasic() generates the following pdf files:

• -basicReproducibility.pdf: correlation dot plot for all the combinations of biological replicates of conditions, based on MS Intensity values using features (peptide+charge)
• -correlationMatrixBR.pdf: It contains 3 pages. Correlation matrix for all the biological replicates using MS Intensity values, Clustering matrix of the MS Intensities and correlation distribution histogram.
• -correlationMatrixBR.pdf: Same as the previous one, but based on MS Intensity values of Conditions
• -IntensityDistributions.pdf: 2 pages. Box-dot plot and Jitter plot of biological replicates based on MS (raw) intensity values.
• -intensityStats.pdf: several pages, including bar plots of Total Sum of Intensities in BioReplicates, Total Sum of Intensities in Conditions, Total Peptide Counts in BioReplicates, Total Peptide Counts in conditions separated by categories (CON: contaminants, PROT peptides, REV reversed sequences used by MaxQuant to estimate the FDR); Box plots of MS Intensity values per biological replicates and conditions; bar plots of total intensity (excluding contaminants) by bioreplicates and conditions; Barplots of total feature counts by bioreplicates and conditions.
• -ptmStats.pdf: If any PTM is selected (PH, UB, AC) an extra pdf file will be generated with stats related to the selected modification, including: bar plot of peptide counts and intensities, broken by PTM/other categories; bar plots of total sum-up of MS intensity values by other/PTM categories.

Check ?artmsQualityControlEvidenceBasic() to find out more options about this function.

4.2 Extended QC (evidence.txt-based)

It takes as input the evidence.txt and keys.txt files as follows:

artmsQualityControlEvidenceExtended(evidence_file = artms_data_ph_evidence,
                                    keys_file = artms_data_ph_keys)

It generates the following QC files:

• plotPSM (QC_Plots_PSM.pdf) : it generates peptide-spectrum-matches (PSMs) statistics plot: Page 1 shows the number of PSMs confidently identified in each BioReplicate. If replicates are present, Page 2 shows the mean number of PSMs per condition with error bar showing the standard error of the mean. Note that potential contaminant proteins are plotted separately.
• plotIONS (QC_Plots_IONS.pdf) : it generates peptide ion statistics plot: A peptide ion is defined in the context of m/z, in other words, an unique peptide sequence may give rise to multiple ions with different charge state and/or amino acid modification. Page 1 shows the number of ions confidently identified in each BioReplicate . If replicates are present, Page 2 shows the mean number of peptide ions per condition with error bar showing the standard error of the mean. Note that potential contaminant proteins are plotted separately.
  
• plotTYPE (QC_Plots_TYPE.pdf): it generates identification type statistics plot: MaxQuant classifies each peptide identification into different categories (e.g., MSMS, MULTI-MSMS, MULTI-SECPEP). Page 1 shows the distribution of identification type in each BioReplicate
• plotPEPTIDES (QC_Plots_PEPTIDES.pdf): it generates peptide statistics plot: Page 1 shows the number of unique peptide sequences (disregard the charge state or amino acid modifications) confidently identified in each BioReplicate. If replicates are present, Page 2 shows the mean number of peptides per condition with error bar showing the standard error of the mean. Note that potential contaminant proteins are plotted separately. Pages 3 and 4 show peptide identification intersection between BioReplicates (the bars are ordered by degree or frequency, respectively), and Page 4 shows the intersections across conditions instead of BioReplicates.
• plotPROTEINS (QC_Plots_PROTEINS.pdf): it generates protein statistics plot: Page 1 shows the number of protein groups confidently identified in each BioReplicate. If replicates are present, Page 2 shows the mean number of protein groups per condition with error bar showing the standard error of the mean. Note that potential contaminant proteins are plotted separately. Pages 3 and 4 show peptide identification intersection between BioReplicates (the bars are ordered by degree or frequency, respectively), and Page 4 shows the intersections across conditions instead of BioReplicates.
• plotPIO (QC_Plots_PepIonOversampling.pdf): it generates oversampling statistics plot: Page 1 shows the proportion of all peptide ions (including peptides matched across runs) fragmented once, twice and thrice or more. Page 2 shows the proportion of peptide ions (with intensity detected) fragmented once, twice and thrice or more. Page 3 shows the proportion of peptide ions (with intensity detected and MS/MS identification) fragmented once, twice and thrice or more
• plotCS (QC_Plots_CHARGESTATE.pdf): it generates charge state plot: Page 1 shows the charge state distribution of PSMs confidently identified in each BioReplicate.
• plotME (QC_Plots_MASSERROR.pdf): it generates precursor mass error plot: Page 1 shows the distribution of precursor error for all PSMs confidently identified in each BioReplicate.
• plotMOCD (QC_Plots_MZ.pdf): it generates precursor mass-over-charge plot: Page 1 shows the distribution of precursor mass-over-charge for all PSMs confidently identified in each BioReplicate.
• plotPEPICV (QC_Plots_PEPINT.pdf): it generates peptide intensity coefficient of variance (CV) plot: The CV is calculated for each feature (peptide ion) identified in more than one replicate. Page 1 shows the distribution of CV’s for each condition, while Page 2 shows the distribution of CV’s within 4 bins of intensity (i.e., 4 quantiles of average intensity).
• plotPEPDETECT (QC_Plots_PepDetect.pdf): it generates peptide detection frequency plot: Page 1 summarizes the frequency that each peptide is detected across BioReplicates of each condition, showing the percentage of peptides detected once, twice, thrice, and so on (for whatever number of replicates each condition has).
• plotPROTICV (QC_Plots_ProtInt.pdf): it generates protein intensity coefficient of variance (CV) plot: The CV is calculated for each protein (after summing the peptide intensities) identified in more than one replicate. Page 1 shows the distribution of CV’s for each condition, while Page 2 shows the distribution of CV’s within 4 bins of intensity (i.e., 4 quantiles of average intensity).
• plotPROTDETECT (QC_Plots_ProtDetect.pdf): it generates protein detection frequency plot: Page 1 summarizes the frequency that each protein group is detected across BioReplicates of each condition, showing the percentage of proteins detected once, twice, thrice, and so on (for whatever number of replicates each condition has). Page 2 shows the feature (peptide ion) intensity distribution within each BioReplicate (potential contaminant proteins are plot separately). Page 3 shows the density of feature intensity for different feature types (i.e., MULTI-MSMS, MULTI-SECPEP).
• plotIDoverlap (QC-ID-Overlap.pdf): it generates pairwise identification heatmap overlap: Pages 1 and 2 show pairwise peptide and protein overlap between any 2 BioReplicates, respectively.
• plotIC (QC-IntCorrelation.pdf): it generates pairwise intensity correlation: Page 1 and 3 show pairwise peptide and protein intensity correlation and scatter plot between any 2 BioReplicates, respectively. Page 2 and 4 show principal component analysis at the intensity level for both peptide and proteins, respectively.
• plotSP (QC-SamplePrep.pdf): it generates sample quality metrics: Page 1 shows missing cleavage distribution of all peptides confidently identified in each BioReplicate. Page 2 shows the fraction of peptides with at least one methionine oxidized in each BioReplicate.

4.3 Extended QC (summary.txt based)

It requires two files:

• keys.txt
• MaxQuant summary.txt file. As described by MaxQuant’s table.pdf, the summary file contains summary information for all the raw files processed with a single MaxQuant run, including statistics on the peak detection. artmsQualityControlSummaryExtended() gathers a quick overview on the quality of every RawFile based on this summary.txt.

Run it as follows:

artmsQualityControlSummaryExtended(summary_file = "summary.txt",
                                    keys_file = artms_data_ph_keys)

It generates the following pdf plots:

• plotMS1SCANS: generates MS1 scan counts plot: Page 1 shows the number of MS1 scans in each BioReplicate. If replicates are present, Page 2 shows the mean number of MS1 scans per condition with error bar showing the standard error of the mean. If isFractions is TRUE, each fraction is a stack on the individual bar graphs.

• plotMS2: generates MS2 scan counts plot: Page 1 shows the number of MSs scans in each BioReplicate. If replicates are present, Page 2 shows the mean number of MS1 scans per condition with error bar showing the standard error of the mean. If isFractions TRUE, each fraction is a stack on the individual bar graphs.

• plotMSMS: generates MS2 identification rate (%) plot: Page 1 shows the fraction of MS2 scans confidently identified in each BioReplicate. If replicates are present, Page 2 shows the mean rate of MS2 scans confidently identified per condition with error bar showing the standard error of the mean. If isFractions TRUE, each fraction is a stack on the individual bar graphs.

• plotISOTOPE: generates Isotope Pattern counts plot: Page 1 shows the number of Isotope Patterns with charge greater than 1 in each BioReplicate. If replicates are present, Page 2 shows the mean number of Isotope Patterns with charge greater than 1 per condition with error bar showing the standard error of the mean. If isFractions TRUE, each fraction is a stack on the individual bar graphs.

5.1 Quantification of Changes in Protein Abundance

It quantifies changes in protein abundance between two different conditions. These are the specific sections that the user has to filled up:

files:
  evidence : /path/to/the/evidence.txt
  keys : /path/to/the/keys.txt
  contrasts : /path/to/the/contrast.txt
  output : /path/to/the/output/results_ptmGlobal/results.txt
  .
  .
  .
data:
  .
  .
  .
  filters:
    modifications : AB 

Make sure that the filter modifications is labeled as AB.

Finally, run the following artMS function:

artmsQuantification(
  yaml_config_file = '/path/to/config/file/artms_ab_config.yaml')

5.2 Quantification of Changes in Global Phosphorylation / Ubiquitination

The global phosphorylation / ubiquitination quantification analysis calculates changes in phosphorylation/ubiquitination at the protein level. This means that all the modified peptides are used to quantify changes in protein phosphorylation/ubiquitination between different conditions. The site-specific (explained next) quantifies changes at the peptide level, i.e., each modified peptide independently between the different conditions.

Only two sections need to be filled up on the default configuration (yaml) file:

files:
  evidence : /path/to/the/evidence.txt
  keys : /path/to/the/keys.txt
  contrasts : /path/to/the/contrast.txt
  output : /path/to/the/output/results_ptmGlobal/results.txt
  .
  .
  .
data:
  .
  .
  .
  filters:
    modifications : PH # Use UB for ubiquination

The remaining options can be left unmodified.

Once the configuration yaml file is ready, run the following command:

artmsQuantification(
  yaml_config_file = '/path/to/config/file/artms_phglobal_config.yaml')

5.3 Site-specific Quantification of Changes in Phosphorylation / Ubiquitination

The site-specific analysis quantifies changes at the modified peptide level. This means that changes in every modified (ph/ub) peptide of a given protein will be quantified individually. The caveat is that the proportion of missing values should increase relative to the global analysis. Both site and global ptm analysis are highly correlated due to the fact that only one or two peptides drive the overall changes in PTMs for every protein.

To run a site specific analysis follow these steps:

1. A pre-processing step is required to be run on the evidence file to enable the site-specific ph analysis.

For phosphorylation

artmsProtein2SiteConversion(
  evidence_file = "/path/to/the/evidence.txt", 
  ref_proteome_file = "/path/to/the/reference_proteome.fasta", 
  output_file = "/path/to/the/output/ph-sites-evidence.txt", 
  mod_type = "PH")

For ubiquitination

artmsProtein2SiteConversion(
  evidence_file = "/path/to/the/evidence.txt", 
  ref_proteome_file = "/path/to/the/reference_proteome.fasta", 
  output_file = "/path/to/the/output/ub-sites-evidence.txt", 
  mod_type = "UB")

2. Generate a new configuration file (phsites_config.yaml or ubsites_config.yaml) as explained above, but using the “new” ph-sites-evidence.txt/ub-sites-evidence.txt file instead of the original evidence.txt file. Only two sections need to be filled up on the default configuration (yaml) file:

files:
  evidence : /path/to/the/evidence-site.txt
  keys : /path/to/the/keys.txt
  contrasts : /path/to/the/contrast.txt
  output : /path/to/the/output/results_ptmSITES/sites-results.txt
  .
  .
  .
data:
  .
  .
  .
  filters:
    modifications : PH # Use UB for ubiquination

Once the new yaml file has been created, execute:

artmsQuantification(
  yaml_config_file = '/path/to/config/file/phsites_config.yaml')

7.1 Annotate table with Gene Symbol and Name based on Uniprot ID(s)

Annotate gene name and symbol based on UniProt ids. It will take the column from your data.frame specified by the columnid argument, search for the gene symbol, name, and entrez based on the species (species argument) and merge the information back to the input data.frame

# This example adds annotations to the evidence file available in
# artMS, based on the column 'Proteins'.

evidence_anno <- artmsAnnotationUniprot(x = artms_data_ph_evidence,
                                        columnid = 'Proteins',
                                        species = 'human')

7.2 Average Intensity, RT, CR

Taking as input the evidence file location, it will summarize and report back the average intensity, average retention time, and the average caliberated retention time for each protein. If a list of proteins is provided, then only those proteins will be summarized and returned. Check ?artmsAvgIntensityRT() to find out more options.

artmsAvgIntensityRT(evidence_file = '/path/to/the/evidence.txt)

7.3 Change Column Name

Change a specific column name in a given data.frame

artms_data_ph_evidence <- artmsChangeColumnName(
                               dataset = artms_data_ph_evidence,
                               oldname = "Phospho..STY.",
                               newname = "PH_STY")

7.4 Individual abundance dot plots

Protein abundance dot plots for each unique uniprot id. It can take a long time

artmsDataPlots(input_file = "results/ab-results-mss-normalized.txt",
               output_file = "results/ab-results-mss-normalized.pdf")

7.5 Enrichment analysis function

Enrichment analysis based on a data.frame with Gene and Comparison/Label protein (i.e, typical MSstats results)

# The data must be annotated (Protein and Gene columns)
data_annotated <- artmsAnnotationUniprot(
                      x = artms_data_ph_msstats_results,
                      columnid = "Protein",
                      species = "human")
# And then the enrichment
enrich_set <- artmsEnrichLog2fc(
                   dataset = data_annotated,
                   species = "human",
                   background = unique(data_annotated$Gene), verbose = FALSE)

## --- No significant results from the enrichment analysis

7.6 Enrichment analysis using gProfileR

Function that simplifies enrichment analysis using gProfileR

# annotate the MSstats results to get the Gene name
data_annotated <- artmsAnnotationUniprot(
                                     x = artms_data_ph_msstats_results,
                                     columnid = "Protein",
                                     species = "human")

# Filter the list of genes with a log2fc > 2
filtered_data <- 
unique(data_annotated$Gene[which(data_annotated$log2FC > 2)])

# And perform enrichment analysis
data_annotated_enrich <- artmsEnrichProfiler(
                                   x = filtered_data,
                                   categorySource = c('KEGG'),
                                   species = "hsapiens",
                                   background = unique(data_annotated$Gene))

## ---+ Enrichment analysis using gProfiler...done!

7.7 MaxQuant evidence file to SAINTexpress format

Converts the MaxQuant evidence file to the 3 required files by SAINTexpress. Choose one of the following quantitative MS metrics:

• MS spectral counts (use msspc)
• MS intensities (use msint)

artmsEvidenceToSaintExpress(evidence_file = "/path/to/evidence.txt", 
                            keys_file = "/path/to/keys.txt", 
                            ref_proteome_file = "/path/to/org.proteome.fasta")

7.8 MaxQuant evidence file to SAINTq format

Converts the MaxQuant evidence file to the required files by SAINTq. The user can filter based on either peptides with spectral counts (use msspc) or all the peptides (use all) for the analysis. The quantitative metric can be also chosen (either MS intensity or spectral counts)

artmsEvidenceToSAINTq(evidence_file = "/path/to/evidence.txt", 
                      keys_file = "/path/to/keys.txt", 
                      output_dir = "saintq_input_files")

7.9 Generate Phosfate input file

It generates the Phosfate input file from the imputedL2fcExtended.txt file resulting from running the artmsAnalysisQuantifications() on a ph-site quantification (see above). Notice that the only species suported by PHOTON is humans.

artmsPhosfateOutput(inputFile = "your-imputedL2fcExtended.txt")

7.10 Generate Photon input file

It generates the Photon input file from the imputedL2fcExtended.txt file resulting from running the artmsAnalysisQuantifications() on a ph-site quantification (see above). Please, notice that the only species suported by PHOTON is humans.

artmsPhotonOutput(inputFile = "your-imputedL2fcExtended.txt")

7.11 Remove contaminants and empty proteins from the MaxQuant evidence file

Remove contaminants and erroneously identified ‘reverse’ sequences by MaxQuant, in addition to empty protein ids

evidencefiltered <- artmsFilterEvidenceContaminants(x = artms_data_ph_evidence)

7.12 Generate ph-site specific evidence file

Generate extended detailed ph-site file, where every line is a ph site instead of a peptide. Therefore, if one peptide has multiple ph sites it will be breaking down in multiple extra lines for each of the sites.

artmsGeneratePhSiteExtended(df = dfobject, 
                            species = "mouse", 
                            ptmType = "ptmsites",
                            output_name = log2fc_file)

8.1 Convert Metabolomics

Pre-process the markview .txt file to generate an “evidence-like” file by running:

artmsConvertMetabolomics(input_file = "markview-output.txt", 
                         out_file = "metabolomics-evidence.txt")

8.2 QC Metabolomics

Perform quality control analysis on the metabolomics data by running:

artmsQualityControlMetabolomics(evidence_file = "metabolomics-evidence.txt",
                                keys_file = "metabolomics-keys.txt")

It generates the following plots:

• plotINTDIST.pdf contains both Box-dot plot and Jitter plot of biological replicates based on MS (raw) intensity values.
• plotREPRO.pdf correlation dotplot for all the combinations of biological replicates of conditions, based on MS Intensity values using features (mz_rt+charge)
• plotCORMAT.pdf, includes up to 3 pdf files for technical replicates, biological replicates, and conditions. Each pdf file contains:
  • Correlation matrix for all the biological replicates using MS Intensity values,
  • Clustering matrix of the MS Intensities and correlation distribution
  • histogram of the distribution of correlations
• plotINTMISC.pdf the pdf contains several pages, including bar plots of Total Sum of Intensities in BioReplicates, Total Sum of Intensities in Conditions, Total Feature Counts in BioReplicates, Total Feature Counts in conditions separated by categories (INT: has a intensity value NOINT: no intensity value ) Box plots of MS Intensity values per biological replicates and conditions; bar plots of total intensity by bioreplicates and conditions; Barplots of total feature counts by bioreplicates and conditions.

8.3 Relative Quantification:

The relative quantification is performed using MSstats. It requires a configuration file (yaml format, please check above). A template can be generated by running: artmsWriteConfigYamlFile(config_file_name = "metab_config.yaml"). The relative quantification is performed by running:

artmsQuantification(yaml_config_file = "metabConfig.yaml")