Workflows: panoply_association_workflow

panoply_association_workflow

Description

This workflow performs association to identify differential markers for classes of interest, and create an interactive report. The workflow executes the following modules:

module	description
panoply_association	performs association analysis, to identify marker genes associated with provided annotations
panoply_accumulate	assembles results from association analysis to run panoply_ssgsea
panoply_ssgsea	Performs ssGSEA on association contrast values
panoply_association_report	creates an interactive R Markdown report of the association results

Association Analysis

Module(s): panoply_association

This module performs association analysis to identify differential markers for classes of interest using a moderated t-test (for binary classes) or F-test (for categorical multi-level classes). The significant markers are then ranked using a combination of p-values and variable importance in accurate classifiers.

Classes used for the analysis are derived from the annotations provided in the groups file, as long as each level in a class has at least 3 samples.

ssGSEA Analysis

Module(s): panoply_accumulate, panoply_ssgsea

ssGSEA is performed on the contrast values (i.e. coefficients of a limma linear model) for all marker features. This analysis is scattered across the results from every class of interest.

Interactive Report

Module(s): panoply_association_report

This module creates an interactive R Markdown report of the GSEA results of the panoply_association module. For each class, for each contrast, an interactive volcano plot shows the Normalized Enrichment Score (NES) vs -log10 of the FDR value for each database pathway analyzed.

Input

Required inputs:

• inputData: (.tar file) tarball from panoply_parse_sm_table or other PANOPLY module;
  (.gct file) normalized/filtered input if standalone is TRUE

• association_groups (.csv file) subset of sample annotations, providing classes for association analysis

• job_identifier: (String) label for job

• type: (String) (proteome) data type

• standalone: (String) set to TRUE to run as a self-contained module;
  if TRUE the analysisDir and groupsFile inputs are required

• yaml: (.yaml file) parameters in yaml format

• geneset_db: (.gmt file) gene set database

Optional inputs:

panoply_association

• sample_na_max: (Float, default = 0.8) maximum allowed fraction of NA values per sample/column; error if violated.
• nmiss_factor: (Float, default = 0.5) features (genes, proteins, PTM sites) with more than nmiss_factor fraction of NA values will be removed from the analysis
• duplicate_gene_policy: (String, default = 'maxvar') method used to combine duplicate genes (when mapping protein accession or PTM site to gene symbols) for running GSEA; possible options are:
  • maxvar: select row with largest variance
  • union: union of binary (0/1) values in all rows (e.g for mutation status)
  • median: median of values in all rows (for each column/sample)
  • mean: mean of values in all rows (for each column/sample)
  • min: minimum of values in all rows (for each column/sample)
• gene_id_col: (String, default = 'geneSymbol') name of sample annotation column containing gene ids.

panoply_association_report

• fdr_value: (Float, default = 0.01) FDR value cutoff to be considered significant.

Output

• outputs: Tarball of files containing the following in the association subdirectory, for each class vector considered for association analysis:
  • List of significant differential markers derived using LIMMA (*-markers-all-fdr*.csv) and p-values for all input features (*-markers-all*.csv)
  • Marker importance for significant markers, along with final rank (*-markerimp-fdr*.csv)
  • Heatmap of significant differential markers (*-markers-heatmap.pdf)
  • Classifier performance contingency tables (*-analysis-model-results.txt)
  • Table of prediction results for training data (*-train-results-*.csv) and testing data (*-test-results.csv) using all classifiers
  • GSEA outputs, along with .gct. and .cls input files, for binary classes (in *-gsea-analysis/ subdirectory).
• contrasts: Tarfile of .gct files containing the contrast values, for each class of interest.
• ssgsea_assoc_tars: Array of tarfiles containing the results of ssGSEA analysis on each of the contrast tarfiles.
• report: Report summarizing the ssGSEA analysis on association results.