The consICa package: User’s manual

Petr V. Nazarov, Tony Kaoma, Maryna Chepeleva

Abstract

This vignette describes the Bioconductor package consICA. We developed a fast parallelized algorithm for deconvolution of omics data, based on the independent component analysis. Additionally, we implemented convenient functionality for the analysis of decomposed signals including ANOVA-based linking to experimental factors, enrichment analysis, and survival analysis. A PDF report can be generated automatically by the package. The package was previously applied to single-cell and bulk-sample transcriptomics data in the field of cancer research. We showed the ability of the approach to (i) separate biologically relevant molecular signals from technical biases, (ii) identify molecular signals specific to cell types and cellular processes, (iii) provide a feature engineering mechanism improving the classification of the samples [1,2,3].

Package

consICA 1.2.0

Contents

This document describes the usage of the functions integrated into the package and is meant to be a reference document for the end-user.

1 Introduction

Independent component analysis (ICA) of omics data can be used for deconvolution of biological signals and technical biases, correction of batch effects, feature engineering for classification and integration of the data [4]. The consICA package allows building robust consensus ICA-based deconvolution of the data as well as running post-hoc biological interpretation of the results. The implementation of parallel computing in the package ensures efficient analysis using modern multicore systems.

2 Installing and loading the package

Load the package with the following command:

if (!requireNamespace("BiocManager", quietly = TRUE)) { 
    install.packages("BiocManager")
}
BiocManager::install("consICA")

library('consICA')

3 Example dataset

The usage of the package functions for an independent component deconvolution is shown for an example set of 472 samples and 2454 most variable genes from the skin cutaneous melanoma (SKCM) TCGA cohort. It stored as a SummarizedExperiment object. In addition the data includes metadata such as age, gender, cancer subtype etc. and survival time-to-event data. Use data as

library(SummarizedExperiment)
data("samples_data")
samples_data
#> class: SummarizedExperiment 
#> dim: 2454 472 
#> metadata(0):
#> assays(1): X
#> rownames(2454): A2ML1 AACSP1 ... ZNF883 ZP1
#> rowData names(0):
#> colnames(472): 3N.A9WB.Metastatic 3N.A9WC.Metastatic ...
#>   Z2.AA3S.Metastatic Z2.AA3V.Metastatic
#> colData names(103): age_at_initial_pathologic_diagnosis gender ... time
#>   event

# According to our terminology expression matrix X of 2454 genes and 472 samples
X <- data.frame(assay(samples_data))
dim(X)
#> [1] 2454  472
# variables described each sample
Var <- data.frame(colData(samples_data))
dim(Var)
#> [1] 472 103
colnames(Var)[1:20] # print first 20
#>  [1] "age_at_initial_pathologic_diagnosis" "gender"                             
#>  [3] "race"                                "Weight"                             
#>  [5] "Height"                              "BMI"                                
#>  [7] "Ethinicity"                          "Cancer.Type.Detailed"               
#>  [9] "Tumor.location.site"                 "ajcc_pathologic_tumor_stage"        
#> [11] "Cancer.stage.M"                      "Cancer.stage.N"                     
#> [13] "Cancer.stage.T"                      "initial_pathologic_dx_year"         
#> [15] "birth_days_to_initial_diagnosis"     "Year.of.Birth"                      
#> [17] "age.at.last.contact"                 "Year.of.last.contact"               
#> [19] "last_contact_days_to"                "age.at.death"
# survival time and event for each sample
Sur <- data.frame(colData(samples_data))[,c("time", "event")]
Var <- Var[,which(colnames(Var) != "time" & colnames(Var) != "event")]

4 Consensus independent component analysis

Independent component analysis (ICA) is an unsupervised method of signal deconvolution [3]. ICA decomposes combined gene expression matrix from multiple samples into meaningful signals in the space of genes (metagenes, S) and weights in the space of samples (weight matrix, M).