Bioc2019pathwayPCA: A Workshop on the pathwayPCA Package

In gabrielodom/Bioc2019pathwayPCA: A workshop on the pathwayPCA package

Description

With the advance in high-throughput technology for molecular assays, multi-omics datasets have become increasingly available. In this workshop, we will demonstrate using the pathwayPCA package to perform integrative pathway-based analyses of multi-omics datasets. In particular, we will demonstrate through three case studies the capabilities of pathwayPCA

1. perform pathway analysis with gene selection,

2. integrate multi-omics datasets to identify driver genes,

3. estimate and visualize sample-specific pathway activities in ovarian cancer, and

4. identify pathways with sex-specific effects in kidney cancer.

pathwayPCA data functions

read_gmt -

imports a .gmt file as a pathway collection

SE2Tidy -

extracts an assay from a SummarizedExperiment object (https://doi.org/10.18129/B9.bioc.SummarizedExperiment) and turns it into a “tidy” data frame

TransposeAssay -

is a variant of the base t function designed specifcially for data frames and tibbles. It preserves row and column names after transposition.

pathwayPCA -Omics functions

CreateOmics -

takes in a collection of pathways, a single -omics assay, and a clinical response data frame and creates a data object of class Omics*

SubsetPathwayData -

can extract the pathway-specific assay values and responses for a given pathway from an Omics* object

pathwayPCA methods

AESPCA_pVals -

takes in an Omics* object and calculates pathway p-values (parametrically or non- parametrically), principal components, and loadings via AESPCA. This returns an object of class aespcOut.

SuperPCA_pVals -

takes in an Omics* object with valid response information and calculates pathway parametric p-values, principal components, and loadings via SuperPCA. This returns an object of class superpcOut.

pathwayPCA results functions

getPathPCLs -

takes in an object of class aespcOut or superpcOut and the TERMS name of a pathway. This function extracts 1) the data frame of principal components and subject IDs for the given pathway, and 2) a data frame of sparse loadings and feature names for the given pathway.

getPathpVals -

takes in an object of class aespcOut or superpcOut and returns a table of the p-values and false discovery rates for each pathway

gabrielodom/Bioc2019pathwayPCA documentation built on June 19, 2019, 7:40 p.m.