docs/presentation.md

TCGA MultiOmics Data Analysis

author: Pablo Riesgo (priesgo@uoc.edu) date: 08/11/2015

The dataset

Retrieval

library("RTCGAToolbox")
firehose_datasets = getFirehoseDatasets()
firehose_dates= getFirehoseRunningDates()
BRCA.dataset = getFirehoseData (dataset="BRCA", runDate=firehose_dates[1], RNAseq2_Gene_Norm = TRUE, RPPA=TRUE)
OV.dataset = getFirehoseData (dataset="OV", runDate=firehose_dates[1], RNAseq2_Gene_Norm = TRUE, RPPA=TRUE)

The dataset

Preprocessing

The dataset

Samples

407 BRCA + 201 ovary cancer samples Samples barplot

Variables

12880 gene expression + 55 protein expression variables are far too many to plot...

Principal component Analysis

Gene expression

Samples barplot

Protein expression

Samples barplot

Principal component Analysis

Joint analysis Samples barplot

Hierarchical clustering

Gene expression

Samples barplot

Protein expression

Samples barplot

Hierarchical clustering

Joint analysis Samples barplot

Multiple Co-Inertia Analysis (MCIA)

The 2 first PCs explain 34.05% and 11.55% of the variance

Samples barplot

Labeled items are the top 100 variables

Samples barplot

sparse Least Square Regression (sPLS)

Samples Samples barplot

Variables Samples barplot

sparse Least Square Regression (sPLS)

Heatmap Samples barplot

Network Samples barplot

Results

Matches between MCIA and sPLS top 100 and known oncogenes and tumor supressor genes

Samples barplot

Matches between MCIA and sPLS enriched pathways in Reactome

Samples barplot

Other methods to study



priesgo/TCGAome documentation built on May 25, 2019, 11:26 a.m.