Sample Level Enrichment Analysis (SLEA) Tutorial and Gitools 1.6.2

As you may have read in the last post, Günes and Nuria presented the Sample Level Enrichment Analysis (SLEA) as a methodology to analyse the transcription level of each sample for groups of genes (like for example pathways, gene signatures, etc.)

An example represantation of the SLEA methodology

A gene-sample matrix is being converted to a gene-module matrix where module can be sets of genes like f.ex. pathways. The transcription level status can be used for stratifying and/or relating with clinical annotation

It is an easy way to stratify the samples into subgroups and/or relate the transcription level status of modules to clinical data. So this last week we have prepared a further video tutorial to show you how to perform SLEA easily with Gitools and gain more insight into your data.

Watch the video below or read the instructions in the fourth step of the Case Study: “Study multi-dimensional cancer data with Gitools”.

With this video tutorial we also release a new version of Gitools, version 1.6.2 so it is possible to have multi-value data matrices as input data for the enrichment analysis. Also we got rid of some bugs.

Download the latest version at www.gitools.org

Sample Level Enrichment Analysis (SLEA) unravels shared stress phenotypes among multiple cancer types

We are happy to share with you the results of a new publication, which has been published today in Genome Medicine:

Gunes Gundem and Nuria Lopez-Bigas. Sample level enrichment analysis (SLEA) unravels shared stress phenotypes among multiple cancer types. Genome Medicine. 4:28 doi:10.1186/gm327

In this manuscript we introduce SLEA, which we have described earlier in this blog, and we use it to explore the interrelation of different stress phenotypes in multiple cancer types. We also ask if these phenotypes could be used to explain prognostic differences among tumor samples.

First we do SLEA using Gitools with the set of genes related to Chromosome Instability (CIN genes) in a breast cancer dataset (Ivishina et al., 2006). Next we use the result of SLEA to stratify the tumors, and find that tumors with upregulation of CIN genes have worse prognosis than the others (see figure in the left). Read the rest of this entry »

Sample Level Enrichment Analysis (SLEA) in Gitools to assess the transcriptional status of pathways per tumor

From an expression profile of a set of tumor samples, in Gitools you can perform SLEA to assess the transcriptional status of modules (ie. pathways) per sample.

The identification of molecular biomarkers from expression data is a major objective in cancer research. It is clear that there is a benefit in pathway biomarkers (ie. measuring the activity of the pathway instead of individual genes). One easy way to analyze the transcriptional status of pathways (or other gene sets) is using Sample Level Enrichment Analysis (SLEA) in Gitools. This way you can assess the status of each pathway in each sample. This can be used to identify tumor subtypes and to correlate molecular features with clinical features.

Read the rest of this entry »