Jan

28

GSVA – gene set variation analysis for microarray and RNA-Seq data

Filed Under Pathway Analysis 

Gene set enrichment (GSE) analysis is a popular framework for condensing information from gene expression profiles into a pathway or signature summary. The strengths of this approach over single gene analysis include noise and dimension reduction, as well as greater biological interpretability. As molecular profiling experiments move beyond simple case-control studies, robust and flexible GSE methodologies are needed that can model pathway activity within highly heterogeneous data sets.

To address this challenge, researchers at Hospital del Mar Medical Research Institute (IMIM),  Spain have developed Gene Set Variation Analysis (GSVA), a GSE method that estimates variation of pathway activity over a sample population in an unsupervised manner. They demonstrate the robustness of GSVA in a comparison with current state of the art sample-wise en-richment methods. Further, they provide examples of its utility in differential pathway activity and survival analysis. Lastly, the researchers show how GSVA works analogously with data from both microarray and RNA-seq experiments.

GSVA provides increased power to detect subtle pathway activity changes over a sample population in comparison to corresponding methods. While GSE methods are generally regarded as end points of a bioinformatic analysis, GSVA constitutes a starting point to build pathway-centric models of biology. Moreover, GSVA contributes to the current need of GSE methods for RNA-seq data. GSVA

Availability – GSVA is an open source software package for R which forms part of the Bioconductor project and can be downloaded at http://www.bioconductor.org/packages/release/bioc/html/GSVA.html.

  • Hänzelmann S, Castelo R, Guinney J. (2013) GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics 14(1), 7. [abstract]

Incoming search terms:

  • gsva seqanswers
  • BMC bioinformatics gene analysis 2013
  • gsva run
  • GSVA: gene set variation analysis for microarray and RNA-Seq data
  • library(gsva)
  • R GSVA
  • survival analysis rsem rna-seq

Comments

Leave a Reply




  • Social Networking Pages

    Linkedin Group

  • Follow Me on Pinterest

  • RSS SEQanswers – RNA Sequencing

    • The Transcript Length from Cufflinks May 25, 2013
      Hi Guys, I'm doing a fungus RNA-Seq.However, the merged transcriptome gave me very long transcripts (generally >2K). I used GeneMarES to do... […]
      hchang10
    • DESeq; can I omit timepoints during dispersal estimation? May 24, 2013
      I have a bacterial timecourse with 2 biological replicates per timepoint. There is a fair bit of variance between my replicates. I have spent the... […]
      amcloon
    • HT Seq Count stranded options May 24, 2013
      I am very new to bioinformatics, so I would be really grateful for some help! I have been using *HTSeq Count v0.5.3* and I am bit confused about... […]
      qwrissie
    • Tophat 2.0.8b installation error May 24, 2013
      I install tophat-2.0.8b to rerun the mapping. but when i make it, the error appears like this. make[1]: Entering directory... […]
      canhu
    • reason for low mapping rate?? May 23, 2013
      we did RNASeq using HiSeq 2000 100PE. When the data were back, I mapping them to the reference sequence, but got very low mapping rate (30-40%). I... […]
      miaom
    • cross-species data - questions about normalization May 23, 2013
      Hi, I have some data form various samples (cell types) in different species. I want to compare and analyze gene expression variability across the... […]
      trelek2

  • RSS Biostar – RNA-Seq

    • Why am I getting so many unmapped reads in STAR, classified as "too short"?
      I am currently using STAR to map several Hi-SEQ mRNA runs. I'm having trouble getting a decent amount of reads to map, but I don't really understand why. I'm hoping you can shed some light :) In the final log, only about 50% (or less) of the reads map to the reference. I'm using a GTF in addition to the genome. The unmapped bin that most […]
    • What are the best practices for SNP identification in RNA seq transcriptome data
      I have 20 RICE RNA seq tranascriptome data hiseq 2000 platform paired end reads. I aligned fasta reads with BWA and remove PCR duplicates with PICARD. Later I call SNP with samtools using various parameters. I would like to clarify what parameters should I used while alinging to reference rice genome for looking SNP location 100 bp upstream and 250 bp downst […]
    • How do TopHat options -g , --supress-hits, and Bowtie options interplay?
      Hi, I am currently using TopHat2 to map RNA-seq runs. I think there have been some changes pertaining the -g option. Does anyone know how it works now? I used to think that setting -g would look for n alignments for a given read, report them [if top-scoring] and discard those reads that had more than g [top scoring] alignments. Now, the description sounds mo […]
    • What happened to -k in TopHat for multiple-mapping reads?
      Selecting -g n in tophat does not discard reads mapping more than n, but instead only reports n alignments for those out all all their TOP scoring alignments. I think there used to be an option -k that would allow one to discard reads that topped x alignments -- whatever happened to that? I only see -g in the tophat 2 manual, no reporting options like before […]
    • Does tophat use the library-type information for mapping, or just for the XS flag?
      When I specify library-type to TopHat, i.e., first-strand, second-strand, unstranded, TopHat appends a value + or - to the XS:A flag, which is useful for subsequent analyses, such as annotation. However, does this information actually influence the "mappability" of reads, or is this unaffected? My thinking is that the information would be considere […]
    • Purpose of Y-shaped adapters in Illumina Sequencing?
      Hi all, Y adapters different sequences to be annealed to the 5' and 3' ends of each molecule in a library. The arms of the Y are unique, and the middle part, connected to the DNA fragment, is complementary. What are the advantages of this? My take of this over having fully-complementary adapters (ADAPTER1 - - - - - ADAPTER1) is that: -Upon primer a […]