#acl CscGroup:read,write,revert = GSEA Gene Set Enrichment Analysis (www.broadinstitute.org/gsea ) = <> *[[attachment:GSEA_paper2005.pdf]] *[[ http://www.broadinstitute.org/gsea/doc/GSEAUserGuideFrame.html | link to GSEA documentation ]]: the format section will explain you how to format your data as .rnk file, a .gmt file or an expression file (.gct). == GSEA enrichment scores and statistics == * which values to consider for interpretating results: || number of gene-set tested || 1 gene-set || few gene-sets (3-10)|| a lot of gene-sets >3000 || || ES || (./) ||not recommended ||not recommended || || NES ||not informative || (./) || (./) || || nom p-value || (./) ||better to use FDR ||better to use FDR || || FDR || not informative || (./) || (./) (*) || (*) FDR values are going to be pessimistic when using the BaderLab geneset files due to the high number of tested gene-sets and therefore the high p-value adjustment needed. * ES (enrichment score): reflects the degree to which a gene-set is overrepresented at the top or bottom of a ranked list of genes. * NES (normalized enrichment score): NES corrects for differences in ES between gene-sets due to differences in gene-set sizes. It enables to compare the scores of the different tested gene-sets with each other. NES = actual ES / mean of all ESs obtained from all random permutations for the single gene-set that is being tested * nom p-value: The nominal p value estimates the statistical significance of the enrichment score for a single gene set. The p-value is calculated from the null distribution. Using gene-set permutation, the null distribution is created by generating, for each permutation, a random gene set the same size as your specified gene set by selecting that number of genes from all of the genes in your expression data set (or pre-ranked list), and then calculating the enrichment score for that randomly selected gene set. The distribution of those enrichment scores across all of the permutations constitutes the null distribution. * FDR: corrects for multiple hypothesis testing and enable a more correct comparison of the different tested gene-sets with each other. * note: for a given gene-set S and observed NES, called NES*, FDR is [% of all NES (including permutations) >= NES*] / [% of all observed NES (=NES for all tested gene-sets) >= NES*] {{attachment:plotGSEA_FDR_pval.png}} === Explanation of GSEA ES score and values from Wang and Murray (BMC Bioinformatics 2013): === * [[attachment:GSEA_explanation_Wang_Murray.pdf | link to explanation of GSEA by Wang and Murray]] * {{attachment:GSEA_explanation_Wang_Murray.png}} == GSEA formats (.GCT, .CLS, .RNK, .GMT) : == * http://www.broadinstitute.org/cancer/software/gsea/wiki/index.php/Data_formats * http://www.broadinstitute.org/cancer/software/genepattern/modules/docs/GSEA/14 == Tips on how to install GSEA locally and launch it from the command line: == * Download and Save the gsea2-2.0.14.jar file in your folder Documents * open your console/terminal window * Type the command for MAC: *"java -Xmx2G -jar ~/Documents/gsea2-2.0.14.jar" * Type the command for Windows: * "cd Documents" * "java -Xmx2G –jar gsea2-2.0.14.jar" == GSEA parameters == TODO == what is single sample GSEA == TODO == Answers to questions == * Question: how can we compare the NES (same gene-sets) between different datasets: * 1) single sample GSEA case: e.g single sample GSEA was used on several patients and then a matrix of NES is created with the gene-sets as rows and patients as column and you want to find out gene-sets that are comparable between patients. A t-test with 1 group could be used to identify the gene-sets with comparable NES throughout samples --> t = mean/ standard error. The gene-sets will get a pvalue close to 0 only for gene-sets with comparable NES across patients (standard error is going to be small) * 2) GSEA has been run using GSEA preranked option, you created a map using the 2 datasets and you see that the map is similar (e.g JAK2 responders versus partial responders) (high correlation throughout the gene-sets for the 2 datasets). You can do a K_S (Kolmogorov–Smirnov) test or a Wilcoxon rank sum test on the NES from the 2 datasets to see if "the 2 maps" are different or not. == Troubleshooting == * out of memory: * running GSEA using the comprehensive Baderlab gene-set is using 1.8 GB of memory: * Check that you have at least 2GB of free memory on your machine * Check that you haved allocated enough memory to GSEA when launching GSEA * using the javaGSEA Desktop Application: Launch with 2GB (for 64-bit Java only) * using the javaGSEA jar file: allocate 2GB of memory using this command: "java '''-Xmx2G''' –jar gsea2-2.0.14.jar" * java version: * GSEA works with java 6 , java 7 and java 8. However, for java 7 and 8, launching GSEA using the javaGSEA Desktop may not work because on some security settings on your computer, in this case try to launch GSEA using the command line.