differential expression and downstream analysis

quick-start.md

@@ -201,7 +201,7 @@ _Untreated_ and _LPS_ as classes. Clicking _OK_
 will call differential gene expression analysis method 
 with [*limma*](https://bioconductor.org/packages/limma) R package.
 
-<img src="images/limma_tool.jpg" width="500px" />
+<img src="images/limma_tool.png" width="500px" />
 
 The rows can be ordered by decreasing *t*-statistic column to see which genes are the most
 up-regulated upon LPS treatment. 

tutorials/differential-expression.files/geneset.txt

@@ -0,0 +1,202 @@
+HALLMARK_INTERFERON_GAMMA_RESPONSE
+> Genes up-regulated in response to IFNG [GeneID=3458].
+ADAR
+APOL6
+ARID5B
+ARL4A
+AUTS2
+B2M
+BANK1
+BATF2
+BPGM
+BST2
+BTG1
+C1R
+C1S
+CASP1
+CASP3
+CASP4
+CASP7
+CASP8
+CCL2
+CCL5
+CCL7
+CD274
+CD38
+CD40
+CD69
+CD74
+CD86
+CDKN1A
+CFB
+CFH
+CIITA
+CMKLR1
+CMPK2
+CMTR1
+CSF2RB
+CXCL10
+CXCL11
+CXCL9
+DDX58
+DDX60
+DHX58
+EIF2AK2
+EIF4E3
+EPSTI1
+FAS
+FCGR1A
+FGL2
+FPR1
+GBP4
+GBP6
+GCH1
+GPR18
+GZMA
+HELZ2
+HERC6
+HIF1A
+HLA-A
+HLA-B
+HLA-DMA
+HLA-DQA1
+HLA-DRB1
+HLA-G
+ICAM1
+IDO1
+IFI27
+IFI30
+IFI35
+IFI44
+IFI44L
+IFIH1
+IFIT1
+IFIT2
+IFIT3
+IFITM2
+IFITM3
+IFNAR2
+IL10RA
+IL15
+IL15RA
+IL18BP
+IL2RB
+IL4R
+IL6
+IL7
+IRF1
+IRF2
+IRF4
+IRF5
+IRF7
+IRF8
+IRF9
+ISG15
+ISG20
+ISOC1
+ITGB7
+JAK2
+KLRK1
+LAP3
+LATS2
+LCP2
+LGALS3BP
+LY6E
+LYSMD2
+MARCHF1
+METTL7B
+MT2A
+MTHFD2
+MVP
+MX1
+MX2
+MYD88
+NAMPT
+NCOA3
+NFKB1
+NFKBIA
+NLRC5
+NMI
+NOD1
+NUP93
+OAS2
+OAS3
+OASL
+OGFR
+P2RY14
+PARP12
+PARP14
+PDE4B
+PELI1
+PFKP
+PIM1
+PLA2G4A
+PLSCR1
+PML
+PNP
+PNPT1
+PSMA2
+PSMA3
+PSMB10
+PSMB2
+PSMB8
+PSMB9
+PSME1
+PSME2
+PTGS2
+PTPN1
+PTPN2
+PTPN6
+RAPGEF6
+RBCK1
+RIPK1
+RIPK2
+RNF213
+RNF31
+RSAD2
+RTP4
+SAMD9L
+SAMHD1
+SECTM1
+SELP
+SERPING1
+SLAMF7
+SLC25A28
+SOCS1
+SOCS3
+SOD2
+SP110
+SPPL2A
+SRI
+SSPN
+ST3GAL5
+ST8SIA4
+STAT1
+STAT2
+STAT3
+STAT4
+TAP1
+TAPBP
+TDRD7
+TNFAIP2
+TNFAIP3
+TNFAIP6
+TNFSF10
+TOR1B
+TRAFD1
+TRIM14
+TRIM21
+TRIM25
+TRIM26
+TXNIP
+UBE2L6
+UPP1
+USP18
+VAMP5
+VAMP8
+VCAM1
+WARS1
+XAF1
+XCL1
+ZBP1
+ZNFX1

tutorials/differential-expression.md

@@ -20,3 +20,182 @@ This is the third of three related modules:
 
 In this module we start with a normalized and filtered dataset, do a differential
 expression analysis and follow it up with pathway enrichment analysis.
+
+You can either continue the session from the previous module, or download
+file [GSE53986_filtered.gct]({{ site.baseurl }}{% link tutorials/exploration.files/GSE53986_filtered.gct%}) 
+and load it into Phantasus.
+
+## Apllying _limma_ tool
+
+Differential expression analysis is one of the most straightforward types of analysis
+to carry out on gene expression data.
+It answers a question what genes a differentially regulated between two conditions
+of interest.
+
+After dataset is properly normalized 
+[*limma*](https://bioconductor.org/packages/limma) R package can be used 
+to do differential expression analysis.
+Open _Tool/Diffential Expression/limma_ menu,
+Choose _treatment_ as a _Field_, with
+_Untreated_ and _LPS_ as classes. 
+
+<img src="differential-expression.files/limma_tool.png" width="400px" />
+
+Clicking _OK_ will add columns like *P-value* or *logFC* to the table.
+The rows can be ordered by decreasing of *t*-statistic column 
+to see which genes are the most up-regulated upon LPS treatment. 
+
+<img src="differential-expression.files/limma_results.png" width="600px" />
+
+## Saving and sharing the results
+
+We have found genes differentially regulated upon LPS treatment. 
+Now we can save the results as a GCT file to able to return to it later
+or share with a colleague.
+The resulting file should be similar to 
+[GSE53986_Ctrl_vs_LPS.gct]({{ site.baseurl }}{% link tutorials/differential-expression.files/GSE53986_Ctrl_vs_LPS.gct%}).
+
+However, in Phantasus you can share the result by providing link to the current
+session. Use *File/Get dataset link* menu.
+After you click on it, a window will appear containing a link, that you can share
+with other people. Accessing it will load an identical session.
+
+<img src="differential-expression.files/dataset_link.png" width="450px" />
+
+## Pathway enrichment with MSigDB
+
+While we now know what genes are differentially regulated
+between our two conditions of interest, these results can be hard to interpret.
+There can be too many or too few differential genes, 
+a gene can multiple functions and so on.
+Naturally, it is much easer to reason about biological processes, or pathways.
+A pathway has many genes, which makes its signal to be more robust and it has a 
+particular function.
+
+There are many ways to analyze pathways, with over-representation analysis 
+being one of the most straightforward. In this analysis we will have a query 
+gene set, such as top 250 up-regulated genes, and we will be looking for biological
+pathways that have a big intersection with our query. 
+For such pathways we can speculate that they are regulated in our process as well.
+
+Let us open a MSigDB web tool for such analysis: <http://software.broadinstitute.org/gsea/msigdb/annotate.jsp>.
+MsigDb requires registration but it is free. 
+After logging in "Investigate Gene Sets" window should appear.
+
+<img src="differential-expression.files/msigdb.png" width="500px" />
+
+Now let us go back to Phantasus where we will be selecting top 250 
+up-regulated genes.
+For this we will us *Tools/Filter* menu.
+There add a top filter by *t* column for 250 genes.
+
+<img src="differential-expression.files/filter_top250.png" width="450px" />
+
+Next close the filter window and select all of the displayed genes. 
+To copy gene symbol do a right-click on any value in *Gene symbol* column
+and select *Copy selected values from Gene symbol* option.
+After copying go back to *Tools/Filter* menu and remove the filter,
+so that all 12000 genes are displayed again.
+
+<img src="differential-expression.files/top250_copy.png" width="600px" />
+
+Paste the copied gene symbols to MSigDB, select proper species (mouse)
+and select *H: hallmark gene sets* and *CP: Canonical pathways* collections.
+Also, you can change parameters to show top 20 gene sets.
+Click on *compute overlaps* to run the analysis.
+
+
+<img src="differential-expression.files/msigdb_genes.png" width="500px" />
+
+The screen features some statistics and the table with pathway p-values.
+All the top pathways are immune singling pathways, in particular
+interferon response, which is not unexpected.
+However, in normal analysis, where the biology is not that well-known,
+such analysis proves to be very useful.
+
+<img src="differential-expression.files/msigdb_results.png" width="500px" />
+
+There is one important moment that should be considered, when MSigDB analysis is executed.
+MSigDB web tool uses hypergeometric test as a statistical measure of overlap
+significance. 
+An essential parameter of the test is the background gene set (also called "universe"),
+from which the query genes were selected.
+In the reported statistics you can see that for the test a universe of 
+38404 genes has been used, however the query was constructed by selecting
+from only 12000 genes. 
+This discrepancy leads to overreported results and the real p-values are
+less significant than the reported ones. 
+Pathways with very low reported q-value, such as 1e-12, won't become
+non-significant after the correction, but pathways with q-value on the
+order of 1e-3--1e-4 should be taken with caution.
+
+
+## Visualizing pathway enrichment
+
+A common way to visualize enrichment of individual pathways is to use
+GSEA (gene set enrichment analysis) plot. In our case let consider Interferon Gamma Response pathway.
+
+First, go to the pathway description: <http://www.gsea-msigdb.org/gsea/msigdb/cards/HALLMARK_INTERFERON_GAMMA_RESPONSE.html>.
+There, click on *text* format in *Download gene set* row, which will get you a
+file with a list of genes in this pathway: 
+[geneset.txt]({{ site.baseurl }}{% link tutorials/differential-expression.files/geneset.txt%}).
+
+Copy the genes from the pathway and paste them in search field in Phantasus. 
+Sort the dataset by *t* column and select *Fit To Windows* zoom option to 
+get an overview of how the whole pathway is regulated.
+You will be able to see that not just the top 250 up-regulated genes
+contain many Interferon Gamma Response pathway genes, but overall
+the genes from this pathway tend to be in the upper part.
+
+<img src="differential-expression.files/ifng_response_overview.png" width="800px" />
+
+To quantify and better show the up-regulation trend GSEA analysis and GSEA
+plot can be used. Go to *Tools/Plot/GSEA plot*. In the tool option select *t*
+column for ranking and select *condition* for annotation.
+This will result in the following plot.
+
+<img src="differential-expression.files/ifng_response_gsea_plot.png" width="600px" />
+
+GSEA plot works as follows. First, the genes are ordered according to some metric,
+such as t-statistic that we used. Next the genes are traversed one by one 
+and if the gene is present in the considered pathway the curve goes up, and it
+goes down otherwise. If the genes of the pathway tend to be up-regulated,
+as in our case, the curve will first go up and then go down. If the 
+pathway is down-regulated, the genes will be shifted towards the other end
+and the curve will first go down and then up. If the pathway is not regulated
+at all, then genes will be approximately uniformly distributed in the
+ranking and the curve will also represent a random walk without any specific trend.
+The displayed P-value shows quantitatively the non-randomness of the behavior.
+An important aspect of GSEA analysis is that it does not require selecting 
+any threshold, unlike hypergeometric based analysis described in the previous section.
+
+## Pathway enrichment with FGSEA
+
+GSEA analysis can be also carried systematically for a collection of pathways
+with _FGSEA_ tool.
+
+Open _Tools/Pathway Analysis/Perform FGSEA_, then select Pathway database, which corresponds specimen used in dataset
+(Mus Musculus in this example), ranking column and column with ENTREZID or Gene IDs.
+
+<img src="differential-expression.files/fgsea_tool.png" width="500px" />
+
+Clicking OK will open new tab with pathways table.
+
+<img src="differential-expression.files/fgsea_result.png" width="800px" />
+
+Clicking on table row will provide additional information on pathway: pathway name, genes in pathway, leading edge.
+You can save result of analysis in `TSV` format.
+
+## Other tools for pathway analysis
+
+Aside mention above tools for pathway enrichment analysis there are few more
+sometimes more suitable tools:
+* [Enrichr](https://amp.pharm.mssm.edu/Enrichr) feature many diverse pathway
+collections, especially interesting is transcription regulation collections. 
+As Enrichr is strict to its input, *Tools/Pathway analysis/Submit to Enrichr* menu
+can be used to simplify access.
+* [WebGestalt](http://www.webgestalt.org/) can do pathway analysis for many organisms.
+Additionally, the background gene set can be specified for over-representation analysis,
+which make results much more accurate.
+* [GeneQuery](https://artyomovlab.wustl.edu/genequery/searcher/) can be used 
+to search for signatures among public datasets from Gene Expression Omnibus.