# # 2-way Venn diagrams # # setlist2 - setlist1:2; OLlist2 - overLapper(setlistsetlist2, sep type"vennsets OLlist2Venn_List; counts - sapply(OLlist2Venn_List, length vennPlot(countscounts) # Plots a non-proportional 2-way Venn diagram.
# In this example, they are appended to the original data frame with the ame function.
#8 by david gilmore nascar 2002 cd key (12:58pm est sun nov 23 2003) hey yall i misplaced my cd key.# The identified row IDs are then used to subset the distance matrix and re-cluster it with hclust.# Removes duplicates and prints the number of unique loci.# For example, if my_infile naruto ninja storm 2 iso contains 'x - c(1:100 x the result for this R expression will be written to 'my_outfile' (or stdout).#if expression where expression can be a number, with the statement returning false for 0, and true for non-zero values.# # 5-way Venn diagrams # # setlist5 - setlist1:5; OLlist5 - overLapper(setlistsetlist5, sep type"vennsets counts - sapply(OLlist5Venn_List, length) vennPlot(countscounts, ccolc(rep(1,30 2 lcex1.5, ccexc(rep(1.5,5 rep(0.6,25.5) # Plots a non-proportional 5-way Venn diagram.# Change Log, all notable changes to this project will be documented in this file.# (2.3) Obtain AffyID-to-GeneID mappings when working with AffyIDs # AffyID2GeneID(map # When working with AffyIDs, this function creates a AffyID-to-GeneID mapping data frame using by default the tair # mappings for the Arabidopsis ATH1 chip.
# (3.1) Reloading required data objects from local files # loadData load(file"MF_node_affy_list load(file"BP_node_affy_list load(file"CC_node_affy_list # This step makes future sessions much faster, since it allows to skip the previous data generation steps (2.1-2.3).
# Provided example with 'wt.# Imports the required overLapper function.' upgrade_warning 0364 Unable to assign default member of symbol 'res'.# Print out row labels in same order as shown in the heatmap.# (4.2) Reduce GO Term Redundancy in 'GOHyperGAll_results' # simplifyDF - gocat"MF cutoff0.001, correctT) # The result data frame 'GOHyperGAll_result' often contains several connected GO terms with significant scores which # can complicate the interpretation of large sample sets.#2 NAS620C4 washer NAS620C47 washer NAS620C5 washer NAS621627D bolt NAS623-2-2 screw NAS62323 screw NAS62512 bolt NAS62623 bolt NAS62936 bolt NAS63 NAS6303-10 bolt NAS6303-4 bolt NAS6303-4Y.# # Reading intensity data # # targets - readTargets Targets.' ' else return tostring(o) end end However, this is not very efficient for big tables because of all the string concatenations involved, and will freak if you have circular references or 'cycles'.
# GO term enrichment analysis with GOstats # # Example of how to test a sample set of probe set keys for over-representation of GO terms using a hypergeometric distribution # test with the function hyperGTest.
# Replace in last step 'exprs(eset)1:40 by matrix of differentially expressed genes from limma analysis.
# Export and other utilities # OLexport - trix(unlist(sapply(OLlist54, paste, collapse" write.
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