suppressPackageStartupMessages({ library(signatureSearch); library(readr); library(dplyr); library(DT) })
This vignette shows the code and results for Gene Expression Signature Searches (GESS)
with the downstream Functional Enrichment Analysis (FEA) of the signatureSearch
package. Here, <gess_method>
is used as GESS method,
The <N_gess_drugs>
top ranking unique hits in the GESS table were then used
for FEA where three different annotation systems were used: GO Molecular
Function (GO MF), GO Biological Process (GO BP) and KEGG pathways.
wf_list <- runWF(drug="<drug>", cell="<cell>", refdb="<refdb>", gess_method="<gess_method>", fea_method="<fea_method>", N_gess_drugs=<N_gess_drugs>)
options(DT.options = list(lengthMenu=c(10, 20, 50), scrollX=TRUE, autoWidth=TRUE)) gess_tb <- suppressMessages(read_tsv("results/<gess_method>_res.tsv")) tar_short <- tarReduce(gess_tb$'t_gn_sym') gess_tb$'t_gn_sym' <- tar_short datatable(gess_tb[1:100, ], filter = 'top', escape=FALSE) %>% formatSignif(columns=sapply(gess_tb, class)=="numeric", digits=3)
mf_tb <- suppressMessages(read_tsv("results/<fea_method>_mf_res.tsv")) datatable(mf_tb[1:50, colnames(mf_tb) != "itemID"], filter = 'top', escape=FALSE) %>% formatSignif(columns=sapply(mf_tb, class)=="numeric", digits=3)
bp_tb <- suppressMessages(read_tsv("results/<fea_method>_bp_res.tsv")) datatable(bp_tb[1:50, colnames(bp_tb) != "itemID"], filter = 'top', escape=FALSE) %>% formatSignif(columns=sapply(bp_tb, class)=="numeric", digits=3)
kegg_tb <- suppressMessages(read_tsv("results/<fea_method>_kegg_res.tsv")) datatable(kegg_tb[1:50, colnames(kegg_tb) != "itemID"], filter = 'top', escape=FALSE) %>% formatSignif(columns=sapply(kegg_tb, class)=="numeric", digits=3)
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