filtsep_bin: Binary converter for PAC_filtsep

In Danis102/seqpac: Seqpac: A Framework for smallRNA analysis in R using Sequence-Based Counts

Binary converter for PAC_filtsep

Description

filtsep_bin Converts PAC_filtsep data.frame output into a binary (hit-no-hit) data.frame

Usage

filtsep_bin(filtsep_out)

Arguments

filtsep_out

PAC_filtsep output data.frame, where each column contains the names of sequences that passed the filter for a specific group specified by a pheno_target object.

Details

Given a PAC_filtsep output data.frame, where each column contains the sequences that passed the filter for a specific group specified in pheno_target, filtsep_bin converts this into a data.frame where sequences are reported as hit (=1) or no hit (=0). Such binary coded group occurrence can for example be used by UpSetR::upset to generate visualization of overlaps using UpSet plots.

Value

data.frame where each uniques sequence (row names) in filtsep_out are reported as hit (=1) or no hit (=0) across samples or sample groups (column names).

See Also

https://github.com/Danis102 for updates on the current package.

Other PAC analysis: PAC_covplot(), PAC_deseq(), PAC_filter(), PAC_filtsep(), PAC_gtf(), PAC_jitter(), PAC_mapper(), PAC_nbias(), PAC_norm(), PAC_pca(), PAC_pie(), PAC_saturation(), PAC_sizedist(), PAC_stackbar(), PAC_summary(), PAC_trna(), as.PAC(), map_rangetype(), tRNA_class()

Examples

load(system.file("extdata", "drosophila_sRNA_pac_filt_anno.Rdata", 
                  package = "seqpac", mustWork = TRUE))

## Keep sequences with 5 counts (threshold) in 100% (coverage) of 
## samples in a group:
 # Use PAC_filtsep to find sequences 
 filtsep <- PAC_filtsep(pac, norm="counts", threshold=5, 
                        coverage=100, pheno_target= list("stage"))
                        
 # Filter by unique sequences passing filtsep  
 filtsep <- unique(do.call("c", as.list(filtsep)))
 pac_filt <- PAC_filter(pac, subset_only = TRUE, anno_target= filtsep)
 
 # Find overlap
 olap <- reshape2::melt(filtsep, 
                        measure.vars = c("Stage1", "Stage3", "Stage5"), 
                        na.rm=TRUE)
                        
## Upset plot using the UpSetR package
 # (when output="binary" PAC_filtsep uses filtsep_bin for binary conversion
 # Use PAC_filtsep with binary output
 filtsep_bin <- PAC_filtsep(pac, norm="counts", threshold=5, 
                            coverage=100, pheno_target= list("stage"), 
                            output="binary")
 
# Plot Venn diagram or UpSetR
#
# plot(venneuler::venneuler(data.frame(olap[,2], olap[,1]))) 
#
# UpSetR::upset(filtsep_bin, sets = colnames(filtsep_bin), 
#              mb.ratio = c(0.55, 0.45), order.by = "freq", keep.order=TRUE)
             

Danis102/seqpac documentation built on Aug. 26, 2023, 10:15 a.m.