PAC_filtsep: Generates filtered sequence name list seperated on group

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

Generates filtered sequence name list seperated on group

Description

PAC_filtsep Group seperated filtered name list

Usage

PAC_filtsep(
  PAC,
  norm = "counts",
  threshold = 10,
  coverage = 100,
  pheno_target = NULL,
  output = "sequence"
)

Arguments

PAC	PAC-list object containing an Pheno data.frame with samples as row names and a Counts table with raw counts or normalized counts table containing for example counts per million (cpm; generated by PAC_norm).
norm	Character specifying if filtering should be done using "counts", "cpm" or another normalized data table in PAC$norm (default="counts").
threshold	Integer giving the threshold in counts PAC$Counts or normalized counts (table in PAC$norm) that needs to be reached for a sequence to be included (default=10).
coverage	Integer giving the percent of independent samples of each group that needs to reach the threshold for a sequence to be included (default=100).
pheno_target	(optional) List with: 1st object being a character vector of target column in Pheno, 2nd object being a character vector of the target group(s) in the target Pheno column (1st object). (default=NULL)
output	Specifies the output format. If output="sequence" (default), then a data.frame is returned where each column contains the sequences names that passed the filter for a specific group specified in pheno_target. If output="binary", then the resulting data.frame will be converted into a binary (hit=1, no hit=0) data.frame. See filtsep_bin.

Details

Given a PAC object the function will extract sequences within the given filter within a given group.

Value

A data.frame (see output for details).

See Also

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

Other PAC analysis: PAC_covplot(), PAC_deseq(), PAC_filter(), 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(), filtsep_bin(), 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 Wenn 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.