PAC_summary: Summarizes PAC objects

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

Summarizes PAC objects

Description

PAC_summary summarizes data stored in a PAC object.

Usage

PAC_summary(
  PAC,
  norm = "counts",
  type = "means",
  pheno_target = NULL,
  rev = FALSE,
  merge_pac = TRUE
)

Arguments

PAC	PAC object containing a Pheno data.frame with samples as row names and a Counts table with raw counts. Optionally, the PAC object may contain normalized counts tables, saved in the norm list ('folder'). Such normalized table can be generated using the PAC_norm function.
norm	Character indicating what type of data to be used. If 'counts', the raw counts in Counts will be used (default). Given any other value, the function will search for the value as a name on a data.frame stored in the normalized list-folder.
type	Character indicating what type of summary to be applied to the data. The function currently supports: type="means" # Group means type="sd" # Group standard deviation type="se" # Group standard error of the mean type="log2FC" # Group log2 fold changes against other groups type="log2FCgrand" # Group log2 fold changes against a grand mean type="percentgrand" # Group log2 fold changes against a grand mean
pheno_target	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).
rev	Logical whether pairwise comparisons (e.g. log2FC) should be reversed (default=FALSE).
merge_pac	Logical whether simplified annotation vector should automatically be added to the Anno object of the input PAC list object (default=TRUE). If merge_pac=FALSE a data.frame is returned.

Details

Given a PAC object this function summarize data in counts(PAC) or in the norm 'folder' according to a grouping column in pheno(PAC).

Value

A PAC object with a pheno_summary folder containing the summarized data in a data.frame. The data.frame will be named according to the pheno_target, type and norm input.

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_trna(), as.PAC(), filtsep_bin(), map_rangetype(), tRNA_class()

Examples

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

PAC_check(pac) # TRUE

# Easy to generate simple group summaries 
pac <- PAC_summary(pac, norm = "cpm", 
                   type = "means", pheno_target=list("stage"))       
pac <- PAC_summary(pac, norm = "cpm", 
                   type = "se", pheno_target=list("stage"))
pac <- PAC_summary(pac, norm = "cpm", 
                   type = "log2FC", pheno_target=list("stage"))

names(summary(pac))               # Names of individual summaries
head(summary(pac)$cpmMeans_stage) # View individual summaries
summary(pac)  # View merge summaries
df <- as.data.frame(tibble::as_tibble(summary(pac))) # Merge multiple summaries
head(df)


# If a pheno_target is left out, a mean of all samples will be returned:
load(system.file("extdata", "drosophila_sRNA_pac_filt_anno.Rdata", 
                 package = "seqpac", mustWork = TRUE))
pac <- PAC_summary(pac, norm = "cpm", type = "mean")  
pac <- PAC_summary(pac, norm = "cpm", type = "percentgrand")
names(summary(pac))
summary(pac)   

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