generate.m.s: Estimates codon enrichment mean, standard deviation and...

In carinelegrand/RiboVIEW: Visualization, Quality and Statistics for Ribosome Profiling

Description

generate.m.s This function takes a list of samples and experimental conditions in input and calculates mean, standard deviation and standard error for codon enrichment of all replicates of the same condition, and for comparison between conditions.

Usage

generate.m.s(XP.conditions, XP.names, pathout, B=1000)

Arguments

XP.conditions	Vector of experimental conditions for each sample
XP.names	Vector of names for each sample
pathout	Address where output files will be written
B	Number of bootstrap resampling occurences

Value

This function writes to the following files, under the address pathout :

Enrichment-per-condition__weighted-mean

for the mean

Enrichment-per-condition__weighted-sdev

for the standard deviation

Enrichment-per-condition__weighted-serr

for the standard error

Condition_*_relative-to_*_mean-and-stderr

for each comparison ratio's mean and standard error

Examples

# Sequenced reads aligned to mRNA (and containing no rRNA, depleted previously),
#   in bam format
readsBAM.1.1  <- paste(system.file(package="RiboVIEW", mustWork = TRUE), 
                                              "/extdata/Cond1-Rep1.bam",sep="")
readsBAM.1.2  <- paste(system.file(package="RiboVIEW", mustWork = TRUE), 
                                              "/extdata/Cond1-Rep2.bam",sep="")
readsBAM.1.3  <- paste(system.file(package="RiboVIEW", mustWork = TRUE), 
                                              "/extdata/Cond1-Rep3.bam",sep="")
readsBAM.2.1  <- paste(system.file(package="RiboVIEW", mustWork = TRUE), 
                                              "/extdata/Cond2-Rep1.bam",sep="")
readsBAM.2.2  <- paste(system.file(package="RiboVIEW", mustWork = TRUE), 
                                              "/extdata/Cond2-Rep2.bam",sep="")
readsBAM.2.3  <- paste(system.file(package="RiboVIEW", mustWork = TRUE), 
                                              "/extdata/Cond2-Rep3.bam",sep="")

list.bam <- list(readsBAM.1.1, readsBAM.1.2, readsBAM.1.3, 
                 readsBAM.2.1, readsBAM.2.2, readsBAM.2.3)


#
## Experimental conditions, in text and as indicators :
#    0 for control
#    1 for a condition, treatment, case, etc...
#    2, 3, etc. for further conditions

XP.conditions   <- c("cond1","cond1","cond1","cond2", "cond2","cond2")
XP.conditions.i <- c( 1,1,1,2,2,2)
XP.names        <- c("C1.R1", "C1.R2", "C1.R3", 
                     "C2.R1", "C2.R2", "C2.R3")

#
## Reference annotation for mRNAs' CDS.
#

refCDS <- paste(system.file(package="RiboVIEW", mustWork = TRUE), "/extdata/synth.tsv", sep="")
# Note : CDS annotation can be obtained from a GTF file, 
#        using gtf2table(my-gtf-file, outfile = my-cds-file)
#        (for example GTF file as provided by Ensembl.org work well with gtf2table)

#
## Reference sequences for mRNAs.
#

refFASTA <- paste(system.file(package="RiboVIEW", mustWork = TRUE), "/extdata/synth.fasta", sep="")

#
## Work and output folder.
#

pathout  <-  paste(tempdir(),"/", sep="")
  ## !! This is a temporary directory, which will be erased when you leave R !!
  ##   For your own analyses you would probably prefer to point to a permanent repository :
  #      pathout <- /home/me/address-to-my-output-repository/ # Define address, 
  #                                                   #including a final slash.
  #      system(paste('mkdir',pathout)) # Create folder at said address.
  #      setwd(pathout)  # Go to this directory. This is useful if you want to 
  #                                         #save additional tables or figures.

# 
## A-site coverage periodicity by length
#

periodicity(list.bam, refCDS, refFASTA, pathout, XP.names, versionStrip = FALSE)

# 
## Select footprint length with sufficient periodicity
#

attach(listminmax <- select.FPlen(list.bam, pathout, XP.names))

#
## Codon occupancy, codon enrichment.
# 

enrichmentNoccupancy(list.bam, refCDS, refFASTA, mini, maxi, XP.names,  
                       pathout, versionStrip = FALSE)
 
#
## Visualisation.
#

generate.m.s(XP.conditions, XP.names, pathout, B=1000)

visu.m.s.enrichmnt.res <- visu.m.s.enrichmnt(XP.conditions, XP.names, pathout)
visu.m.s.enrichmnt.res

visu.tracks.res <- visu.tracks(XP.conditions, XP.names, pathout, refCDS, 
                               mRNA="random", 
                               codon.labels=FALSE, codon.col="darkslateblue")
visu.tracks.res

Venn.all.res <- Venn.all(XP.names, pathout)
Venn.all.res

enricht.aroundA.res <- enricht.aroundA(XP.conditions, 
  XP.names, pathout)
enricht.aroundA.res

carinelegrand/RiboVIEW documentation built on July 17, 2020, 3:02 p.m.