ScreenBEAM: Gene-level meta-analysis of high-throughput functional...

In jyyu/ScreenBEAM: Gene-level meta-analysis of high-throughput functional genomics (RNAi or CRISPR) screens.

Description

ScreenBEAM is an R package to do gene-level meta-anlaysis of high-throughput functional genomics RNAi or CRISPR screening data. Both microarray and NGS data are supported.

Usage

ScreenBEAM(input.file, control.samples, case.samples,
  data.type = c("microarray", "NGS"), control.groupname = "control",
  case.groupname = "treatment", gene.columnId = 2,
  do.normalization = FALSE, filterLowCount = FALSE, filterBy = "control",
  count.cutoff = 4, nitt = 15000, burnin = 5000, ...)

Arguments

input.file	input file in tab-separated format, with the first two columns as sh/sgRNA id, gene id, followed by samples
control.samples	column names of control samples
case.samples	column names of case/treated samples
data.type	data type, eith microarray or NGS param control.groupname group name for control samples, control as default
case.groupname	group name for control samples, treatment as default
gene.columnId	which column is for gene id, 2 as default
do.normalization	whether to do normalization on the data or not, default is FALSE. If TRUE, scale normalization will be applied to NGS data and quantile normalization will applied to microarray data.
filterLowCount	for NGS data only, whether to filter out low count sh/sgRNAs
filterBy,count.cutoff	if filterLowCount as true, filterBy which group (control as default) at which threhsold (4 as default)
nitt	number of MCMC iterations, 15000 as default
burnin	number of burnin for MCMC sampling, 5000 as default

Value

A data.frame table with the following columns: gene (gene id), n.sh_sgRNAs.raw (number of sh/sgRNAs targeting this gene before data filtering), n.sh_sgRNAs.passFilter (number of sh/sgRNAs targeting this gene after data filtering),B (coefficient of the gene-level effect or fixed effects in the mixture model, indicating the gene-level effects),z (z-score of the gene-level effect),pval (p-value of gene-level effect),FDR (False Discovery Rate),B.sd (standard deviation of gene-level coefficient)

Author(s)

Jiyang Yu

References

Yu J, Silva, J, Califano A. ScreenBEAM: a Novel Meta-Analysis Algorithm for Functional Genomics Screens via Bayesian Hierarchical Modeling. Bioinformatics (In revision), 2015.

Examples

library(ScreenBEAM)
#NGS data
r<-ScreenBEAM(

  ###input format
  input.file=system.file("extdata", "microarray.example.tsv", package = "ScreenBEAM")
  ,
  control.samples=c('T0_A','T0_B','T0_C')
  ,
  case.samples=c('T16_A','T16_B','T16_C')
  ,
  control.groupname='T0'
  ,
  case.groupname='T16'
  ,

  ###data pre-processing
  data.type='NGS'
  ,
  do.normalization=TRUE
  ,
  filterLowCount=TRUE
  ,
  filterBy = 'control'
  ,
  count.cutoff=4
  ,

  ###Bayesian computing
  nitt=1500,#number of MCMC iterations, use small number here for testing, please use larger number in real data, 15000 is default
  burnin=500#number of burnin in MCMC sampling, 5000 is default

)

###microarray data
r<-ScreenBEAM(

  ###input format
  input.file=system.file("extdata", "microarray.example.tsv", package = "ScreenBEAM")
  ,
  control.samples=c('T0_A','T0_B','T0_C')
  ,
  case.samples=c('T16_A','T16_B','T16_C')
  ,
  control.groupname='T0'
  ,
  case.groupname='T16'
  ,

  ###data pre-processing
  data.type='microarray'
  ,
  do.normalization=FALSE
  ,

  ###Bayesian computing
  nitt=1500,#number of MCMC iterations, use small number here for testing, please use larger number in real data, 15000 is default
  burnin=500#number of burnin in MCMC sampling, 5000 is default

)

head(r)


###save your results
#write.csv(r,file=file.path('results.ScreenBEAM.csv'),row.names=FALSE,na='')

jyyu/ScreenBEAM documentation built on May 20, 2019, 6:28 a.m.