RNAi {cancerGI}R Documentation

Molecular phenotypes from single and double knockdowns in RNAi screen

Description

Single and double siRNA knockdowns were performed for genes and gene pairs. Multiple molecular phenotypes, such as the number of cells, cell size, nucleus size, etc., were measured.

Format

A data matrix with each row a knockdown experiment.

References

Wang, X., Fu, A. Q., McNerney, M. and White, K. P. (2014). Widespread genetic epistasis among breast cancer genes. Nature Communications. 5 4828. doi: 10.1038/ncomms5828

Examples

## Not run: 
library (systemfit)
library (qvalue)

data (RNAi)
data (tested_pairs) # gene pairs tested in the RNAi knockdown assay

# extract gene names and put in a vector
genelist <- union(unique(RNAi$template_gene),unique(RNAi$query_gene))
genelist <- genelist[!((genelist=="empty")|(genelist=="NT"))]

# create the interaction terms for linear model
sorted_tested_pairs <- apply(tested_pairs,1,
	function(x){if (x[1]>x[2]) return (c(x[2],x[1])) 
	else return(c(x[1],x[2]))})
pairs_names <- apply(sorted_tested_pairs,2,
	function(x) {paste(x[1],x[2],sep=":")})

# create vector of covariates
# using batch3 as baseline
regressors <- c("batch1","batch2","batch4",genelist,pairs_names)

# construct the design matrix
my_matrix=constructDesignMatrix(data=RNAi, covariates=regressors)

# n (cell number) and csize (cell size) are on log2 scale already
# need to transform nsize (nucleus size) to original scale
RNAi.tmp <- RNAi
RNAi$nsize <- 2^RNAi.tmp$nsize
rm (RNAi.tmp)

# create formula from column names
eqlog2n <- as.formula (paste ("RNAi$n ~ ", 
	paste (colnames (my_matrix), collapse="+"), sep=''))
eqlog2csize <- as.formula (paste ("RNAi$csize ~ ", 
	paste (colnames (my_matrix), collapse="+"), sep=''))
eqnsize <- as.formula (paste ("RNAi$nsize ~ ", 
	paste (colnames (my_matrix), collapse="+"), sep=''))
system <- list (cell.number = eqlog2n, cell.size = eqlog2csize, nuc.size=eqnsize)

# perform seemingly unrelated regression
fitsur <- systemfit (system, "SUR", data=cbind (RNAi, my_matrix), maxit=100)

# extract coefficient estimates
log2n_fitsur_coef <- coef (summary (fitsur$eq[[1]]))
log2csize_fitsur_coef <- coef (summary (fitsur$eq[[2]]))
nsize_fitsur_coef <- coef (summary (fitsur$eq[[3]]))

# compute q values
log2n_coef_q <- qvalue (log2n_fitsur_coef[,4])$qvalues
log2csize_coef_q <- qvalue (log2csize_fitsur_coef[,4])$qvalues
nsize_coef_q <- qvalue (nsize_fitsur_coef[,4])$qvalues

# build three matrices of results
log2n_fitsur_coef <- data.frame (log2n_fitsur_coef, qvalue=log2n_coef_q)
colnames (log2n_fitsur_coef) <- c("Estimate", "StdError", "tValue", "pValue", "qValue")
log2csize_fitsur_coef <- data.frame (log2csize_fitsur_coef, qvalue=log2csize_coef_q)
colnames (log2csize_fitsur_coef) <- c("Estimate", "StdError", "tValue", "pValue", "qValue")
nsize_fitsur_coef <- data.frame (nsize_fitsur_coef, qvalue=nsize_coef_q)
colnames (nsize_fitsur_coef) <- c("Estimate", "StdError", "tValue", "pValue", "qValue")

## End(Not run)

[Package cancerGI version 1.0.0 Index]