Name Mode Size
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GeneData-PoolScreenExp-method.Rd 100755 1 kb
GeneData.Rd 100755 1 kb
PoolScreenExp-class.Rd 100755 1 kb
ResultsTable.Rd 100755 1 kb
RunGscreend.Rd 100755 1 kb
assignGeneData.Rd 100755 0 kb
calculateIntervalFits.Rd 100755 1 kb
calculateLFC.Rd 100755 0 kb
calculatePValues.Rd 100755 0 kb
createPoolScreenExp.Rd 100755 1 kb
createPoolScreenExpFromSE.Rd 100755 0 kb
defineFittingIntervals.Rd 100755 0 kb
fit_least_quantile.Rd 100755 1 kb
normalizePoolScreenExp.Rd 100755 0 kb
plotModelParameters.Rd 100755 1 kb
plotReplicateCorrelation.Rd 100755 1 kb
sgRNAData-PoolScreenExp-method.Rd 100755 1 kb
sgRNAData.Rd 100755 1 kb
README.md
# gscreend - analysis of pooled CRISPR screens ### Run gscreend, as also explained in the vignette section Set up a SummarizedExperiment object containing a matrix of raw count data, rowData on gRNAs and genes and colData on the sample type. ```{r} counts_matrix <- cbind(raw_counts$library0, raw_counts$R0_0, raw_counts$R1_0) rowData <- data.frame(sgRNA_id = raw_counts$sgrna_id, gene = raw_counts$Gene) colData <- data.frame(samplename = c("library", "R1", "R2"), # timepoint naming convention: # T0 -> reference, # T1 -> selected timepoint = c("T0", "T1", "T1")) se <- SummarizedExperiment(assays=list(counts=counts_matrix), rowData=rowData, colData=colData) ``` Create a PoolScreenExp object ```{r} pse <- createPoolScreenExp(se) ``` Run gscreend ```{r} pse <- RunGscreend(pse) ```