CRISPR/shRNA screens are performed with the goal to identify genes that upon perturbation (knock-down, knock-out or activation) result in a phenotype of interest, for example genes that upon knockout enhance the effect of a drug.
After sequence deconvolution of the screen, the count data are use as input for the analysis pipeline. The first step in the pipeline is the normalization for sequence depth. For quality control we generate plots for clustering of the samples, the correlation between replicates and the separation of the distribution of the positive and negative controls.
Statistical analysis is performed by a differential analysis on the level of individual sgRNAs between two conditions of interest, for which we use DESeq21.The results are sorted by the DESeq2 statistic and used as input for step 2. In this step we test whether the sgRNAs targeting a specific gene are enriched towards the top of the list using MAGeCK’s Robust Rank Algorithm2. For visualization volcano plots and bubble plots are used.
Compound screens are performed with the goal to identify drugs that alone or in combination with another drug result in a (enhanced) phenotype of interest, for example killing a cancer cell line with (a) specific genetic alteration(s).
The initial quality of a plate is determined with the z’ factor, a metric indicating the separation in the distribution of the positive and negative controls. To check for any plate effects, we generate per plate a heatmap of the data. In order to make values over plates comparable, we use Normalized Percentage Inhibition (NPI). In case of a depletion screen it is done such that the mean of the positive controls become 0 and those of the negative controls 1. In case of the presence of replicates, correlation plots between the replicates are generated.
Statistical analysis is performed using Strictly Standardized Mean Difference (SSMD), t-test Wilcoxon test or an estimated p-value based on a NULL distribution. In case a p-value is calculated, multiple testing is applied using the Benjamini-Hochberg method.
For synergy analysis between two compounds we offer a 384 assay for six separate experiments on one plate in a five by five doses matrix. Synergy is calculated based on Loewe, Combination Index and BLISS. We also offer a 96 well format, with BLISS score calculation alone.
(1) Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15, 550.
(2) Li, et al. (2014) MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens. Genome Biology 15, 554
