A pipeline for the analysis of CRISPR edited data. It allows the evaluation of the quality of gene editing experiments using targeted next generation sequencing (NGS) data (`targeted`) as well as the discovery of important genes from knock-out or activation CRISPR-Cas9 screens using CRISPR pooled DNA (`screening`).
Maybe this isn't the typical case for most users, but my experiment has multiple replicates of treatment and control. If I wanted to check how concordant my replicates were, and how separated my treatment from control conditions were, I'd likely attempt a PCA based on the raw or normalized counts produced by MAGeCK, coloring the points both by sample and condition (similar to DESeq2 output in nf-core/rnaseq). This could also help identify whether any sample(s) were outliers compared to others, which would be supported by the countsummary table--ie perhaps one sample was an outlier from others in PC space and also has a lower-than-usual mapping rate or very high Gini index etc.
I'd also plot the cross-sample pairwise correlations of normalized counts as a (n * n) heatmap, which likely also would be a useful output
Description of feature
Maybe this isn't the typical case for most users, but my experiment has multiple replicates of treatment and control. If I wanted to check how concordant my replicates were, and how separated my treatment from control conditions were, I'd likely attempt a PCA based on the raw or normalized counts produced by MAGeCK, coloring the points both by sample and condition (similar to
DESeq2output in nf-core/rnaseq). This could also help identify whether any sample(s) were outliers compared to others, which would be supported by thecountsummarytable--ie perhaps one sample was an outlier from others in PC space and also has a lower-than-usual mapping rate or very high Gini index etc.I'd also plot the cross-sample pairwise correlations of normalized counts as a (
n * n) heatmap, which likely also would be a useful output