QC Report

In gimap: Calculate Genetic Interactions for Paired CRISPR Targets

knitr::opts_chunk$set(echo = FALSE, warning = FALSE, message = FALSE)
knitr::opts_knit$set(progress = TRUE, verbose = FALSE)
library(gimap)
library(ggplot2)
library(purrr)

Unfiltered/raw data

Counts CDF

qc_cdf(gimap_dataset)

Histogram of cpm values by sample

qc_sample_hist(gimap_dataset)

Sample Correlation heatmap (unfiltered data)

qc_cor_heatmap(gimap_dataset)

Variance within replicates

qc_variance_hist(gimap_dataset, filter_replicates_target_col = filter_replicates_target_col)

Plasmid expression (log 2 cpm values for Day 0 sample/time point)

qc_plasmid_histogram(gimap_dataset, filter_plasmid_target_col = filter_plasmid_target_col)

Applying potential filters

Filter pgRNAs where there is a count of 0 for any sample/time point

Replicates with a pgRNA count of 0

qc_constructs_countzero_bar(gimap_dataset, filter_zerocount_target_col = filter_zerocount_target_col, filter_replicates_target_col = filter_replicates_target_col)

If this filter is applied, this is the number of pgRNAs that would be filtered out

potentialFilter1 <- qc_filter_zerocounts(gimap_dataset, filter_zerocount_target_col = filter_zerocount_target_col)

potentialFilter1$reportdf

Filter pgRNAs where there is a low log2 CPM value for the plasmid sample/time point

If this filter is applied, this is the number of pgRNAs that would be filtered out

potentialFilter2 <- qc_filter_plasmid(gimap_dataset, filter_plasmid_target_col = filter_plasmid_target_col)

potentialFilter2$reportdf

If both filters are applied

combined_filters <- reduce(list(potentialFilter1$filter, potentialFilter2$filter), cbind)

| Which Filter(s) | Number of pgRNAs flagged for removal | Percent of total pgRNA constructs | |:---------------|:------------------------------------|:----------| | Zero count, but not low plasmid CPM | r sum(combined_filters[,1] == TRUE & combined_filters[,2] == FALSE) | r round(sum(combined_filters[,1] == TRUE & combined_filters[,2] == FALSE) / nrow(combined_filters) * 100, 2)| | Low plasmid CPM, but not zero count | r sum(combined_filters[,2] == TRUE & combined_filters[,1] == FALSE) | r round(sum(combined_filters[,2] == TRUE & combined_filters[,1] == FALSE) / nrow(combined_filters) * 100, 2) | | Either Zero count or Low plasmid CPM or both | r sum(rowSums(combined_filters) >= 1)| r round(sum(rowSums(combined_filters) >= 1) / nrow(combined_filters) * 100, 2) | | Both Zero count and Low plasmid CPM | r sum(rowSums(combined_filters) == 2) | r round(sum(rowSums(combined_filters) == 2) / nrow(combined_filters) * 100, 2) | | Remaining pgRNAs flagged by no filters | r sum(rowSums(combined_filters) == 0) | r round(sum(rowSums(combined_filters) == 0) / nrow(combined_filters) * 100, 2) |

Session Info

sessionInfo()

gimap documentation built on June 8, 2025, 10:13 a.m.