knitr::opts_chunk$set(tidy=FALSE, cache=TRUE, dev="png", message=FALSE, error=FALSE, warning=TRUE)
Note: if you use MAGeCKFlute in published research, please cite: Binbin Wang, Mei Wang, Wubing Zhang. "Integrative analysis of pooled CRISPR genetic screens using MAGeCKFlute." Nature Protocols (2019), doi: 10.1038/s41596-018-0113-7.
if(!"MAGeCKFlute" %in% installed.packages()) BiocManager::install("MAGeCKFlute") if(!"clusterProfiler" %in% installed.packages()) BiocManager::install("clusterProfiler") if(!"ggplot2" %in% installed.packages()) BiocManager::install("ggplot2") library(MAGeCKFlute) library(clusterProfiler) library(ggplot2)
The MAGeCK (mageck test
) uses Robust Rank Aggregation (RRA) for robust identification of CRISPR-screen hits, and outputs the summary results at both sgRNA and gene level. Before performing the downstream analysis, please make sure you have got the gene summary and sgRNA summary results from mageck test
. MAGeCKFlute incorporates an example dataset [@Pan2018], which studies the gene functions in T cell mediate tumor killing by performing CRISPR screens in B16F10 cell lines co-cultured with Pmel-1 T cells. There are three samples in the data, including Pmel1_Input (B16F10 cells without T cell co-culture), Pmel1_Ctrl (B16F10 cells co-cultured with control T cells), and Pmel1 (B16F10 cells co-cultured with antigen specific T cells). We compared Pmel1
with Pmel1_Ctrl
using mageck test
, which identifies genes associated with T cell mediated tumor killing.
MAGeCKFlute processes MAGeCK RRA results ("gene_summary" and "sgrna_summary") with the function FluteRRA, which identifies positively and negatively selected genes and performs functional analysis.
## path to the gene summary file (required) file1 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/rra.gene_summary.txt") ## path to the sgRNA summary file (optional) file2 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/rra.sgrna_summary.txt") # Run FluteRRA with only gene summary file FluteRRA(file1, proj="Pmel1", organism="mmu", outdir = "./") # Run FluteRRA with both gene summary file and sgRNA summary file FluteRRA(file1, file2, proj="Pmel1", organism="mmu", outdir = "./")
incorporateDepmap
will allow users to compare the selected genes in the input data and Depmap data, which could clean the selected genes by eliminating lethal genes (false positive hits in some CRISPR screens) and then perform downstream enrichment analysis. You can set the incorporateDepmap
to be TRUE
if you want to include this analysis. Users could specify Depmap data of specific cell lines by setting the cell_lines
and lineages
.FluteRRA(file1, proj="Pmel1", organism="mmu", incorporateDepmap = TRUE, outdir = "./")
omitEssential
allows users to eliminate all common essential genes selected in the Depmap from all of the downstream analysis.FluteRRA(gdata, proj="Pmel1", organism="mmu", omitEssential = TRUE, outdir = "./")
Hints: all figures and intermediate data are saved into local directory "./MAGeCKFlute_Test/", and all figures are integrated into file "FluteRRA_Test.pdf".
For more available parameters in FluteRRA
, please read the documentation using the command ?FluteRRA
.
The MAGeCK-VISPR (mageck mle
) utilizes a maximum likelihood estimation (MLE) for robust identification of CRISPR screen hits. It outputs beta scores and the associated statistics in multiple conditions. The beta score describes how a gene is selected: a positive beta score indicates positive selection, and a negative beta score indicates negative selection. Before using FluteMLE
, you should first get gene summary result from MAGeCK-VISPR (mageck mle
). MAGeCKFlute uses the same dataset as before for demonstration. Using mageck mle
, we removed the baseline effect (plasmid sample) from all the three samples, including Pmel1_Input (B16F10 cells without T cell co-culture), Pmel1_Ctrl (B16F10 cells co-cultured with control T cells), and Pmel1 (B16F10 cells co-cultured with antigen specific T cells).
MAGeCKFlute processes MAGeCK MLE results ("gene_summary") with the function FluteMLE, which performs QC and data normalization based on the beta score, identifies essential genes by comparing control and treatment samples, and finally performs functional enrichment analysis.
file3 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/mle.gene_summary.txt") FluteMLE(file3, treatname="Pmel1", ctrlname="Pmel1_Ctrl", proj="Pmel1", organism="mmu")
incorporateDepmap
will allow users to compare the selected genes in the input data and Depmap data, which could clean the selected genes by eliminating lethal genes (false positive hits in some CRISPR screens) and then perform downstream enrichment analysis. You can set the incorporateDepmap
to be TRUE
if you want to include this analysis. Users could specify Depmap data of specific cell lines by setting the cell_lines
and lineages
.## Take Depmap screen as control FluteMLE(gdata, treatname="Pmel1_Ctrl", ctrlname="Depmap", proj="Pmel1", organism="mmu", incorporateDepmap = TRUE)
omitEssential
allows users to eliminate all common essential genes selected in the Depmap from all of the downstream analysis.FluteMLE(gdata, treatname="Pmel1", ctrlname="Pmel1_Ctrl", proj="Pmel1", organism="mmu", omitEssential = TRUE)
Hint: All pipeline results are written into local directory "./MAGeCKFlute_Test/", and all figures are integrated into file "FluteMLE_Test.pdf".
For more available parameters in FluteMLE
, please read the documentation using the command ?FluteMLE
.
MAGeCK/MAGeCK-VISPR outputs a count summary file, which summarizes some basic QC scores at raw count level, including map ratio, Gini index, and NegSelQC. MAGeCKFlute incorporates an example dataset [@Ophir2014] for demonstration.
file4 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/countsummary.txt") countsummary = read.delim(file4, check.names = FALSE) head(countsummary)
# Gini index BarView(countsummary, x = "Label", y = "GiniIndex", ylab = "Gini index", main = "Evenness of sgRNA reads") # Missed sgRNAs countsummary$Missed = log10(countsummary$Zerocounts) BarView(countsummary, x = "Label", y = "Missed", fill = "#394E80", ylab = "Log10 missed gRNAs", main = "Missed sgRNAs") # Read mapping MapRatesView(countsummary) # Or countsummary$Unmapped = countsummary$Reads - countsummary$Mapped gg = reshape2::melt(countsummary[, c("Label", "Mapped", "Unmapped")], id.vars = "Label") gg$variable = factor(gg$variable, levels = c("Unmapped", "Mapped")) gg = gg[order(gg$Label, gg$variable), ] p = BarView(gg, x = "Label", y = "value", fill = "variable", position = "stack", xlab = NULL, ylab = "Reads", main = "Map ratio") p + scale_fill_manual(values = c("#9BC7E9", "#1C6DAB"))
For CRISPR/Cas9 screens with two experimental conditions, MAGeCK-RRA is available for identification of essential genes. In MAGeCK-RRA results, the sgRNA summary and gene summary file summarizes the statistical significance of positive selections and negative selections at sgRNA level and gene level.
file1 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/rra.gene_summary.txt") gdata = ReadRRA(file1) head(gdata) file2 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/rra.sgrna_summary.txt") sdata = ReadsgRRA(file2) head(sdata)
gdata$HumanGene = TransGeneID(gdata$id, fromType = "symbol", toType = "symbol", fromOrg = "mmu", toOrg = "hsa") sdata$HumanGene = TransGeneID(sdata$Gene, fromType = "symbol", toType = "symbol", fromOrg = "mmu", toOrg = "hsa")
## Remove missing or duplicate human genes idx = duplicated(gdata$HumanGene)|is.na(gdata$HumanGene) gdata = gdata[!idx, ] depmap_similarity = ResembleDepmap(gdata, symbol = "HumanGene", score = "Score") head(depmap_similarity)
gdata = OmitCommonEssential(gdata, symbol = "HumanGene") sdata = OmitCommonEssential(sdata, symbol = "HumanGene")
gdata$LogFDR = -log10(gdata$FDR) p1 = ScatterView(gdata, x = "Score", y = "LogFDR", label = "id", model = "volcano", top = 5) print(p1) # Or p2 = VolcanoView(gdata, x = "Score", y = "FDR", Label = "id") print(p2)
Rank all the genes based on their scores and label genes in the rank plot.
gdata$Rank = rank(gdata$Score) p1 = ScatterView(gdata, x = "Rank", y = "Score", label = "id", top = 5, auto_cut_y = TRUE, ylab = "Log2FC", groups = c("top", "bottom")) print(p1)
Label interested hits using parameter toplabels
(in ScatterView) and genelist
(in RankView).
ScatterView(gdata, x = "Rank", y = "Score", label = "id", top = 5, auto_cut_y = TRUE, groups = c("top", "bottom"), ylab = "Log2FC", toplabels = c("Pbrm1", "Arid2", "Brd7"))
You can also use the function RankView
to draw the figure.
geneList= gdata$Score names(geneList) = gdata$id p2 = RankView(geneList, top = 5, bottom = 10) print(p2) RankView(geneList, top = 0, bottom = 0, genelist = c("Pbrm1", "Arid2", "Brd7"))
Visualize negative and positive selected genes separately.
gdata$RandomIndex = sample(1:nrow(gdata), nrow(gdata)) gdata = gdata[order(-gdata$Score), ] gg = gdata[gdata$Score>0, ] p1 = ScatterView(gg, x = "RandomIndex", y = "Score", label = "id", y_cut = CutoffCalling(gdata$Score,2), groups = "top", top = 5, ylab = "Log2FC") p1 gg = gdata[gdata$Score<0, ] p2 = ScatterView(gg, x = "RandomIndex", y = "Score", label = "id", y_cut = CutoffCalling(gdata$Score,2), groups = "bottom", top = 5, ylab = "Log2FC") p2
p2 = sgRankView(sdata, top = 4, bottom = 4) print(p2)
For more information about functional enrichment analysis in MAGeCKFlute, please read the MAGeCKFlute_enrichment document, in which we introduce all the available options and methods.
geneList= gdata$Score names(geneList) = gdata$id enrich_pos = EnrichAnalyzer(geneList = geneList[geneList>0.5], method = "HGT", type = "KEGG") enrich_neg = EnrichAnalyzer(geneList = geneList[geneList< -0.5], method = "HGT", type = "KEGG")
EnrichedView(enrich_pos, mode = 1, top = 5, bottom = 0) EnrichedView(enrich_pos, mode = 2, top = 5, bottom = 0) EnrichedView(enrich_neg, mode = 2, top = 0, bottom = 5)
The MAGeCK-VISPR (mageck mle
) computes beta scores and the associated statistics for all genes in multiple conditions. The beta score describes how the gene is selected: a positive beta score indicates a positive selection, and a negative beta score indicates a negative selection. Before using FluteMLE
, you should first get gene summary result from MAGeCK-VISPR (mageck mle
). MAGeCKFlute incorporates an example datasets for demonstration.
Is there batch effects? This is a commonly asked question before perform later analysis. In our package, we provide HeatmapView
to ensure whether the batch effect exists in data and use BatchRemove
to remove easily if same batch samples cluster together.
##Before batch removal edata = matrix(c(rnorm(2000, 5), rnorm(2000, 8)), 1000) colnames(edata) = paste0("s", 1:4) HeatmapView(cor(edata)) ## After batch removal batchMat = data.frame(sample = colnames(edata), batch = rep(1:2, each = 2)) edata1 = BatchRemove(edata, batchMat) head(edata1$data) print(edata1$p)
file3 = file.path(system.file("extdata", package = "MAGeCKFlute"), "testdata/mle.gene_summary.txt") # Read and visualize the file format gdata = ReadBeta(file3) head(gdata)
It is difficult to control all samples with a consistent cell cycle in a CRISPR screen experiment with multi conditions. Besides, beta score among different states with an inconsistent cell cycle is incomparable. So it is necessary to do the normalization when comparing the beta scores in different conditions. Essential genes are those genes that are indispensable for its survival. The effect generated by knocking out these genes in different cell types is consistent. Based on this, we developed the cell cycle normalization method to shorten the gap of the cell cycle in different conditions.
ctrlname = "Pmel1_Ctrl" treatname = "Pmel1" gdata$HumanGene = TransGeneID(gdata$Gene, fromType = "symbol", toType = "symbol", fromOrg = "mmu", toOrg = "hsa") gdata_cc = NormalizeBeta(gdata, id = "HumanGene", samples=c(ctrlname, treatname), method="cell_cycle") head(gdata_cc)
After normalization, the distribution of beta scores in different conditions should be similar. We can evaluate the distribution of beta scores using the function ‘DensityView’, and ‘ConsistencyView’.
DensityView(gdata_cc, samples=c(ctrlname, treatname)) ConsistencyView(gdata_cc, ctrlname, treatname) # Another option MAView MAView(gdata_cc, ctrlname, treatname)
gdata_cc$Control = rowMeans(gdata_cc[,ctrlname, drop = FALSE]) gdata_cc$Treatment = rowMeans(gdata_cc[,treatname, drop = FALSE]) p1 = ScatterView(gdata_cc, "Control", "Treatment", label = "Gene", auto_cut_diag = TRUE, display_cut = TRUE, groups = c("top", "bottom"), toplabels = c("Pbrm1", "Brd7", "Arid2", "Jak1", "Stat1", "B2m")) print(p1)
gdata_cc$Diff = gdata_cc$Treatment - gdata_cc$Control gdata_cc$Rank = rank(gdata_cc$Diff) p1 = ScatterView(gdata_cc, x = "Rank", y = "Diff", label = "Gene", top = 5, auto_cut_y = TRUE, groups = c("top", "bottom")) print(p1) # Or rankdata = gdata_cc$Treatment - gdata_cc$Control names(rankdata) = gdata_cc$Gene RankView(rankdata)
p1 = ScatterView(gdata_cc, x="Pmel1_Ctrl", y="Pmel1", label = "Gene", model = "ninesquare", top = 5, display_cut = TRUE, force = 2) print(p1)
Customize the cutoff
p1 = ScatterView(gdata_cc, x="Pmel1_Ctrl", y="Pmel1", label = "Gene", model = "ninesquare", top = 5, display_cut = TRUE, x_cut = c(-1,1), y_cut = c(-1,1)) print(p1)
Or
p2 = SquareView(gdata_cc, label = "Gene", x_cutoff = CutoffCalling(gdata_cc$Control, 2), y_cutoff = CutoffCalling(gdata_cc$Treatment, 2)) print(p2)
# 9-square groups Square9 = p1$data idx=Square9$group=="topcenter" geneList = Square9$Diff[idx] names(geneList) = Square9$Gene[idx] universe = Square9$Gene # Enrichment analysis kegg1 = EnrichAnalyzer(geneList = geneList, universe = universe) EnrichedView(kegg1, top = 6, bottom = 0)
Also, pathway visualization can be done using function KeggPathwayView
[@Luo2013].
gdata_cc$Entrez = TransGeneID(gdata_cc$Gene, "symbol", "entrez", organism = "mmu") genedata = gdata_cc[, c("Entrez", "Control","Treatment")] arrangePathview(genedata, pathways = "mmu04630", organism = "mmu", sub = NULL)
sessionInfo()
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