Contents

Citation: 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.t

1 Input data - weighted gene list

library(MAGeCKFlute)
file1 = file.path(system.file("extdata", package = "MAGeCKFlute"),
                  "testdata/rra.gene_summary.txt")
gdata = ReadRRA(file1)
genelist = gdata$Score
names(genelist) = gdata$id
genelist[1:5]
##   CREBBP    EP300      CHD C16orf72   CACNB2 
##  0.96608  1.02780  0.59265  0.82307  0.39268

2 Enrichment analysis methods

MAGeCKFlute incorporates three enrichment methods, including Over-Representation Test (ORT), Gene Set Enrichment Analysis (GSEA), and Hypergeometric test (HGT). Here, ORT and GSEA are borrowed from R package clusterProfiler (Yu et al. 2012).

2.1 Hypergeometric test

# Alternative functions EnrichAnalyzer and enrich.HGT.
hgtRes1 = EnrichAnalyzer(genelist[genelist< -1], method = "HGT")
head(hgtRes1@result)
##                                ID
## REACTOME_2467813 REACTOME_2467813
## REACTOME_72163     REACTOME_72163
## GO:0006364             GO:0006364
## GO:1901990             GO:1901990
## GO:0031145             GO:0031145
## REACTOME_68949     REACTOME_68949
##                                                             Description
## REACTOME_2467813                        Separation of Sister Chromatids
## REACTOME_72163                            mRNA Splicing - Major Pathway
## GO:0006364                                              rRNA processing
## GO:1901990            regulation of mitotic cell cycle phase transition
## GO:0031145       anaphase-promoting complex-dependent catabolic process
## REACTOME_68949                              Orc1 removal from chromatin
##                         NES       pvalue     p.adjust GeneRatio   BgRatio
## REACTOME_2467813 -10.350541 1.109937e-14 2.239853e-11    24/292 191/16544
## REACTOME_72163   -10.123151 3.683095e-14 3.716243e-11    23/292 183/16544
## GO:0006364        -8.586255 1.556246e-12 1.046835e-09    19/292 143/16544
## GO:1901990        -7.571253 1.344431e-11 6.782654e-09    14/292  80/16544
## GO:0031145        -7.591227 2.346509e-11 9.470511e-09    14/292  83/16544
## REACTOME_68949    -7.619360 3.195559e-11 1.074773e-08    13/292  71/16544
##                                                                                                                                          geneID
## REACTOME_2467813 5518/5686/5688/5689/5691/5692/5695/5702/5708/5347/29945/5885/701/8243/8697/9184/1778/10403/11243/147841/25936/54820/81930/9212
## REACTOME_72163   9775/1479/29894/23517/25804/5433/5435/10262/10523/10772/1665/23020/27339/3192/51340/56949/6426/6632/8175/1660/23398/54883/7536
## GO:0006364                          2091/23160/51367/55127/92856/6187/6209/9775/23517/5394/54512/56915/10969/23481/54555/81887/9136/54853/55661
## GO:1901990                                                                2068/5686/5688/5689/5691/5692/5695/5702/5708/5347/29945/701/8697/9184
## GO:0031145                                                                5686/5688/5689/5691/5692/5695/5702/5708/5347/29945/701/8697/9184/9212
## REACTOME_68949                                                                 4171/4175/5686/5688/5689/5691/5692/5695/5702/5708/9978/23594/890
##                                                                                                                                                          geneName
## REACTOME_2467813  PPP2R1A/PSMA5/PSMA7/PSMB1/PSMB3/PSMB4/PSMB7/PSMC3/PSMD2/PLK1/ANAPC4/RAD21/BUB1B/SMC1A/CDC23/BUB3/DYNC1H1/NDC80/PMF1/SPC24/NSL1/NDE/KIF18A/AURKB
## REACTOME_72163   EIF4A3/CSTF3/CPSF1/SKIV2L2/LSM4/POLR2D/POLR2F/SF3B4/CHERP/SRSF10/DHX15/SNRNP200/PRPF19/HNRNPU/CRNKL1/XAB2/SRSF1/SNRPD/SF3A2/DHX9/PPWD1/CWC25/SF1
## GO:0006364                                  FBL/WDR43/POP5/HEATR1/IMP4/RPS2/RPS15/EIF4A3/SKIV2L2/EXOSC10/EXOSC4/EXOSC5/EBNA1BP2/PES1/DDX49/LAS1L/RRP9/WDR55/DDX27
## GO:1901990                                                                     ERCC2/PSMA5/PSMA7/PSMB1/PSMB3/PSMB4/PSMB7/PSMC3/PSMD2/PLK1/ANAPC4/BUB1B/CDC23/BUB3
## GO:0031145                                                                     PSMA5/PSMA7/PSMB1/PSMB3/PSMB4/PSMB7/PSMC3/PSMD2/PLK1/ANAPC4/BUB1B/CDC23/BUB3/AURKB
## REACTOME_68949                                                                          MCM2/MCM6/PSMA5/PSMA7/PSMB1/PSMB3/PSMB4/PSMB7/PSMC3/PSMD2/RBX1/ORC6/CCNA2
##                  Count
## REACTOME_2467813    24
## REACTOME_72163      23
## GO:0006364          19
## GO:1901990          14
## GO:0031145          14
## REACTOME_68949      13
# hgtRes2 = enrich.HGT(genelist[genelist< -1])
# head(hgtRes2@result)

2.2 Over-representation test

# Alternative functions EnrichAnalyzer and enrich.ORT.
ortRes1 = EnrichAnalyzer(genelist[genelist< -1], method = "ORT")
head(ortRes1@result)
##                                ID
## REACTOME_2467813 REACTOME_2467813
## REACTOME_72163     REACTOME_72163
## GO:0006364             GO:0006364
## GO:1901990             GO:1901990
## GO:0031145             GO:0031145
## REACTOME_68949     REACTOME_68949
##                                                             Description
## REACTOME_2467813                        Separation of Sister Chromatids
## REACTOME_72163                            mRNA Splicing - Major Pathway
## GO:0006364                                              rRNA processing
## GO:1901990            regulation of mitotic cell cycle phase transition
## GO:0031145       anaphase-promoting complex-dependent catabolic process
## REACTOME_68949                              Orc1 removal from chromatin
##                         NES       pvalue     p.adjust GeneRatio   BgRatio
## REACTOME_2467813 -10.350541 4.600475e-14 9.283758e-11    24/292 191/16544
## REACTOME_72163   -10.123151 1.575004e-13 1.589179e-10    23/292 183/16544
## GO:0006364        -8.586255 7.998992e-12 5.380656e-09    19/292 143/16544
## GO:1901990        -7.571253 1.107947e-10 5.589591e-08    14/292  80/16544
## GO:0031145        -7.591227 1.853006e-10 7.478733e-08    14/292  83/16544
## REACTOME_68949    -7.619360 2.875071e-10 9.669822e-08    13/292  71/16544
##                                                                                                                                          geneID
## REACTOME_2467813 29945/5708/54820/5689/5695/5518/701/25936/5688/8697/81930/5702/5692/5347/10403/5691/147841/9212/11243/5885/1778/8243/5686/9184
## REACTOME_72163   6426/1479/56949/5435/6632/27339/23398/7536/54883/29894/5433/51340/10523/8175/10262/9775/1660/1665/3192/25804/10772/23020/23517
## GO:0006364                          23481/10969/2091/54555/23160/9136/51367/6187/55661/55127/6209/56915/54512/9775/54853/81887/5394/92856/23517
## GO:1901990                                                                2068/29945/5708/5689/5695/701/5688/8697/5702/5692/5347/5691/5686/9184
## GO:0031145                                                                29945/5708/5689/5695/701/5688/8697/5702/5692/5347/5691/9212/5686/9184
## REACTOME_68949                                                                 5708/5689/5695/5688/4175/5702/5692/5691/4171/890/9978/5686/23594
##                                                                                                                                                          geneName
## REACTOME_2467813 ANAPC4/PSMD2/NDE1/PSMB1/PSMB7/PPP2R1A/BUB1B/NSL1/PSMA7/CDC23/KIF18A/PSMC3/PSMB4/PLK1/NDC80/PSMB3/SPC24/AURKB/PMF1/RAD21/DYNC1H1/SMC1A/PSMA5/BUB3
## REACTOME_72163    SRSF1/CSTF3/XAB2/POLR2F/SNRPD1/PRPF19/PPWD1/SF1/CWC25/CPSF1/POLR2D/CRNKL1/CHERP/SF3A2/SF3B4/EIF4A3/DHX9/DHX15/HNRNPU/LSM4/SRSF10/SNRNP200/MTREX
## GO:0006364                                    PES1/EBNA1BP2/FBL/DDX49/WDR43/RRP9/POP5/RPS2/DDX27/HEATR1/RPS15/EXOSC5/EXOSC4/EIF4A3/WDR55/LAS1L/EXOSC10/IMP4/MTREX
## GO:1901990                                                                     ERCC2/ANAPC4/PSMD2/PSMB1/PSMB7/BUB1B/PSMA7/CDC23/PSMC3/PSMB4/PLK1/PSMB3/PSMA5/BUB3
## GO:0031145                                                                     ANAPC4/PSMD2/PSMB1/PSMB7/BUB1B/PSMA7/CDC23/PSMC3/PSMB4/PLK1/PSMB3/AURKB/PSMA5/BUB3
## REACTOME_68949                                                                          PSMD2/PSMB1/PSMB7/PSMA7/MCM6/PSMC3/PSMB4/PSMB3/MCM2/CCNA2/RBX1/PSMA5/ORC6
##                  Count
## REACTOME_2467813    24
## REACTOME_72163      23
## GO:0006364          19
## GO:1901990          14
## GO:0031145          14
## REACTOME_68949      13
# ortRes2 = enrich.ORT(genelist[genelist< -1])
# head(ortRes2@result)

2.3 Gene set enrichment analysis

# Alternative functions EnrichAnalyzer and enrich.GSE.
gseRes1 = EnrichAnalyzer(genelist, method = "GSEA")
## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.63% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.
head(gseRes1@result)
##                                ID                             Description
## REACTOME_2467813 REACTOME_2467813         Separation of Sister Chromatids
## REACTOME_72163     REACTOME_72163           mRNA Splicing - Major Pathway
## GO:0006364             GO:0006364                         rRNA processing
## REACTOME_2500257 REACTOME_2500257 Resolution of Sister Chromatid Cohesion
## KEGG_hsa03013       KEGG_hsa03013                           RNA transport
## KEGG_hsa03040       KEGG_hsa03040                             Spliceosome
##                        NES       pvalue     p.adjust
## REACTOME_2467813 -2.346482 1.382185e-17 1.240925e-13
## REACTOME_72163   -2.299303 6.817860e-16 3.060537e-12
## GO:0006364       -2.243248 1.493703e-12 3.635892e-09
## REACTOME_2500257 -2.228855 1.619912e-12 3.635892e-09
## KEGG_hsa03013    -2.216234 4.329000e-12 7.773153e-09
## KEGG_hsa03040    -2.202327 1.166840e-11 1.745982e-08
##                                                                                                                                                                                                                                                                                  geneID
## REACTOME_2467813                    5719/23244/9735/11130/54821/55746/5709/79019/6396/23047/10197/1062/1063/57405/3796/113130/10274/5690/11004/9861/5905/701/5518/5702/8697/54820/9184/5686/81930/5885/29945/1778/5692/5695/5708/147841/25936/5688/8243/9212/10403/5689/5347/11243/5691
## REACTOME_72163   8449/6432/1655/9410/9092/25949/22827/55749/79869/1994/6628/6434/10450/23524/5432/5356/9939/9129/9785/51690/1477/10465/24148/26121/10250/8175/23398/51340/54883/5435/10262/10523/6426/9775/29894/1665/23020/1660/23517/25804/27339/7536/10772/1479/3192/5433/6632/56949
## GO:0006364                                                             79863/23246/317781/51202/79039/705/28987/55759/27042/29960/88745/22984/51118/10171/79707/10521/65083/54555/56915/6209/23160/2091/55661/9775/10969/5394/6187/51367/23481/54853/23517/92856/54512/9136/55127/81887
## REACTOME_2500257                                                                                                 23244/9735/11130/54821/55746/79019/6396/23047/1062/1063/57405/3796/113130/10274/11004/5905/701/5518/54820/9184/81930/5885/1778/147841/25936/8243/9212/10403/5347/11243
## KEGG_hsa03013                                                                                                               9972/55746/11097/6396/2733/9669/9939/25929/1975/11218/5976/10799/8891/5905/10250/9984/79897/9775/8892/51367/80145/8890/3837/60528/79833/8661/1983/1964/4927
## KEGG_hsa03040                                                                      8449/6432/1655/9410/9092/25949/22827/153527/8559/6628/6434/10450/5356/9939/9129/9785/51690/10465/24148/26121/8175/51340/10262/10523/6426/9984/9775/1665/23020/1659/25804/27339/10772/3192/6632/56949
##                                                                                                                                                                                                                                                                                                               geneName
## REACTOME_2467813                   PSMD13/PDS5A/KNTC1/ZWINT/ERCC6L/NUP133/PSMD3/CENPM/SEC13/PDS5B/PSME3/CENPE/CENPF/SPC25/KIF2A/CDCA5/STAG1/PSMB2/KIF2C/PSMD6/RANGAP1/BUB1B/PPP2R1A/PSMC3/CDC23/NDE1/BUB3/PSMA5/KIF18A/RAD21/ANAPC4/DYNC1H1/PSMB4/PSMB7/PSMD2/SPC24/NSL1/PSMA7/SMC1A/AURKB/NDC80/PSMB1/PLK1/PMF1/PSMB3
## REACTOME_72163   DHX16/SRSF7/DDX5/SNRNP40/SART1/SYF2/PUF60/CCAR1/CPSF7/ELAVL1/SNRPB/TRA2B/PPIE/SRRM2/POLR2C/PLRG1/RBM8A/PRPF3/DHX38/LSM7/CSTF1/PPIH/PRPF6/PRPF31/SRRM1/SF3A2/PPWD1/CRNKL1/CWC25/POLR2F/SF3B4/CHERP/SRSF1/EIF4A3/CPSF1/DHX15/SNRNP200/DHX9/MTREX/LSM4/PRPF19/SF1/SRSF10/CSTF3/HNRNPU/POLR2D/SNRPD1/XAB2
## GO:0006364                                                                                          RBFA/BOP1/DDX51/DDX47/DDX54/BYSL/NOB1/WDR12/UTP25/MRM2/RRP36/PDCD11/UTP11/RCL1/NOL9/DDX17/NOL6/DDX49/EXOSC5/RPS15/WDR43/FBL/DDX27/EIF4A3/EBNA1BP2/EXOSC10/RPS2/POP5/PES1/WDR55/MTREX/IMP4/EXOSC4/RRP9/HEATR1/LAS1L
## REACTOME_2500257                                                                                                               PDS5A/KNTC1/ZWINT/ERCC6L/NUP133/CENPM/SEC13/PDS5B/CENPE/CENPF/SPC25/KIF2A/CDCA5/STAG1/KIF2C/RANGAP1/BUB1B/PPP2R1A/NDE1/BUB3/KIF18A/RAD21/DYNC1H1/SPC24/NSL1/SMC1A/AURKB/NDC80/PLK1/PMF1
## KEGG_hsa03013                                                                                                                     NUP153/NUP133/NUP42/SEC13/GLE1/EIF5B/RBM8A/GEMIN5/EIF4B/DDX20/UPF1/RPP40/EIF2B3/RANGAP1/SRRM1/THOC1/RPP21/EIF4A3/EIF2B2/POP5/THOC7/EIF2B4/KPNB1/ELAC2/GEMIN6/EIF3A/EIF5/EIF1AX/NUP88
## KEGG_hsa03040                                                                             DHX16/SRSF7/DDX5/SNRNP40/SART1/SYF2/PUF60/ZMAT2/PRPF18/SNRPB/TRA2B/PPIE/PLRG1/RBM8A/PRPF3/DHX38/LSM7/PPIH/PRPF6/PRPF31/SF3A2/CRNKL1/SF3B4/CHERP/SRSF1/THOC1/EIF4A3/DHX15/SNRNP200/DHX8/LSM4/PRPF19/SRSF10/HNRNPU/SNRPD1/XAB2
##                  Count
## REACTOME_2467813    45
## REACTOME_72163      48
## GO:0006364          36
## REACTOME_2500257    30
## KEGG_hsa03013       29
## KEGG_hsa03040       36
# gseRes2 = enrich.GSE(genelist)
# head(gseRes2@result)

2.4 Visualize enrichment results.

2.4.1 Barplot

require(ggplot2)
df = hgtRes1@result
df$logFDR = -log10(df$p.adjust)
p = BarView(df[1:5,], "Description", 'logFDR')
p = p + labs(x = NULL) + coord_flip()
p

# Or use function barplot from enrichplot package
barplot(hgtRes1, showCategory = 5)

2.4.2 Dot plot

## top: up-regulated pathways; 
## bottom: down-regulated pathways
EnrichedView(hgtRes1, top = 0, bottom = 5, mode = 1)

EnrichedView(hgtRes1, top = 0, bottom = 5, mode = 2)

dotplot(hgtRes1, showCategory = 5)

2.4.3 Other visualization functions from enrichplot (Yu 2018).

hgtRes1@result$geneID = hgtRes1@result$geneName
cnetplot(hgtRes1, 2)
heatplot(hgtRes1, showCategory = 3, foldChange=genelist)
emapplot(hgtRes1, layout="kk")

2.4.4 Visulization for GSEA enriched categories

#gseaplot
gseaplot(gseRes1, geneSetID = 1, title = gseRes1$Description[1])

gseaplot(gseRes1, geneSetID = 1, by = "runningScore", title = gseRes1$Description[1])

gseaplot(gseRes1, geneSetID = 1, by = "preranked", title = gseRes1$Description[1])

#or
gseaplot2(gseRes1, geneSetID = 1:3)

2.5 Type of gene sets for enrichment analysis

For enrichment analysis, MAGeCKFlute signifies the public available gene sets, including Pathways (PID, KEGG, REACTOME, BIOCARTA, C2CP), GO terms (GOBP, GOCC, GOMF), Complexes (CORUM) and molecular signature from MsigDB (c1, c2, c3, c4, c5, c6, c7, HALLMARK).

2.5.1 Functional enrichment analysis on GO terms and pathways

Analysis of high-throughput data increasingly relies on pathway annotation and functional information derived from Gene Ontology, which is also useful in the analysis of CRISPR screens.

## KEGG and REACTOME pathways
enrich = EnrichAnalyzer(geneList = genelist[genelist< -1], type = "KEGG+REACTOME")
EnrichedView(enrich, bottom = 5)

## Only KEGG pathways
enrich = EnrichAnalyzer(geneList = genelist[genelist< -1], type = "KEGG")
EnrichedView(enrich, bottom = 5)

## Gene ontology
enrichGo = EnrichAnalyzer(genelist[genelist< -1], type = "GOBP+GOMF")
EnrichedView(enrichGo, bottom = 5)

2.5.2 Protein complex analysis

Functional annotations from the pathways and GO are powerful in the context of network dynamics. However, the approach has limitations in particular for the analysis of CRISPR screenings, in which elements within a protein complex rather than complete pathways might have a strong selection. So we incorporate protein complex resource from CORUM database, which enable identification of essential protein complexes from the CRISPR screens.

enrichPro = EnrichAnalyzer(genelist[genelist< -1], type = "CORUM")
EnrichedView(enrichPro, bottom = 5)

2.5.3 Enrichment analysis on the combination of the gene sets

enrichComb = EnrichAnalyzer(genelist[genelist< -1], type = "GOBP+KEGG")
EnrichedView(enrichComb, bottom = 5)

2.6 Limit the size of gene sets for testing

enrich = EnrichAnalyzer(genelist[genelist< -1], type = "GOBP", limit = c(50, 500))
EnrichedView(enrich, bottom = 5)

2.7 Remove redundant results using EnrichedFilter.

enrich1 = EnrichAnalyzer(genelist[genelist< -1], type = "GOMF+GOBP")
enrich2 = EnrichAnalyzer(genelist[genelist< -1], type = "GOMF+GOBP", filter = TRUE)
enrich3 = EnrichedFilter(enrich1)
EnrichedView(enrich1, bottom = 15)

EnrichedView(enrich2, bottom = 15)

EnrichedView(enrich3, bottom = 15)

3 Session info

sessionInfo()
## R version 4.1.0 (2021-05-18)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.2 LTS
## 
## Matrix products: default
## BLAS:   /home/biocbuild/bbs-3.13-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.13-bioc/R/lib/libRlapack.so
## 
## locale:
##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
##  [3] LC_TIME=en_GB              LC_COLLATE=C              
##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
##  [9] LC_ADDRESS=C               LC_TELEPHONE=C            
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       
## 
## attached base packages:
## [1] stats     graphics  grDevices utils     datasets  methods   base     
## 
## other attached packages:
## [1] ggplot2_3.3.3      MAGeCKFlute_1.12.0 BiocStyle_2.20.0  
## 
## loaded via a namespace (and not attached):
##   [1] fgsea_1.18.0           colorspace_2.0-1       ggtree_3.0.0          
##   [4] ellipsis_0.3.2         qvalue_2.24.0          XVector_0.32.0        
##   [7] aplot_0.0.6            farver_2.1.0           graphlayouts_0.7.1    
##  [10] ggrepel_0.9.1          bit64_4.0.5            AnnotationDbi_1.54.0  
##  [13] fansi_0.4.2            scatterpie_0.1.6       codetools_0.2-18      
##  [16] splines_4.1.0          cachem_1.0.5           GOSemSim_2.18.0       
##  [19] knitr_1.33             polyclip_1.10-0        jsonlite_1.7.2        
##  [22] annotate_1.70.0        GO.db_3.13.0           png_0.1-7             
##  [25] pheatmap_1.0.12        ggforce_0.3.3          msigdbr_7.4.1         
##  [28] BiocManager_1.30.15    compiler_4.1.0         httr_1.4.2            
##  [31] rvcheck_0.1.8          assertthat_0.2.1       Matrix_1.3-3          
##  [34] fastmap_1.1.0          lazyeval_0.2.2         limma_3.48.0          
##  [37] tweenr_1.0.2           htmltools_0.5.1.1      tools_4.1.0           
##  [40] igraph_1.2.6           gtable_0.3.0           glue_1.4.2            
##  [43] GenomeInfoDbData_1.2.6 reshape2_1.4.4         DO.db_2.9             
##  [46] dplyr_1.0.6            fastmatch_1.1-0        Rcpp_1.0.6            
##  [49] enrichplot_1.12.0      Biobase_2.52.0         jquerylib_0.1.4       
##  [52] vctrs_0.3.8            Biostrings_2.60.0      babelgene_21.4        
##  [55] ape_5.5                nlme_3.1-152           ggraph_2.0.5          
##  [58] xfun_0.23              stringr_1.4.0          lifecycle_1.0.0       
##  [61] clusterProfiler_4.0.0  XML_3.99-0.6           DOSE_3.18.0           
##  [64] edgeR_3.34.0           zlibbioc_1.38.0        MASS_7.3-54           
##  [67] scales_1.1.1           tidygraph_1.2.0        parallel_4.1.0        
##  [70] RColorBrewer_1.1-2     yaml_2.2.1             memoise_2.0.0         
##  [73] gridExtra_2.3          downloader_0.4         sass_0.4.0            
##  [76] stringi_1.6.2          RSQLite_2.2.7          genefilter_1.74.0     
##  [79] highr_0.9              S4Vectors_0.30.0       tidytree_0.3.3        
##  [82] BiocGenerics_0.38.0    BiocParallel_1.26.0    GenomeInfoDb_1.28.0   
##  [85] matrixStats_0.58.0     rlang_0.4.11           pkgconfig_2.0.3       
##  [88] bitops_1.0-7           evaluate_0.14          lattice_0.20-44       
##  [91] purrr_0.3.4            treeio_1.16.0          patchwork_1.1.1       
##  [94] labeling_0.4.2         cowplot_1.1.1          shadowtext_0.0.8      
##  [97] bit_4.0.4              tidyselect_1.1.1       plyr_1.8.6            
## [100] magrittr_2.0.1         bookdown_0.22          R6_2.5.0              
## [103] IRanges_2.26.0         magick_2.7.2           generics_0.1.0        
## [106] DBI_1.1.1              mgcv_1.8-35            pillar_1.6.1          
## [109] withr_2.4.2            survival_3.2-11        KEGGREST_1.32.0       
## [112] RCurl_1.98-1.3         tibble_3.1.2           crayon_1.4.1          
## [115] utf8_1.2.1             rmarkdown_2.8          viridis_0.6.1         
## [118] locfit_1.5-9.4         grid_4.1.0             sva_3.40.0            
## [121] data.table_1.14.0      blob_1.2.1             digest_0.6.27         
## [124] xtable_1.8-4           tidyr_1.1.3            stats4_4.1.0          
## [127] munsell_0.5.0          viridisLite_0.4.0      bslib_0.2.5.1

References

Wei Li, Han Xu, Johannes Köster, and X. Shirley Liu. 2015. “Quality control, modeling, and visualization of CRISPR screens with MAGeCK-VISPR.” https://genomebiology.biomedcentral.com/articles/10.1186/s13059-015-0843-6.

Wei Li, Tengfei Xiao, Han Xu, and X Shirley Liu. 2014. “MAGeCK enables robust identification of essential genes from genome-scale CRISPR/Cas9 knockout screens.” https://genomebiology.biomedcentral.com/articles/10.1186/s13059-014-0554-4.

Yu, Guangchuang. 2018. Enrichplot: Visualization of Functional Enrichment Result. https://github.com/GuangchuangYu/enrichplot.

Yu, Guangchuang, Li-Gen Wang, Yanyan Han, and Qing-Yu He. 2012. “ClusterProfiler: An R Package for Comparing Biological Themes Among Gene Clusters.” OMICS: A Journal of Integrative Biology 16 (5): 284–87. https://doi.org/10.1089/omi.2011.0118.