MAGeCKFlute - Functional enrichment analysis in MAGeCKFlute
27 April 2020
Abstract
CRISPR (clustered regularly interspaced short palindrome repeats) coupled with nuclease Cas9 (CRISPR/Cas9) screens represent a promising technology to systematically evaluate gene functions. Data analysis for CRISPR/Cas9 screens is a critical process that includes identifying screen hits and exploring biological functions for these hits in downstream analysis. We have previously developed two algorithms, MAGeCK (Wei Li and Liu. 2014) and MAGeCK-VISPR (Wei Li and Liu. 2015), to analyze CRISPR/Cas9 screen data in various scenarios. These two algorithms allow users to perform quality control, read count generation and normalization, and calculate beta score to evaluate gene selection performance. In downstream analysis, the biological functional analysis is required for understanding biological functions of these identified genes with different screening purposes. Here, We developed MAGeCKFlute for supporting downstream analysis. MAGeCKFlute provides several strategies to remove potential biases within sgRNA-level read counts and gene-level beta scores. The downstream analysis with the package includes identifying essential, non-essential, and target-associated genes, and performing biological functional category analysis, pathway enrichment analysis and protein complex enrichment analysis of these genes. The package also visualizes genes in multiple ways to benefit users exploring screening data. Collectively, MAGeCKFlute enables accurate identification of essential, non-essential, and targeted genes, as well as their related biological functions. This vignette explains the use of the package and demonstrates typical workflows.Contents
- 1 Input data - weighted gene list
- 2 Enrichment analysis methods
- 3 Session info
- References
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 0.392682 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_TRNA_METABOLIC_PROCESS GO_TRNA_METABOLIC_PROCESS
## GO_SISTER_CHROMATID_SEGREGATION GO_SISTER_CHROMATID_SEGREGATION
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS
## REACTOME_68949 REACTOME_68949
## Description
## REACTOME_2467813 Separation of Sister Chromatids
## REACTOME_72163 mRNA Splicing - Major Pathway
## GO_TRNA_METABOLIC_PROCESS TRNA METABOLIC PROCESS
## GO_SISTER_CHROMATID_SEGREGATION SISTER CHROMATID SEGREGATION
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS ANAPHASE PROMOTING COMPLEX DEPENDENT CATABOLIC PROCESS
## REACTOME_68949 Orc1 removal from chromatin
## NES
## REACTOME_2467813 -10.350541
## REACTOME_72163 -10.123151
## GO_TRNA_METABOLIC_PROCESS -10.970698
## GO_SISTER_CHROMATID_SEGREGATION -9.637773
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS -7.591227
## REACTOME_68949 -7.619360
## pvalue
## REACTOME_2467813 5.258300e-14
## REACTOME_72163 1.844729e-13
## GO_TRNA_METABOLIC_PROCESS 8.226663e-13
## GO_SISTER_CHROMATID_SEGREGATION 2.943300e-12
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 4.637988e-11
## REACTOME_68949 8.565588e-11
## p.adjust
## REACTOME_2467813 4.306548e-11
## REACTOME_72163 7.554166e-11
## GO_TRNA_METABOLIC_PROCESS 2.245879e-10
## GO_SISTER_CHROMATID_SEGREGATION 6.026407e-10
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 7.597025e-09
## REACTOME_68949 1.069499e-08
## GeneRatio BgRatio
## REACTOME_2467813 24/190 190/190
## REACTOME_72163 23/183 183/183
## GO_TRNA_METABOLIC_PROCESS 22/178 178/178
## GO_SISTER_CHROMATID_SEGREGATION 22/189 189/189
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 14/81 81/81
## REACTOME_68949 13/71 71/71
## geneID
## REACTOME_2467813 29945/5708/54820/5689/5695/5518/701/25936/5688/8697/81930/5702/5692/5347/10403/5691/147841/9212/100527963/5885/1778/8243/5686/9184
## 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_TRNA_METABOLIC_PROCESS 79897/4677/51637/285381/1615/8565/57505/51520/60528/51367/29894/90353/10056/283989/3035/112858/51067/7407/348180/54938/3028/55226
## GO_SISTER_CHROMATID_SEGREGATION 29945/79643/79075/5518/701/25936/8697/81930/55294/23397/5347/10403/11331/9212/7153/5885/79892/27243/8243/3192/9184/3837
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 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/NDE/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/SNRPD/PRPF19/PPWD1/SF1/CWC25/CPSF1/POLR2D/CRNKL1/CHERP/SF3A2/SF3B4/EIF4A3/DHX9/DHX15/HNRNPU/LSM4/SRSF10/SNRNP200/SKIV2L2
## GO_TRNA_METABOLIC_PROCESS RPP21/NARS/C14orf166/DPH3/DARS/YARS/AARS2/LARS/ELAC2/POP5/CPSF1/CTU1/FARSB/TSEN54/HARS/TP53RK/YARS2/VARS/CTU2/SYS/HSD17B10/NAT10
## GO_SISTER_CHROMATID_SEGREGATION ANAPC4/CHMP6/DSCC1/PPP2R1A/BUB1B/NSL1/CDC23/KIF18A/CDC4/NCAPH/PLK1/NDC80/PHB2/AURKB/TOP2A/RAD21/MCMBP/CHMP2A/SMC1A/HNRNPU/BUB3/KPNB1
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 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_TRNA_METABOLIC_PROCESS 22
## GO_SISTER_CHROMATID_SEGREGATION 22
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 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_TRNA_METABOLIC_PROCESS GO_TRNA_METABOLIC_PROCESS
## GO_SISTER_CHROMATID_SEGREGATION GO_SISTER_CHROMATID_SEGREGATION
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS
## GO_RNA_3_END_PROCESSING GO_RNA_3_END_PROCESSING
## Description
## REACTOME_2467813 Separation of Sister Chromatids
## REACTOME_72163 mRNA Splicing - Major Pathway
## GO_TRNA_METABOLIC_PROCESS TRNA METABOLIC PROCESS
## GO_SISTER_CHROMATID_SEGREGATION SISTER CHROMATID SEGREGATION
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS ANAPHASE PROMOTING COMPLEX DEPENDENT CATABOLIC PROCESS
## GO_RNA_3_END_PROCESSING RNA 3 END PROCESSING
## NES
## REACTOME_2467813 -10.350541
## REACTOME_72163 -10.123151
## GO_TRNA_METABOLIC_PROCESS -10.970698
## GO_SISTER_CHROMATID_SEGREGATION -9.637773
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS -7.591227
## GO_RNA_3_END_PROCESSING -8.926127
## pvalue
## REACTOME_2467813 1.542034e-13
## REACTOME_72163 5.619434e-13
## GO_TRNA_METABOLIC_PROCESS 2.572894e-12
## GO_SISTER_CHROMATID_SEGREGATION 8.679735e-12
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 2.956773e-10
## GO_RNA_3_END_PROCESSING 3.467555e-10
## p.adjust
## REACTOME_2467813 4.259099e-10
## REACTOME_72163 7.760439e-10
## GO_TRNA_METABOLIC_PROCESS 2.368777e-09
## GO_SISTER_CHROMATID_SEGREGATION 5.993357e-09
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 1.596231e-07
## GO_RNA_3_END_PROCESSING 1.596231e-07
## GeneRatio BgRatio
## REACTOME_2467813 24/288 190/15323
## REACTOME_72163 23/288 183/15323
## GO_TRNA_METABOLIC_PROCESS 22/288 178/15323
## GO_SISTER_CHROMATID_SEGREGATION 22/288 189/15323
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 14/288 81/15323
## GO_RNA_3_END_PROCESSING 18/288 148/15323
## geneID
## REACTOME_2467813 29945/5708/54820/5689/5695/5518/701/25936/5688/8697/81930/5702/5692/5347/10403/5691/147841/9212/100527963/5885/1778/8243/5686/9184
## 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_TRNA_METABOLIC_PROCESS 79897/4677/51637/285381/1615/8565/57505/51520/60528/51367/29894/90353/10056/283989/3035/112858/51067/7407/348180/54938/3028/55226
## GO_SISTER_CHROMATID_SEGREGATION 29945/79643/79075/5518/701/25936/8697/81930/55294/23397/5347/10403/11331/9212/7153/5885/79892/27243/8243/3192/9184/3837
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 29945/5708/5689/5695/701/5688/8697/5702/5692/5347/5691/9212/5686/9184
## GO_RNA_3_END_PROCESSING 580/64852/6426/170506/1479/2091/60528/9984/114034/80145/29894/5433/55656/56915/54512/9775/134353/3028
## geneName
## REACTOME_2467813 ANAPC4/PSMD2/NDE1/PSMB1/PSMB7/PPP2R1A/BUB1B/NSL1/PSMA7/CDC23/KIF18A/PSMC3/PSMB4/PLK1/NDC80/PSMB3/SPC24/AURKB/PMF-1/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_TRNA_METABOLIC_PROCESS RPP21/NARS1/RTRAF/DPH3/DARS1/YARS1/AARS2/LARS1/ELAC2/POP5/CPSF1/CTU1/FARSB/TSEN54/HARS1/TP53RK/YARS2/VARS1/CTU2/SARS2/HSD17B10/NAT10
## GO_SISTER_CHROMATID_SEGREGATION ANAPC4/CHMP6/DSCC1/PPP2R1A/BUB1B/NSL1/CDC23/KIF18A/FBXW7/NCAPH/PLK1/NDC80/PHB2/AURKB/TOP2A/RAD21/MCMBP/CHMP2A/SMC1A/HNRNPU/BUB3/KPNB1
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS ANAPC4/PSMD2/PSMB1/PSMB7/BUB1B/PSMA7/CDC23/PSMC3/PSMB4/PLK1/PSMB3/AURKB/PSMA5/BUB3
## GO_RNA_3_END_PROCESSING BARD1/PAPD2/SRSF1/DHX36/CSTF3/FBL/ELAC2/THOC1/TOE1/THOC7/CPSF1/POLR2D/INTS8/EXOSC5/EXOSC4/EIF4A3/LSM11/HSD17B10
## Count
## REACTOME_2467813 24
## REACTOME_72163 23
## GO_TRNA_METABOLIC_PROCESS 22
## GO_SISTER_CHROMATID_SEGREGATION 22
## GO_ANAPHASE_PROMOTING_COMPLEX_DEPENDENT_CATABOLIC_PROCESS 14
## GO_RNA_3_END_PROCESSING 18# 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 .GSEA(geneList = geneList, exponent = exponent, minGSSize =
## minGSSize, : We do not recommend using nPerm parameter incurrent and future
## releases## Warning in fgsea(pathways = geneSets, stats = geneList, nperm = nPerm, minSize
## = minGSSize, : You are trying to run fgseaSimple. It is recommended to use
## fgseaMultilevel. To run fgseaMultilevel, you need to remove the nperm argument
## in the fgsea function call.## Warning in preparePathwaysAndStats(pathways, stats, minSize, maxSize, gseaParam, : There are ties in the preranked stats (2.56% of the list).
## The order of those tied genes will be arbitrary, which may produce unexpected results.head(gseRes1@result)## ID
## GO_REGULATION_OF_LIPID_BIOSYNTHETIC_PROCESS GO_REGULATION_OF_LIPID_BIOSYNTHETIC_PROCESS
## GO_CELLULAR_KETONE_METABOLIC_PROCESS GO_CELLULAR_KETONE_METABOLIC_PROCESS
## GO_PROTEIN_ACETYLATION GO_PROTEIN_ACETYLATION
## GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN
## GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR
## REACTOME_2467813 REACTOME_2467813
## Description
## GO_REGULATION_OF_LIPID_BIOSYNTHETIC_PROCESS REGULATION OF LIPID BIOSYNTHETIC PROCESS
## GO_CELLULAR_KETONE_METABOLIC_PROCESS CELLULAR KETONE METABOLIC PROCESS
## GO_PROTEIN_ACETYLATION PROTEIN ACETYLATION
## GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN ANTIGEN PROCESSING AND PRESENTATION OF PEPTIDE ANTIGEN
## GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR REGULATION OF SIGNAL TRANSDUCTION BY P53 CLASS MEDIATOR
## REACTOME_2467813 Separation of Sister Chromatids
## NES
## GO_REGULATION_OF_LIPID_BIOSYNTHETIC_PROCESS -1.693697
## GO_CELLULAR_KETONE_METABOLIC_PROCESS -1.705535
## GO_PROTEIN_ACETYLATION -1.712543
## GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN -1.914588
## GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR -1.693346
## REACTOME_2467813 -2.318692
## pvalue
## GO_REGULATION_OF_LIPID_BIOSYNTHETIC_PROCESS 0.0005246590
## GO_CELLULAR_KETONE_METABOLIC_PROCESS 0.0005254861
## GO_PROTEIN_ACETYLATION 0.0005260389
## GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN 0.0005274262
## GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR 0.0005274262
## REACTOME_2467813 0.0005299417
## p.adjust
## GO_REGULATION_OF_LIPID_BIOSYNTHETIC_PROCESS 0.03139187
## GO_CELLULAR_KETONE_METABOLIC_PROCESS 0.03139187
## GO_PROTEIN_ACETYLATION 0.03139187
## GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN 0.03139187
## GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR 0.03139187
## REACTOME_2467813 0.03139187
## geneID
## GO_REGULATION_OF_LIPID_BIOSYNTHETIC_PROCESS 4802/4800/51085/672/6309/11218/6713/2222/6647/2194/10654/3837/10399/4047/2475/7536/3157/3692
## GO_CELLULAR_KETONE_METABOLIC_PROCESS 5693/133/84274/6770/5707/2739/5719/5709/10197/5690/9861/51805/7167/5702/5686/5692/5695/5708/5688/2821/2475/5689/5691
## GO_PROTEIN_ACETYLATION 54386/339287/2648/117143/6877/51616/10847/8260/54934/672/64769/8089/8607/80155/2959/84148/79075/86/80018/1111/3054/122830/6880
## GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN 10120/5707/10437/5719/5709/6396/10197/1062/3796/10671/51164/84516/5690/11004/9861/11258/829/5702/5686/81930/1778/5692/5695/5708/5688/5689/5691
## GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR 10524/1655/83990/79915/1263/23246/80279/960/6874/9219/83695/6877/51616/672/5982/64769/6749/22974/112858/6118/5810/23028/4839/55702/9212/83860/1111/580/6880
## REACTOME_2467813 5719/23244/9735/11130/54821/55746/5709/79019/6396/23047/10197/1062/1063/57405/3796/113130/10274/5690/11004/9861/5905/701/5518/5702/8697/54820/9184/5686/81930/5885/29945/1778/5692/5695/5708/147841/25936/5688/8243/9212/10403/5689/5347/100527963/5691
## geneName
## GO_REGULATION_OF_LIPID_BIOSYNTHETIC_PROCESS NFYC/NFYA/MLXIPL/BRCA1/SC5D/DDX20/SQLE/FDFT1/SOD1/FASN/PMVK/KPNB1/RACK1/LSS/MTOR/SF1/HMGCS1/EIF6
## GO_CELLULAR_KETONE_METABOLIC_PROCESS PSMB5/ADM/COQ5/STAR/PSMD1/GLO1/PSMD13/PSMD3/PSME3/PSMB2/PSMD6/COQ3/TPI1/PSMC3/PSMA5/PSMB4/PSMB7/PSMD2/PSMA7/GPI/MTOR/PSMB1/PSMB3
## GO_PROTEIN_ACETYLATION TERF2IP/MSL1/KAT2A/TADA1/TAF5/TAF9B/SRCAP/NAA10/KANSL2/BRCA1/MEAF6/YEATS4/RUVBL1/NAA15/GTF2B/KAT8/DSCC1/ACTL6A/NAA25/CHEK1/HCFC1/NAA30/TAF9
## GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN ACTR1B/PSMD1/IFI30/PSMD13/PSMD3/SEC13/PSME3/CENPE/KIF2A/DCTN6/DCTN4/DCTN5/PSMB2/KIF2C/PSMD6/DCTN3/CAPZA1/PSMC3/PSMA5/KIF18A/DYNC1H1/PSMB4/PSMB7/PSMD2/PSMA7/PSMB1/PSMB3
## GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR KAT5/DDX5/BRIP1/ATAD5/PLK3/BOP1/CDK5RAP3/CD44/TAF4/MTA2/RHNO1/TAF5/TAF9B/BRCA1/RFC2/MEAF6/SSRP1/TPX2/TP53RK/RPA2/RAD1/KDM1A/NOP2/YJU2/AURKB/TAF3/CHEK1/BARD1/TAF9
## 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/PMF-1/PSMB3
## Count
## GO_REGULATION_OF_LIPID_BIOSYNTHETIC_PROCESS 18
## GO_CELLULAR_KETONE_METABOLIC_PROCESS 23
## GO_PROTEIN_ACETYLATION 23
## GO_ANTIGEN_PROCESSING_AND_PRESENTATION_OF_PEPTIDE_ANTIGEN 27
## GO_REGULATION_OF_SIGNAL_TRANSDUCTION_BY_P53_CLASS_MEDIATOR 29
## REACTOME_2467813 45# 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
EnrichedView(hgtRes1, bottom = 5, mode = 1)
EnrichedView(hgtRes1, bottom = 5, mode = 2)
dotplot(hgtRes1, showCategory = 5)
2.4.3 Visualization functions from enrichplot (Yu 2018).
hgtRes1@result$geneID = hgtRes1@result$geneName
#cnetplot
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(1, 80))
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 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 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_3.3.0 MAGeCKFlute_1.8.0 BiocStyle_2.16.0
##
## loaded via a namespace (and not attached):
## [1] fastmatch_1.1-0 BiocFileCache_1.12.0 plyr_1.8.6
## [4] igraph_1.2.5 splines_4.0.0 BiocParallel_1.22.0
## [7] pathview_1.28.0 sva_3.36.0 urltools_1.7.3
## [10] digest_0.6.25 htmltools_0.4.0 GOSemSim_2.14.0
## [13] viridis_0.5.1 magick_2.3 GO.db_3.10.0
## [16] magrittr_1.5 memoise_1.1.0 limma_3.44.0
## [19] Biostrings_2.56.0 annotate_1.66.0 graphlayouts_0.7.0
## [22] matrixStats_0.56.0 askpass_1.1 enrichplot_1.8.0
## [25] prettyunits_1.1.1 colorspace_1.4-1 blob_1.2.1
## [28] rappdirs_0.3.1 ggrepel_0.8.2 xfun_0.13
## [31] dplyr_0.8.5 crayon_1.3.4 RCurl_1.98-1.2
## [34] jsonlite_1.6.1 graph_1.66.0 scatterpie_0.1.4
## [37] genefilter_1.70.0 survival_3.1-12 glue_1.4.0
## [40] polyclip_1.10-0 gtable_0.3.0 zlibbioc_1.34.0
## [43] XVector_0.28.0 Rgraphviz_2.32.0 BiocGenerics_0.34.0
## [46] scales_1.1.0 DOSE_3.14.0 msigdbr_7.0.1
## [49] pheatmap_1.0.12 DBI_1.1.0 edgeR_3.30.0
## [52] Rcpp_1.0.4.6 viridisLite_0.3.0 xtable_1.8-4
## [55] progress_1.2.2 gridGraphics_0.5-0 bit_1.1-15.2
## [58] europepmc_0.3 stats4_4.0.0 httr_1.4.1
## [61] fgsea_1.14.0 RColorBrewer_1.1-2 ellipsis_0.3.0
## [64] pkgconfig_2.0.3 XML_3.99-0.3 farver_2.0.3
## [67] dbplyr_1.4.3 locfit_1.5-9.4 ggplotify_0.0.5
## [70] tidyselect_1.0.0 labeling_0.3 rlang_0.4.5
## [73] reshape2_1.4.4 AnnotationDbi_1.50.0 munsell_0.5.0
## [76] tools_4.0.0 downloader_0.4 RSQLite_2.2.0
## [79] ggridges_0.5.2 evaluate_0.14 stringr_1.4.0
## [82] yaml_2.2.1 knitr_1.28 bit64_0.9-7
## [85] tidygraph_1.1.2 purrr_0.3.4 KEGGREST_1.28.0
## [88] dendextend_1.13.4 ggraph_2.0.2 nlme_3.1-147
## [91] KEGGgraph_1.48.0 DO.db_2.9 xml2_1.3.2
## [94] biomaRt_2.44.0 compiler_4.0.0 curl_4.3
## [97] png_0.1-7 ggsignif_0.6.0 tibble_3.0.1
## [100] tweenr_1.0.1 stringi_1.4.6 lattice_0.20-41
## [103] Matrix_1.2-18 ggsci_2.9 vctrs_0.2.4
## [106] pillar_1.4.3 lifecycle_0.2.0 BiocManager_1.30.10
## [109] triebeard_0.3.0 data.table_1.12.8 cowplot_1.0.0
## [112] bitops_1.0-6 qvalue_2.20.0 R6_2.4.1
## [115] bookdown_0.18 gridExtra_2.3 IRanges_2.22.0
## [118] codetools_0.2-16 MASS_7.3-51.6 assertthat_0.2.1
## [121] openssl_1.4.1 withr_2.2.0 S4Vectors_0.26.0
## [124] mgcv_1.8-31 parallel_4.0.0 hms_0.5.3
## [127] clusterProfiler_3.16.0 grid_4.0.0 tidyr_1.0.2
## [130] rmarkdown_2.1 rvcheck_0.1.8 ggpubr_0.2.5
## [133] ggforce_0.3.1 Biobase_2.48.0References
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.