Integrative analysis pipeline for pooled CRISPR functional genetic screens - MAGeCKFlute
WubingZhang, Feizhen Wu, and Binbin Wang
25 October 2018
Abstract
Genome-wide 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 and MAGeCK-VISPR, 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, 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, utilizing the data provided through MAGeCK and MAGeCK-VISPR. 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 and pathway enrichment analysis of these genes. The package also visualizes genes in the context of pathways 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. MAGeCKFlute package version: 1.2.0- How to get help for MAGeCKFlute
- Input data
- Quick start
- Section I: Run pipeline from gene summary file generated by MAGeCK MLE
- Gene summary file
- Read beta scores
- Batch effect removal
- Normalization of beta scores
- Distribution of all gene beta scores
- Estimate cell cycle time by linear fitting
- Positive selection and negative selection
- Functional analysis of selected genes
- Identify treatment-associated genes using 9-square model
- Functional analysis for treatment-associated genes
- Section II: Run pipeline from gene summary file generated by MAGeCK RRA
- Session info
- References
Note: if you use MAGeCKFlute in published research, please cite:
How to get help for MAGeCKFlute
Any and all MAGeCKFlute questions should be posted to the Bioconductor support site, which serves as a searchable knowledge base of questions and answers:
https://support.bioconductor.org
Posting a question and tagging with “MAGeCKFlute” will automatically send an alert to the package authors to respond on the support site. See the first question in the list of Frequently Asked Questions (FAQ) for information about how to construct an informative post.
You should not email your question to the package authors, as we will just reply that the question should be posted to the Bioconductor support site.
Input data
As input, the MAGeCKFlute package expects gene summary file as obtained by running commands mageck test or mageck mle in MAGeCK (Wei Li and Liu. 2014) and MAGeCK-VISPR (Wei Li and Liu. 2015), which are developed by our lab previously, to analyze CRISPR/Cas9 screen data in different scenarios(Tim Wang 2014, Hiroko Koike-Yusa (2014), Ophir Shalem1 (2014), Luke A.Gilbert (2014), Silvana Konermann (2015)). Both algorithms use negative binomial models to model the variances of sgRNAs, and use Robust Rank Aggregation (for MAGeCK) or maximum likelihood framework (for MAGeCK-VISPR) for a robust identification of selected genes.
MAGeCK-MLE can be used to analyze CRISPR screen data from multi-conditioned experiments. MAGeCK-MLE also normalizes the data across multiple samples, making them comparable to each other. The most important ouput of MAGeCK MLE is “gene_summary” file, which includes the beta scores of multiple conditions and the associated statistics. The ‘beta score’ for each gene describes how the gene is selected: a positive beta score indicates a positive selection, and a negative beta score indicates a negative selection.
MAGeCK RRA allows for the comparison between two experimental conditions. It can identify genes and sgRNAs are significantly selected between the two conditions. The most important output of MAGeCK RRA is the file “gene_summary.txt”. MAGeCK RRA will output both the negative score and positive score for each gene. A smaller score indicates higher gene importance. MAGeCK RRA will also output the statistical value for the scores of each gene. Genes that are significantly positively and negatively selected can be identified based on the p-value or FDR.
Quick start
Here we show the most basic steps for integrative analysis pipeline using gene summary file. Before using MAGeCKFlute, analysing CRISPR/Cas9 screen data using MAGeCK RRA (in MAGeCK (Wei Li and Liu. 2014)) or MAGeCK MLE (in MAGeCK-VISPR (Wei Li and Liu. 2015)) is neccessary, which result in the generation of the gene summary file.
To run MAGeCKFlute pipeline, we need gene summary file generated by running MAGeCK RRA or MAGeCK MLE. MAGeCKFlute package provides two example data, one is MLE_Data and the other is RRA_Data. We will work with them in this document.
Downstream analysis pipeline for MAGeCK MLE
library(MAGeCKFlute)
##Load gene summary data in MAGeCK MLE results
data("MLE_Data")
##Run the downstream analysis pipeline for MAGeCK MLE
FluteMLE(MLE_Data, ctrlname=c("D7_R1", "D7_R2"), treatname=c("PLX7_R1","PLX7_R2"), prefix="BRAF_", organism="hsa")All pipeline results are written into local directory “./BRAF_Flute_Results/”, and all figures are integrated into file “BRAF_Flute.mle_summary.pdf”.
Downstream analysis pipeline for MAGeCK RRA
##Load gene summary data in MAGeCK RRA results
data("RRA_Data")
##Run the downstream analysis pipeline for MAGeCK RRA
FluteRRA(RRA_Data, prefix="BRAF", organism="hsa")All pipeline results are written into local directory “./BRAF_Flute_Results/” too, and all figures are integrated into file “BRAF_Flute.rra_summary.pdf”.
Section I: Run pipeline from gene summary file generated by MAGeCK MLE
Gene summary file
As the input of MAGeCKFlute package, the gene summary file in MAGeCK-MLE results includes beta scores of all genes in multiple condition samples. Use function ‘data’ to load the dataset, and have a look at the file with a text editor to see how it is formatted.
## Gene D7_R1.beta D7_R2.beta PLX7_R1.beta PLX7_R2.beta
## 1 SMPD2 -0.458770 -0.514690 -0.67770 -0.510360
## 2 MIXL1 0.094571 0.122780 -0.14383 -0.119670
## 3 ENPEP -0.082621 -0.082305 -0.11271 -0.061796
## 4 VAV2 -0.024270 -0.034077 0.14537 -0.015095
## 5 C5orf24 0.452310 0.226940 0.15809 0.191940
## 6 TLCD1 -0.883320 -0.779930 -0.80811 -0.854900Read beta scores
Then, extract beta scores of control and treatment samples from the gene summary table(can be a file path of ‘gene_summary’ or data frame), and have a look at the beta score matrix.
data("MLE_Data")
gene_summary = MLE_Data
ctrlname = c("D7_R1", "D7_R2")
treatname = c("PLX7_R1", "PLX7_R2")
#Read beta scores from gene summary table in MAGeCK MLE results
dd=ReadBeta(gene_summary, organism="hsa")
head(dd)## Gene EntrezID D7_R1 D7_R2 PLX7_R1 PLX7_R2
## 6610 SMPD2 6610 -0.458770 -0.514690 -0.67770 -0.510360
## 83881 MIXL1 83881 0.094571 0.122780 -0.14383 -0.119670
## 2028 ENPEP 2028 -0.082621 -0.082305 -0.11271 -0.061796
## 7410 VAV2 7410 -0.024270 -0.034077 0.14537 -0.015095
## 134553 C5orf24 134553 0.452310 0.226940 0.15809 0.191940
## 116238 TLCD1 116238 -0.883320 -0.779930 -0.80811 -0.854900Batch effect removal
Is there batch effect? This is a common asked question before perform later analysis. In our package, we provide HeatmapView to ensure whether batch effect exists in data, and use BatchRemove to remove easily if same batch samples cluster together.
batchMat = data.frame(samples = c(ctrlname, treatname), batch = c(1,2,1,2), cov = c(1,1,2,2))
dd1 = BatchRemove(dd[,c(ctrlname, treatname)], batchMat)$data## Standardizing Data across genes
Normalization of beta scores
It is difficult to control all samples with a consistent cell cycle in an CRISPR screen experiment with multi conditions. Besides, beta score among different conditions with an inconsistent cell cycle are incomparable. So it is necessary to do the normalization when comparing the beta scores in different conditions. Essential genesare thosegenes 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 cell cycle normalization method to shorten the gap of cell cycle in different conditions. In addition, a previous normalization method called loess normalization is available in this package.(Laurent Gautier 2004)
dd_essential = NormalizeBeta(dd, samples=c(ctrlname, treatname), method="cell_cycle")
head(dd_essential)## Gene EntrezID D7_R1 D7_R2 PLX7_R1 PLX7_R2
## 6610 SMPD2 6610 -0.50382174 -0.58514097 -0.8888801 -0.66908767
## 83881 MIXL1 83881 0.10385798 0.13958618 -0.1886493 -0.15688871
## 2028 ENPEP 2028 -0.09073448 -0.09357094 -0.1478319 -0.08101525
## 7410 VAV2 7410 -0.02665334 -0.03874147 0.1906692 -0.01978971
## 134553 C5orf24 134553 0.49672736 0.25800364 0.2073529 0.25163549
## 116238 TLCD1 116238 -0.97006304 -0.88668713 -1.0599276 -1.12078346## Gene EntrezID D7_R1 D7_R2 PLX7_R1 PLX7_R2
## 6610 SMPD2 6610 -0.44038017 -0.50825938 -0.6938220 -0.51905843
## 83881 MIXL1 83881 0.09383891 0.13556615 -0.1515824 -0.12397162
## 2028 ENPEP 2028 -0.08218047 -0.07375746 -0.1186463 -0.06484778
## 7410 VAV2 7410 -0.02259079 -0.02142822 0.1343865 -0.01843950
## 134553 C5orf24 134553 0.45749684 0.25157985 0.1396790 0.18052426
## 116238 TLCD1 116238 -0.84112255 -0.77043580 -0.8356798 -0.87902190Distribution of all gene beta scores
After normalization, the distribution of beta scores in different conditions should be similar. We can evaluate the distribution of beta scores using function ‘ViolinView’, ‘DensityView’, and ‘DensityDiffView’.
#we can also use function 'MAView' to evaluate the data quality of normalized
#beta score profile.
MAView(dd_essential, ctrlname, treatname, cex=1, main="Cell cycle normalized")
Estimate cell cycle time by linear fitting
After normalization, the cell cycle time in different condition should be almost consistent. Here we use linear fitting to estimate the cell cycle time, and use function CellCycleView to view the cell cycle time of all samples.
##Fitting beta score of all genes
CellCycleView(dd_essential, ctrlname, treatname, main="Cell cycle normalized")
Positive selection and negative selection
The function ScatterView can group all genes into three groups, positive selection genes (GroupA), negative selection genes (GroupB), and others, and visualize these three grouped genes in scatter plot. We can also use function RankView to rank the beta score deviation between control and treatment and mark top selected genes in figure.
## Add column of 'diff'
dd_essential$Control = rowMeans(dd_essential[,ctrlname])
dd_essential$Treatment = rowMeans(dd_essential[,treatname])
rankdata = dd_essential$Treatment - dd_essential$Control
names(rankdata) = rownames(dd_essential)
p2 = RankView(rankdata, main="Cell cycle normalized")
print(p2)
Functional analysis of selected genes
For gene set enrichment analysis, we provide three methods in this package, including “ORT”(Over-Representing Test (Guangchuang Yu and He. 2012)), “GSEA”(Gene Set Enrichment Analysis (Aravind Subramanian and Mesirov. 2005)), and “HGT”(HyperGemetric test), which can be performed on annotations of Gene ontology(GO) terms (Consortium. 2014), Kyoto encyclopedia of genes and genomes (KEGG) pathways (Minoru Kanehisa 2014), MsigDB gene sets, or custom gene sets. The enrichment analysis can be done easily using function enrichment_analysis, which return a list containing gridPlot (ggplot object) and enrichRes (enrichResult instance). Alternatively, you can do enrichment analysis using function enrich.ORT for “ORT”, enrich.GSE for GSEA, and enrich.HGT for “HGT”, which return an enrichResult instance. Function EnrichedView and EnrichedGSEView (for enrich.GSE) can be used to generate gridPlot from enrichReseasily, as shown below.
## Get information of positive and negative selection genes
groupAB = p1$data
## select positive selection genes
idx1=groupAB$group=="up"
genes=rownames(groupAB)[idx1]
geneList=groupAB$diff[idx1]
names(geneList)=genes
geneList = sort(geneList, decreasing = TRUE)
universe=rownames(groupAB)
## Do enrichment analysis using HGT method
keggA = enrich.HGT(geneList[1:100], universe, organism = "human", limit = c(3, 50))
keggA_grid = EnrichedView(keggA@result, plotTitle = "Positive selection")
## look at the results
head(keggA@result)## ID
## 405 GO_HISTONE_H3_ACETYLATION
## 110 CRGAARNNNNCGA_UNKNOWN
## 168 GO_PROTEIN_NEDDYLATION
## 197 GO_NEGATIVE_REGULATION_OF_SMALL_GTPASE_MEDIATED_SIGNAL_TRANSDUCTION
## 48 PID_ERB_GENOMIC_PATHWAY
## 229 GO_NEGATIVE_REGULATION_OF_CELL_MATRIX_ADHESION
## Description
## 405 http://www.broadinstitute.org/gsea/msigdb/cards/GO_CARDIAC_CHAMBER_DEVELOPMENT
## 110 http://www.broadinstitute.org/gsea/msigdb/cards/BIOCARTA_THELPER_PATHWAY
## 168 http://www.broadinstitute.org/gsea/msigdb/cards/GO_DNA_TEMPLATED_TRANSCRIPTIONAL_PREINITIATION_COMPLEX_ASSEMBLY
## 197 http://www.broadinstitute.org/gsea/msigdb/cards/GO_MEMBRANE_LIPID_CATABOLIC_PROCESS
## 48 http://www.broadinstitute.org/gsea/msigdb/cards/GSE29949_CD8_POS_DC_SPLEEN_VS_DC_BRAIN_UP
## 229 http://www.broadinstitute.org/gsea/msigdb/cards/GO_PROTEASOME_ASSEMBLY
## NES pvalue p.adjust GeneRatio BgRatio
## 405 21.195116 7.744210e-09 2.001878e-06 6/46 46/17304
## 110 14.474263 2.172780e-07 3.744424e-05 5/45 45/17304
## 168 12.853735 4.468965e-09 2.001878e-06 4/12 12/17304
## 197 9.026295 6.664960e-05 4.307230e-03 3/38 38/17304
## 48 9.003448 1.364866e-06 1.411272e-04 3/15 15/17304
## 229 8.204543 5.523581e-04 9.336792e-03 2/28 28/17304
## geneID geneName
## 405 112869/27097/10629/10474/117143/1810 SGF29/TAF5L/TAF6L/TADA3/TADA1/DR1
## 110 7178/1387/9040/9968/9049 TPT1/CREBBP/UBE2M/MED12/AIP
## 168 9039/8883/9616/9040 UBA3/NAE1/RNF7/UBE2M
## 197 10253/8452/4763 SPRY2/CUL3/NF1
## 48 9039/6598/9040 UBA3/SMARCB1/UBE2M
## 229 4771/4763 NF2/NF1
## Count
## 405 6
## 110 5
## 168 4
## 197 3
## 48 3
## 229 2
## Do enrichment analysis using GSEA method
gseA = enrich.GSE(geneList, type = "KEGG", organism = "human", pvalueCutoff = 1)
gseA_grid = EnrichedGSEView(gseA@result, plotTitle = "Positive selection")
#should same as
head(gseA@result)## ID Description setSize
## hsa00190 hsa00190 Oxidative phosphorylation 45
## hsa05010 hsa05010 Alzheimer disease 45
## hsa05012 hsa05012 Parkinson disease 42
## hsa04932 hsa04932 Non-alcoholic fatty liver disease (NAFLD) 39
## hsa05169 hsa05169 Epstein-Barr virus infection 29
## hsa03010 hsa03010 Ribosome 39
## enrichmentScore NES pvalue p.adjust qvalues rank
## hsa00190 0.4989677 2.124260 0.001022495 0.06038136 0.05731490 654
## hsa05010 0.5049680 2.149805 0.001022495 0.06038136 0.05731490 654
## hsa05012 0.5155812 2.169594 0.001026694 0.06038136 0.05731490 654
## hsa04932 0.4908035 2.045632 0.001028807 0.06038136 0.05731490 654
## hsa05169 0.5255877 2.051231 0.001059322 0.06038136 0.05731490 670
## hsa03010 0.4438049 1.849745 0.002057613 0.08526552 0.08093532 981
## leading_edge
## hsa00190 tags=56%, list=23%, signal=43%
## hsa05010 tags=56%, list=23%, signal=43%
## hsa05012 tags=57%, list=23%, signal=45%
## hsa04932 tags=54%, list=23%, signal=42%
## hsa05169 tags=59%, list=24%, signal=45%
## hsa03010 tags=64%, list=35%, signal=42%
## core_enrichment
## hsa00190 10063/1327/9377/7381/6392/4715/506/4708/4702/1355/4701/10632/1329/498/7386/29796/513/7385/6389/1337/1340/4716/4719/4709/55967
## hsa05010 3028/1327/8883/9377/7381/6392/4715/506/4708/4702/4701/1329/498/7386/29796/513/7385/6389/1337/1340/2906/4716/4719/4709/55967
## hsa05012 1327/9377/7381/6392/4715/506/4708/4702/7332/4701/1329/498/7386/29796/513/7385/6389/1337/1340/4716/4719/4709/135/55967
## hsa04932 1050/1327/9377/7381/6392/4715/4708/4702/4701/1329/7186/7386/29796/7385/6389/1337/1340/4716/4719/4709/55967
## hsa05169 1460/1387/7979/5608/5434/3312/3305/171568/5704/51728/7186/3106/6693/10621/890/9612/3133
## hsa03010 63875/6183/64979/6182/6230/6208/51021/6191/28998/51073/63931/51116/54948/124995/55168/29088/55052/6181/29093/6150/64968/6146/51318/6169/6193
## geneName
## hsa00190 COX17/COX4I1/COX5A/UQCRB/SDHD/NDUFB9/ATP5F1B/NDUFB2/NDUFA8/COX15/NDUFA7/ATP5MG/COX5B/ATP5F1A/UQCRFS1/UQCR10/ATP5F1D/UQCRC2/SDHA/COX6A1/COX6B1/NDUFB10/NDUFS1/NDUFB3/NDUFA12
## hsa05010 HSD17B10/COX4I1/NAE1/COX5A/UQCRB/SDHD/NDUFB9/ATP5F1B/NDUFB2/NDUFA8/NDUFA7/COX5B/ATP5F1A/UQCRFS1/UQCR10/ATP5F1D/UQCRC2/SDHA/COX6A1/COX6B1/GRIN2D/NDUFB10/NDUFS1/NDUFB3/NDUFA12
## hsa05012 COX4I1/COX5A/UQCRB/SDHD/NDUFB9/ATP5F1B/NDUFB2/NDUFA8/UBE2L3/NDUFA7/COX5B/ATP5F1A/UQCRFS1/UQCR10/ATP5F1D/UQCRC2/SDHA/COX6A1/COX6B1/NDUFB10/NDUFS1/NDUFB3/ADORA2A/NDUFA12
## hsa04932 CEBPA/COX4I1/COX5A/UQCRB/SDHD/NDUFB9/NDUFB2/NDUFA8/NDUFA7/COX5B/TRAF2/UQCRFS1/UQCR10/UQCRC2/SDHA/COX6A1/COX6B1/NDUFB10/NDUFS1/NDUFB3/NDUFA12
## hsa05169 CSNK2B/CREBBP/SEM1/MAP2K6/POLR2E/HSPA8/HSPA1L/POLR3H/PSMC4/POLR3K/TRAF2/HLA-B/SPN/POLR3F/CCNA2/NCOR2/HLA-E
## hsa03010 MRPL17/MRPS12/MRPL36/MRPL12/RPS25/RPS14/MRPS16/RPS4X/MRPL13/MRPL4/MRPS14/MRPS2/MRPL16/MRPL10/MRPS18A/MRPL15/MRPL20/RPLP2/MRPL22/MRPL23/MRPS6/RPL22/MRPL35/RPL38/RPS5
For enriched pathways, we can use function KeggPathwayView to visualize the beta score level in control and treatment on pathway map.(Weijun Luo 2013)
genedata = dd_essential[,c("Control","Treatment")]
keggID = gseA@result$ID[1]
#The pathway map will be located on current workspace
KeggPathwayView(gene.data = genedata, pathway.id = keggID, species="hsa")
##Read the figure into R
pngname=paste0(keggID, ".pathview.multi.png")
grid.arrange(grid::rasterGrob(png::readPNG(pngname)))
## [1] TRUE TRUE TRUEIdentify treatment-associated genes using 9-square model
Considering the difference of beta scores in control and treatment sample, we developed a 9-square model, which group all genes into several subgroups. Among these subgroups, four subgroup genes are treatment-associated, which correspond to specific functions. Group1 and Group3 genes are not selected in control sample, while they are significantly selected in treatment sample, so they may related to drug resistance. Group2 and Group4 genes are selected in control, but they are not selected in treatment, so maybe these genes are associated with drug targets.
In this package, use function SquareView can select these four treatment-associated subgroup genes and view them in 9-Square scatter plot.
Functional analysis for treatment-associated genes
Same as section above. We can do enrichment analysis for treatment-associated genes.
##Get information of treatment-associated genes
Square9 = p3$data
##==select group1 genes in 9-Square
idx=Square9$group=="Group1"
geneList = (Square9$Treatment - Square9$Control)[idx]
names(geneList) = rownames(Square9)[idx]
universe=rownames(Square9)
#====KEGG_enrichment=====
kegg1=enrich.ORT(geneList = geneList, universe = universe, type = "KEGG", limit = c(3, 50))
## look at the results
head(kegg1@result)## ID Description
## hsa00030 hsa00030 Pentose phosphate pathway
## hsa00130 hsa00130 Ubiquinone and other terpenoid-quinone biosynthesis
## hsa00480 hsa00480 Glutathione metabolism
## hsa00970 hsa00970 Aminoacyl-tRNA biosynthesis
## hsa00270 hsa00270 Cysteine and methionine metabolism
## hsa00280 hsa00280 Valine, leucine and isoleucine degradation
## NES pvalue p.adjust GeneRatio BgRatio
## hsa00030 0.24490684 0.01225853 0.5148581 5/339 28/6677
## hsa00130 0.14887484 0.01191997 0.5148581 3/339 10/6677
## hsa00480 0.03605360 0.47008429 0.9268424 3/339 50/6677
## hsa00970 0.02548853 0.17253001 0.9268424 4/339 43/6677
## hsa00270 0.02544361 0.89436894 0.9268424 1/339 43/6677
## hsa00280 0.02162495 0.91436495 0.9268424 1/339 47/6677
## geneID geneName Count
## hsa00030 226/7086/5226/5213/51071 ALDOA/TKT/PGD/PFKM/DERA 5
## hsa00130 84274/27235/51805 COQ5/COQ2/COQ3 3
## hsa00480 6723/5226/9027 SRM/PGD/NAT8 3
## hsa00970 10352/57176/92935/57505 WARS2/VARS2/MARS2/AARS2 4
## hsa00270 6723 SRM 1
## hsa00280 3028 HSD17B10 1
Also, pathway visualization can be done using function KeggPathwayView, the same as section above.
genedata = dd_essential[, c("Control","Treatment")]
keggID = kegg1$enrichRes@result$ID[1]
KeggPathwayView(gene.data = genedata, pathway.id = keggID, species="hsa")
##Read the figure into R
pngname=paste0(keggID, ".pathview.multi.png")
grid.arrange(grid::rasterGrob(png::readPNG(pngname)))
file.remove(paste0(keggID, c(".pathview.multi.png", ".png", ".xml")))Section II: Run pipeline from gene summary file generated by MAGeCK RRA
Gene summary file
For experiments with two experimental conditions, we recommend to use MAGeCK-RRA to identify essential genes from CRISPR/Cas9 knockout screens and tests the statistical significance of each observed change between two conditions. Gene summary file in MAGeCK-RRA results summarize the statistical significance of positive selection and negative selection. Use function ‘data’ to load the dataset, and have a look at the file with a text editor to see how it is formatted.
## id neg.fdr pos.fdr
## 1 CMIP 0.001650 0.977480
## 2 MCL1 0.001650 1.000000
## 3 ITGB2 0.001650 0.999967
## 4 SCN2A 0.003713 0.886655
## 5 GRB2 0.048680 0.984303
## 6 EGFR 0.048680 0.993803Read FDR of negative selection and positive selection
Then, extract “neg.fdr” and “pos.fdr” from the gene summary table.
## Official neg.fdr pos.fdr ENTREZID
## 1 CMIP 0.001650 0.977480 80790
## 2 MCL1 0.001650 1.000000 4170
## 3 ITGB2 0.001650 0.999967 3689
## 4 SCN2A 0.003713 0.886655 6326
## 5 GRB2 0.048680 0.984303 2885
## 6 EGFR 0.048680 0.993803 1956Negative selection and positive selection
Take 0.05 as cutoff, get negative selection and positive selection genes and do enrichment analysis on KEGG pathway and GO BP terms.
##Negative selection
universe=dd.rra$ENTREZID
idx = dd.rra$neg.fdr<0.05
geneList= -log10(dd.rra[idx, "neg.fdr"])
names(geneList) = dd.rra$ENTREZID[idx]
kegg.neg=enrichment_analysis(geneList = geneList, universe=universe,
type = "KEGG", plotTitle="KEGG: neg")
bp.neg=enrichment_analysis(geneList = geneList, universe=universe,
type = "BP", plotTitle="BP: neg")
print(kegg.neg$gridPlot)
##Positive selection
universe = dd.rra$ENTREZID
idx = dd.rra$pos.fdr<0.05
geneList = -log10(dd.rra[idx, "pos.fdr"])
names(geneList) = dd.rra$ENTREZID[idx]
kegg.pos=enrichment_analysis(geneList = geneList, universe=universe,
type = "KEGG", plotTitle="KEGG: pos")
bp.pos=enrichment_analysis(geneList = geneList, universe=universe,
type = "BP", plotTitle="BP: pos")
print(kegg.pos$gridPlot)
Session info
## R version 3.5.1 Patched (2018-07-12 r74967)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 16.04.5 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.8-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.8-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] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] MAGeCKFlute_1.2.0 gridExtra_2.3 pathview_1.22.0
## [4] org.Hs.eg.db_3.7.0 AnnotationDbi_1.44.0 IRanges_2.16.0
## [7] S4Vectors_0.20.0 Biobase_2.42.0 BiocGenerics_0.28.0
## [10] ggplot2_3.1.0
##
## loaded via a namespace (and not attached):
## [1] fgsea_1.8.0 colorspace_1.3-2 ggridges_0.5.1
## [4] rprojroot_1.3-2 qvalue_2.14.0 XVector_0.22.0
## [7] farver_1.0 urltools_1.7.1 ggrepel_0.8.0
## [10] bit64_0.9-7 xml2_1.2.0 codetools_0.2-15
## [13] splines_3.5.1 GOSemSim_2.8.0 knitr_1.20
## [16] jsonlite_1.5 annotate_1.60.0 GO.db_3.7.0
## [19] png_0.1-7 pheatmap_1.0.10 graph_1.60.0
## [22] ggforce_0.1.3 shiny_1.1.0 compiler_3.5.1
## [25] httr_1.3.1 rvcheck_0.1.1 backports_1.1.2
## [28] assertthat_0.2.0 Matrix_1.2-14 lazyeval_0.2.1
## [31] limma_3.38.0 later_0.7.5 tweenr_1.0.0
## [34] htmltools_0.3.6 prettyunits_1.0.2 tools_3.5.1
## [37] bindrcpp_0.2.2 igraph_1.2.2 gtable_0.2.0
## [40] glue_1.3.0 reshape2_1.4.3 DO.db_2.9
## [43] dplyr_0.7.7 fastmatch_1.1-0 Rcpp_0.12.19
## [46] enrichplot_1.2.0 Biostrings_2.50.0 nlme_3.1-137
## [49] ggraph_1.0.2 stringr_1.3.1 mime_0.6
## [52] miniUI_0.1.1.1 clusterProfiler_3.10.0 XML_3.98-1.16
## [55] DOSE_3.8.0 europepmc_0.3 zlibbioc_1.28.0
## [58] MASS_7.3-51 scales_1.0.0 hms_0.4.2
## [61] promises_1.0.1 KEGGgraph_1.42.0 RColorBrewer_1.1-2
## [64] yaml_2.2.0 memoise_1.1.0 UpSetR_1.3.3
## [67] biomaRt_2.38.0 triebeard_0.3.0 ggExtra_0.8
## [70] stringi_1.2.4 RSQLite_2.1.1 genefilter_1.64.0
## [73] BiocParallel_1.16.0 matrixStats_0.54.0 rlang_0.3.0.1
## [76] pkgconfig_2.0.2 bitops_1.0-6 evaluate_0.12
## [79] lattice_0.20-35 purrr_0.2.5 bindr_0.1.1
## [82] labeling_0.3 cowplot_0.9.3 bit_1.1-14
## [85] tidyselect_0.2.5 ggsci_2.9 plyr_1.8.4
## [88] magrittr_1.5 R6_2.3.0 DBI_1.0.0
## [91] mgcv_1.8-25 pillar_1.3.0 withr_2.1.2
## [94] units_0.6-1 survival_2.43-1 KEGGREST_1.22.0
## [97] RCurl_1.95-4.11 tibble_1.4.2 crayon_1.3.4
## [100] rmarkdown_1.10 viridis_0.5.1 progress_1.2.0
## [103] grid_3.5.1 sva_3.30.0 data.table_1.11.8
## [106] blob_1.1.1 Rgraphviz_2.26.0 digest_0.6.18
## [109] xtable_1.8-3 tidyr_0.8.2 httpuv_1.4.5
## [112] gridGraphics_0.3-0 munsell_0.5.0 viridisLite_0.3.0
## [115] ggplotify_0.0.3References
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