MotifAnalysis.RmdFor peak-related data, there are two levels of motif analysis:
DEbPeak provides MotifDiscovery to perfrom de novo motif discovery with STREME and MotifEnrich to perform de novo motif discovery and motif enrichment with Homer. While Homer only supports human, mouse, rat, frog, zebrafish, fly, worm, yeast and arabidopsis, DEbPeak provides the MotifCompare to map motifs provided by users against de novo motif discovered by MotifDiscovery.
The data used here are from In vivo CD8+ T cell CRISPR screening reveals control by Fli1 in infection and cancer:
To simplify the steps of obtaining differential expression analysis results, DEbPeak provides ConductDESeq2 to perfrom all steps of differential expression analysis, including quality control, principal component analysis, differential expression analysis and functional enrichment analysis.
peak.matrix.file <- system.file("extdata", "RA_ATAC_count.txt", package = "DEbPeak")
peak.meta.file <- system.file("extdata", "RA_ATAC_meta.txt", package = "DEbPeak")
ConductDESeq2(count.matrix.file = peak.matrix.file, meta.file = peak.meta.file, data.type = "ATAC",
gene.type = "SYMBOL", outlier.detection = F, min.count = 0,
peak.anno.key = "All", group.key = "condition", ref.group = "WT",
out.folder = '/home/songyabing/R/learn/tmp/DEbPeak/ATACDE',
signif = "padj", signif.threshold = 0.05, l2fc.threshold = 0, gmt.file = '')## Create DESeqDataSet from count matrix!## Warning in DESeqDataSet(se, design = design, ignoreRank): some variables in
## design formula are characters, converting to factors## Conduct Quality Control on Counts!## Differential expression analysis with DESeq2!## [1] "Warning: 7 features with 0 counts in all samples are to be removed for this analysis."
## [1] "Count distributions are to be computed for:"
## [1] "Fli1KO_1" "Fli1KO_2" "Fli1KO_3" "Fli1KO_4" "WT_1" "WT_2"## Differential expression analysis with DESeq2!## Raw gene number: 101921## Gene number after filtered with counts bigger than 0 : 101921## Conduct Quality Control on Samples!## Differential expression analysis with DESeq2!## Conduct PCA analysis!
## Conduct PCA analysis!## Differential expression analysis with DESeq2!## Use all genes for PCA!## Coordinate system already present. Adding new coordinate system, which will
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## Selecting by Loadding
## Selecting by Loadding
## Differential expression analysis with DESeq2!
##
## Selecting by Loadding
## Selecting by Loadding
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## 载入程辑包：'BiocGenerics'
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## The following objects are masked from 'package:stats':
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## IQR, mad, sd, var, xtabs
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## The following objects are masked from 'package:base':
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## anyDuplicated, append, as.data.frame, basename, cbind, colnames,
## dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep,
## grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget,
## order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,
## rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,
## union, unique, unsplit, which.max, which.min
##
##
## Welcome to Bioconductor
##
## Vignettes contain introductory material; view with
## 'browseVignettes()'. To cite Bioconductor, see
## 'citation("Biobase")', and for packages 'citation("pkgname")'.
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## 载入程辑包：'S4Vectors'
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## The following object is masked from 'package:base':
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## Conduct Functional Enrichment on PC Loading genes!
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## Selecting by Loadding
## Selecting by Loadding
## Convert SYMBOL to ENTREZID!
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## 'select()' returned 1:1 mapping between keys and columns
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## conduct ALL GO enrichment analysis on: PC1_Positive
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## conduct KEGG enrichment analysis: PC1_Positive
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## Reading KEGG annotation online: "https://rest.kegg.jp/link/mmu/pathway"...
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## Reading KEGG annotation online: "https://rest.kegg.jp/list/pathway/mmu"...
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## Convert SYMBOL to ENTREZID!
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## 'select()' returned 1:1 mapping between keys and columns
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## conduct ALL GO enrichment analysis on: PC1_Negative
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## conduct KEGG enrichment analysis: PC1_Negative
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## Convert SYMBOL to ENTREZID!
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## 'select()' returned 1:1 mapping between keys and columns
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## conduct ALL GO enrichment analysis on: PC2_Positive
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## conduct KEGG enrichment analysis: PC2_Positive
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## Convert SYMBOL to ENTREZID!
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## 'select()' returned 1:1 mapping between keys and columns
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## conduct ALL GO enrichment analysis on: PC2_Negative
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## conduct KEGG enrichment analysis: PC2_Negative
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## 'select()' returned 1:1 mapping between keys and columns
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## conduct ALL GO enrichment analysis on: PC3_Positive
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## conduct ALL GO enrichment analysis on: PC3_Negative
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## conduct ALL GO enrichment analysis on: PC4_Positive
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## conduct ALL GO enrichment analysis on: PC5_Positive
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## conduct ALL GO enrichment analysis on: PC5_Negative
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## Conduct Differential Expression Analysis!
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## estimating size factors
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## estimating dispersions
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## gene-wise dispersion estimates
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## mean-dispersion relationship
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## -- note: fitType='parametric', but the dispersion trend was not well captured by the
## function: y = a/x + b, and a local regression fit was automatically substituted.
## specify fitType='local' or 'mean' to avoid this message next time.
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## final dispersion estimates
##
## fitting model and testing
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## Conduct Gene ID Conversion!
##
## 'select()' returned 1:many mapping between keys and columns## Warning in clusterProfiler::bitr(de.df[, "Gene2Convert"], fromType =
## gene.type, : 1.63% of input gene IDs are fail to map...## Visualize Differential Analysis Results!
## Differential expression analysis with DESeq2!
## Differential expression analysis with DESeq2!
## Differential expression analysis with DESeq2!
## Differential expression analysis with DESeq2!
## Differential expression analysis with DESeq2!
## Differential expression analysis with DESeq2!
## Conduct Functional Enrichment Analysis!
## Differential expression analysis with DESeq2!
## Convert SYMBOL to ENTREZID!
## 'select()' returned 1:1 mapping between keys and columns
## conduct ALL GO enrichment analysis on: UP
## conduct KEGG enrichment analysis: UP
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## conduct ALL GO enrichment analysis on: DOWN
## conduct KEGG enrichment analysis: DOWN
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## Scale for y is already present.
## Adding another scale for y, which will replace the existing scale.Gene Set Enrichment Analysis is not available for ChIP-seq or ATAC-seq data!
## All Analysis Done!The structure of result directory:
tree /home/songyabing/R/learn/tmp/DEbPeak/ATACDE## /home/songyabing/R/learn/tmp/DEbPeak/ATACDE
## ├── CountQC_CPM.pdf
## ├── CountQC_saturation.pdf
## ├── DA
## │ ├── Condition_KO_WT_all.csv
## │ ├── Condition_KO_WT_padj0.05_FC0.csv
## │ ├── DA_GenePlot.pdf
## │ ├── DA_Heatmap.pdf
## │ ├── DA_MAPlot.pdf
## │ ├── DA_RankPlot.pdf
## │ ├── DA_ScatterPlot.pdf
## │ ├── DA_VolcanoPlot.pdf
## │ └── normalized_counts.csv
## ├── DEG
## │ ├── Condition_KO_WT_all.csv
## │ ├── Condition_KO_WT_padj0.05_FC0.csv
## │ ├── DEG_GenePlot.pdf
## │ ├── DEG_Heatmap.pdf
## │ ├── DEG_MAPlot.pdf
## │ ├── DEG_RankPlot.pdf
## │ ├── DEG_ScatterPlot.pdf
## │ ├── DEG_VolcanoPlot.pdf
## │ └── normalized_counts.csv
## ├── FE
## │ ├── DOWN_ALL_GO.csv
## │ ├── DOWN_Biological_Process.png
## │ ├── DOWN_Cellular_Component.png
## │ ├── DOWN_Functional_Enrichment.pdf
## │ ├── DOWN_KEGG.csv
## │ ├── DOWN_KEGG_Enrichment.png
## │ ├── DOWN_Molecular_Function.png
## │ ├── UP_ALL_GO.csv
## │ ├── UP_Biological_Process.png
## │ ├── UP_Cellular_Component.png
## │ ├── UP_Functional_Enrichment.pdf
## │ ├── UP_KEGG.csv
## │ ├── UP_KEGG_Enrichment.png
## │ └── UP_Molecular_Function.png
## ├── PCA
## │ ├── PC1
## │ │ ├── Negative
## │ │ │ ├── PC1_Negative_ALL_GO.csv
## │ │ │ └── PC1_Negative_KEGG.csv
## │ │ └── Positive
## │ │ ├── PC1_Positive_ALL_GO.csv
## │ │ └── PC1_Positive_KEGG.csv
## │ ├── PC1_Negative_Biological_Process.png
## │ ├── PC1_Negative_Cellular_Component.png
## │ ├── PC1_Negative_Functional_Enrichment.pdf
## │ ├── PC1_Negative_KEGG_Enrichment.png
## │ ├── PC1_Negative_Molecular_Function.png
## │ ├── PC1_Positive_Biological_Process.png
## │ ├── PC1_Positive_Cellular_Component.png
## │ ├── PC1_Positive_Functional_Enrichment.pdf
## │ ├── PC1_Positive_KEGG_Enrichment.png
## │ ├── PC1_Positive_Molecular_Function.png
## │ ├── PC2
## │ │ ├── Negative
## │ │ │ ├── PC2_Negative_ALL_GO.csv
## │ │ │ └── PC2_Negative_KEGG.csv
## │ │ └── Positive
## │ │ ├── PC2_Positive_ALL_GO.csv
## │ │ └── PC2_Positive_KEGG.csv
## │ ├── PC2_Negative_Biological_Process.png
## │ ├── PC2_Negative_Cellular_Component.png
## │ ├── PC2_Negative_Functional_Enrichment.pdf
## │ ├── PC2_Negative_KEGG_Enrichment.png
## │ ├── PC2_Negative_Molecular_Function.png
## │ ├── PC2_Positive_Biological_Process.png
## │ ├── PC2_Positive_Cellular_Component.png
## │ ├── PC2_Positive_Functional_Enrichment.pdf
## │ ├── PC2_Positive_KEGG_Enrichment.png
## │ ├── PC2_Positive_Molecular_Function.png
## │ ├── PC3
## │ │ ├── Negative
## │ │ │ ├── PC3_Negative_ALL_GO.csv
## │ │ │ └── PC3_Negative_KEGG.csv
## │ │ └── Positive
## │ │ ├── PC3_Positive_ALL_GO.csv
## │ │ └── PC3_Positive_KEGG.csv
## │ ├── PC3_Negative_Biological_Process.png
## │ ├── PC3_Negative_Cellular_Component.png
## │ ├── PC3_Negative_Functional_Enrichment.pdf
## │ ├── PC3_Negative_KEGG_Enrichment.png
## │ ├── PC3_Negative_Molecular_Function.png
## │ ├── PC3_Positive_Biological_Process.png
## │ ├── PC3_Positive_Cellular_Component.png
## │ ├── PC3_Positive_Functional_Enrichment.pdf
## │ ├── PC3_Positive_KEGG_Enrichment.png
## │ ├── PC3_Positive_Molecular_Function.png
## │ ├── PC4
## │ │ ├── Negative
## │ │ │ ├── PC4_Negative_ALL_GO.csv
## │ │ │ └── PC4_Negative_KEGG.csv
## │ │ └── Positive
## │ │ ├── PC4_Positive_ALL_GO.csv
## │ │ └── PC4_Positive_KEGG.csv
## │ ├── PC4_Negative_Biological_Process.png
## │ ├── PC4_Negative_Cellular_Component.png
## │ ├── PC4_Negative_Functional_Enrichment.pdf
## │ ├── PC4_Negative_KEGG_Enrichment.png
## │ ├── PC4_Negative_Molecular_Function.png
## │ ├── PC4_Positive_Biological_Process.png
## │ ├── PC4_Positive_Cellular_Component.png
## │ ├── PC4_Positive_Functional_Enrichment.pdf
## │ ├── PC4_Positive_KEGG_Enrichment.png
## │ ├── PC4_Positive_Molecular_Function.png
## │ ├── PC5
## │ │ ├── Negative
## │ │ │ ├── PC5_Negative_ALL_GO.csv
## │ │ │ └── PC5_Negative_KEGG.csv
## │ │ └── Positive
## │ │ ├── PC5_Positive_ALL_GO.csv
## │ │ └── PC5_Positive_KEGG.csv
## │ ├── PC5_Negative_Biological_Process.png
## │ ├── PC5_Negative_Cellular_Component.png
## │ ├── PC5_Negative_Functional_Enrichment.pdf
## │ ├── PC5_Negative_KEGG_Enrichment.png
## │ ├── PC5_Negative_Molecular_Function.png
## │ ├── PC5_Positive_Biological_Process.png
## │ ├── PC5_Positive_Cellular_Component.png
## │ ├── PC5_Positive_Functional_Enrichment.pdf
## │ ├── PC5_Positive_KEGG_Enrichment.png
## │ ├── PC5_Positive_Molecular_Function.png
## │ ├── PCA_3DPCA.pdf
## │ ├── PCA_biplot.pdf
## │ ├── PCA_loading_bar.pdf
## │ ├── PCA_loading_heat.pdf
## │ ├── PCA_overview.pdf
## │ ├── PCA_pairs_plot.pdf
## │ ├── PCA_screen_plot.pdf
## │ └── PCA_scree_plot.pdf
## └── SampleQC_dist_pcc.pdf
##
## 19 directories, 113 filesRead the results:
dds.peak.results.ordered = read.csv( "/home/songyabing/R/learn/tmp/DEbPeak/ATACDE/DEG/Condition_KO_WT_all.csv", row.names = 1)
head(dds.peak.results.ordered)## baseMean log2FoldChange lfcSE
## chr8:94825359-94825432|Ciapin1|P 12.106577 6.439216 1.527019
## chr3:105628355-105628501|Ddx20|I 11.180004 6.323179 1.523447
## chr1:118440678-118440831|2900060B14Rik|I 7.836836 5.790918 1.569651
## chr1:165368382-165368517|Dcaf6|I 6.140173 5.439287 1.619636
## chr16:59434969-59435053|Gabrr3|I 5.706767 5.348676 1.631421
## chr11:20780454-20780545|Aftph|DI 5.390470 5.269629 1.636609
## stat pvalue padj
## chr8:94825359-94825432|Ciapin1|P 4.216854 2.477343e-05 0.001265734
## chr3:105628355-105628501|Ddx20|I 4.150575 3.316417e-05 0.001598853
## chr1:118440678-118440831|2900060B14Rik|I 3.689304 2.248687e-04 0.006892699
## chr1:165368382-165368517|Dcaf6|I 3.358339 7.841226e-04 0.017866061
## chr16:59434969-59435053|Gabrr3|I 3.278539 1.043461e-03 0.021915949
## chr11:20780454-20780545|Aftph|DI 3.219846 1.282594e-03 0.025309971
## SYMBOL ENSEMBL
## chr8:94825359-94825432|Ciapin1|P Ciapin1 ENSMUSG00000031781
## chr3:105628355-105628501|Ddx20|I Ddx20 ENSMUSG00000027905
## chr1:118440678-118440831|2900060B14Rik|I 2900060B14Rik ENSMUSG00000107722
## chr1:165368382-165368517|Dcaf6|I Dcaf6 ENSMUSG00000026571
## chr16:59434969-59435053|Gabrr3|I Gabrr3 ENSMUSG00000074991
## chr11:20780454-20780545|Aftph|DI Aftph ENSMUSG00000049659
## ENTREZID
## chr8:94825359-94825432|Ciapin1|P 109006
## chr3:105628355-105628501|Ddx20|I 53975
## chr1:118440678-118440831|2900060B14Rik|I 68204
## chr1:165368382-165368517|Dcaf6|I 74106
## chr16:59434969-59435053|Gabrr3|I 328699
## chr11:20780454-20780545|Aftph|DI 216549Here, we will perform de novo motif discovery with STREME. There are three categories for users to choose to perform de novo motif discovery: Up_regulated, Down_regulated, Not_regulated. Here, we will use Up_regulated as example.
# this step is time consuming!
MotifDiscovery(diff.peak = dds.peak.results.ordered, peak.motif.key = "Up_regulated",
genome = '/path/to/genome.fa', streme.path = "~/data/software/meme-5.5.2/bin/streme",
samtools.path = "/home/songyabing/data/software/samtools-1.17/samtools",
out.folder = "/home/songyabing/R/learn/tmp/DEbPeak/DAdenovo", show.html = FALSE)
# after running, there will be a browser pop-up window to show the results.In this step, we will map de novo motifs against motifs provided by users (known motif database).
verteb.motifs = system.file("extdata", "homer_verteb.motifs.meme", package = "DEbPeak")
motif.cmp = MotifCompare(motif.folder = "/home/songyabing/R/learn/tmp/DEbPeak/DAdenovo",
known.motif = verteb.motifs,
tomtom.path = '~/data/software/meme-5.5.2/bin/tomtom',
out.folder = "/home/songyabing/R/learn/tmp/DEbPeak/DAdenovo", show.html = FALSE)
# after running, there will be a browser pop-up window to show the results.There are three categories for users to choose to perform motif enrichment: Up_regulated, Down_regulated, Not_regulated. Here, we will use Up_regulated as example.
# this step is time consuming!
da.peak.motif = MotifEnrich(de.res = dds.peak.results.ordered, reg.type = "Up_regulated",
homer.motif.path = '~/anaconda3/bin/findMotifsGenome.pl',
out.folder = "/home/songyabing/R/learn/tmp/DEbPeak/DAmotif",
other.paras = "-len 8,10,12 -size -100,50 -S 25")The results will contains de novo motif results and known motif enrichment analysis results.
## R version 4.0.3 (2020-10-10)
## Platform: x86_64-conda-linux-gnu (64-bit)
## Running under: CentOS Linux 7 (Core)
##
## Matrix products: default
## BLAS/LAPACK: /home/softwares/anaconda3/envs/r4.0/lib/libopenblasp-r0.3.12.so
##
## locale:
## [1] LC_CTYPE=zh_CN.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=zh_CN.UTF-8 LC_COLLATE=zh_CN.UTF-8
## [5] LC_MONETARY=zh_CN.UTF-8 LC_MESSAGES=zh_CN.UTF-8
## [7] LC_PAPER=zh_CN.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=zh_CN.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] org.Mm.eg.db_3.12.0 AnnotationDbi_1.52.0 IRanges_2.24.1
## [4] S4Vectors_0.28.1 Biobase_2.50.0 BiocGenerics_0.42.0
## [7] DEbPeak_1.4.0
##
## loaded via a namespace (and not attached):
## [1] rsvd_1.0.3
## [2] ggvenn_0.1.9
## [3] apeglm_1.12.0
## [4] Rsamtools_2.6.0
## [5] rsvg_2.1
## [6] foreach_1.5.1
## [7] rprojroot_2.0.2
## [8] crayon_1.4.1
## [9] V8_3.4.2
## [10] MASS_7.3-58
## [11] nlme_3.1-152
## [12] backports_1.2.1
## [13] sva_3.38.0
## [14] GOSemSim_2.25.0
## [15] rlang_1.1.0
## [16] XVector_0.30.0
## [17] readxl_1.4.2
## [18] irlba_2.3.5
## [19] limma_3.46.0
## [20] GOstats_2.56.0
## [21] BiocParallel_1.24.1
## [22] rjson_0.2.20
## [23] bit64_4.0.5
## [24] glue_1.6.2
## [25] DiffBind_3.0.15
## [26] mixsqp_0.3-43
## [27] pheatmap_1.0.12
## [28] parallel_4.0.3
## [29] DEFormats_1.18.0
## [30] base64url_1.4
## [31] tcltk_4.0.3
## [32] DOSE_3.23.2
## [33] haven_2.5.2
## [34] tidyselect_1.2.0
## [35] SummarizedExperiment_1.20.0
## [36] rio_0.5.27
## [37] XML_3.99-0.6
## [38] tidyr_1.3.0
## [39] ggpubr_0.4.0
## [40] GenomicAlignments_1.26.0
## [41] xtable_1.8-4
## [42] ggnetwork_0.5.12
## [43] magrittr_2.0.3
## [44] evaluate_0.14
## [45] ggplot2_3.4.2
## [46] cli_3.6.1
## [47] zlibbioc_1.36.0
## [48] hwriter_1.3.2
## [49] rstudioapi_0.14
## [50] bslib_0.3.1
## [51] GreyListChIP_1.22.0
## [52] fastmatch_1.1-3
## [53] BiocSingular_1.6.0
## [54] xfun_0.30
## [55] askpass_1.1
## [56] clue_0.3-59
## [57] gson_0.0.9
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## [59] caTools_1.18.2
## [60] tidygraph_1.2.0
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## [62] ggrepel_0.9.1
## [63] Biostrings_2.58.0
## [64] png_0.1-7
## [65] withr_2.5.0
## [66] bitops_1.0-6
## [67] ggforce_0.3.3
## [68] RBGL_1.66.0
## [69] plyr_1.8.6
## [70] cellranger_1.1.0
## [71] GSEABase_1.52.1
## [72] pcaPP_2.0-1
## [73] dqrng_0.2.1
## [74] coda_0.19-4
## [75] pillar_1.9.0
## [76] gplots_3.1.1
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## [78] cachem_1.0.4
## [79] GenomicFeatures_1.42.2
## [80] fs_1.5.0
## [81] GetoptLong_1.0.5
## [82] clusterProfiler_4.7.1
## [83] DelayedMatrixStats_1.12.3
## [84] vctrs_0.6.2
## [85] generics_0.1.0
## [86] plot3D_1.4
## [87] tools_4.0.3
## [88] foreign_0.8-81
## [89] NOISeq_2.34.0
## [90] munsell_0.5.0
## [91] tweenr_1.0.2
## [92] fgsea_1.16.0
## [93] DelayedArray_0.16.3
## [94] fastmap_1.1.0
## [95] compiler_4.0.3
## [96] abind_1.4-5
## [97] rtracklayer_1.50.0
## [98] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [99] GenomeInfoDbData_1.2.4
## [100] gridExtra_2.3
## [101] edgeR_3.32.1
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## [103] ggnewscale_0.4.7
## [104] AnnotationForge_1.32.0
## [105] utf8_1.2.1
## [106] dplyr_1.1.2
## [107] BiocFileCache_1.14.0
## [108] jsonlite_1.8.4
## [109] scales_1.2.1
## [110] graph_1.68.0
## [111] carData_3.0-4
## [112] sparseMatrixStats_1.2.1
## [113] TFEA.ChIP_1.10.0
## [114] genefilter_1.72.1
## [115] car_3.0-11
## [116] doParallel_1.0.16
## [117] latticeExtra_0.6-29
## [118] R.utils_2.12.0
## [119] brew_1.0-6
## [120] checkmate_2.0.0
## [121] rmarkdown_2.14
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## [123] pkgdown_1.6.1
## [124] cowplot_1.1.1
## [125] textshaping_0.3.6
## [126] forcats_1.0.0
## [127] downloader_0.4
## [128] BSgenome_1.58.0
## [129] igraph_1.4.99.9024
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## [131] numDeriv_2016.8-1.1
## [132] yaml_2.2.1
## [133] plotrix_3.8-2
## [134] systemfonts_1.0.4
## [135] ashr_2.2-47
## [136] SQUAREM_2021.1
## [137] htmltools_0.5.2
## [138] memoise_2.0.0
## [139] VariantAnnotation_1.36.0
## [140] locfit_1.5-9.4
## [141] graphlayouts_0.7.1
## [142] batchtools_0.9.15
## [143] PCAtools_2.2.0
## [144] viridisLite_0.4.0
## [145] rrcov_1.7-0
## [146] digest_0.6.27
## [147] assertthat_0.2.1
## [148] rappdirs_0.3.3
## [149] emdbook_1.3.12
## [150] RSQLite_2.2.5
## [151] amap_0.8-18
## [152] yulab.utils_0.0.4
## [153] debugme_1.1.0
## [154] misc3d_0.9-1
## [155] data.table_1.14.2
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## [157] R.oo_1.24.0
## [158] ragg_0.4.0
## [159] labeling_0.4.2
## [160] splines_4.0.3
## [161] Cairo_1.5-12.2
## [162] ggupset_0.3.0
## [163] RCurl_1.98-1.3
## [164] broom_1.0.4
## [165] hms_1.1.3
## [166] colorspace_2.0-0
## [167] BiocManager_1.30.16
## [168] GenomicRanges_1.42.0
## [169] shape_1.4.6
## [170] sass_0.4.1
## [171] GEOquery_2.58.0
## [172] Rcpp_1.0.9
## [173] mvtnorm_1.1-2
## [174] circlize_0.4.15
## [175] enrichplot_1.10.2
## [176] fansi_0.4.2
## [177] tzdb_0.3.0
## [178] truncnorm_1.0-8
## [179] ChIPseeker_1.33.0.900
## [180] R6_2.5.0
## [181] grid_4.0.3
## [182] lifecycle_1.0.3
## [183] ShortRead_1.48.0
## [184] zip_2.1.1
## [185] curl_4.3
## [186] ggsignif_0.6.3
## [187] jquerylib_0.1.3
## [188] robustbase_0.95-0
## [189] DO.db_2.9
## [190] Matrix_1.5-4
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## [192] desc_1.3.0
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## [194] RColorBrewer_1.1-2
## [195] iterators_1.0.13
## [196] stringr_1.5.0
## [197] DOT_0.1
## [198] ggpie_0.2.5
## [199] beachmat_2.6.4
## [200] polyclip_1.10-0
## [201] biomaRt_2.46.3
## [202] purrr_1.0.1
## [203] shadowtext_0.0.9
## [204] gridGraphics_0.5-1
## [205] mgcv_1.8-34
## [206] ComplexHeatmap_2.13.1
## [207] openssl_1.4.3
## [208] patchwork_1.0.0
## [209] bdsmatrix_1.3-4
## [210] codetools_0.2-18
## [211] matrixStats_0.58.0
## [212] invgamma_1.1
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## [214] gtools_3.8.2
## [215] prettyunits_1.1.1
## [216] dbplyr_2.3.2
## [217] R.methodsS3_1.8.1
## [218] GenomeInfoDb_1.26.7
## [219] gtable_0.3.0
## [220] DBI_1.1.1
## [221] ggfun_0.0.6
## [222] httr_1.4.5
## [223] KernSmooth_2.23-18
## [224] stringi_1.5.3
## [225] progress_1.2.2
## [226] reshape2_1.4.4
## [227] farver_2.1.0
## [228] annotate_1.68.0
## [229] viridis_0.6.1
## [230] Rgraphviz_2.34.0
## [231] xml2_1.3.4
## [232] bbmle_1.0.24
## [233] systemPipeR_1.24.3
## [234] boot_1.3-28
## [235] readr_2.1.4
## [236] geneplotter_1.68.0
## [237] ggplotify_0.1.0
## [238] Category_2.56.0
## [239] DEoptimR_1.0-11
## [240] DESeq2_1.30.1
## [241] bit_4.0.4
## [242] scatterpie_0.1.7
## [243] jpeg_0.1-8.1
## [244] MatrixGenerics_1.2.1
## [245] ggraph_2.0.5
## [246] pkgconfig_2.0.3
## [247] rstatix_0.7.0
## [248] knitr_1.37