# ChIPseeker: an R package for ChIP peak Annotation, Comparison and Visualization
Author: Guangchuang Yu (guangchuangyu@gmail.com) School of Public Health, The University of Hong Kong
Date: 2018-07-21

\ Guangchuang Yu (<guangchuangyu@gmail.com>)\ School of Public Health, The University of Hong Kong

#### 2018-07-21

# Contents

# 1 Abstract

ChIPseeker is an R package for annotating ChIP-seq data analysis. It supports annotating ChIP peaks and provides functions to visualize ChIP peaks coverage over chromosomes and profiles of peaks binding to TSS regions. Comparison of ChIP peak profiles and annotation are also supported. Moreover, it supports evaluating significant overlap among ChIP-seq datasets. Currently, ChIPseeker contains 17,000 bed file information from GEO database. These datasets can be downloaded and compare with user’s own data to explore significant overlap datasets for inferring co-regulation or transcription factor complex for further investigation.

# 2 Citation

If you use ChIPseeker 1 in published research, please cite:

G Yu, LG Wang, QY He. ChIPseeker: an R/Bioconductor package for ChIP peak annotation, comparison and visualization. Bioinformatics 2015, 31(14):2382-2383. doi:[10.1093/bioinformatics/btv145](http://dx.doi.org/10.1093/bioinformatics/btv145)

# 3 Introduction

Chromatin immunoprecipitation followed by high-throughput sequencing (ChIP-seq) has become standard technologies for genome wide identification of DNA-binding protein target sites. After read mappings and peak callings, the peak should be annotated to answer the biological questions. Annotation also create the possibility of integrating expression profile data to predict gene expression regulation. ChIPseeker 1 was developed for annotating nearest genes and genomic features to peaks.

ChIP peak data set comparison is also very important. We can use it as an index to estimate how well biological replications are. Even more important is applying to infer cooperative regulation. If two ChIP seq data, obtained by two different binding proteins, overlap significantly, these two proteins may form a complex or have interaction in regulation chromosome remodelling or gene expression. ChIPseeker 1 support statistical testing of significant overlap among ChIP seq data sets, and incorporate open access database GEO for users to compare their own dataset to those deposited in database. Protein interaction hypothesis can be generated by mining data deposited in database. Converting genome coordinations from one genome version to another is also supported, making this comparison available for different genome version and different species.

Several visualization functions are implemented to visualize the coverage of the ChIP seq data, peak annotation, average profile and heatmap of peaks binding to TSS region.

Functional enrichment analysis of the peaks can be performed by my Bioconductor packages DOSE 2, ReactomePA 3, clusterProfiler 4.

    ## loading packages
    library(ChIPseeker)
    library(TxDb.Hsapiens.UCSC.hg19.knownGene)
    txdb <- TxDb.Hsapiens.UCSC.hg19.knownGene
    library(clusterProfiler)

# 4 ChIP profiling

The datasets CBX6 and CBX7 in this vignettes were downloaded from GEO (GSE40740)5 while ARmo_0M, ARmo_1nM and ARmo_100nM were downloaded from GEO (GSE48308)6 . ChIPseeker provides `readPeakFile` to load the peak and store in `GRanges` object.

    files <- getSampleFiles()
    print(files)

    ## $ARmo_0M
    ## [1] "/tmp/RtmpLh0KEr/Rinst3e496f20405e/ChIPseeker/extdata/GEO_sample_data/GSM1174480_ARmo_0M_peaks.bed.gz"
    ##
    ## $ARmo_1nM
    ## [1] "/tmp/RtmpLh0KEr/Rinst3e496f20405e/ChIPseeker/extdata/GEO_sample_data/GSM1174481_ARmo_1nM_peaks.bed.gz"
    ##
    ## $ARmo_100nM
    ## [1] "/tmp/RtmpLh0KEr/Rinst3e496f20405e/ChIPseeker/extdata/GEO_sample_data/GSM1174482_ARmo_100nM_peaks.bed.gz"
    ##
    ## $CBX6_BF
    ## [1] "/tmp/RtmpLh0KEr/Rinst3e496f20405e/ChIPseeker/extdata/GEO_sample_data/GSM1295076_CBX6_BF_ChipSeq_mergedReps_peaks.bed.gz"
    ##
    ## $CBX7_BF
    ## [1] "/tmp/RtmpLh0KEr/Rinst3e496f20405e/ChIPseeker/extdata/GEO_sample_data/GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz"

    peak <- readPeakFile(files[[4]])
    peak

    ## GRanges object with 1331 ranges and 2 metadata columns:
    ##          seqnames              ranges strand |             V4        V5
    ##             <Rle>           <IRanges>  <Rle> |       <factor> <numeric>
    ##      [1]     chr1       815093-817883      * |    MACS_peak_1    295.76
    ##      [2]     chr1     1243288-1244338      * |    MACS_peak_2     63.19
    ##      [3]     chr1     2979977-2981228      * |    MACS_peak_3    100.16
    ##      [4]     chr1     3566182-3567876      * |    MACS_peak_4    558.89
    ##      [5]     chr1     3816546-3818111      * |    MACS_peak_5     57.57
    ##      ...      ...                 ...    ... .            ...       ...
    ##   [1327]     chrX 135244783-135245821      * | MACS_peak_1327     55.54
    ##   [1328]     chrX 139171964-139173506      * | MACS_peak_1328    270.19
    ##   [1329]     chrX 139583954-139586126      * | MACS_peak_1329    918.73
    ##   [1330]     chrX 139592002-139593238      * | MACS_peak_1330    210.88
    ##   [1331]     chrY   13845134-13845777      * | MACS_peak_1331     58.39
    ##   -------
    ##   seqinfo: 24 sequences from an unspecified genome; no seqlengths

## 4.1 ChIP peaks coverage plot

After peak calling, we would like to know the peak locations over the whole genome, `covplot` function calculates the coverage of peak regions over chromosomes and generate a figure to visualize. GRangesList is also supported and can be used to compare coverage of multiple bed files.

    covplot(peak, weightCol="V5")

    covplot(peak, weightCol="V5", chrs=c("chr17", "chr18"), xlim=c(4.5e7, 5e7))

## 4.2 Profile of ChIP peaks binding to TSS regions

First of all, for calculating the profile of ChIP peaks binding to TSS regions, we should prepare the TSS regions, which are defined as the flanking sequence of the TSS sites. Then align the peaks that are mapping to these regions, and generate the tagMatrix.

    ## promoter <- getPromoters(TxDb=txdb, upstream=3000, downstream=3000)
    ## tagMatrix <- getTagMatrix(peak, windows=promoter)
    ##
    ## to speed up the compilation of this vignettes, we use a precalculated tagMatrix
    data("tagMatrixList")
    tagMatrix <- tagMatrixList[[4]]

In the above code, you should notice that tagMatrix is not restricted to TSS regions. The regions can be other types that defined by the user.

### 4.2.1 Heatmap of ChIP binding to TSS regions

    tagHeatmap(tagMatrix, xlim=c(-3000, 3000), color="red")

Figure 1: Heatmap of ChIP peaks binding to TSS regions

ChIPseeker provide a one step function to generate this figure from bed file. The following function will generate the same figure as above.

    peakHeatmap(files[[4]], TxDb=txdb, upstream=3000, downstream=3000, color="red")

### 4.2.2 Average Profile of ChIP peaks binding to TSS region

    plotAvgProf(tagMatrix, xlim=c(-3000, 3000),
                xlab="Genomic Region (5'->3')", ylab = "Read Count Frequency")

Figure 2: Average Profile of ChIP peaks binding to TSS region

The function `plotAvgProf2` provide a one step from bed file to average profile plot. The following command will generate the same figure as shown above.

    plotAvgProf2(files[[4]], TxDb=txdb, upstream=3000, downstream=3000,
                 xlab="Genomic Region (5'->3')", ylab = "Read Count Frequency")

Confidence interval estimated by bootstrap method is also supported for characterizing ChIP binding profiles.

    plotAvgProf(tagMatrix, xlim=c(-3000, 3000), conf = 0.95, resample = 1000)

## 4.3 Profile of ChIP peaks binding to start site of Exon/Intron

Referring to the issue #16, we developed `getBioRegion` function to support centering all peaks to the start region of Exon/Intron. Users can also create heatmap or average profile of ChIP peaks binding to these regions.

# 5 Peak Annotation

    peakAnno <- annotatePeak(files[[4]], tssRegion=c(-3000, 3000),
                             TxDb=txdb, annoDb="org.Hs.eg.db")

    ## >> loading peak file...               2018-07-21 08:02:46 PM
    ## >> preparing features information...      2018-07-21 08:02:46 PM
    ## >> identifying nearest features...        2018-07-21 08:02:48 PM
    ## >> calculating distance from peak to TSS...   2018-07-21 08:02:48 PM
    ## >> assigning genomic annotation...        2018-07-21 08:02:48 PM
    ## >> adding gene annotation...          2018-07-21 08:03:06 PM
    ## >> assigning chromosome lengths           2018-07-21 08:03:06 PM
    ## >> done...                    2018-07-21 08:03:06 PM

Peak Annotation is performed by `annotatePeak`. User can define TSS (transcription start site) region, by default TSS is defined from -3kb to +3kb. The output of `annotatePeak` is `csAnno` instance. ChIPseeker provides `as.GRanges` to convert `csAnno` to `GRanges` instance, and `as.data.frame` to convert `csAnno` to `data.frame` which can be exported to file by `write.table`.

`TxDb` object contained transcript-related features of a particular genome. Bioconductor provides several package that containing `TxDb` object of model organisms with multiple commonly used genome version, for instance TxDb.Hsapiens.UCSC.hg38.knownGene, TxDb.Hsapiens.UCSC.hg19.knownGene for human genome hg38 and hg19, TxDb.Mmusculus.UCSC.mm10.knownGene and TxDb.Mmusculus.UCSC.mm9.knownGene for mouse genome mm10 and mm9, etc. User can also prepare their own `TxDb` object by retrieving information from UCSC Genome Bioinformatics and BioMart data resources by R function `makeTxDbFromBiomart` and `makeTxDbFromUCSC`. `TxDb` object should be passed for peak annotation.

All the peak information contained in peakfile will be retained in the output of `annotatePeak`. The position and strand information of nearest genes are reported. The distance from peak to the TSS of its nearest gene is also reported. The genomic region of the peak is reported in annotation column. Since some annotation may overlap, ChIPseeker adopted the following priority in genomic annotation.

- Promoter
- 5’ UTR
- 3’ UTR
- Exon
- Intron
- Downstream
- Intergenic

Downstream is defined as the downstream of gene end.

ChIPseeker also provides parameter genomicAnnotationPriority for user to prioritize this hierachy.

`annotatePeak` report detail information when the annotation is Exon or Intron, for instance “Exon (uc002sbe.3/9736, exon 69 of 80)”, means that the peak is overlap with an Exon of transcript uc002sbe.3, and the corresponding Entrez gene ID is 9736 (Transcripts that belong to the same gene ID may differ in splice events), and this overlaped exon is the 69th exon of the 80 exons that this transcript uc002sbe.3 prossess.

Parameter annoDb is optional, if provided, extra columns including SYMBOL, GENENAME, ENSEMBL/ENTREZID will be added. The geneId column in annotation output will be consistent with the geneID in TxDb. If it is ENTREZID, ENSEMBL will be added if annoDb is provided, while if it is ENSEMBL ID, ENTREZID will be added.

## 5.1 Visualize Genomic Annotation

To annotate the location of a given peak in terms of genomic features, `annotatePeak` assigns peaks to genomic annotation in “annotation” column of the output, which includes whether a peak is in the TSS, Exon, 5’ UTR, 3’ UTR, Intronic or Intergenic. Many researchers are very interesting in these annotations. TSS region can be defined by user and `annotatePeak` output in details of which exon/intron of which genes as illustrated in previous section.

Pie and Bar plot are supported to visualize the genomic annotation.

    plotAnnoPie(peakAnno)

Figure 3: Genomic Annotation by pieplot

    plotAnnoBar(peakAnno)

Figure 4: Genomic Annotation by barplot

Since some annotation overlap, user may interested to view the full annotation with their overlap, which can be partially resolved by `vennpie` function.

    vennpie(peakAnno)

Figure 5: Genomic Annotation by vennpie

We extend UpSetR to view full annotation overlap. User can user `upsetplot` function.

    upsetplot(peakAnno)

We can combine `vennpie` with `upsetplot` by setting vennpie = TRUE.

    upsetplot(peakAnno, vennpie=TRUE)

## 5.2 Visualize distribution of TF-binding loci relative to TSS

The distance from the peak (binding site) to the TSS of the nearest gene is calculated by `annotatePeak` and reported in the output. We provide `plotDistToTSS` to calculate the percentage of binding sites upstream and downstream from the TSS of the nearest genes, and visualize the distribution.

    plotDistToTSS(peakAnno,
                  title="Distribution of transcription factor-binding loci\nrelative to TSS")

Figure 6: Distribution of Binding Sites

# 6 Functional enrichment analysis

Once we have obtained the annotated nearest genes, we can perform functional enrichment analysis to identify predominant biological themes among these genes by incorporating biological knowledge provided by biological ontologies. For instance, Gene Ontology (GO)7 annotates genes to biological processes, molecular functions, and cellular components in a directed acyclic graph structure, Kyoto Encyclopedia of Genes and Genomes (KEGG)8 annotates genes to pathways, Disease Ontology (DO)9 annotates genes with human disease association, and Reactome10 annotates gene to pathways and reactions.

ChIPseeker also provides a function, seq2gene, for linking genomc regions to genes in a many-to-many mapping. It consider host gene (exon/intron), promoter region and flanking gene from intergenic region that may under control via cis-regulation. This function is designed to link both coding and non-coding genomic regions to coding genes and facilitate functional analysis.

Enrichment analysis is a widely used approach to identify biological themes. I have developed several Bioconductor packages for investigating whether the number of selected genes associated with a particular biological term is larger than expected, including DOSE 2 for Disease Ontology, ReactomePA for reactome pathway, clusterProfiler 4 for Gene Ontology and KEGG enrichment analysis.

    library(ReactomePA)

    pathway1 <- enrichPathway(as.data.frame(peakAnno)$geneId)
    head(pathway1, 2)

    ##                          ID
    ## R-HSA-186712   R-HSA-186712
    ## R-HSA-3769402 R-HSA-3769402
    ##                                                            Description
    ## R-HSA-186712                       Regulation of beta-cell development
    ## R-HSA-3769402 Deactivation of the beta-catenin transactivating complex
    ##               GeneRatio  BgRatio       pvalue     p.adjust       qvalue
    ## R-HSA-186712     12/456 33/10561 5.644654e-09 4.905204e-06 4.905204e-06
    ## R-HSA-3769402     9/456 42/10561 6.019542e-05 2.615491e-02 2.615491e-02
    ##                                                                          geneID
    ## R-HSA-186712  2494/5080/3651/3175/6928/390874/3642/4821/4825/2255/222546/389692
    ## R-HSA-3769402                   607/55553/6662/6657/51176/83595/64321/7088/6658
    ##               Count
    ## R-HSA-186712     12
    ## R-HSA-3769402     9

    gene <- seq2gene(peak, tssRegion = c(-1000, 1000), flankDistance = 3000, TxDb=txdb)
    pathway2 <- enrichPathway(gene)
    head(pathway2, 2)

    ##                          ID                           Description GeneRatio
    ## R-HSA-186712   R-HSA-186712   Regulation of beta-cell development    10/370
    ## R-HSA-8943724 R-HSA-8943724 Regulation of PTEN gene transcription    10/370
    ##                BgRatio       pvalue     p.adjust       qvalue
    ## R-HSA-186712  33/10561 1.111462e-07 9.069527e-05 8.868294e-05
    ## R-HSA-8943724 61/10561 4.511578e-05 1.257097e-02 1.229205e-02
    ##                                                                   geneID
    ## R-HSA-186712       2494/3651/4821/4825/2255/222546/389692/5080/6928/3642
    ## R-HSA-8943724 64121/100532731/23466/2122/7101/57167/648/84733/57332/8535
    ##               Count
    ## R-HSA-186712     10
    ## R-HSA-8943724    10

    dotplot(pathway2)

More information can be found in the vignettes of Bioconductor packages DOSE 2, ReactomePA, clusterProfiler 4, which also provide several methods to visualize enrichment results. The clusterProfiler 4 is designed for comparing and visualizing functional profiles among gene clusters, and can directly applied to compare biological themes at GO, DO, KEGG, Reactome perspective.

# 7 ChIP peak data set comparison

## 7.1 Profile of several ChIP peak data binding to TSS region

Function `plotAvgProf` and `tagHeatmap` can accept a list of `tagMatrix` and visualize profile or heatmap among several ChIP experiments, while `plotAvgProf2` and `peakHeatmap` can accept a list of bed files and perform the same task in one step.

### 7.1.1 Average profiles

    ## promoter <- getPromoters(TxDb=txdb, upstream=3000, downstream=3000)
    ## tagMatrixList <- lapply(files, getTagMatrix, windows=promoter)
    ##
    ## to speed up the compilation of this vigenette, we load a precaculated tagMatrixList
    data("tagMatrixList")
    plotAvgProf(tagMatrixList, xlim=c(-3000, 3000))

Figure 7: Average Profiles of ChIP peaks among different experiments

    plotAvgProf(tagMatrixList, xlim=c(-3000, 3000), conf=0.95,resample=500, facet="row")

### 7.1.2 Peak heatmaps

    tagHeatmap(tagMatrixList, xlim=c(-3000, 3000), color=NULL)

Figure 8: Heatmap of ChIP peaks among different experiments

## 7.2 ChIP peak annotation comparision

The `plotAnnoBar` and `plotDistToTSS` can also accept input of a named list of annotated peaks (output of `annotatePeak`).

    peakAnnoList <- lapply(files, annotatePeak, TxDb=txdb,
                           tssRegion=c(-3000, 3000), verbose=FALSE)

We can use `plotAnnoBar` to comparing their genomic annotation.

    plotAnnoBar(peakAnnoList)

Figure 9: Genomic Annotation among different ChIPseq data

R function `plotDistToTSS` can use to comparing distance to TSS profiles among ChIPseq data.

    plotDistToTSS(peakAnnoList)

Figure 10: Distribution of Binding Sites among different ChIPseq data

## 7.3 Functional profiles comparison

As shown in section 4, the annotated genes can analyzed by `r Biocpkg("clusterProfiler")`4, `r Biocpkg("DOSE")`2, meshes and `r Biocpkg("ReactomePA")` for Gene Ontology, KEGG, Disease Ontology, MeSH and Reactome Pathway enrichment analysis.

The clusterProfiler 4 package provides `compareCluster` function for comparing biological themes among gene clusters, and can be easily adopted to compare different ChIP peak experiments.

    genes = lapply(peakAnnoList, function(i) as.data.frame(i)$geneId)
    names(genes) = sub("_", "\n", names(genes))
    compKEGG <- compareCluster(geneCluster   = genes,
                             fun           = "enrichKEGG",
                             pvalueCutoff  = 0.05,
                             pAdjustMethod = "BH")
    dotplot(compKEGG, showCategory = 15, title = "KEGG Pathway Enrichment Analysis")

## 7.4 Overlap of peaks and annotated genes

User may want to compare the overlap peaks of replicate experiments or from different experiments. ChIPseeker provides `peak2GRanges` that can read peak file and stored in GRanges object. Several files can be read simultaneously using lapply, and then passed to `vennplot` to calculate their overlap and draw venn plot.

`vennplot` accept a list of object, can be a list of GRanges or a list of vector. Here, I will demonstrate using `vennplot` to visualize the overlap of the nearest genes stored in peakAnnoList.

    genes= lapply(peakAnnoList, function(i) as.data.frame(i)$geneId)
    vennplot(genes)

Figure 11: Overlap of annotated genes

# 8 Statistical testing of ChIP seq overlap

Overlap is very important, if two ChIP experiment by two different proteins overlap in a large fraction of their peaks, they may cooperative in regulation. Calculating the overlap is only touch the surface. ChIPseeker implemented statistical methods to measure the significance of the overlap.

## 8.1 Shuffle genome coordination

    p <- GRanges(seqnames=c("chr1", "chr3"),
                 ranges=IRanges(start=c(1, 100), end=c(50, 130)))
    shuffle(p, TxDb=txdb)

    ## GRanges object with 2 ranges and 0 metadata columns:
    ##       seqnames              ranges strand
    ##          <Rle>           <IRanges>  <Rle>
    ##   [1]     chr1 178542505-178542554      *
    ##   [2]     chr3   21036248-21036278      *
    ##   -------
    ##   seqinfo: 2 sequences from an unspecified genome; no seqlengths

We implement the `shuffle` function to randomly permute the genomic locations of ChIP peaks defined in a genome which stored in `TxDb` object.

## 8.2 Peak overlap enrichment analysis

With the ease of this `shuffle` method, we can generate thousands of random ChIP data and calculate the background null distribution of the overlap among ChIP data sets.

    enrichPeakOverlap(queryPeak     = files[[5]],
                      targetPeak    = unlist(files[1:4]),
                      TxDb          = txdb,
                      pAdjustMethod = "BH",
                      nShuffle      = 50,
                      chainFile     = NULL,
                      verbose       = FALSE)

    ##                                                       qSample
    ## ARmo_0M    GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz
    ## ARmo_1nM   GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz
    ## ARmo_100nM GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz
    ## CBX6_BF    GSM1295077_CBX7_BF_ChipSeq_mergedReps_peaks.bed.gz
    ##                                                       tSample qLen tLen N_OL
    ## ARmo_0M                       GSM1174480_ARmo_0M_peaks.bed.gz 1663  812    0
    ## ARmo_1nM                     GSM1174481_ARmo_1nM_peaks.bed.gz 1663 2296    8
    ## ARmo_100nM                 GSM1174482_ARmo_100nM_peaks.bed.gz 1663 1359    3
    ## CBX6_BF    GSM1295076_CBX6_BF_ChipSeq_mergedReps_peaks.bed.gz 1663 1331  968
    ##                pvalue   p.adjust
    ## ARmo_0M    0.92156863 0.92156863
    ## ARmo_1nM   0.25490196 0.50980392
    ## ARmo_100nM 0.39215686 0.52287582
    ## CBX6_BF    0.01960784 0.07843137

Parameter queryPeak is the query ChIP data, while targetPeak is bed file name or a vector of bed file names from comparison; nShuffle is the number to shuffle the peaks in targetPeak. To speed up the compilation of this vignettes, we only set nShuffle to 50 as an example for only demonstration. User should set the number to 1000 or above for more robust result. Parameter chainFile are chain file name for mapping the targetPeak to the genome version consistent with queryPeak when their genome version are different. This creat the possibility of comparison among different genome version and cross species.

In the output, qSample is the name of queryPeak and qLen is the the number of peaks in queryPeak. N_OL is the number of overlap between queryPeak and targetPeak.

# 9 Data Mining with ChIP seq data deposited in GEO

There are many ChIP seq data sets that have been published and deposited in GEO database. We can compare our own dataset to those deposited in GEO to search for significant overlap data. Significant overlap of ChIP seq data by different binding proteins may be used to infer cooperative regulation and thus can be used to generate hypotheses.

We collect about 17,000 bed files deposited in GEO, user can use `getGEOspecies` to get a summary based on speices.

## 9.1 GEO data collection

    getGEOspecies()

    ##                                               species  Freq
    ## 1                                       Aedes aegypti    11
    ## 2                                            Anabaena     6
    ## 3                                 Anolis carolinensis     5
    ## 4                                   Anopheles gambiae     2
    ## 5                                      Apis mellifera     5
    ## 6                           Apis mellifera scutellata     1
    ## 7                                  Arabidopsis lyrata     4
    ## 8                                Arabidopsis thaliana   209
    ## 9                                Atelerix albiventris     2
    ## 10                                         Bos taurus    28
    ## 11                          Branchiostoma lanceolatum    62
    ## 12                                      Brassica rapa     8
    ## 13                             Caenorhabditis elegans   164
    ## 14                                   Candida albicans    25
    ## 15                               Candida dubliniensis    20
    ## 16                             Canis lupus familiaris     7
    ## 17                          Chlamydomonas reinhardtii    51
    ## 18                               Chlorocebus aethiops     2
    ## 19                                 Cleome hassleriana     1
    ## 20                                      Columba livia     6
    ## 21                                  Crassostrea gigas     1
    ## 22                            Cryptococcus neoformans    51
    ## 23                                    Cyprinus carpio    40
    ## 24                                        Danio rerio   164
    ## 25                                 Drosophila busckii     1
    ## 26                            Drosophila melanogaster   734
    ## 27            Drosophila melanogaster;\tSindbis virus     3
    ## 28                                 Drosophila miranda     2
    ## 29                           Drosophila pseudoobscura     7
    ## 30                                Drosophila simulans    12
    ## 31                                 Drosophila virilis    26
    ## 32                              Drosophila willistoni     1
    ## 33                                  Drosophila yakuba     8
    ## 34                                     Equus caballus     1
    ## 35                                   Escherichia coli    13
    ## 36                           Escherichia coli BW25113     4
    ## 37                              Escherichia coli K-12     2
    ## 38          Escherichia coli str. K-12 substr. MG1655     8
    ## 39                                      Gallus gallus    55
    ## 40                       Geobacter sulfurreducens PCA     3
    ## 41                                    Gorilla gorilla     2
    ## 42                                  Histophilus somni     1
    ## 43                                       Homo sapiens 10083
    ## 44                 Homo sapiens;\tHuman herpesvirus 8     6
    ## 45                               Human herpesvirus 6B     2
    ## 46                                Human herpesvirus 8     6
    ## 47                                Larimichthys crocea     7
    ## 48                             Legionella pneumophila     5
    ## 49                             Leishmania amazonensis     4
    ## 50                                   Leishmania major     2
    ## 51                   Leishmania major strain Friedlin     4
    ## 52                              Leishmania tarentolae    15
    ## 53                                     Macaca mulatta   116
    ## 54                              Monodelphis domestica     8
    ## 55                         Moraxella catarrhalis O35E     6
    ## 56                                       Mus musculus  7596
    ## 57                         Mus musculus x Mus spretus     1
    ## 58                         Mycobacterium tuberculosis     2
    ## 59                                    Myotis brandtii    15
    ## 60                              Naumovozyma castellii     1
    ## 61                             Nematostella vectensis    23
    ## 62                              Oreochromis niloticus     1
    ## 63                           Ornithorhynchus anatinus     5
    ## 64                                       Oryza sativa    23
    ## 65                                    Oryzias latipes     2
    ## 66                                    Pan troglodytes    93
    ## 67                                       Papio anubis     1
    ## 68                              Plasmodium falciparum   105
    ## 69                          Plasmodium falciparum 3D7    29
    ## 70                          Pseudomonas putida KT2440     2
    ## 71                                 Pseudozyma aphidis    11
    ## 72                                Pyrococcus furiosus     4
    ## 73                                  Rattus norvegicus    57
    ## 74                         Rhodopseudomonas palustris     6
    ## 75                  Rhodopseudomonas palustris CGA009     3
    ## 76                           Saccharomyces cerevisiae   586
    ## 77 Saccharomyces cerevisiae x Saccharomyces paradoxus    16
    ## 78            Saccharomyces cerevisiae;\tMus musculus    12
    ## 79                         Saccharomyces kudriavzevii     1
    ## 80                            Saccharomyces paradoxus     8
    ## 81                               Saccharomyces uvarum     1
    ## 82                      Schizosaccharomyces japonicus     2
    ## 83                          Schizosaccharomyces pombe   130
    ## 84                             Schmidtea mediterranea     7
    ## 85                                    Sorghum bicolor     2
    ## 86                      Streptomyces coelicolor A3(2)     6
    ## 87                                         Sus scrofa    23
    ## 88                                Taeniopygia guttata     1
    ## 89                                   Tupaia chinensis     7
    ## 90                                    Vibrio cholerae     8
    ## 91                      Xenopus (Silurana) tropicalis    62
    ## 92                                     Xenopus laevis    10
    ## 93                                           Zea mays    63

The summary can also based on genome version as illustrated below:

    getGEOgenomeVersion()

    ##                         organism genomeVersion Freq
    ## 1            Anolis carolinensis       anoCar2    5
    ## 2                     Bos taurus       bosTau4    2
    ## 3                     Bos taurus       bosTau6   24
    ## 4                     Bos taurus       bosTau7    2
    ## 5         Caenorhabditis elegans          ce10    4
    ## 6         Caenorhabditis elegans           ce6   64
    ## 7                    Danio rerio       danRer6    6
    ## 8                    Danio rerio       danRer7   61
    ## 9        Drosophila melanogaster           dm3  502
    ## 10                 Gallus gallus       galGal3   32
    ## 11                 Gallus gallus       galGal4   15
    ## 12                  Homo sapiens          hg18 2512
    ## 13                  Homo sapiens          hg19 6876
    ## 14                  Homo sapiens          hg38   43
    ## 15                  Mus musculus          mm10  214
    ## 16                  Mus musculus           mm8  507
    ## 17                  Mus musculus           mm9 6289
    ## 18         Monodelphis domestica       monDom5    8
    ## 19               Pan troglodytes       panTro3   48
    ## 20               Pan troglodytes       panTro4   42
    ## 21                Macaca mulatta       rheMac2   81
    ## 22                Macaca mulatta       rheMac3   31
    ## 23             Rattus norvegicus           rn5    3
    ## 24      Saccharomyces cerevisiae       sacCer2  141
    ## 25      Saccharomyces cerevisiae       sacCer3  227
    ## 26                    Sus scrofa       susScr2   17
    ## 27 Xenopus (Silurana) tropicalis       xenTro3    3

User can access the detail information by `getGEOInfo`, for each genome version.

    hg19 <- getGEOInfo(genome="hg19", simplify=TRUE)
    head(hg19)

    ##     series_id        gsm     organism
    ## 111  GSE16256  GSM521889 Homo sapiens
    ## 112  GSE16256  GSM521887 Homo sapiens
    ## 113  GSE16256  GSM521883 Homo sapiens
    ## 114  GSE16256 GSM1010966 Homo sapiens
    ## 115  GSE16256  GSM896166 Homo sapiens
    ## 116  GSE16256  GSM910577 Homo sapiens
    ##                                                                                                       title
    ## 111          Reference Epigenome: ChIP-Seq Analysis of H3K27me3 in IMR90 Cells; renlab.H3K27me3.IMR90-02.01
    ## 112            Reference Epigenome: ChIP-Seq Analysis of H3K27ac in IMR90 Cells; renlab.H3K27ac.IMR90-03.01
    ## 113            Reference Epigenome: ChIP-Seq Analysis of H3K14ac in IMR90 Cells; renlab.H3K14ac.IMR90-02.01
    ## 114                      polyA RNA sequencing of STL003 Pancreas Cultured Cells; polyA-RNA-seq_STL003PA_r1a
    ## 115          Reference Epigenome: ChIP-Seq Analysis of H4K8ac in hESC H1 Cells; renlab.H4K8ac.hESC.H1.01.01
    ## 116 Reference Epigenome: ChIP-Seq Analysis of H3K4me1 in Human Spleen Tissue; renlab.H3K4me1.STL003SX.01.01
    ##                                                                                                     supplementary_file
    ## 111         ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM521nnn/GSM521889/suppl/GSM521889_UCSD.IMR90.H3K27me3.SK05.bed.gz
    ## 112          ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM521nnn/GSM521887/suppl/GSM521887_UCSD.IMR90.H3K27ac.YL58.bed.gz
    ## 113          ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM521nnn/GSM521883/suppl/GSM521883_UCSD.IMR90.H3K14ac.SK17.bed.gz
    ## 114 ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM1010nnn/GSM1010966/suppl/GSM1010966_UCSD.Pancreas.mRNA-Seq.STL003.bed.gz
    ## 115              ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM896nnn/GSM896166/suppl/GSM896166_UCSD.H1.H4K8ac.AY17.bed.gz
    ## 116       ftp://ftp.ncbi.nlm.nih.gov/geo/samples/GSM910nnn/GSM910577/suppl/GSM910577_UCSD.Spleen.H3K4me1.STL003.bed.gz
    ##     genomeVersion pubmed_id
    ## 111          hg19  19829295
    ## 112          hg19  19829295
    ## 113          hg19  19829295
    ## 114          hg19  19829295
    ## 115          hg19  19829295
    ## 116          hg19  19829295

If simplify is set to FALSE, extra information including source_name, extract_protocol, description, data_processing and submission_date will be incorporated.

## 9.2 Download GEO ChIP data sets

ChIPseeker provide function `downloadGEObedFiles` to download all the bed files of a particular genome.

    downloadGEObedFiles(genome="hg19", destDir="hg19")

Or a vector of GSM accession number by `downloadGSMbedFiles`.

    gsm <- hg19$gsm[sample(nrow(hg19), 10)]
    downloadGSMbedFiles(gsm, destDir="hg19")

## 9.3 Overlap significant testing

After download the bed files from GEO, we can pass them to `enrichPeakOverlap` for testing the significant of overlap. Parameter targetPeak can be the folder, e.g. hg19, that containing bed files. `enrichPeakOverlap` will parse the folder and compare all the bed files. It is possible to test the overlap with bed files that are mapping to different genome or different genome versions, `enrichPeakOverlap` provide a parameter chainFile that can pass a chain file and liftOver the targetPeak to the genome version consistent with queryPeak. Signifcant overlap can be use to generate hypothesis of cooperative regulation.By mining the data deposited in GEO, we can identify some putative complex or interacted regulators in gene expression regulation or chromsome remodelling for further validation.

# 10 Need helps?

If you have questions/issues, please visit ChIPseeker homepage first. Your problems are mostly documented. If you think you found a bug, please follow the guide and provide a reproducible example to be posted on github issue tracker. For questions, please post to Bioconductor support site and tag your post with ChIPseeker.

For Chinese user, you can follow me on WeChat (微信).

# 11 Session Information

Here is the output of `sessionInfo()` on the system on which this document was compiled:

    ## R version 3.5.1 RC (2018-06-24 r74929)
    ## Platform: x86_64-pc-linux-gnu (64-bit)
    ## Running under: Ubuntu 16.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS: /home/biocbuild/bbs-3.7-bioc/R/lib/libRblas.so
    ## LAPACK: /home/biocbuild/bbs-3.7-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] stats4    parallel  stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] bindrcpp_0.2.2
    ##  [2] ChIPseeker_1.16.1
    ##  [3] ReactomePA_1.24.0
    ##  [4] clusterProfiler_3.8.1
    ##  [5] ggplot2_3.0.0
    ##  [6] org.Hs.eg.db_3.6.0
    ##  [7] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
    ##  [8] GenomicFeatures_1.32.0
    ##  [9] AnnotationDbi_1.42.1
    ## [10] Biobase_2.40.0
    ## [11] GenomicRanges_1.32.6
    ## [12] GenomeInfoDb_1.16.0
    ## [13] IRanges_2.14.10
    ## [14] S4Vectors_0.18.3
    ## [15] BiocGenerics_0.26.0
    ## [16] BiocStyle_2.8.2
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] fgsea_1.6.0                 colorspace_1.3-2
    ##   [3] ggridges_0.5.0              rprojroot_1.3-2
    ##   [5] qvalue_2.12.0               XVector_0.20.0
    ##   [7] ggrepel_0.8.0               bit64_0.9-7
    ##   [9] splines_3.5.1               GOSemSim_2.6.0
    ##  [11] knitr_1.20                  Rsamtools_1.32.2
    ##  [13] gridBase_0.4-7              GO.db_3.6.0
    ##  [15] graph_1.58.0                ggforce_0.1.3
    ##  [17] graphite_1.26.1             compiler_3.5.1
    ##  [19] httr_1.3.1                  rvcheck_0.1.0
    ##  [21] backports_1.1.2             assertthat_0.2.0
    ##  [23] Matrix_1.2-14               lazyeval_0.2.1
    ##  [25] tweenr_0.1.5                htmltools_0.3.6
    ##  [27] prettyunits_1.0.2           tools_3.5.1
    ##  [29] igraph_1.2.1                gtable_0.2.0
    ##  [31] glue_1.3.0                  GenomeInfoDbData_1.1.0
    ##  [33] reshape2_1.4.3              DO.db_2.9
    ##  [35] dplyr_0.7.6                 rappdirs_0.3.1
    ##  [37] fastmatch_1.1-0             Rcpp_0.12.17
    ##  [39] enrichplot_1.0.2            Biostrings_2.48.0
    ##  [41] gdata_2.18.0                rtracklayer_1.40.3
    ##  [43] ggraph_1.0.2                xfun_0.3
    ##  [45] stringr_1.3.1               gtools_3.8.1
    ##  [47] XML_3.98-1.12               DOSE_3.6.1
    ##  [49] zlibbioc_1.26.0             MASS_7.3-50
    ##  [51] scales_0.5.0                reactome.db_1.64.0
    ##  [53] hms_0.4.2                   SummarizedExperiment_1.10.1
    ##  [55] RColorBrewer_1.1-2          yaml_2.1.19
    ##  [57] memoise_1.1.0               gridExtra_2.3
    ##  [59] UpSetR_1.3.3                biomaRt_2.36.1
    ##  [61] stringi_1.2.4               RSQLite_2.1.1
    ##  [63] highr_0.7                   plotrix_3.7-2
    ##  [65] checkmate_1.8.5             caTools_1.17.1.1
    ##  [67] boot_1.3-20                 BiocParallel_1.14.2
    ##  [69] rlang_0.2.1                 pkgconfig_2.0.1
    ##  [71] matrixStats_0.53.1          bitops_1.0-6
    ##  [73] evaluate_0.11               lattice_0.20-35
    ##  [75] purrr_0.2.5                 bindr_0.1.1
    ##  [77] labeling_0.3                GenomicAlignments_1.16.0
    ##  [79] cowplot_0.9.3               bit_1.1-14
    ##  [81] tidyselect_0.2.4            plyr_1.8.4
    ##  [83] magrittr_1.5                bookdown_0.7
    ##  [85] R6_2.2.2                    gplots_3.0.1
    ##  [87] DelayedArray_0.6.1          DBI_1.0.0
    ##  [89] pillar_1.3.0                withr_2.1.2
    ##  [91] units_0.6-0                 RCurl_1.95-4.11
    ##  [93] tibble_1.4.2                crayon_1.3.4
    ##  [95] KernSmooth_2.23-15          rmarkdown_1.10
    ##  [97] viridis_0.5.1               progress_1.2.0
    ##  [99] grid_3.5.1                  data.table_1.11.4
    ## [101] blob_1.1.1                  digest_0.6.15
    ## [103] tidyr_0.8.1                 munsell_0.5.0
    ## [105] viridisLite_0.3.0

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