FindIT2:Find influential TF and influential Target
30 December 2021
Package
FindIT2 1.0.3
1 Basics
1.1 Install FindIT2
FindIT2 is available on Bioconductor repository for
packages, you can install it by:
if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("FindIT2")
# Check that you have a valid Bioconductor installation
BiocManager::valid()1.2 Citation
citation("FindIT2")
#>
#> To cite package 'FindIT2' in publications use:
#>
#> Guandong Shang (2021). FindIT2: find influential TF and Target based
#> on multi-omics data. R package version 1.0.3.
#> https://github.com/shangguandong1996/FindIT2
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Manual{,
#> title = {FindIT2: find influential TF and Target based on multi-omics data},
#> author = {Guandong Shang},
#> year = {2021},
#> note = {R package version 1.0.3},
#> url = {https://github.com/shangguandong1996/FindIT2},
#> }1.3 Acknowledgments
I benefited a lot from X. Shirley Liu lab’s tools. The integrate_ChIP_RNA model
borrow the idea from BETA(Wang et al. 2013) and cistromeGO
(Li et al. 2019). The calcRP model borrow the idea from regulation
potential(Wang et al. 2016). And the FindIT_regionRP model borrow idea from
lisa(Qin et al. 2020).
I also want to thanks the Allen Lynch in Liu lab, it is please to talk with him
on the github about lisa.
I want to thanks for the memberships in our lab. Many ideas in this packages appeared when I talk with them.
1.4 Introduction
The origin name of FindIT2 is MPMG(Multi Peak Multi Gene) :), which means this package origin purpose is to do mutli-peak-multi-gene annotation. But as the diversity of analysis increase, it gradually extend its function and rename into FindIT2(Find influential TF and Target). But the latter function are still build on the MPMG. Now, it have five module:
- Multi-peak multi-gene annotaion(mmPeakAnno module)
- Calculate regulation potential(calcRP module)
- Find influential Target based on ChIP-Seq and RNA-Seq data(Find influential Target module)
- Find influential TF based on different input(Find influential TF module)
- Calculate peak-gene or peak-peak correlation(peakGeneCor module)
And there are also some other useful function like integrate different source information, calculate jaccard similarity for your TF. I will introduce all these function in below manual. And for each part, I will also show the file type you may need prepare, which can help you prepare the correct file format.
The ChIP and ATAC datasets in this vignettes are from (Wang et al. 2020). For the speed, I only use the data in chrosome 5.
# load packages
# If you want to run this manual, please check you have install below packages.
library(FindIT2)
library(TxDb.Athaliana.BioMart.plantsmart28)
library(SummarizedExperiment)
library(dplyr)
library(ggplot2)
# because of the fa I use, I change the seqlevels of Txdb to make the chrosome levels consistent
Txdb <- TxDb.Athaliana.BioMart.plantsmart28
seqlevels(Txdb) <- c(paste0("Chr", 1:5), "M", "C")
all_geneSet <- genes(Txdb)2 Multi-peak multi-gene annotation
The multi-peak multi-gene annotation(mmPeakAnno) is the basic of this package. Most function will use the result of mmPeakAnno. So I explain them first.
The object you may need
- peak_GR: a GRange object with a column named feature_id
FindIT2 provides loadPeakFile to load peak and store in GRanges object.
Meanwhile, it also rename one of your GRange column name into feature_id. The
feature_id is the most important column in FindIT2, which will be used as a
link to join information from different source. The feature_id column
represents your peak name, which is often the forth column in bed file and the
first column in GRange metadata column . If you have a GRange without
feature_id column, you can rename your first metadata column or just add a
column named feature_id like below
# when you make sure your first metadata column is peak name
colnames(mcols(yourGR))[1] <- "feature_id"
# or you just add a column
yourGR$feature_id <- paste0("peak_", seq_len(length(yourGR)))
- Txdb: a Txdb object that manages genomic locations and the relationships between genomic feature
you can see the detailed Txdb description in Making and Utilizing TxDb Objects
- input_genes: gene id
Here I take the ChIP-Seq data as example.
# load the test ChIP peak bed
ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
# you can see feature_id is in your first column of metadata
ChIP_peak_GR
#> GRanges object with 4288 ranges and 2 metadata columns:
#> seqnames ranges strand | feature_id score
#> <Rle> <IRanges> <Rle> | <character> <numeric>
#> [1] Chr5 6236-6508 * | peak_14125 27
#> [2] Chr5 7627-8237 * | peak_14126 51
#> [3] Chr5 9730-10211 * | peak_14127 32
#> [4] Chr5 12693-12867 * | peak_14128 22
#> [5] Chr5 13168-14770 * | peak_14129 519
#> ... ... ... ... . ... ...
#> [4284] Chr5 26937822-26938526 * | peak_18408 445
#> [4285] Chr5 26939152-26939267 * | peak_18409 21
#> [4286] Chr5 26949581-26950335 * | peak_18410 263
#> [4287] Chr5 26952230-26952558 * | peak_18411 30
#> [4288] Chr5 26968877-26969091 * | peak_18412 26
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths2.1 annotate peak using nearest mode
The nearest mode is the most widely used annotation mode. It will link the peak
to its nearest gene, which means every peak only have one related gene. The
disadvantage is sometimes you can not link the correct gene for your peak because
of the complexity in the genomic feature. But this annotation mode is simple
enough and at most time, for most peak, the result is correct.
The skeleton function is distanceToNearest from GenomicRanges. I add some
modification so that it will output more human readable result.
mmAnno_nearestgene <- mm_nearestGene(peak_GR = ChIP_peak_GR,
Txdb = Txdb)
#> >> checking seqlevels match... 2021-12-30 10:33:23 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> ------------
#> annotating Peak using nearest gene mode begins
#> >> preparing gene features information... 2021-12-30 10:33:23 AM
#> >> finding nearest gene and calculating distance... 2021-12-30 10:33:25 AM
#> >> dealing with gene strand ... 2021-12-30 10:33:25 AM
#> >> merging all info together ... 2021-12-30 10:33:25 AM
#> >> done 2021-12-30 10:33:25 AM
mmAnno_nearestgene
#> GRanges object with 4288 ranges and 4 metadata columns:
#> seqnames ranges strand | feature_id score gene_id
#> <Rle> <IRanges> <Rle> | <character> <numeric> <character>
#> [1] Chr5 6236-6508 * | peak_14125 27 AT5G01015
#> [2] Chr5 7627-8237 * | peak_14126 51 AT5G01020
#> [3] Chr5 9730-10211 * | peak_14127 32 AT5G01030
#> [4] Chr5 12693-12867 * | peak_14128 22 AT5G01030
#> [5] Chr5 13168-14770 * | peak_14129 519 AT5G01040
#> ... ... ... ... . ... ... ...
#> [4284] Chr5 26937822-26938526 * | peak_18408 445 AT5G67510
#> [4285] Chr5 26939152-26939267 * | peak_18409 21 AT5G67520
#> [4286] Chr5 26949581-26950335 * | peak_18410 263 AT5G67560
#> [4287] Chr5 26952230-26952558 * | peak_18411 30 AT5G67570
#> [4288] Chr5 26968877-26969091 * | peak_18412 26 AT5G67630
#> distanceToTSS
#> <numeric>
#> [1] -344
#> [2] 206
#> [3] 0
#> [4] 2823
#> [5] 1402
#> ... ...
#> [4284] 0
#> [4285] 0
#> [4286] 0
#> [4287] 0
#> [4288] 302
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengthsYou can also use this the annotation result to check your TF type using
plot_annoDistance. For most TF, the distance density plot maybe like below,
which means your TF is promoter-type. But for some TF, its density plot will be
different, like GATA4, MYOD1(Li et al. 2019).
plot_annoDistance(mmAnno = mmAnno_nearestgene)
Sometimes, you may interested in the number peaks of each gene linked. Or
reciprocally, how many genes of each peak link. you can use the
getAssocPairNumber to see the deatailed number or summary.
getAssocPairNumber(mmAnno = mmAnno_nearestgene)
#> # A tibble: 2,757 x 2
#> gene_id peakNumber
#> <chr> <int>
#> 1 AT5G01015 1
#> 2 AT5G01020 1
#> 3 AT5G01030 2
#> 4 AT5G01040 2
#> 5 AT5G01050 2
#> 6 AT5G01070 1
#> 7 AT5G01090 1
#> 8 AT5G01100 1
#> 9 AT5G01170 1
#> 10 AT5G01175 3
#> # ... with 2,747 more rows
getAssocPairNumber(mmAnno = mmAnno_nearestgene,
output_summary = TRUE)
#> # A tibble: 8 x 2
#> N gene_freq
#> <fct> <int>
#> 1 1 1793
#> 2 2 606
#> 3 3 229
#> 4 4 75
#> 5 5 38
#> 6 6 9
#> 7 7 4
#> 8 >=8 3
# you can see all peak's related gene number is 1 because I use the nearest gene mode
getAssocPairNumber(mmAnno_nearestgene, output_type = "feature_id")
#> # A tibble: 4,288 x 2
#> feature_id geneNumber
#> <chr> <int>
#> 1 peak_14125 1
#> 2 peak_14126 1
#> 3 peak_14127 1
#> 4 peak_14128 1
#> 5 peak_14129 1
#> 6 peak_14130 1
#> 7 peak_14131 1
#> 8 peak_14132 1
#> 9 peak_14133 1
#> 10 peak_14134 1
#> # ... with 4,278 more rows
getAssocPairNumber(mmAnno = mmAnno_nearestgene,
output_type = "feature_id",
output_summary = TRUE)
#> # A tibble: 1 x 2
#> N feature_freq
#> <fct> <int>
#> 1 1 4288And if you want the summary plot, you can use the plot_peakGeneAlias_summary
function.
plot_peakGeneAlias_summary(mmAnno_nearestgene)
plot_peakGeneAlias_summary(mmAnno_nearestgene, output_type = "feature_id")
2.2 find realted peak using gene Bound mode
The mm_geneBound function is designed for finding related peak for your
input_genes.Because we do the nearest gene mode to annotate peak, once a peak
is linked by nearest gene, it will not be linked by another gene even if another
gene is very close to your peak. So this function is very useful when you want
to plot peak heatmap or volcano plot. When ploting these plot, you often have a
interesting gene set, and want to plot related peak. If we just filter gene id
in the nearest result,many of your interesting gene will not have related peak.
After all, each peak will be assigned once.
For mm_geneBound, it will output realted peak for all your input_gene as long
as your input_genes in your Txdb. The model behind mm_geneBound is simple, it
will do mm_nearestgene first and scan nearest peak for these genes which do not
have related peak.
# The genes_Chr5 is all gene set in Chr5
genes_Chr5 <- names(all_geneSet[seqnames(all_geneSet) == "Chr5"])
# The genes_Chr5_notAnno is gene set which is not linked by peak
genes_Chr5_notAnno <- genes_Chr5[!genes_Chr5 %in% unique(mmAnno_nearestgene$gene_id)]
# The genes_Chr5_tAnno is gene set which is linked by peak
genes_Chr5_Anno <- unique(mmAnno_nearestgene$gene_id)
# mm_geneBound will tell you there 5 genes in your input_genes not be annotated
# and it will use the distanceToNearest to find nearest peak of these genes
mmAnno_geneBound <- mm_geneBound(peak_GR = ChIP_peak_GR,
Txdb = Txdb,
input_genes = c(genes_Chr5_Anno[1:5], genes_Chr5_notAnno[1:5]))
#> >> checking seqlevels match... 2021-12-30 10:33:27 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> >> using mm_nearestGene to annotate Peak... 2021-12-30 10:33:27 AM
#> >> checking seqlevels match... 2021-12-30 10:33:27 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> ------------
#> annotating Peak using nearest gene mode begins
#> >> preparing gene features information... 2021-12-30 10:33:27 AM
#> >> finding nearest gene and calculating distance... 2021-12-30 10:33:29 AM
#> >> dealing with gene strand ... 2021-12-30 10:33:29 AM
#> >> merging all info together ... 2021-12-30 10:33:29 AM
#> >> done 2021-12-30 10:33:29 AM
#> It seems that there 5 genes have not been annotated by nearestGene mode
#> >> using distanceToNearest to find nearest peak of these genes... 2021-12-30 10:33:30 AM
#> >> merging all anno... 2021-12-30 10:33:30 AM
#> >> done 2021-12-30 10:33:30 AM
# all of your input_genes have related peaks
mmAnno_geneBound
#> # A tibble: 13 x 3
#> feature_id gene_id distanceToTSS_abs
#> <chr> <chr> <dbl>
#> 1 peak_14125 AT5G01015 344
#> 2 peak_14126 AT5G01020 206
#> 3 peak_14127 AT5G01030 0
#> 4 peak_14128 AT5G01030 2823
#> 5 peak_14129 AT5G01040 1402
#> 6 peak_14130 AT5G01040 0
#> 7 peak_14131 AT5G01050 571
#> 8 peak_14132 AT5G01050 94
#> 9 peak_14125 AT5G01010 1174
#> 10 peak_14132 AT5G01060 2022
#> 11 peak_14133 AT5G01075 949
#> 12 peak_14134 AT5G01080 2339
#> 13 peak_14135 AT5G01110 66232.3 find related peak using gene scan mode
mm_geneScan is the most important annotation mode. Strictly, it is not a peak
annotation mode. The function will define a TSS scan region for each gene
according to your upstream and downstream parameters. Then it will fish all peaks
located in scan region and link gene with peak scaned. For these peak not
locating in the scan region, it will use the distanceToNearest to find nearest
gene. After these steps, each peak will have at least one gene. But not all genes
on your Txdb will have at least one peak, after all, there maybe no peak locating
in scan region for these gene. Now, compared with mm_nearestgene result, gene
may be linked by more than one peak, and peak maybe linked by more than one gene.
The mm_geneScan can be used in many conditions. For example, after differential
peak analysis, you may have 300 diff peaks. Or if your ChIP-Seq peak experiment
not work very well, you only have 100 peaks. You do not want to use the
mm_nearestgene because you do not want to lose some important candidate gene
and you also do not want to see each peak in IGV. Now you can use mm_geneScan,
just set parameters like upstream=500 , everything will be different. This
function is especially useful for small genome because the complexity in genomic
feature location. Expect the origin nearest mode result, the final result will
output other peak-gene links.
But I do not recommend you use the mm_geneScan mode or set
upstream/downstream to big when you have too many peaks, it will make your
final result messy. For this condition, you can use the mm_nearestgene or set
upstream/downstream small.
The true power of mm_geneScan is that it is the foundation of other module,
like calcRP, FindIT. And in these condition, the upstream and downstream
parameter should be set big, like 2e4, 2e5, 2e6 and so on.
mmAnno_geneScan <- mm_geneScan(peak_GR = ChIP_peak_GR,
Txdb = Txdb,
upstream = 2e4,
downstream = 2e4)
#> >> checking seqlevels match... 2021-12-30 10:33:30 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> ------------
#> annotatePeak using geneScan mode begins
#> >> preparing gene features information and scan region... 2021-12-30 10:33:30 AM
#> >> some scan range may cross Chr bound, trimming... 2021-12-30 10:33:31 AM
#> >> finding overlap peak in gene scan region... 2021-12-30 10:33:31 AM
#> >> dealing with left peak not your gene scan region... 2021-12-30 10:33:31 AM
#> >> merging two set peaks... 2021-12-30 10:33:31 AM
#> >> calculating distance and dealing with gene strand... 2021-12-30 10:33:31 AM
#> >> merging all info together ... 2021-12-30 10:33:31 AM
#> >> done 2021-12-30 10:33:31 AM
mmAnno_geneScan
#> GRanges object with 50662 ranges and 4 metadata columns:
#> seqnames ranges strand | feature_id score gene_id
#> <Rle> <IRanges> <Rle> | <character> <numeric> <character>
#> [1] Chr5 6236-6508 * | peak_14125 27 AT5G01010
#> [2] Chr5 7627-8237 * | peak_14126 51 AT5G01010
#> [3] Chr5 9730-10211 * | peak_14127 32 AT5G01010
#> [4] Chr5 12693-12867 * | peak_14128 22 AT5G01010
#> [5] Chr5 13168-14770 * | peak_14129 519 AT5G01010
#> ... ... ... ... . ... ... ...
#> [50658] Chr5 26949581-26950335 * | peak_18410 263 AT5G67630
#> [50659] Chr5 26952230-26952558 * | peak_18411 30 AT5G67630
#> [50660] Chr5 26968877-26969091 * | peak_18412 26 AT5G67630
#> [50661] Chr5 26952230-26952558 * | peak_18411 30 AT5G67640
#> [50662] Chr5 26968877-26969091 * | peak_18412 26 AT5G67640
#> distanceToTSS
#> <numeric>
#> [1] -1174
#> [2] -2565
#> [3] -4668
#> [4] -7631
#> [5] -8106
#> ... ...
#> [50658] 19058
#> [50659] 16835
#> [50660] 302
#> [50661] 18082
#> [50662] 1549
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengthsyou can also apply below function in the result of mm_geneScan.
getAssocPairNumber(mmAnno_geneScan)
#> # A tibble: 6,783 x 2
#> gene_id peakNumber
#> <chr> <int>
#> 1 AT5G01010 8
#> 2 AT5G01015 8
#> 3 AT5G01020 9
#> 4 AT5G01030 9
#> 5 AT5G01040 10
#> 6 AT5G01050 11
#> 7 AT5G01060 11
#> 8 AT5G01070 11
#> 9 AT5G01075 10
#> 10 AT5G01080 9
#> # ... with 6,773 more rows
getAssocPairNumber(mmAnno_geneScan, output_type = "feature_id")
#> # A tibble: 4,288 x 2
#> feature_id geneNumber
#> <chr> <int>
#> 1 peak_14125 8
#> 2 peak_14126 9
#> 3 peak_14127 10
#> 4 peak_14128 11
#> 5 peak_14129 11
#> 6 peak_14130 11
#> 7 peak_14131 12
#> 8 peak_14132 12
#> 9 peak_14133 12
#> 10 peak_14134 12
#> # ... with 4,278 more rowsplot_peakGeneAlias_summary(mmAnno_geneScan)
plot_peakGeneAlias_summary(mmAnno_geneScan, output_type = "feature_id")
3 Calculate regulation potential(RP)
regulation potential(RP) is a simple but powerful theory to convert peak level information into gene level. After this transform, analysis will be much easier. After all, peak do not have id while gene have. The detailed theory about RP can be seen in (Wang et al. 2016), (Li et al. 2019), (Qin et al. 2020).
The object you may need:
- mmAnno: the result from mm_geneScan
The upstream/downstream parameters of mm_geneScan should be big enough. The RP
model actually consider all peaks in TSS scan region. And each peak will be
assigned a weight when calculating final RP. The weight decreases with peak
distance from the TSS of gene. For Arabidopsis thaliana, I set the parameter is
2e4. Because it is the longest interaction distance in HiC
data(Liu et al. 2016). For human or mouse data, you can set 100kb(1e5). It
is the origin parameters in paper.
Actually, the upstream/downstream parameters can be arbitrary because it only
influence the number of scaned peak. The another important parameter is
decay_dist, which control the weight of peak. If you set decay_dist to 1000,
a peak 1kb from the TSS contribute one-half of that at TSS. For example, if a
value of peak is 100, and its distance to TSS is 1000, so the final value
contributing to the gene will be 100 * 2 ^ -(1000 / 1000) = 50.
- Txdb
3.1 calculate RP using mmAnno
The calcRP_TFHit here is to calculate RP according to your TF ChIP-seq
annotation result. The theory behind this is that if there are more peaks near
your gene, then your gene is more likely to be the target. You can use the
result to decide your TF target gene or combine with RNA-Seq data using integrate_ChIP_RNA
to infer direct target genes more accurately.
The object you may need to consider:
- decay_dist:
You can set decay_dist to 1000 for promoter-type TF and 10 kb for enhancer-type
TF. But you can set the decay_dist by yourself. You can use the plot from
plot_annoDistance(mmAnno_nearestgene) to decide your TF type.
- mmAnno
The result from mm_geneScan. calcRP_TFHit will use the peak-gene pair in
mmAnno to calculate the contribution of each peak to the final RP of the gene.
The detailed formula used in calcRP_TFHit shows below.
\[ \begin{equation} RP_{gene_{g}}=\sum_{p=1}^{k}RP_{peak_p, gene_g} \tag{1} \end{equation} \]
\[ \begin{equation} RP_{peak_p, gene_g} = score_{peak_p} * 2^{\frac{-d_{i}}{d_0}} \tag{2} \end{equation} \]
The parameter \(d_0\) is the half_decay distance(decay_dist).
All k binding sites in the scan region of gene g(within the
upstram-TSS-downstream) will be used in the calculation, \(d_i\) is the distance
between the ith peak’s center and TSS. The \(score_{peak_{p}}\) represent your
feature_score column if your origin GRange have a column named
feature_score, otherwise, it will be 1.
The feature_score always be the fifth column in bed file and maybe the second
column in your GRange metadata column.
# Here you can see the score column in metadata
ChIP_peak_GR
#> GRanges object with 4288 ranges and 2 metadata columns:
#> seqnames ranges strand | feature_id score
#> <Rle> <IRanges> <Rle> | <character> <numeric>
#> [1] Chr5 6236-6508 * | peak_14125 27
#> [2] Chr5 7627-8237 * | peak_14126 51
#> [3] Chr5 9730-10211 * | peak_14127 32
#> [4] Chr5 12693-12867 * | peak_14128 22
#> [5] Chr5 13168-14770 * | peak_14129 519
#> ... ... ... ... . ... ...
#> [4284] Chr5 26937822-26938526 * | peak_18408 445
#> [4285] Chr5 26939152-26939267 * | peak_18409 21
#> [4286] Chr5 26949581-26950335 * | peak_18410 263
#> [4287] Chr5 26952230-26952558 * | peak_18411 30
#> [4288] Chr5 26968877-26969091 * | peak_18412 26
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths
# I can rename it into feature_score
colnames(mcols(ChIP_peak_GR))[2] <- "feature_score"
ChIP_peak_GR
#> GRanges object with 4288 ranges and 2 metadata columns:
#> seqnames ranges strand | feature_id feature_score
#> <Rle> <IRanges> <Rle> | <character> <numeric>
#> [1] Chr5 6236-6508 * | peak_14125 27
#> [2] Chr5 7627-8237 * | peak_14126 51
#> [3] Chr5 9730-10211 * | peak_14127 32
#> [4] Chr5 12693-12867 * | peak_14128 22
#> [5] Chr5 13168-14770 * | peak_14129 519
#> ... ... ... ... . ... ...
#> [4284] Chr5 26937822-26938526 * | peak_18408 445
#> [4285] Chr5 26939152-26939267 * | peak_18409 21
#> [4286] Chr5 26949581-26950335 * | peak_18410 263
#> [4287] Chr5 26952230-26952558 * | peak_18411 30
#> [4288] Chr5 26968877-26969091 * | peak_18412 26
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengthsFor the normal ChIP-seq data, adding or not this column will not make much
difference to the result. Because peaks which are closer to the TSS always have
big feature_score. But for those tag or GR-induced ChIP-seq data, the above
assumptions may not be satisfied. In this condition, you can add a column named feature_score
representing your confidence about each peak. And feature_score
in this situation may not be the second column in your GRange metadata column. You should decide it by yourself.
There are advantages and disadvantages to adding feature_score.
On the one hand, you can add your confidence to make the final TF target result
more credible. On the other hand, adding this column will make your result less
human-readable. And if you want to adjust your TF result considering the
background from batch existing ChIP-seq data to get the more accurate and
specific function of the TF. You should not add the feature_score column
because different scoure ChIP-Seq data have different bias(the background data
will be be ready soon).
# if you just want to get RP_df, you can set report_fullInfo FALSE
fullRP_hit <- calcRP_TFHit(mmAnno = mmAnno_geneScan,
Txdb = Txdb,
decay_dist = 1000,
report_fullInfo = TRUE)
#> >> calculating peakCenter to TSS using peak-gene pair... 2021-12-30 10:33:33 AM
#> >> calculating RP using centerToTSS and TF hit 2021-12-30 10:33:34 AM
#> >> merging all info together 2021-12-30 10:33:35 AM
#> >> done 2021-12-30 10:33:35 AM
# if you set report_fullInfo to TRUE, the result will be a GRange object
# it maintain the mmAnno_geneScan result and add other column, which represent
# the contribution of each peak to the final RP of the gene
fullRP_hit
#> GRanges object with 50662 ranges and 6 metadata columns:
#> seqnames ranges strand | feature_id score gene_id
#> <Rle> <IRanges> <Rle> | <character> <numeric> <character>
#> [1] Chr5 6236-6508 * | peak_14125 27 AT5G01010
#> [2] Chr5 7627-8237 * | peak_14126 51 AT5G01010
#> [3] Chr5 9730-10211 * | peak_14127 32 AT5G01010
#> [4] Chr5 12693-12867 * | peak_14128 22 AT5G01010
#> [5] Chr5 13168-14770 * | peak_14129 519 AT5G01010
#> ... ... ... ... . ... ... ...
#> [50658] Chr5 26949581-26950335 * | peak_18410 263 AT5G67630
#> [50659] Chr5 26952230-26952558 * | peak_18411 30 AT5G67630
#> [50660] Chr5 26968877-26969091 * | peak_18412 26 AT5G67630
#> [50661] Chr5 26952230-26952558 * | peak_18411 30 AT5G67640
#> [50662] Chr5 26968877-26969091 * | peak_18412 26 AT5G67640
#> distanceToTSS centerToTSS RP
#> <numeric> <integer> <numeric>
#> [1] -1174 1310 0.40332088
#> [2] -2565 2870 0.13678671
#> [3] -4668 4908 0.03330771
#> [4] -7631 7718 0.00474953
#> [5] -8106 8907 0.00208318
#> ... ... ... ...
#> [50658] 19058 19435 1.41085e-06
#> [50659] 16835 16999 7.63468e-06
#> [50660] 302 409 7.53145e-01
#> [50661] 18082 18246 3.21667e-06
#> [50662] 1549 1656 3.17318e-01
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths
# or you can directly extract from metadata of your result
peakRP_gene <- metadata(fullRP_hit)$peakRP_gene
# The result is ordered by the sumRP and you can decide the target threshold by yourself
peakRP_gene
#> # A tibble: 6,783 x 4
#> gene_id withPeakN sumRP RP_rank
#> <chr> <int> <dbl> <dbl>
#> 1 AT5G67190 14 4.31 1
#> 2 AT5G05140 25 3.56 2
#> 3 AT5G62280 12 3.51 3
#> 4 AT5G44380 10 3.38 4
#> 5 AT5G58750 13 3.36 5
#> 6 AT5G66590 13 3.36 6
#> 7 AT5G02760 12 3.24 7
#> 8 AT5G41071 8 3.23 8
#> 9 AT5G64100 18 3.21 9
#> 10 AT5G24030 12 3.19 10
#> # ... with 6,773 more rows3.2 Calculate RP using bw file
The calcRP_coverage here is to calculate RP based on the ATAC or other histone
modification bigwig file.
The object you may need to consider:
- bwFile:
A bigwig file. And if you want to compare gene RP between samples, the bigwig file should be normalized.
- decay_dist:
You can set 10kb for human/mouse data and set 1kb for Arabidopsis thaliana data.
- scan_dist:
You can set 100kb for human/mouse data and set 20kb for Arabidopsis thaliana data.
- Chrs_included:
The Chromosomes where you want to calculate gene RP in. Here I set Chr5 because I only use the test data in Chr5. Sometimes, we just want to the calculate gene RP in some chromosomes. For example, we do not want to calculate gene RP in mitochondrion. The less chrom you select, the faster function calculates.
It can be applied in the condition that you just have bigwig files from GEO. The purpose here is not to identify the target of TF ChIP-Seq. The real purpose is to summarize the ATAC, H3K27ac, or other histone modification profiles and convert into gene level information. The RP score can be a useful predictor of gene expression changes and a summary representing histone modification in your gene. You can compare gene RP in different samples and explore the RP trend. Or you can use RP in the identification of key tissue-specific genes. The detailed application can be seen in (Wang et al. 2016).
The detailed formula used in calcRP_coverage is a little different from the
previous (1), (2).
\[
\begin{equation}
RP_{gene_{g}}=\sum_{i\in[t_g-L,tg+L]}w_iS_i
\tag{3}
\end{equation}
\]
\[
\begin{equation}
w_i=\frac {2e^{-\mu d}} {1+e^{-\mu d}}
\tag{4}
\end{equation}
\]
\(L\) is set to scan_dist, and \(w_i\) is a weight representing the regulatory
influence of a locus at position \(i\) on the TSS of gene \(g\) at genomic position
\(t_k\). \(d = |i − t_{g}|/L\), and \(i\) stands for ith nucleotide position within
the \([−L, L]\) genomic interval centered on the TSS at \(t_g\). \(s_i\) is the signal
of at position \(i\). μ is the parameter to determine the decay rate of the
weight, which is
defined as \(\mu = -ln\frac{1}{3} * (L/\Delta)\). \(\Delta\) is set to decay_dist.
bwFile <- system.file("extdata", "E50h_sampleChr5.bw", package = "FindIT2")
RP_df <- calcRP_coverage(bwFile = bwFile,
Txdb = Txdb,
Chrs_included = "Chr5")
#> >> preparing gene features information... 2021-12-30 10:33:35 AM
#> >> some scan range may cross Chr bound, trimming... 2021-12-30 10:33:35 AM
#> >> preparing weight info... 2021-12-30 10:33:35 AM
#> >> loading E50h_sampleChr5.bw info... 2021-12-30 10:33:35 AM
#> ------------
#> >> extracting and calcluating Chr5 signal... 2021-12-30 10:33:36 AM
#> >> dealing with Chr5 left gene signal... 2021-12-30 10:33:37 AM
#> >> norming Chr5RP accoring to the whole Chr RP... 2021-12-30 10:33:37 AM
#> >> merging all Chr RP together... 2021-12-30 10:33:37 AM
#> >> done 2021-12-30 10:33:37 AM
head(RP_df)
#> gene_id sumRP
#> 1 AT5G01050 -0.8437482
#> 2 AT5G01060 0.6576913
#> 3 AT5G01070 3.5641593
#> 4 AT5G01075 3.2401437
#> 5 AT5G01080 1.6852928
#> 6 AT5G01090 -1.32545543.3 Calculate RP using mmAnno result and peakScore matrix
The calcRP_region here is to calculate RP according to your ATAC peak file and
ATAC norm Count matrix.
The object you may need to consider:
- peak_GR:
if you have several samples, it should be the merge peak set from these samples
- peakScoreMt:
the ATAC norm Count matrix. you can use different normalized ways to norm the origin peak count matrix, like CPM, FPKM, quantile, DESeq2, edgeR and so on.
- Chrs_included:
The Chromosomes where you want to calculate gene RP in. Here I set Chr5 because I only use the test data in Chr5. It do not have a effect on the speed. If your peak all on the Chr5, and I set Chrs_included to Chr1 and Chr5, then all gene RP in Chr1 will be filled with 0.
The calculation formula is same as (1), (2). But
it do not use the feature_score in peak_GR. Instead, it will use the count in
peakScoreMt. THis is why the count matrix should be normalized firstly. The
class of calcRP_region result is a
MultiAssayExperiment object containing detailed peak-RP-gene relationship and
sumRP info. The calcRP_region result can be as the input of findIT_regionRP
to find the influential TF.
data("ATAC_normCount")
# This ATAC peak is the merge peak set from E50h-72h
ATAC_peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
ATAC_peak_GR <- loadPeakFile(ATAC_peak_path)
mmAnno_regionRP <- mm_geneScan(ATAC_peak_GR,
Txdb,
upstream = 2e4,
downstream = 2e4)
#> >> checking seqlevels match... 2021-12-30 10:33:38 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> ------------
#> annotatePeak using geneScan mode begins
#> >> preparing gene features information and scan region... 2021-12-30 10:33:38 AM
#> >> some scan range may cross Chr bound, trimming... 2021-12-30 10:33:39 AM
#> >> finding overlap peak in gene scan region... 2021-12-30 10:33:39 AM
#> >> dealing with left peak not your gene scan region... 2021-12-30 10:33:39 AM
#> >> merging two set peaks... 2021-12-30 10:33:39 AM
#> >> calculating distance and dealing with gene strand... 2021-12-30 10:33:39 AM
#> >> merging all info together ... 2021-12-30 10:33:39 AM
#> >> done 2021-12-30 10:33:39 AM
# This ATAC_normCount is the peak count matrix normilzed by DESeq2
calcRP_region(mmAnno = mmAnno_regionRP,
peakScoreMt = ATAC_normCount,
Txdb = Txdb,
Chrs_included = "Chr5") -> regionRP
#> >> calculating peakCenter to TSS using peak-gene pair... 2021-12-30 10:33:39 AM
#> >> pre-filling 446 noAssoc peak gene's RP with 0... 2021-12-30 10:33:40 AM
#> >> calculating RP using centerToTSS and peak score2021-12-30 10:33:40 AM
#> >> merging all info together 2021-12-30 10:33:42 AM
#> >> done 2021-12-30 10:33:43 AM
# The sumRP is a matrix representing your geneRP in every samples
sumRP <- assays(regionRP)$sumRP
head(sumRP)
#> E5_0h_R1 E5_0h_R2 E5_4h_R1 E5_4h_R2 E5_8h_R1 E5_8h_R2 E5_16h_R1
#> AT5G01010 222.2079 212.7673 218.1653 213.6052 249.9372 206.0400 254.9596
#> AT5G01015 208.7814 203.1862 203.0282 203.9045 224.0673 192.7383 240.3690
#> AT5G01020 433.2978 439.1758 414.0660 446.9142 370.8003 419.2071 507.6845
#> AT5G01030 417.1240 422.5180 395.8684 429.5824 349.7193 401.1142 486.7315
#> AT5G01040 398.0912 398.0977 321.6067 351.3717 243.5476 236.7626 367.2205
#> AT5G01050 269.0832 285.3329 117.0695 102.0756 109.3351 101.0077 135.9758
#> E5_16h_R2 E5_24h_R1 E5_24h_R2 E5_48h_R1 E5_48h_R2 E5_48h_R3 E5_72h_R1
#> AT5G01010 294.6202 271.1531 262.5603 216.3946 214.0887 226.4749 183.1970
#> AT5G01015 270.5445 248.4688 241.9568 196.3324 194.7707 203.5344 168.8261
#> AT5G01020 524.0937 489.1091 473.2037 338.7931 370.7956 356.9675 332.7021
#> AT5G01030 499.3811 467.3236 452.1052 322.8229 353.5329 339.1584 320.1101
#> AT5G01040 342.5372 360.6502 355.6829 293.5963 295.9778 290.2336 379.6624
#> AT5G01050 139.9906 154.1518 129.9962 134.2388 113.4725 138.8942 161.2767
#> E5_72h_R2 E5_72h_R3
#> AT5G01010 201.5551 232.2543
#> AT5G01015 188.3035 213.0661
#> AT5G01020 382.2974 405.6337
#> AT5G01030 366.3332 389.1395
#> AT5G01040 342.0031 381.4891
#> AT5G01050 174.7018 176.5979
# The fullRP is a detailed peak-RP-gene relationship
fullRP <- assays(regionRP)$fullRP
head(fullRP)
#> E5_0h_R1 E5_0h_R2 E5_4h_R1 E5_4h_R2
#> WFX_peak_19629:AT5G01010 3.2505507 2.94366371 4.91215176 3.2210557
#> WFX_peak_19630:AT5G01010 28.3673260 25.49941664 33.09901392 26.4569161
#> WFX_peak_19631:AT5G01010 11.3199591 8.66507551 7.69205730 10.1043687
#> WFX_peak_19632:AT5G01010 140.0066186 135.72131106 135.00039379 133.1331029
#> WFX_peak_19633:AT5G01010 38.7559482 39.48821374 37.10950497 40.2704327
#> WFX_peak_19634:AT5G01010 0.1620359 0.09696071 0.05939023 0.1040209
#> E5_8h_R1 E5_8h_R2 E5_16h_R1 E5_16h_R2
#> WFX_peak_19629:AT5G01010 4.63291194 3.40978941 3.4267032 5.0733396
#> WFX_peak_19630:AT5G01010 33.35699057 35.98214354 31.2959400 37.9690563
#> WFX_peak_19631:AT5G01010 10.00585368 10.17992743 15.1914895 20.1385141
#> WFX_peak_19632:AT5G01010 169.10492510 118.32576124 158.9697269 184.2512264
#> WFX_peak_19633:AT5G01010 32.53901650 37.84312560 45.6184227 46.7559771
#> WFX_peak_19634:AT5G01010 0.08068857 0.09220271 0.1290225 0.1258721
#> E5_24h_R1 E5_24h_R2 E5_48h_R1 E5_48h_R2
#> WFX_peak_19629:AT5G01010 4.7636561 3.6203395 3.1645878 3.61212739
#> WFX_peak_19630:AT5G01010 41.7795900 37.5422424 27.0006566 32.58453567
#> WFX_peak_19631:AT5G01010 16.1608755 14.0341872 8.6911928 13.21967538
#> WFX_peak_19632:AT5G01010 164.3433125 164.7184992 147.3949774 131.34350008
#> WFX_peak_19633:AT5G01010 43.6546465 42.2005830 29.7367518 32.99092356
#> WFX_peak_19634:AT5G01010 0.1348949 0.1294783 0.1536341 0.07837431
#> E5_48h_R3 E5_72h_R1 E5_72h_R2 E5_72h_R3
#> WFX_peak_19629:AT5G01010 3.44344427 2.7374503 2.61457722 2.6626243
#> WFX_peak_19630:AT5G01010 28.19522111 25.5527166 27.32943247 32.1836089
#> WFX_peak_19631:AT5G01010 17.57139155 11.1322592 9.29903999 13.0251325
#> WFX_peak_19632:AT5G01010 145.44860373 113.7699899 127.73896923 147.9226052
#> WFX_peak_19633:AT5G01010 31.46275054 29.5742561 34.19603669 35.9567241
#> WFX_peak_19634:AT5G01010 0.09615333 0.0841232 0.06690242 0.16839094 Find influential target
integrate_ChIP_RNA can combine ChIP-Seq with RNA-Seq data to find target gene
more accurately.
The object you may need:
- result_geneRP:
The data.frame object representing target rank from calcRP_TFHit
(setreport_fullInfo=FALSE) or metadata(fullRP_hit)$peakRP_gene.
- result_geneDiff:
The RNA-Seq diff data.frame, it should be have three column:gene_id, log2FoldChange, padj.
Differential expression analysis result between TF perturbations (i.e. stimulation, repression, knock-down or knockout) and controls is an alternative approach for predicting TF targets. However, it is difficult to determine whether the differentially expressed genes in such experiments are direct targets of the TF using expression profile only. Therefore, ChIP-seq peaks adding differential expression information upon TF perturbation could be used to discriminate between directly regulated genes and secondary effects more accurately.
The theory behind integrate_ChIP_RNA is simple. It will firstly rank the diff
result according to the padj value then integrate the ChIP and RNA data using
the rank-product. If a gene is in the top rank of calcRP_TFHit and RNA-Seq,
then it will be the top target in final result. The integrate_ChIP_RNA will
also predict your TF function. It will divide genes into three groups according
to expression pattern, up-regulated, down-regulated or unchanged. The threshold
deciding groups is lfc_threshold and padj_threshold parameters.
data("RNADiff_LEC2_GR")
integrate_ChIP_RNA(result_geneRP = peakRP_gene,
result_geneDiff = RNADiff_LEC2_GR) -> merge_result
# you can see compared with down gene, there are more up gene on the top rank in ChIP-Seq data
# In the meanwhile, the number of down gene is less than up
merge_result
# if you want to extract merge target data
target_result <- merge_result$data
target_result
#> # A tibble: 6,783 x 10
#> gene_id withPeakN sumRP RP_rank log2FoldChange padj diff_rank
#> <chr> <int> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AT5G23370 9 2.84 23 3.56 2.33e- 46 12
#> 2 AT5G13910 11 2.30 140 4.20 1.36e- 85 2
#> 3 AT5G23350 10 1.68 525 3.20 1.34e-121 1
#> 4 AT5G23360 10 2.12 200 2.99 2.24e- 64 6
#> 5 AT5G52882 17 2.50 83 1.63 1.93e- 36 15
#> 6 AT5G05180 21 2.61 52 1.22 3.58e- 23 27
#> 7 AT5G11320 8 2.56 62 2.56 6.28e- 22 29
#> 8 AT5G02760 12 3.24 7 0.719 4.66e- 2 319
#> 9 AT5G55620 5 1.49 759 2.60 5.78e- 68 4
#> 10 AT5G60760 14 2.67 43 1.09 6.47e- 11 71
#> # ... with 6,773 more rows, and 3 more variables: rankProduct <dbl>,
#> # rankOf_rankProduct <dbl>, gene_category <fct>5 Find influential TF
Find influential TF contains some function to help you find TF based on your input or analysis type. You can find detailed case in each function section.
The object you may need
input_genes: the gene set you want to find infulential TF for
input_feature_id: the peak set you want to find infulential TF for
The input_feature_id should be a part of the total peak set. You can use methods such as kmeans or differential analysis to get the feature id set you are interested in.
- peak_GR:a GRange object with a column named feature_id
The peak GRange can be from ATAC, H3K27ac, or some other histone modification
peak data which you believe TF hit in. input_feature_id should be a part of
this GRange’s feature id set.
- TF_GR_database: a GRange object with a column named TF_id
The TF_GR_database can be from public TF database, or motif scan in ATAC/H3K27ac data. If your data is from model species, like human/mouse or A. thaliana, there are some wonderful public ChIP-Seq database, like cistrome, Remap, unibind. For those species that do not have good database, you can use motif scan tools like memesuite, HOMER, GimmeMotifs, motifmatchr to find your motif location in your ATAC peak to represent TF occupy. And if you do not have TF database or ATAC-Seq in hand, you can also try some other database like PLAZA, plantTFDB, which use the evolutionary conservation to find the motif occupy. But I do not recommend it, it can not represent your sample specific TF occupy profile.
Regardless of whether you use public TF ChIP-Seq or motif scan result, all you
need to do is to import the bed file like above and rename one of column into
TF_id. The TF_id is same as feature_id, which always the forth column in
bed file and the first column in GRange metadata column. For TF_GR_database,
each site is not important, what is important is the set of sites represented by
each TF_id. The TF_id is important column when using findIT module, so
please make sure add correctly.
# Here I take the top50 gene from integrate_ChIP_RNA as my interesting gene set.
input_genes <- target_result$gene_id[1:50]
# I use mm_geneBound to find related peak, which I will take as my interesting peak set.
related_peaks <- mm_geneBound(peak_GR = ATAC_peak_GR,
Txdb = Txdb,
input_genes = input_genes)
#> >> checking seqlevels match... 2021-12-30 10:33:44 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> >> using mm_nearestGene to annotate Peak... 2021-12-30 10:33:45 AM
#> >> checking seqlevels match... 2021-12-30 10:33:45 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> ------------
#> annotating Peak using nearest gene mode begins
#> >> preparing gene features information... 2021-12-30 10:33:45 AM
#> >> finding nearest gene and calculating distance... 2021-12-30 10:33:46 AM
#> >> dealing with gene strand ... 2021-12-30 10:33:46 AM
#> >> merging all info together ... 2021-12-30 10:33:46 AM
#> >> done 2021-12-30 10:33:46 AM
#> It seems that there 7 genes have not been annotated by nearestGene mode
#> >> using distanceToNearest to find nearest peak of these genes... 2021-12-30 10:33:47 AM
#> >> merging all anno... 2021-12-30 10:33:47 AM
#> >> done 2021-12-30 10:33:47 AM
input_feature_id <- unique(related_peaks$feature_id)
# AT1G28300 is LEC2 tair ID
# I add a column named TF_id into my ChIP Seq GR
ChIP_peak_GR$TF_id <- "AT1G28300"
# And I also add some other public ChIP-Seq data
TF_GR_database_path <- system.file("extdata", "TF_GR_database.bed.gz", package = "FindIT2")
TF_GR_database <- loadPeakFile(TF_GR_database_path)
TF_GR_database
#> GRanges object with 4223 ranges and 2 metadata columns:
#> seqnames ranges strand | feature_id score
#> <Rle> <IRanges> <Rle> | <character> <numeric>
#> [1] Chr5 9865-10065 * | AT3G59060 1
#> [2] Chr5 14244-14444 * | AT2G36270 1
#> [3] Chr5 14911-15154 * | AT2G36270 1
#> [4] Chr5 17037-17257 * | AT3G59060 1
#> [5] Chr5 20654-21105 * | AT2G36270 1
#> ... ... ... ... . ... ...
#> [4219] Chr5 26934932-26935211 * | AT2G36270 1
#> [4220] Chr5 26938020-26938219 * | AT5G24110 1
#> [4221] Chr5 26947440-26947615 * | AT5G24110 1
#> [4222] Chr5 26949899-26950521 * | AT3G59060 1
#> [4223] Chr5 26967024-26967348 * | AT2G36270 1
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths
# rename feature_id column into TF_id
# because the true thing I am interested in is TF set, not each TF binding site.
colnames(mcols(TF_GR_database))[1] <- "TF_id"
# merge LEC2 ChIP GR
TF_GR_database <- c(TF_GR_database, ChIP_peak_GR)
TF_GR_database
#> GRanges object with 8511 ranges and 4 metadata columns:
#> seqnames ranges strand | TF_id score feature_id
#> <Rle> <IRanges> <Rle> | <character> <numeric> <character>
#> [1] Chr5 9865-10065 * | AT3G59060 1 <NA>
#> [2] Chr5 14244-14444 * | AT2G36270 1 <NA>
#> [3] Chr5 14911-15154 * | AT2G36270 1 <NA>
#> [4] Chr5 17037-17257 * | AT3G59060 1 <NA>
#> [5] Chr5 20654-21105 * | AT2G36270 1 <NA>
#> ... ... ... ... . ... ... ...
#> [8507] Chr5 26937822-26938526 * | AT1G28300 NA peak_18408
#> [8508] Chr5 26939152-26939267 * | AT1G28300 NA peak_18409
#> [8509] Chr5 26949581-26950335 * | AT1G28300 NA peak_18410
#> [8510] Chr5 26952230-26952558 * | AT1G28300 NA peak_18411
#> [8511] Chr5 26968877-26969091 * | AT1G28300 NA peak_18412
#> feature_score
#> <numeric>
#> [1] NA
#> [2] NA
#> [3] NA
#> [4] NA
#> [5] NA
#> ... ...
#> [8507] 445
#> [8508] 21
#> [8509] 263
#> [8510] 30
#> [8511] 26
#> -------
#> seqinfo: 1 sequence from an unspecified genome; no seqlengths5.1 Find IT of input peak based on wilcox test
Compared with background peak, if TF in input_feature_id has more TF hit, this
TF may be important in your input_feature_id.
If your TF_GR_database is from motif scan result and have a column named TF_score, findIT_enrichWilcox will consider it to improve the accuracy. The TF_score always be the fifth column in your motif scan bed file and it represent your motif hit confidence in the location.
Here is the example bed output from gimmeMotif scan. The fifth column can be
treated as TF_score. You can directly load this bed file and rename or add meta column
just like feature_score before.
Chr1 2982 2989 MA0982.1_DOF2.4 5.817207239414311 +
Chr1 3085 3097 MA1044.1_NAC92 8.87118934508003 -
Chr1 3146 3165 MA1062.2_TCP15 7.842209471388505 +
Chr1 3146 3165 MA1065.2_TCP20 7.86289776912883 +findIT_enrichWilcox(input_feature_id = input_feature_id,
peak_GR = ATAC_peak_GR,
TF_GR_database = TF_GR_database) -> result_enrichWilcox
# you can see AT1G28300 is top1
result_enrichWilcox
#> # A tibble: 4 x 7
#> TF_id input_meanMotifScore bg_meanMotifSco~ pvalue padj qvalue rank
#> <chr> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
#> 1 AT1G28300 1.22 0.486 3.43e-18 1.37e-17 NA 1
#> 2 AT2G36270 0.494 0.301 4.51e- 3 1.35e- 2 NA 2
#> 3 AT3G59060 0.338 0.243 2.60e- 2 5.20e- 2 NA 3
#> 4 AT5G24110 0.0130 0.0347 8.44e- 1 8.44e- 1 NA 45.2 Find IT of input peak based on fisher test
You can also find the enrichment of TF using findIT_enrichFisher, it use the
same theory like GO-enrich analysis. The background is total ATAC peak, and the
select set is your input_feature_id. Compared with findIT_enrichWilcox
above, its runs more quickly. But it will have a little problem when using
motif scan result as TF_GR_database. A TF may hit more than one time in a
peak, however, here I treat it as one because I want the whole frame to be
more like GO enrichment analysis. Actually, the TF hit number can offer some
other useful information, which you can see in findIT_MARA. But it will do
not have a big effect on the final result. After all, what we really need is TF rank instead of p-value.
findIT_enrichFisher(input_feature_id = input_feature_id,
peak_GR = ATAC_peak_GR,
TF_GR_database = TF_GR_database) -> result_enrichFisher
# you can see AT1G28300 is top1
result_enrichFisher
#> # A tibble: 4 x 9
#> TF_id num_TFHit_input~ inputRatio bgRatio pvalue odds_ratio padj qvalue
#> <chr> <int> <chr> <chr> <dbl> <dbl> <dbl> <lgl>
#> 1 AT1G2~ 64 64/77 2486/5~ 1.15e-13 6.91 4.61e-13 NA
#> 2 AT2G3~ 30 30/77 1605/5~ 1.59e- 2 1.72 4.76e- 2 NA
#> 3 AT3G5~ 24 24/77 1332/5~ 5.02e- 2 1.56 1.00e- 1 NA
#> 4 AT5G2~ 1 1/77 190/59~ 9.21e- 1 0.392 9.21e- 1 NA
#> # ... with 1 more variable: rank <dbl>In the meanwhile, you can parse your result using jaccard_findIT_enrichFisher,
which can help you find co-occupy TF in your input_feature_id. But please note
you should not input too much TF_id in input_TF_id because it will run slowly.
You can use the top rank gene as input_TF_id.
# Here I use the top 4 TF id to calculate jaccard similarity of TF
jaccard_findIT_enrichFisher(input_feature_id = input_feature_id,
peak_GR = ATAC_peak_GR,
TF_GR_database = TF_GR_database,
input_TF_id = result_enrichFisher$TF_id[1:4]) -> enrichAll_jaccard
# it report the jaccard similarity of TF you input
# but here I make the TF's own jaccard similarity 0, which is useful for heatmap
# If you want to convert it to 1, you can just use
# diag(enrichAll_jaccard) <- 1
enrichAll_jaccard
#> AT1G28300 AT2G36270 AT3G59060 AT5G24110
#> AT1G28300 0.0000000 0.4242424 0.3750000 0.015625
#> AT2G36270 0.4242424 0.0000000 0.2857143 0.000000
#> AT3G59060 0.3750000 0.2857143 0.0000000 0.000000
#> AT5G24110 0.0156250 0.0000000 0.0000000 0.0000005.3 Find IT of input genes based on fisher test
The findIT_TTPair also use the fisher test like findIT_enrichFisher. The
difference is your input set is gene id instead of feature id. And it means that
your database should be the TF_target_database like this.
data("TF_target_database")
# it should have two column named TF_id and target_gene.
head(TF_target_database)
#> TF_id target_gene
#> 1 AT1G28300 AT1G50650
#> 2 AT3G23250 AT2G05940
#> 3 AT1G28300 AT4G01720
#> 4 AT5G24110 AT4G04540
#> 5 AT3G23250 AT5G36925
#> 6 AT5G24110 AT5G41750This function is very useful when you have a interesting gene set producing from
some analysis like k-means in RNA-Seq data, WGCNA, single cell analysis. The
test TF_target_database here is downloaded from
iGRN.
# By default, TTpair will consider all target gene as background
# Because I just use part of true TF_target_database, the background calculation
# is not correct.
# so I use all gene in Txdb as gene_background.
result_TTpair <- findIT_TTPair(input_genes = input_genes,
TF_target_database = TF_target_database,
gene_background = names(all_geneSet))
# you can see AT1G28300 is top1
result_TTpair
#> # A tibble: 3 x 9
#> TF_id num_TFHit_input inputRatio bgRatio pvalue odds_ratio padj qvalue
#> <chr> <int> <chr> <chr> <dbl> <dbl> <dbl> <lgl>
#> 1 AT1G28300 14 14/50 1950/3~ 6.08e-7 6.35 1.82e-6 NA
#> 2 AT3G23250 11 11/50 2781/3~ 2.22e-3 3.13 4.44e-3 NA
#> 3 AT5G24110 6 6/50 2275/3~ 1.20e-1 1.88 1.20e-1 NA
#> # ... with 1 more variable: rank <dbl>You can parse your result_TT using jaccard_findIT_TTpair.
# Here I use the all TF_id because I just have three TF in result_TTpair
# For you, you can select top N TF_id as input_TF_id
jaccard_findIT_TTpair(input_genes = input_genes,
TF_target_database = TF_target_database,
input_TF_id = result_TTpair$TF_id) -> TTpair_jaccard
# Here I make the TF's own jaccard similarity 0, which is useful for heatmap
# If you want to convert it to 1, you can just use
# diag(TTpair_jaccard) <- 1
TTpair_jaccard
#> AT1G28300 AT3G23250 AT5G24110
#> AT1G28300 0.0000000 0.1363636 0.1111111
#> AT3G23250 0.1363636 0.0000000 0.1333333
#> AT5G24110 0.1111111 0.1333333 0.00000005.4 Find IT of input genes based on TF hit
Even though findIT_TTpaior is a very useful tool for finding TF when you have
a interesting gene set. But for most species, it do not have a database like
TF_target_database, so I write findIT_TFHit. You can think it run
calcRP_TFhit for each TF in your TF_GR_database. Compared with background
gene, the TF have a effect on your input_genes will produce more significant
p-value.
# For repeatability of results, you should set seed.
set.seed(20160806)
# the meaning of scan_dist and decay_dist is same as calcRP_TFHit
# the Chrs_included control the chromosome your background in
# the background_number control the number of background gene
# If you want to compare the TF enrichment in your input_genes with other gene set
# you can input other gene set id into background_genes
result_TFHit <- findIT_TFHit(input_genes = input_genes,
Txdb = Txdb,
TF_GR_database = TF_GR_database,
scan_dist = 2e4,
decay_dist = 1e3,
Chrs_included = "Chr5",
background_number = 3000)
#> >> preparing gene features information... 2021-12-30 10:33:48 AM
#> >> some scan range may cross Chr bound, trimming... 2021-12-30 10:33:50 AM
#> >> calculating p-value for each TF, which may be time consuming... 2021-12-30 10:33:50 AM
#> >> merging all info together... 2021-12-30 10:33:51 AM
#> >> done 2021-12-30 10:33:51 AM
# you can see AT1G28300 is top1
result_TFHit
#> # A tibble: 4 x 7
#> TF_id mean_value TFPeak_number pvalue padj qvalue rank
#> <chr> <dbl> <dbl> <dbl> <dbl> <lgl> <dbl>
#> 1 AT1G28300 1.88 4288 1.40e-30 5.58e-30 NA 1
#> 2 AT2G36270 0.268 1987 7.84e- 5 2.35e- 4 NA 2
#> 3 AT3G59060 0.172 1712 1.82e- 4 3.65e- 4 NA 3
#> 4 AT5G24110 0.0324 524 7.74e- 2 7.74e- 2 NA 45.5 Find IT of input genes based on region RP
Do you remember the regionRP we calculated earlier in (section 3.3?) Now
we use the result to find TF for your input_genes. Compared with findIT_TFHit,
it use the RP information and calculate each TF influence on each input_genes,
and then compare the influence distribution of input genes with background
genes. The advantage of findIT_regionRP is that it it provides richer
information for user. The theory behind of findIT_regionRP is from
lisa.
# For repeatability of results, you should set seed.
set.seed(20160806)
result_findIT_regionRP <- findIT_regionRP(regionRP = regionRP,
Txdb = Txdb,
TF_GR_database = TF_GR_database,
input_genes = input_genes,
background_number = 3000)
#> >> extracting RP info from regionRP... 2021-12-30 10:33:52 AM
#> >> dealing with TF_GR_databse... 2021-12-30 10:33:52 AM
#> >> calculating percent and p-value... 2021-12-30 10:33:52 AM
#> >> dealing withE5_0h_R1... 2021-12-30 10:33:52 AM
#> >> dealing withE5_0h_R2... 2021-12-30 10:33:52 AM
#> >> dealing withE5_4h_R1... 2021-12-30 10:33:52 AM
#> >> dealing withE5_4h_R2... 2021-12-30 10:33:52 AM
#> >> dealing withE5_8h_R1... 2021-12-30 10:33:52 AM
#> >> dealing withE5_8h_R2... 2021-12-30 10:33:52 AM
#> >> dealing withE5_16h_R1... 2021-12-30 10:33:53 AM
#> >> dealing withE5_16h_R2... 2021-12-30 10:33:53 AM
#> >> dealing withE5_24h_R1... 2021-12-30 10:33:53 AM
#> >> dealing withE5_24h_R2... 2021-12-30 10:33:53 AM
#> >> dealing withE5_48h_R1... 2021-12-30 10:33:53 AM
#> >> dealing withE5_48h_R2... 2021-12-30 10:33:53 AM
#> >> dealing withE5_48h_R3... 2021-12-30 10:33:53 AM
#> >> dealing withE5_72h_R1... 2021-12-30 10:33:54 AM
#> >> dealing withE5_72h_R2... 2021-12-30 10:33:54 AM
#> >> dealing withE5_72h_R3... 2021-12-30 10:33:54 AM
#> >> merging all info together... 2021-12-30 10:33:54 AM
#> >> done 2021-12-30 10:33:54 AM
# The result object of findIT_regionRP is MultiAssayExperiment, same as calcRP_region
# TF_percentMean is the mean influence of TF on input genes minus background,
# which represent the total influence of specific TF on your input genes
TF_percentMean <- assays(result_findIT_regionRP)$TF_percentMean
TF_pvalue <- assays(result_findIT_regionRP)$TF_pvalueThe true power of findIT_regionRP is that it provide multidimensional data:
gene_id, TF_id, feature_id and sample_id. You can fold, unfold and combine with
them in different ways.
- row is TF_id，column is sample_id，the value in matrix is TF percentMean
In this condition, we can see the each TF total influence trend on input genes set between samples
TF_percentMean
#> E5_0h_R1 E5_0h_R2 E5_4h_R1 E5_4h_R2 E5_8h_R1
#> AT3G59060 0.09925941 0.09712202 0.089206825 0.099649297 0.096098115
#> AT2G36270 0.13635214 0.13608772 0.148323105 0.143089355 0.137866627
#> AT5G24110 -0.01007736 -0.01091903 -0.006733566 -0.007131005 -0.007922378
#> AT1G28300 0.41689657 0.42042912 0.412106202 0.413097486 0.415512274
#> E5_8h_R2 E5_16h_R1 E5_16h_R2 E5_24h_R1 E5_24h_R2
#> AT3G59060 0.094740188 0.09649221 0.09919591 0.09970536 0.095745267
#> AT2G36270 0.134958108 0.13582114 0.13321162 0.13815229 0.137683060
#> AT5G24110 -0.007370023 -0.01071986 -0.01018437 -0.01129071 -0.009585838
#> AT1G28300 0.411671138 0.41310189 0.40991275 0.41507176 0.413921240
#> E5_48h_R1 E5_48h_R2 E5_48h_R3 E5_72h_R1 E5_72h_R2
#> AT3G59060 0.097940897 0.09357488 0.10074538 0.09559483 0.09547439
#> AT2G36270 0.139019907 0.13253096 0.13648215 0.13210676 0.13327821
#> AT5G24110 -0.009450179 -0.01094474 -0.01260133 -0.01273412 -0.01376151
#> AT1G28300 0.410781266 0.41151891 0.41379095 0.40970512 0.41691670
#> E5_72h_R3
#> AT3G59060 0.10141545
#> AT2G36270 0.13397782
#> AT5G24110 -0.01202533
#> AT1G28300 0.41128926
heatmap(TF_percentMean, Colv = NA, scale = "none")
- select specific sample_id，row is gene_id, column is TF_id，the value in matrix is each TF percent on each gene in specific sample
In this condition, we can see the influence of each TF on each gene in the specific sample.
metadata(result_findIT_regionRP)$percent_df %>%
filter(sample == "E5_0h_R1") %>%
select(gene_id, percent, TF_id) %>%
tidyr::pivot_wider(values_from = percent, names_from = gene_id) -> E50h_TF_percent
E50h_TF_mt <- as.matrix(E50h_TF_percent[, -1])
rownames(E50h_TF_mt) <- E50h_TF_percent$TF_id
E50h_TF_mt
#> AT5G02760 AT5G04820 AT5G05140 AT5G05180 AT5G07500
#> AT3G59060 0.6185198 9.510659e-01 0.9998695196 0.8599037413 3.548629e-06
#> AT2G36270 0.2870298 4.323193e-02 0.4996276611 0.0006340383 9.553795e-01
#> AT5G24110 0.0000000 2.684875e-06 0.0000148582 0.1379194448 0.000000e+00
#> AT1G28300 0.9804750 9.997659e-01 0.9999273678 0.9995774791 8.719047e-01
#> AT5G08460 AT5G11320 AT5G13790 AT5G13910 AT5G13990
#> AT3G59060 1.244017e-06 5.733276e-06 0.05908985 0.2153231 0.8272742766
#> AT2G36270 3.609851e-06 0.000000e+00 0.05080496 0.2152208 0.0431370565
#> AT5G24110 4.909990e-01 0.000000e+00 0.00000000 0.2840863 0.0000111638
#> AT1G28300 9.999935e-01 9.045044e-01 0.89659323 0.4993108 0.9941978731
#> AT5G14070 AT5G14120 AT5G15830 AT5G16110 AT5G17810 AT5G19250
#> AT3G59060 9.350048e-06 3.318164e-06 0.01088969 0.1929898 0.0004321823 0.5861229
#> AT2G36270 6.456453e-04 9.264010e-01 0.09777221 0.8694675 0.8128045462 0.0000000
#> AT5G24110 0.000000e+00 0.000000e+00 0.00000000 0.0000000 0.0000000000 0.0000000
#> AT1G28300 4.191532e-01 9.264015e-01 0.57300981 0.9888982 0.9548497704 0.7455370
#> AT5G20045 AT5G20050 AT5G20670 AT5G22390 AT5G23000 AT5G23350
#> AT3G59060 0.02123381 0.004773372 0.0007349553 0.0000000 0.0005218444 0.9443291
#> AT2G36270 0.74664572 0.816722826 0.0006931239 0.9148911 0.0000911175 0.2057015
#> AT5G24110 0.00000000 0.000000000 0.0000000000 0.0000000 0.0000911175 0.1534872
#> AT1G28300 0.97875473 0.995219102 0.9339122498 0.9148932 0.4641437769 0.9663600
#> AT5G23360 AT5G23370 AT5G24600 AT5G36250 AT5G41460 AT5G44260
#> AT3G59060 0.70760567 0.070780064 0.4331444 0.0000000 0.5784376826 0.2686116
#> AT2G36270 0.34142821 0.868768341 0.9262625 0.0000000 0.9084167040 0.9396472
#> AT5G24110 0.06999959 0.007002021 0.0000000 0.0000000 0.0008762819 0.0000000
#> AT1G28300 0.96984817 0.947063959 0.9312906 0.9983315 0.9920151017 0.9426889
#> AT5G44380 AT5G46060 AT5G46950 AT5G48420 AT5G49100
#> AT3G59060 0.64877424 0.0007712204 0.03947753 7.957429e-04 0.9579552
#> AT2G36270 0.03806873 0.0268983512 0.04003764 4.624518e-06 0.7438419
#> AT5G24110 0.00000000 0.0565108962 0.00000000 7.723619e-03 0.0000000
#> AT1G28300 0.90013014 0.9939655268 0.21824707 9.914729e-01 0.9989870
#> AT5G52860 AT5G52882 AT5G54230 AT5G55620 AT5G56075 AT5G58350
#> AT3G59060 2.496830e-03 0.1726205 0.0000000 0.007848307 0.00512347 0.9168544
#> AT2G36270 1.542930e-01 0.1722801 0.7925334 0.306030309 0.08232805 0.9774949
#> AT5G24110 3.115976e-05 0.0000000 0.0000000 0.007844281 0.00000000 0.0000000
#> AT1G28300 2.999216e-01 0.9826116 0.7670893 0.986513106 0.92178007 0.9983444
#> AT5G58660 AT5G58910 AT5G60760 AT5G62000 AT5G62280 AT5G64100
#> AT3G59060 0.974813541 0.2213277 0.9083193 0.9990986 0.405752767 0.5866519
#> AT2G36270 0.974811032 0.3780437 0.9421843 0.9990986 0.001213448 0.6241822
#> AT5G24110 0.006108379 0.0000000 0.0000000 0.0000000 0.001213448 0.0000000
#> AT1G28300 0.999982735 0.6159790 0.9980976 0.9990986 0.126204232 0.6370139
#> AT5G64530 AT5G64570 AT5G65100 AT5G66360 AT5G67190
#> AT3G59060 0.8879179 0.01827701 0.985395178 8.932819e-01 0.07999059
#> AT2G36270 0.9926487 0.89624267 0.988307943 4.201213e-02 0.81500186
#> AT5G24110 0.0000000 0.00000000 0.005207579 1.862933e-05 0.01015147
#> AT1G28300 0.8879424 0.99968452 0.999970308 9.343217e-01 0.92825295
heatmap(E50h_TF_mt, scale = "none")
- select specific TF_id，row is gene_id, column is sample_id，the value in matrix is specific TF percent on each gene in each sample
In this condition, we can see the influence trend of specific TF on each gene between samples.
metadata(result_findIT_regionRP)
#> $percent_df
#> # A tibble: 3,200 x 4
#> gene_id percent TF_id sample
#> <chr> <dbl> <chr> <chr>
#> 1 AT5G02760 0.619 AT3G59060 E5_0h_R1
#> 2 AT5G04820 0.951 AT3G59060 E5_0h_R1
#> 3 AT5G05140 1.00 AT3G59060 E5_0h_R1
#> 4 AT5G05180 0.860 AT3G59060 E5_0h_R1
#> 5 AT5G07500 0.00000355 AT3G59060 E5_0h_R1
#> 6 AT5G08460 0.00000124 AT3G59060 E5_0h_R1
#> 7 AT5G11320 0.00000573 AT3G59060 E5_0h_R1
#> 8 AT5G13790 0.0591 AT3G59060 E5_0h_R1
#> 9 AT5G13910 0.215 AT3G59060 E5_0h_R1
#> 10 AT5G13990 0.827 AT3G59060 E5_0h_R1
#> # ... with 3,190 more rows
#>
#> $hits_df
#> # A tibble: 5,578 x 2
#> TF_id feature_id
#> <chr> <chr>
#> 1 AT3G59060 WFX_peak_19633
#> 2 AT2G36270 WFX_peak_19634
#> 3 AT2G36270 WFX_peak_19635
#> 4 AT3G59060 WFX_peak_19636
#> 5 AT2G36270 WFX_peak_19637
#> 6 AT3G59060 WFX_peak_19638
#> 7 AT2G36270 WFX_peak_19639
#> 8 AT2G36270 WFX_peak_19641
#> 9 AT2G36270 WFX_peak_19645
#> 10 AT3G59060 WFX_peak_19645
#> # ... with 5,568 more rows
metadata(result_findIT_regionRP)$percent_df %>%
filter(TF_id == "AT1G28300") %>%
select(-TF_id) %>%
tidyr::pivot_wider(names_from = sample, values_from = percent) -> LEC2_percent_df
LEC2_percent_mt <- as.matrix(LEC2_percent_df[, -1])
rownames(LEC2_percent_mt) <- LEC2_percent_df$gene_id
heatmap(LEC2_percent_mt, Colv = NA, scale = "none")
If above analysis is too complex for you, I also provide the shiny function
shinyParse_findIT_regionRP from
InteractiveFindIT2 to help you
explore the result interactively.
# Before using shiny function, you should merge the regionRP and result_findIT_regionRP firstly.
merge_result <- c(regionRP, result_findIT_regionRP)
InteractiveFindIT2::shinyParse_findIT_regionRP(merge_result = merge_result, mode = "gene")
InteractiveFindIT2::shinyParse_findIT_regionRP(merge_result = merge_result,mode = "TF")5.6 Find IT of input genes based on motif activity response
findIT_regionRP is a useful tool, but I find for small genome like Arabidopsis
thaliana, it can not provide much information about TF total influence trend on
input genes set between samples. So I write findIT_MARA to see the TF
influence trend between samples. The advantage is that it can provide more
valuable result compared with findIT_regionRP when you want to see the total
trend. But the disadvantage is that it can not offer you the detailed informatin
on each gene. And the most important thing is it use the input_feature_id as
input, so you should use mm_geneBound, peakGeneCor, enhancerPromoterCor to
find related peak for your input genes.
The theory behind findIT_regionRP is from Motif Activity Response
Analysis(The FANTOM Consortium and Riken Omics Science Center 2009). And I also borrow the idea
from gimmeMotifs maelstrom(Bruse and Heeringen 2018).
And please note that the TF_GR_database here should be the motif scan in your
ATAC peak instead of public ChIP-Seq!!!. Because I use the linear function to
combine with TF, which means TF will influence each other. And for other
function in findIT module, each TF result is orthogonal with each other.
If you have a column named TF_score in TF_GR_database, findIT_MARA will
consider it to improve the accuracy. The TF_score always be the fifth column
in your motif scan bed file and it represent your motif hit confidence in the
location.
Here is the example bed output from gimmeMotif scan. The fifth column can be
treated as TF_score.
Chr1 2982 2989 MA0982.1_DOF2.4 5.817207239414311 +
Chr1 3085 3097 MA1044.1_NAC92 8.87118934508003 -
Chr1 3146 3165 MA1062.2_TCP15 7.842209471388505 +
Chr1 3146 3165 MA1065.2_TCP20 7.86289776912883 +
# For repeatability of results, you should set seed.
set.seed(20160806)
findIT_MARA(input_feature_id = input_feature_id,
peak_GR = ATAC_peak_GR,
peakScoreMt = ATAC_normCount,
TF_GR_database = TF_GR_database,
log = TRUE,
meanScale = TRUE) -> result_findIT_MARA
#> >> dealing with TF_GR_database... 2021-12-30 10:33:56 AM
#> >> calculating coef and converting into z-score using INT... 2021-12-30 10:33:57 AM
#> >> dealing withE5_0h_R1... 2021-12-30 10:33:57 AM
#> >> dealing withE5_0h_R2... 2021-12-30 10:33:57 AM
#> >> dealing withE5_4h_R1... 2021-12-30 10:33:57 AM
#> >> dealing withE5_4h_R2... 2021-12-30 10:33:57 AM
#> >> dealing withE5_8h_R1... 2021-12-30 10:33:57 AM
#> >> dealing withE5_8h_R2... 2021-12-30 10:33:57 AM
#> >> dealing withE5_16h_R1... 2021-12-30 10:33:57 AM
#> >> dealing withE5_16h_R2... 2021-12-30 10:33:58 AM
#> >> dealing withE5_24h_R1... 2021-12-30 10:33:58 AM
#> >> dealing withE5_24h_R2... 2021-12-30 10:33:58 AM
#> >> dealing withE5_48h_R1... 2021-12-30 10:33:58 AM
#> >> dealing withE5_48h_R2... 2021-12-30 10:33:58 AM
#> >> dealing withE5_48h_R3... 2021-12-30 10:33:58 AM
#> >> dealing withE5_72h_R1... 2021-12-30 10:33:58 AM
#> >> dealing withE5_72h_R2... 2021-12-30 10:33:58 AM
#> >> dealing withE5_72h_R3... 2021-12-30 10:33:58 AM
#> >> merging all info together... 2021-12-30 10:33:58 AM
#> >> done 2021-12-30 10:33:58 AM
# Please note that you should add the total motif scan data in TF_GR_database
# Here I just use the test public ChIP-Seq data, so the result is not valuable
result_findIT_MARA
#> # A tibble: 4 x 17
#> TF_id E5_0h_R1 E5_0h_R2 E5_4h_R1 E5_4h_R2 E5_8h_R1 E5_8h_R2 E5_16h_R1
#> <chr> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl> <dbl>
#> 1 AT3G59060 1.15 1.15 -1.15 -0.319 1.15 1.15 -0.319
#> 2 AT2G36270 -0.319 -0.319 0.319 1.15 -0.319 -0.319 -1.15
#> 3 AT5G24110 -1.15 -1.15 1.15 -1.15 -1.15 -1.15 1.15
#> 4 AT1G28300 0.319 0.319 -0.319 0.319 0.319 0.319 0.319
#> # ... with 9 more variables: E5_16h_R2 <dbl>, E5_24h_R1 <dbl>, E5_24h_R2 <dbl>,
#> # E5_48h_R1 <dbl>, E5_48h_R2 <dbl>, E5_48h_R3 <dbl>, E5_72h_R1 <dbl>,
#> # E5_72h_R2 <dbl>, E5_72h_R3 <dbl># when you get the zscale value from findIT_MARA,
# you can use integrate_replicates to integrate replicate zscale by setting type as "rank_zscore"
# Here each replicate are combined using Stouffer’s method
MARA_mt <- as.matrix(result_findIT_MARA[, -1])
rownames(MARA_mt) <- result_findIT_MARA$TF_id
MARA_colData <- data.frame(row.names = colnames(MARA_mt),
type = gsub("_R[0-9]", "", colnames(MARA_mt))
)
integrate_replicates(mt = MARA_mt,
colData = MARA_colData,
type = "rank_zscore")
#> E5_0h E5_4h E5_8h E5_16h E5_24h E5_48h
#> AT3G59060 1.6268397 -1.038732 1.6268397 0.5881078 -1.6268397 -1.9924636
#> AT2G36270 -0.4506241 1.038732 -0.4506241 -1.0387319 -0.4506241 0.5518996
#> AT5G24110 -1.6268397 0.000000 -1.6268397 0.0000000 1.6268397 1.1443425
#> AT1G28300 0.4506241 0.000000 0.4506241 0.4506241 0.4506241 0.2962215
#> E5_72h
#> AT3G59060 0.5518996
#> AT2G36270 1.1443425
#> AT5G24110 -0.6641545
#> AT1G28300 -1.03208765.7 integrate result
If you have p-value or rank value from different source, you can combine them
using integrate_replicates.
list(TF_Hit = result_TFHit,
enrichFisher = result_enrichFisher,
wilcox = result_enrichWilcox,
TT_pair = result_TTpair
) -> rank_merge_list
purrr::map(names(rank_merge_list), .f = function(x){
data <- rank_merge_list[[x]]
data %>%
select(TF_id, rank) %>%
mutate(source = x) -> data
return(data)
}) %>%
do.call(rbind, .) %>%
tidyr::pivot_wider(names_from = source, values_from = rank) -> rank_merge_df
rank_merge_df
#> # A tibble: 5 x 5
#> TF_id TF_Hit enrichFisher wilcox TT_pair
#> <chr> <dbl> <dbl> <dbl> <dbl>
#> 1 AT1G28300 1 1 1 1
#> 2 AT2G36270 2 2 2 NA
#> 3 AT3G59060 3 3 3 NA
#> 4 AT5G24110 4 4 4 3
#> 5 AT3G23250 NA NA NA 2
# we only select TF which appears in all source
rank_merge_df <- rank_merge_df[rowSums(is.na(rank_merge_df)) == 0, ]
rank_merge_mt <- as.matrix(rank_merge_df[, -1])
rownames(rank_merge_mt) <- rank_merge_df$TF_id
colData <- data.frame(row.names = colnames(rank_merge_mt),
type = rep("source", ncol(rank_merge_mt)))
integrate_replicates(mt = rank_merge_mt, colData = colData, type = "rank")
#> source
#> AT1G28300 1
#> AT5G24110 26 Calculate feature correlation
6.1 Calculate peak gene correlation
data("RNA_normCount")
peak_GR <- loadPeakFile(ATAC_peak_path)[1:100]
mmAnno <- mm_geneScan(peak_GR,Txdb)
#> >> checking seqlevels match... 2021-12-30 10:33:59 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> ------------
#> annotatePeak using geneScan mode begins
#> >> preparing gene features information and scan region... 2021-12-30 10:33:59 AM
#> >> some scan range may cross Chr bound, trimming... 2021-12-30 10:34:00 AM
#> >> finding overlap peak in gene scan region... 2021-12-30 10:34:00 AM
#> >> dealing with left peak not your gene scan region... 2021-12-30 10:34:00 AM
#> >> merging two set peaks... 2021-12-30 10:34:00 AM
#> >> calculating distance and dealing with gene strand... 2021-12-30 10:34:00 AM
#> >> merging all info together ... 2021-12-30 10:34:00 AM
#> >> done 2021-12-30 10:34:00 AM
ATAC_colData <- data.frame(row.names = colnames(ATAC_normCount),
type = gsub("_R[0-9]", "", colnames(ATAC_normCount))
)
integrate_replicates(ATAC_normCount, ATAC_colData) -> ATAC_normCount_merge
RNA_colData <- data.frame(row.names = colnames(RNA_normCount),
type = gsub("_R[0-9]", "", colnames(RNA_normCount))
)
integrate_replicates(RNA_normCount, RNA_colData) -> RNA_normCount_merge
peakGeneCor(mmAnno = mmAnno,
peakScoreMt = ATAC_normCount_merge,
geneScoreMt = RNA_normCount_merge,
parallel = FALSE) -> mmAnnoCor
#> Good, your two matrix colnames matchs
#> Warning: some gene_id or feature_id in your mmAnno is not in your geneScoreMt or peakScore Mt, final cor and pvalue of these gene_id or feature_id pair will be NA
#> >> calculating cor and pvalue, which may be time consuming... 2021-12-30 10:34:01 AM
#> >> merging all info together... 2021-12-30 10:34:01 AM
#> >> done 2021-12-30 10:34:01 AMsubset(mmAnnoCor, cor > 0.8) %>%
getAssocPairNumber()
#> # A tibble: 10 x 2
#> gene_id peakNumber
#> <chr> <int>
#> 1 AT5G01075 2
#> 2 AT5G01175 1
#> 3 AT5G01180 1
#> 4 AT5G01230 1
#> 5 AT5G01340 1
#> 6 AT5G01590 1
#> 7 AT5G01600 2
#> 8 AT5G01620 2
#> 9 AT5G01720 1
#> 10 AT5G01890 1plot_peakGeneCor(mmAnnoCor = mmAnnoCor,
select_gene = "AT5G01075")
#> Joining, by = "feature_id"
#> Joining, by = "feature_id"
#> `geom_smooth()` using formula 'y ~ x'
plot_peakGeneCor(mmAnnoCor = subset(mmAnnoCor, cor > 0.95),
select_gene = "AT5G01075")
#> Joining, by = "feature_id"
#> Joining, by = "feature_id"
#> `geom_smooth()` using formula 'y ~ x'
plot_peakGeneCor(mmAnnoCor = subset(mmAnnoCor, cor > 0.95),
select_gene = "AT5G01075") +
geom_point(aes(color = time_point))
#> Joining, by = "feature_id"
#> Joining, by = "feature_id"
#> `geom_smooth()` using formula 'y ~ x'
plot_peakGeneAlias_summary(mmAnno = mmAnnoCor,
mmAnno_corFilter = subset(mmAnnoCor, cor > 0.8))
the shiny function shinyParse_peakGeneCor from
InteractiveFindIT2 to help you
explore the result interactively
InteractiveFindIT2::shinyParse_peakGeneCor(mmAnnoCor)6.2 Calculate enhancer promoter correlation
enhancerPromoterCor(peak_GR = peak_GR[1:100],
Txdb = Txdb,
peakScoreMt = ATAC_normCount,
up_scanPromoter = 500,
down_scanPromoter = 500,
up_scanEnhancer = 2000,
down_scanEnhacner = 2000,
parallel = FALSE) -> mmAnnoCor_linkEP
#> >> checking seqlevels match... 2021-12-30 10:34:03 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> >> using scanPromoter parameter to scan promoter for each gene... 2021-12-30 10:34:03 AM
#> >> checking seqlevels match... 2021-12-30 10:34:03 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> >> some scan range may cross Chr bound, trimming... 2021-12-30 10:34:04 AM
#> >> there are 85 gene have scaned promoter
#> >> using scanEnhancer parameter to scan Enhancer for each gene... 2021-12-30 10:34:04 AM
#> >> checking seqlevels match... 2021-12-30 10:34:05 AM
#> >> your peak_GR seqlevel:Chr5...
#> >> your Txdb seqlevel:Chr1 Chr2 Chr3 Chr4 Chr5 M C...
#> Good, your Chrs in peak_GR is all in Txdb
#> >> some scan range may cross Chr bound, trimming... 2021-12-30 10:34:05 AM
#> >> calculating cor and pvalue, which may be time consuming... 2021-12-30 10:34:05 AM
#> >> merging all info together... 2021-12-30 10:34:05 AM
#> >> done 2021-12-30 10:34:06 AMplot_peakGeneCor(mmAnnoCor = mmAnnoCor_linkEP,
select_gene = "AT5G01075") -> p
#> Joining, by = "feature_id"
#> Joining, by = "feature_id"
p
#> `geom_smooth()` using formula 'y ~ x'
p$data$type <- gsub("_R[0-9]", "", p$data$time_point)
p$data$type <- factor(p$data$type, levels = unique(p$data$type))
p +
ggplot2::geom_point(aes(color = type))
#> `geom_smooth()` using formula 'y ~ x'
plot_peakGeneAlias_summary(mmAnno = mmAnnoCor_linkEP,
mmAnno_corFilter = subset(mmAnnoCor_linkEP, cor > 0.8))
the shiny function shinyParse_peakGeneCor from
InteractiveFindIT2 to help you
explore the result interactively
InteractiveFindIT2::shinyParse_peakGeneCor(mmAnnoCor_linkEP)7 Integrate result
You have seen integrate_replicates in (section 5.7,
6.1), 5.6). But actually, integrate_replicates
can do more. The integrate_replicates has four basic mode: value, rank,
rank_zscore and p-value. For each mode, it use different function.
8 Session info
#> - Session info -------------------------------------------------------------------------------------------------------
#> setting value
#> version R version 4.1.2 (2021-11-01)
#> os Windows Server 2012 R2 x64 (build 9600)
#> system x86_64, mingw32
#> ui RTerm
#> language (EN)
#> collate C
#> ctype English_United States.1252
#> tz America/New_York
#> date 2021-12-30
#> pandoc 2.7.3 @ C:/PROGRA~1/Pandoc/ (via rmarkdown)
#>
#> - Packages -----------------------------------------------------------------------------------------------------------
#> package * version date (UTC) lib source
#> AnnotationDbi * 1.56.2 2021-12-30 [2] Bioconductor
#> assertthat 0.2.1 2019-03-21 [2] CRAN (R 4.1.1)
#> Biobase * 2.54.0 2021-12-30 [2] Bioconductor
#> BiocFileCache 2.2.0 2021-12-30 [2] Bioconductor
#> BiocGenerics * 0.40.0 2021-12-30 [2] Bioconductor
#> BiocIO 1.4.0 2021-12-30 [2] Bioconductor
#> BiocManager 1.30.16 2021-06-15 [2] CRAN (R 4.1.1)
#> BiocParallel 1.28.3 2021-12-30 [2] Bioconductor
#> BiocStyle * 2.22.0 2021-12-30 [2] Bioconductor
#> biomaRt 2.50.1 2021-12-30 [2] Bioconductor
#> Biostrings 2.62.0 2021-12-30 [2] Bioconductor
#> bit 4.0.4 2020-08-04 [2] CRAN (R 4.1.1)
#> bit64 4.0.5 2020-08-30 [2] CRAN (R 4.1.1)
#> bitops 1.0-7 2021-04-24 [2] CRAN (R 4.1.1)
#> blob 1.2.2 2021-07-23 [2] CRAN (R 4.1.1)
#> bookdown 0.24 2021-09-02 [2] CRAN (R 4.1.1)
#> bslib 0.3.1 2021-10-06 [2] CRAN (R 4.1.1)
#> cachem 1.0.6 2021-08-19 [2] CRAN (R 4.1.1)
#> cli 3.1.0 2021-10-27 [2] CRAN (R 4.1.1)
#> codetools 0.2-18 2020-11-04 [2] CRAN (R 4.1.2)
#> colorspace 2.0-2 2021-06-24 [2] CRAN (R 4.1.1)
#> crayon 1.4.2 2021-10-29 [2] CRAN (R 4.1.1)
#> curl 4.3.2 2021-06-23 [2] CRAN (R 4.1.1)
#> DBI 1.1.2 2021-12-20 [2] CRAN (R 4.1.2)
#> dbplyr 2.1.1 2021-04-06 [2] CRAN (R 4.1.1)
#> DelayedArray 0.20.0 2021-12-30 [2] Bioconductor
#> digest 0.6.29 2021-12-01 [2] CRAN (R 4.1.2)
#> dplyr * 1.0.7 2021-06-18 [2] CRAN (R 4.1.1)
#> ellipsis 0.3.2 2021-04-29 [2] CRAN (R 4.1.1)
#> evaluate 0.14 2019-05-28 [2] CRAN (R 4.1.1)
#> fansi 0.5.0 2021-05-25 [2] CRAN (R 4.1.1)
#> farver 2.1.0 2021-02-28 [2] CRAN (R 4.1.1)
#> fastmap 1.1.0 2021-01-25 [2] CRAN (R 4.1.1)
#> filelock 1.0.2 2018-10-05 [2] CRAN (R 4.1.1)
#> FindIT2 * 1.0.3 2021-12-30 [1] Bioconductor
#> foreach 1.5.1 2020-10-15 [2] CRAN (R 4.1.1)
#> generics 0.1.1 2021-10-25 [2] CRAN (R 4.1.1)
#> GenomeInfoDb * 1.30.0 2021-12-30 [2] Bioconductor
#> GenomeInfoDbData 1.2.7 2021-11-09 [2] Bioconductor
#> GenomicAlignments 1.30.0 2021-12-30 [2] Bioconductor
#> GenomicFeatures * 1.46.3 2021-12-30 [2] Bioconductor
#> GenomicRanges * 1.46.1 2021-12-30 [2] Bioconductor
#> ggplot2 * 3.3.5 2021-06-25 [2] CRAN (R 4.1.1)
#> ggrepel 0.9.1 2021-01-15 [2] CRAN (R 4.1.1)
#> glmnet 4.1-3 2021-11-02 [2] CRAN (R 4.1.2)
#> glue 1.6.0 2021-12-17 [2] CRAN (R 4.1.2)
#> gtable 0.3.0 2019-03-25 [2] CRAN (R 4.1.1)
#> highr 0.9 2021-04-16 [2] CRAN (R 4.1.1)
#> hms 1.1.1 2021-09-26 [2] CRAN (R 4.1.1)
#> htmltools 0.5.2 2021-08-25 [2] CRAN (R 4.1.1)
#> httr 1.4.2 2020-07-20 [2] CRAN (R 4.1.1)
#> IRanges * 2.28.0 2021-12-30 [2] Bioconductor
#> iterators 1.0.13 2020-10-15 [2] CRAN (R 4.1.1)
#> jquerylib 0.1.4 2021-04-26 [2] CRAN (R 4.1.1)
#> jsonlite 1.7.2 2020-12-09 [2] CRAN (R 4.1.1)
#> KEGGREST 1.34.0 2021-12-30 [2] Bioconductor
#> knitr 1.37 2021-12-16 [2] CRAN (R 4.1.2)
#> labeling 0.4.2 2020-10-20 [2] CRAN (R 4.1.1)
#> lattice 0.20-45 2021-09-22 [2] CRAN (R 4.1.2)
#> lifecycle 1.0.1 2021-09-24 [2] CRAN (R 4.1.1)
#> magrittr 2.0.1 2020-11-17 [2] CRAN (R 4.1.1)
#> Matrix 1.4-0 2021-12-08 [2] CRAN (R 4.1.2)
#> MatrixGenerics * 1.6.0 2021-12-30 [2] Bioconductor
#> matrixStats * 0.61.0 2021-09-17 [2] CRAN (R 4.1.1)
#> memoise 2.0.1 2021-11-26 [2] CRAN (R 4.1.2)
#> mgcv 1.8-38 2021-10-06 [2] CRAN (R 4.1.2)
#> MultiAssayExperiment 1.20.0 2021-12-30 [2] Bioconductor
#> munsell 0.5.0 2018-06-12 [2] CRAN (R 4.1.1)
#> nlme 3.1-153 2021-09-07 [2] CRAN (R 4.1.2)
#> patchwork 1.1.1 2020-12-17 [2] CRAN (R 4.1.1)
#> pillar 1.6.4 2021-10-18 [2] CRAN (R 4.1.1)
#> pkgconfig 2.0.3 2019-09-22 [2] CRAN (R 4.1.1)
#> plyr 1.8.6 2020-03-03 [2] CRAN (R 4.1.1)
#> png 0.1-7 2013-12-03 [2] CRAN (R 4.1.1)
#> prettyunits 1.1.1 2020-01-24 [2] CRAN (R 4.1.1)
#> progress 1.2.2 2019-05-16 [2] CRAN (R 4.1.1)
#> purrr 0.3.4 2020-04-17 [2] CRAN (R 4.1.1)
#> qvalue 2.26.0 2021-12-30 [2] Bioconductor
#> R6 2.5.1 2021-08-19 [2] CRAN (R 4.1.1)
#> rappdirs 0.3.3 2021-01-31 [2] CRAN (R 4.1.1)
#> Rcpp 1.0.7 2021-07-07 [2] CRAN (R 4.1.1)
#> RCurl 1.98-1.5 2021-09-17 [2] CRAN (R 4.1.1)
#> reshape2 1.4.4 2020-04-09 [2] CRAN (R 4.1.1)
#> restfulr 0.0.13 2017-08-06 [2] CRAN (R 4.1.1)
#> rjson 0.2.20 2018-06-08 [2] CRAN (R 4.1.1)
#> rlang 0.4.12 2021-10-18 [2] CRAN (R 4.1.1)
#> rmarkdown 2.11 2021-09-14 [2] CRAN (R 4.1.1)
#> Rsamtools 2.10.0 2021-12-30 [2] Bioconductor
#> RSQLite 2.2.9 2021-12-06 [2] CRAN (R 4.1.2)
#> rtracklayer 1.54.0 2021-12-30 [2] Bioconductor
#> S4Vectors * 0.32.3 2021-12-30 [2] Bioconductor
#> sass 0.4.0 2021-05-12 [2] CRAN (R 4.1.1)
#> scales 1.1.1 2020-05-11 [2] CRAN (R 4.1.1)
#> sessioninfo * 1.2.2 2021-12-06 [2] CRAN (R 4.1.2)
#> shape 1.4.6 2021-05-19 [2] CRAN (R 4.1.1)
#> stringi 1.7.6 2021-11-29 [2] CRAN (R 4.1.2)
#> stringr 1.4.0 2019-02-10 [2] CRAN (R 4.1.1)
#> SummarizedExperiment * 1.24.0 2021-12-30 [2] Bioconductor
#> survival 3.2-13 2021-08-24 [2] CRAN (R 4.1.2)
#> tibble 3.1.6 2021-11-07 [2] CRAN (R 4.1.2)
#> tidyr 1.1.4 2021-09-27 [2] CRAN (R 4.1.1)
#> tidyselect 1.1.1 2021-04-30 [2] CRAN (R 4.1.1)
#> TxDb.Athaliana.BioMart.plantsmart28 * 3.2.2 2021-11-09 [2] Bioconductor
#> utf8 1.2.2 2021-07-24 [2] CRAN (R 4.1.1)
#> vctrs 0.3.8 2021-04-29 [2] CRAN (R 4.1.1)
#> withr 2.4.3 2021-11-30 [2] CRAN (R 4.1.2)
#> xfun 0.29 2021-12-14 [2] CRAN (R 4.1.2)
#> XML 3.99-0.8 2021-09-17 [2] CRAN (R 4.1.1)
#> xml2 1.3.3 2021-11-30 [2] CRAN (R 4.1.2)
#> XVector 0.34.0 2021-12-30 [2] Bioconductor
#> yaml 2.2.1 2020-02-01 [2] CRAN (R 4.1.1)
#> zlibbioc 1.40.0 2021-12-30 [2] Bioconductor
#>
#> [1] C:/Users/biocbuild/bbs-3.14-bioc/tmpdir/RtmpCsvUSk/Rinstf5c3ac0562
#> [2] C:/Users/biocbuild/bbs-3.14-bioc/R/library
#>
#> ----------------------------------------------------------------------------------------------------------------------References
Bruse, Niklas, and Simon J. van Heeringen. 2018. “GimmeMotifs: An Analysis Framework for Transcription Factor Motif Analysis.” Preprint. Bioinformatics. https://doi.org/10.1101/474403.
Li, Shaojuan, Changxin Wan, Rongbin Zheng, Jingyu Fan, Xin Dong, Clifford A Meyer, and X Shirley Liu. 2019. “Cistrome-GO: A Web Server for Functional Enrichment Analysis of Transcription Factor ChIP-Seq Peaks.” Nucleic Acids Research 47 (July): W206–W211. https://doi.org/10.1093/nar/gkz332.
Liu, Chang, Congmao Wang, George Wang, Claude Becker, Maricris Zaidem, and Detlef Weigel. 2016. “Genome-Wide Analysis of Chromatin Packing in Arabidopsis Thaliana at Single-Gene Resolution.” Genome Research 26 (August): 1057–68. https://doi.org/10.1101/gr.204032.116.
Qin, Qian, Jingyu Fan, Rongbin Zheng, Changxin Wan, Shenglin Mei, Qiu Wu, Hanfei Sun, et al. 2020. “Lisa: Inferring Transcriptional Regulators Through Integrative Modeling of Public Chromatin Accessibility and ChIP-Seq Data.” Genome Biology 21 (December): 32. https://doi.org/10.1186/s13059-020-1934-6.
The FANTOM Consortium, and Riken Omics Science Center. 2009. “The Transcriptional Network That Controls Growth Arrest and Differentiation in a Human Myeloid Leukemia Cell Line.” Nature Genetics 41 (May): 553–62. https://doi.org/10.1038/ng.375.
Wang, Fu-Xiang, Guan-Dong Shang, Lian-Yu Wu, Zhou-Geng Xu, Xin-Yan Zhao, and Jia-Wei Wang. 2020. “Chromatin Accessibility Dynamics and a Hierarchical Transcriptional Regulatory Network Structure for Plant Somatic Embryogenesis.” Developmental Cell 54 (September): 742–757.e8. https://doi.org/10.1016/j.devcel.2020.07.003.
Wang, Su, Hanfei Sun, Jian Ma, Chongzhi Zang, Chenfei Wang, Juan Wang, Qianzi Tang, Clifford A Meyer, Yong Zhang, and X Shirley Liu. 2013. “Target Analysis by Integration of Transcriptome and ChIP-Seq Data with BETA.” Nature Protocols 8 (December): 2502–15. https://doi.org/10.1038/nprot.2013.150.
Wang, Su, Chongzhi Zang, Tengfei Xiao, Jingyu Fan, Shenglin Mei, Qian Qin, Qiu Wu, et al. 2016. “Modeling Cis-Regulation with a Compendium of Genome-Wide Histone H3K27ac Profiles.” Genome Research 26 (October): 1417–29. https://doi.org/10.1101/gr.201574.115.