Package 'FindIT2' reference manual

Title:	find influential TF and Target based on multi-omics data
Description:	This package implements functions to find influential TF and target based on different input type. 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.
Authors:	Guandong Shang [aut, cre]
Maintainer:	Guandong Shang <[email protected]>
License:	Artistic-2.0
Version:	1.13.0
Built:	2024-12-19 03:24:53 UTC
Source:	https://github.com/bioc/FindIT2

ATAC normCount of E50h-72h in Chr5

Description

ATAC normCount of E50h-72h in Chr5

Usage

data(ATAC_normCount)
data(ATAC_normCount)

Format

A matrix

Source

https://doi.org/10.1016/j.devcel.2020.07.003

calcRP_coverage

Description

calculate regulatory potential using big wig files, which is useful for ATAC or H3K27ac histone modification data.

Usage

calcRP_coverage(
  bwFile,
  Txdb,
  gene_included,
  Chrs_included,
  decay_dist = 1000,
  scan_dist = 20000,
  verbose = TRUE
)
calcRP_coverage(
  bwFile,
  Txdb,
  gene_included,
  Chrs_included,
  decay_dist = 1000,
  scan_dist = 20000,
  verbose = TRUE
)

Arguments

`bwFile`	bw file
`Txdb`	Txdb
`gene_included`	a character vector which represent gene set which you want to calculate RP for
`Chrs_included`	a character vector which represent chromosomes where you want to calculate gene RP in
`decay_dist`	decay distance
`scan_dist`	scan distance
`verbose`	whether you want to report detailed running message

Details

Please note that because of rtracklayer::import has some issue on 32 bit R of windows, so the calcRP_coverage can not work on this system. But if your R is 64 bit, which now be applied on the most windows R, this function still work.

Value

data.frame

Examples

if (.Platform$OS.type != "windows" & require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    bwFile <- system.file("extdata", "E50h_sampleChr5.bw", package = "FindIT2")

    RP_df <- calcRP_coverage(
        bwFile = bwFile,
        Txdb = Txdb,
        Chrs_included = "Chr5"
    )

}
if (.Platform$OS.type != "windows" & require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    bwFile <- system.file("extdata", "E50h_sampleChr5.bw", package = "FindIT2")

    RP_df <- calcRP_coverage(
        bwFile = bwFile,
        Txdb = Txdb,
        Chrs_included = "Chr5"
    )

}

calcRP_region

Description

calculate regulatory potential based on mm_geneScan result and peakCount matrix, which is useful for ATAC or H3K27ac histone modification data.

Usage

calcRP_region(
  mmAnno,
  peakScoreMt,
  Txdb,
  Chrs_included,
  decay_dist = 1000,
  log_transform = FALSE,
  verbose = TRUE
)
calcRP_region(
  mmAnno,
  peakScoreMt,
  Txdb,
  Chrs_included,
  decay_dist = 1000,
  log_transform = FALSE,
  verbose = TRUE
)

Arguments

`mmAnno`	the annotated GRange object from mm_geneScan
`peakScoreMt`	peak count matrix. The rownames are feature_id in mmAnno, while the colnames are sample names
`Txdb`	Txdb
`Chrs_included`	a character vector which represent chromosome where you want to calculate gene RP in. If Chromosome is not be set, it will calculate gene RP in all chromosomes in Txdb.
`decay_dist`	decay distance
`log_transform`	whether you want to log and norm your RP
`verbose`	whether you want to report detailed running message

Value

a MultiAssayExperiment object containg detailed peak-RP-gene relationship and sumRP info

Examples


if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("ATAC_normCount")
    library(SummarizedExperiment)
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    regionRP <- calcRP_region(
        mmAnno = mmAnno,
        peakScoreMt = ATAC_normCount,
        Txdb = Txdb,
        Chrs_included = "Chr5"
    )

    sumRP <- assays(regionRP)$sumRP
    fullRP <- assays(regionRP)$fullRP
}

if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("ATAC_normCount")
    library(SummarizedExperiment)
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    regionRP <- calcRP_region(
        mmAnno = mmAnno,
        peakScoreMt = ATAC_normCount,
        Txdb = Txdb,
        Chrs_included = "Chr5"
    )

    sumRP <- assays(regionRP)$sumRP
    fullRP <- assays(regionRP)$fullRP
}

calcRP_TFHit

Description

calculate regulatory potential based on ChIP-Seq peak data, which is useful for TF ChIP-seq data.

Usage

calcRP_TFHit(
  mmAnno,
  Txdb,
  decay_dist = 1000,
  report_fullInfo = FALSE,
  verbose = TRUE
)
calcRP_TFHit(
  mmAnno,
  Txdb,
  decay_dist = 1000,
  report_fullInfo = FALSE,
  verbose = TRUE
)

Arguments

`mmAnno`	the annotated GRange object from mm_geneScan
`Txdb`	Txdb
`decay_dist`	decay distance
`report_fullInfo`	whether you want to report full peak-RP-gene info
`verbose`	whether you want to report detailed running message

Details

If your origin peak_GR of mmAnno have column named feature_score, calcRP_TFHit will consider this column when calculating sumRP. Otherwise, it will consider all peak Hit feature_score is 1.

Value

if report_fullInfo is TRUE, it will output GRanges with detailed info. While FALSE, it will output data frame

Examples

if (require(TxDb.Athaliana.BioMart.plantsmart28)){
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    # if you just want to get RP_df, you can set report_fullInfo FALSE
    fullRP_hit <- calcRP_TFHit(
        mmAnno = mmAnno,
        Txdb = Txdb,
        report_fullInfo = TRUE
    )

    RP_df <- metadata(fullRP_hit)$peakRP_gene

}
if (require(TxDb.Athaliana.BioMart.plantsmart28)){
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    # if you just want to get RP_df, you can set report_fullInfo FALSE
    fullRP_hit <- calcRP_TFHit(
        mmAnno = mmAnno,
        Txdb = Txdb,
        report_fullInfo = TRUE
    )

    RP_df <- metadata(fullRP_hit)$peakRP_gene

}

enhancerPromoterCor

Description

enhancerPromoterCor

Usage

enhancerPromoterCor(
  peak_GR,
  Txdb,
  up_scanPromoter = 500,
  down_scanPromoter = 500,
  up_scanEnhancer = 20000,
  down_scanEnhacner = 20000,
  peakScoreMt,
  parallel = FALSE,
  verbose = TRUE
)
enhancerPromoterCor(
  peak_GR,
  Txdb,
  up_scanPromoter = 500,
  down_scanPromoter = 500,
  up_scanEnhancer = 20000,
  down_scanEnhacner = 20000,
  peakScoreMt,
  parallel = FALSE,
  verbose = TRUE
)

Arguments

`peak_GR`	peak GRange with a column named feature_id representing you peak name
`Txdb`	Txdb
`up_scanPromoter`	the scan distance which is used to scan nearest promoter
`down_scanPromoter`	the scan distance which is used to scan nearest promoter
`up_scanEnhancer`	the scan distance which is used to scan feature
`down_scanEnhacner`	the scan distance which is used to scan feature
`peakScoreMt`	peak count matrix. The rownames are feature_id in peak_GR
`parallel`	whether you want to parallel to speed up
`verbose`	whether you want to report detailed running message

Value

mmAnno with Cor, pvalue,padj,qvalue column

Examples


if (require(TxDb.Athaliana.BioMart.plantsmart28)){
    data("ATAC_normCount")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)[1:100]
    mm_ePLink <- enhancerPromoterCor(
    peak_GR = peak_GR,
    Txdb = Txdb,
    peakScoreMt = ATAC_normCount,
    parallel = FALSE)
}


if (require(TxDb.Athaliana.BioMart.plantsmart28)){
    data("ATAC_normCount")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)[1:100]
    mm_ePLink <- enhancerPromoterCor(
    peak_GR = peak_GR,
    Txdb = Txdb,
    peakScoreMt = ATAC_normCount,
    parallel = FALSE)
}

findI(nfluential)T(F)_enrichFisher

Description

find influential TF of your input peak set compared with your whole peak sets based on TF ChIP-Seq or motif data.

Usage

findIT_enrichFisher(input_feature_id, peak_GR, TF_GR_database)
findIT_enrichFisher(input_feature_id, peak_GR, TF_GR_database)

Arguments

`input_feature_id`	a character vector which represent peaks set which you want to find influential TF for
`peak_GR`	a GRange object represent your whole feature location with a column named feature_id, which your input_feature_id should a part of it.
`TF_GR_database`	TF peak GRange with a column named TF_id representing you TF name

Value

data.frame

Examples

data("test_featureSet")
peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
peak_GR <- loadPeakFile(peak_path)
ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
ChIP_peak_GR$TF_id <- "AT1G28300"

 result_findIT_enrichFisher <- findIT_enrichFisher(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    TF_GR_database = ChIP_peak_GR
)
data("test_featureSet")
peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
peak_GR <- loadPeakFile(peak_path)
ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
ChIP_peak_GR$TF_id <- "AT1G28300"

 result_findIT_enrichFisher <- findIT_enrichFisher(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    TF_GR_database = ChIP_peak_GR
)

findIT_enrichWilcox

Description

findIT_enrichWilcox

Usage

findIT_enrichWilcox(
  input_feature_id,
  peak_GR,
  TF_GR_database,
  background_peaks = NULL,
  background_number = 3000
)
findIT_enrichWilcox(
  input_feature_id,
  peak_GR,
  TF_GR_database,
  background_peaks = NULL,
  background_number = 3000
)

Arguments

`input_feature_id`	a character vector which represent peaks set which you want to find influential TF for
`peak_GR`	a GRange object represent your whole feature location with a column named feature_id, which your input_feature_id should a part of it.
`TF_GR_database`	TF peak GRange with a column named TF_id representing you TF name
`background_peaks`	a character vector which represent background peak set. If you do not assign background peaks, program will sample background_number peaks as background peaks from all feature_id in your peak_GR
`background_number`	background peaks number

Value

data.frame

Examples

data("test_featureSet")
peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
peak_GR <- loadPeakFile(peak_path)
ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
ChIP_peak_GR$TF_id <- "AT1G28300"

result_findIT_enrichWilcox <- findIT_enrichWilcox(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    TF_GR_database = ChIP_peak_GR
)
data("test_featureSet")
peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
peak_GR <- loadPeakFile(peak_path)
ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
ChIP_peak_GR$TF_id <- "AT1G28300"

result_findIT_enrichWilcox <- findIT_enrichWilcox(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    TF_GR_database = ChIP_peak_GR
)

findIT_MARA

Description

findIT_MARA

Usage

findIT_MARA(
  input_feature_id,
  peak_GR,
  peakScoreMt,
  TF_GR_database,
  log = TRUE,
  meanScale = TRUE,
  output = c("coef", "cor"),
  verbose = TRUE
)
findIT_MARA(
  input_feature_id,
  peak_GR,
  peakScoreMt,
  TF_GR_database,
  log = TRUE,
  meanScale = TRUE,
  output = c("coef", "cor"),
  verbose = TRUE
)

Arguments

`input_feature_id`	a character vector which represent peaks set which you want to find influential TF for
`peak_GR`	a GRange object represent your whole feature location with a column named feature_id, which your input_feature_id should a part of it.
`peakScoreMt`	peak count matrix.
`TF_GR_database`	TF peak GRange with a column named TF_id representing you TF name. If you have TF_score column, MARA will consider it. otherwise, MARA will consider each hit is 1.
`log`	whether you want to log your peakScoreMt
`meanScale`	whether you want to mean-centered per row
`output`	one of 'coef' and 'cor'. Default is coef
`verbose`	whether you want to report detailed running message

Value

a data.frame

Examples


data("ATAC_normCount")
data("test_featureSet")

peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
peak_GR <- loadPeakFile(peak_path)

ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
ChIP_peak_GR$TF_id <- "AT1G28300"

set.seed(20160806)

result_findIT_MARA <- findIT_MARA(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    peakScoreMt = ATAC_normCount,
    TF_GR_database = ChIP_peak_GR
)

data("ATAC_normCount")
data("test_featureSet")

peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
peak_GR <- loadPeakFile(peak_path)

ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
ChIP_peak_GR$TF_id <- "AT1G28300"

set.seed(20160806)

result_findIT_MARA <- findIT_MARA(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    peakScoreMt = ATAC_normCount,
    TF_GR_database = ChIP_peak_GR
)

findI(nfluential)T(F)_regionRP

Description

find Influential TF of your input gene set based on regulatory potential data and TF ChIP-Seq or motif data

Usage

findIT_regionRP(
  regionRP,
  Txdb,
  TF_GR_database,
  input_genes,
  background_genes = NULL,
  background_number = 3000,
  verbose = TRUE
)
findIT_regionRP(
  regionRP,
  Txdb,
  TF_GR_database,
  input_genes,
  background_genes = NULL,
  background_number = 3000,
  verbose = TRUE
)

Arguments

`regionRP`	the MultiAssayExperiment object from calcRP_region
`Txdb`	Txdb
`TF_GR_database`	TF peak GRange with a column named TF_id representing you TF name
`input_genes`	a character vector which represent genes set which you want to find influential TF for
`background_genes`	a character vector which represent background genes set. If you do not assign background gene , program will sample background_number genes as background genes from all gene sets.
`background_number`	background genes number
`verbose`	whether you want to report detailed running message

Value

a MultiAssayExperiment object containg detailed TF-percent and TF-pvalue

Examples

if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("ATAC_normCount")
    data("test_geneSet")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)

    ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
    ChIP_peak_GR$TF_id <- "AT1G28300"

    mmAnno <- mm_geneScan(peak_GR, Txdb)

    regionRP <- calcRP_region(
        mmAnno = mmAnno,
        peakScoreMt = ATAC_normCount,
        Txdb = Txdb,
        Chrs_included = "Chr5"
    )

    set.seed(20160806)
    result_findIT_regionRP <- findIT_regionRP(
        regionRP = regionRP,
        Txdb = Txdb,
        TF_GR_database = ChIP_peak_GR,
        input_genes = test_geneSet,
        background_number = 3000
    )

}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("ATAC_normCount")
    data("test_geneSet")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)

    ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
    ChIP_peak_GR$TF_id <- "AT1G28300"

    mmAnno <- mm_geneScan(peak_GR, Txdb)

    regionRP <- calcRP_region(
        mmAnno = mmAnno,
        peakScoreMt = ATAC_normCount,
        Txdb = Txdb,
        Chrs_included = "Chr5"
    )

    set.seed(20160806)
    result_findIT_regionRP <- findIT_regionRP(
        regionRP = regionRP,
        Txdb = Txdb,
        TF_GR_database = ChIP_peak_GR,
        input_genes = test_geneSet,
        background_number = 3000
    )

}

findI(nfluential)T(F)_TFHit

Description

find influential TF of your input gene set based on TF ChIP-Seq or motif data

Usage

findIT_TFHit(
  input_genes,
  Txdb,
  TF_GR_database,
  scan_dist = 20000,
  decay_dist = 1000,
  Chrs_included,
  background_genes = NULL,
  background_number = 3000,
  verbose = TRUE
)
findIT_TFHit(
  input_genes,
  Txdb,
  TF_GR_database,
  scan_dist = 20000,
  decay_dist = 1000,
  Chrs_included,
  background_genes = NULL,
  background_number = 3000,
  verbose = TRUE
)

Arguments

`input_genes`	a character vector which represent genes set which you want to find influential TF for
`Txdb`	Txdb
`TF_GR_database`	TF peak GRange with a column named TF_id representing you TF name
`scan_dist`	scan distance
`decay_dist`	decay distance
`Chrs_included`	a character vector represent chromosomes which you want to sample background genes from
`background_genes`	a character vector which represent background genes set. If you do not assign background gene , program will sample background_number genes as background genes from all gene sets.
`background_number`	background genes number
`verbose`	whether you want to report detailed running message

Value

data.frame

Examples


if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("test_geneSet")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
    ChIP_peak_GR$TF_id <- "AT1G28300"

    set.seed(20160806)
    result_findIT_TFHit <- findIT_TFHit(
        input_genes = test_geneSet,
        Txdb = Txdb,
        TF_GR_database = ChIP_peak_GR
    )

}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("test_geneSet")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
    ChIP_peak_GR$TF_id <- "AT1G28300"

    set.seed(20160806)
    result_findIT_TFHit <- findIT_TFHit(
        input_genes = test_geneSet,
        Txdb = Txdb,
        TF_GR_database = ChIP_peak_GR
    )

}

findI(nfluential)T(F)_T(F)T(arget)Pair

Description

find influential TF of your input gene set based on public TF-Target data

Usage

findIT_TTPair(
  input_genes,
  TF_target_database,
  gene_background = NULL,
  TFHit_min = 5,
  TFHit_max = 10000
)
findIT_TTPair(
  input_genes,
  TF_target_database,
  gene_background = NULL,
  TFHit_min = 5,
  TFHit_max = 10000
)

Arguments

`input_genes`	a character vector which represent genes set which you want to find influential TF for
`TF_target_database`	TF_target pair data with two column named TF_id and target_gene
`gene_background`	a character vector represent your bakcaground gene. If you do not assign background gene, program will consider all target gene as background
`TFHit_min`	minimal size of target gene regulated by TF
`TFHit_max`	maximal size of target gene regulated by TF

Value

data.frame

Examples

data("TF_target_database")
data("test_geneSet")

result_findIT_TTPair <- findIT_TTPair(
    input_genes = test_geneSet,
    TF_target_database = TF_target_database
)
data("TF_target_database")
data("test_geneSet")

result_findIT_TTPair <- findIT_TTPair(
    input_genes = test_geneSet,
    TF_target_database = TF_target_database
)

getAssocPairNumber

Description

get associated peak number of gene and vice verse.

Usage

getAssocPairNumber(
  mmAnno,
  output_type = c("gene_id", "feature_id"),
  output_summary = FALSE
)
getAssocPairNumber(
  mmAnno,
  output_type = c("gene_id", "feature_id"),
  output_summary = FALSE
)

Arguments

`mmAnno`	the annotated GRange object from mm_geneScan or mm_nearestGene
`output_type`	one of 'gene_id' or 'feature_id'
`output_summary`	whether you want to detailed info

Value

data.frame

Examples

if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_nearestGene(peak_GR, Txdb)

    getAssocPairNumber(peakAnno)

}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_nearestGene(peak_GR, Txdb)

    getAssocPairNumber(peakAnno)

}

integrate_ChIP_RNA

Description

integrate ChIP-Seq and RNA-Seq data to find TF target genes

Usage

integrate_ChIP_RNA(
  result_geneRP,
  result_geneDiff,
  lfc_threshold = 1,
  padj_threshold = 0.05
)
integrate_ChIP_RNA(
  result_geneRP,
  result_geneDiff,
  lfc_threshold = 1,
  padj_threshold = 0.05
)

Arguments

`result_geneRP`	the simplify result from calcRP_TFHit(report_fullInfo = FALSE) or RP_df <- metadata(fullRP_hit)$peakRP_gene.
`result_geneDiff`	the result from RNA diff result with three column gene_id, log2FoldChange, padj
`lfc_threshold`	the threshold which decide significant genes
`padj_threshold`	the threshold which decide significant genes

Value

a ggplot object if having significant genes in your result. If not, it will report a data.frame with integrated info.

Examples

if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("RNADiff_LEC2_GR")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    result_geneRP <- calcRP_TFHit(
        mmAnno = mmAnno,
        Txdb = Txdb
    )
    # output a plot
    merge_data <- integrate_ChIP_RNA(
        result_geneRP = result_geneRP,
        result_geneDiff = RNADiff_LEC2_GR
    )
    # if you want to extract merge target data
    target_data <- merge_data$data

}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("RNADiff_LEC2_GR")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    result_geneRP <- calcRP_TFHit(
        mmAnno = mmAnno,
        Txdb = Txdb
    )
    # output a plot
    merge_data <- integrate_ChIP_RNA(
        result_geneRP = result_geneRP,
        result_geneDiff = RNADiff_LEC2_GR
    )
    # if you want to extract merge target data
    target_data <- merge_data$data

}

integrate_replicates

Description

integrate value from replicates

Usage

integrate_replicates(
  mt,
  colData,
  fun = NULL,
  type = c("value", "rank", "rank_zscore", "pvalue")
)
integrate_replicates(
  mt,
  colData,
  fun = NULL,
  type = c("value", "rank", "rank_zscore", "pvalue")
)

Arguments

`mt`	value matrix
`colData`	a data.frame with a single column named with "type". Rows of colData correspond to columns of mt.
`fun`	the function you want to use. If set NULL, program will decide integrate method according to your 'type' parameter.
`type`	one of 'value', 'rank', 'rank_zscore', pvalue'. value will use mean to integrate replicates, rank will use product, rank_zscore will use Stouffer's method and pvalue will use CCT(Cauchy distribution)

Value

matrix

Examples

mt <- matrix(runif(100, 0, 1), nrow = 10)
colnames(mt) <- paste0(paste0("type", 1:5), "_", rep(1:2, 5))
rownames(mt) <- paste0("TF", 1:10)

colData <- data.frame(
    type = gsub("_[0-9]", "", colnames(mt)),
    row.names = colnames(mt)
)


integrate_replicates(mt, colData, type = "value")
mt <- matrix(runif(100, 0, 1), nrow = 10)
colnames(mt) <- paste0(paste0("type", 1:5), "_", rep(1:2, 5))
rownames(mt) <- paste0("TF", 1:10)

colData <- data.frame(
    type = gsub("_[0-9]", "", colnames(mt)),
    row.names = colnames(mt)
)


integrate_replicates(mt, colData, type = "value")

jaccard_findIT_enrichFisher

Description

jaccard_findIT_enrichFisher

Usage

jaccard_findIT_enrichFisher(
  input_feature_id,
  peak_GR,
  TF_GR_database,
  input_TF_id
)
jaccard_findIT_enrichFisher(
  input_feature_id,
  peak_GR,
  TF_GR_database,
  input_TF_id
)

Arguments

`input_feature_id`	a character vector which represent peaks set which you want to find influential TF for (same as your find_IT_enrichFisher parameter)
`peak_GR`	a GRange object represent your whole feature location with a column named feature_id, which your input_feature_id should a part of it.
`TF_GR_database`	TF peak GRange with a column named TF_id representing you TF name
`input_TF_id`	TF_id which you want to calculate jaccard index for

Value

jaccard similarity matrix

Examples

data("test_featureSet")
peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
peak_GR <- loadPeakFile(peak_path)

ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
ChIP_peak_GR$TF_id <- "AT1G28300"
result_findIT_enrichFisher <- findIT_enrichFisher(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    TF_GR_database = ChIP_peak_GR
)

jaccard_findIT_enrichFisher(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    TF_GR_database = ChIP_peak_GR,
    input_TF_id = result_findIT_enrichFisher$TF_id[1]
)
data("test_featureSet")
peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
peak_GR <- loadPeakFile(peak_path)

ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
ChIP_peak_GR$TF_id <- "AT1G28300"
result_findIT_enrichFisher <- findIT_enrichFisher(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    TF_GR_database = ChIP_peak_GR
)

jaccard_findIT_enrichFisher(
    input_feature_id = test_featureSet,
    peak_GR = peak_GR,
    TF_GR_database = ChIP_peak_GR,
    input_TF_id = result_findIT_enrichFisher$TF_id[1]
)

jaccard_findIT_TTpair

Description

jaccard_findIT_TTpair

Usage

jaccard_findIT_TTpair(input_genes, TF_target_database, input_TF_id)
jaccard_findIT_TTpair(input_genes, TF_target_database, input_TF_id)

Arguments

`input_genes`	a character vector which represent gene set which you want to find influential TF for (same as your find_IT_TTpair parameter)
`TF_target_database`	TF_target pair data
`input_TF_id`	TF_id which you want to calculate jaccard index for

Value

jaccard similarity matrix

Examples


data("TF_target_database")
data("test_geneSet")
result_findIT_TTPair <- findIT_TTPair(
    input_genes = test_geneSet,
    TF_target_database = TF_target_database
)

jaccard_findIT_TTpair(
    input_genes = test_geneSet,
    TF_target_database = TF_target_database,
    input_TF_id = result_findIT_TTPair$TF_id[1:3]
)
data("TF_target_database")
data("test_geneSet")
result_findIT_TTPair <- findIT_TTPair(
    input_genes = test_geneSet,
    TF_target_database = TF_target_database
)

jaccard_findIT_TTpair(
    input_genes = test_geneSet,
    TF_target_database = TF_target_database,
    input_TF_id = result_findIT_TTPair$TF_id[1:3]
)

loadPeakFile

Description

read peak file and transform it into GRanges object

Usage

loadPeakFile(filePath, TFBS_database = FALSE)
loadPeakFile(filePath, TFBS_database = FALSE)

Arguments

`filePath`	peak Path
`TFBS_database`	whether your peak file is a TFBS database file. If you want the final GRanges have a column named "TF_id", you should set TFBS_database TRUE. The GRanges with TF_id can be applied in "TF_GR_database" parameter of findIT_TFHit, findIT_enrichFisher, findIT_enrichWilcox, findIT_regionRP. If FALSE, the GRanges will have a column named "feature_id", which always be the input of "peak_GR" parameter.

Details

The GRanges with TF_id always be the input of "TF_GR_database" parameter. It represents the TFBS database like motif scan result, public database ChIP-seq site and so on.

The GRanges with feature_id always be the input of "peak_GR" parameter.

Value

GRanges object with a column named feature_id or TF_id

Examples

peakfile <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
loadPeakFile(peakfile)

peakfile <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
loadPeakFile(peakfile)

mm_geneBound

Description

find related peaks of your input genes, which is useful when you want to plot volcano plot or heatmap of peaks.

Usage

mm_geneBound(peak_GR, Txdb, input_genes, verbose = TRUE, ...)
mm_geneBound(peak_GR, Txdb, input_genes, verbose = TRUE, ...)

Arguments

`peak_GR`	peak GRange with a column named feature_id representing you peak name
`Txdb`	Txdb
`input_genes`	a character vector which represent genes set which you want to find related peak for
`verbose`	whether you want to report detailed running message
`...`	additional arguments in distanceToNearest

Value

data.frame with three column: related peak id, your input gene id, and distance

Examples

if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peak_pair <- mm_geneBound(peak_GR, Txdb, c("AT5G01015", "AT5G67570"))
    peak_pair
}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peak_pair <- mm_geneBound(peak_GR, Txdb, c("AT5G01015", "AT5G67570"))
    peak_pair
}

mm_geneScan

Description

Annotate peaks using geneScan mode, which means every peak have more than one related genes.

Usage

mm_geneScan(
  peak_GR,
  Txdb,
  upstream = 3000,
  downstream = 3000,
  reportGeneInfo = FALSE,
  verbose = TRUE,
  ...
)
mm_geneScan(
  peak_GR,
  Txdb,
  upstream = 3000,
  downstream = 3000,
  reportGeneInfo = FALSE,
  verbose = TRUE,
  ...
)

Arguments

`peak_GR`	peak GRange with a column named feature_id representing you peak name
`Txdb`	Txdb
`upstream`	distance to start site(upstream)
`downstream`	distance to start site(downstream)
`reportGeneInfo`	whether you want to add gene info
`verbose`	whether you want to report detailed running message
`...`	additional arguments in findOverlaps

Value

Granges object with annotated info

Examples

if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_geneScan(peak_GR, Txdb)
    peakAnno
}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_geneScan(peak_GR, Txdb)
    peakAnno
}

mm_nearestGene

Description

Annotate peaks using nearest gene mode, which means every peak only have one related gene.

Usage

mm_nearestGene(peak_GR, Txdb, reportGeneInfo = FALSE, verbose = TRUE, ...)
mm_nearestGene(peak_GR, Txdb, reportGeneInfo = FALSE, verbose = TRUE, ...)

Arguments

`peak_GR`	peak GRange with a column named feature_id representing you peak name
`Txdb`	Txdb
`reportGeneInfo`	whether you want to report full gene info
`verbose`	whether you want to report detailed running message
`...`	additional arguments in distanceToNearest

Value

Granges object with annotated info

Examples


if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_nearestGene(peak_GR, Txdb)
    peakAnno
}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_nearestGene(peak_GR, Txdb)
    peakAnno
}

peakGeneCor

Description

peakGeneCor

Usage

peakGeneCor(mmAnno, peakScoreMt, geneScoreMt, parallel = FALSE, verbose = TRUE)
peakGeneCor(mmAnno, peakScoreMt, geneScoreMt, parallel = FALSE, verbose = TRUE)

Arguments

`mmAnno`	the annotated GRange object from mm_geneScan or mm_nearestGene
`peakScoreMt`	peak count matrix. The rownames are feature_id in mmAnno, while the colnames are sample names.
`geneScoreMt`	gene count matirx. The rownames are gene_id in mmAnno, while the colnames are sample names.
`parallel`	whehter you want to using bplapply to speed up calculation
`verbose`	whether you want to report detailed running message

Value

mmAnno with Cor, pvalue,padj,qvalue column

Examples


if (require(TxDb.Athaliana.BioMart.plantsmart28)){
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    data("RNA_normCount")
    data("ATAC_normCount")
    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)[1:100]
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    ATAC_colData <- data.frame(
        row.names = colnames(ATAC_normCount),
        type = gsub("_R[0-9]", "", colnames(ATAC_normCount))
    )

    ATAC_normCount_merge <- integrate_replicates(ATAC_normCount, ATAC_colData)
    RNA_colData <- data.frame(
        row.names = colnames(RNA_normCount),
        type = gsub("_R[0-9]", "", colnames(RNA_normCount))
    )
    RNA_normCount_merge <- integrate_replicates(RNA_normCount, RNA_colData)
    mmAnnoCor <- peakGeneCor(
        mmAnno = mmAnno,
        peakScoreMt = ATAC_normCount_merge,
        geneScoreMt = RNA_normCount_merge,
        parallel = FALSE
    )

    mmAnnoCor

}
if (require(TxDb.Athaliana.BioMart.plantsmart28)){
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    data("RNA_normCount")
    data("ATAC_normCount")
    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)[1:100]
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    ATAC_colData <- data.frame(
        row.names = colnames(ATAC_normCount),
        type = gsub("_R[0-9]", "", colnames(ATAC_normCount))
    )

    ATAC_normCount_merge <- integrate_replicates(ATAC_normCount, ATAC_colData)
    RNA_colData <- data.frame(
        row.names = colnames(RNA_normCount),
        type = gsub("_R[0-9]", "", colnames(RNA_normCount))
    )
    RNA_normCount_merge <- integrate_replicates(RNA_normCount, RNA_colData)
    mmAnnoCor <- peakGeneCor(
        mmAnno = mmAnno,
        peakScoreMt = ATAC_normCount_merge,
        geneScoreMt = RNA_normCount_merge,
        parallel = FALSE
    )

    mmAnnoCor

}

plot_annoDistance

Description

plot the distance distribution of mmAnno from mm_nearestGene, which helps you decide whehter your TF is promoter or enhancer dominant

Usage

plot_annoDistance(mmAnno, quantile = c(0.01, 0.99))
plot_annoDistance(mmAnno, quantile = c(0.01, 0.99))

Arguments

`mmAnno`	the annotated GRange object from mm_nearestGene
`quantile`	the quantile of distanceToTSS you want to show

Value

a ggplot2 object

Examples

if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_nearestGene(peak_GR, Txdb)
    plot_annoDistance(peakAnno)

}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_nearestGene(peak_GR, Txdb)
    plot_annoDistance(peakAnno)

}

plot_peakGeneAlias_summary

Description

plot_peakGeneAlias_summary

Usage

plot_peakGeneAlias_summary(
  mmAnno,
  mmAnno_corFilter = NULL,
  output_type = c("gene_id", "feature_id"),
  fillColor = "#ca6b67"
)
plot_peakGeneAlias_summary(
  mmAnno,
  mmAnno_corFilter = NULL,
  output_type = c("gene_id", "feature_id"),
  fillColor = "#ca6b67"
)

Arguments

`mmAnno`	the annotated GRange object from mm_geneScan or mm_nearestGene
`mmAnno_corFilter`	the filter mmAnno object according to p-value or cor, defalut is NULL
`output_type`	one of 'gene_id' or 'feature_id'
`fillColor`	the bar plot color

Value

a ggplot object

Examples

if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_nearestGene(peak_GR, Txdb)

    plot_peakGeneAlias_summary(peakAnno)

}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))

    peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)
    peakAnno <- mm_nearestGene(peak_GR, Txdb)

    plot_peakGeneAlias_summary(peakAnno)

}

plot_peakGeneCor

Description

plot_peakGeneCor

Usage

plot_peakGeneCor(
  mmAnnoCor,
  select_gene,
  addLine = TRUE,
  addFullInfo = TRUE,
  sigShow = c("pvalue", "padj", "qvalue")
)
plot_peakGeneCor(
  mmAnnoCor,
  select_gene,
  addLine = TRUE,
  addFullInfo = TRUE,
  sigShow = c("pvalue", "padj", "qvalue")
)

Arguments

`mmAnnoCor`	the annotated GRange object from peakGeneCor or enhancerPromoterCor
`select_gene`	a gene_id which you want to show
`addLine`	whether add cor line
`addFullInfo`	whether add full feature info on plot
`sigShow`	one of 'pvalue' 'padj' 'qvalue'

Value

ggplot2 object

Examples


if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("RNA_normCount")
    data("ATAC_normCount")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)[1:100]
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    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
    mmAnnoCor <- peakGeneCor(
        mmAnno = mmAnno,
        peakScoreMt = ATAC_normCount_merge,
        geneScoreMt = RNA_normCount_merge,
        parallel = FALSE
    )

    plot_peakGeneCor(mmAnnoCor, select_gene = "AT5G01010")

}
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    data("RNA_normCount")
    data("ATAC_normCount")
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    peak_GR <- loadPeakFile(peak_path)[1:100]
    mmAnno <- mm_geneScan(peak_GR, Txdb)

    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
    mmAnnoCor <- peakGeneCor(
        mmAnno = mmAnno,
        peakScoreMt = ATAC_normCount_merge,
        geneScoreMt = RNA_normCount_merge,
        parallel = FALSE
    )

    plot_peakGeneCor(mmAnnoCor, select_gene = "AT5G01010")

}

RNA normCount of E50h-72h in Chr5

Description

RNA normCount of E50h-72h in Chr5

Usage

data(RNA_normCount)
data(RNA_normCount)

Format

A matrix

Source

https://doi.org/10.1016/j.devcel.2020.07.003

RNA diff result from LEC2_GR VS LEC2_DMSO

Description

RNA diff result from LEC2_GR VS LEC2_DMSO

Usage

data(RNADiff_LEC2_GR)
data(RNADiff_LEC2_GR)

Format

a data frame

Source

https://doi.org/10.1016/j.devcel.2020.07.003

test_featureSet

Description

test_featureSet

Usage

data(test_featureSet)
data(test_featureSet)

Format

character vector represent your interesting feature_id set

Details

For the detailed progress producing input_feature_id, you can see ?test_geneSet

test_geneSet

Description

test_geneSet

Usage

data(test_geneSet)
data(test_geneSet)

Format

character vector represent your interesting gene set

Examples

## Not run: 
# source
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    library(FindIT2)
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
    ATAC_peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    ATAC_peak_GR <- loadPeakFile(ATAC_peak_path)

    mmAnno_geneScan <- mm_geneScan(
        peak_GR = ChIP_peak_GR,
        Txdb = Txdb,
        upstream = 2e4,
        downstream = 2e4
    )

    peakRP_gene <- calcRP_TFHit(
        mmAnno = mmAnno_geneScan,
        Txdb = Txdb,
        report_fullInfo = FALSE
    )

    data("RNADiff_LEC2_GR")
     merge_result <- integrate_ChIP_RNA(
        result_geneRP = peakRP_gene,
        result_geneDiff = RNADiff_LEC2_GR
    )

    target_result <- merge_result$data
    test_geneSet <- target_result$gene_id[1:50]

    related_peaks <- mm_geneBound(
        peak_GR = ATAC_peak_GR,
        Txdb = Txdb,
        input_genes = test_geneSet
    )
    test_featureSet <- unique(related_peaks$feature_id)
    # save(test_geneSet, file = "data/test_geneSet.rda", version = 2)
    # save(test_featureSet, file = "data/test_featureSet.rda", version = 2)
}

## End(Not run)

## Not run: 
# source
if (require(TxDb.Athaliana.BioMart.plantsmart28)) {
    library(FindIT2)
    Txdb <- TxDb.Athaliana.BioMart.plantsmart28
    seqlevels(Txdb) <- paste0("Chr", c(1:5, "M", "C"))
    ChIP_peak_path <- system.file("extdata", "ChIP.bed.gz", package = "FindIT2")
    ChIP_peak_GR <- loadPeakFile(ChIP_peak_path)
    ATAC_peak_path <- system.file("extdata", "ATAC.bed.gz", package = "FindIT2")
    ATAC_peak_GR <- loadPeakFile(ATAC_peak_path)

    mmAnno_geneScan <- mm_geneScan(
        peak_GR = ChIP_peak_GR,
        Txdb = Txdb,
        upstream = 2e4,
        downstream = 2e4
    )

    peakRP_gene <- calcRP_TFHit(
        mmAnno = mmAnno_geneScan,
        Txdb = Txdb,
        report_fullInfo = FALSE
    )

    data("RNADiff_LEC2_GR")
     merge_result <- integrate_ChIP_RNA(
        result_geneRP = peakRP_gene,
        result_geneDiff = RNADiff_LEC2_GR
    )

    target_result <- merge_result$data
    test_geneSet <- target_result$gene_id[1:50]

    related_peaks <- mm_geneBound(
        peak_GR = ATAC_peak_GR,
        Txdb = Txdb,
        input_genes = test_geneSet
    )
    test_featureSet <- unique(related_peaks$feature_id)
    # save(test_geneSet, file = "data/test_geneSet.rda", version = 2)
    # save(test_featureSet, file = "data/test_featureSet.rda", version = 2)
}

## End(Not run)

TF-target database

Description

TF-target database

Usage

data(TF_target_database)
data(TF_target_database)

Format

a data frame

Source

http://bioinformatics.psb.ugent.be/webtools/iGRN/pages/download

Examples

## Not run: 
# source
library(dplyr)
data <- read.table("~/reference/annoation/Athaliana/TF_target/iGRN_network_full.txt",
                  sep = "\t",
                  stringsAsFactors = FALSE)

data %>%
rename(TF_id = V1, target_gene = V2) %>%
select(TF_id, target_gene) %>%
TF_target_database <- filter(TF_id %in% c("AT1G28300",
"AT5G63790", "AT5G24110", "AT3G23250")) %>%
as.data.frame()

save(TF_target_database, file = "inst/extdata/TF_target_database.rda", version = 2,
     compress = "bzip2")


## End(Not run)

## Not run: 
# source
library(dplyr)
data <- read.table("~/reference/annoation/Athaliana/TF_target/iGRN_network_full.txt",
                  sep = "\t",
                  stringsAsFactors = FALSE)

data %>%
rename(TF_id = V1, target_gene = V2) %>%
select(TF_id, target_gene) %>%
TF_target_database <- filter(TF_id %in% c("AT1G28300",
"AT5G63790", "AT5G24110", "AT3G23250")) %>%
as.data.frame()

save(TF_target_database, file = "inst/extdata/TF_target_database.rda", version = 2,
     compress = "bzip2")


## End(Not run)

Package 'FindIT2'

Help Index

ATAC normCount of E50h-72h in Chr5

Description

Usage

Format

Source

calcRP_coverage

Description

Usage

Arguments

Details

Value

Examples

calcRP_region

Description

Usage

Arguments

Value

Examples

calcRP_TFHit

Description

Usage

Arguments

Details

Value

Examples

enhancerPromoterCor

Description

Usage

Arguments

Value

Examples

findI(nfluential)T(F)_enrichFisher

Description

Usage

Arguments

Value

Examples

findIT_enrichWilcox

Description

Usage

Arguments

Value

Examples

findIT_MARA

Description

Usage

Arguments

Value

Examples

findI(nfluential)T(F)_regionRP

Description

Usage

Arguments

Value

Examples

findI(nfluential)T(F)_TFHit

Description

Usage

Arguments

Value

Examples

findI(nfluential)T(F)_T(F)T(arget)Pair

Description

Usage

Arguments

Value

Examples

getAssocPairNumber

Description

Usage

Arguments

Value

Examples

integrate_ChIP_RNA

Description

Usage

Arguments

Value