Package 'fCCAC'

Title: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets
Description: Computational evaluation of variability across DNA or RNA sequencing datasets is a crucial step in genomics, as it allows both to evaluate reproducibility of replicates, and to compare different datasets to identify potential correlations. fCCAC applies functional Canonical Correlation Analysis to allow the assessment of: (i) reproducibility of biological or technical replicates, analyzing their shared covariance in higher order components; and (ii) the associations between different datasets. fCCAC represents a more sophisticated approach that complements Pearson correlation of genomic coverage.
Authors: Pedro Madrigal [aut, cre]
Maintainer: Pedro Madrigal <[email protected]>
License: Artistic-2.0
Version: 1.33.0
Built: 2024-12-02 06:32:24 UTC
Source: https://github.com/bioc/fCCAC

Help Index

functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets

Description

An application of functional canonical correlation analysis to assess covariance of nucleic acid sequencing datasets such as chromatin immunoprecipitation followed by deep sequencing (ChIP-seq).

Details

Package: fCCAC
Type: Package
Version: 1.23.1
Date: 2022-05-28
License: Artistic-2.0
LazyLoad: yes

Author(s)

Pedro Madrigal,

Maintainer: Pedro Madrigal [email protected]

References

Madrigal P (2017) fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets. Bioinformatics: http://doi.org/10.1093/bioinformatics/btw724.

Examples

## hg19. chr21:40000000-48129895 H3K4me3 data from Bertero et al. (2015)
    if (.Platform$OS.type == "unix") {
  
        owd <- setwd(tempdir())

        bigwig1 <- "chr21_H3K4me3_1.bw"
        bigwig2 <- "chr21_H3K4me3_2.bw"
        bigwig3 <- "chr21_H3K4me3_3.bw"
        peakFile <- "chr21_merged_ACT_K4.bed"
        labels <- c( "H3K4me3", "H3K4me3","H3K4me3" )
        ti <- "H3K4me3 peaks"

        r1 <- system.file("extdata", bigwig1,  package="fCCAC",mustWork = TRUE)
        r2 <- system.file("extdata", bigwig2,  package="fCCAC",mustWork = TRUE)
        r3 <- system.file("extdata", bigwig3,  package="fCCAC",mustWork = TRUE)
        r4 <- system.file("extdata", peakFile, package="fCCAC",mustWork = TRUE)

        fc <- fccac(bar=NULL, main=ti, peaks=r4, bigwigs=c(r1,r2,r3), labels=labels, splines=15, nbins=100, ncan=15) 

        head(fc)

        setwd(owd)
    }

functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets

Description

functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets.

Usage

fccac(peaks, bigwigs, labels, splines=10, nbins=100, ncan=5 , tf=c(), main="", bar=NULL, outFiles=FALSE )

Arguments

peaks

BED file. Column 1: chr, Column 2: start, Column 3: end (Required).

bigwigs

A vector of characters containing the path to bigwigs files. Replicates of the same samples should be entered consecutive one another (Required).

labels

IDs for each sample. Replicates should have the same label and be ordered (vector of characters, Required)

splines

Number of cubic B-splines used to smooth the data and to estimate the canonical variate weight functions (default: 15)

nbins

Integer value representing the number of bins that should be used for each window (default: 100)

ncan

Number of canonical components to report in the results. It cannot be higher than number of splines or the number of peaks (default: 15)

tf

Plot results involving only this TF or TF-replicate (character). Eg., "SOX2" or "SOX2\_Rep1" (default: empty vector. plot all)

main

Title of the plot generated (default: no title)

bar

In the barplot, plot only first bar[1] and last bar[2] interactions after ranking by F-value (default: NULL, plots all the combinations).

outFiles

If TRUE, the function writes two files in the working directory, fCCAC.pdf and fCCAC.txt (tabulated text-file with results). (default: FALSE)

Details

Detailed information about the methodology can be found in Madrigal (2016).

Value

The function reports a dataframe with the following columns: pairwise samples, F value, k (order of the first canonical correlation), and value of the first canonical correlation.

Author(s)

Pedro Madrigal, [email protected]

References

Madrigal P (2016) fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets. Bioinformatics: http://doi.org/10.1093/bioinformatics/btw724.

See Also

fCCAC-package

Examples

## hg19. chr21:40000000-48129895 H3K4me3 data from Bertero et al. (2015)
  if (.Platform$OS.type == "unix") {
  
    owd <- setwd(tempdir())

    bigwig1 <- "chr21_H3K4me3_1.bw"
    bigwig2 <- "chr21_H3K4me3_2.bw"
    bigwig3 <- "chr21_H3K4me3_3.bw"
    peakFile <- "chr21_merged_ACT_K4.bed"
    labels <- c( "H3K4me3", "H3K4me3","H3K4me3" )

    r1 <- system.file("extdata", bigwig1,  package="fCCAC",mustWork = TRUE)
    r2 <- system.file("extdata", bigwig2,  package="fCCAC",mustWork = TRUE)
    r3 <- system.file("extdata", bigwig3,  package="fCCAC",mustWork = TRUE)
    r4 <- system.file("extdata", peakFile, package="fCCAC",mustWork = TRUE)
    ti <- "H3K4me3 peaks"

    fc <- fccac(bar=NULL, main=ti, peaks=r4, bigwigs=c(r1,r2,r3), labels=labels, splines=15, nbins=100, ncan=15) 

    head(fc)

    setwd(owd)

  }

Heatmap of F values obtained by Canonical Correlation Analysis

Description

Heatmap of F values obtained by Canonical Correlation Analysis. This function can only be used if all pairwise comparisons were computed previously with the function 'fccac', i.e., using "tf=c()".

Usage

heatmapfCCAC(fc)

Arguments

fc

Output of the function 'fccac'.

Value

Plots a Heatmap of F values using the package 'ComplexHeatmap'.

Author(s)

Pedro Madrigal, [email protected]

References

Madrigal P (2017) fCCAC: functional canonical correlation analysis to evaluate covariance between nucleic acid sequencing datasets. Bioinformatics: http://doi.org/10.1093/bioinformatics/btw724.

See Also

fccac

Examples

## hg19. chr21:40000000-48129895 H3K4me3 data from Bertero et al. (2015)
  if (.Platform$OS.type == "unix") {
  
    owd <- setwd(tempdir())

    bigwig1 <- "chr21_H3K4me3_1.bw"
    bigwig2 <- "chr21_H3K4me3_2.bw"
    bigwig3 <- "chr21_H3K4me3_3.bw"
    peakFile <- "chr21_merged_ACT_K4.bed"
    labels <- c( "H3K4me3", "H3K4me3","H3K4me3" )

    r1 <- system.file("extdata", bigwig1,  package="fCCAC",mustWork = TRUE)
    r2 <- system.file("extdata", bigwig2,  package="fCCAC",mustWork = TRUE)
    r3 <- system.file("extdata", bigwig3,  package="fCCAC",mustWork = TRUE)
    r4 <- system.file("extdata", peakFile, package="fCCAC",mustWork = TRUE)
    ti <- "H3K4me3 peaks"

    fc <- fccac(bar=NULL, main=ti, peaks=r4, bigwigs=c(r1,r2,r3), labels=labels, splines=15, nbins=100, ncan=15) 

    head(fc)

    heatmapfCCAC(fc)

    setwd(owd)
    
  }