fccac: functional Canonical Correlation Analysis to evaluate...

In pmb59/fCCAC: functional Canonical Correlation Analysis to evaluate Covariance between nucleic acid sequencing datasets

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, [e-mail]

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)

  }

pmb59/fCCAC documentation built on May 31, 2022, 4:38 a.m.