fccac {fCCAC} | R Documentation

## 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, dnaseiseq@gmail.com

### 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)
    }

[Package fCCAC version 1.6.0 Index]