Description Usage Arguments Details Value Author(s) References See Also Examples
Import and preprocess all or subset of bin-level ChIP-sep data, including ChIP data, matched control data, mappability score, GC content score, and sequence ambiguity score.
1 2 |
type |
Character vector indicating data types to be imported.
This vector can contain |
fileName |
Character vector of file names, each of which matches each element of |
dataType |
How reads were processed? Possible values are
either |
rounding |
How are mappability score and GC content score rounded? Default is 100 and this indicates rounding of mappability score and GC content score to the nearest hundredth. |
parallel |
Utilize multiple CPUs for parallel computing using |
nCore |
Number of CPUs when parallel computing is utilized. |
Bin-level ChIP and matched control data can be generated
from the aligned read files for your samples using the method constructBins
.
In mosaics
package companion website, http://www.stat.wisc.edu/~keles/Software/mosaics/,
we provide preprocessed mappability score, GC content score,
and sequence ambiguity score files for diverse reference genomes.
Please check the website and the vignette for further details.
The imported data type constraints the analysis that can be implemented.
If type=c("chip", "input")
or c("chip", "input", "N")
,
only two-sample analysis without using mappability and GC content is allowed.
For type=c("chip", "input", "M", "GC", "N")
,
user can do the one- or two-sample analysis.
If type=c("chip", "M", "GC", "N")
, only one-sample analysis is permitted.
See help page of mosaicsFit
.
When the data contains multiple chromosomes,
parallel computing can be utilized for faster preprocessing
if parallel=TRUE
and parallel
package is loaded.
nCore
determines number of CPUs used for parallel computing.
Construct BinData
class object.
Dongjun Chung, Pei Fen Kuan, Rene Welch, Sunduz Keles
Kuan, PF, D Chung, G Pan, JA Thomson, R Stewart, and S Keles (2011), "A Statistical Framework for the Analysis of ChIP-Seq Data", Journal of the American Statistical Association, Vol. 106, pp. 891-903.
Chung, D, Zhang Q, and Keles S (2014), "MOSAiCS-HMM: A model-based approach for detecting regions of histone modifications from ChIP-seq data", Datta S and Nettleton D (eds.), Statistical Analysis of Next Generation Sequencing Data, Springer.
constructBins
, mosaicsFit
, BinData
.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 | ## Not run:
library(mosaicsExample)
constructBins( infile=system.file( file.path("extdata","wgEncodeSydhTfbsGm12878Stat1StdAlnRep1_chr22_sorted.bam"), package="mosaicsExample"),
fileFormat="bam", outfileLoc="~/",
PET=FALSE, fragLen=200, binSize=200, capping=0 )
constructBins( infile=system.file( file.path("extdata","wgEncodeSydhTfbsGm12878InputStdAlnRep1_chr22_sorted.bam"), package="mosaicsExample"),
fileFormat="bam", outfileLoc="~/",
PET=FALSE, fragLen=200, binSize=200, capping=0 )
binTFBS <- readBins( type=c("chip","input"),
fileName=c( "~/wgEncodeSydhTfbsGm12878Stat1StdAlnRep1_chr22_sorted.bam_fragL200_bin200.txt",
"~/wgEncodeSydhTfbsGm12878InputStdAlnRep1_chr22_sorted.bam_fragL200_bin200.txt" ) )
## End(Not run)
|
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.