Package: CSSQ 1.17.0

Fan Lab at Georgia Institute of Technology

CSSQ: Chip-seq Signal Quantifier Pipeline

This package is desgined to perform statistical analysis to identify statistically significant differentially bound regions between multiple groups of ChIP-seq dataset.

Authors:Ashwath Kumar [aut], Michael Y Hu [aut], Yajun Mei [aut], Yuhong Fan [aut]

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CSSQ.pdf |CSSQ.html✨
CSSQ/json (API)

# Install 'CSSQ' in R:

install.packages('CSSQ', repos = c('https://bioc.r-universe.dev', 'https://cloud.r-project.org'))

On BioConductor:CSSQ-1.17.0(bioc 3.20)CSSQ-1.16.0(bioc 3.19)

This package does not link to any Github/Gitlab/R-forge repository. No issue tracker or development information is available.

bioconductor-package

9 exports 0.49 score 87 dependencies

Last updated 2 months agofrom:f7f6b2d487

Exports:ansTransform DBAnalyze getBgSubVal getRegionCounts kmeansNormalize loadCountData normalizeData plotDist preprocessData

Dependencies:abind AnnotationDbi askpass BH Biobase BiocGenerics BiocIO BiocParallel Biostrings bit bit64 bitops blob cachem cli codetools colorspace cpp11 crayon curl DBI DelayedArray fansi farver fastmap formatR futile.logger futile.options GenomeInfoDb GenomeInfoDbData GenomicAlignments GenomicFeatures GenomicRanges ggplot2 glue gtable httr IRanges isoband jsonlite KEGGREST labeling lambda.r lattice lifecycle magrittr MASS Matrix MatrixGenerics matrixStats memoise mgcv mime munsell nlme openssl pillar pkgconfig plogr png R6 RColorBrewer RCurl restfulr Rhtslib rjson rlang Rsamtools RSQLite rtracklayer S4Arrays S4Vectors scales snow SparseArray SummarizedExperiment sys tibble UCSC.utils utf8 vctrs viridisLite withr XML XVector yaml zlibbioc

ChIP-seq signal quantifier (CSSQ)

Ashwath Kumar, Yajun Mei, Yuhong Fan

Rendered fromCSSQ.Rmdusingknitr::knitron Jul 05 2024.

Last update: 2019-12-04
Started: 2019-11-13

Citation

Development and contributors

Readme and manuals

Help Manual

Help page	Topics
Perform quantification and normalization of count data	ansTransform
Calculates P-value for the regions	calculatePvalue
Calculates modified T-statistics values for the given label and comparison	calculateTvalue
Performs differential binding analysis	DBAnalyze
Perform quantification and normalization of count data	getBgSubVal
Identify possible combinations	getComparisons
Labels the samples according to the combinations from 'getComparisons'	getNewLabels
Quantify region level count data	getRegionCounts
Perform k-means clustering, normalize anscombe data and calculate cluster variances for a sample.	kmeansNormalize
Load count data from input file.	loadCountData
Normalize anscombe transformed data	normalizeData
Plot data distribution histograms	plotDist
Wrapper function to preprocess the data	preprocessData