# Introducing the csaw package

Aaron Lun1

1Walter and Eliza Hall Institute for Medical Research, Melbourne, Australia

#### Revised: 17 February 2019

#### Package

csaw 1.38.0

# Contents

# 1 Introduction

The csaw package is designed for the de novo detection of differentially bound regions from ChIP-seq data. It uses a sliding window approach to count reads across the genome from sorted and indexed BAM files. Each window is then tested for significant differences between libraries, using the methods in the edgeR package. It implements statistical methods for:

- normalization of window counts between libraries
- independent filtering of uninteresting windows
- controlling the false discovery rate across aggregated windows

csaw can be applied to any data set containing multiple conditions with biological replication. While intended for ChIP-seq data, the methods in this package can also be applied to any type of sequencing data where changes in genomic coverage are of interest.

# 2 Documentation

The full user’s guide is available as part of the online documentation in the csawBook package. It can be obtained by typing:

    library(csaw)
    if (interactive()) csawUsersGuide()

Documentation for speicific functions is available through the usual R help system, e.g., `?windowCounts`. Further questions about the package should be directed to the Bioconductor support site.

# 3 Session information

    sessionInfo()

    ## R version 4.4.0 beta (2024-04-15 r86425)
    ## Platform: x86_64-pc-linux-gnu
    ## Running under: Ubuntu 22.04.4 LTS
    ##
    ## Matrix products: default
    ## BLAS:   /home/biocbuild/bbs-3.19-bioc/R/lib/libRblas.so
    ## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.10.0
    ##
    ## locale:
    ##  [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C
    ##  [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8
    ##  [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8
    ##  [7] LC_PAPER=en_US.UTF-8       LC_NAME=C
    ##  [9] LC_ADDRESS=C               LC_TELEPHONE=C
    ## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
    ##
    ## time zone: America/New_York
    ## tzcode source: system (glibc)
    ##
    ## attached base packages:
    ## [1] stats4    stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ##  [1] csaw_1.38.0                 SummarizedExperiment_1.34.0
    ##  [3] Biobase_2.64.0              MatrixGenerics_1.16.0
    ##  [5] matrixStats_1.3.0           GenomicRanges_1.56.0
    ##  [7] GenomeInfoDb_1.40.0         IRanges_2.38.0
    ##  [9] S4Vectors_0.42.0            BiocGenerics_0.50.0
    ## [11] BiocStyle_2.32.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] sass_0.4.9              SparseArray_1.4.0       bitops_1.0-7
    ##  [4] lattice_0.22-6          digest_0.6.35           evaluate_0.23
    ##  [7] grid_4.4.0              bookdown_0.39           fastmap_1.1.1
    ## [10] jsonlite_1.8.8          Matrix_1.7-0            limma_3.60.0
    ## [13] BiocManager_1.30.22     httr_1.4.7              UCSC.utils_1.0.0
    ## [16] Biostrings_2.72.0       codetools_0.2-20        jquerylib_0.1.4
    ## [19] abind_1.4-5             cli_3.6.2               rlang_1.1.3
    ## [22] crayon_1.5.2            XVector_0.44.0          cachem_1.0.8
    ## [25] DelayedArray_0.30.0     yaml_2.3.8              metapod_1.12.0
    ## [28] S4Arrays_1.4.0          tools_4.4.0             parallel_4.4.0
    ## [31] BiocParallel_1.38.0     locfit_1.5-9.9          GenomeInfoDbData_1.2.12
    ## [34] Rsamtools_2.20.0        R6_2.5.1                lifecycle_1.0.4
    ## [37] zlibbioc_1.50.0         edgeR_4.2.0             bslib_0.7.0
    ## [40] Rcpp_1.0.12             statmod_1.5.0           xfun_0.43
    ## [43] knitr_1.46              htmltools_0.5.8.1       rmarkdown_2.26
    ## [46] compiler_4.4.0