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 csawUsersGuide workflow package. It can be obtained by typing:

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

In addition, several end-to-end usage examples are provided by the chipseqDB workflow package. This is less comprehensive but more concise than the user’s guide.

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 3.6.0 (2019-04-26)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows Server 2012 R2 x64 (build 9600)
## 
## Matrix products: default
## 
## locale:
## [1] LC_COLLATE=C                          
## [2] LC_CTYPE=English_United States.1252   
## [3] LC_MONETARY=English_United States.1252
## [4] LC_NUMERIC=C                          
## [5] LC_TIME=English_United States.1252    
## 
## attached base packages:
## [1] parallel  stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] csaw_1.18.0                 SummarizedExperiment_1.14.0
##  [3] DelayedArray_0.10.0         BiocParallel_1.18.0        
##  [5] matrixStats_0.54.0          Biobase_2.44.0             
##  [7] GenomicRanges_1.36.0        GenomeInfoDb_1.20.0        
##  [9] IRanges_2.18.0              S4Vectors_0.22.0           
## [11] BiocGenerics_0.30.0         knitr_1.22                 
## [13] BiocStyle_2.12.0           
## 
## loaded via a namespace (and not attached):
##  [1] progress_1.2.0           locfit_1.5-9.1          
##  [3] xfun_0.6                 lattice_0.20-38         
##  [5] htmltools_0.3.6          rtracklayer_1.44.0      
##  [7] yaml_2.2.0               GenomicFeatures_1.36.0  
##  [9] blob_1.1.1               XML_3.98-1.19           
## [11] rlang_0.3.4              DBI_1.0.0               
## [13] bit64_0.9-7              GenomeInfoDbData_1.2.1  
## [15] stringr_1.4.0            zlibbioc_1.30.0         
## [17] Biostrings_2.52.0        evaluate_0.13           
## [19] memoise_1.1.0            biomaRt_2.40.0          
## [21] AnnotationDbi_1.46.0     Rcpp_1.0.1              
## [23] edgeR_3.26.0             BiocManager_1.30.4      
## [25] limma_3.40.0             XVector_0.24.0          
## [27] bit_1.1-14               Rsamtools_2.0.0         
## [29] hms_0.4.2                digest_0.6.18           
## [31] stringi_1.4.3            bookdown_0.9            
## [33] grid_3.6.0               tools_3.6.0             
## [35] bitops_1.0-6             magrittr_1.5            
## [37] RCurl_1.95-4.12          RSQLite_2.1.1           
## [39] crayon_1.3.4             pkgconfig_2.0.2         
## [41] Matrix_1.2-17            prettyunits_1.0.2       
## [43] assertthat_0.2.1         rmarkdown_1.12          
## [45] httr_1.4.0               R6_2.4.0                
## [47] GenomicAlignments_1.20.0 compiler_3.6.0