Miscellaneous epiwraps functions

Abstract

This vignette introduces some more isolated epiwraps functions.

Quality control

Coverage statistics

Coverage statistics give an overview of how the reads are distributed across the genome (or more precisely, across a large number of random regions). The getCovStats will compute such statistics from bam or bigwig files (from bigwig files will be considerably faster, but if the files are normalized the coverage/density will be relative).

Because our example data spans only part of a chromosome, we’ll exclude completely empty regions using the exclude parameter, which would normally be used to exclude regions likely to be technical artefacts (e.g. blacklisted regions).

suppressPackageStartupMessages(library(epiwraps))
# get the path to an example bigwig file:
bwf <- system.file("extdata/example_atac.bw", package="epiwraps")
cs <- getCovStats(bwf, exclude=GRanges("1", IRanges(1, 4300000)))
plotCovStats(cs)

Panel A shows the proportion of sampled regions which are above a certain read density (relative because this is a normalized bigwig file, would be coverage otherwise). This shows us, for example, that as expected only a minority of regions have any reads at all (indicating that the reads are not randomly distributed). Panel B is what is sometimes called a fingerprint plot. It similarly shows us that the reads are concentrated in very few regions, since the vast majority of regions have only a very low fraction of the coverage of a few high-density regions. Randomly distributed reads would go along the diagonal, but one normally has a curve somewhere between this line and the lower-right corner – the farther away from the diagonal, to more strongly enriched the data is.

This can be done for multiple files simultaneously. If we have several files, we can also use the coverage in the random windows to computer their similarity (see ?plotCorFromCovStats).

Fragment length distributions

Given one or more paired-end bam files, we can extract and plot the fragment length distribution using:

fragSizesDist(bam, what=100)

TSS enrichment

The TSS enrichment can also be calculated and plotted using the TSSenrichment function.

Peak calling

A very experimental peak calling function can be used, either against an input control or against local or global backgrounds:

p <- callPeaks(bam, fragLength=50)

Note that this function is still under heavy development, and its usage at the moment is discouraged!

Region overlapping

The GenomicRanges package offers fast and powerful functions for overlapping genomic regions. epiwraps includes wrappers around those for common tasks, such as calculating and visualizing overlaps across multiple sets of regions (see ?regionUpset, ?regionOverlaps, and ?regionCAT).

Session information

sessionInfo()

## R version 4.4.1 (2024-06-14)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 22.04.5 LTS
## 
## Matrix products: default
## BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
## LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.20.so;  LAPACK version 3.10.0
## 
## locale:
##  [1] LC_CTYPE=C.UTF-8       LC_NUMERIC=C           LC_TIME=C.UTF-8       
##  [4] LC_COLLATE=C.UTF-8     LC_MONETARY=C.UTF-8    LC_MESSAGES=C.UTF-8   
##  [7] LC_PAPER=C.UTF-8       LC_NAME=C              LC_ADDRESS=C          
## [10] LC_TELEPHONE=C         LC_MEASUREMENT=C.UTF-8 LC_IDENTIFICATION=C   
## 
## time zone: UTC
## tzcode source: system (glibc)
## 
## attached base packages:
## [1] grid      stats4    stats     graphics  grDevices utils     datasets 
## [8] methods   base     
## 
## other attached packages:
##  [1] epiwraps_0.99.99            EnrichedHeatmap_1.34.0     
##  [3] ComplexHeatmap_2.20.0       SummarizedExperiment_1.34.0
##  [5] Biobase_2.64.0              GenomicRanges_1.56.2       
##  [7] GenomeInfoDb_1.40.1         IRanges_2.38.1             
##  [9] S4Vectors_0.42.1            BiocGenerics_0.50.0        
## [11] MatrixGenerics_1.16.0       matrixStats_1.4.1          
## [13] BiocStyle_2.32.1           
## 
## loaded via a namespace (and not attached):
##   [1] RColorBrewer_1.1-3       rstudioapi_0.17.1        jsonlite_1.8.9          
##   [4] shape_1.4.6.1            magrittr_2.0.3           GenomicFeatures_1.56.0  
##   [7] farver_2.1.2             rmarkdown_2.28           GlobalOptions_0.1.2     
##  [10] fs_1.6.4                 BiocIO_1.14.0            zlibbioc_1.50.0         
##  [13] ragg_1.3.3               vctrs_0.6.5              memoise_2.0.1           
##  [16] Rsamtools_2.20.0         RCurl_1.98-1.16          base64enc_0.1-3         
##  [19] htmltools_0.5.8.1        S4Arrays_1.4.1           progress_1.2.3          
##  [22] curl_5.2.3               SparseArray_1.4.8        Formula_1.2-5           
##  [25] sass_0.4.9               bslib_0.8.0              htmlwidgets_1.6.4       
##  [28] desc_1.4.3               plyr_1.8.9               Gviz_1.48.0             
##  [31] httr2_1.0.5              cachem_1.1.0             GenomicAlignments_1.40.0
##  [34] lifecycle_1.0.4          iterators_1.0.14         pkgconfig_2.0.3         
##  [37] Matrix_1.7-0             R6_2.5.1                 fastmap_1.2.0           
##  [40] GenomeInfoDbData_1.2.12  clue_0.3-65              digest_0.6.37           
##  [43] colorspace_2.1-1         AnnotationDbi_1.66.0     textshaping_0.4.0       
##  [46] Hmisc_5.1-3              RSQLite_2.3.7            labeling_0.4.3          
##  [49] filelock_1.0.3           fansi_1.0.6              httr_1.4.7              
##  [52] abind_1.4-8              compiler_4.4.1           withr_3.0.1             
##  [55] bit64_4.5.2              doParallel_1.0.17        backports_1.5.0         
##  [58] htmlTable_2.4.3          BiocParallel_1.38.0      DBI_1.2.3               
##  [61] UpSetR_1.4.0             highr_0.11               biomaRt_2.60.1          
##  [64] rappdirs_0.3.3           DelayedArray_0.30.1      rjson_0.2.23            
##  [67] tools_4.4.1              foreign_0.8-86           nnet_7.3-19             
##  [70] glue_1.8.0               restfulr_0.0.15          checkmate_2.3.2         
##  [73] cluster_2.1.6            generics_0.1.3           gtable_0.3.5            
##  [76] BSgenome_1.72.0          ensembldb_2.28.1         data.table_1.16.2       
##  [79] hms_1.1.3                xml2_1.3.6               utf8_1.2.4              
##  [82] XVector_0.44.0           foreach_1.5.2            pillar_1.9.0            
##  [85] stringr_1.5.1            circlize_0.4.16          dplyr_1.1.4             
##  [88] BiocFileCache_2.12.0     lattice_0.22-6           deldir_2.0-4            
##  [91] rtracklayer_1.64.0       bit_4.5.0                biovizBase_1.52.0       
##  [94] tidyselect_1.2.1         locfit_1.5-9.10          pbapply_1.7-2           
##  [97] Biostrings_2.72.1        knitr_1.48               gridExtra_2.3           
## [100] bookdown_0.41            ProtGenerics_1.36.0      xfun_0.48               
## [103] stringi_1.8.4            UCSC.utils_1.0.0         lazyeval_0.2.2          
## [106] yaml_2.3.10              evaluate_1.0.1           codetools_0.2-20        
## [109] interp_1.1-6             GenomicFiles_1.40.0      tibble_3.2.1            
## [112] BiocManager_1.30.25      cli_3.6.3                rpart_4.1.23            
## [115] systemfonts_1.1.0        munsell_0.5.1            jquerylib_0.1.4         
## [118] dichromat_2.0-0.1        Rcpp_1.0.13              dbplyr_2.5.0            
## [121] png_0.1-8                XML_3.99-0.17            parallel_4.4.1          
## [124] pkgdown_2.1.1            ggplot2_3.5.1            blob_1.2.4              
## [127] prettyunits_1.2.0        jpeg_0.1-10              latticeExtra_0.6-30     
## [130] AnnotationFilter_1.28.0  bitops_1.0-9             viridisLite_0.4.2       
## [133] VariantAnnotation_1.50.0 scales_1.3.0             crayon_1.5.3            
## [136] GetoptLong_1.0.5         rlang_1.1.4              cowplot_1.1.3           
## [139] KEGGREST_1.44.1