inst/doc/bisplotti.md

In huishenlab/bisplotti: Plotting package for bisulfite sequencing data

title: "Bisplotti User Guide" date: "21 February 2023" package: "bisplotti 0.0.18" output: BiocStyle::html_document: highlight: pygments toc_float: true fig_width: 8 git_height: 6 vignette: > %\VignetteEngine{knitr::rmarkdown} %\VignetteIndexEntry{Bisplotti User Guide} %\VignetteEncoding[utf8]{inputenc}

Bisplotti

bisplotti is a package to generate commonly produced plots in DNA methylation sequencing analyses. It creates plots from standard Bioconductor formats (i.e., GRanges) and the commonly used BSseq format.

Quick Start

Installing

A development version is available on GitHub and can be installed via:

if (!requireNamespace("BiocManager", quietly=TRUE)) {
    install.packages("BiocManager")
}

BiocManager::install("huishenlab/bisplotti")

library(bisplotti)

Create Plots

Epiread

To create the lollipop epiread plots, two commands are needed. First, use the GRanges output from biscuiteer::readEpibed() as input to tabulateEpibed, which turns the GRanges into a convenient matrix format. The second command, plotEpiread uses the matrix output from tabulateEpibed as the input to create the lollipop plot. epistateCaller() can take the output of tabulateEpibed() to cluster the epireads on both CpG and GpC methylation. Note, epistateCaller() is under development and is not currently suggested for use in publication level analyses.

epibed.nome     <- system.file("extdata", "hct116.nome.epibed.gz", package="biscuiteer")
epibed.nome.gr  <- readEpibed(epibed = epibed.nome, genome = "hg19", chr = "chr1")
epibed.tab.nome <- tabulateEpibed(epibed.nome.gr)
plotEpiread(epibed.tab.nome$gc_table)

## Error in plotEpiread(epibed.tab.nome$gc_table): could not find function "plotEpiread"

#epistateCaller(epibed.tab.nome)

Multiscale

The multiscale plot is based on Figures 2 and 6 of Zhou et al., Nature Genetics, 2018. It shows the average methylation levels across varying size genomic windows. The example shows a small portion of chromosome 16 for window sizes running from 1 Mb to 10 Mb.

files.loc <- system.file("extdata", package="bisplotti")

files <- lapply(
    list.files(files.loc, pattern="Heyn_2012_100yr", full.names=TRUE), function(x) {
        return(rtracklayer::import(x, format="bedGraph"))
    }
)
cnames <- list.files(files.loc, pattern="Heyn_2012_100yr")
cnames <- gsub("Heyn_2012_100yr", "100yr", cnames)
cnames <- gsub(".bed.gz", "", cnames)
names(files) <- cnames

files.grl <- as(files, "GRangesList")

multiscaleMethylationPlot(files.grl)

1D Methylation Level Density

The 1D methylation level density plot shows the density of the methylation levels for all CpGs in your dataset. An example of creating this plot is:

bed <- system.file("extdata", "MCF7_Cunha_chr11p15.bed.gz", package="biscuiteer")
vcf <- system.file("extdata", "MCF7_Cunha_header_only.vcf.gz", package="biscuiteer")
bisc <- readBiscuit(BEDfile = bed, VCFfile = vcf, merged = FALSE)

meth1DDensity(bisc)

A matrix of methylation levels (beta values) can also be used as input to meth1DDensity(). This method assumes the column names of your matrix are the samples in the matrix. The row names can either be NULL or the CpG loci/probes.

2D Methylation Level Density

The 2D methylation level density plot compares the density of average methylation levels in provided bins across two samples. An example of creating this plot is:

orig_bed <- system.file("extdata", "MCF7_Cunha_chr11p15.bed.gz", package="biscuiteer")
orig_vcf <- system.file("extdata", "MCF7_Cunha_header_only.vcf.gz", package="biscuiteer")
bisc1    <- readBiscuit(BEDfile = orig_bed, VCFfile = orig_vcf, merged = FALSE)

shuf_bed <- system.file("extdata", "MCF7_Cunha_chr11p15_shuffled.bed.gz", package="biscuiteer")
shuf_vcf <- system.file("extdata", "MCF7_Cunha_shuffled_header_only.vcf.gz", package="biscuiteer")
bisc2    <- readBiscuit(BEDfile = shuf_bed, VCFfile = shuf_vcf, merged = FALSE)

comb <- unionize(bisc1, bisc2)

meth2DDensity(comb, chr="chr11", sample_1 = "MCF7_Cunha", sample_2 = "MCF7_Cunha_shuffled")

A matrix of methylation levels (beta values) can also be used as input to meth2DDensity(). This method assumes the column names of your matrix are the samples in the matrix. The row names must be the CpG loci/probes (i.e., the result of rownames(mat) <- as.character(granges(gr)). In either input case, there must be at least two samples in your input object.

Session Info

sessionInfo()

## R version 4.2.1 (2022-06-23)
## Platform: x86_64-apple-darwin17.0 (64-bit)
## Running under: macOS Big Sur ... 10.16
## 
## Matrix products: default
## BLAS:   /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.2/Resources/lib/libRlapack.dylib
## 
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
## 
## attached base packages:
## [1] stats4    stats     graphics  grDevices utils     datasets  methods  
## [8] base     
## 
## other attached packages:
##  [1] bisplotti_0.0.18            RColorBrewer_1.1-3         
##  [3] ggplot2_3.4.1               biscuiteer_1.13.0          
##  [5] bsseq_1.34.0                SummarizedExperiment_1.28.0
##  [7] Biobase_2.58.0              MatrixGenerics_1.10.0      
##  [9] matrixStats_0.63.0          GenomicRanges_1.50.2       
## [11] GenomeInfoDb_1.34.9         IRanges_2.32.0             
## [13] S4Vectors_0.36.1            biscuiteerData_1.12.0      
## [15] ExperimentHub_2.6.0         AnnotationHub_3.6.0        
## [17] BiocFileCache_2.6.1         dbplyr_2.3.0               
## [19] BiocGenerics_0.44.0        
## 
## loaded via a namespace (and not attached):
##   [1] plyr_1.8.8                               
##   [2] splines_4.2.1                            
##   [3] BiocParallel_1.32.5                      
##   [4] listenv_0.9.0                            
##   [5] CGHcall_2.60.0                           
##   [6] digest_0.6.31                            
##   [7] foreach_1.5.2                            
##   [8] htmltools_0.5.4                          
##   [9] viridis_0.6.2                            
##  [10] GO.db_3.16.0                             
##  [11] fansi_1.0.4                              
##  [12] magrittr_2.0.3                           
##  [13] memoise_2.0.1                            
##  [14] BSgenome_1.66.3                          
##  [15] tzdb_0.3.0                               
##  [16] limma_3.54.1                             
##  [17] readr_2.1.4                              
##  [18] globals_0.16.2                           
##  [19] Biostrings_2.66.0                        
##  [20] R.utils_2.12.2                           
##  [21] prettyunits_1.1.1                        
##  [22] colorspace_2.1-0                         
##  [23] blob_1.2.3                               
##  [24] rappdirs_0.3.3                           
##  [25] xfun_0.37                                
##  [26] dplyr_1.1.0                              
##  [27] crayon_1.5.2                             
##  [28] RCurl_1.98-1.10                          
##  [29] org.Mm.eg.db_3.16.0                      
##  [30] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2  
##  [31] graph_1.76.0                             
##  [32] annotatr_1.24.0                          
##  [33] Mus.musculus_1.3.1                       
##  [34] impute_1.72.3                            
##  [35] iterators_1.0.14                         
##  [36] VariantAnnotation_1.44.1                 
##  [37] glue_1.6.2                               
##  [38] gtable_0.3.1                             
##  [39] zlibbioc_1.44.0                          
##  [40] XVector_0.38.0                           
##  [41] DelayedArray_0.24.0                      
##  [42] dmrseq_1.18.0                            
##  [43] Rhdf5lib_1.20.0                          
##  [44] future.apply_1.10.0                      
##  [45] HDF5Array_1.26.0                         
##  [46] scales_1.2.1                             
##  [47] rngtools_1.5.2                           
##  [48] DBI_1.1.3                                
##  [49] CGHbase_1.58.0                           
##  [50] Rcpp_1.0.10                              
##  [51] viridisLite_0.4.1                        
##  [52] xtable_1.8-4                             
##  [53] progress_1.2.2                           
##  [54] bumphunter_1.40.0                        
##  [55] bit_4.0.5                                
##  [56] OrganismDbi_1.40.0                       
##  [57] httr_1.4.4                               
##  [58] ellipsis_0.3.2                           
##  [59] farver_2.1.1                             
##  [60] pkgconfig_2.0.3                          
##  [61] XML_3.99-0.13                            
##  [62] R.methodsS3_1.8.2                        
##  [63] locfit_1.5-9.7                           
##  [64] utf8_1.2.3                               
##  [65] DNAcopy_1.72.3                           
##  [66] labeling_0.4.2                           
##  [67] reshape2_1.4.4                           
##  [68] tidyselect_1.2.0                         
##  [69] rlang_1.0.6                              
##  [70] later_1.3.0                              
##  [71] AnnotationDbi_1.60.0                     
##  [72] munsell_0.5.0                            
##  [73] BiocVersion_3.16.0                       
##  [74] tools_4.2.1                              
##  [75] cachem_1.0.6                             
##  [76] cli_3.6.0                                
##  [77] generics_0.1.3                           
##  [78] RSQLite_2.3.0                            
##  [79] evaluate_0.20                            
##  [80] stringr_1.5.0                            
##  [81] fastmap_1.1.0                            
##  [82] yaml_2.3.7                               
##  [83] outliers_0.15                            
##  [84] org.Hs.eg.db_3.16.0                      
##  [85] knitr_1.42                               
##  [86] bit64_4.0.5                              
##  [87] purrr_1.0.1                              
##  [88] KEGGREST_1.38.0                          
##  [89] doRNG_1.8.6                              
##  [90] nlme_3.1-162                             
##  [91] RBGL_1.74.0                              
##  [92] future_1.31.0                            
##  [93] sparseMatrixStats_1.10.0                 
##  [94] mime_0.12                                
##  [95] R.oo_1.25.0                              
##  [96] xml2_1.3.3                               
##  [97] biomaRt_2.54.0                           
##  [98] BiocStyle_2.26.0                         
##  [99] compiler_4.2.1                           
## [100] filelock_1.0.2                           
## [101] curl_5.0.0                               
## [102] png_0.1-8                                
## [103] interactiveDisplayBase_1.36.0            
## [104] marray_1.76.0                            
## [105] tibble_3.1.8                             
## [106] Homo.sapiens_1.3.1                       
## [107] stringi_1.7.12                           
## [108] QDNAseq_1.34.0                           
## [109] highr_0.10                               
## [110] GenomicFeatures_1.50.4                   
## [111] lattice_0.20-45                          
## [112] Matrix_1.5-3                             
## [113] permute_0.9-7                            
## [114] vctrs_0.5.2                              
## [115] pillar_1.8.1                             
## [116] lifecycle_1.0.3                          
## [117] rhdf5filters_1.10.0                      
## [118] BiocManager_1.30.19                      
## [119] cowplot_1.1.1                            
## [120] data.table_1.14.8                        
## [121] bitops_1.0-7                             
## [122] TxDb.Mmusculus.UCSC.mm10.knownGene_3.10.0
## [123] httpuv_1.6.9                             
## [124] rtracklayer_1.58.0                       
## [125] R6_2.5.1                                 
## [126] BiocIO_1.8.0                             
## [127] promises_1.2.0.1                         
## [128] gridExtra_2.3                            
## [129] KernSmooth_2.23-20                       
## [130] parallelly_1.34.0                        
## [131] codetools_0.2-19                         
## [132] MASS_7.3-58.2                            
## [133] gtools_3.9.4                             
## [134] assertthat_0.2.1                         
## [135] qualV_0.3-4                              
## [136] rhdf5_2.42.0                             
## [137] rjson_0.2.21                             
## [138] withr_2.5.0                              
## [139] regioneR_1.30.0                          
## [140] GenomicAlignments_1.34.0                 
## [141] Rsamtools_2.14.0                         
## [142] GenomeInfoDbData_1.2.9                   
## [143] parallel_4.2.1                           
## [144] hms_1.1.2                                
## [145] grid_4.2.1                               
## [146] rmarkdown_2.20                           
## [147] DelayedMatrixStats_1.20.0                
## [148] shiny_1.7.4                              
## [149] restfulr_0.0.15

huishenlab/bisplotti documentation built on Sept. 20, 2023, 10:13 p.m.