root.dir <- here::here()
knitr::opts_chunk$set(echo = TRUE, root.dir=root.dir, dpi = 400)
knitr::opts_knit$set(root.dir = root.dir)
source(here::here("code","utils.R"))keys <- create_keys()Data previously processed in data/enrichment.Rmd script.
gr.chip <- readRDS(here::here("data","gr.chip.hg38_cutoff5_standardised.rds")) |>
GenomicRanges::GRangesList()## Loading required package: rtracklayer## Loading required package: GenomicRanges## Loading required package: stats4## Loading required package: BiocGenerics##
## Attaching package: 'BiocGenerics'## The following objects are masked from 'package:stats':
##
## IQR, mad, sd, var, xtabs## The following objects are masked from 'package:base':
##
## anyDuplicated, aperm, append, as.data.frame, basename, cbind,
## colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find,
## get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply,
## match, mget, order, paste, pmax, pmax.int, pmin, pmin.int,
## Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort,
## table, tapply, union, unique, unsplit, which.max, which.min## Loading required package: S4Vectors##
## Attaching package: 'S4Vectors'## The following object is masked from 'package:utils':
##
## findMatches## The following objects are masked from 'package:base':
##
## expand.grid, I, unname## Loading required package: IRanges## Loading required package: GenomeInfoDbviewpoint <- 166169213
gr.span <- GenomicRanges::GRanges(
seqnames = "chr6",
ranges = IRanges::IRanges(
start = viewpoint - 1000000,
end = viewpoint + 1000000
)
)Create a table of metadata from the peakfile names, and ensure
standardised nomenclature
(i.e.Â
meta <- create_metadata(nms = names(gr.chip))
names(gr.chip) <- meta$name2Filter to only ranges within the predefinedTBXT window. Only include samples with >1 ranges.
#### Filter to just the target region ####
gr.chip_sub <- GenomicRanges::pintersect(
x = GenomicRanges::GRangesList(gr.chip),
y = gr.span,
drop.nohit.ranges=TRUE)
lengths <- sapply(gr.chip_sub, length)
print(lengths)## A549_H3K27ac_R1 A549_H3K27ac_R2 A549_H3K27me3_R1 A549_H3K27me3_R2
## 26 31 10 20
## A549_H3K4me3_R1 A549_H3K4me3_R2 A549_H3K4me3_R3 A549_H3K9me3_R1
## 24 19 27 67
## H358_H3K27ac_R1 H358_H3K27me3_R1 H358_H3K4me3_R1 H358_H3K9me3_R1
## 19 1 15 0
## H460_H3K4me3_R1 H460_H3K4me3_R2 H460_H3K27ac_R1 H460_H3K27ac_R2
## 43 36 56 57
## H460_H3K9me3_R1 H460_H3K27me3_R1
## 14 25gr.chip_sub <- gr.chip_sub[lengths>0]bins <- EpiCompare::rebin_peaks(peakfiles = gr.chip_sub,
genome_build = "hg38",
intensity_cols = "score",
bin_size = 1000,
keep_chr = "chr6",
workers = 10)## ## Warning: replacing previous import 'Biostrings::pattern' by 'grid::pattern'
## when loading 'genomation'## Standardising peak files in 170,806 bins of 1,000 bp.## Merging data into matrix.## Converting to sparse matrix.## Binned matrix size: 170,806 x 17## Matrix sparsity: 0.9997bins <- prepare_bins(bins = bins) ## 170806 empty bins dropped.## Filtered matrix sparsity: 0.881cor_mat <- cor(as.matrix(bins))
utils::write.csv(cor_mat,
file = here::here("results","rebinned_correlations.csv"),
row.names = TRUE)file <- here::here("results",paste0("heatmap_locus",c(".png",".pdf")))
hm <- heatmaply::heatmaply(x = cor_mat,
row_side_colors = meta[colnames(cor_mat),
c("Cell","Histone")],
row_side_palette = c(keys$cell_key,keys$color_key),
plot_method = "plotly",
file = file,
key.title = "Pearson r",
height = 600
) ## Warning: 'plotly::orca' is deprecated.
## Use 'kaleido' instead.
## See help("Deprecated")## Warning in value[[3L]](cond): plotly::orca failed:
## Error: The orca command-line utility is required for this functionality.
##
## Please follow the installation instructions here -- https://github.com/plotly/orca#installation## Warning: 'plotly::export' is deprecated.
## Use 'orca' instead.
## See help("Deprecated")## Warning: 'plotly::orca' is deprecated.
## Use 'kaleido' instead.
## See help("Deprecated")## Warning in value[[3L]](cond): plotly::orca failed:
## Error: The orca command-line utility is required for this functionality.
##
## Please follow the installation instructions here -- https://github.com/plotly/orca#installation## Warning: 'plotly::export' is deprecated.
## Use 'orca' instead.
## See help("Deprecated")# plotly::export(p = hm,
# file = file[1],
# vwidth = 500*3,
# vheight = 500*3,
# zoom = 1)
hmCompute correlations across all of Chromosome 6.
bins <- EpiCompare::rebin_peaks(peakfiles = gr.chip,
genome_build = "hg38",
intensity_cols = "score",
bin_size = 1000,
keep_chr = "chr6",
workers = 10)## Standardising peak files in 170,806 bins of 1,000 bp.## Merging data into matrix.## Converting to sparse matrix.## Binned matrix size: 170,806 x 18## Matrix sparsity: 0.9819bins <- prepare_bins(bins = bins) ## 170806 empty bins dropped.## Filtered matrix sparsity: 0.899cor_mat <- cor(as.matrix(bins))
utils::write.csv(cor_mat,
file = here::here("results","rebinned_correlations.chr6.csv"),
row.names = TRUE) file <- here::here("results",paste0("heatmap_chr6",c(".png",".pdf")))
hm <- heatmaply::heatmaply(x = cor_mat,
row_side_colors = meta[colnames(cor_mat),
c("Cell","Histone")],
row_side_palette = c(keys$cell_key,keys$color_key),
plot_method = "plotly",
file = file,
key.title = "Pearson r",
height = 600
) ## Warning: 'plotly::orca' is deprecated.
## Use 'kaleido' instead.
## See help("Deprecated")## Warning in value[[3L]](cond): plotly::orca failed:
## Error: The orca command-line utility is required for this functionality.
##
## Please follow the installation instructions here -- https://github.com/plotly/orca#installation## Warning: 'plotly::export' is deprecated.
## Use 'orca' instead.
## See help("Deprecated")## Warning: 'plotly::orca' is deprecated.
## Use 'kaleido' instead.
## See help("Deprecated")## Warning in value[[3L]](cond): plotly::orca failed:
## Error: The orca command-line utility is required for this functionality.
##
## Please follow the installation instructions here -- https://github.com/plotly/orca#installation## Warning: 'plotly::export' is deprecated.
## Use 'orca' instead.
## See help("Deprecated")hmutils::sessionInfo()## R version 4.3.1 (2023-06-16)
## Platform: aarch64-apple-darwin20 (64-bit)
## Running under: macOS Ventura 13.6
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/4.3-arm64/Resources/lib/libRlapack.dylib; LAPACK version 3.11.0
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## time zone: Europe/Rome
## tzcode source: internal
##
## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
##
## other attached packages:
## [1] rtracklayer_1.61.1 GenomicRanges_1.53.2 GenomeInfoDb_1.37.6
## [4] IRanges_2.35.2 S4Vectors_0.39.3 BiocGenerics_0.47.0
##
## loaded via a namespace (and not attached):
## [1] splines_4.3.1
## [2] later_1.3.1
## [3] BiocIO_1.11.0
## [4] bitops_1.0-7
## [5] ggplotify_0.1.2
## [6] filelock_1.0.2
## [7] tibble_3.2.1
## [8] polyclip_1.10-6
## [9] XML_3.99-0.14
## [10] lifecycle_1.0.3
## [11] rprojroot_2.0.3
## [12] processx_3.8.2
## [13] lattice_0.21-9
## [14] pals_1.8
## [15] MASS_7.3-60
## [16] crosstalk_1.2.0
## [17] dendextend_1.17.1
## [18] magrittr_2.0.3
## [19] plotly_4.10.2
## [20] sass_0.4.7
## [21] rmarkdown_2.25
## [22] BSgenome.Hsapiens.UCSC.hg38_1.4.5
## [23] plotrix_3.8-2
## [24] jquerylib_0.1.4
## [25] yaml_2.3.7
## [26] BRGenomics_1.12.0
## [27] httpuv_1.6.11
## [28] cowplot_1.1.1
## [29] mapproj_1.2.11
## [30] DBI_1.1.3
## [31] RColorBrewer_1.1-3
## [32] maps_3.4.1
## [33] abind_1.4-5
## [34] zlibbioc_1.47.0
## [35] purrr_1.0.2
## [36] ggraph_2.1.0
## [37] RCurl_1.98-1.12
## [38] yulab.utils_0.1.0
## [39] tweenr_2.0.2
## [40] rappdirs_0.3.3
## [41] seriation_1.5.1
## [42] GenomeInfoDbData_1.2.10
## [43] enrichplot_1.20.3
## [44] ggrepel_0.9.4
## [45] tidytree_0.4.5
## [46] ChIPseeker_1.36.0
## [47] codetools_0.2-19
## [48] DelayedArray_0.27.10
## [49] DOSE_3.26.2
## [50] xml2_1.3.5
## [51] ggforce_0.4.1
## [52] tidyselect_1.2.0
## [53] aplot_0.2.2
## [54] farver_2.1.1
## [55] viridis_0.6.4
## [56] TSP_1.2-4
## [57] matrixStats_1.0.0
## [58] BiocFileCache_2.9.1
## [59] webshot_0.5.5
## [60] GenomicAlignments_1.37.0
## [61] jsonlite_1.8.7
## [62] ellipsis_0.3.2
## [63] tidygraph_1.2.3
## [64] iterators_1.0.14
## [65] foreach_1.5.2
## [66] tools_4.3.1
## [67] progress_1.2.2
## [68] treeio_1.25.4
## [69] TxDb.Hsapiens.UCSC.hg19.knownGene_3.2.2
## [70] Rcpp_1.0.11
## [71] glue_1.6.2
## [72] gridExtra_2.3
## [73] SparseArray_1.1.12
## [74] xfun_0.40
## [75] here_1.0.1
## [76] DESeq2_1.40.2
## [77] qvalue_2.32.0
## [78] MatrixGenerics_1.13.1
## [79] ca_0.71.1
## [80] dplyr_1.1.3
## [81] withr_2.5.1
## [82] BiocManager_1.30.22
## [83] fastmap_1.1.1
## [84] boot_1.3-28.1
## [85] fansi_1.0.5
## [86] callr_3.7.3
## [87] caTools_1.18.2
## [88] digest_0.6.33
## [89] R6_2.5.1
## [90] mime_0.12
## [91] gridGraphics_0.5-1
## [92] seqPattern_1.32.0
## [93] colorspace_2.1-0
## [94] GO.db_3.18.0
## [95] gtools_3.9.4
## [96] dichromat_2.0-0.1
## [97] biomaRt_2.57.1
## [98] RSQLite_2.3.1
## [99] utf8_1.2.3
## [100] tidyr_1.3.0
## [101] generics_0.1.3
## [102] data.table_1.14.8
## [103] htmlwidgets_1.6.2
## [104] prettyunits_1.2.0
## [105] graphlayouts_1.0.1
## [106] httr_1.4.7
## [107] S4Arrays_1.1.6
## [108] downloadthis_0.3.2
## [109] scatterpie_0.2.1
## [110] pkgconfig_2.0.3
## [111] gtable_0.3.4
## [112] registry_0.5-1
## [113] blob_1.2.4
## [114] impute_1.75.1
## [115] XVector_0.41.1
## [116] shadowtext_0.1.2
## [117] htmltools_0.5.6.1
## [118] fgsea_1.26.0
## [119] scales_1.2.1
## [120] Biobase_2.61.0
## [121] png_0.1-8
## [122] EpiCompare_1.3.4
## [123] ggfun_0.1.3
## [124] knitr_1.44
## [125] rstudioapi_0.15.0
## [126] tzdb_0.4.0
## [127] reshape2_1.4.4
## [128] rjson_0.2.21
## [129] nlme_3.1-163
## [130] curl_5.1.0
## [131] cachem_1.0.8
## [132] stringr_1.5.0
## [133] KernSmooth_2.23-22
## [134] BiocVersion_3.18.0
## [135] parallel_4.3.1
## [136] HDO.db_0.99.1
## [137] AnnotationDbi_1.63.2
## [138] restfulr_0.0.15
## [139] pillar_1.9.0
## [140] grid_4.3.1
## [141] vctrs_0.6.4
## [142] gplots_3.1.3
## [143] promises_1.2.1
## [144] dbplyr_2.3.4
## [145] xtable_1.8-4
## [146] evaluate_0.22
## [147] readr_2.1.4
## [148] GenomicFeatures_1.53.2
## [149] cli_3.6.1
## [150] locfit_1.5-9.8
## [151] compiler_4.3.1
## [152] Rsamtools_2.17.0
## [153] rlang_1.1.1
## [154] crayon_1.5.2
## [155] heatmaply_1.5.0
## [156] ps_1.7.5
## [157] plyr_1.8.9
## [158] fs_1.6.3
## [159] stringi_1.7.12
## [160] gridBase_0.4-7
## [161] genomation_1.32.0
## [162] viridisLite_0.4.2
## [163] BiocParallel_1.35.4
## [164] assertthat_0.2.1
## [165] munsell_0.5.0
## [166] Biostrings_2.69.2
## [167] lazyeval_0.2.2
## [168] GOSemSim_2.26.1
## [169] Matrix_1.6-1.1
## [170] BSgenome_1.69.0
## [171] hms_1.1.3
## [172] patchwork_1.1.3
## [173] bit64_4.0.5
## [174] ggplot2_3.4.4
## [175] KEGGREST_1.41.4
## [176] shiny_1.7.5
## [177] SummarizedExperiment_1.31.1
## [178] interactiveDisplayBase_1.39.0
## [179] AnnotationHub_3.9.2
## [180] igraph_1.5.1
## [181] memoise_2.0.1.9000
## [182] bslib_0.5.1
## [183] ggtree_3.9.1
## [184] fastmatch_1.1-4
## [185] bit_4.0.5
## [186] ape_5.7-1