FootprintCharter
Introduction
This vignette exemplifies how to perform unsupervised footprint detection and quantification using FootprintCharter as per Baderna & Barzaghi et al., 2024 and Barzaghi et al., 2024.
FootprintCharter partitions molecules by their methylation patterns without relying on orthogonal genomic annotations such as TF motifs.
Loading libraries
Methylation = qs::qread(system.file("extdata", "Methylation_4.qs", package="SingleMoleculeFootprinting"))
RegionOfInterest = GRanges("chr6", IRanges(88106000, 88106500))
TFBSs = qs::qread(system.file("extdata", "TFBSs_1.qs", package="SingleMoleculeFootprinting"))
PlotAvgSMF(MethGR = Methylation[[1]], RegionOfInterest = RegionOfInterest, TFBSs = TFBSs)
## No sorted reads passed...plotting counts from all reads
sessionInfo
## R Under development (unstable) (2024-10-26 r87273 ucrt)
## Platform: x86_64-w64-mingw32/x64
## Running under: Windows Server 2022 x64 (build 20348)
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## Matrix products: default
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## locale:
## [1] LC_COLLATE=C
## [2] LC_CTYPE=English_United States.utf8
## [3] LC_MONETARY=English_United States.utf8
## [4] LC_NUMERIC=C
## [5] LC_TIME=English_United States.utf8
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## time zone: America/New_York
## tzcode source: internal
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] BSgenome.Mmusculus.UCSC.mm10_1.4.3 BSgenome_1.75.0
## [3] rtracklayer_1.67.0 BiocIO_1.17.0
## [5] Biostrings_2.75.0 XVector_0.47.0
## [7] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
## [9] IRanges_2.41.0 S4Vectors_0.45.0
## [11] BiocGenerics_0.53.1 generics_0.1.3
## [13] SingleMoleculeFootprinting_2.1.0
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## loaded via a namespace (and not attached):
## [1] RColorBrewer_1.1-3 jsonlite_1.8.9
## [3] magrittr_2.0.3 GenomicFeatures_1.59.0
## [5] farver_2.1.2 rmarkdown_2.28
## [7] zlibbioc_1.53.0 vctrs_0.6.5
## [9] memoise_2.0.1 Rsamtools_2.23.0
## [11] RCurl_1.98-1.16 QuasR_1.47.0
## [13] ggpointdensity_0.1.0 htmltools_0.5.8.1
## [15] S4Arrays_1.7.1 progress_1.2.3
## [17] curl_5.2.3 SparseArray_1.7.0
## [19] sass_0.4.9 bslib_0.8.0
## [21] plyr_1.8.9 httr2_1.0.5
## [23] cachem_1.1.0 GenomicAlignments_1.43.0
## [25] lifecycle_1.0.4 pkgconfig_2.0.3
## [27] Matrix_1.7-1 R6_2.5.1
## [29] fastmap_1.2.0 GenomeInfoDbData_1.2.13
## [31] MatrixGenerics_1.19.0 digest_0.6.37
## [33] colorspace_2.1-1 ShortRead_1.65.0
## [35] patchwork_1.3.0 AnnotationDbi_1.69.0
## [37] RSQLite_2.3.7 hwriter_1.3.2.1
## [39] labeling_0.4.3 filelock_1.0.3
## [41] fansi_1.0.6 httr_1.4.7
## [43] abind_1.4-8 compiler_4.5.0
## [45] Rbowtie_1.47.0 bit64_4.5.2
## [47] withr_3.0.2 BiocParallel_1.41.0
## [49] viridis_0.6.5 DBI_1.2.3
## [51] qs_0.27.2 highr_0.11
## [53] biomaRt_2.63.0 rappdirs_0.3.3
## [55] DelayedArray_0.33.1 rjson_0.2.23
## [57] tools_4.5.0 glue_1.8.0
## [59] restfulr_0.0.15 grid_4.5.0
## [61] gtable_0.3.6 tidyr_1.3.1
## [63] RApiSerialize_0.1.4 hms_1.1.3
## [65] xml2_1.3.6 stringfish_0.16.0
## [67] utf8_1.2.4 ggrepel_0.9.6
## [69] pillar_1.9.0 stringr_1.5.1
## [71] dplyr_1.1.4 BiocFileCache_2.15.0
## [73] lattice_0.22-6 bit_4.5.0
## [75] deldir_2.0-4 tidyselect_1.2.1
## [77] knitr_1.48 gridExtra_2.3
## [79] SummarizedExperiment_1.37.0 xfun_0.49
## [81] Biobase_2.67.0 matrixStats_1.4.1
## [83] stringi_1.8.4 UCSC.utils_1.3.0
## [85] yaml_2.3.10 evaluate_1.0.1
## [87] codetools_0.2-20 interp_1.1-6
## [89] GenomicFiles_1.43.0 tibble_3.2.1
## [91] cli_3.6.3 RcppParallel_5.1.9
## [93] munsell_0.5.1 jquerylib_0.1.4
## [95] Rcpp_1.0.13-1 dbplyr_2.5.0
## [97] tidyverse_2.0.0 png_0.1-8
## [99] XML_3.99-0.17 parallel_4.5.0
## [101] ggplot2_3.5.1 blob_1.2.4
## [103] prettyunits_1.2.0 latticeExtra_0.6-30
## [105] jpeg_0.1-10 plyranges_1.27.0
## [107] bitops_1.0-9 pwalign_1.3.0
## [109] txdbmaker_1.3.0 viridisLite_0.4.2
## [111] VariantAnnotation_1.53.0 scales_1.3.0
## [113] purrr_1.0.2 crayon_1.5.3
## [115] rlang_1.1.4 KEGGREST_1.47.0