if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("SingleCellMultiModal")library(SingleCellMultiModal)
library(MultiAssayExperiment)The dataset was graciously provided by Argelaguet et al. (2019).
Scripts used to process the raw data were written and maintained by Argelaguet and colleagues and reside on GitHub: https://github.com/rargelaguet/scnmt_gastrulation
For more information on the protocol, see Clark et al. (2018).
The user can see the available dataset by using the default options
scNMT("mouse_gastrulation", mode = "*", dry.run = TRUE)## Available data modes for
## mouse_gastrulation:
## acc_CTCF, acc_DHS, acc_cgi, acc_genebody,
## acc_p300, acc_promoter, met_CTCF,
## met_DHS, met_cgi, met_genebody, met_p300,
## met_promoter, rnaOr by simply running:
scNMT("mouse_gastrulation")## Available data modes for
## mouse_gastrulation:
## acc_CTCF, acc_DHS, acc_cgi, acc_genebody,
## acc_p300, acc_promoter, met_CTCF,
## met_DHS, met_cgi, met_genebody, met_p300,
## met_promoter, rnaExample with actual data:
nmt <- scNMT("mouse_gastrulation", mode = c("*_DHS", "*_cgi", "*_genebody"),
dry.run = FALSE)
nmt## A MultiAssayExperiment object of 6 listed
## experiments with user-defined names and respective classes.
## Containing an ExperimentList class object of length 6:
## [1] acc_cgi: matrix with 4459 rows and 826 columns
## [2] acc_DHS: matrix with 290 rows and 826 columns
## [3] acc_genebody: matrix with 17139 rows and 826 columns
## [4] met_cgi: matrix with 5536 rows and 826 columns
## [5] met_DHS: matrix with 66 rows and 826 columns
## [6] met_genebody: matrix with 15837 rows and 826 columns
## Features:
## experiments() - obtain the ExperimentList instance
## colData() - the primary/phenotype DFrame
## sampleMap() - the sample availability DFrame
## `$`, `[`, `[[` - extract colData columns, subset, or experiment
## *Format() - convert into a long or wide DFrame
## assays() - convert ExperimentList to a SimpleList of matricesCheck row annotations:
rownames(nmt)## CharacterList of length 6
## [["acc_cgi"]] CGI_5278 CGI_6058 CGI_10627 ... CGI_7832 CGI_11329 CGI_10964
## [["acc_DHS"]] ESC_DHS_118970 ESC_DHS_118919 ... ESC_DHS_68996 ESC_DHS_109494
## [["acc_genebody"]] ENSMUSG00000036181 ENSMUSG00000071862 ... ENSMUSG00000025576
## [["met_cgi"]] CGI_3481 CGI_8941 CGI_956 CGI_9461 ... CGI_2867 CGI_3499 CGI_365
## [["met_DHS"]] ESC_DHS_20778 ESC_DHS_14504 ... ESC_DHS_72133 ESC_DHS_72129
## [["met_genebody"]] ENSMUSG00000059334 ENSMUSG00000024026 ... ENSMUSG00000078302Take a peek at the sampleMap:
sampleMap(nmt)## DataFrame with 4956 rows and 3 columns
## assay primary colname
## <factor> <character> <character>
## 1 met_genebody E4.5-5.5_new_Plate1_A02 E4.5-5.5_new_Plate1_A02
## 2 met_genebody E4.5-5.5_new_Plate1_A04 E4.5-5.5_new_Plate1_A04
## 3 met_genebody E4.5-5.5_new_Plate1_A07 E4.5-5.5_new_Plate1_A07
## 4 met_genebody E4.5-5.5_new_Plate1_A08 E4.5-5.5_new_Plate1_A08
## 5 met_genebody E4.5-5.5_new_Plate1_A12 E4.5-5.5_new_Plate1_A12
## ... ... ... ...
## 4952 acc_DHS PS_VE_Plate9_G05 PS_VE_Plate9_G05
## 4953 acc_DHS PS_VE_Plate9_G08 PS_VE_Plate9_G08
## 4954 acc_DHS PS_VE_Plate9_G09 PS_VE_Plate9_G09
## 4955 acc_DHS PS_VE_Plate9_G12 PS_VE_Plate9_G12
## 4956 acc_DHS PS_VE_Plate9_H08 PS_VE_Plate9_H08See the accessibilty levels (as proportions) for DNase Hypersensitive Sites:
head(assay(nmt, "acc_DHS"))[, 1:4]## E4.5-5.5_new_Plate1_A02 E4.5-5.5_new_Plate1_A04
## ESC_DHS_118970 0.66666667 NA
## ESC_DHS_118919 0.76190476 NA
## ESC_DHS_66330 0.81818182 0.7142857
## ESC_DHS_43318 NA 0.8000000
## ESC_DHS_6229 0.85714286 0.8000000
## ESC_DHS_9413 0.06666667 0.6800000
## E4.5-5.5_new_Plate1_A07 E4.5-5.5_new_Plate1_A08
## ESC_DHS_118970 NA 0.2631579
## ESC_DHS_118919 0.3636364 0.8421053
## ESC_DHS_66330 0.7391304 0.6086957
## ESC_DHS_43318 0.5000000 0.8888889
## ESC_DHS_6229 0.3333333 0.7142857
## ESC_DHS_9413 0.2142857 0.5217391See the methylation percentage / proportion:
head(assay(nmt, "met_DHS"))[, 1:4]## E4.5-5.5_new_Plate1_A02 E4.5-5.5_new_Plate1_A04
## ESC_DHS_20778 0.8000000 NA
## ESC_DHS_14504 0.8000000 0.8
## ESC_DHS_112143 NA 0.4
## ESC_DHS_34593 0.6666667 0.6
## ESC_DHS_20747 0.4000000 0.6
## ESC_DHS_33671 NA 0.6
## E4.5-5.5_new_Plate1_A07 E4.5-5.5_new_Plate1_A08
## ESC_DHS_20778 0.8571429 0.8000000
## ESC_DHS_14504 0.8000000 0.6000000
## ESC_DHS_112143 0.5714286 0.5000000
## ESC_DHS_34593 0.7142857 0.8000000
## ESC_DHS_20747 NA 0.6000000
## ESC_DHS_33671 0.8333333 0.6666667For protocol information, see the references below.
sessionInfo()## R version 4.0.0 (2020-04-24)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 18.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.11-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.11-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_US.UTF-8 LC_COLLATE=C
## [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
## [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
## [9] LC_ADDRESS=C LC_TELEPHONE=C
## [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] SingleCellMultiModal_1.0.0 MultiAssayExperiment_1.14.0
## [3] SummarizedExperiment_1.18.1 DelayedArray_0.14.0
## [5] matrixStats_0.56.0 Biobase_2.48.0
## [7] GenomicRanges_1.40.0 GenomeInfoDb_1.24.0
## [9] IRanges_2.22.1 S4Vectors_0.26.0
## [11] BiocGenerics_0.34.0 BiocStyle_2.16.0
##
## loaded via a namespace (and not attached):
## [1] Rcpp_1.0.4.6 lattice_0.20-41
## [3] assertthat_0.2.1 digest_0.6.25
## [5] mime_0.9 BiocFileCache_1.12.0
## [7] R6_2.4.1 RSQLite_2.2.0
## [9] evaluate_0.14 httr_1.4.1
## [11] pillar_1.4.4 zlibbioc_1.34.0
## [13] rlang_0.4.6 curl_4.3
## [15] blob_1.2.1 Matrix_1.2-18
## [17] rmarkdown_2.1 AnnotationHub_2.20.0
## [19] stringr_1.4.0 RCurl_1.98-1.2
## [21] bit_1.1-15.2 shiny_1.4.0.2
## [23] compiler_4.0.0 httpuv_1.5.2
## [25] xfun_0.13 pkgconfig_2.0.3
## [27] htmltools_0.4.0 tidyselect_1.0.0
## [29] tibble_3.0.1 GenomeInfoDbData_1.2.3
## [31] interactiveDisplayBase_1.26.0 bookdown_0.18
## [33] crayon_1.3.4 dplyr_0.8.5
## [35] dbplyr_1.4.3 later_1.0.0
## [37] bitops_1.0-6 rappdirs_0.3.1
## [39] grid_4.0.0 xtable_1.8-4
## [41] lifecycle_0.2.0 DBI_1.1.0
## [43] magrittr_1.5 stringi_1.4.6
## [45] XVector_0.28.0 promises_1.1.0
## [47] ellipsis_0.3.0 vctrs_0.2.4
## [49] tools_4.0.0 bit64_0.9-7
## [51] glue_1.4.0 BiocVersion_3.11.1
## [53] purrr_0.3.4 fastmap_1.0.1
## [55] yaml_2.2.1 AnnotationDbi_1.50.0
## [57] ExperimentHub_1.14.0 BiocManager_1.30.10
## [59] memoise_1.1.0 knitr_1.28Argelaguet, Ricard, Stephen J Clark, Hisham Mohammed, L Carine Stapel, Christel Krueger, Chantriolnt-Andreas Kapourani, Ivan Imaz-Rosshandler, et al. 2019. “Multi-Omics Profiling of Mouse Gastrulation at Single-Cell Resolution.” Nature 576 (7787):487–91.
Clark, Stephen J, Ricard Argelaguet, Chantriolnt-Andreas Kapourani, Thomas M Stubbs, Heather J Lee, Celia Alda-Catalinas, Felix Krueger, et al. 2018. “scNMT-seq Enables Joint Profiling of Chromatin Accessibility DNA Methylation and Transcription in Single Cells.” Nat. Commun. 9 (1):781.