Visualizing Large-scale Copy Number Variation in Single-Cell RNA-Seq Expression Data
2022-04-26
Abstract
InferCNV is used to explore tumor single cell RNA-Seq data to identify evidence for large-scale chromosomal copy number variations, such as gains or deletions of entire chromosomes or large segments of chromosomes. This is done by exploring expression intensity of genes across positions of the genome in comparison to the average or a set of reference ‘normal’ cells. A heatmap is generated illustrating the relative expression intensities across each chromosome, and it becomes readily apparent as to which regions of the genome are over-abundant or less-abundant as compared to normal cells (or the average, if reference normal cells are not provided).Package
infercnv 1.12.0
Contents
1 Installation
1.1 Required dependencies
inferCNV uses the R packages ape, BiocGenerics, binhf, caTools, coda, coin, dplyr, doparallel, edgeR, fastcluster, fitdistrplus, foreach, futile.logger, future, gplots, ggplot2, HiddenMarkov, leiden, phyclust, RANN, reshape, rjags, RColorBrewer, SingleCellExperiment, SummarizedExperiment, tidyr and imports functions from the archived GMD.
1.2 Installing
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("infercnv")1.3 Optional extension
If you want to use the interactive heatmap visualization, please check the add-on packge R inferCNV_NGCHM after installing the packages tibble, tsvio and NGCHMR. To install optional packages, type the following in an R command window:
install.packages("tibble")
install.packages("devtools")
devtools::install_github("bmbroom/tsvio")
devtools::install_github("bmbroom/NGCHMR", ref="stable")
devtools::install_github("broadinstitute/inferCNV_NGCHM")And download the NGCHM java application by typing the following in a regular shell:
wget http://tcga.ngchm.net/NGCHM/ShaidyMapGen.jar2 Running InferCNV
2.1 Create the InferCNV Object
Reading in the raw counts matrix and meta data, populating the infercnv object
infercnv_obj = CreateInfercnvObject(
raw_counts_matrix="../inst/extdata/oligodendroglioma_expression_downsampled.counts.matrix.gz",
annotations_file="../inst/extdata/oligodendroglioma_annotations_downsampled.txt",
delim="\t",
gene_order_file="../inst/extdata/gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt",
ref_group_names=c("Microglia/Macrophage","Oligodendrocytes (non-malignant)"))## INFO [2022-04-26 16:56:34] Parsing matrix: ../inst/extdata/oligodendroglioma_expression_downsampled.counts.matrix.gz
## INFO [2022-04-26 16:56:37] Parsing gene order file: ../inst/extdata/gencode_downsampled.EXAMPLE_ONLY_DONT_REUSE.txt
## INFO [2022-04-26 16:56:37] Parsing cell annotations file: ../inst/extdata/oligodendroglioma_annotations_downsampled.txt
## INFO [2022-04-26 16:56:37] ::order_reduce:Start.
## INFO [2022-04-26 16:56:37] .order_reduce(): expr and order match.
## INFO [2022-04-26 16:56:38] ::process_data:order_reduce:Reduction from positional data, new dimensions (r,c) = 10338,184 Total=18322440.6799817 Min=0 Max=34215.
## INFO [2022-04-26 16:56:38] num genes removed taking into account provided gene ordering list: 399 = 3.8595473012188% removed.
## INFO [2022-04-26 16:56:38] -filtering out cells < 100 or > Inf, removing 0 % of cells
## WARN [2022-04-26 16:56:38] Please use "options(scipen = 100)" before running infercnv if you are using the analysis_mode="subclusters" option or you may encounter an error while the hclust is being generated.
## INFO [2022-04-26 16:56:38] validating infercnv_obj2.2 Running the full default analysis
out_dir = tempfile()
infercnv_obj_default = infercnv::run(
infercnv_obj,
cutoff=1, # cutoff=1 works well for Smart-seq2, and cutoff=0.1 works well for 10x Genomics
out_dir=out_dir,
cluster_by_groups=TRUE,
plot_steps=FALSE,
denoise=TRUE,
HMM=FALSE,
no_prelim_plot=TRUE,
png_res=60
)Basic ouput from running inferCNV.
## Warning in knitr::include_graphics(paste(out_dir, "infercnv.png", sep = "/")):
## It is highly recommended to use relative paths for images. You had absolute
## paths: "/tmp/RtmpSaDg5B/file41064102f924c/infercnv.png"
3 Additional Information
3.1 Online Documentation
For additional explanations on files, usage, and a tutorial please visit the wiki.
3.2 TrinityCTAT
This tool is a part of the TrinityCTAT toolkit focused on leveraging the use of RNA-Seq to better understand cancer transcriptomes. To find out more please visit TrinityCTAT
3.3 Applications
This methodology was used in:
4 Session info
## R version 4.2.0 RC (2022-04-19 r82224)
## Platform: x86_64-pc-linux-gnu (64-bit)
## Running under: Ubuntu 20.04.4 LTS
##
## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.15-bioc/R/lib/libRblas.so
## LAPACK: /home/biocbuild/bbs-3.15-bioc/R/lib/libRlapack.so
##
## locale:
## [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
## [3] LC_TIME=en_GB 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] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] infercnv_1.12.0 BiocStyle_2.24.0
##
## loaded via a namespace (and not attached):
## [1] TH.data_1.1-1 colorspace_2.0-3
## [3] ellipsis_0.3.2 modeltools_0.2-23
## [5] futile.logger_1.4.3 XVector_0.36.0
## [7] GenomicRanges_1.48.0 leiden_0.3.9
## [9] listenv_0.8.0 fansi_1.0.3
## [11] mvtnorm_1.1-3 coin_1.4-2
## [13] codetools_0.2-18 splines_4.2.0
## [15] doParallel_1.0.17 libcoin_1.0-9
## [17] knitr_1.38 jsonlite_1.8.0
## [19] argparse_2.1.5 rjags_4-13
## [21] png_0.1-7 BiocManager_1.30.17
## [23] compiler_4.2.0 assertthat_0.2.1
## [25] Matrix_1.4-1 fastmap_1.1.0
## [27] limma_3.52.0 cli_3.3.0
## [29] formatR_1.12 htmltools_0.5.2
## [31] tools_4.2.0 igraph_1.3.1
## [33] coda_0.19-4 gtable_0.3.0
## [35] glue_1.6.2 GenomeInfoDbData_1.2.8
## [37] RANN_2.6.1 dplyr_1.0.8
## [39] Rcpp_1.0.8.3 Biobase_2.56.0
## [41] jquerylib_0.1.4 vctrs_0.4.1
## [43] ape_5.6-2 nlme_3.1-157
## [45] iterators_1.0.14 xfun_0.30
## [47] fastcluster_1.2.3 stringr_1.4.0
## [49] globals_0.14.0 lifecycle_1.0.1
## [51] gtools_3.9.2 future_1.25.0
## [53] edgeR_3.38.0 zlibbioc_1.42.0
## [55] MASS_7.3-57 zoo_1.8-10
## [57] scales_1.2.0 MatrixGenerics_1.8.0
## [59] parallel_4.2.0 SummarizedExperiment_1.26.0
## [61] sandwich_3.0-1 lambda.r_1.2.4
## [63] RColorBrewer_1.1-3 SingleCellExperiment_1.18.0
## [65] yaml_2.3.5 reticulate_1.24
## [67] gridExtra_2.3 ggplot2_3.3.5
## [69] sass_0.4.1 reshape_0.8.9
## [71] stringi_1.7.6 highr_0.9
## [73] S4Vectors_0.34.0 foreach_1.5.2
## [75] caTools_1.18.2 BiocGenerics_0.42.0
## [77] GenomeInfoDb_1.32.0 rlang_1.0.2
## [79] pkgconfig_2.0.3 matrixStats_0.62.0
## [81] bitops_1.0-7 parallelDist_0.2.6
## [83] evaluate_0.15 lattice_0.20-45
## [85] purrr_0.3.4 tidyselect_1.1.2
## [87] parallelly_1.31.1 plyr_1.8.7
## [89] magrittr_2.0.3 bookdown_0.26
## [91] R6_2.5.1 IRanges_2.30.0
## [93] gplots_3.1.3 generics_0.1.2
## [95] multcomp_1.4-19 DelayedArray_0.22.0
## [97] DBI_1.1.2 pillar_1.7.0
## [99] fitdistrplus_1.1-8 survival_3.3-1
## [101] RCurl_1.98-1.6 tibble_3.1.6
## [103] crayon_1.5.1 futile.options_1.0.1
## [105] phyclust_0.1-30 KernSmooth_2.23-20
## [107] utf8_1.2.2 rmarkdown_2.14
## [109] locfit_1.5-9.5 grid_4.2.0
## [111] digest_0.6.29 tidyr_1.2.0
## [113] RcppParallel_5.1.5 stats4_4.2.0
## [115] munsell_0.5.0 bslib_0.3.1