Resolution of curse of dimensionality (RECODE) is a noise reduction method for single-cell sequencing data. This vignette briefly demonstrates how to run RECODE on single-cell RNA-seq data and store the result in Seurat object.
if (!require(RECODE)) devtools::install_github("yusuke-imoto-lab/RECODE/R")Though we use Seurat v4 in this tutorial, we have checked Seurat v3 also works.
library(Seurat)
library(Matrix)
library(RECODE)Sample data： For this tutorial, we use sample 10k Human PBMCs, 3’ v3.1, Chromium Controller (11,485 cells and 36,601 genes) in 10X Genomics Datasets. The test data is deposited as Feature / cell matrix HDF5 (filterd) here (registration required).
seurat <- Read10X_h5("10k_PBMC_3p_nextgem_Chromium_Controller_filtered_feature_bc_matrix.h5")## Warning in sparseMatrix(i = indices[] + 1, p = indptr[], x = as.numeric(x = counts[]), : 'giveCsparse' has been deprecated; setting 'repr = "T"' for youseurat <- CreateSeuratObject(seurat)We apply RECODE on a count matrix where each row is a gene and each column is a cell. The output is a denoised count matrix.
data <- as.matrix(seurat[["RNA"]]@counts)
data_RECODE <- RECODE(data)## --START RECODE--## I. Normalizing## II. Projecting to PCA space## III. Modifying eigenvalues## ell = 166## Reducing noise## VI. Reversing to original space## --END RECODE--We store the denoised count matrix in seurat[["RECODE"]]@counts.
seurat[["RECODE"]] <- CreateAssayObject(counts = Matrix(data_RECODE, sparse = TRUE))
DefaultAssay(seurat) <- "RECODE"sessionInfo()## R version 4.0.2 (2020-06-22)
## Platform: x86_64-w64-mingw32/x64 (64-bit)
## Running under: Windows 10 x64 (build 19044)
##
## Matrix products: default
##
## locale:
## [1] LC_COLLATE=Japanese_Japan.932 LC_CTYPE=Japanese_Japan.932 LC_MONETARY=Japanese_Japan.932 LC_NUMERIC=C LC_TIME=Japanese_Japan.932
##
## attached base packages:
## [1] stats graphics grDevices utils datasets methods base
##
## other attached packages:
## [1] ggplot2_3.3.5 Matrix_1.3-4 SeuratObject_4.0.2 Seurat_4.0.4 RECODE_1.0.0 rmarkdown_2.8 RevoUtils_11.0.2
## [8] RevoUtilsMath_11.0.0
##
## loaded via a namespace (and not attached):
## [1] plyr_1.8.6 igraph_1.2.5 lazyeval_0.2.2 splines_4.0.2 listenv_0.8.0 scattermore_0.7 usethis_2.0.1
## [8] digest_0.6.25 htmltools_0.5.2 viridis_0.5.1 fansi_0.4.1 magrittr_2.0.1 memoise_2.0.0 tensor_1.5
## [15] cluster_2.1.0 ROCR_1.0-11 remotes_2.4.0 globals_0.14.0 matrixStats_0.61.0 spatstat.sparse_2.0-0 prettyunits_1.1.1
## [22] colorspace_1.4-1 ggrepel_0.8.2 xfun_0.23 dplyr_1.0.7 callr_3.7.0 crayon_1.4.1 jsonlite_1.7.0
## [29] spatstat.data_2.1-0 survival_3.1-12 zoo_1.8-8 glue_1.4.1 polyclip_1.10-0 gtable_0.3.0 leiden_0.3.9
## [36] pkgbuild_1.2.0 future.apply_1.8.1 abind_1.4-5 scales_1.1.1 DBI_1.1.1 miniUI_0.1.1.1 Rcpp_1.0.7
## [43] viridisLite_0.3.0 xtable_1.8-4 reticulate_1.16 spatstat.core_2.3-0 bit_4.0.4 htmlwidgets_1.5.4 httr_1.4.2
## [50] RColorBrewer_1.1-2 ellipsis_0.3.2 ica_1.0-2 pkgconfig_2.0.3 farver_2.0.3 sass_0.4.0 uwot_0.1.10
## [57] deldir_0.2-10 utf8_1.2.2 tidyselect_1.1.1 labeling_0.4.2 rlang_0.4.11 reshape2_1.4.4 later_1.3.0
## [64] munsell_0.5.0 tools_4.0.2 cachem_1.0.6 cli_3.0.1 generics_0.1.0 devtools_2.4.2 ggridges_0.5.3
## [71] evaluate_0.14 stringr_1.4.0 fastmap_1.1.0 yaml_2.2.1 goftest_1.2-2 processx_3.5.2 knitr_1.34
## [78] bit64_4.0.5 fs_1.5.0 fitdistrplus_1.1-5 purrr_0.3.4 RANN_2.6.1 dendextend_1.15.1 pbapply_1.5-0
## [85] future_1.22.1 nlme_3.1-148 mime_0.11 hdf5r_1.3.4 compiler_4.0.2 plotly_4.9.4.1 png_0.1-7
## [92] testthat_3.0.4 spatstat.utils_2.2-0 tibble_3.0.3 bslib_0.3.0 stringi_1.4.6 highr_0.9 ps_1.6.0
## [99] RSpectra_0.16-0 desc_1.3.0 lattice_0.20-41 vctrs_0.3.8 pillar_1.6.2 lifecycle_1.0.0 spatstat.geom_2.2-2
## [106] lmtest_0.9-37 jquerylib_0.1.4 RcppAnnoy_0.0.18 data.table_1.12.8 cowplot_1.1.1 irlba_2.3.3 httpuv_1.6.3
## [113] patchwork_1.0.1 R6_2.4.1 promises_1.2.0.1 KernSmooth_2.23-17 gridExtra_2.3 parallelly_1.28.1 sessioninfo_1.1.1
## [120] codetools_0.2-16 MASS_7.3-51.6 assertthat_0.2.1 pkgload_1.2.2 rprojroot_2.0.2 withr_2.4.2 sctransform_0.3.2
## [127] mgcv_1.8-31 parallel_4.0.2 grid_4.0.2 rpart_4.1-15 tidyr_1.1.0 Rtsne_0.15 shiny_1.5.0