Last updated: 2019-07-16
Checks: 6 1
Knit directory: MAGL/
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File | Version | Author | Date | Message |
---|---|---|---|---|
Rmd | 244411f | HelenaLC | 2019-07-11 | update lps analysis to include removal of doublets |
html | 244411f | HelenaLC | 2019-07-11 | update lps analysis to include removal of doublets |
Rmd | f10fe14 | HelenaLC | 2019-06-17 | rmv old |
html | 08bf260 | HelenaLC | 2019-06-17 | update |
Rmd | 61c0b06 | HelenaLC | 2019-05-20 | update |
html | 3c5aa5f | HelenaLC | 2019-05-03 | add MAGL prepro,clust,anno |
Rmd | 10c7525 | HelenaLC | 2019-05-03 | add MAGL prepro,clust,anno |
Rmd | eaed7ec | HelenaLC | 2019-03-11 | initial commit |
# increase future's maximum allowed size of objects
# to be exported from default of 500 MB to 2 GB
options(future.globals.maxSize = 2048 * 1024 ^ 2)
library(cowplot)
library(Seurat)
library(SingleCellExperiment)
sce <- readRDS(file.path("output", "MAGL-SCE.rds"))
# create SeuratObject
so <- CreateSeuratObject(
counts = counts(sce),
meta.data = data.frame(colData(sce)),
project = "10xMAGL")
# split by sample
cells_by_sample <- split(colnames(sce), sce$sample_id)
so <- lapply(cells_by_sample, function(i)
SubsetData(so, cells = i))
Warning: 'SubsetData' is deprecated.
Use 'subset' instead.
See help("Deprecated")
Warning: 'OldWhichCells' is deprecated.
Use 'WhichCells' instead.
See help("Deprecated")
Warning: 'SubsetData' is deprecated.
Use 'subset' instead.
See help("Deprecated")
Warning: 'OldWhichCells' is deprecated.
Use 'WhichCells' instead.
See help("Deprecated")
Warning: 'SubsetData' is deprecated.
Use 'subset' instead.
See help("Deprecated")
Warning: 'OldWhichCells' is deprecated.
Use 'WhichCells' instead.
See help("Deprecated")
Warning: 'SubsetData' is deprecated.
Use 'subset' instead.
See help("Deprecated")
Warning: 'OldWhichCells' is deprecated.
Use 'WhichCells' instead.
See help("Deprecated")
Warning: 'SubsetData' is deprecated.
Use 'subset' instead.
See help("Deprecated")
Warning: 'OldWhichCells' is deprecated.
Use 'WhichCells' instead.
See help("Deprecated")
Warning: 'SubsetData' is deprecated.
Use 'subset' instead.
See help("Deprecated")
Warning: 'OldWhichCells' is deprecated.
Use 'WhichCells' instead.
See help("Deprecated")
Warning: 'SubsetData' is deprecated.
Use 'subset' instead.
See help("Deprecated")
Warning: 'OldWhichCells' is deprecated.
Use 'WhichCells' instead.
See help("Deprecated")
Warning: 'SubsetData' is deprecated.
Use 'subset' instead.
See help("Deprecated")
Warning: 'OldWhichCells' is deprecated.
Use 'WhichCells' instead.
See help("Deprecated")
# normalize, find variable genes, and scale
so <- lapply(so, NormalizeData, verbose = FALSE)
so <- lapply(so, FindVariableFeatures, nfeatures = 2e3,
selection.method = "vst", do.plot = FALSE, verbose = FALSE)
so <- lapply(so, ScaleData, verbose = FALSE)
# find anchors & integrate
as <- FindIntegrationAnchors(so, verbose = FALSE)
so <- IntegrateData(anchorset = as, dims = seq_len(30), verbose = FALSE)
# scale integrated data
DefaultAssay(so) <- "integrated"
so <- ScaleData(so, display.progress = FALSE)
Centering and scaling data matrix
so <- RunPCA(so, npcs = 30, verbose = FALSE)
so <- RunTSNE(so, reduction = "pca", dims = seq_len(20),
seed.use = 1, do.fast = TRUE, verbose = FALSE)
so <- RunUMAP(so, reduction = "pca", dims = seq_len(20),
seed.use = 1, verbose = FALSE)
so <- FindNeighbors(so, reduction = "pca", dims = seq_len(20), verbose = FALSE)
for (res in c(0.1, 0.2, 0.4, 0.8, 1, 1.2, 2))
so <- FindClusters(so, resolution = res, random.seed = 1, verbose = FALSE)
thm <- theme(aspect.ratio = 1, legend.position = "none")
ps <- lapply(c("sample_id", "group_id", "ident"), function(u) {
p1 <- DimPlot(so, reduction = "tsne", group.by = u) + thm
p2 <- DimPlot(so, reduction = "umap", group.by = u)
lgd <- get_legend(p2)
p2 <- p2 + thm
list(p1, p2, lgd)
plot_grid(p1, p2, lgd, nrow = 1,
rel_widths = c(1, 1, 0.5))
})
plot_grid(plotlist = ps, ncol = 1)
Version | Author | Date |
---|---|---|
244411f | HelenaLC | 2019-07-11 |
saveRDS(so, file.path("output", "MAGL-SeuratObject.rds"))
sessionInfo()
R version 3.6.0 (2019-04-26)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 16.04.5 LTS
Matrix products: default
BLAS: /usr/local/R/R-3.6.0/lib/libRblas.so
LAPACK: /home/HelenaLC/soft/miniconda3/lib/libmkl_rt.so
locale:
[1] LC_CTYPE=en_CA.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_CA.UTF-8 LC_COLLATE=en_CA.UTF-8
[5] LC_MONETARY=en_CA.UTF-8 LC_MESSAGES=en_CA.UTF-8
[7] LC_PAPER=en_CA.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_CA.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] SingleCellExperiment_1.6.0 SummarizedExperiment_1.14.0
[3] DelayedArray_0.10.0 BiocParallel_1.18.0
[5] matrixStats_0.54.0 Biobase_2.44.0
[7] GenomicRanges_1.36.0 GenomeInfoDb_1.20.0
[9] IRanges_2.18.1 S4Vectors_0.22.0
[11] BiocGenerics_0.30.0 Seurat_3.0.2
[13] cowplot_0.9.4 ggplot2_3.2.0
loaded via a namespace (and not attached):
[1] Rtsne_0.15 colorspace_1.4-1 ggridges_0.5.1
[4] rprojroot_1.3-2 XVector_0.24.0 fs_1.3.1
[7] listenv_0.7.0 npsurv_0.4-0 ggrepel_0.8.1
[10] codetools_0.2-16 splines_3.6.0 R.methodsS3_1.7.1
[13] lsei_1.2-0 knitr_1.23 jsonlite_1.6
[16] workflowr_1.4.0 ica_1.0-2 cluster_2.1.0
[19] png_0.1-7 R.oo_1.22.0 sctransform_0.2.0
[22] compiler_3.6.0 httr_1.4.0 backports_1.1.4
[25] assertthat_0.2.1 Matrix_1.2-17 lazyeval_0.2.2
[28] htmltools_0.3.6 tools_3.6.0 rsvd_1.0.1
[31] igraph_1.2.4.1 gtable_0.3.0 glue_1.3.1
[34] GenomeInfoDbData_1.2.1 RANN_2.6.1 reshape2_1.4.3
[37] dplyr_0.8.3 Rcpp_1.0.1 gdata_2.18.0
[40] ape_5.3 nlme_3.1-140 gbRd_0.4-11
[43] lmtest_0.9-37 xfun_0.8 stringr_1.4.0
[46] globals_0.12.4 irlba_2.3.3 gtools_3.8.1
[49] future_1.14.0 MASS_7.3-51.4 zlibbioc_1.30.0
[52] zoo_1.8-6 scales_1.0.0 RColorBrewer_1.1-2
[55] yaml_2.2.0 reticulate_1.12 pbapply_1.4-0
[58] gridExtra_2.3 stringi_1.4.3 highr_0.8
[61] caTools_1.17.1.2 bibtex_0.4.2 Rdpack_0.11-0
[64] SDMTools_1.1-221.1 rlang_0.4.0 pkgconfig_2.0.2
[67] bitops_1.0-6 evaluate_0.14 lattice_0.20-38
[70] ROCR_1.0-7 purrr_0.3.2 htmlwidgets_1.3
[73] labeling_0.3 tidyselect_0.2.5 plyr_1.8.4
[76] magrittr_1.5 R6_2.4.0 gplots_3.0.1.1
[79] pillar_1.4.2 whisker_0.3-2 withr_2.1.2
[82] fitdistrplus_1.0-14 survival_2.44-1.1 RCurl_1.95-4.12
[85] tibble_2.1.3 future.apply_1.3.0 tsne_0.1-3
[88] crayon_1.3.4 KernSmooth_2.23-15 plotly_4.9.0
[91] rmarkdown_1.13 grid_3.6.0 data.table_1.12.2
[94] git2r_0.26.1 metap_1.1 digest_0.6.20
[97] tidyr_0.8.3 R.utils_2.9.0 munsell_0.5.0
[100] viridisLite_0.3.0