BIOS_sctransform-normalisation
Ruqian Lyu
11/30/2020
Last updated: 2020-11-30
Checks: 6 1
Knit directory: bios_2020_single-cell-workshop-svi/
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| File | Version | Author | Date | Message |
|---|---|---|---|---|
| Rmd | eab0b17 | rlyu | 2020-11-16 | finalising analysis workflow and sctransform |
| html | eab0b17 | rlyu | 2020-11-16 | finalising analysis workflow and sctransform |
| Rmd | 110616a | rlyu | 2020-11-12 | updat workflow, still need to do varying Ks |
| Rmd | 9bd198a | rlyu | 2020-11-12 | update analysis workflow |
| Rmd | 11c8318 | rlyu | 2020-11-12 | Adding more descriptions |
sctransform
Different from the size_factor-based normalisation method that we used before, sctransform is based on probabilistic models for normalisation and variance stablisation of UMI-count data from scRNAseq. Instead of using a constant factor for normalising all genes for one cell, sctransform (Hafemeister and Satija 2019) models and scales each gene individually.
sctransform fits a generalised linear model with a negative binomial error model for each gene with the sequencing depth (library size) as a covariate followed by a regularisation procedure to control overfitting. The residuals from the regularised regression model is then treated as normalised expression levels with variation caused by sequencing depth removed.
suppressPackageStartupMessages({
library(sctransform)
library(ggplot2)
library(scater)
library(scran)
}
)
sce.pbmc <- readRDS(file = "raw_data/sce_pbmc.rds")We use the log10_umi as the latent variable that will be regressed out in the normalised gene expression values.
set.seed(10000)
colData(sce.pbmc)$log10_umi <- log(colData(sce.pbmc)$total,base=10)
pbmc.sctrans <- suppressWarnings(sctransform::vst(assay(sce.pbmc,"counts"),
cell_attr = colData(sce.pbmc),
latent_var = "log10_umi", verbosity = FALSE))
## Warning related issue: https://github.com/ChristophH/sctransform/issues/25Add to assay field
pbmc.sctrans stores returned value from running variance stablisation using sctransform. The normalised values are stored in the matrix y. We now add y to the sce object in the assay field with asaay name “SCT”. This is equivalant to the logcounts assay.
Select highly variable genes
Feature selection after sctransform normalisation is straightforward. We can just select the top genes that have a high residual variance which contribute the most biological sources of variation.
detection_rate gmean variance residual_mean residual_variance
IGKC 0.27 0.60 184.08 2.89 135.91
S100A8 0.62 2.15 633.20 3.75 125.33
S100A9 0.70 2.70 921.73 3.68 113.23
GNLY 0.24 0.45 74.24 2.05 96.29
LYZ 0.65 2.82 1042.16 3.70 82.80
IGLC3 0.10 0.16 590.98 1.11 71.41
IGLC2 0.19 0.26 16209.67 1.16 69.35
NKG7 0.38 0.84 45.18 1.76 41.94
PPBP 0.03 0.05 10.27 0.55 41.88
PF4 0.03 0.04 3.27 0.55 41.33
CCL5 0.35 0.85 38.41 1.70 32.14
SDPR 0.02 0.03 1.20 0.44 31.22
GNG11 0.04 0.04 1.01 0.43 30.53
HIST1H2AC 0.06 0.06 2.78 0.42 27.31
GZMB 0.10 0.14 9.18 0.62 27.26
TUBB1 0.02 0.03 1.27 0.39 27.14
CD74 0.92 6.11 1597.07 1.75 26.45
FTL 0.99 12.79 1475.18 1.45 22.33
JCHAIN 0.07 0.09 12.44 0.40 21.84
S100A12 0.26 0.49 16.41 0.95 21.73
KLRB1 0.24 0.46 15.92 0.99 21.64
CST3 0.56 1.77 306.70 1.51 21.15select 3,000 highly variable genes for downstream analysis
runPCA with HVGs
Next, we runPCA with the selected number of highly variable genes.
Generate TSNE plot using PCs
Clustering
The remaining steps are similar to those presented in the main workflow.
Plot Clusters
| Version | Author | Date |
|---|---|---|
| eab0b17 | rlyu | 2020-11-16 |
| Version | Author | Date |
|---|---|---|
| eab0b17 | rlyu | 2020-11-16 |
plotExpression(sce.pbmc, features=c("CD14", "CD68",
"MNDA", "FCGR3A"), x="label", colour_by="label",exprs_values = "SCT")
| Version | Author | Date |
|---|---|---|
| eab0b17 | rlyu | 2020-11-16 |
More info
sctransform is also integrated and interfaced with Seurat package which you can find more information here: https://satijalab.org/seurat/v3.2/sctransform_vignette.html
References
SessionInfo
R version 4.0.3 (2020-10-10)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Red Hat Enterprise Linux
Matrix products: default
BLAS/LAPACK: /usr/lib64/libopenblasp-r0.3.3.so
locale:
[1] LC_CTYPE=en_AU.UTF-8 LC_NUMERIC=C
[3] LC_TIME=en_AU.UTF-8 LC_COLLATE=en_AU.UTF-8
[5] LC_MONETARY=en_AU.UTF-8 LC_MESSAGES=en_AU.UTF-8
[7] LC_PAPER=en_AU.UTF-8 LC_NAME=C
[9] LC_ADDRESS=C LC_TELEPHONE=C
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C
attached base packages:
[1] parallel stats4 stats graphics grDevices utils datasets
[8] methods base
other attached packages:
[1] scran_1.18.0 scater_1.18.0
[3] SingleCellExperiment_1.12.0 SummarizedExperiment_1.20.0
[5] Biobase_2.50.0 GenomicRanges_1.42.0
[7] GenomeInfoDb_1.26.0 IRanges_2.24.0
[9] S4Vectors_0.28.0 BiocGenerics_0.36.0
[11] MatrixGenerics_1.2.0 matrixStats_0.57.0
[13] ggplot2_3.3.2 sctransform_0.3.1
loaded via a namespace (and not attached):
[1] bitops_1.0-6 fs_1.5.0
[3] rprojroot_1.3-2 tools_4.0.3
[5] backports_1.2.0 R6_2.5.0
[7] irlba_2.3.3 vipor_0.4.5
[9] uwot_0.1.8 colorspace_1.4-1
[11] withr_2.3.0 tidyselect_1.1.0
[13] gridExtra_2.3 compiler_4.0.3
[15] git2r_0.27.1 BiocNeighbors_1.8.0
[17] DelayedArray_0.16.0 labeling_0.4.2
[19] scales_1.1.1 stringr_1.4.0
[21] digest_0.6.27 rmarkdown_2.5
[23] XVector_0.30.0 pkgconfig_2.0.3
[25] htmltools_0.5.0 parallelly_1.21.0
[27] sparseMatrixStats_1.2.0 limma_3.46.0
[29] rlang_0.4.8 rstudioapi_0.11
[31] FNN_1.1.3 DelayedMatrixStats_1.12.0
[33] farver_2.0.3 generics_0.1.0
[35] BiocParallel_1.24.0 dplyr_1.0.2
[37] RCurl_1.98-1.2 magrittr_1.5
[39] BiocSingular_1.6.0 GenomeInfoDbData_1.2.4
[41] scuttle_1.0.0 Matrix_1.2-18
[43] Rcpp_1.0.5 ggbeeswarm_0.6.0
[45] munsell_0.5.0 viridis_0.5.1
[47] lifecycle_0.2.0 stringi_1.5.3
[49] whisker_0.4 yaml_2.2.1
[51] edgeR_3.32.0 MASS_7.3-53
[53] zlibbioc_1.36.0 Rtsne_0.15
[55] plyr_1.8.6 grid_4.0.3
[57] listenv_0.8.0 promises_1.1.1
[59] dqrng_0.2.1 crayon_1.3.4
[61] lattice_0.20-41 cowplot_1.1.0
[63] beachmat_2.6.0 locfit_1.5-9.4
[65] knitr_1.30 pillar_1.4.6
[67] igraph_1.2.6 future.apply_1.6.0
[69] reshape2_1.4.4 codetools_0.2-16
[71] glue_1.4.2 evaluate_0.14
[73] vctrs_0.3.4 httpuv_1.5.4
[75] gtable_0.3.0 purrr_0.3.4
[77] future_1.20.1 xfun_0.19
[79] rsvd_1.0.3 RSpectra_0.16-0
[81] later_1.1.0.1 viridisLite_0.3.0
[83] tibble_3.0.4 beeswarm_0.2.3
[85] workflowr_1.6.2 statmod_1.4.35
[87] bluster_1.0.0 globals_0.13.1
[89] ellipsis_0.3.1 Hafemeister, Christoph, and Rahul Satija. 2019. “Normalization and Variance Stabilization of Single-Cell RNA-seq Data Using Regularized Negative Binomial Regression.” Genome Biol. 20 (1): 296.
─ Session info ───────────────────────────────────────────────────────────────
setting value
version R version 4.0.3 (2020-10-10)
os Red Hat Enterprise Linux
system x86_64, linux-gnu
ui X11
language (EN)
collate en_AU.UTF-8
ctype en_AU.UTF-8
tz Australia/Melbourne
date 2020-11-30
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