Last updated: 2020-08-20
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Knit directory: single-cell-topics/analysis/
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TO DO: Add introductory text here.
Load the packages used in the analysis below.
library(dplyr)
library(fastTopics)
library(ggplot2)
library(cowplot)
source("../code/plots.R")Load the sample annotations. (The count data are no longer needed at this stage of the analysis.)
load("../data/pbmc_purified.RData")
samples_purified <- samples
load("../data/pbmc_68k.RData")
samples_68k <- samples
rm(genes,counts)Load the \(k = 6\) Poisson NMF model fits for the 68k and mixture of FACS-purified PBMC data sets.
fit_68k <- readRDS("../output/pbmc-68k/rds/fit-pbmc-68k-scd-ex-k=6.rds")$fit
fit_purified <-
readRDS("../output/pbmc-purified/rds/fit-pbmc-purified-scd-ex-k=6.rds")$fitLoadings plot:
p1 <- loadings_plot(poisson2multinom(fit),samples$celltype)PCA plot:
clrs <- c("forestgreen", # CD14+ Monocyte
"dodgerblue", # CD19+ B
"darkmagenta", # CD34+"
"yellowgreen", # CD4+ T Helper2
"gold", # CD4+/CD25 T Reg
"limegreen", # CD4+/CD45RA+/CD25- Naive T
"orange", # CD4+/CD45RO+ Memory"
"gray", # CD56+ NK
"tomato", # CD8+ Cytotoxic T
"magenta", # CD8+/CD45RA+ Naive Cytotoxic"
"darkblue") # Dendritic"
fit2 <- poisson2multinom(fit)
pca <- prcomp(fit2$L)
pdat <- cbind(samples,pca$x)
ggplot(pdat,aes(x = PC3,y = PC4,fill = celltype)) +
geom_point(shape = 21,color = "white",size = 1.5) +
scale_fill_manual(values = clrs) +
theme_cowplot(font_size = 10)t-SNE plot:
set.seed(1)
p2 <- tsne_plot(fit,n = 8000,num_threads = 4)Differential count analysis:
diff_count_res <- diff_count_analysis(fit,counts)Volcano plots:
p3 <- volcano_plot(diff_count_res,labels = genes$symbol,
label_above_quantile = 0.995)Structure plots:
set.seed(1)
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD19+ B"))
p4 <- structure_plot(fit2,n = 2000,num_threads = 4) # B-cells.
set.seed(1)
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD56+ NK"))
p5 <- structure_plot(fit2,n = 2000,num_threads = 4) # NK cells.
set.seed(1)
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD34+"))
p6 <- structure_plot(fit2,num_threads = 4,perplexity = 50) # CD34+ cells
set.seed(1)
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD14+ Monocyte"))
p7 <- structure_plot(fit2,num_threads = 4) # CD14+ monocytes
set.seed(1)
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "Dendritic"))
p8 <- structure_plot(fit2,num_threads = 4) # dendritic cells
plot_grid(p7,p8,nrow = 2)
set.seed(1)
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD4+ T Helper2"))
p9 <- structure_plot(fit2,num_threads = 4,perplexity = 30) +
ggtitle("CD4+ T Helper2") +
set.seed(1)
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD4+/CD45RA+/CD25- Naive T"))
p10 <- structure_plot(fit2,num_threads = 4) +
ggtitle("CD4+/CD45RA+/CD25- Naive T")
set.seed(1)
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD4+/CD45RO+ Memory"))
p11 <- structure_plot(fit2,num_threads = 4) +
ggtitle("CD4+/CD45RO+ Memory")
set.seed(1)
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD4+/CD25 T Reg"))
p12 <- structure_plot(fit2,num_threads = 4) +
ggtitle("CD4+/CD25 T Reg")
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD8+/CD45RA+ Naive Cytotoxic"))
p13 <- structure_plot(fit2,num_threads = 4) +
ggtitle("CD8+/CD45RA+ Naive Cytotoxic")
fit2 <- select(poisson2multinom(fit),
loadings = which(samples$celltype == "CD8+ Cytotoxic T"))
p14 <- structure_plot(fit2,num_threads = 4) +
ggtitle("CD8+ Cytotoxic T")
plot_grid(p9,p10,p11,p12,p13,p14,nrow = 6)Another structure plot:
p15 <- structure_plot(fit,num_threads = 4)
sessionInfo()
# R version 3.6.2 (2019-12-12)
# Platform: x86_64-apple-darwin15.6.0 (64-bit)
# Running under: macOS Catalina 10.15.5
#
# Matrix products: default
# BLAS: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRblas.0.dylib
# LAPACK: /Library/Frameworks/R.framework/Versions/3.6/Resources/lib/libRlapack.dylib
#
# locale:
# [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
#
# attached base packages:
# [1] stats graphics grDevices utils datasets methods base
#
# other attached packages:
# [1] cowplot_1.0.0 ggplot2_3.3.0 fastTopics_0.3-163 dplyr_0.8.3
#
# loaded via a namespace (and not attached):
# [1] progress_1.2.2 tidyselect_0.2.5 xfun_0.11
# [4] purrr_0.3.3 lattice_0.20-38 vctrs_0.2.1
# [7] colorspace_1.4-1 viridisLite_0.3.0 htmltools_0.4.0
# [10] yaml_2.2.0 MCMCpack_1.4-5 plotly_4.9.2
# [13] rlang_0.4.5 later_1.0.0 pillar_1.4.3
# [16] withr_2.1.2 glue_1.3.1 lifecycle_0.1.0
# [19] stringr_1.4.0 MatrixModels_0.4-1 munsell_0.5.0
# [22] gtable_0.3.0 workflowr_1.6.2.9000 htmlwidgets_1.5.1
# [25] coda_0.19-3 evaluate_0.14 knitr_1.26
# [28] SparseM_1.78 httpuv_1.5.2 quantreg_5.54
# [31] irlba_2.3.3 Rcpp_1.0.3 promises_1.1.0
# [34] backports_1.1.5 scales_1.1.0 RcppParallel_4.4.4
# [37] jsonlite_1.6 fs_1.3.1 mcmc_0.9-6
# [40] hms_0.5.2 digest_0.6.23 stringi_1.4.3
# [43] Rtsne_0.15 ggrepel_0.9.0 grid_3.6.2
# [46] rprojroot_1.3-2 quadprog_1.5-8 tools_3.6.2
# [49] magrittr_1.5 lazyeval_0.2.2 tibble_2.1.3
# [52] zeallot_0.1.0 tidyr_1.0.0 crayon_1.3.4
# [55] whisker_0.4 pkgconfig_2.0.3 MASS_7.3-51.4
# [58] Matrix_1.2-18 prettyunits_1.1.1 data.table_1.12.8
# [61] assertthat_0.2.1 rmarkdown_2.3 httr_1.4.1
# [64] R6_2.4.1 git2r_0.26.1 compiler_3.6.2