PCA of gene expression in CTCs from NSG-CDX-BR16 mice
Francesc Castro-Giner
2022-02-23
Last updated: 2022-05-12
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diamantopoulou-ctc-dynamics/
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Load libraries, additional functions and data
Setup environment
knitr::opts_chunk$set(results='asis', echo=TRUE, message=FALSE, warning=FALSE, error=FALSE, fig.align = 'center', fig.width = 3.5, fig.asp = 0.618, dpi = 600, dev = c("png", "pdf"), fig.showtext = TRUE)
options(stringsAsFactors = FALSE)Load packages
library(tidyverse)
library(showtext)
library(scater)
library(scran)
library(PCAtools)
library(cowplot)
library(ggalt)
library(grid)
library(gridExtra)
library(knitr)
library(kableExtra)Set font family for figures
font_add("Helvetica", "./configuration/fonts/Helvetica.ttc")
showtext_auto()Load ggplot theme
source("./configuration/rmarkdown/ggplot_theme.R")Load color palettes
source("./configuration/rmarkdown/color_palettes.R")Load functions
source('./code/R-functions/pca_tools.r')
source('./code/R-functions/color_tools.r')Load SingleCellExpression data
use_sce <- readRDS(file.path(params$sce_dir, 'sce_br16.rds'))PCA analysis Data
Configuration
use_metavars <- c(`Library size` = "sum",
`Detected genes` = "detected",
`Mitochondrial\nproportion` = "subsets_Mito_percent",
`CTC type` = "sample_type",
`Number of CTC` = "ctc_n",
`Time point` = "timepoint",
`G1 score` = 'G1_score',
`G2M score` = 'G2M_score',
`S score` = 'S_score')Quantify per-gene variation and select top-500 highly variable genes
fit_res <- modelGeneVar(use_sce, assay.type = "logcpm")
fit_md <- metadata(fit_res)
hvg_var_bio <- getTopHVGs(fit_res, n=500)
use_genes <- hvg_var_bioGenerate PCA object and calculate elbow point.
mat <- logcounts(use_sce)[use_genes,]
rownames(mat) <- rowData(use_sce[use_genes,])$gene_name
p <- PCAtools::pca(mat, metadata = colData(use_sce) %>% data.frame)
p$metadata$timepointf <- factor(p$metadata$timepoint, levels = c('active', 'resting'))
p$metadata$timepoint_sample_type_f <- factor(
p$metadata$timepoint_sample_type,
levels = c('active_ctc_single', 'active_ctc_cluster', 'active_ctc_cluster_wbc', 'resting_ctc_single', 'resting_ctc_cluster', 'resting_ctc_cluster_wbc'))Calculate elbow point
elbow_point <- findElbowPoint(p$variance)Add additional metadata to PCA object
p$metadata <- p$metadata %>%
mutate(`Library size` = sum,
`Detected genes` = detected,
`Mitochondrial\nproportion` = subsets_Mito_percent,
`CTC type` = sample_type,
`Number of CTC` = ctc_n,
`Time point` = timepoint,
`G1 score` = G1_score,
`G2M score` = G2M_score,
`S score` = S_score)Correlation of PC eigenvectors with metadata
Heatmap showing the Pearson’s correlation coefficient of PC1-7 eigenvectors from gene expression with technical and biological variables in BR16-CDX CTCs. P values by two-sided Pearson’s correlation test (*P < 0.01, **P <0.001, ***P <0.0001).
use_cex <- 8/12
eigencorplot(
p,
components = getComponents(p, 1:elbow_point),
metavars = names(use_metavars),
col = c( "blue2", "blue1", "black", "red1", "red2"),
colCorval = 'white',
scale = TRUE,
main = 'PCs clinical correlations',
plotRsquared = FALSE,
signifSymbols = c("***", "**", "*", ""),
signifCutpoints = c(0, 0.0001, 0.001, 0.01, 1),
cexTitleX= use_cex,
cexTitleY= use_cex,
cexLabX = use_cex,
cexLabY = use_cex,
cexMain = use_cex,
cexLabColKey = use_cex,
cexCorval = use_cex
)
| Version | Author | Date |
|---|---|---|
| 1006c84 | fcg-bio | 2022-04-26 |
Table : Percentage of variance associated to each PC
p$variance[1:elbow_point] %>% data.frame %>% set_names('Variance') %>%
kable(caption = "Percentage of variance associated to each PC") %>%
kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)| Variance | |
|---|---|
| PC1 | 32.706864 |
| PC2 | 7.518723 |
| PC3 | 4.742565 |
| PC4 | 3.000690 |
| PC5 | 1.904646 |
| PC6 | 1.600260 |
| PC7 | 1.303003 |
Table : Pearson r values
pca_cor_val <- pca_eigencorplot(p, components = getComponents(p, 1:elbow_point), metavars = names(use_metavars), returnPlot = FALSE)
pca_cor_val$corvals %>% t %>%
kable(caption = "Pearson r values correlation values") %>%
kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | |
|---|---|---|---|---|---|---|---|
| Library size | -0.4348691 | -0.2465262 | 0.0909495 | -0.2155405 | -0.1511203 | 0.0953848 | 0.0261902 |
| Detected genes | -0.6456730 | -0.6200878 | 0.2337476 | -0.1128454 | -0.0422498 | 0.1290895 | -0.0146735 |
| Mitochondrial proportion | -0.0053273 | -0.1536387 | -0.0656106 | 0.2049580 | -0.0788361 | 0.0458012 | 0.1228353 |
| CTC type | -0.1591137 | -0.3877828 | 0.2147562 | -0.0602830 | -0.0106637 | 0.1025418 | -0.1101724 |
| Number of CTC | -0.2725151 | -0.3508628 | 0.1484451 | -0.2680048 | -0.1754515 | 0.1533324 | -0.0316719 |
| Time point | -0.2473968 | 0.0222874 | 0.1293707 | -0.5776125 | -0.4448752 | 0.2867533 | -0.0336370 |
| G1 score | 0.4284753 | 0.0194577 | 0.2475039 | -0.0065806 | 0.1332250 | -0.0370506 | -0.1149801 |
| G2M score | -0.5215066 | 0.4957481 | 0.4399587 | 0.1521718 | 0.0625063 | -0.0885702 | -0.0039446 |
| S score | 0.7091394 | -0.3425337 | -0.4171847 | -0.0468110 | 0.0256570 | 0.0423829 | 0.0103013 |
Table : Pearson correlation P-values
pca_cor_val$pvals_format <- apply(pca_cor_val$pvals, 2, format.pval, digits = 2)
dimnames(pca_cor_val$pvals_format) <- dimnames(pca_cor_val$pvals)
pca_cor_val$pvals_format %>% t %>%
kable(caption = "Pearson correlation P-values") %>%
kable_styling(bootstrap_options = c("striped", "hover"), full_width = F)| PC1 | PC2 | PC3 | PC4 | PC5 | PC6 | PC7 | |
|---|---|---|---|---|---|---|---|
| Library size | 9.8e-08 | 0.0036 | 0.2887 | 0.0111 | 0.0768 | 0.2658 | 0.7604 |
| Detected genes | < 2e-16 | 5.1e-16 | 0.0058 | 0.1876 | 0.6227 | 0.1313 | 0.8644 |
| Mitochondrial proportion | 0.951 | 0.072 | 0.445 | 0.016 | 0.358 | 0.594 | 0.151 |
| CTC type | 0.062 | 2.6e-06 | 0.011 | 0.482 | 0.901 | 0.231 | 0.198 |
| Number of CTC | 0.0012 | 2.5e-05 | 0.0823 | 0.0015 | 0.0396 | 0.0726 | 0.7123 |
| Time point | 0.00344 | 0.79527 | 0.13046 | 1.2e-13 | 4.6e-08 | 0.00065 | 0.69531 |
| G1 score | 1.6e-07 | 0.8208 | 0.0034 | 0.9389 | 0.1193 | 0.6662 | 0.1793 |
| G2M score | 5.4e-11 | 6.3e-10 | 6.7e-08 | 0.075 | 0.466 | 0.302 | 0.963 |
| S score | < 2e-16 | 3.9e-05 | 3.6e-07 | 0.59 | 0.77 | 0.62 | 0.90 |
Biplot PC4 and PC5
Plot showing the principal components PC4 and PC5 of gene expression in CTCs from NSG-CDX-BR16 mice. Upper and right panels show the density of values for active (blue) and rest phase (red).
PCx <- 'PC4'
PCy<- 'PC5'
zt_sample_type_legend_palette_t <- sapply(zt_sample_type_legend_palette, transparent_col, percent = 50)
use_palette <- c(zt_sample_type_legend_palette, timepoint_palette)
use_shapes <- c(
'ZT16 Single CTCs' = 16,
'ZT16 CTC-Clusters' = 17,
'ZT16 CTC-WBC Clusters' = 15,
'ZT4 Single CTCs' = 16,
'ZT4 CTC-Clusters' = 17,
'ZT4 CTC-WBC Clusters' = 15,
active = 1,
resting = 1
)
use_palette_sel <- c(
'ZT16 Single CTCs' = use_palette['active'] %>% unname,
'ZT16 CTC-Clusters' = use_palette['active'] %>% unname,
'ZT16 CTC-WBC Clusters' = use_palette['active'] %>% unname,
'ZT4 Single CTCs' = use_palette['resting'] %>% unname,
'ZT4 CTC-Clusters' = use_palette['resting'] %>% unname,
'ZT4 CTC-WBC Clusters' = use_palette['resting'] %>% unname,
use_palette['active'],
use_palette['resting']
)
circle_data <- cbind(p$metadata,
x = p$rotated[,PCx],
y = p$rotated[,PCy])
xlab_name <- paste0(PCx,', ', p$variance[PCx] %>% round(2) %>% unname, '% variation')
ylab_name <- paste0(PCy,', ', p$variance[PCy] %>% round(2) %>% unname, '% variation')
biplot_res <- circle_data %>%
ggplot(aes(x, y)) +
geom_point(
aes(fill = zt_sample_type_legend, color = zt_sample_type_legend, shape = zt_sample_type_legend),
alpha = 0.4, size = 3
) +
geom_encircle(
aes(color = timepoint),
alpha = 0.6,
size = 1.5,
s_shape = 1.5,
show.legend = FALSE,
na.rm = TRUE,
expand = 0) +
scale_color_manual(values = use_palette_sel) +
scale_fill_manual(values = use_palette_sel) +
scale_shape_manual(values = use_shapes) +
theme_cowplot(font_family = "Helvetica", font_size = 8, rel_small = 8/8, rel_tiny = 8/8, rel_large = 8/8) +
theme (
axis.line = element_line(size = rel(0.25)),
axis.ticks = element_line(size = rel(0.25)),
panel.border = element_rect(size = rel(1), fill = NA, colour = "black")
) +
labs(
x = xlab_name,
y = ylab_name
)Main plot
x_density_plot <- ggplot(circle_data, aes(x = x, fill = timepoint)) +
geom_density(alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values = timepoint_palette) +
theme (
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank()
)
y_density_plot <- ggplot(circle_data, aes(x = y, fill = timepoint)) +
geom_density(alpha = 0.5, show.legend = FALSE) +
scale_fill_manual(values = timepoint_palette) +
theme (
axis.line = element_blank(),
axis.ticks = element_blank(),
axis.text = element_blank(),
axis.title = element_blank()
) +
coord_flip()
plot_grid(
x_density_plot, NULL, NULL,
NULL, NULL, NULL,
biplot_res + theme(legend.position = "none"), NULL, y_density_plot,
nrow = 3,
ncol = 3,
align="hv",
axis = "tblr",
rel_heights = c(1.1, -0.45, 3),
rel_widths = c(3, -0.45, 1)
)
| Version | Author | Date |
|---|---|---|
| 1006c84 | fcg-bio | 2022-04-26 |
Plot legend
legend <- cowplot::get_legend(biplot_res)
grid.newpage()
grid.draw(legend)
sessionInfo()R version 4.1.0 (2021-05-18) Platform: x86_64-apple-darwin17.0 (64-bit) Running under: macOS Big Sur 10.16
Matrix products: default BLAS: /Library/Frameworks/R.framework/Versions/4.1/Resources/lib/libRblas.dylib LAPACK: /Library/Frameworks/R.framework/Versions/4.1/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] grid parallel stats4 stats graphics
grDevices utils
[8] datasets methods base
other attached packages: [1] lattice_0.20-45 kableExtra_1.3.4
[3] knitr_1.36 gridExtra_2.3
[5] ggalt_0.4.0 cowplot_1.1.1
[7] PCAtools_2.4.0 ggrepel_0.9.1
[9] scran_1.20.1 scater_1.20.1
[11] scuttle_1.2.1 SingleCellExperiment_1.14.1 [13]
SummarizedExperiment_1.22.0 Biobase_2.52.0
[15] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
[17] IRanges_2.26.0 S4Vectors_0.30.2
[19] BiocGenerics_0.38.0 MatrixGenerics_1.4.3
[21] matrixStats_0.61.0 showtext_0.9-4
[23] showtextdb_3.0 sysfonts_0.8.5
[25] forcats_0.5.1 stringr_1.4.0
[27] dplyr_1.0.7 purrr_0.3.4
[29] readr_2.0.2 tidyr_1.1.4
[31] tibble_3.1.5 ggplot2_3.3.5
[33] tidyverse_1.3.1 workflowr_1.6.2
loaded via a namespace (and not attached): [1] readxl_1.3.1
backports_1.3.0
[3] systemfonts_1.0.2 plyr_1.8.6
[5] igraph_1.2.7 BiocParallel_1.26.2
[7] digest_0.6.28 htmltools_0.5.2
[9] viridis_0.6.2 fansi_0.5.0
[11] magrittr_2.0.1 ScaledMatrix_1.0.0
[13] cluster_2.1.2 tzdb_0.2.0
[15] limma_3.48.3 modelr_0.1.8
[17] extrafont_0.17 extrafontdb_1.0
[19] svglite_2.0.0 colorspace_2.0-2
[21] rvest_1.0.2 haven_2.4.3
[23] xfun_0.27 crayon_1.4.2
[25] RCurl_1.98-1.5 jsonlite_1.7.2
[27] glue_1.4.2 gtable_0.3.0
[29] zlibbioc_1.38.0 XVector_0.32.0
[31] webshot_0.5.2 DelayedArray_0.18.0
[33] proj4_1.0-10.1 BiocSingular_1.8.1
[35] Rttf2pt1_1.3.9 maps_3.4.0
[37] scales_1.1.1 DBI_1.1.1
[39] edgeR_3.34.1 Rcpp_1.0.7
[41] viridisLite_0.4.0 dqrng_0.3.0
[43] rsvd_1.0.5 metapod_1.0.0
[45] httr_1.4.2 RColorBrewer_1.1-2
[47] ellipsis_0.3.2 farver_2.1.0
[49] pkgconfig_2.0.3 sass_0.4.0
[51] dbplyr_2.1.1 locfit_1.5-9.4
[53] utf8_1.2.2 labeling_0.4.2
[55] tidyselect_1.1.1 rlang_0.4.12
[57] reshape2_1.4.4 later_1.3.0
[59] munsell_0.5.0 cellranger_1.1.0
[61] tools_4.1.0 cli_3.1.0
[63] generics_0.1.1 broom_0.7.10
[65] evaluate_0.14 fastmap_1.1.0
[67] yaml_2.2.1 fs_1.5.0
[69] sparseMatrixStats_1.4.2 whisker_0.4
[71] ash_1.0-15 xml2_1.3.2
[73] compiler_4.1.0 rstudioapi_0.13
[75] beeswarm_0.4.0 reprex_2.0.1
[77] statmod_1.4.36 bslib_0.3.1
[79] stringi_1.7.5 highr_0.9
[81] bluster_1.2.1 Matrix_1.3-4
[83] vctrs_0.3.8 pillar_1.6.4
[85] lifecycle_1.0.1 jquerylib_0.1.4
[87] BiocNeighbors_1.10.0 bitops_1.0-7
[89] irlba_2.3.3 httpuv_1.6.3
[91] R6_2.5.1 promises_1.2.0.1
[93] KernSmooth_2.23-20 vipor_0.4.5
[95] MASS_7.3-54 assertthat_0.2.1
[97] rprojroot_2.0.2 withr_2.4.2
[99] GenomeInfoDbData_1.2.6 hms_1.1.1
[101] beachmat_2.8.1 rmarkdown_2.11
[103] DelayedMatrixStats_1.14.3 git2r_0.28.0
[105] lubridate_1.8.0 ggbeeswarm_0.6.0