PCA of gene expression in CTCs from NSG-CDX-BR16 mice

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Last updated: 2022-04-26

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Knit directory: 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_bio

Generate 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
)

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)

Percentage of variance associated to each PC

	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)

Pearson r values correlation values

	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)

Pearson correlation P-values

	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

Scatter plot showing the principal component PC4 and PC5 of gene expression in CTCs from NSG-CDX-BR16 mice. Upper (PC4) and right (PC5) panels show the density of PC values for active (blue) and rest phase (red) samples.

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")
    ) +
  # guides(shape = FALSE) +
  # background_grid() +
  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)
  )

Plot legend

legend <- cowplot::get_legend(biplot_res)
grid.newpage()
grid.draw(legend)

Session information

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