Last updated: 2022-05-12

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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(ComplexHeatmap)
library(circlize)
library(RColorBrewer)
library(DT)

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 SingleCellExpression data

use_sce <- readRDS(file.path(params$sce_dir, 'sce_br16.rds'))

Load results from differential gene expression analyses

dge_all <- readRDS(file.path(params$dge_dir, 'br16', 'dge_edgeR_QLF_robust.rds'))
dge_cluster_g <- readRDS(file.path(params$dge_dir,'br16-ctc_cluster_and_wbc', 'dge_edgeR_QLF_robust.rds'))
dge_single <- readRDS(file.path(params$dge_dir,'br16-ctc_single', 'dge_edgeR_QLF_robust.rds'))

Genes differentially expressed in CTCs of NSG-CDX-BR16

Genes differentially expressed in CTCs of NSG-CDX-BR16 mice during the rest phase versus active phase. Table listing the differentially expressed genes comparing CTCs obtained in the rest phase (n = 65) versus the active phase (n = 73) of NSG-CDX-BR16 mice. All genes evaluated are included in the table (n = 12,261). Fold-change and P values were computed with the quasi-likelihood (QL) approach from edgeR using robust dispersion estimates. For fold-change calculation, active phase samples were used in the denominator.

dge_all$results %>% 
  dplyr::select(gene_name, gene_type, logFC, logCPM, PValue, FDR, description) %>% 
  rownames_to_column('Ensemble ID') %>% 
  mutate(
    logFC = round(logFC, 2),
    logCPM = round(logCPM, 2),
    PValue = format.pval(PValue, digits = 2),
    FDR = format.pval(FDR, digits = 2),
    description = gsub(" \\[.*\\]", "", description)
  ) %>% 
  dplyr::rename(
    `Gene name` = gene_name,
    `Gene type` = gene_type
  ) %>% 
  datatable(., 
            rownames = FALSE, 
            filter = 'top', 
            caption = 'Genes differentially expressed in CTCs of NSG-CDX-BR16 mice during the rest phase versus active phase.',
            extensions = 'Buttons', 
            options = list(
              dom = 'Blfrtip',
              buttons = c('csv', 'excel'),
              title = paste('', params$prefix)
              ))

Heatmap differential expression NSG-CDX-BR16 mice

Heatmap showing expression levels (row scaled z-scores using normalized counts) of differentially-expressed genes between rest and active phase (absolute log2 fold change ≥ 0.5 and FDR ≤ 0.05) in CTCs from NSG-BR16-CDX mice.

dge <- dge_all
use_genes <- dge$results %>% filter(FDR <= 0.05 & abs(logFC) >= 0.5) %>% collect %>% .[['feature']]
use_genes_name <- rowData(use_sce[use_genes,]) %>% data.frame %>% collect %>% .[['gene_name']]
n_up <- dge$results %>% filter(FDR <= 0.05 & logFC >= 0.5) %>% nrow
n_down <- dge$results %>% filter(FDR <= 0.05 & logFC <= -0.5) %>% nrow
expr_values <- logcounts(use_sce[use_genes,])
heat_values <- t(apply(expr_values, 1, scale, center = TRUE, scale = TRUE)) # Z-score
rownames(heat_values) <- use_genes_name
colnames(heat_values) <- colnames(expr_values)

coldata_ord <- colData(use_sce) %>% data.frame %>% arrange(zt_sample_type_legend) 
heat_values <- heat_values[,coldata_ord$sample_alias]

ha_top <- HeatmapAnnotation(
  show_legend = FALSE,
  `CTC type` = coldata_ord$zt_sample_type_legend,
  col = list(`CTC type` = zt_sample_type_legend_palette),
  annotation_legend_param = list(
    title = NULL,
    title_gp = gpar(fontsize = 8),
    labels_gp = gpar(col = "black", fontsize = 8),
    grid_width = unit(3, "mm")
    ),
  show_annotation_name = FALSE,
  simple_anno_size = unit(3, "mm")
  )

zlim <- c(-3, 3)
heat_values[heat_values < zlim[1]] <- zlim[1]
heat_values[heat_values > zlim[2]] <- zlim[2]

col_fun <-  colorRamp2(seq(zlim[1], zlim[2], length.out = 11), rev(brewer.pal(n = 11, name ="BrBG")))

Heatmap(
  heat_values,
  name = 'z\nscore',
  col =  col_fun,
  row_split = 2, 
  row_gap = unit(2, "mm"),
  cluster_columns = FALSE,
  column_title = NULL,
  row_title = NULL,
  show_column_dend = FALSE,
  show_column_names = FALSE, 
  show_row_dend = FALSE,
  show_row_names = FALSE, 
  top_annotation = ha_top,
  left_annotation = rowAnnotation(foo = anno_block(
        labels = c(
          paste0("Upregulated in\nZT4 (N=", n_up, ")"), 
          paste0("Upregulated in\nZT16 (N=", n_down, ")")
        ),
        labels_gp = gpar(col = "black", fontsize = 8),
        gp = gpar(lwd = 0, lty = 0)
        )
  ),
  heatmap_legend_param = list(
    title_gp = gpar(fontsize = 8),
    labels_gp = gpar(fontsize = 8),
    grid_width = unit(3, "mm")
    )
)

Version Author Date
1006c84 fcg-bio 2022-04-26 
use_genes <- dge$results %>% filter(FDR <= 0.05 & abs(logFC) >= 0.5) %>% collect %>% .[['feature']]
use_genes_name <- rowData(use_sce[use_genes,]) %>% data.frame %>% collect %>% .[['gene_name']]
n_up <- dge$results %>% filter(FDR <= 0.05 & logFC >= 0.5) %>% nrow
n_down <- dge$results %>% filter(FDR <= 0.05 & logFC <= -0.5) %>% nrow
expr_values <- logcounts(use_sce[use_genes,])
heat_values <- t(apply(expr_values, 1, scale, center = TRUE, scale = TRUE)) # Z-score
rownames(heat_values) <- use_genes_name
colnames(heat_values) <- colnames(expr_values)

coldata_ord <- colData(use_sce) %>% data.frame %>% arrange(zt_sample_type_legend) 
heat_values <- heat_values[,coldata_ord$sample_alias]

ha_top <- HeatmapAnnotation(
  show_legend = TRUE,
  `CTC type` = coldata_ord$zt_sample_type_legend,
  col = list(`CTC type` = zt_sample_type_legend_palette),
  annotation_legend_param = list(
    title = NULL,
    title_gp = gpar(fontsize = 8),
    labels_gp = gpar(col = "black", fontsize = 8),
    grid_width = unit(3, "mm")
    ),
  show_annotation_name = FALSE,
  simple_anno_size = unit(3, "mm")
  )

zlim <- c(-3, 3)
heat_values[heat_values < zlim[1]] <- zlim[1]
heat_values[heat_values > zlim[2]] <- zlim[2]

col_fun <-  colorRamp2(seq(zlim[1], zlim[2], length.out = 11), rev(RColorBrewer::brewer.pal(n = 11, name ="BrBG")))


Heatmap(
  heat_values,
  name = 'z\nscore',
  col =  col_fun,
  row_split = 2, 
  row_gap = unit(2, "mm"),
  cluster_columns = FALSE,
  column_title = NULL,
  row_title = NULL,
  show_column_dend = FALSE,
  show_column_names = FALSE, 
  show_row_dend = FALSE,
  show_row_names = FALSE, 
  top_annotation = ha_top,
  left_annotation = rowAnnotation(foo = anno_block(
        labels = c(
          paste0("Upregulated in\nrest phase\n(N=", n_up, ")"), 
          paste0("Upregulated in\nactive phase\n(N=", n_down, ")")
        ),
        labels_gp = gpar(col = "black", fontsize = 8),
        gp = gpar(lwd = 0, lty = 0)
        )
  ),
  heatmap_legend_param = list(
    title_gp = gpar(fontsize = 8),
    labels_gp = gpar(fontsize = 8),
    grid_width = unit(3, "mm")
    )
)

Version Author Date
1006c84 fcg-bio 2022-04-26

Correlation DEG single CTC versus CTC clusters and CTC-WBC

Scatter plot showing the correlation of the fold-change between active and rest phase in single CTC (Y-axis) versus CTC clusters and CTC-WBC (X-axis), using genes with FDR ≤ 0.05 in any of the two sets (two-sided Pearson’s correlation coefficient 0.57, P value ≤ 2.22e-16). Points are colored according to the dataset where they were found with a FDR ≤ 0.05 (both, single CTC or CTC clusters and CTC-WBC clusters). The dashed red line represents the linear regression line using all the points in the plot.

# Fold-change correlation
res_cl <- dge_cluster_g$results %>% dplyr::select(feature, gene_name, description, logFC, PValue, FDR) %>% mutate(logFDR = -log10(FDR), logPValue = -log10(PValue))
res_s <- dge_single$results %>% dplyr::select(feature, logFC, PValue, FDR) %>% mutate(logFDR = -log10(FDR), logPValue = -log10(PValue))

data_corrset <- res_cl %>% left_join(res_s, by = 'feature', suffix = c(".cl", ".s")) %>% 
  filter(FDR.s <= 0.05 | FDR.cl <= 0.05) %>% 
  mutate(
    sign = ifelse(FDR.s <= 0.05 & FDR.cl <= 0.05, 'Both sets', NA),
    sign = ifelse(is.na(sign) & FDR.cl <= 0.05, 'CTC clusters and CTC-WBC clusters', sign),
    sign = ifelse(is.na(sign) & FDR.s <= 0.05, 'Single CTCs', sign),
    sign = factor(sign, levels = c('CTC clusters and CTC-WBC clusters', 'Both sets', 'Single CTCs'))
    ) %>% 
  na.omit()

# Generate plot
maxlogFC <- max(abs(c(data_corrset$logFC.cl, data_corrset$logFC.s)), na.rm = TRUE)
use_palette <- c('#1b9e77', '#e6ab02', '#7570b3') %>% set_names(data_corrset$sign %>% levels)
fc_plot <- data_corrset %>% 
  ggplot(aes(logFC.cl,logFC.s, color = sign, label = gene_name)) +
  geom_point(size = 2, alpha = 0.3) +
  geom_hline(yintercept = 0, lty = 3, color = 'grey80') +
  geom_vline(xintercept = 0, lty = 3, color = 'grey80') +
  geom_smooth(method = lm, se = FALSE, inherit.aes = FALSE, aes(logFC.cl, logFC.s), color = 'firebrick', lty = 2, fullrange = TRUE, size = 0.5) +
  geom_point(size = 2, alpha = 1, pch = 16, data = data_corrset[data_corrset$sign != 'CTC clusters and CTC-WBC clusters', ]) +
  scale_color_manual(values = use_palette) +
  xlim(c(-maxlogFC, maxlogFC)) +
  ylim(c(-maxlogFC, maxlogFC)) +
  labs(
    x = expression(paste("lo", g[2],"(Fold change) in CTC clusters and CTC-WBC clusters")),
    y = expression(paste("lo", g[2],"(Fold change) in Single CTCs")),
    color = 'FDR <= 0.05'
  ) +
  guides(alpha = "none")

fc_plot_2_legend <- data_corrset %>% 
  ggplot(aes(logFC.s,logFC.cl, color = sign, label = gene_name)) +
  geom_point(size = 1.5, alpha = 0.8) +
  scale_color_manual(values = use_palette)

fc_plot + theme(legend.position = "none")

Version Author Date
1006c84 fcg-bio 2022-04-26 
legend <- cowplot::get_legend(fc_plot_2_legend)

grid.newpage()
grid.draw(legend)

Version Author Date
1006c84 fcg-bio 2022-04-26

Compare DEG CTC versus CTC clusters and CTC-WBC

Bar plot showing the number of differentially expressed genes (absolute log2 fold change ≥ 0.5 and FDR ≤ 0.05) using all the samples (‘All’), using clustered CTCs (CTC clusters and CTC-WBC clusters) and using single CTCs.

results_comb <- rbind(
  dge_all$results %>% filter(FDR <= 0.05 & abs(logFC) >= 0.5) %>% mutate(sample_set = 'All'),
  dge_cluster_g$results %>% filter(FDR <= 0.05 & abs(logFC) >= 0.5) %>% mutate(sample_set = 'CTC clusters and\nCTC-WBC clusters'),
  dge_single$results %>% filter(FDR <= 0.05 & abs(logFC) >= 0.5) %>% mutate(sample_set = 'Single CTCs')
)
results_comb_Nlabels <- results_comb$sample_set %>% table %>% data.frame %>% set_names(c('sample_set', 'Freq'))

results_comb %>% 
  ggplot(aes(sample_set)) +
  geom_bar() +
  ylim(c(0, 50+(results_comb_Nlabels$Freq %>% max))) +
  geom_text(aes(y=Freq, label=Freq), vjust=-0.05, color="black", size=geom_text_size, data = results_comb_Nlabels) +
  labs(
    x = '',
    y = 'Number of\ndifferential expressed genes'
  ) + 
  scale_y_continuous(expand = c(0, 0), limits = c(0, 305)) +
  theme(
      panel.grid.major.y   = element_line(colour = 'grey90', size = 0.5),
      axis.ticks        = element_line(colour = 'grey90', size = 0.5),
      axis.line.x       = element_line(colour = 'grey90', size = 0.5),
      axis.line.y       = element_blank(),
    )

Version Author Date
1006c84 fcg-bio 2022-04-26 

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] cowplot_1.1.1 DT_0.19
[3] RColorBrewer_1.1-2 circlize_0.4.13
[5] ComplexHeatmap_2.8.0 scater_1.20.1
[7] scuttle_1.2.1 SingleCellExperiment_1.14.1 [9] SummarizedExperiment_1.22.0 Biobase_2.52.0
[11] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
[13] IRanges_2.26.0 S4Vectors_0.30.2
[15] BiocGenerics_0.38.0 MatrixGenerics_1.4.3
[17] matrixStats_0.61.0 showtext_0.9-4
[19] showtextdb_3.0 sysfonts_0.8.5
[21] forcats_0.5.1 stringr_1.4.0
[23] dplyr_1.0.7 purrr_0.3.4
[25] readr_2.0.2 tidyr_1.1.4
[27] tibble_3.1.5 ggplot2_3.3.5
[29] tidyverse_1.3.1 workflowr_1.6.2

loaded via a namespace (and not attached): [1] ggbeeswarm_0.6.0 colorspace_2.0-2
[3] rjson_0.2.20 ellipsis_0.3.2
[5] rprojroot_2.0.2 XVector_0.32.0
[7] GlobalOptions_0.1.2 BiocNeighbors_1.10.0
[9] fs_1.5.0 clue_0.3-60
[11] rstudioapi_0.13 farver_2.1.0
[13] fansi_0.5.0 lubridate_1.8.0
[15] xml2_1.3.2 splines_4.1.0
[17] codetools_0.2-18 sparseMatrixStats_1.4.2
[19] doParallel_1.0.16 knitr_1.36
[21] jsonlite_1.7.2 Cairo_1.5-12.2
[23] broom_0.7.10 cluster_2.1.2
[25] dbplyr_2.1.1 png_0.1-7
[27] compiler_4.1.0 httr_1.4.2
[29] backports_1.3.0 assertthat_0.2.1
[31] Matrix_1.3-4 fastmap_1.1.0
[33] cli_3.1.0 later_1.3.0
[35] BiocSingular_1.8.1 htmltools_0.5.2
[37] tools_4.1.0 rsvd_1.0.5
[39] gtable_0.3.0 glue_1.4.2
[41] GenomeInfoDbData_1.2.6 Rcpp_1.0.7
[43] cellranger_1.1.0 jquerylib_0.1.4
[45] vctrs_0.3.8 nlme_3.1-153
[47] crosstalk_1.1.1 iterators_1.0.13
[49] DelayedMatrixStats_1.14.3 xfun_0.27
[51] beachmat_2.8.1 rvest_1.0.2
[53] lifecycle_1.0.1 irlba_2.3.3
[55] zlibbioc_1.38.0 scales_1.1.1
[57] hms_1.1.1 promises_1.2.0.1
[59] yaml_2.2.1 gridExtra_2.3
[61] sass_0.4.0 stringi_1.7.5
[63] highr_0.9 foreach_1.5.1
[65] ScaledMatrix_1.0.0 BiocParallel_1.26.2
[67] shape_1.4.6 rlang_0.4.12
[69] pkgconfig_2.0.3 bitops_1.0-7
[71] evaluate_0.14 lattice_0.20-45
[73] labeling_0.4.2 htmlwidgets_1.5.4
[75] tidyselect_1.1.1 magrittr_2.0.1
[77] R6_2.5.1 generics_0.1.1
[79] DelayedArray_0.18.0 DBI_1.1.1
[81] mgcv_1.8-38 pillar_1.6.4
[83] haven_2.4.3 whisker_0.4
[85] withr_2.4.2 RCurl_1.98-1.5
[87] modelr_0.1.8 crayon_1.4.2
[89] utf8_1.2.2 tzdb_0.2.0
[91] rmarkdown_2.11 GetoptLong_1.0.5
[93] viridis_0.6.2 readxl_1.3.1
[95] git2r_0.28.0 reprex_2.0.1
[97] digest_0.6.28 httpuv_1.6.3
[99] munsell_0.5.0 beeswarm_0.4.0
[101] viridisLite_0.4.0 vipor_0.4.5
[103] bslib_0.3.1