Last updated: 2022-04-26

Checks: 7 0

Knit directory: diamantopoulou-ctc-dynamics/

This reproducible R Markdown analysis was created with workflowr (version 1.6.2). The Checks tab describes the reproducibility checks that were applied when the results were created. The Past versions tab lists the development history.

R Markdown file: up-to-date

Great! Since the R Markdown file has been committed to the Git repository, you know the exact version of the code that produced these results.

Environment: empty

Great job! The global environment was empty. Objects defined in the global environment can affect the analysis in your R Markdown file in unknown ways. For reproduciblity it’s best to always run the code in an empty environment.

Seed: set.seed(20220425)

The command set.seed(20220425) was run prior to running the code in the R Markdown file. Setting a seed ensures that any results that rely on randomness, e.g. subsampling or permutations, are reproducible.

Session information: recorded

Great job! Recording the operating system, R version, and package versions is critical for reproducibility.

Cache: none

Nice! There were no cached chunks for this analysis, so you can be confident that you successfully produced the results during this run.

File paths: relative

Great job! Using relative paths to the files within your workflowr project makes it easier to run your code on other machines.

Repository version: 545ee28

Great! You are using Git for version control. Tracking code development and connecting the code version to the results is critical for reproducibility.

The results in this page were generated with repository version 545ee28. See the Past versions tab to see a history of the changes made to the R Markdown and HTML files.

Note that you need to be careful to ensure that all relevant files for the analysis have been committed to Git prior to generating the results (you can use wflow_publish or wflow_git_commit). workflowr only checks the R Markdown file, but you know if there are other scripts or data files that it depends on. Below is the status of the Git repository when the results were generated: 


Ignored files:
    Ignored:    .Rhistory
    Ignored:    .Rproj.user/

Untracked files:
    Untracked:  analysis/0_differential_expression_gsea_gsva.md
    Untracked:  analysis/about.md
    Untracked:  analysis/br16_dge.md
    Untracked:  analysis/br16_pca.md
    Untracked:  analysis/core_gene_sets.md
    Untracked:  analysis/gsea_across_models.md
    Untracked:  analysis/index.md
    Untracked:  analysis/license.md
    Untracked:  analysis/patients_ctc_counts_distribution.md
    Untracked:  data/differential_expression/
    Untracked:  data/patients/
    Untracked:  data/resources/
    Untracked:  data/sce/

Note that any generated files, e.g. HTML, png, CSS, etc., are not included in this status report because it is ok for generated content to have uncommitted changes.

These are the previous versions of the repository in which changes were made to the R Markdown (analysis/br16_dge.Rmd) and HTML (docs/br16_dge.html) files. If you’ve configured a remote Git repository (see ?wflow_git_remote), click on the hyperlinks in the table below to view the files as they were in that past version.

File Version Author Date Message
Rmd 545ee28 fcg-bio 2022-04-26 release v1.0
html 74b1891 fcg-bio 2022-04-26 Build site.
html c0865c6 fcg-bio 2022-04-26 Build site.
html a136590 fcg-bio 2022-04-26 Build site.
html bfb622b fcg-bio 2022-04-26 Build site.
html 1006c84 fcg-bio 2022-04-26 Build site.
Rmd 0ded9f5 fcg-bio 2022-04-26 added final code

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