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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(cowplot)
library(scater)
library(ggbeeswarm)
library(ggpubr)
library(ggrepel)

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 shared variables

source("./configuration/rmarkdown/shared_variables.R")

Load functions

source('./code/R-functions/gse_report.r')
clean_msigdb_names <- function(x) x %>% gsub('REACTOME_', '', .) %>% gsub('WP_', '', .) %>% gsub('BIOCARTA_', '', .) %>% gsub('KEGG_', '', .) %>% gsub('PID_', '', .) %>% gsub('GOBP_', '', .) %>% gsub('_', ' ', .)

Load NSG-BR16 data

sce_br16 <- readRDS(file.path(params$sce_dir, 'sce_br16.rds'))
sce_br16$sample_type <- recode(sce_br16$sample_type, ctc_single = 'Single CTCs', ctc_cluster = 'CTC-clusters', ctc_cluster_wbc = 'CTC-WBC Clusters')
dge_br16 <- readRDS(file.path('./data/differential_expression/br16', 'dge_edgeR_QLF_robust.rds'))
dge_br16 <- dge_br16$results

Load SingleCellExpression raw data

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

Core circadian genes in NSG-CDX-BR16

Initial configuration

use_sce <- sce_br16
use_dge <- dge_br16

Read core circadian genes list

key_circadian_genes_sel <- key_circadian_genes[key_circadian_genes %in% rowData(use_sce)$gene_name]
key_circadian_genes_ens <- rowData(use_sce)[match(key_circadian_genes_sel, rowData(use_sce)$gene_name), 'gene_id'] %>% set_names(names(key_circadian_genes_sel)) %>% gsub("\\.[0-9]+", "", .)

Subset of SCE and DGE objects

use_sce <- use_sce[key_circadian_genes_ens,]
rownames(use_sce) <- names(key_circadian_genes_sel)
use_dge <- use_dge[key_circadian_genes_ens,]
rownames(use_dge) <- names(key_circadian_genes_sel)
use_dge$gene <- names(key_circadian_genes_sel)
use_dge <- use_dge %>%
  mutate(group1 = 'active', group2 = 'resting') %>% # for stat_pvalue_manual
  arrange(PValue) %>% 
  mutate(
    gene = factor(gene, levels = gene)
  )

Expression distribution of core circadian genes in NSG-CDX-BR16

Plot showing the expression distribution of core circadian genes in CTCs from NSG-CDX-BR16 mice. The fold change (FC, in log2 scale) and P value from the differential expression analysis are shown for each gene.

expr_long <- logcounts(use_sce) %>% data.frame %>% rownames_to_column('gene') %>% pivot_longer(-gene, names_to = 'sample_alias', values_to = 'exprs')
use_data <- colData(use_sce) %>% 
  data.frame %>% 
  dplyr::select(sample_alias, timepoint, sample_type) %>% 
  left_join(expr_long) %>% 
  mutate(
    gene = factor(gene, levels = use_dge$gene),
    timepoint = recode(timepoint, resting = 'Rest phase', active = 'Active phase')
    )


use_ylim <- c(0, 2 + max(use_data$exprs))
use_breaks <- seq(use_ylim[1],  max(use_data$exprs), by = 2)
use_dge <- use_dge %>%
  mutate(
    group1 = 'Rest phase', group2 = 'Active phase',
    label = paste0('FC=', round(logFC,2),", P= ", format.pval(PValue, 1))
  )
timepoint_palette['Rest phase'] <- timepoint_palette['resting']
timepoint_palette['Active phase'] <- timepoint_palette['active']

use_data %>% 
  ggplot(aes(timepoint, exprs, color = timepoint)) +
  geom_quasirandom(alpha = 0.6, wdth = 0.4, groupOnX=TRUE, bandwidth=1) +
  geom_violin(color = 'black', alpha = 0, scale = "width", width = 0.8, draw_quantiles = 0.5) +
  scale_color_manual(values =timepoint_palette) +
  facet_wrap(~gene, ncol = 3) +
  stat_pvalue_manual(use_dge, label = "label", y.position = 9, size = geom_text_size) +
  scale_y_continuous(limits = use_ylim, breaks = use_breaks) +
  guides(color = FALSE) +
  labs(
    x = '',
    y = expression(paste("lo", g[2],"(Normalized counts)"))
  ) +
  background_grid(minor = 'none', major = 'y', size.major = 0.2)

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

Density plot of core circadian genes in NSG-CDX-BR16

Density plot showing the distribution of the average expression (log2 counts per million) of genes in CTCs from NSG-CDX-BR16 mice. Core circadian genes are labeled in the X-axis.

avg_counts <- data.frame(
  median_expr = logcounts(sce_br16) %>% rowMedians,
  mean_expr = logcounts(sce_br16) %>% rowMeans,
  gene_name = rowData(sce_br16)$gene_name
  )

avg_counts_circadian <- data.frame(
  median_expr = logcounts(use_sce) %>% rowMedians,
  mean_expr = logcounts(use_sce) %>% rowMeans
  ) %>% 
  rownames_to_column('gene_name')

ggplot(avg_counts, aes(x = median_expr)) +
  geom_density(fill="#dbd8be") +
  geom_vline(aes(xintercept=median(median_expr)), color="#043665", linetype="dashed", size=0.5)  +
  geom_point(
    data = avg_counts_circadian, 
    mapping = aes(y = 0.01, x = median_expr, label = gene_name, color = keep), 
    alpha = 0.8, 
    color = '#d37d0a') +
  geom_text_repel(
    data = avg_counts_circadian, 
    mapping = aes(y = 0.01, x = median_expr, label = gene_name),
    force_pull   = 0, # do not pull toward data points
    nudge_y      = 0.02,
    direction    = "x",
    angle        = 90,
    hjust        = 0,
    segment.size = 0.2,
    max.iter = 1e4, 
    max.time = 1,
    size = geom_text_size
    ) +
  labs(
    x = 'Median expression (logcounts)',
    y = 'Density',
    caption = 'Blue dashed line represents the median across all genes'
  )

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

Expression of TEAD genes in CTCs from NSG-CDX-BR16

Plot showing the expression distribution of TEAD genes in CTCs from NSG-CDX-BR16 mice. The fold change (FC, in log2 scale) and P value from the differential expression analysis are shown for each gene.

use_sce <- sce_br16
use_dge <- dge_br16

use_genes_name <- grep('TEAD[0-9]', rowData(use_sce)$gene_name, value = TRUE)
use_rows <- grepl('TEAD[0-9]', rowData(use_sce)$gene_name)
sel_sce <- use_sce[use_rows,]
use_features <- rownames(sel_sce)
rownames(sel_sce) <- use_genes_name

sel_dge <- use_dge[use_features,]
rownames(sel_dge) <- use_genes_name
sel_dge$gene <- rownames(sel_dge)
sel_dge <- sel_dge %>%
  mutate(group1 = 'active', group2 = 'resting') %>% # for stat_pvalue_manual
  arrange(PValue) %>%
  mutate(
    gene = factor(gene, levels = gene)
  )

expr_long <- logcounts(sel_sce) %>% data.frame %>% rownames_to_column('gene') %>% pivot_longer(-gene, names_to = 'sample_alias', values_to = 'exprs')
use_data <- colData(sel_sce) %>% 
  data.frame %>% 
  dplyr::select(sample_alias, timepoint, sample_type) %>% 
  left_join(expr_long) %>% 
  mutate(
    gene = factor(gene, levels = sel_dge$gene),
    timepoint = recode(timepoint, resting = 'Rest phase', active = 'Active phase')
    )


use_ylim <- c(0, 1 + max(use_data$exprs))
use_breaks <- seq(use_ylim[1],  max(use_data$exprs), by = 2)
sel_dge <- sel_dge %>%
  mutate(
    group1 = 'Rest phase', group2 = 'Active phase',
    label = paste0('FC=', round(logFC,2),", P= ", format.pval(PValue, 1))
  )
timepoint_palette['Rest phase'] <- timepoint_palette['resting']
timepoint_palette['Active phase'] <- timepoint_palette['active']

use_data %>% 
  ggplot(aes(timepoint, exprs, color = timepoint)) +
  geom_quasirandom(alpha = 0.6, wdth = 0.4, groupOnX=TRUE, bandwidth=1) +
  geom_violin(color = 'black', alpha = 0, scale = "width", width = 0.8, draw_quantiles = 0.5) +
  scale_color_manual(values =timepoint_palette) +
  facet_wrap(~gene, ncol = 3) +
  stat_pvalue_manual(sel_dge, label = "label", y.position = 0.5+max(use_data$exprs), size = geom_text_size) +
  scale_y_continuous(limits = use_ylim, breaks = use_breaks) +
  guides(color = 'none') +
  labs(
    x = '',
    y = expression(paste("lo", g[2],"(Normalized counts)"))
  ) +
  background_grid(minor = 'none', major = 'y', size.major = 0.2)

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

Expression of receptors activated by circadian rhythm regulated ligands

Density plots showing the distribution of the average expression (log2 counts per million) of genes encoding for receptors of circadian-regulated hormones, growth factors or molecules in CTCs from NSG-CDX-BR16 mice, NSG-LM2 mice and patients with breast cancer. Genes for the glucocorticoid receptor, androgen receptor and insulin receptor are labeled in the X-axis.

Load list of genes

use_genes_1 <- read_csv(file =  './data/resources/HGNC/group-71-nuclear_hormone_receptors.csv', skip = 1)$`Approved symbol`
use_genes_2 <- read_tsv(file =  './data/resources/user_input/circadian_regulated_hormones_and_gf.txt', col_names = 'genes')$genes
use_genes <- c(use_genes_1, use_genes_2, c('INSR', 'IGF1R', 'IGF2R', 'NR2C2', 'AR')) %>% unique
rm_genes <- use_genes[!use_genes %in% rowData(sce_raw)$gene_name]

Plot expression distributions

use_rows <- rowData(sce_raw)$gene_name %in% use_genes
use_sce <- sce_raw[use_rows,]
rownames(use_sce) <- rowData(use_sce)$gene_name

use_data <- assay(use_sce, 'logcpm') %>% data.frame %>% rownames_to_column('gene_name') %>% pivot_longer(cols =  -gene_name, names_to = 'sample_alias', values_to = 'exprs') %>% 
  left_join(colData(use_sce) %>% data.frame) %>% 
  mutate(
    genes_sel = gene_name %in% c('INSR', 'IGF1R', 'IGF2R', 'NR2C2', 'AR')
    )

mean_data <- use_data %>% group_by(donor, gene_name) %>% summarise(avrg_exprs = mean(exprs)) 
mean_data_selected <- mean_data %>% filter(gene_name %in% c('INSR', 'IGF1R', 'IGF2R', 'NR2C2', 'AR'))
mean_data %>% 
  ggplot(aes(avrg_exprs)) +
  geom_density(fill="#dbd8be") +
  geom_vline(aes(xintercept=mean(avrg_exprs)), color="#043665", linetype="dashed", size=0.5)  +
  facet_grid(rows = vars(donor), scales = 'free') +
  geom_point(
    data = mean_data_selected, 
    mapping = aes(y = 0.01, x = avrg_exprs, label = gene_name), 
    alpha = 0.8,
    color = '#d37d0a') +
  geom_text_repel(
    mean_data_selected, 
    mapping = aes(y = 0.01, x = avrg_exprs, label = gene_name),
    force_pull   = 0, # do not pull toward data points
    nudge_y      = 0.4,
    direction    = "x",
    angle        = 90,
    hjust        = 0,
    segment.size = 0.2,
    max.iter = 1e4, 
    max.time = 1,
    size = geom_text_size) +
  labs(
    x = expression(paste("Average lo", g[2],"(counts per million reads)")),
    y = 'Density',
    caption = 'Blue dashed line represents the average across all genes'
  )

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] parallel stats4 stats graphics grDevices utils datasets [8] methods base

other attached packages: [1] ggrepel_0.9.1 ggpubr_0.4.0
[3] ggbeeswarm_0.6.0 scater_1.20.1
[5] scuttle_1.2.1 SingleCellExperiment_1.14.1 [7] SummarizedExperiment_1.22.0 Biobase_2.52.0
[9] GenomicRanges_1.44.0 GenomeInfoDb_1.28.4
[11] IRanges_2.26.0 S4Vectors_0.30.2
[13] BiocGenerics_0.38.0 MatrixGenerics_1.4.3
[15] matrixStats_0.61.0 cowplot_1.1.1
[17] showtext_0.9-4 showtextdb_3.0
[19] sysfonts_0.8.5 forcats_0.5.1
[21] stringr_1.4.0 dplyr_1.0.7
[23] purrr_0.3.4 readr_2.0.2
[25] tidyr_1.1.4 tibble_3.1.5
[27] ggplot2_3.3.5 tidyverse_1.3.1
[29] workflowr_1.6.2

loaded via a namespace (and not attached): [1] colorspace_2.0-2 ggsignif_0.6.3
[3] rio_0.5.27 ellipsis_0.3.2
[5] rprojroot_2.0.2 XVector_0.32.0
[7] BiocNeighbors_1.10.0 fs_1.5.0
[9] rstudioapi_0.13 farver_2.1.0
[11] bit64_4.0.5 fansi_0.5.0
[13] lubridate_1.8.0 xml2_1.3.2
[15] sparseMatrixStats_1.4.2 knitr_1.36
[17] jsonlite_1.7.2 broom_0.7.10
[19] dbplyr_2.1.1 compiler_4.1.0
[21] httr_1.4.2 backports_1.3.0
[23] assertthat_0.2.1 Matrix_1.3-4
[25] fastmap_1.1.0 cli_3.1.0
[27] later_1.3.0 BiocSingular_1.8.1
[29] htmltools_0.5.2 tools_4.1.0
[31] rsvd_1.0.5 gtable_0.3.0
[33] glue_1.4.2 GenomeInfoDbData_1.2.6
[35] Rcpp_1.0.7 carData_3.0-4
[37] cellranger_1.1.0 jquerylib_0.1.4
[39] vctrs_0.3.8 DelayedMatrixStats_1.14.3 [41] xfun_0.27 openxlsx_4.2.4
[43] beachmat_2.8.1 rvest_1.0.2
[45] lifecycle_1.0.1 irlba_2.3.3
[47] rstatix_0.7.0 zlibbioc_1.38.0
[49] scales_1.1.1 vroom_1.5.5
[51] hms_1.1.1 promises_1.2.0.1
[53] curl_4.3.2 yaml_2.2.1
[55] gridExtra_2.3 sass_0.4.0
[57] stringi_1.7.5 highr_0.9
[59] ScaledMatrix_1.0.0 zip_2.2.0
[61] BiocParallel_1.26.2 rlang_0.4.12
[63] pkgconfig_2.0.3 bitops_1.0-7
[65] evaluate_0.14 lattice_0.20-45
[67] labeling_0.4.2 bit_4.0.4
[69] tidyselect_1.1.1 magrittr_2.0.1
[71] R6_2.5.1 generics_0.1.1
[73] DelayedArray_0.18.0 DBI_1.1.1
[75] foreign_0.8-81 pillar_1.6.4
[77] haven_2.4.3 whisker_0.4
[79] withr_2.4.2 abind_1.4-5
[81] RCurl_1.98-1.5 car_3.0-11
[83] modelr_0.1.8 crayon_1.4.2
[85] utf8_1.2.2 tzdb_0.2.0
[87] rmarkdown_2.11 viridis_0.6.2
[89] grid_4.1.0 readxl_1.3.1
[91] data.table_1.14.2 git2r_0.28.0
[93] reprex_2.0.1 digest_0.6.28
[95] httpuv_1.6.3 munsell_0.5.0
[97] beeswarm_0.4.0 viridisLite_0.4.0
[99] vipor_0.4.5 bslib_0.3.1