Last updated: 2022-07-06

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File Version Author Date Message
Rmd dd28879 Steve Pederson 2022-07-06 Setup initial DGE after restructure
html dd28879 Steve Pederson 2022-07-06 Setup initial DGE after restructure 
library(ngsReports)
library(tidyverse)
library(yaml)
library(scales)
library(pander)
library(glue)
library(plotly)
library(edgeR)
library(ggfortify)
library(AnnotationHub)
library(ensembldb)
library(magrittr)
library(BSgenome.Rnorvegicus.UCSC.rn6)
library(broom)
library(ggrepel)
panderOptions("table.split.table", Inf)
panderOptions("big.mark", ",")
theme_set(theme_bw())
suffix <- "_L001"
pattern <- paste0("_CB2YGANXX_.+fastq.gz")
sp <- "Rnorvegicus"
rn6 <- BSgenome.Rnorvegicus.UCSC.rn6
samples <- "data/targets.csv" %>%
  here::here() %>%
  read_csv() %>% 
    mutate(
      Filename = paste0(File, suffix)
    )
group_cols <- hcl.colors(
  n = length(unique(samples$group)), 
  palette = "Zissou 1"
  ) %>%
  setNames(unique(samples$group))

Annotations

Annotation Setup

ah <- AnnotationHub() %>%
  subset(rdataclass == "EnsDb") %>%
  subset(species == "Rattus norvegicus") %>% 
  subset(str_detect(description, "96"))
ensDb <- ah[[1]]
genesGR <- genes(ensDb) %>% 
  keepStandardChromosomes(species = "Rattus_norvegicus", pruning.mode = "coarse") %>% 
  sortSeqlevels() 
transGR <- transcripts(ensDb) %>% 
  keepStandardChromosomes(species = "Rattus_norvegicus", pruning.mode = "coarse") %>% 
  sortSeqlevels() 
exonGR <- exonsBy(ensDb, "tx") %>% 
  reduce() %>% 
  keepStandardChromosomes(species = "Rattus_norvegicus", pruning.mode = "coarse") %>% 
  sortSeqlevels() 
exonGR_UCSC <- exonGR
seqlevels(exonGR_UCSC) <- paste0("chr", seqlevels(exonGR_UCSC)) %>% 
  str_replace("chrMT", "chrM")
genome(exonGR_UCSC) <- genome(rn6)
exonSeq <- getSeq(rn6, exonGR_UCSC) %>% 
  lapply(unlist) %>% 
  as("DNAStringSet")
transGC <- exonSeq %>% 
  letterFrequency("GC", as.prob = TRUE) %>% 
  .[,1] %>% 
  setNames(names(exonSeq))
transGR$gc_content <- transGC[names(transGR)]
mcols(transGR) <- mcols(transGR) %>%
  cbind(
    transcriptLengths(ensDb)[rownames(.), c("nexon", "tx_len")]
  )
mcols(genesGR) <- mcols(genesGR) %>%
  as.data.frame() %>%
  dplyr::select(
    gene_id, gene_name, gene_biotype, entrezid
  ) %>%
  left_join(
    mcols(transGR) %>%
      as.data.frame() %>%
      mutate(
        tx_support_level = case_when(
          is.na(tx_support_level) ~ 1L, 
          TRUE ~ tx_support_level
        )
      ) %>%
      group_by(gene_id) %>%
      summarise(
        n_tx = n(),
        longest_tx = max(tx_len),
        ave_tx_len = mean(tx_len),
        gc_content = sum(tx_len*gc_content) / sum(tx_len)
      ) %>%
      mutate(
        bin_length = cut(
          x = ave_tx_len,
          labels = seq_len(10),
          breaks = quantile(ave_tx_len, probs = seq(0, 1, length.out = 11)),
          include.lowest = TRUE
        ),
        bin_gc = cut(
          x = gc_content,
          labels = seq_len(10),
          breaks = quantile(gc_content, probs = seq(0, 1, length.out = 11)),
          include.lowest = TRUE
        ),
        bin = paste(bin_gc, bin_length, sep = "_")
      ),
    by = "gene_id"
  ) %>%
  set_rownames(.$gene_id) %>%
  as("DataFrame")
trans2Gene <- mcols(transGR) %>%
    as.data.frame() %>%
    dplyr::select(tx_id, gene_id) %>%
    dplyr::filter(!is.na(tx_id), !is.na(gene_id)) %>%
    as_tibble()

Annotation data was loaded as an EnsDb object, using Ensembl release 96. Transcript level gene lengths and GC content was converted to gene level values using:

  • GC Content: The total GC content divided by the total length of transcripts
  • Gene Length: The mean transcript length
write_rds(genesGR, here::here("output/genesGR.rds"), compress = "gz")
counts <- list.files(here::here("data/3_kallisto"), full.names = TRUE) %>% 
    catchKallisto()
dge <- counts$counts %>%
    as.data.frame() %>%
    rownames_to_column("tx_id") %>%
    as_tibble() %>%
    set_colnames(basename(colnames(.))) %>%
    set_colnames(str_remove(colnames(.),"_CB2Y.+")) %>%
    mutate(tx_id = str_remove(tx_id, "\\.[0-9]+")) %>% 
  dplyr::filter(tx_id %in% trans2Gene$tx_id) %>% 
  pivot_longer(cols = all_of(samples$Rat), names_to = "Rat", values_to = "count") %>% 
    left_join(trans2Gene) %>%
    group_by(Rat, gene_id) %>%
    summarise(count = sum(count)) %>% 
  pivot_wider(names_from = "Rat", values_from = "count") %>% 
    dplyr::filter(grepl("ENSRNOG", gene_id)) %>%
    as.data.frame() %>% 
    column_to_rownames("gene_id") %>% 
    DGEList() 
dge$samples %<>%
    mutate(Rat = rownames(.)) %>%
  dplyr::select(-group) %>% 
    left_join(samples, by = "Rat") %>%
    set_rownames(.$Rat)
dge$genes <- genesGR[rownames(dge)] %>% 
  mcols()

Read Assignment To Genes

Library Sizes

dge$samples %>% 
  mutate(lib.size = lib.size / 1e6) %>% 
  ggplot(aes(Rat, lib.size, fill = group)) +
  geom_col() +
  facet_wrap(~group, scales = "free_x") +
  scale_y_continuous(expand = expansion(c(0, .05))) +
  scale_fill_manual(values = group_cols) +
  labs(
    x = "Sample", y = "Library Size (millions)",
    fill = "Group"
  )
Library sizes after summarising to gene-level counts.

Library sizes after summarising to gene-level counts.

After assignment to genes, library sizes ranged between 6,934,169 and 14,552,053 reads, with a median library size of 10,208,651 reads.

Count Assignment Rates

trimFqc <- here::here("data/1_trimmedData/FastQC") %>%
  list.files(pattern = "zip", full.names = TRUE) %>%
  FastqcDataList()
trimFqc %>% 
  getModule("Basic") %>% 
  mutate(Filename = str_remove_all(Filename, "_R1.fastq.gz")) %>% 
  left_join(dge$samples, by = "Filename") %>% 
  mutate(`% Assigned To Genes` = lib.size / Total_Sequences) %>% 
  ggplot(aes(Rat, `% Assigned To Genes`, fill = group)) +
  geom_col() +
  facet_wrap(~group, scales = "free_x") +
  scale_fill_manual(values = group_cols) +
  scale_y_continuous(labels = percent, expand = expansion(c(0, 0.05))) +
  labs(fill = "Group")
Assignment rate for reads to genes. All samples showed that a large proportion of reads were not derived from mRNA transcripts, and as such, were not informative for the analysis.

Assignment rate for reads to genes. All samples showed that a large proportion of reads were not derived from mRNA transcripts, and as such, were not informative for the analysis.

Total Detected Genes

  • Of the 23,706 genes defined in this annotation build, 1,621 genes had no reads assigned in any samples.
dge$counts %>%
  as_tibble() %>%
  mutate(
    across(everything(), as.logical)
  ) %>%
  summarise(
    across(everything(), sum)
  ) %>%
  pivot_longer(
    everything(), names_to = "Rat", values_to = "Detected"
  ) %>%
  left_join(samples)%>%
  ggplot(aes(group, Detected, colour = group)) +
  geom_point() +
  geom_segment(
    aes(xend = group, y = 0, yend = Detected),
    data = . %>% 
      group_by(group) %>%
      summarise(Detected = min(Detected)),
    colour = "black", size = 1/4) +
  scale_y_continuous(labels = comma, expand = expansion(c(0, 0.05))) +
  scale_colour_manual(values = group_cols) +
  labs(
    x = "Group", 
    y = "Genes Detected",
    colour = "Group"
  )
*Total numbers of genes detected across all samples and groups.*

Total numbers of genes detected across all samples and groups.

Version Author Date
dd28879 Steve Pederson 2022-07-06 
plotly::ggplotly(
  dge$counts %>%
    is_greater_than(0) %>%
    rowSums() %>%
    table() %>%
    enframe(name = "n_samples", value = "n_genes") %>%
    mutate(
      n_samples = as.integer(n_samples),
      n_genes = as.integer(n_genes),
    ) %>%
    arrange(desc(n_samples)) %>%
    mutate(
      Detectable = cumsum(n_genes),
      Undetectable = sum(n_genes) - Detectable
    ) %>%
    pivot_longer(
      cols = ends_with("table"),
      names_to = "Status",
      values_to = "Number of Genes"
    ) %>%
    dplyr::rename(
      `Number of Samples` = n_samples,
    ) %>%
    ggplot(aes(`Number of Samples`, `Number of Genes`, colour = Status)) +
    geom_line() +
    geom_vline(
      aes(xintercept = `Mean Sample Number`),
      data = . %>%
        summarise(`Mean Sample Number` = mean(`Number of Samples`)),
      linetype = 2,
      colour = "grey50"
    ) +
    scale_x_continuous(expand = expansion(c(0.01, 0.01))) +
    scale_y_continuous(labels = comma) +
    scale_colour_manual(values = c(rgb(0.1, 0.7, 0.2), rgb(0.7, 0.1, 0.1))) +
    labs(
      x = "Samples > 0"
    )
)

Total numbers of genes detected shown against the number of samples with at least one read assigned to each gene.

PCA

Sample Similarity

pca <- dge$counts %>%
  .[rowSums(. == 0) < ncol(.)/2,] %>%
  cpm(log = TRUE) %>%
  t() %>%
  prcomp()

A PCA was performed using logCPM values from the subset of 18,017 genes with at least one read in more than half of the samples.

showLabel <- nrow(samples) <= 20
pca %>% 
tidy() %>% 
  dplyr::rename(Rat = row) %>% 
  left_join(dge$samples, by = "Rat") %>% 
  dplyr::filter(PC %in% 1:2) %>% 
  pivot_wider(names_from = "PC", names_prefix = "PC", values_from = "value") %>% 
  ggplot(
    aes(PC1, PC2, colour = group, size = lib.size/1e6)
  ) +
  geom_point() +
  geom_text_repel(aes(label = Rat), show.legend = FALSE) +
  scale_colour_manual(values = group_cols) +
  scale_size_continuous(limits = c(5, 15), breaks = seq(5, 15, by = 5)) +
  labs(
    x = glue("PC1 ({percent(pca$sdev[[1]]^2 / sum(pca$sdev^2), 0.1)})"),
    y = glue("PC2 ({percent(pca$sdev[[2]]^2 / sum(pca$sdev^2), 0.1)})"),
    colour = "Group",
    size = "Library Size\n(millions)"
  )
*PCA plot of all samples. PC1 is most strongly correlated with library size, whilst PC2 appears to capture the majority of the biological variability*

PCA plot of all samples. PC1 is most strongly correlated with library size, whilst PC2 appears to capture the majority of the biological variability

Version Author Date
dd28879 Steve Pederson 2022-07-06

GC and Length Biases

mcols(genesGR) %>%
  as.data.frame() %>%
  dplyr::filter(gene_id %in% rownames(pca$rotation)) %>%
  as_tibble() %>%
  mutate(
    bin_length = cut(
      x = ave_tx_len,
      labels = seq_len(10),
      breaks = quantile(ave_tx_len, probs = seq(0, 1, length.out = 11)),
      include.lowest = TRUE
    ),
    bin_gc = cut(
      x = gc_content,
      labels = seq_len(10),
      breaks = quantile(gc_content, probs = seq(0, 1, length.out = 11)),
      include.lowest = TRUE
    ),
    bin = paste(bin_gc, bin_length, sep = "_")
  ) %>%
  dplyr::select(gene_id, contains("bin")) %>%
  mutate(
    PC1 = pca$rotation[gene_id, "PC1"],
    PC2 = pca$rotation[gene_id, "PC2"]
  ) %>%
  pivot_longer(
    cols = c("PC1", "PC2"),
    names_to = "PC",
    values_to = "value"
  ) %>%
  group_by(PC, bin_gc, bin_length, bin) %>%
  summarise(
    Size = n(),
    mean = mean(value),
    sd = sd(value),
    t = t.test(value)$statistic,
    p = t.test(value)$p.value,
    adjP = p.adjust(p, method = "bonf")
  ) %>%
  ggplot(
    aes(bin_length, bin_gc, colour = t, alpha = -log10(adjP), size = Size)
  ) +
  geom_point() +
  facet_wrap(~PC) +
  scale_colour_gradient2() +
  scale_size_continuous(range = c(1, 10)) +
  labs(
    x = "Average Transcript Length",
    y = "GC Content",
    alpha = expression(paste(-log[10], p[adj]))) +
  theme(
    panel.grid = element_blank(),
    legend.position = "bottom"
    )
*Contribution of each GC/Length Bin to PC1 and PC2. Fill colours indicate the t-statistic, with tranparency denoting significance as -log10(p), using Bonferroni-adjusted p-values.*

Contribution of each GC/Length Bin to PC1 and PC2. Fill colours indicate the t-statistic, with tranparency denoting significance as -log10(p), using Bonferroni-adjusted p-values.

Version Author Date
dd28879 Steve Pederson 2022-07-06 
write_rds(dge, here::here("output/dge.rds"))

sessionInfo()
R version 4.2.0 (2022-04-22)
Platform: x86_64-pc-linux-gnu (64-bit)
Running under: Ubuntu 20.04.4 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/blas/libblas.so.3.9.0
LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.9.0

locale:
 [1] LC_CTYPE=en_AU.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_AU.UTF-8        LC_COLLATE=en_AU.UTF-8    
 [5] LC_MONETARY=en_AU.UTF-8    LC_MESSAGES=en_AU.UTF-8   
 [7] LC_PAPER=en_AU.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_AU.UTF-8 LC_IDENTIFICATION=C       

attached base packages:
[1] stats4    stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
 [1] ggrepel_0.9.1                       broom_0.8.0                        
 [3] BSgenome.Rnorvegicus.UCSC.rn6_1.4.1 BSgenome_1.64.0                    
 [5] rtracklayer_1.56.0                  Biostrings_2.64.0                  
 [7] XVector_0.36.0                      magrittr_2.0.3                     
 [9] ensembldb_2.20.1                    AnnotationFilter_1.20.0            
[11] GenomicFeatures_1.48.0              AnnotationDbi_1.58.0               
[13] Biobase_2.56.0                      GenomicRanges_1.48.0               
[15] GenomeInfoDb_1.32.1                 IRanges_2.30.0                     
[17] S4Vectors_0.34.0                    AnnotationHub_3.4.0                
[19] BiocFileCache_2.4.0                 dbplyr_2.1.1                       
[21] ggfortify_0.4.14                    edgeR_3.38.0                       
[23] limma_3.52.0                        plotly_4.10.0                      
[25] glue_1.6.2                          pander_0.6.5                       
[27] scales_1.2.0                        yaml_2.3.5                         
[29] forcats_0.5.1                       stringr_1.4.0                      
[31] dplyr_1.0.9                         purrr_0.3.4                        
[33] readr_2.1.2                         tidyr_1.2.0                        
[35] tidyverse_1.3.1                     ngsReports_1.13.0                  
[37] tibble_3.1.7                        ggplot2_3.3.6                      
[39] BiocGenerics_0.42.0                 workflowr_1.7.0                    

loaded via a namespace (and not attached):
  [1] readxl_1.4.0                  backports_1.4.1              
  [3] lazyeval_0.2.2                crosstalk_1.2.0              
  [5] BiocParallel_1.30.0           digest_0.6.29                
  [7] htmltools_0.5.2               fansi_1.0.3                  
  [9] memoise_2.0.1                 tzdb_0.3.0                   
 [11] modelr_0.1.8                  matrixStats_0.62.0           
 [13] vroom_1.5.7                   prettyunits_1.1.1            
 [15] colorspace_2.0-3              blob_1.2.3                   
 [17] rvest_1.0.2                   rappdirs_0.3.3               
 [19] haven_2.5.0                   xfun_0.30                    
 [21] callr_3.7.0                   crayon_1.5.1                 
 [23] RCurl_1.98-1.6                jsonlite_1.8.0               
 [25] zoo_1.8-10                    gtable_0.3.0                 
 [27] zlibbioc_1.42.0               DelayedArray_0.22.0          
 [29] Rhdf5lib_1.18.0               DBI_1.1.2                    
 [31] Rcpp_1.0.8.3                  viridisLite_0.4.0            
 [33] xtable_1.8-4                  progress_1.2.2               
 [35] bit_4.0.4                     DT_0.22                      
 [37] htmlwidgets_1.5.4             httr_1.4.3                   
 [39] ellipsis_0.3.2                farver_2.1.0                 
 [41] pkgconfig_2.0.3               XML_3.99-0.9                 
 [43] sass_0.4.1                    here_1.0.1                   
 [45] locfit_1.5-9.5                utf8_1.2.2                   
 [47] labeling_0.4.2                tidyselect_1.1.2             
 [49] rlang_1.0.2                   later_1.3.0                  
 [51] munsell_0.5.0                 BiocVersion_3.15.2           
 [53] cellranger_1.1.0              tools_4.2.0                  
 [55] cachem_1.0.6                  cli_3.3.0                    
 [57] generics_0.1.2                RSQLite_2.2.13               
 [59] evaluate_0.15                 fastmap_1.1.0                
 [61] ggdendro_0.1.23               processx_3.5.3               
 [63] knitr_1.39                    bit64_4.0.5                  
 [65] fs_1.5.2                      KEGGREST_1.36.0              
 [67] whisker_0.4                   mime_0.12                    
 [69] xml2_1.3.3                    biomaRt_2.52.0               
 [71] compiler_4.2.0                rstudioapi_0.13              
 [73] filelock_1.0.2                curl_4.3.2                   
 [75] png_0.1-7                     interactiveDisplayBase_1.34.0
 [77] reprex_2.0.1                  bslib_0.3.1                  
 [79] stringi_1.7.6                 highr_0.9                    
 [81] ps_1.7.0                      lattice_0.20-45              
 [83] ProtGenerics_1.28.0           Matrix_1.4-1                 
 [85] vctrs_0.4.1                   rhdf5filters_1.8.0           
 [87] pillar_1.7.0                  lifecycle_1.0.1              
 [89] BiocManager_1.30.17           jquerylib_0.1.4              
 [91] data.table_1.14.2             bitops_1.0-7                 
 [93] httpuv_1.6.5                  R6_2.5.1                     
 [95] BiocIO_1.6.0                  promises_1.2.0.1             
 [97] gridExtra_2.3                 MASS_7.3-57                  
 [99] assertthat_0.2.1              rhdf5_2.40.0                 
[101] SummarizedExperiment_1.26.1   rprojroot_2.0.3              
[103] rjson_0.2.21                  withr_2.5.0                  
[105] GenomicAlignments_1.32.0      Rsamtools_2.12.0             
[107] GenomeInfoDbData_1.2.8        parallel_4.2.0               
[109] hms_1.1.1                     grid_4.2.0                   
[111] rmarkdown_2.14                MatrixGenerics_1.8.0         
[113] git2r_0.30.1                  getPass_0.2-2                
[115] shiny_1.7.1                   lubridate_1.8.0              
[117] restfulr_0.0.13