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rough-draft_evaluate-categories_expression.R
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#!/usr/bin/env Rscript
# rough-draft_evaluate-categories_expression.R
# KA
# Initialize arguments =======================================================
samples <- "ovation" #ARGUMENT
# samples <- "ovation_tecan_test" #ARGUMENT
# samples <- "ovation_tecan_test_rrp6∆" #ARGUMENT
# samples <- "ovation_tecan_updated" #ARGUMENT
# samples <- "n3d_od" #ARGUMENT
# samples <- "r6n" #ARGUMENT
# samples <- "r6n_timecourse" #ARGUMENT
# samples <- "Rrp6∆.timecourse-G1-Q.SS"
tx <- "coding-non-pa-ncRNA" #ARGUMENT #IMPORTANT #NOTE i.e., "R64"
# tx <- "noncoding-collapsed" #ARGUMENT #IMPORTANT
# tx <- "noncoding-non-collapsed" #ARGUMENT
# tx <- "Trinity-G1" #ARGUMENT
# tx <- "Trinity-Q" #ARGUMENT
# Get situated ===============================================================
suppressMessages(library(ggplot2))
suppressMessages(library(ggpubr))
suppressMessages(library(PCAtools))
suppressMessages(library(rstatix))
suppressMessages(library(tidyverse))
suppressMessages(library(treemap))
options(scipen = 999)
options(ggrepel.max.overlaps = Inf)
if(stringr::str_detect(getwd(), "kalavattam")) {
p_local <- "/Users/kalavattam/Dropbox/FHCC"
} else {
p_local <- "/Users/kalavatt/projects-etc"
}
p_wd <- "2022-2023_RRP6-NAB3/results/2023-0215"
setwd(paste(p_local, p_wd, sep = "/"))
getwd()
rm(p_local, p_wd)
# Initialize functions -------------------------------------------------------
`%notin%` <- Negate(`%in%`)
read_in_counts_matrix <- function(x) {
# ...
#
# :param x: counts matrix from htseq-count
# :return y: counts matrix as tibble
y <- readr::read_tsv(x, show_col_types = FALSE) %>%
dplyr::rename(gene_id = ...1)
return(y)
}
filter_process_counts_matrix <- function(
counts_matrix,
named_character_vector
) {
# ...
#
# :param counts_matrix: counts matrix from htseq-count
# :param named_character_vector: ...
# :return df: counts matrix as tibble
# Test
# counts_matrix <- t_cm
# named_character_vector <- col_cor
df <- dplyr::bind_cols(
counts_matrix[, 1],
counts_matrix[
, colnames(counts_matrix) %in% named_character_vector
]
)
df <- dplyr::bind_cols(
df[, 1],
df[, 2:ncol(df)][
, match(named_character_vector, colnames(df)[2:ncol(df)])
]
)
names(df)[2:ncol(df)] <- names(named_character_vector)
return(df)
}
derive_summary_metrics <- function(uni_multi, summary, counts) {
# Debug
run <- FALSE
if(base::isTRUE(run)) {
uni_multi <- uni_multi_etc
summary <- underscore
counts <- t_cm
}
# Step 1: Get multimappers associated with "Mito-KL-20S"
`Mito-KL-20S_multi` <- uni_multi[
stringr::str_detect(uni_multi[["sample"]], "Mito-KL-20S_multi"),
2:ncol(uni_multi)
]
if(base::isTRUE(run)) print(`Mito-KL-20S_multi`)
# Step 2: Get unimappers associated with "Mito-KL-20S"
`Mito-KL-20S_uni` <- uni_multi[
stringr::str_detect(uni_multi[["sample"]], "Mito-KL-20S_uni"),
2:ncol(uni_multi)
]
if(base::isTRUE(run)) print(`Mito-KL-20S_uni`)
# Step 3: Get unimappers associated with "SC-I-XVI"
`SC-I-XVI_uni` <- uni_multi[
stringr::str_detect(uni_multi[["sample"]], "SC-I-XVI_uni"),
2:ncol(uni_multi)
]
if(base::isTRUE(run)) print(`SC-I-XVI_uni`)
# Step 4:
#+ - Isolate and define `__alignment_not_unique`
#+ - Define and calculate `__alignment_not_unique_I-XVI` by subtracting
#+ `Mito-KL-20S_multi` from `__alignment_not_unique`
`__alignment_not_unique` <- summary[
stringr::str_detect(summary[["gene_id"]], "__alignment_not_unique"),
2:ncol(summary)
] %>%
tibble::as_tibble()
`__alignment_not_unique_I-XVI` <-
`__alignment_not_unique` - `Mito-KL-20S_multi`
`__alignment_not_unique_I-XVI` <- `__alignment_not_unique_I-XVI` %>%
tibble::as_tibble()
if(base::isTRUE(run)) print(`__alignment_not_unique`)
if(base::isTRUE(run)) print(`__alignment_not_unique_I-XVI`)
# Step 5:
#+ - Isolate and define `__no_feature`
#+ - Define and calculate `__no_feature_I-XVI` by subtracting
#+ `Mito-KL-20S_uni` from "`__no_feature`"
`__no_feature` <- summary[
stringr::str_detect(summary[["gene_id"]], "__no_feature"),
2:ncol(summary)
]
`__no_feature_I-XVI` <- `__no_feature` - `Mito-KL-20S_uni`
`__no_feature_I-XVI` <- `__no_feature_I-XVI` %>% tibble::as_tibble()
if(base::isTRUE(run)) print(`__no_feature`)
if(base::isTRUE(run)) print(`__no_feature_I-XVI`)
# Step 6: Define `__sum_I-XVI` by summing the following:
#+ - "`__no_feature_I-XVI`"
#+ - "`__ambiguous`"
#+ - "`__alignment_not_unique_I-XVI`"
#+ - "`__valid_counts`"
`__ambiguous` <- summary[
stringr::str_detect(summary[["gene_id"]], "__ambiguous"),
2:ncol(summary)
]
`__valid_counts` <- sapply(counts[, -1], sum) %>%
t() %>%
tibble::as_tibble()
`__sum_I-XVI` <- `__no_feature_I-XVI` +
`__ambiguous` +
`__alignment_not_unique_I-XVI` +
`__valid_counts`
`__sum_I-XVI` <- `__sum_I-XVI` %>% tibble::as_tibble()
if(base::isTRUE(run)) print(`__ambiguous`)
if(base::isTRUE(run)) print(`__valid_counts`)
if(base::isTRUE(run)) print(`__sum_I-XVI`)
# Step 7: Tally all counts associated with "SC-I-XVI" (`SC-I-XVI_all`)
`SC-I-XVI_all` <-
uni_multi[
stringr::str_detect(uni_multi[["sample"]], "SC-I-XVI_all"),
2:ncol(uni_multi)
]
`SC-I-XVI_all` <- `SC-I-XVI_all` %>% tibble::as_tibble()
if(base::isTRUE(run)) print(`SC-I-XVI_all`)
# Step 8: Check that `__sum_I-XVI` and `SC-I-XVI_all` are equal; if not,
#+ then there is a problem and troubleshooting needs to occur
identical(
as.numeric(`__sum_I-XVI`[, 2:ncol(`__sum_I-XVI`)]),
as.numeric(`SC-I-XVI_all`[, 2:ncol(`SC-I-XVI_all`)])
) # [1] TRUE
# Step 9: Give all of the new tibbles appropriate "gene_id" columns
`Mito-KL-20S_multi` <- `Mito-KL-20S_multi` %>%
dplyr::mutate(gene_id = "Mito-KL-20S_multi") %>%
dplyr::select(gene_id, dplyr::everything())
`Mito-KL-20S_uni` <- `Mito-KL-20S_uni` %>%
dplyr::mutate(gene_id = "Mito-KL-20S_uni") %>%
dplyr::select(gene_id, dplyr::everything())
`SC-I-XVI_uni` <- `SC-I-XVI_uni` %>%
dplyr::mutate(gene_id = "SC-I-XVI_uni") %>%
dplyr::select(gene_id, dplyr::everything())
`__alignment_not_unique` <- `__alignment_not_unique` %>%
dplyr::mutate(gene_id = "__alignment_not_unique") %>%
dplyr::select(gene_id, dplyr::everything())
`__alignment_not_unique_I-XVI` <- `__alignment_not_unique_I-XVI` %>%
dplyr::mutate(gene_id = "__alignment_not_unique_I-XVI") %>%
dplyr::select(gene_id, dplyr::everything())
`__no_feature` <- `__no_feature` %>%
dplyr::mutate(gene_id = "__no_feature") %>%
dplyr::select(gene_id, dplyr::everything())
`__no_feature_I-XVI` <- `__no_feature_I-XVI` %>%
tibble::as_tibble() %>%
dplyr::mutate(gene_id = "__no_feature_I-XVI") %>%
dplyr::select(gene_id, dplyr::everything())
`__ambiguous` <- `__ambiguous` %>%
dplyr::mutate(gene_id = "__ambiguous") %>%
dplyr::select(gene_id, dplyr::everything())
`__valid_counts` <- `__valid_counts` %>%
dplyr::mutate(gene_id = "__valid_counts") %>%
dplyr::select(gene_id, dplyr::everything())
`__sum_I-XVI` <- `__sum_I-XVI` %>%
dplyr::mutate(gene_id = "__sum_I-XVI") %>%
dplyr::select(gene_id, dplyr::everything())
`SC-I-XVI_all` <- `SC-I-XVI_all` %>%
dplyr::mutate(gene_id = "SC-I-XVI_all") %>%
dplyr::select(gene_id, dplyr::everything())
# Step 10: Create a tibble of "thorough" summary metrics
summary_thorough <- dplyr::bind_rows(
`Mito-KL-20S_multi`, `Mito-KL-20S_uni`, `SC-I-XVI_uni`,
`__alignment_not_unique`, `__alignment_not_unique_I-XVI`,
`__no_feature`, `__no_feature_I-XVI`, `__ambiguous`, `__valid_counts`,
`__sum_I-XVI`, `SC-I-XVI_all`
)
# Step 11: Create a tibble of "relevant" summary metrics
exclude <- c(
"__no_feature", "__too_low_aQual", "__not_aligned",
"__alignment_not_unique", "sample_total", "Mito-KL-20S_all",
"Mito-KL-20S_uni", "Mito-KL-20S_multi", "SC-I-XVI_all", "SC-I-XVI_uni",
"SC-I-XVI_multi", "__sum_I-XVI"
)
summary_relevant <- summary_thorough[
summary_thorough$gene_id %notin% exclude,
]
# Step 12: Return the "thorough" and "relevant" tibbles
results_list <- list()
results_list[["summary_thorough"]] <- summary_thorough
results_list[["summary_relevant"]] <- summary_relevant
return(results_list)
}
pivot_on_columns <- function(
tbl,
vec_col,
rename_n = "samples",
rename_v = "counts"
) {
# ...
#
# :param tbl: tibble/dataframe
# :param vec_col: a vector of column names to long-pivot on
# :param rename_n: what to rename new column "name"
# :param rename_v: what to rename new column "value"
# # Test
# tbl <- summary
# vec_col <- col_piv
# rename_n <- "samples"
# rename_v <- "counts"
# # rm(tbl, vec_col, rename_n, rename_v)
if(length(vec_col) <= 1) {
stop("Vector of column names must be greater than 1")
} else if(isFALSE(any(vec_col %in% colnames(tbl)))) {
stop("No column-name elements match tibble/dataframe column names")
}
tbl <- tbl %>%
tidyr::pivot_longer(cols = vec_col) %>%
dplyr::rename(
!! dplyr::quo_name(rename_n) := name,
!! dplyr::quo_name(rename_v) := value
)
return(tbl)
}
`plot-rep-prop_by-sample_stacked` <- function(tbl) {
# ...
# :param tbl: Pivoted summary tibble
# :return plot: ...
plot <- tbl %>%
ggplot(aes(x = samples, y = counts, fill = category)) +
geom_bar(position = "fill", stat = "identity") +
theme_slick +
ggpubr::rotate_x_text(45) +
ggtitle(
"Sample-wise proportions of counts",
subtitle = "Stacked"
)
return(plot)
}
`plot-rep-prop_by-category_stacked` <- function(tbl) {
# ...
# :param tbl: Pivoted summary tibble
# :return plot: ...
plot <- tbl %>%
ggplot(aes(x = category, y = counts, fill = samples)) +
geom_bar(position = "fill", stat = "identity") +
theme_slick +
ggpubr::rotate_x_text(45) +
ggtitle(
"Category-wise proportions of counts",
subtitle = "Stacked"
)
return(plot)
}
# Set up custom ggplot2 plot themes ------------------------------------------
theme_slick <- theme_classic() +
theme(
panel.grid.major = ggplot2::element_line(linewidth = 0.4),
panel.grid.minor = ggplot2::element_line(linewidth = 0.2),
axis.line = ggplot2::element_line(linewidth = 0.2),
axis.ticks = ggplot2::element_line(linewidth = 0.4),
axis.text = ggplot2::element_text(color = "black"),
axis.title.x = ggplot2::element_text(),
axis.title.y = ggplot2::element_text(),
plot.title = ggplot2::element_text(),
text = element_text(family = "")
)
theme_slick_no_legend <- theme_slick + theme(legend.position = "none")
theme_AG <- theme_classic() +
theme(
panel.grid.major = ggplot2::element_line(linewidth = 3),
panel.grid.minor = ggplot2::element_line(linewidth = 2),
axis.line = ggplot2::element_line(linewidth = 0.5),
axis.ticks = ggplot2::element_line(linewidth = 1.0),
axis.text = ggplot2::element_text(
color = "black", size = 20, face = "bold"
),
axis.title.x = ggplot2::element_text(size = 25, face = "bold"),
axis.title.y = ggplot2::element_text(size = 25, face = "bold"),
plot.title = ggplot2::element_text(size = 20),
text = element_text(family = "")
)
theme_AG_boxed <- theme_AG +
theme(
axis.line = ggplot2::element_line(linewidth = 0),
panel.border = element_rect(linewidth = 2, color = "black", fill = NA)
)
theme_slick_no_legend <- theme_slick + theme(legend.position = "none")
theme_AG_no_legend <- theme_AG + theme(legend.position = "none")
theme_AG_boxed_no_legend <- theme_AG_boxed + theme(legend.position = "none")
# Load and process gtf, counts matrix, and summary metrics ===================
# Load gtf file of interest --------------------------------------------------
# ...which consists of collapsed/merged pa-ncRNAs and "processed" features
if(base::isFALSE(tx %in% c(
"coding-non-pa-ncRNA", "noncoding-non-collapsed",
"noncoding-collapsed", "Trinity-G1", "Trinity-Q"
))) {
stop(paste(
"Argument \"tx\" must be one of either \"coding\",",
"\"noncoding-non-collapsed\", or \"noncoding-collapsed\""
))
}
if(tx == "coding-non-pa-ncRNA") {
p_gtf <- "outfiles_gtf-gff3/representation"
f_gtf <- "Greenlaw-et-al_representative-coding-non-pa-ncRNA-transcriptome.gtf"
} else if(tx == "noncoding-non-collapsed") {
p_gtf <- "outfiles_gtf-gff3/representation"
f_gtf <- "Greenlaw-et-al_non-collapsed-non-coding-transcriptome.gtf" #DONE #IMPORTANT #FUTURE
} else if(tx == "noncoding-collapsed") {
p_gtf <- "outfiles_gtf-gff3/representation"
f_gtf <- "Greenlaw-et-al_representative-non-coding-transcriptome.gtf" #TODO #TOCOMPLETE
} else if(tx == "Trinity-G1") {
p_gtf <- "outfiles_gtf-gff3/Trinity-GG/G_N/filtered/locus"
f_gtf <- "G1_mkc-4_gte-pctl-25.clean.gtf"
} else if(tx == "Trinity-Q") {
p_gtf <- "outfiles_gtf-gff3/Trinity-GG/Q_N/filtered/locus"
f_gtf <- "Q_mkc-4_gte-pctl-25.clean.gtf"
}
#DONOTUSE #TBD
# f_gtf <- "Greenlaw-et-al_representative-coding-pa-ncRNA-transcriptome.gtf"
# f_gtf <- "Greenlaw-et-al_representative-coding-ncRNA-transcriptome.gtf"
run <- FALSE
if(base::isTRUE(run)) {
dir.exists(p_gtf)
file.exists(paste(p_gtf, f_gtf, sep = "/"))
}
t_gtf <- paste(p_gtf, f_gtf, sep = "/") %>%
rtracklayer::import() %>%
tibble::as_tibble() %>%
dplyr::arrange(seqnames, start) %>%
dplyr::select(-c(width, score, phase))
if(tx %in% c("coding-non-pa-ncRNA", "noncoding-non-collapsed")) {
t_gtf <- t_gtf %>% dplyr::rename(category = type.1)
}
t_gtf[t_gtf == "NA"] <- NA_character_
# t_gtf
rm(p_gtf)
# Load counts matrix file of interest -----------------------------------------
# ...which consists of collapsed/merged pa-ncRNAs and "processed" features
if(f_gtf == "Greenlaw-et-al_representative-coding-pa-ncRNA-transcriptome.gtf") {
p_cm <- "outfiles_htseq-count/representation/UT_prim_UMI"
f_cm <- "representative-coding-pa-ncRNA-transcriptome.hc-strd-eq.union-none.tsv"
# f_cm <- "representative-coding-pa-ncRNA-transcriptome.hc-strd-eq.union-fraction.tsv"
} else if(f_gtf == "Greenlaw-et-al_representative-coding-non-pa-ncRNA-transcriptome.gtf") {
p_cm <- "outfiles_htseq-count/representation/UT_prim_UMI"
f_cm <- "representative-coding-non-pa-ncRNA-transcriptome.hc-strd-eq.union-none.tsv"
# f_cm <- "representative-coding-non-pa-ncRNA-transcriptome.hc-strd-eq.union-fraction.tsv"
} else if(f_gtf == "Greenlaw-et-al_representative-coding-ncRNA-transcriptome.gtf") {
p_cm <- "outfiles_htseq-count/representation/UT_prim_UMI"
f_cm <- "representative-coding-ncRNA-transcriptome.hc-strd-eq.union-none.tsv"
} else if(f_gtf == "Greenlaw-et-al_non-collapsed-non-coding-transcriptome.gtf") { #DONE
p_cm <- "outfiles_htseq-count/representation/UT_prim_UMI"
f_cm <- "non-collapsed-non-coding-transcriptome.hc-strd-eq.tsv" #TODO #IMPORTANT
} else if(f_gtf == "Greenlaw-et-al_representative-non-coding-transcriptome.gtf") {
p_cm <- "outfiles_htseq-count/representation/UT_prim_UMI"
f_cm <- "representative-non-coding-transcriptome.hc-strd-eq.tsv"
} else if(f_gtf == "G1_mkc-4_gte-pctl-25.clean.gtf") {
p_cm <- "outfiles_htseq-count/Trinity-GG/G_N/filtered/locus"
f_cm <- "G1_mkc-4_gte-pctl-25.clean.hc-strd-eq.tsv"
} else if(f_gtf == "Q_mkc-4_gte-pctl-25.clean.gtf") {
p_cm <- "outfiles_htseq-count/Trinity-GG/Q_N/filtered/locus"
f_cm <- "Q_mkc-4_gte-pctl-25.clean.hc-strd-eq.tsv"
}
run <- FALSE
if(base::isTRUE(run)) {
dir.exists(p_cm) %>% print()
file.exists(paste(p_cm, f_cm, sep = "/")) %>% print()
}
t_cm <- read_in_counts_matrix(paste(p_cm, f_cm, sep = "/"))
if(base::isFALSE(nrow(unique(t_cm[1])) == nrow(t_cm))) {
stop(paste(
"There are non-unique elements in the \"gene_id\" column (i.e., the",
"feature name vector). This will result in complications when running",
"subsequent code/analyses. Stopping the script."
))
}
# Clean up the tibble of counts ----------------------------------------------
# Clean up, correct, and abbreviate sample names
colnames(t_cm) <- colnames(t_cm) %>%
gsub("bams_renamed/UT_prim_UMI/", "", .) %>%
gsub("\\.UT_prim_UMI\\.bam", "", .)
colnames(t_cm)
col_cor <- setNames(
c(
"n3-d_Q_day7_tcn_N_aux-T_tc-F_rep1_tech1",
"n3-d_Q_day7_tcn_N_aux-T_tc-F_rep2_tech1",
"n3-d_Q_day7_tcn_N_aux-T_tc-F_rep3_tech1", #EXCLUDE
"n3-d_Q_day7_tcn_SS_aux-T_tc-F_rep1_tech1",
"n3-d_Q_day7_tcn_SS_aux-T_tc-F_rep2_tech1",
"n3-d_Q_day7_tcn_SS_aux-T_tc-F_rep3_tech1", #EXCLUDE
"o-d_Q_day7_tcn_N_aux-T_tc-F_rep1_tech1",
"o-d_Q_day7_tcn_N_aux-T_tc-F_rep2_tech1",
"o-d_Q_day7_tcn_SS_aux-T_tc-F_rep1_tech1",
"o-d_Q_day7_tcn_SS_aux-T_tc-F_rep2_tech1",
"r1-n_Q_day8_tcn_N_aux-F_tc-F_rep1_tech1", #FIXME* ∆ rep1 → rep2
"r1-n_Q_day8_tcn_N_aux-F_tc-F_rep2_tech1", #FIXME* ∆ rep2 → rep1
"r1-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1", #FIXME* ∆ rep1 → rep2
"r1-n_Q_day8_tcn_SS_aux-F_tc-F_rep2_tech1", #FIXME* ∆ rep2 → rep1
"r6-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep1_tech1", #FIXME* ∆ rep1 → rep2
"r6-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep2_tech1", #FIXME* ∆ rep2 → rep1
"r6-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep1_tech1", #FIXME* ∆ rep1 → rep2
"r6-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep2_tech1", #FIXME* ∆ rep2 → rep1
"r6-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep1_tech1", #FIXME* ∆ rep1 → rep2
"r6-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep2_tech1", #FIXME* ∆ rep2 → rep1
"r6-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1", #FIXME* ∆ rep1 → rep2
"r6-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep2_tech1", #FIXME* ∆ rep2 → rep1 #FIXME‡ ∆ tech1 → tech2
"r6-n_G1_day1_tcn_SS_aux-F_tc-F_rep1_tech1", #FIXME* ∆ rep1 → rep2 #FIXME‡ ∆ tech1 → tech2
"r6-n_G1_day1_tcn_SS_aux-F_tc-F_rep2_tech1", #FIXME* ∆ rep2 → rep1 #FIXME‡ ∆ tech1 → tech2
"r6-n_Q_day8_tcn_N_aux-F_tc-F_rep1_tech1", #FIXME* ∆ rep1 → rep2
"r6-n_Q_day8_tcn_N_aux-F_tc-F_rep2_tech1", #FIXME* ∆ rep2 → rep1
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1", #FIXME* ∆ rep1 → rep2 #OK
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech2", #FIXME* ∆ rep1 → rep2 #OK
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep2_tech1", #FIXME* ∆ rep2 → rep1
"t4-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"t4-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"t4-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep1_tech1",
"t4-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep2_tech1",
"t4-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"t4-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"t4-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1",
"t4-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep2_tech1",
"WT_DSm2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSm2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"WT_DSp24_day3_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSp24_day3_tcn_SS_aux-F_tc-T_rep2_tech1",
"WT_DSp2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSp2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1", #OK
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech2", #OK
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep2_tech1",
"WT_G1_day1_ovn_N_aux-F_tc-F_rep1_tech1",
"WT_G1_day1_ovn_N_aux-F_tc-F_rep2_tech1",
"WT_G1_day1_ovn_SS_aux-F_tc-F_rep1_tech1",
"WT_G1_day1_ovn_SS_aux-F_tc-F_rep2_tech1",
"WT_G1_day1_tcn_SS_aux-F_tc-F_rep1_tech1", #FIXME‡ ∆ tech1 → tech2
"WT_G1_day1_tcn_SS_aux-F_tc-F_rep2_tech1", #FIXME‡ ∆ tech1 → tech2
"WT_Q_day7_ovn_N_aux-F_tc-F_rep1_tech1",
"WT_Q_day7_ovn_N_aux-F_tc-F_rep2_tech1",
"WT_Q_day7_ovn_SS_aux-F_tc-F_rep1_tech1",
"WT_Q_day7_ovn_SS_aux-F_tc-F_rep2_tech1",
"WT_Q_day7_tcn_N_aux-F_tc-F_rep2_tech1", #FIXME† Duplicated #1
"WT_Q_day7_tcn_SS_aux-F_tc-F_rep2_tech1", #FIXME† Duplicated #2
"WT_Q_day8_tcn_N_aux-F_tc-F_rep1_tech1",
"WT_Q_day8_tcn_N_aux-F_tc-F_rep2_tech1", #FIXME† Duplicated #1
"WT_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1",
"WT_Q_day8_tcn_SS_aux-F_tc-F_rep2_tech1" #FIXME† Duplicated #2
),
c(
"n3d_Q_N_rep1_tech1",
"n3d_Q_N_rep2_tech1",
"n3d_Q_N_rep3_tech1", #EXCLUDE
"n3d_Q_SS_rep1_tech1",
"n3d_Q_SS_rep2_tech1",
"n3d_Q_SS_rep3_tech1", #EXCLUDE
"od_Q_N_rep1_tech1",
"od_Q_N_rep2_tech1",
"od_Q_SS_rep1_tech1",
"od_Q_SS_rep2_tech1",
"r1n_Q_N_rep2_tech1", #DONE* ∆ rep1 → rep2
"r1n_Q_N_rep1_tech1", #DONE* ∆ rep2 → rep1
"r1n_Q_SS_rep2_tech1", #DONE* ∆ rep1 → rep2
"r1n_Q_SS_rep1_tech1", #DONE* ∆ rep2 → rep1
"r6n_DSm2_SS_rep2_tech1", #DONE* ∆ rep1 → rep2
"r6n_DSm2_SS_rep1_tech1", #DONE* ∆ rep2 → rep1
"r6n_DSp24_SS_rep2_tech1", #DONE* ∆ rep1 → rep2
"r6n_DSp24_SS_rep1_tech1", #DONE* ∆ rep2 → rep1
"r6n_DSp2_SS_rep2_tech1", #DONE* ∆ rep1 → rep2
"r6n_DSp2_SS_rep1_tech1", #DONE* ∆ rep2 → rep1
"r6n_DSp48_SS_rep2_tech1", #DONE* ∆ rep1 → rep2
"r6n_DSp48_SS_rep1_tech2", #DONE* ∆ rep2 → rep1 #DONE‡ ∆ tech1 → tech2
"r6n_G1_SS_rep2_tech2", #DONE* ∆ rep1 → rep2 #DONE‡ ∆ tech1 → tech2
"r6n_G1_SS_rep1_tech2", #DONE* ∆ rep2 → rep1 #DONE‡ ∆ tech1 → tech2
"r6n_Q_N_rep2_tech1", #DONE* ∆ rep1 → rep2
"r6n_Q_N_rep1_tech1", #DONE* ∆ rep2 → rep1
"r6n_Q_SS_rep2_tech1", #DONE* ∆ rep1 → rep2 #OK
"r6n_Q_SS_rep2_tech2", #DONE* ∆ rep1 → rep2 #OK
"r6n_Q_SS_rep1_tech1", #DONE* ∆ rep2 → rep1
"t4n_DSm2_SS_rep1_tech1",
"t4n_DSm2_SS_rep2_tech1",
"t4n_DSp24_SS_rep1_tech1",
"t4n_DSp24_SS_rep2_tech1",
"t4n_DSp2_SS_rep1_tech1",
"t4n_DSp2_SS_rep2_tech1",
"t4n_DSp48_SS_rep1_tech1",
"t4n_DSp48_SS_rep2_tech1",
"WT_DSm2_SS_rep1_tech1",
"WT_DSm2_SS_rep2_tech1",
"WT_DSp24_SS_rep1_tech1",
"WT_DSp24_SS_rep2_tech1",
"WT_DSp2_SS_rep1_tech1",
"WT_DSp2_SS_rep2_tech1",
"WT_DSp48_SS_rep1_tech1", #OK
"WT_DSp48_SS_rep1_tech2", #OK
"WT_DSp48_SS_rep2_tech1",
"WTovn_G1_N_rep1_tech1",
"WTovn_G1_N_rep2_tech1",
"WTovn_G1_SS_rep1_tech1",
"WTovn_G1_SS_rep2_tech1",
"WT_G1_SS_rep1_tech2", #DONE‡ ∆ tech1 → tech2
"WT_G1_SS_rep2_tech2", #DONE‡ ∆ tech1 → tech2
"WTovn_Q_N_rep1_tech1",
"WTovn_Q_N_rep2_tech1",
"WTovn_Q_SS_rep1_tech1",
"WTovn_Q_SS_rep2_tech1",
"WTtest_Q_N_rep2_tech1", #DONE† Duplicated #1
"WTtest_Q_SS_rep2_tech1", #DONE† Duplicated #2
"WT_Q_N_rep1_tech1",
"WT_Q_N_rep2_tech1", #DONE† Duplicated #1
"WT_Q_SS_rep1_tech1",
"WT_Q_SS_rep2_tech1" #DONE† Duplicated #2
)
)
run <- TRUE
if(base::isTRUE(run)) {
t_cm.bak <- t_cm
# t_cm <- t_cm.bak
}
t_cm <- filter_process_counts_matrix(t_cm, col_cor)
# Extract htseq-count summary metrics from t_cm ------------------------------
# They are at the end of the matrices and have names that begin with two
#+ underscore characters
underscore <- t_cm[stringr::str_detect(t_cm$gene_id, "^__[a-zA-Z0-9_]*$"), ]
# Exclude htseq-count summary metrics from t_cm ------------------------------
t_cm <- t_cm[!stringr::str_detect(t_cm$gene_id, "^__[a-zA-Z0-9_]*$"), ]
run <- FALSE
if(base::isTRUE(run)) t_cm %>% tail(10)
# Read in, process metrics from work_calculate_uni-multimappers-etc.md -------
p_uni_multi_etc <- "outfiles_htseq-count"
f_uni_multi_etc <- "calculate_uni-multimappers-etc.UT_prim_UMI.txt"
uni_multi_etc <- readr::read_tsv(
paste(p_uni_multi_etc, f_uni_multi_etc, sep = "/"),
show_col_types = FALSE
)
rm(f_uni_multi_etc, p_uni_multi_etc)
# Clean up the tibble of uni_multi_etc counts --------------------------------
# Clean up, correct, and abbreviate sample names
run <- TRUE
if(base::isTRUE(run)) {
uni_multi_etc.bak <- uni_multi_etc
# uni_multi_etc <- uni_multi_etc.bak
}
uni_multi_etc$sample[match(col_cor, uni_multi_etc$sample)] <- names(col_cor)
# Transpose the dataframe, converting row #1 to column names
uni_multi_etc <- uni_multi_etc %>%
t() %>%
as.data.frame() %>%
tibble::rownames_to_column() %>%
tibble::as_tibble(.name_repair = "unique") %>%
`colnames<-`(.[1, ]) %>%
.[-1,]
# Convert counts from type character to type numeric
uni_multi_etc[, 2:length(uni_multi_etc)] <- tibble::as_tibble(sapply(
uni_multi_etc[, 2:length(uni_multi_etc)], as.numeric
))
# Generate tibbles of thorough and relevant summary metrics ------------------
summaries <- derive_summary_metrics(uni_multi_etc, underscore, t_cm)
summary_thorough <- summaries$summary_thorough
summary_relevant <- summaries$summary_relevant
rm(summaries)
# Associate the counts-matrix features with appropriate gtf metadata ---------
if(tx == "coding-non-pa-ncRNA") {
t_full <- dplyr::full_join(
t_gtf[, c(1:4, 7, 9, 10)],
t_cm,
by = "gene_id"
)
} else if(tx == "noncoding-non-collapsed") {
t_full <- dplyr::full_join(
t_gtf[, c(1:4, 14, 9, 7)] %>%
dplyr::rename(
"original" = "gene_id",
"gene_id" = "complete"
),
t_cm,
by = "gene_id"
)
} else if(tx == "noncoding-collapsed") {
t_full <- dplyr::full_join(
t_gtf[, c(1:4, 7, 9, 11)] %>%
dplyr::rename(
"category" = "details_type_alpha",
"original" = "details_id"
),
t_cm,
by = "gene_id"
)
# table(t_gtf[, c(1:4, 7, 9, 11)]$gene_id %in% t_cm$gene_id)
} else if(tx %in% c("Trinity-G1", "Trinity-Q")) { #TODO Need to build out code
t_full <- dplyr::full_join(
t_gtf[, c(1:4, 7, 10, 13)] %>% #TODO Check that this is correct
dplyr::rename(
"gene_id" = "locus_id",
"category" = "type.1",
"R64_overlap" = "category_detailed"
),
t_cm,
by = "gene_id"
)
# Simplify the "coding" categories
t_full$category <- ifelse(
t_full$category %in% c(
"coding: partial", "coding: multiple, partial", "coding: multiple"
),
"coding: miscellaneous",
t_full$category
)
}
# Row-bind tibbles t_full and summary_relevant -------------------------------
t_full <- dplyr::bind_rows(t_full, summary_relevant)
# Checks
# nrow(t_full)
# tail(t_full)
# tail(t_full[, -c(1:7)])
# tail(t_full[-c((nrow(t_full) - 3):nrow(t_full)), -c(1:7)])
# Important check: Do sample-wise tallies equal __valid_counts? --------------
test_A <- sapply(
t_full[-c((nrow(t_full) - 3):nrow(t_full)), -c(1:7)], sum
) %>%
t() %>%
tibble::as_tibble()
test_B <- summary_relevant[
stringr::str_detect(summary_relevant$gene_id, "valid"),
][
, 2:ncol(summary_relevant)
]
if(base::isFALSE(identical(test_A, test_B))) {
stop(paste(
"Sample-wise tallies do not equal __valid_counts. Stopping the",
"script."
))
}
rm(test_A, test_B)
# Plot category proportions for the Ovation samples ==========================
# Isolate categories and relevant samples, then process them -----------------
#TODO May need to be adapted for...
# - "Greenlaw-et-al_non-collapsed-non-coding-transcriptome.gtf"
# - "non-collapsed-non-coding-transcriptome.hc-strd-eq.tsv"
tmp <- t_full[-nrow(t_full), -c(1:5, 7)]
# colnames(tmp)
if(samples == "ovation") {
tmp_samples <- tmp[, stringr::str_detect(colnames(tmp), "ovn")]
} else if(samples == "ovation_tecan_test") {
tmp_samples <- tmp[, stringr::str_detect(colnames(tmp), "ovn|test")]
} else if(samples == "ovation_tecan_test_rrp6∆") {
tmp_samples <- tmp[, stringr::str_detect(
colnames(tmp), "r6n_Q|r6n_G1|WT_Q|WT_G1|WTovn|WTtest"
)]
} else if(samples == "ovation_tecan_updated") {
tmp_samples <- tmp[, stringr::str_detect(colnames(tmp), "ovn|test")] %>%
dplyr::select(-WTovn_Q_SS_rep1_tech1)
} else if(samples == "n3d_od") {
tmp_samples <- tmp[, stringr::str_detect(colnames(tmp), "n3d|od")] %>%
dplyr::select(-dplyr::contains("rep3"))
} else if(samples == "r6n") {
tmp_samples <- tmp[, stringr::str_detect(colnames(tmp), "r6n|WT")] %>%
dplyr::select(-dplyr::contains("DS")) %>%
dplyr::select(-dplyr::contains("ovn")) %>%
dplyr::select(-dplyr::contains("test")) %>%
dplyr::select(-dplyr::contains("_N_"))
} else if(samples == "r6n_timecourse") {
tmp_samples <- tmp[, stringr::str_detect(colnames(tmp), "DS")] %>%
dplyr::select(-dplyr::contains("t4n"))
} else if(samples == "Rrp6∆.timecourse-G1-Q.SS") {
tmp_samples <- tmp[, stringr::str_detect(colnames(tmp), "r6n|WT|DS")] %>%
dplyr::select(-dplyr::contains("t4n")) %>%
dplyr::select(-dplyr::contains("ovn")) %>%
dplyr::select(-dplyr::contains("test")) %>%
dplyr::select(-dplyr::contains("_N_"))
}
# colnames(tmp_samples)
t_rel <- dplyr::bind_cols(tmp[, 1], tmp_samples)
t_rel[(nrow(t_rel) - 2), 1] <- "multimapper"
t_rel[(nrow(t_rel) - 1), 1] <- "no feature"
t_rel[nrow(t_rel), 1] <- "ambiguous"
if(base::isTRUE(colnames(t_rel)[1] != "category")) {
colnames(t_rel)[1] <- "category"
}
# tail(t_rel)
# head(t_rel)
# Rename category "PG" to "pseudogene" (if applicable)
if(tx == "coding-non-pa-ncRNA") {
t_rel$category[stringr::str_detect(t_rel$category, "PG")] <- "pseudogene"
}
# Further clean up the column names
if(samples %in% c("ovation", "n3d_od")) {
colnames(t_rel) <- colnames(t_rel) %>%
stringr::str_remove("ovn") %>%
stringr::str_remove("_tech1")
} else if(samples %in% c("ovation_tecan_test", "ovation_tecan_updated")) {
colnames(t_rel) <- colnames(t_rel) %>%
gsub("WTovn", "WT_ovn", .) %>%
gsub("WTtest", "WT_tcn", .) %>%
gsub("_tech1", "", .)
} else if(samples == "ovation_tecan_test_rrp6∆") {
colnames(t_rel) <- colnames(t_rel) %>%
gsub("WT_", "WT_tcn_", .) %>%
gsub("r6n_", "r6n_tcn_", .) %>%
gsub("WTovn", "WT_ovn", .) %>%
gsub("WTtest", "WT_test", .)
}
if(samples == "ovation") {
t_rel_summarize <- t_rel %>%
dplyr::group_by(category) %>%
dplyr::summarize(
sum_WT_G1_N_rep1 = sum(WT_G1_N_rep1),
sum_WT_G1_N_rep2 = sum(WT_G1_N_rep2),
sum_WT_G1_SS_rep1 = sum(WT_G1_SS_rep1),
sum_WT_G1_SS_rep2 = sum(WT_G1_SS_rep2),
sum_WT_Q_N_rep1 = sum(WT_Q_N_rep1),
sum_WT_Q_N_rep2 = sum(WT_Q_N_rep2),
sum_WT_Q_SS_rep1 = sum(WT_Q_SS_rep1),
sum_WT_Q_SS_rep2 = sum(WT_Q_SS_rep2),
number_of_features = dplyr::n()
)
} else if(samples == "ovation_tecan_test") {
t_rel_summarize <- t_rel %>%
dplyr::group_by(category) %>%
dplyr::summarize(
sum_WT_ovn_G1_N_rep1 = sum(WT_ovn_G1_N_rep1),
sum_WT_ovn_G1_N_rep2 = sum(WT_ovn_G1_N_rep2),
sum_WT_ovn_G1_SS_rep1 = sum(WT_ovn_G1_SS_rep1),
sum_WT_ovn_G1_SS_rep2 = sum(WT_ovn_G1_SS_rep2),
sum_WT_ovn_Q_N_rep1 = sum(WT_ovn_Q_N_rep1),
sum_WT_ovn_Q_N_rep2 = sum(WT_ovn_Q_N_rep2),
sum_WT_tcn_Q_N_rep2 = sum(WT_tcn_Q_N_rep2),
sum_WT_ovn_Q_SS_rep1 = sum(WT_ovn_Q_SS_rep1),
sum_WT_ovn_Q_SS_rep2 = sum(WT_ovn_Q_SS_rep2),
sum_WT_tcn_Q_SS_rep2 = sum(WT_tcn_Q_SS_rep2),
number_of_features = dplyr::n()
)
} else if(samples == "ovation_tecan_test_rrp6∆") {
t_rel_summarize <- t_rel %>%
dplyr::group_by(category) %>%
dplyr::summarize(
sum_WT_ovn_G1_N_rep2_tech1 = sum(WT_ovn_G1_N_rep2_tech1),
sum_WT_ovn_G1_N_rep1_tech1 = sum(WT_ovn_G1_N_rep1_tech1),
sum_WT_ovn_G1_SS_rep1_tech1 = sum(WT_ovn_G1_SS_rep1_tech1),
sum_WT_ovn_G1_SS_rep2_tech1 = sum(WT_ovn_G1_SS_rep2_tech1),
sum_WT_tcn_G1_SS_rep1_tech2 = sum(WT_tcn_G1_SS_rep1_tech2),
sum_WT_tcn_G1_SS_rep2_tech2 = sum(WT_tcn_G1_SS_rep2_tech2),
sum_WT_ovn_Q_N_rep1_tech1 = sum(WT_ovn_Q_N_rep1_tech1),
sum_WT_ovn_Q_N_rep2_tech1 = sum(WT_ovn_Q_N_rep2_tech1),
sum_WT_test_Q_N_rep2_tech1 = sum(WT_test_Q_N_rep2_tech1),
sum_WT_tcn_Q_N_rep1_tech1 = sum(WT_tcn_Q_N_rep1_tech1),
sum_WT_tcn_Q_N_rep2_tech1 = sum(WT_tcn_Q_N_rep2_tech1),
sum_WT_ovn_Q_SS_rep1_tech1 = sum(WT_ovn_Q_SS_rep1_tech1),
sum_WT_ovn_Q_SS_rep2_tech1 = sum(WT_ovn_Q_SS_rep2_tech1),
sum_WT_test_Q_SS_rep2_tech1 = sum(WT_test_Q_SS_rep2_tech1),
sum_WT_tcn_Q_SS_rep1_tech1 = sum(WT_tcn_Q_SS_rep1_tech1),
sum_WT_tcn_Q_SS_rep2_tech1 = sum(WT_tcn_Q_SS_rep2_tech1),
sum_r6n_tcn_G1_SS_rep2_tech2 = sum(r6n_tcn_G1_SS_rep2_tech2),
sum_r6n_tcn_G1_SS_rep1_tech2 = sum(r6n_tcn_G1_SS_rep1_tech2),
sum_r6n_tcn_Q_N_rep2_tech1 = sum(r6n_tcn_Q_N_rep2_tech1),
sum_r6n_tcn_Q_N_rep1_tech1 = sum(r6n_tcn_Q_N_rep1_tech1),
sum_r6n_tcn_Q_SS_rep2_tech1 = sum(r6n_tcn_Q_SS_rep2_tech1),
sum_r6n_tcn_Q_SS_rep2_tech2 = sum(r6n_tcn_Q_SS_rep2_tech2),
sum_r6n_tcn_Q_SS_rep1_tech1 = sum(r6n_tcn_Q_SS_rep1_tech1),
number_of_features = dplyr::n()
)
} else if(samples == "ovation_tecan_updated") {
t_rel_summarize <- t_rel %>%
dplyr::group_by(category) %>%
dplyr::summarize(
sum_WT_ovn_G1_N_rep1 = sum(WT_ovn_G1_N_rep1),
sum_WT_ovn_G1_N_rep2 = sum(WT_ovn_G1_N_rep2),
sum_WT_ovn_G1_SS_rep1 = sum(WT_ovn_G1_SS_rep1),
sum_WT_ovn_G1_SS_rep2 = sum(WT_ovn_G1_SS_rep2),
sum_WT_ovn_Q_N_rep1 = sum(WT_ovn_Q_N_rep1),
sum_WT_ovn_Q_N_rep2 = sum(WT_ovn_Q_N_rep2),
sum_WT_tcn_Q_N_rep2 = sum(WT_tcn_Q_N_rep2),
sum_WT_ovn_Q_SS_rep2 = sum(WT_ovn_Q_SS_rep2),
sum_WT_tcn_Q_SS_rep2 = sum(WT_tcn_Q_SS_rep2),
number_of_features = dplyr::n()
)
} else if(samples == "n3d_od") {
t_rel <- t_rel %>% dplyr::relocate(
c("n3d_Q_N_rep1", "n3d_Q_N_rep2"),
.after = "od_Q_N_rep2"
) %>%
dplyr::relocate(
c("n3d_Q_SS_rep1", "n3d_Q_SS_rep2"),
.after = "od_Q_SS_rep2"
)
t_rel_summarize <- t_rel %>%
dplyr::group_by(category) %>%
dplyr::summarize(
sum_od_Q_N_rep1 = sum(od_Q_N_rep1),
sum_od_Q_N_rep2 = sum(od_Q_N_rep2),
sum_n3d_Q_N_rep1 = sum(n3d_Q_N_rep1),
sum_n3d_Q_N_rep2 = sum(n3d_Q_N_rep2),
sum_od_Q_SS_rep1 = sum(od_Q_SS_rep1),
sum_od_Q_SS_rep2 = sum(od_Q_SS_rep2),
sum_n3d_Q_SS_rep1 = sum(n3d_Q_SS_rep1),
sum_n3d_Q_SS_rep2 = sum(n3d_Q_SS_rep2),
number_of_features = dplyr::n()
)
} else if(samples == "r6n") {
t_rel <- t_rel %>%
dplyr::relocate(
c(
"WT_G1_SS_rep1_tech2", "WT_G1_SS_rep2_tech2",
"r6n_G1_SS_rep1_tech2", "r6n_G1_SS_rep2_tech2"
),
.after = "category"
) %>%
dplyr::relocate(
c(
"WT_Q_SS_rep1_tech1", "WT_Q_SS_rep2_tech1",
"r6n_Q_SS_rep1_tech1", "r6n_Q_SS_rep2_tech1",
"r6n_Q_SS_rep2_tech2"
),
.after = "r6n_G1_SS_rep2_tech2"
)
t_rel_summarize <- t_rel %>%
dplyr::group_by(category) %>%
dplyr::summarize(
sum_WT_G1_SS_rep1_tech2 = sum(WT_G1_SS_rep1_tech2),
sum_WT_G1_SS_rep2_tech2 = sum(WT_G1_SS_rep2_tech2),
sum_r6n_G1_SS_rep1_tech2 = sum(r6n_G1_SS_rep1_tech2),
sum_r6n_G1_SS_rep2_tech2 = sum(r6n_G1_SS_rep2_tech2),
sum_WT_Q_SS_rep1_tech1 = sum(WT_Q_SS_rep1_tech1),
sum_WT_Q_SS_rep2_tech1 = sum(WT_Q_SS_rep2_tech1),
sum_r6n_Q_SS_rep1_tech1 = sum(r6n_Q_SS_rep1_tech1),
sum_r6n_Q_SS_rep2_tech1 = sum(r6n_Q_SS_rep2_tech1),
sum_r6n_Q_SS_rep2_tech2 = sum(r6n_Q_SS_rep2_tech2),
number_of_features = dplyr::n()
)
} else if(samples == "r6n_timecourse") {
t_rel <- t_rel %>% dplyr::relocate(
c("r6n_DSm2_SS_rep1_tech1", "r6n_DSm2_SS_rep2_tech1"),
.after = "WT_DSm2_SS_rep2_tech1"
) %>%
# dplyr::relocate(
# c("r6n_DSp2_SS_rep1_tech1", "r6n_DSp2_SS_rep2_tech1"),
# .after = "WT_DSp2_SS_rep2_tech1"
# ) %>%
dplyr::relocate(
c("r6n_DSp24_SS_rep1_tech1", "r6n_DSp24_SS_rep2_tech1"),
.after = "WT_DSp24_SS_rep2_tech1"
) %>%
dplyr::relocate(
c("r6n_DSp48_SS_rep1_tech2", "r6n_DSp48_SS_rep2_tech1"),
.after = "WT_DSp48_SS_rep2_tech1"
) %>%
dplyr::relocate(
c(
"WT_DSp2_SS_rep1_tech1",
"WT_DSp2_SS_rep2_tech1",
"r6n_DSp2_SS_rep1_tech1",
"r6n_DSp2_SS_rep2_tech1"
),
.before = "WT_DSp24_SS_rep1_tech1"
)
t_rel_summarize <- t_rel %>%
dplyr::group_by(category) %>%
dplyr::summarize(
sum_WT_DSm2_SS_rep1_tech1 = sum(WT_DSm2_SS_rep1_tech1),
sum_WT_DSm2_SS_rep2_tech1 = sum(WT_DSm2_SS_rep2_tech1),
sum_r6n_DSm2_SS_rep1_tech1 = sum(r6n_DSm2_SS_rep1_tech1),
sum_r6n_DSm2_SS_rep2_tech1 = sum(r6n_DSm2_SS_rep2_tech1),
sum_WT_DSp2_SS_rep1_tech1 = sum(WT_DSp2_SS_rep1_tech1),
sum_WT_DSp2_SS_rep2_tech1 = sum(WT_DSp2_SS_rep2_tech1),
sum_r6n_DSp2_SS_rep1_tech1 = sum(r6n_DSp2_SS_rep1_tech1),
sum_r6n_DSp2_SS_rep2_tech1 = sum(r6n_DSp2_SS_rep2_tech1),
sum_WT_DSp24_SS_rep1_tech1 = sum(WT_DSp24_SS_rep1_tech1),