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rough-draft_run-analyses_rlog-PCA_write-rds.scraps.R
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#!/usr/bin/env Rscript
# rough-draft_run-analyses_rlog-PCA_write_rds.scraps.R
# KA
if(samples == "test.Ovation_Rrp6∆") {
# Strip various strings from column names
colnames(t_mat) <- colnames(t_mat) %>%
gsub("WT_", "WT_tcn_", .) %>%
gsub("r6n_", "r6n_tcn_", .) %>%
gsub("ovn", "_ovn", .)
# Create a metadata matrix for DESeq2, PCAtools, etc.
t_meta <- colnames(t_mat)[12:ncol(t_mat)] %>%
stringr::str_split("_") %>%
as.data.frame() %>%
t() %>%
tibble::as_tibble(.name_repair = "unique") %>%
dplyr::rename(
genotype = ...1, kit = ...2, state = ...3,
transcription = ...4, replicate = ...5, technical = ...6
) %>%
dplyr::mutate(rownames = colnames(t_mat)[12:ncol(t_mat)]) %>%
tibble::column_to_rownames("rownames") %>% # DESeq2 requires rownames
dplyr::mutate(
genotype = factor(genotype, level = c("WT", "r6n")),
no_genotype = as.numeric(sapply(
as.character(genotype),
switch,
"WT" = 0,
"r6n" = 1,
USE.NAMES = FALSE
)),
gt_tx = factor(paste(genotype, transcription, sep = "_")),
no_gt_tx = as.numeric(sapply(
as.character(gt_tx),
switch,
"r6n_SS" = 3,
"r6n_N" = 2,
"WT_SS" = 1,
"WT_N" = 0,
USE.NAMES = FALSE
)),
st_gt_tx = factor(
paste(state, genotype, transcription, sep = "_")
),
no_st_gt_tx = as.numeric(sapply(
as.character(st_gt_tx),
switch,
"G1_r6n_SS" = 7,
"G1_r6n_N" = 6,
"Q_r6n_SS" = 5,
"Q_r6n_N" = 4,
"G1_WT_SS" = 3,
"G1_WT_N" = 2,
"Q_WT_SS" = 1,
"Q_WT_N" = 0,
USE.NAMES = FALSE
)),
kit = factor(kit, levels = c("tcn", "ovn")),
no_kit = as.numeric(sapply(
as.character(kit),
switch,
"tcn" = 0,
"ovn" = 1,
USE.NAMES = FALSE
)),
state = factor(state, levels = c("G1", "Q")),
no_state = as.numeric(sapply(
as.character(state),
switch,
"G1" = 0,
"Q" = 1,
USE.NAMES = FALSE
)),
transcription = factor(transcription, levels = c("N", "SS")),
no_transcription = as.numeric(sapply(
as.character(transcription),
switch,
"N" = 0,
"SS" = 1,
USE.NAMES = FALSE
)),
replicate = factor(replicate, levels = c("rep1", "rep2")),
no_replicate = as.numeric(sapply(
as.character(replicate),
switch,
"rep1" = 0,
"rep2" = 1,
USE.NAMES = FALSE
)),
technical = factor(technical, levels = c("tech1", "tech2")),
no_technical = as.numeric(sapply(
as.character(technical),
switch,
"tech1" = 0,
"tech2" = 1,
USE.NAMES = FALSE
))
)
# For the sample with unexpected projected coordinates, add two extra
#+ variables: "strange" and "no_strange"
t_meta$strange <- ifelse(
t_meta$state == "Q" &
t_meta$genotype == "WT" &
t_meta$transcription == "SS" &
t_meta$kit == "ovn" &
t_meta$replicate == "rep1" &
t_meta$technical == "tech1",
"strange",
"not_strange"
)
t_meta$no_strange = as.numeric(sapply(
as.character(t_meta$strange),
switch,
"not_strange" = 0,
"strange" = 1,
USE.NAMES = FALSE
))
t_meta <- t_meta %>%
dplyr::relocate(
c(
genotype, gt_tx, st_gt_tx, kit, state, transcription,
replicate, technical, strange, no_genotype, no_gt_tx,
no_st_gt_tx, no_kit, no_state, no_transcription, no_replicate,
no_technical
),
.before = no_strange
)
} else if(samples == "test.Ovation_Tecan") {
# If "test" is in column name, then convert label "rep2" to "rep2-Tecan"
colnames(t_mat) <- ifelse(
stringr::str_detect(colnames(t_mat), "test"),
stringr::str_replace_all(
colnames(t_mat)[stringr::str_detect(colnames(t_mat), "test")],
"rep2",
"rep2-Tecan"
),
colnames(t_mat)
)
# Strip strings "ovn" and "test" from column names
colnames(t_mat) <- colnames(t_mat) %>%
gsub("ovn", "", .) %>%
gsub("test", "", .)
# Create a metadata matrix for DESeq2, PCAtools, etc.
t_meta <- colnames(t_mat)[12:ncol(t_mat)] %>%
stringr::str_split("_") %>%
as.data.frame() %>%
t() %>%
tibble::as_tibble(.name_repair = "unique") %>%
dplyr::rename(
genotype = ...1, state = ...2, transcription = ...3,
replicate = ...4, technical = ...5
) %>%
dplyr::mutate(rownames = colnames(t_mat)[12:ncol(t_mat)]) %>%
tibble::column_to_rownames("rownames") %>% # DESeq2 requires rownames
dplyr::mutate(
genotype = factor(genotype, level = "WT"),
no_genotype = as.numeric(sapply(
as.character(genotype),
switch,
"WT" = 0,
USE.NAMES = FALSE
)),
state = factor(state, levels = c("G1", "Q")),
no_state = as.numeric(sapply(
as.character(state),
switch,
"G1" = 0,
"Q" = 1,
USE.NAMES = FALSE
)),
transcription = factor(transcription, levels = c("N", "SS")),
no_transcription = as.numeric(sapply(
as.character(transcription),
switch,
"N" = 0,
"SS" = 1,
USE.NAMES = FALSE
)),
st_tx = factor(
paste(state, transcription, sep = "_")
),
no_st_tx = as.numeric(sapply(
as.character(st_tx),
switch,
"G1_SS" = 3,
"G1_N" = 2,
"Q_SS" = 1,
"Q_N" = 0,
USE.NAMES = FALSE
)),
replicate = factor(
replicate, levels = c("rep1", "rep2", "rep2-Tecan")
),
no_replicate = as.numeric(sapply(
as.character(replicate),
switch,
"rep1" = 0,
"rep2" = 1,
"rep2-Tecan" = 2,
USE.NAMES = FALSE
)),
technical = factor(technical, levels = "tech1"),
no_technical = as.numeric(sapply(
as.character(technical),
switch,
"tech1" = 0,
USE.NAMES = FALSE
)),
# Add additional variables for library-prep kit
kit = factor(
ifelse(replicate != "rep2-Tecan", "ovn", "tcn"),
levels = c("ovn", "tcn")
),
no_kit = as.numeric(sapply(
as.character(kit),
switch,
"tcn" = 1,
"ovn" = 0,
USE.NAMES = FALSE
))
) %>% dplyr::relocate(
c(
genotype, state, transcription, st_tx, replicate, technical,
kit, no_genotype, no_state, no_transcription, no_st_tx,
no_replicate, no_technical
),
.before = no_kit
)
}
# If necessary, remove batch effects
if(samples == "test.Ovation_Rrp6∆") {
norm_r <- limma::removeBatchEffect(
SummarizedExperiment::assay(rld),
# batch = rld$technical,
# batch = rld$replicate,
# batch = rld$kit,
batch = rld$strange,
design = model.matrix(
~1,
# SummarizedExperiment::colData(rld)[, 1:6] # technical
# SummarizedExperiment::colData(rld)[, c(1:5, 7)] # replicate
# SummarizedExperiment::colData(rld)[, c(1:9)] # kit
SummarizedExperiment::colData(rld)[, c(1:8)] # strange
)
) %>%
as.data.frame()
} else {
norm_r <- SummarizedExperiment::assay(rld) %>% as.data.frame()
}