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work_evaluation-etc_variables_pairwise-groupwise.tmp-gw.R
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# tmp-gw.R
# KA
Q_N_wt_r1_r6 <- c(
"WT_Q_day8_tcn_N_aux-F_tc-F_rep1_tech1",
"WT_Q_day8_tcn_N_aux-F_tc-F_rep2_tech1",
"r1-n_Q_day8_tcn_N_aux-F_tc-F_rep1_tech1",
"r1-n_Q_day8_tcn_N_aux-F_tc-F_rep2_tech1",
"r6-n_Q_day8_tcn_N_aux-F_tc-F_rep1_tech1",
"r6-n_Q_day8_tcn_N_aux-F_tc-F_rep2_tech1"
)
Q_SS_wt_r1_r6 <- c( #BATCH
"WT_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1",
"WT_Q_day8_tcn_SS_aux-F_tc-F_rep2_tech1",
"r1-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1",
"r1-n_Q_day8_tcn_SS_aux-F_tc-F_rep2_tech1",
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1",
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech2",
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep2_tech1"
)
Q_SS_wt_ot_r1_r6_n3 <- c( #BATCH
"WT_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1",
"WT_Q_day8_tcn_SS_aux-F_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_SS_aux-F_tc-F_rep1_tech1",
"r1-n_Q_day8_tcn_SS_aux-F_tc-F_rep2_tech1",
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1",
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech2",
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep2_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",
"n3-d_Q_day7_tcn_SS_aux-T_tc-F_rep1_tech1"
)
Q_N_wt_ot_r1_r6_n3 <- c(
"WT_Q_day8_tcn_N_aux-F_tc-F_rep1_tech1",
"WT_Q_day8_tcn_N_aux-F_tc-F_rep2_tech1",
"o-d_Q_day7_tcn_N_aux-T_tc-F_rep1_tech1",
"o-d_Q_day7_tcn_N_aux-T_tc-F_rep2_tech1",
"r1-n_Q_day8_tcn_N_aux-F_tc-F_rep1_tech1",
"r1-n_Q_day8_tcn_N_aux-F_tc-F_rep2_tech1",
"r6-n_Q_day8_tcn_N_aux-F_tc-F_rep1_tech1",
"r6-n_Q_day8_tcn_N_aux-F_tc-F_rep2_tech1",
"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"
)
tc_SS_wt_r6 <- c( #BATCH
"WT_DSm2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSm2_day2_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_DSp24_day3_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSp24_day3_tcn_SS_aux-F_tc-T_rep2_tech1",
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech2",
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep2_tech1"
)
tc_SS_wt_t4_r6 <- c( #BATCH
"WT_DSm2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSm2_day2_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_DSp24_day3_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSp24_day3_tcn_SS_aux-F_tc-T_rep2_tech1",
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech2",
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep2_tech1",
"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_DSp2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"t4-n_DSp2_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_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1",
"t4-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep2_tech1"
)
Q_tc_SS_wt_ot_r1_r6_n3 <- c( #BATCH
"WT_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1",
"WT_Q_day8_tcn_SS_aux-F_tc-F_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_DSp2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSp2_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_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1",
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech2",
"WT_DSp48_day4_tcn_SS_aux-F_tc-T_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_SS_aux-F_tc-F_rep1_tech1",
"r1-n_Q_day8_tcn_SS_aux-F_tc-F_rep2_tech1",
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech1",
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep1_tech2",
"r6-n_Q_day8_tcn_SS_aux-F_tc-F_rep2_tech1",
"r6-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSm2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSp2_day2_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSp24_day3_tcn_SS_aux-F_tc-T_rep2_tech1",
"r6-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep1_tech1",
"r6-n_DSp48_day4_tcn_SS_aux-F_tc-T_rep2_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",
"n3-d_Q_day7_tcn_SS_aux-T_tc-F_rep1_tech1"
)
G1_Q_SS_N_wt <- c(
"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_ovn_N_aux-F_tc-F_rep1_tech1",
"WT_G1_day1_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_ovn_N_aux-F_tc-F_rep1_tech1",
"WT_Q_day7_ovn_N_aux-F_tc-F_rep2_tech1"
)
# Make a named list
datasets_gw <- list(
"Q_N_wt_r1_r6" = Q_N_wt_r1_r6,
"Q_SS_wt_r1_r6" = Q_SS_wt_r1_r6,
"Q_SS_wt_ot_r1_r6_n3" = Q_SS_wt_ot_r1_r6_n3,
"Q_N_wt_ot_r1_r6_n3" = Q_N_wt_ot_r1_r6_n3,
"tc_SS_wt_r6" = tc_SS_wt_r6,
"tc_SS_wt_t4_r6" = tc_SS_wt_t4_r6,
"Q_tc_SS_wt_ot_r1_r6_n3" = Q_tc_SS_wt_ot_r1_r6_n3,
"G1_Q_SS_N_wt" = G1_Q_SS_N_wt
)
res_gw <- list()
for (i in 1:length(datasets_gw)) {
# i <- 1
cat(paste0(
"# --------------------------------------------------------\n",
"# Working with '", names(datasets_gw)[[i]], "'\n",
"# --------------------------------------------------------\n\n"
))
res_gw[[names(datasets_gw)[[i]]]] <- list()
# 0. Assign datasets -----------------------------------------------------
cat("#+ 0. Datasets are...\n\n")
print(datasets_gw[[i]])
cat("\n")
# Initialize variable 'datasets'
datasets <- datasets_gw[[i]]
res_gw[[names(datasets_gw)[[i]]]][["n00_datasets"]] <- datasets
# 1. Create 'counts_data' matrix -----------------------------------------
cat(paste0(
"#+ 1. Create 'counts_data' matrix for '",
names(datasets_gw)[[i]],
"'\n\n"
))
counts_data <- t_hc[, colnames(t_hc) %in% datasets] %>%
as.data.frame()
counts_data <- sapply(counts_data, as.numeric)
res_gw[[names(datasets_gw)[[i]]]][["n01_counts_data"]] <- counts_data
# 2. Isolate datasets of interest ----------------------------------------
cat(paste0(
"#+ 2. Isolate datasets of interest for '",
names(datasets_gw)[[i]],
"' ('col_data')\n\n"
))
col_data <- samples[samples$keys %in% datasets, ] %>%
as.data.frame() %>% #IMPORTANT Output a dataframe, not a tibble
tibble::column_to_rownames(., var = "keys") %>% #IMPORTANT Have row names
droplevels()
col_data #TBD
#NOTE Version that drops hyphens from values
# col_data <- samples[samples$keys %in% datasets, ] %>%
# lapply(., gsub, pattern = "-", replacement = "") %>% # Get rid of hyphens
# as.data.frame() %>% #IMPORTANT Output a dataframe, not a tibble
# tibble::column_to_rownames(., var = "keys") %>% #IMPORTANT Have row names
# droplevels() # Drop any unused factor levels
res_gw[[names(datasets_gw)[[i]]]][["n02_col_data"]] <- col_data
# 3, 4. Do explicit ordering of relevant factors, etc. -------------------
cat(paste0(
"#+ 3, 4. Do explicit ordering of relevant factors for '",
names(datasets_gw)[[i]],
"', including factor-to-integer conversions\n\n"
))
if(isTRUE(names(datasets_gw)[[i]] == "Q_N_wt_r1_r6")) {
# datasets_gw[[1]]
strain_1 <- "WT"
strain_2 <- "r1-n"
strain_3 <- "r6-n"
col_data$strain <- factor(
col_data$strain, levels = c(strain_1, strain_2, strain_3)
)
string_strain <- paste(levels(col_data$strain), collapse = " ")
rep_1 <- "rep1"
rep_2 <- "rep2"
col_data$replicate <- factor(
col_data$replicate, levels = c(rep_1, rep_2)
)
string_replicate <- paste(levels(col_data$replicate), collapse = " ")
res_gw[[names(datasets_gw)[[i]]]][["n03_string_strain"]] <- string_strain
res_gw[[names(datasets_gw)[[i]]]][["n03_string_replicate"]] <- string_replicate
col_data$no_strain <- sapply(
as.character(col_data$strain),
switch,
"WT" = 1,
"r1-n" = 2,
"r6-n" = 3,
USE.NAMES = FALSE
)
col_data$no_replicate <- sapply(
as.character(col_data$replicate),
switch,
"rep1" = 1,
"rep2" = 2,
USE.NAMES = FALSE
)
res_gw[[names(datasets_gw)[[i]]]][["n04_col_data"]] <- col_data
} else if(isTRUE(names(datasets_gw)[[i]] == "Q_SS_wt_r1_r6")) {
# datasets_gw[[2]]
strain_1 <- "WT"
strain_2 <- "r1-n"
strain_3 <- "r6-n"
col_data$strain <- factor(
col_data$strain, levels = c(strain_1, strain_2, strain_3)
)
string_strain <- paste(levels(col_data$strain), collapse = " ")
rep_1 <- "rep1"
rep_2 <- "rep2"
col_data$replicate <- factor(
col_data$replicate, levels = c(rep_1, rep_2)
)
string_replicate <- paste(levels(col_data$replicate), collapse = " ")
tech_1 <- "tech1"
tech_2 <- "tech2"
col_data$technical <- factor(
col_data$technical, levels = c(tech_1, tech_2)
)
string_tech <- paste(levels(col_data$technical), collapse = " ")
res_gw[[names(datasets_gw)[[i]]]][["n03_string_strain"]] <- string_strain
res_gw[[names(datasets_gw)[[i]]]][["n03_string_replicate"]] <- string_replicate
res_gw[[names(datasets_gw)[[i]]]][["n03_string_tech"]] <- string_tech
col_data$no_strain <- sapply(
as.character(col_data$strain),
switch,
"WT" = 1,
"r1-n" = 2,
"r6-n" = 3,
USE.NAMES = FALSE
)
col_data$no_replicate <- sapply(
as.character(col_data$replicate),
switch,
"rep1" = 1,
"rep2" = 2,
USE.NAMES = FALSE
)
col_data$no_technical <- sapply(
as.character(col_data$technical),
switch,
"tech1" = 1,
"tech2" = 2,
USE.NAMES = FALSE
)
res_gw[[names(datasets_gw)[[i]]]][["n04_col_data"]] <- col_data
cat(paste0(
"#+ 5. Make the DESeqDataSet (dds) object for '",
names(datasets_gw)[[i]],
"'\n\n"
))
} else if(isTRUE(names(datasets_gw)[[i]] == "Q_SS_wt_ot_r1_r6_n3")) {
# datasets_gw[[3]]
strain_1 <- "WT"
strain_2 <- "o-d"
strain_3 <- "r1-n"
strain_4 <- "r6-n"
strain_5 <- "n3-d"
col_data$strain <- factor(
col_data$strain, levels = c(
strain_1, strain_2, strain_3, strain_4, strain_5
)
)
string_strain <- paste(levels(col_data$strain), collapse = " ")
tech_1 <- "tech1"
tech_2 <- "tech2"
col_data$technical <- factor(
col_data$technical, levels = c(tech_1, tech_2)
)
string_tech <- paste(levels(col_data$technical), collapse = " ")
res_gw[[names(datasets_gw)[[i]]]][["n03_string_strain"]] <- string_strain
res_gw[[names(datasets_gw)[[i]]]][["n03_string_tech"]] <- string_tech
col_data$no_strain <- sapply(
as.character(col_data$strain),
switch,
"WT" = 1,
"o-d" = 2,
"r1-n" = 3,
"r6-n" = 4,
"n3-d" = 5,
USE.NAMES = FALSE
)
col_data$no_technical <- sapply(
as.character(col_data$technical),
switch,
"tech1" = 1,
"tech2" = 2,
USE.NAMES = FALSE
)
res_gw[[names(datasets_gw)[[i]]]][["n04_col_data"]] <- col_data
} else if(isTRUE(names(datasets_gw)[[i]] == "Q_N_wt_ot_r1_r6_n3")) {
# datasets_gw[[4]]
strain_1 <- "WT"
strain_2 <- "o-d"
strain_3 <- "r1-n"
strain_4 <- "r6-n"
strain_5 <- "n3-d"
col_data$strain <- factor(
col_data$strain, levels = c(
strain_1, strain_2, strain_3, strain_4, strain_5
)
)
string_strain <- paste(levels(col_data$strain), collapse = " ")
res_gw[[names(datasets_gw)[[i]]]][["n03_string_strain"]] <- string_strain
col_data$no_strain <- sapply(
as.character(col_data$strain),
switch,
"WT" = 1,
"o-d" = 2,
"r1-n" = 3,
"r6-n" = 4,
"n3-d" = 5,
USE.NAMES = FALSE
)
res_gw[[names(datasets_gw)[[i]]]][["n04_col_data"]] <- col_data
} else if(isTRUE(names(datasets_gw)[[i]] == "tc_SS_wt_r6")) {
# datasets_gw[[5]]
strain_1 <- "WT"
strain_2 <- "r6-n"
col_data$strain <- factor(
col_data$strain, levels = c(strain_1, strain_2)
)
string_strain <- paste(levels(col_data$strain), collapse = " ")
tech_1 <- "tech1"
tech_2 <- "tech2"
col_data$technical <- factor(
col_data$technical, levels = c(tech_1, tech_2)
)
string_tech <- paste(levels(col_data$technical), collapse = " ")
state_1 <- "DSm2"
state_2 <- "DSp2"
state_3 <- "DSp24"
state_4 <- "DSp48"
col_data$state <- factor(
col_data$state, levels = c(state_1, state_2, state_3, state_4)
)
string_state <- paste(levels(col_data$state), collapse = " ")
res_gw[[names(datasets_gw)[[i]]]][["n03_string_strain"]] <- string_strain
res_gw[[names(datasets_gw)[[i]]]][["n03_string_tech"]] <- string_tech
res_gw[[names(datasets_gw)[[i]]]][["n03_string_state"]] <- string_state
col_data$no_strain <- sapply(
as.character(col_data$strain),
switch,
"WT" = 1,
"r6-n" = 2,
USE.NAMES = FALSE
)
col_data$no_technical <- sapply(
as.character(col_data$technical),
switch,
"tech1" = 1,
"tech2" = 2,
USE.NAMES = FALSE
)
col_data$no_state <- sapply(
as.character(col_data$state),
switch,
"DSm2" = 1,
"DSp2" = 2,
"DSp24" = 3,
"DSp48" = 4,
USE.NAMES = FALSE
)
res_gw[[names(datasets_gw)[[i]]]][["n04_col_data"]] <- col_data
} else if(isTRUE(names(datasets_gw)[[i]] == "tc_SS_wt_t4_r6")) {
# datasets_gw[[6]]
strain_1 <- "WT"
strain_2 <- "t4-n"
strain_3 <- "r6-n"
col_data$strain <- factor(
col_data$strain, levels = c(strain_1, strain_2, strain_3)
)
string_strain <- paste(levels(col_data$strain), collapse = " ")
tech_1 <- "tech1"
tech_2 <- "tech2"
col_data$technical <- factor(
col_data$technical, levels = c(tech_1, tech_2)
)
string_tech <- paste(levels(col_data$technical), collapse = " ")
state_1 <- "DSm2"
state_2 <- "DSp2"
state_3 <- "DSp24"
state_4 <- "DSp48"
col_data$state <- factor(
col_data$state, levels = c(state_1, state_2, state_3, state_4)
)
string_state <- paste(levels(col_data$state), collapse = " ")
res_gw[[names(datasets_gw)[[i]]]][["n03_string_strain"]] <- string_strain
res_gw[[names(datasets_gw)[[i]]]][["n03_string_tech"]] <- string_tech
res_gw[[names(datasets_gw)[[i]]]][["n03_string_state"]] <- string_state
col_data$no_strain <- sapply(
as.character(col_data$strain),
switch,
"WT" = 1,
"t4-n" = 2,
"r6-n" = 3,
USE.NAMES = FALSE
)
col_data$no_technical <- sapply(
as.character(col_data$technical),
switch,
"tech1" = 1,
"tech2" = 2,
USE.NAMES = FALSE
)
col_data$no_state <- sapply(
as.character(col_data$state),
switch,
"DSm2" = 1,
"DSp2" = 2,
"DSp24" = 3,
"DSp48" = 4,
USE.NAMES = FALSE
)
res_gw[[names(datasets_gw)[[i]]]][["n04_col_data"]] <- col_data
} else if(isTRUE(names(datasets_gw)[[i]] == "Q_tc_SS_wt_ot_r1_r6_n3")) {
# datasets_gw[[7]]
strain_1 <- "WT"
strain_2 <- "o-d"
strain_3 <- "r1-n"
strain_4 <- "r6-n"
strain_5 <- "n3-d"
col_data$strain <- factor(
col_data$strain, levels = c(
strain_1, strain_2, strain_3, strain_4, strain_5
)
)
string_strain <- paste(levels(col_data$strain), collapse = " ")
tech_1 <- "tech1"
tech_2 <- "tech2"
col_data$technical <- factor(
col_data$technical, levels = c(tech_1, tech_2)
)
string_tech <- paste(levels(col_data$technical), collapse = " ")
state_1 <- "Q"
state_2 <- "DSm2"
state_3 <- "DSp2"
state_4 <- "DSp24"
state_5 <- "DSp48"
col_data$state <- factor(
col_data$state, levels = c(
state_1, state_2, state_3, state_4, state_5
)
)
string_state <- paste(levels(col_data$state), collapse = " ")
res_gw[[names(datasets_gw)[[i]]]][["n03_string_strain"]] <- string_strain
res_gw[[names(datasets_gw)[[i]]]][["n03_string_tech"]] <- string_tech
res_gw[[names(datasets_gw)[[i]]]][["n03_string_state"]] <- string_state
col_data$no_strain <- sapply(
as.character(col_data$strain),
switch,
"WT" = 1,
"o-d" = 2,
"r1-n" = 3,
"r6-n" = 4,
"n3-d" = 5,
USE.NAMES = FALSE
)
col_data$no_technical <- sapply(
as.character(col_data$technical),
switch,
"tech1" = 1,
"tech2" = 2,
USE.NAMES = FALSE
)
col_data$no_state <- sapply(
as.character(col_data$state),
switch,
"Q" = 1,
"DSm2" = 2,
"DSp2" = 3,
"DSp24" = 4,
"DSp48" = 5,
USE.NAMES = FALSE
)
res_gw[[names(datasets_gw)[[i]]]][["n04_col_data"]] <- col_data
} else if(isTRUE(names(datasets_gw)[[i]] == "G1_Q_SS_N_wt")){
# datasets_gw[[8]]
strain_1 <- "WT"
col_data$strain <- factor(
col_data$strain, levels = c(strain_1)
)
string_strain <- paste(levels(col_data$strain), collapse = " ")
state_1 <- "G1"
state_2 <- "Q"
col_data$state <- factor(col_data$state, levels = c(state_1, state_2))
string_state <- paste(levels(col_data$state), collapse = " ")
tx_1 <- "SS"
tx_2 <- "N"
col_data$transcription <- factor(
col_data$transcription, levels = c(tx_1, tx_2)
)
string_tx <- paste(levels(col_data$transcription), collapse = " ")
res_gw[[names(datasets_gw)[[i]]]][["n03_string_strain"]] <- string_strain
res_gw[[names(datasets_gw)[[i]]]][["n03_string_state"]] <- string_state
res_gw[[names(datasets_gw)[[i]]]][["n03_string_tx"]] <- string_tx
col_data$no_strain <- sapply(
as.character(col_data$strain),
switch,
"WT" = 1,
USE.NAMES = FALSE
)
col_data$no_state <- sapply(
as.character(col_data$state),
switch,
"G1" = 1,
"Q" = 2,
USE.NAMES = FALSE
)
col_data$no_transcription <- sapply(
as.character(col_data$transcription),
switch,
"SS" = 1,
"N" = 2,
USE.NAMES = FALSE
)
res_gw[[names(datasets_gw)[[i]]]][["n04_col_data"]] <- col_data
}
model_linear <- "~ 1"
res_gw[[names(datasets_gw)[[i]]]] [["n03_model_linear"]] <- model_linear
# 5. Make the DESeqDataSet (dds) object ----------------------------------
cat(paste0(
"#+ 5. Make the DESeqDataSet (dds) object for '",
names(datasets_gw)[[i]],
"'\n\n"
))
dds <- DESeq2::DESeqDataSetFromMatrix(
countData = counts_data,
colData = col_data,
design = ~ 1,
rowRanges = pos_info
)
res_gw[[names(datasets_gw)[[i]]]][["n05_dds"]] <- dds
res_gw[[names(datasets_gw)[[i]]]][["n05_design"]] <- dds@design
}
# # 6 ------------------------------
# cat(paste0(
# "#+ 6. Create normalized counts for '",
# names(datasets_gw)[[i]], "'\n\n"
# ))
#
# if(isTRUE(names(datasets_gw)[[i]] == "Q_N_wt_r1_r6")) {
# datasets_gw[[1]]
#
# # ----------------
# vsd <- DESeq2::vst(
# dds[dds@rowRanges$genome == "S_cerevisiae", ],
# blind = FALSE
# ) %>%
# SummarizedExperiment::assay() %>%
# as.data.frame()
# vsd$feature_init <- dds@rowRanges$feature_init[
# dds@rowRanges$genome == "S_cerevisiae"
# ]
#
# # Associate normalized values with feature metadata
# norm_v <- dplyr::full_join(
# vsd,
# t_hc[t_hc$genome == "S_cerevisiae", 1:9],
# by = "feature_init"
# ) %>%
# dplyr::as_tibble()
#
# rm(vsd)
# res_gw[[names(datasets_gw)[[i]]]][["n06_norm_v"]] <- norm_v
#
# # ----------------
# rld <- DESeq2::rlog(
# dds[dds@rowRanges$genome == "S_cerevisiae", ],
# blind = FALSE
# ) %>%
# SummarizedExperiment::assay() %>%
# as.data.frame()
# rld$feature_init <- dds@rowRanges$feature_init[
# dds@rowRanges$genome == "S_cerevisiae"
# ]
#
# # Associate normalized values with feature metadata
# norm_r <- dplyr::full_join(
# rld,
# t_hc[t_hc$genome == "S_cerevisiae", 1:9],
# by = "feature_init"
# ) %>%
# dplyr::as_tibble()
#
# rm(rld)
# res_gw[[names(datasets_gw)[[i]]]][["n06_norm_r"]] <- norm_r
#
# #TODO GeTMM, TPM normalizations
# } else if(isTRUE(names(datasets_gw)[[i]] == "Q_SS_wt_r1_r6")) {
# datasets_gw[[2]]
#
# # ----------------
# vsd <- DESeq2::vst(
# dds[dds@rowRanges$genome == "S_cerevisiae", ],
# blind = FALSE
# )
#
# norm_v <- limma::removeBatchEffect(
# SummarizedExperiment::assay(vsd),
# batch = vsd$technical,
# design = model.matrix(~strain, SummarizedExperiment::colData(vsd))
# ) %>%
# as.data.frame()
# norm_v$feature_init <- dds@rowRanges$feature_init[
# dds@rowRanges$genome == "S_cerevisiae"
# ]
#
# # Associate normalized values with feature metadata
# norm_v <- dplyr::full_join(
# norm_v,
# t_hc[t_hc$genome == "S_cerevisiae", 1:9],
# by = "feature_init"
# ) %>%
# dplyr::as_tibble()
#
# rm(vsd)
#
#
# # ----------------
# rld <- DESeq2::rlog(
# dds[dds@rowRanges$genome == "S_cerevisiae", ],
# blind = FALSE
# )
#
# norm_r <- limma::removeBatchEffect(
# SummarizedExperiment::assay(rld),
# batch = rld$technical,
# design = model.matrix(~strain, SummarizedExperiment::colData(rld))
# ) %>%
# as.data.frame()
# norm_r$feature_init <- dds@rowRanges$feature_init[
# dds@rowRanges$genome == "S_cerevisiae"
# ]
#
# # Associate normalized values with feature metadata
# norm_r <- dplyr::full_join(
# norm_r,
# t_hc[t_hc$genome == "S_cerevisiae", 1:9],
# by = "feature_init"
# ) %>%
# dplyr::as_tibble()
#
# rm(rld)
# } else if(isTRUE(names(datasets_gw)[[i]] == "Q_SS_wt_ot_r1_r6_n3")) {
# datasets_gw[[3]]
# } else if(isTRUE(names(datasets_gw)[[i]] == "Q_N_wt_ot_r1_r6_n3")) {
# datasets_gw[[4]]
# } else if(isTRUE(names(datasets_gw)[[i]] == "tc_SS_wt_r6")) {
# datasets_gw[[5]]
# } else if(isTRUE(names(datasets_gw)[[i]] == "tc_SS_wt_t4_r6")) {
# datasets_gw[[6]]
# } else if(isTRUE(names(datasets_gw)[[i]] == "Q_tc_SS_wt_ot_r1_r6_n3")) {
# datasets_gw[[7]]
# } else if(isTRUE(names(datasets_gw)[[i]] == "G1_Q_SS_N_wt")){
# datasets_gw[[8]]
# }
#
# # 7 ------------------------------
# cat(paste0(
# "#+ 7. Create a PCAtools 'pca' S4 object for the normalized counts ",
# "associated with '", names(datasets_gw)[[i]], "'\n\n"
# ))
# norm <- norm_r
#
# # Create a PCAtools "pca" S4 object for the normalized counts ------------
# #+ Assign unique row names too
# col_samp_TF <- stringr::str_detect(colnames(norm), "_tech")
# col_samp_TF_N <- length(col_samp_TF[col_samp_TF == TRUE])
#
# obj_pca <- PCAtools::pca(
# norm[, 1:col_samp_TF_N],
# metadata = dds[dds@rowRanges$genome != "K_lactis", ]@colData
# )
# rownames(obj_pca$loadings) <- make.names(
# dds[dds@rowRanges$genome != "K_lactis", ]@rowRanges$feature,
# unique = TRUE
# )
# # colnames(obj_pca$loadings)
#
# # 8 ------------------------------
# cat(paste0(
# "#+ 8. Determine significant PCs for '",
# names(datasets_gw)[[i]], "'\n\n"
# ))
# # Determine "significant" PCs with Horn's parallel analysis
# #+ See Horn, 1965
# horn <- PCAtools::parallelPCA(mat = norm[, 1:col_samp_TF_N])
# # horn$n
#
#
# # Determine "significant" principle components with the elbow method
# #+ See Buja and Eyuboglu, 1992
# elbow <- PCAtools::findElbowPoint(obj_pca$variance)
# # elbow
#
# # 9 ------------------------------
# cat(paste0(
# "#+ 9. Draw a scree plot to evaluate cumulative proportion of ",
# "explained variance for '", names(datasets_gw)[[i]], "'\n\n"
# ))
# # Evaluate cumulative prop. of explained variance with a scree plot
# scree <- draw_scree_plot(obj_pca, horn = horn$n, elbow = elbow)
# scree
# # save_title <- paste0("panel-plot", ".", "scree", ".pdf")
# # ggplot2::ggsave(paste0(args$directory_out, "/", save_title), scree)
# #TODO Work up some logic for location(s) for outfiles
#
# # 10 -----------------------------
# cat(paste0(
# "#+ 10. Evaluate the component loading vectors for the number of ",
# "'significant' PCs for '", names(datasets_gw)[[i]], "'\n\n"
# ))
#
# # Save component loading vectors in their own data frame
# loadings <- as.data.frame(obj_pca$loadings)
#
# # Evaluate the component loading vectors for the number of significant PCs
# #+ identified via the elbow method plus two
# PCs <- paste0("PC", 1:(as.numeric(elbow) + 2))
# top_loadings_all <- lapply(
# PCs, get_top_loadings, x = loadings, z = "all", a = TRUE
# )
# top_loadings_pos <- lapply(
# PCs, get_top_loadings, x = loadings, z = "pos", a = TRUE
# )
# top_loadings_neg <- lapply(
# PCs, get_top_loadings, x = loadings, z = "neg", a = TRUE
# )
#
# names(top_loadings_all) <-
# names(top_loadings_pos) <-
# names(top_loadings_neg) <-
# PCs
# # rm(PCs)
# # top_loadings_all$PC1 %>% head(n = 20)
# # top_loadings_pos$PC1 %>% head(n = 20)
# # top_loadings_neg$PC1 %>% head(n = 20)
#
# # 11 -----------------------------
# cat(paste0(
# "#+ 11. Analyze positive, negative loadings on axes of biplots for ",
# "'", names(datasets_gw)[[i]], "'\n\n"
# ))
#
# # Look at the top 15 loading vectors per axis
# images <- list()
# mat <- combn(PCs, 2)
# for(i in 1:ncol(mat)) {
# # i <- 1
# j <- mat[, i]
#
# PC_x <- x_label <- j[1]
# PC_y <- y_label <- j[2]
#
# images[[paste0("PCAtools.", PC_x, ".v.", PC_y)]] <- plot_biplot(
# pca = obj_pca,
# PC_x = PC_x,
# PC_y = PC_y,
# loadings_show = FALSE,
# loadings_n = 0,
# meta_color = "strain",
# meta_shape = "technical",
# x_min = -100, # -120000, # -66, # -20,
# x_max = 100, # 120000, # 66, # 20,
# y_min = -100, # -50000, # -50, # -20,
# y_max = 100 # 50000 # 50 # 20
# )
#
# #HERE
# images[[paste0("KA.", PC_x, ".v.", PC_y)]] <-
# plot_pos_neg_loadings_each_axis(
# df_all = loadings,
# df_pos = top_loadings_pos,
# df_neg = top_loadings_neg,
# PC_x = PC_x,
# PC_y = PC_y,
# row_start = 1,
# row_end = 15, # 30
# x_min = -0.15, # -0.15, # -1.0,
# x_max = 0.15, # 0.15, # 1.0,
# y_min = -0.15, # -0.1, # -0.5,
# y_max = 0.15, # 0.1, # 0.5,
# x_nudge = 0.02, # 0.02, # 0.04,
# y_nudge = 0.04, # 0.04, # 0.02,
# x_label = x_label,
# y_label = y_label,
# col_line_pos = "black",
# col_line_neg = "red",
# col_seg_pos = "grey",
# col_seg_neg = "grey"
# )
#
# images[[paste0("KA.", PC_x, ".v.", PC_y)]]
# }
#
# # How do things look?
# images$PCAtools.PC1.v.PC2
# images$KA.PC1.v.PC2$PC_x_pos
# images$KA.PC1.v.PC2$PC_x_neg
# images$KA.PC1.v.PC2$PC_y_pos
# images$KA.PC1.v.PC2$PC_y_neg
#
# images$PCAtools.PC1.v.PC3
# images$KA.PC1.v.PC3$PC_x_pos
# images$KA.PC1.v.PC3$PC_x_neg
# images$KA.PC1.v.PC3$PC_y_pos
# images$KA.PC1.v.PC3$PC_y_neg
#
# # images$PCAtools.PC1.v.PC4
# # images$KA.PC1.v.PC4$PC_x_pos
# # images$KA.PC1.v.PC4$PC_x_neg
# # images$KA.PC1.v.PC4$PC_y_pos
# # images$KA.PC1.v.PC4$PC_y_neg
# #
# # images$PCAtools.PC2.v.PC3
# # images$KA.PC2.v.PC3$PC_x_pos
# # images$KA.PC2.v.PC3$PC_x_neg
# # images$KA.PC2.v.PC3$PC_y_pos
# # images$KA.PC2.v.PC3$PC_y_neg
# #
# # images$PCAtools.PC2.v.PC4
# # images$KA.PC2.v.PC4$PC_x_pos
# # images$KA.PC2.v.PC4$PC_x_neg
# # images$KA.PC2.v.PC4$PC_y_pos
# # images$KA.PC2.v.PC4$PC_y_neg
# #
# # images$PCAtools.PC3.v.PC4
# # images$KA.PC3.v.PC4$PC_x_pos
# # images$KA.PC3.v.PC4$PC_x_neg
# # images$KA.PC3.v.PC4$PC_y_pos
# # images$KA.PC3.v.PC4$PC_y_neg
#
# # images$PCAtools.PC1.v.PC3
# # images$KA.PC1.v.PC3
# # images$PCAtools.PC1.v.PC4
# # images$KA.PC1.v.PC4
# # images$PCAtools.PC2.v.PC3
# # images$KA.PC2.v.PC3
#
# # for(i in 1:length(names(images))) {
# # # i <- 2
# # vector_names <- names(images) %>% stringr::str_split("\\.")
# #
# # if(vector_names[[i]][1] == "PCAtools") {
# # save_title <- paste0("panel-plot", ".", names(images)[i], ".pdf")
# # ggplot2::ggsave(
# # paste0(args$directory_out, "/", save_title), images[[i]]
# # )
# # } else if(vector_names[[i]][1] == "KA") {
# # save_title <- paste0(
# # "panel-plot", ".", names(images)[i], ".1-x-positive.pdf"
# # )
# # ggplot2::ggsave(
# # paste0(args$directory_out, "/", save_title), images[[i]][[1]]
# # )
# #
# # save_title <- paste0(
# # "panel-plot", ".", names(images)[i], ".2-y-positive.pdf"
# # )
# # ggplot2::ggsave(
# # paste0(args$directory_out, "/", save_title), images[[i]][[2]]
# # )
# #
# # save_title <- paste0(
# # "panel-plot", ".", names(images)[i], ".3-x-negative.pdf"
# # )
# # ggplot2::ggsave(
# # paste0(args$directory_out, "/", save_title), images[[i]][[3]]
# # )
# #
# # save_title <- paste0(
# # "panel-plot", ".", names(images)[i], ".4-y-negative.pdf"
# # )
# # ggplot2::ggsave(
# # paste0(args$directory_out, "/", save_title), images[[i]][[4]]
# # )
# # }
# # }
# #TODO Work up some logic for location(s) for outfiles
#
#
# # Plot the top features on an axis of component loading range ----------------
# #+ ...to visualize the top variables (features) that drive variance among
# #+ principal components of interest
# p_loadings <- PCAtools::plotloadings(
# obj_pca,
# components = getComponents(obj_pca, 1),
# # components = getComponents(obj_pca, 1:5),
# rangeRetain = 0.05,
# absolute = FALSE,
# col = c("#785EF075", "#FFFFFF75", "#FE610075"),
# title = "Loadings plot",
# subtitle = "Top 5% of variables (i.e., features)",
# # shapeSizeRange = c(4, 16),
# borderColour = "#000000",
# borderWidth = 0.2,
# gridlines.major = TRUE,
# gridlines.minor = TRUE,