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extract_data.R
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{
to.win.dir <- function(dir) {
return(stringi::stri_replace_all(dir, fixed = "\\", replacement = "/"))
}
if (exists("config.list"))
attach(config.list, warn.conflicts = F)
dir_0 <-
to.win.dir(paste(choose.dir(default = "Z:/"), "/combined/txt/", sep = ""))
# config.dir <- paste(dir_0,"config.list",sep = "")
# setwd(dir_0)
if (!exists("config.list")) {
config.file <-
list.files(path = dir_0,
recursive = T,
full.names = T)[grep(
list.files(
path = dir_0,
recursive = T,
full.names = T
),
pattern = "config.list",
fixed = T
)]
if (length(config.file) != 0) {
message("file < config.list > exists, choose file ?\n\n")
for (f in 1:length(config.file)) {
message(paste(" [", f, "] ", config.file[f], "\n\n", sep = ""))
}
n <- as.numeric(readline(prompt = " choose = "))
if (!is.na(n)) {
load(file = config.file[n], verbose = T)
} else{
message("\nOR source new configuration\n")
}
} else{
message("\nOR source new configuration\n")
}
}
}
if (exists("config.list"))
attach(config.list, warn.conflicts = F)
{
species_info <- list(
HUMAN = list(
OrgDb = "org.Hs.eg.db",
organism = "hsa",
species = 9606
),
MOUSE = list(
OrgDb = "org.Mm.eg.db",
organism = "mmu",
species = 10090
),
RAT = list(
OrgDb = "org.Rn.eg.db",
organism = "rno",
species = 10116
),
DOG = list(
OrgDb = "org.Cf.eg.db",
organism = "cfa",
species = 9615
)
)
OrgDb = species_info[[SPECIES_INFO]][[1]]
organism = species_info[[SPECIES_INFO]][[2]]
species = species_info[[SPECIES_INFO]][[3]]
fca <- quant.type == "count"
dir_1 <- paste(dir_0, run, "/", sep = "")
if (!dir.exists(dir_1))
{
dir.create(dir_1)
}
}
{
lib.loc <- "C:/Program Files/R/R-3.4.3/library/"
library("Biobase", lib.loc = lib.loc)
library("reshape2", lib.loc = lib.loc)
library("ggplot2", lib.loc = lib.loc)
library("gplots", lib.loc = lib.loc)
library("stringi", lib.loc = lib.loc)
library("RColorBrewer", lib.loc = lib.loc)
library("extrafont", lib.loc = lib.loc)
suppressMessages(loadfonts(device = "win"))
library("siggenes", lib.loc = lib.loc)
library("dplyr", lib.loc = lib.loc)
}
{
# setwd(dir_0 )
protein <-
read.table(
blank.lines.skip = T,
file = paste(dir_0, "proteinGroups.txt", sep = ""),
header = T,
sep = "\t",
quote = "",
stringsAsFactors = F
)
major.n <-
stri_split_fixed(protein$Majority.protein.IDs,
pattern = ";",
simplify = T)
major.500 <- major.n[, 1:min(ncol(major.n), 500)]
protein$Majority.protein.IDs <-
apply(major.500, 1, function(x)
paste(x[x != ""], collapse = ";"))
peptide <-
read.table(
blank.lines.skip = T,
file = paste(dir_0, "peptides.txt", sep = ""),
header = T,
sep = "\t",
quote = "",
stringsAsFactors = F
)
# names(protein)
}
{
pt <-
c("Reporter.intensity.corrected.",
"iBAQ.",
"LFQ.intensity.",
"MS.MS.count.")
names(pt) <- c("TMT", "iBAQ", "LFQ", "count")
reporter <-
names(protein)[grep(pattern = pt[quant.type], x = names(x = protein))]
}
#### BIO GROUP ############################################################################################
# setwd(dir_1)
sample.list.dir <- paste(dir_1, "sample.list", sep = "")
if (file.exists(sample.list.dir))
{
message("\n< sample.list exsits >, loading it? [Y] / [N]\n")
load.file <- readline(prompt = ">>\n")
if (load.file %in% c("y", "Y", ""))
{
load(file = sample.list.dir, verbose = T)
Group.all <- sample.list$bioID
reporter <- sample.list$raw
}
} else{
load.file <- "N"
}
if (!file.exists(sample.list.dir) | load.file %in% c("N", "n"))
{
check <- T
while (check)
{
samples.info <- data.frame(raw = reporter, stringsAsFactors = F)
message("======= sample ID structure =======\n")
print(samples.info)
print(data.frame(up = config.list$up, down = config.list$dn))
message("===================================\n")
message(
"\n please input bioGroup pattern regular expression ### '[:alnum:]+[.]{1}' for 'BioGroup.' ###\n"
)
pattern.read <-
readline(prompt = "new regular expression pattern = \ndefault pattern = [:alnum:]+")
if (pattern.read == "")
{
group.pattern <- "[:alnum:]+"
message(paste("\n=====", group.pattern, "=====\n", sep = " "))
} else
{
group.pattern <- pattern.read
message(paste("\n=====", group.pattern, "=====\n", sep = " "))
}
# message(
# "\n please input bioGroup pattern regular expression position ### '[Enter] for prefix, [0] for postfix' ###\n"
# )
# position.read <- readline(prompt = "pattern position = \ndefault position = prefix")
if (T) {
group.mode <- "first" ### first/last
# message("\n ===== prefix =====")
} else{
group.mode <- "last"
message("\n ===== postfix =====")
}
sample.number <- length(reporter)
message(
"\nplease input [y] to assign new bioID for each reporter, OR press [Enter] directly if use raw ID"
)
if (readline() == "")
{
samples.info$bioID <- reporter
samples.info$group <-
stri_extract(str = samples.info$bioID,
regex = group.pattern,
mode = group.mode)
message("========== use raw bioID ==========\n")
print(samples.info)
message("===================================\n")
} else
{
if (is.null(samples.info$bioID))
{
samples.info$bioID <- ""
}
info.file <- paste(dir_1, ".info.file", sep = "")
if (!file.exists(info.file))
{
write.table(
x = samples.info,
file = info.file,
quote = F,
sep = "\t",
row.names = F
)
}
shell(cmd = paste('start excel ', info.file, sep = ""))
.readline <- readline(prompt = "Enter to continue ...")
samples.info <-
read.table(
info.file,
sep = "\t" ,
header = T,
stringsAsFactors = F
)
# samples.info <- na.omit(edit(name = samples.info))
samples.info.na <-
stri_detect(samples.info$bioID, regex = "[:graph:]")
# samples.info.na <- !stri_detect(samples.info$bioID,regex = "[:space:]")
reporter <- reporter[samples.info.na]
samples.info.1 <- samples.info[samples.info.na,]
samples.info.1$group <-
stri_extract(str = samples.info.1$bioID,
regex = group.pattern,
mode = group.mode)
message("========== use new bioID ==========\n")
print(samples.info.1)
message("===================================\n")
}
message(
"\ncheck bioID assignment, press [Enter] if done, else press [N] to reassign bioID\n"
)
check <- ifelse(readline() == "", F, T)
if (!check)
{
message("======== assign bioID done ========\n")
}
}
sample.list <- as.list(samples.info.1)
sample.list[["group.mode"]] <- group.mode
sample.list[["group.pattern"]] <- group.pattern
Group.all <- sample.list$bioID
}
{
sample.list[["group.names.order"]] <- unique(c(up, dn))
sample.list[["replicates"]] <- table(sample.list$group)
}
#### configure pv.mt structure ####
group.sep="-"
if (length(sample.list$group.names.order) >= 2)
{
up.vs.dn <- paste(up, dn, sep = group.sep) ### compare selected
sample.list[["up.vs.dn"]] <- up.vs.dn
cpr <-
cbind.data.frame(
vs = stri_split_fixed(
str = up.vs.dn,
pattern = group.sep,
simplify = T
),
stringsAsFactors = F
)
cpr$fc <- paste(cpr$vs.1, group.sep, cpr$vs.2, sep = "")
cpr$imputation <-
paste("im..", cpr$vs.1, group.sep, cpr$vs.2, sep = "")
cpr$names <- sample.list$up.vs.dn
cpr$pv <- paste("pv..", sample.list$up.vs.dn, sep = "")
cpr$class <- paste("class..", cpr$fc, sep = "")
# cpr$ratio <-
sample.list[["contrast"]] <- cpr
}
################################################################################################
################################################################################################
{
# names(protein)
if (all(c("Gene.names") %in% names(protein)))
{
column <-
c(
"Majority.protein.IDs",
"Gene.names",
# "Protein.names",
"Number.of.proteins",
"Unique.peptides",
"Sequence.coverage....",
"MS.MS.count",
"Q.value",
"Score",
reporter,
"iBAQ"
)
prot <- protein[, column]
nrow(prot)
gene.names.na.bl <- prot$Gene.names == "" |
!isUnique(
stri_split_fixed(
str = prot$Gene.names,
pattern = ";",
n = 2,
simplify = T
)[, 1])
prot$Symbol <-
stri_split_fixed(
str = prot$Gene.names,
pattern = ";",
n = 2,
simplify = T
)[, 1]
prot$Symbol[gene.names.na.bl] <-
stri_split_fixed(
str = prot$Majority.protein.IDs,
pattern = ";",
n = 2,
simplify = T
)[, 1][gene.names.na.bl]
prot$Rank <- rank(x = -prot$iBAQ)
}else
{
column <-
c(
"Majority.protein.IDs",
# "Gene.names",
# "Protein.names",
"Number.of.proteins",
"Unique.peptides",
"Sequence.coverage....",
"MS.MS.count",
"Q.value",
"Score",
reporter,
"iBAQ"
)
prot <- protein[, column]
prot$Symbol <-
stri_extract_first(prot$Majority.protein.IDs, regex = "[:alnum:]+[-]{0,1}")
prot$Rank <- rank(x = -prot$iBAQ)
}
if (!fca)
{
if (length(grep("CON_", x = prot$Majority.protein.IDs)) > 0)
{
prot <- prot[-grep("CON_", x = prot$Majority.protein.IDs), ]
} else
{
prot <- prot
}
}
if (length(grep("REV_", x = prot$Majority.protein.IDs)) > 0)
{
prot <- prot[-grep("REV_", x = prot$Majority.protein.IDs), ]
} else
{
prot <- prot
}
prot_bak <- prot
if (quant.type %in% c("count")) {
prot <- prot[rowSums(prot[reporter] > 0) >= 1,] ## 20180116 hq
} else{
prot <-
prot[rowSums(prot[reporter] > 0) >= length(reporter) * missing.ratio,] ## 20171228 hq
}
prot[Group.all] <- prot[c(reporter)]
# names(prot)
{
if (quant.type %in% c("TMT", "iBAQ")) {
### CAL FACTORS: sum median custom ####
prot_rpt_sub <- prot[reporter]
median_factor <-
apply(X = prot_rpt_sub,
MARGIN = 2,
FUN = median) /
(apply(
X = prot_rpt_sub,
MARGIN = 2,
FUN = median
)[1])
nor_mt <-
t(t(prot_rpt_sub) / median_factor) ### < CONFIG > ###
colMeans(nor_mt, na.rm = T)
prot[sample.list$bioID] <- nor_mt
}
}
nrow(prot)
#### get ratio ####
#### imputation ####
if (quant.type %in% c("LFQ", "iBAQ", "TMT"))
{
prot_imp <- log2(prot[Group.all])
prot_imp.bl <-
as.data.frame(prot[Group.all] == 0, optional = T)
prot_imp.df <- as.data.frame(melt(prot_imp), optional = T)
prot_imp.df$value[is.infinite(prot_imp.df$value)] <- NA
prot_imp.df$imputation <- F
prot_imp.df$imputation[is.na(prot_imp.df$value)] <- T
if (imp.mode == "s")
{
imp.avg <-
acast(
prot_imp.df,
formula = . ~ variable,
fun.aggregate = mean,
na.rm = T
)
imp.sd <-
acast(
prot_imp.df,
formula = . ~ variable,
fun.aggregate = sd,
na.rm = T
)
m <- imp.avg - imp.downshift * imp.sd
s <- imp.width * imp.sd
for (im in sample.list$bioID)
{
prot_imp[prot_imp.bl[, im], im] <-
rnorm(n = sum(prot_imp.bl[, im]),
sd = s[, im],
mean = m[, im])
}
} else
if (imp.mode == "a")
{
imp.avg <- mean(prot_imp.df$value, na.rm = T)
imp.sd <- sd(prot_imp.df$value, na.rm = T)
m <- imp.avg - imp.downshift * imp.sd
s <- imp.width * imp.sd
for (im in sample.list$bioID)
{
prot_imp[prot_imp.bl[, im], im] <-
rnorm(n = sum(prot_imp.bl[, im]),
sd = s,
mean = m)
}
}
row.names(prot_imp.bl) <- prot$Majority.protein.IDs
prot_imp.bl$Majority.protein.IDs <- row.names(prot_imp.bl)
prot_in <- prot[c("Majority.protein.IDs", sample.list$bioID)]
prot_in[Group.all] <- 2 ^ (prot_imp[Group.all])
} else
if (quant.type %in% c("count"))
{
{
prot_imp <- log2(prot[Group.all])
prot_imp.bl <-
as.data.frame(prot[Group.all] == 0, optional = T)
prot_imp.df <- as.data.frame(melt(prot_imp), optional = T)
prot_imp.df$imputation <- F
row.names(prot_imp.bl) <- prot$Majority.protein.IDs
prot_imp.bl$Majority.protein.IDs <- row.names(prot_imp.bl)
}
prot_in <- prot[c("Majority.protein.IDs", sample.list$bioID)]
names(prot_in)
}
rownames(prot) <- prot$Majority.protein.IDs
rownames(prot_in) <- prot_in$Majority.protein.IDs
}
nrow(prot_in)
nrow(prot_imp.bl)
#### FC / imputation AND/OR CV ####
{
{
pro_imp_m <- melt(
data = prot_imp.bl,
id.vars = "Majority.protein.IDs",
variable.name = "replicates",
as.is = T
)
pro_imp_m$group <-
stri_extract(
str = pro_imp_m$replicates,
regex = sample.list$group.pattern,
simplify = T,
mode = sample.list$group.mode
)
pro_imp_m$group <-
factor(pro_imp_m$group,
levels = sample.list$group.names.order,
ordered = T)
imp_mt <-
acast(
data = pro_imp_m,
formula = Majority.protein.IDs ~ group,
fun.aggregate = mean
)
imp_df <- as.data.frame(x = imp_mt, optional = T)
}
{
pro_m_ <- melt(data = prot_in,
variable.name = "replicates",
as.is = T)
pro_m <- pro_m_
pro_m$group <-
stri_extract(
str = pro_m$replicates,
regex = sample.list$group.pattern,
simplify = T,
mode = sample.list$group.mode
)
pro_m$group <-
factor(pro_m$group,
levels = sample.list$group.names.order,
ordered = T)
avg_mt <-
acast(
data = pro_m,
formula = Majority.protein.IDs ~ group,
fun.aggregate = mean
)
avg_df <- as.data.frame(x = avg_mt, optional = T)
# names(avg_df)
test.num <- length(unique(pro_m$group))
}
{
if (all(sample.list$replicates > 2))
{
cv_mt <-
acast(
data = na.omit(pro_m),
formula = Majority.protein.IDs ~ group,
fun.aggregate = function(x)
100 * sd(x) / mean(x)
)
cv_df <- as.data.frame(cv_mt, optional = T)
sd_mt <-
acast(
data = na.omit(pro_m),
formula = Majority.protein.IDs ~ group,
fun.aggregate = sd
)
sd_df <- as.data.frame(sd_mt, optional = T)
}
}
}
#### T.TEST OR ANOVA #########################################################
if (all(sample.list$replicates > 2))
{
pv_list <- list()
qv_list <- list()
fdr_list <- list()
sam.out_list <- list()
fdr.cl.all <-
factor(
stri_extract(
str = names(prot_in)[-1],
regex = sample.list$group.pattern,
simplify = T,
mode = sample.list$group.mode
),
levels = sample.list$group.names.order,
ordered = T
)
# test.num <- length(unique(pro_m$group))
## tow case SAM
if (test.num == 2 | use.t == use.t)
{
for (cpr.name in cpr$names)
{
fdr.cl.idx.x <- fdr.cl.all %in% cpr[match(cpr.name, cpr$names), 1:2]
fdr.cl.x <- fdr.cl.all[fdr.cl.idx.x]
fdr.dt.x <- prot_in[-1][fdr.cl.idx.x]
sam.out_list[[cpr.name]] <-
sam(
data = scale(log(fdr.dt.x)),
var.equal = F,
cl = fdr.cl.x,
rand = 123
)
pv_list[[cpr.name]] <- sam.out_list[[cpr.name]]@p.value
qv_list[[cpr.name]] <- sam.out_list[[cpr.name]]@q.value
if (sum(summary(sam.out_list[[cpr.name]])@mat.fdr[, "Called"]) == 0) {
fdr_co_idx <- rep(T, nrow(fdr.dt.x))
} else{
delta.fdr.0 <- findDelta(sam.out_list[[cpr.name]], fdr = fdr_co)
delta.fdr <-
rbind(delta.fdr.0, delta.fdr.0) ### bug fixed for if delta.fdr.0 has only one row
delta_min <- min(as.data.frame(delta.fdr)$Delta)
row.sig_vt <-
summary(sam.out_list[[cpr.name]], delta_min)@row.sig.genes
fdr_co_idx <- rep(F, nrow(prot_in))
fdr_co_idx[row.sig_vt] <- T
}
fdr_list[[cpr.name]] <- fdr_co_idx
}
pv_df <- as.data.frame(pv_list, optional = T)
rownames(pv_df) <- prot_in[, "Majority.protein.IDs"]
qv_df <- as.data.frame(qv_list, optional = T)
rownames(qv_df) <- prot_in[, "Majority.protein.IDs"]
fdr_df <- as.data.frame(fdr_list, optional = T)
rownames(fdr_df) <- prot_in[, "Majority.protein.IDs"]
} else if (test.num > 2) {
## three or more case SAM & anova-TukeyHSD
cpr.name <- paste(levels(x = fdr.cl.all), collapse = "-")
fdr.dt.all <- prot_in[-1][names(prot_in)[-1]]
sam.out_list[[cpr.name]] <-
sam(data = log(fdr.dt.all),
cl = fdr.cl.all,
rand = 123)
plot(sam.out_list[[cpr.name]], 5)
pv_list[[cpr.name]] <- sam.out_list[[cpr.name]]@p.value
# pv_df0 <- as.data.frame(pv_list, optional = T)
# rownames(pv_df0) <- prot_in["Majority.protein.IDs"]
qv_list[[cpr.name]] <- sam.out_list[[cpr.name]]@q.value
if (sum(summary(sam.out_list[[cpr.name]])@mat.fdr[, "Called"]) == 0) {
fdr_co_idx <- rep(T, nrow(fdr.dt.all))
} else{
delta.fdr <- findDelta(sam.out_list[[cpr.name]], fdr = fdr_co)
delta_min <- min(as.data.frame(delta.fdr)$Delta)
row.sig_vt <-
summary(sam.out_list[[cpr.name]], delta_min)@row.sig.genes
fdr_co_idx <- rep(F, nrow(prot_in))
fdr_co_idx[row.sig_vt] <- T
}
fdr_list[[cpr.name]] <- fdr_co_idx
qv_df <- as.data.frame(qv_list, optional = T)
rownames(qv_df) <- prot_in[, "Majority.protein.IDs"]
fdr_df <- as.data.frame(fdr_list, optional = T)
rownames(fdr_df) <- prot_in[, "Majority.protein.IDs"]
anova.df <- function(data.df, cl.vt, ...) {
Tukey.pv <- as.data.frame(optional = T, t(apply(
X = data.df,
MARGIN = 1,
FUN = function(.x = X,
.cl.vt = cl.vt,
...) {
aov.lm <-
aov(
formula = value ~ variable,
data = data.frame(value = .x, variable = .cl.vt)
)
TukeyHSD(aov.lm)$variable[, "p adj"]
}
)))
return(Tukey.pv)
}
pv_df <- anova.df(fdr.dt.all, fdr.cl.all)
# anova.df(data.df = fdr.dt.all[1:14, ], factor(c("a", "b", "c", "a", "b", "c")))
}
}
#### average fc ####
fc_list <- list()
im_list <- list()
if (fca) {
FCa <- function(idx = 1, df = df) {
#### Nj and Nx with coexist protiens ####
coexist <-
rowSums(df[c(cpr$vs.1[idx], cpr$vs.2[idx])] >= fca.spc) == 2
Nj <- sum(df[coexist, cpr$vs.1[idx]])
Nx <- sum(df[coexist, cpr$vs.2[idx]])
Cix <- df[cpr$vs.2[idx]] / Nx
n <- 1
b <- 1
a <- b / ave(Nx)
Ci <- (1 / n) * Cix
Tij <- df[cpr$vs.1[idx]] / Nj
FCij <-
((Tij + a) / (Ci + a)) * (rowSums((df[c(cpr$vs.1[idx], cpr$vs.2[idx])])) > 0)
return(log2(FCij))
}
for (j in 1:nrow(cpr)) {
fc_list[cpr$fc[j]] <- FCa(idx = j, df = avg_df)
}
# names(fc_list)
fc_df <-
as.data.frame(x = fc_list,
optional = T,
row.names = row.names(avg_df))
# names(fc_df)
class_df <- fc_df
prot.o <- prot[rownames(fc_df),]
for (.f in 1:nrow(cpr)) {
stopifnot(all.equal.character(rownames(fc_df), rownames(prot.o), rowname.vt))
f <- cpr$fc[.f]
vs.1 <- cpr$vs.1[.f]
vs.2 <- cpr$vs.2[.f]
idxT <-
which(
fc_df[, f] >= lr_co &
prot.o$Unique.peptides >= unique_co &
(avg_df[, vs.1] > spc_co | avg_df[, vs.2] > spc_co)
)
idxC <-
which((fc_df[, f]) <= -lr_co &
prot.o$Unique.peptides >= unique_co &
(avg_df[, vs.1] > spc_co | avg_df[, vs.2] > spc_co)
)
idxNA <- which((fc_df[, f]) == -Inf)
class_df[, f] <- "T&C"
class_df[idxT , f] <- "T"
class_df[idxC , f] <- "C"
class_df[idxNA, f] <- NA
}
# names(class_df)
} else{
for (j in 1:nrow(cpr)) {
fc_list[[cpr$fc[j]]] <-
avg_df[, cpr$vs.1[j]] / avg_df[, cpr$vs.2[j]]
im_list[[cpr$im[j]]] <-
imp_df[, cpr$vs.1[j]] < imp_co |
imp_df[, cpr$vs.2[j]] < imp_co
}
# names(fc_list)
fc_df <-
as.data.frame(x = fc_list,
optional = T,
row.names = row.names(avg_df))
im_df.rs <-
as.data.frame(x = im_list,
optional = T,
row.names = row.names(imp_df))
}
####### merge data #######
rowname.vt <- rownames(avg_df)
prot.od <- prot[rowname.vt,]
prot_in.od <- prot_in[rowname.vt,]
prot_imp.od <- prot_imp.bl[rowname.vt, -length(prot_imp.bl)]
names(prot_imp.od) <- paste("im.", names(prot_imp), sep = ".")
if (all(sample.list$replicates > 2)) {
# pv_df.od <- pv_df[rowname.vt, ]
fc_df.od <- fc_df
cv_df.od <- cv_df
names(fc_df.od) <- paste("fc.", names(fc_df), sep = ".")
names(cv_df.od) <- paste("cv.", names(cv_df), sep = ".")
pv_df.. <- pv_df
qv_df.. <- qv_df
fdr_df.. <- fdr_df
names(pv_df..) <- paste("pv.", names(pv_df), sep = ".")
names(qv_df..) <- paste("qv.", names(qv_df), sep = ".")
names(fdr_df..) <- paste("fd.", names(fdr_df), sep = ".")
pv_qv_fdr.od <- cbind.data.frame(pv_df.., qv_df..)[rowname.vt, ]
if (all(
all.equal.character(rowname.vt, rownames(cv_df.od)),
all.equal.character(rowname.vt, rownames(fc_df.od)),
# all.equal.character(rowname.vt, rownames(pv_df.od)),
all.equal.character(rowname.vt, rownames(pv_qv_fdr.od)),
all.equal.character(rowname.vt, rownames(prot.od)),
all.equal.character(rowname.vt, rownames(prot_imp.od)),
all.equal.character(rowname.vt, rownames(prot_in.od))
)) {
message("\n===== all rownames equal =====\n")
}
pro_quant_df <-
cbind.data.frame(prot.od[!names(prot.od) %in% sample.list$bioID]
,
prot_in.od[Group.all]
,
prot_imp.od
,
avg_df
,
cv_df.od
,
fc_df.od
# , pv_df.od
,
pv_qv_fdr.od)
} else if (fca) {
fc_df.od <- fc_df
class_df.od <- class_df
names(fc_df.od) <- paste("fc.", names(fc_df), sep = ".")
names(class_df.od) <- paste("class.", names(class_df), sep = ".")
if (all(
all.equal.character(rowname.vt, rownames(prot_imp.od)),
all.equal.character(rowname.vt, rownames(fc_df.od)),
all.equal.character(rowname.vt, rownames(class_df.od)),
all.equal.character(rowname.vt, rownames(prot.od)),
all.equal.character(rowname.vt, rownames(prot_in.od))
)) {
message("\n===== all rownames equal =====\n")
}
pro_quant_df <-
cbind.data.frame(prot.od[!names(prot.od) %in% sample.list$bioID]
,
prot_in.od[Group.all]
,
prot_imp.od
,
avg_df
,
fc_df.od
,
class_df.od)
} else
{
fc_df.od <- fc_df
names(fc_df.od) <- paste("fc.", names(fc_df), sep = ".")
if (all(
all.equal.character(rowname.vt, rownames(prot_imp.od)),
all.equal.character(rowname.vt, rownames(fc_df.od)),
all.equal.character(rowname.vt, rownames(prot.od)),
all.equal.character(rowname.vt, rownames(prot_in.od)))
) {
message("\n===== all rownames equal =====\n")
}
pro_quant_df <-
cbind.data.frame(prot.od[!names(prot.od) %in% sample.list$bioID]
, prot_in.od[Group.all]
, prot_imp.od
, avg_df
, fc_df.od)
}
#### UNIPROT MAPPING ####
{
if (!exists("uniprot.tab"))
{
.tab <-
paste("Y:/anno/", dir("Y:/anno/")[grep(dir("Y:/anno/"),
pattern = SPECIES_INFO,
ignore.case = T)], sep = "")
if (length(.tab) == 1) {
uniprot.tab <-
read.table(
file = .tab,
header = T,
sep = "\t",
stringsAsFactors = F)
} else if (length(.tab) > 1) {
uniprot.tab <-
read.table(
file = choose.files(
default = "Y:/anno/*.tab",
caption = paste("Choose uniprot annotation file", SPECIES_INFO, sep = "--"))
,
header = T,
sep = "\t",
stringsAsFactors = F
)
} else{
uniprot.tab <-
read.table(
file = choose.files(
default = "*.tab",
caption = paste("Choose uniprot annotation file", SPECIES_INFO, sep = "--")
),
header = T,
sep = "\t",
stringsAsFactors = F
)
}
}
# my_gene2(str = pro_quant_df$Majority.protein.IDs[4])
my_gene2 <- function(str, ...)
{
vt <-
stri_extract_all_regex(
str = str,
pattern = "[:alnum:]+(-*)[:alnum:]*",
simplify = T,
omit_no_match = F
)[1, ]
# vt <- vt0[idx,]
gi <-
subset.data.frame(x = uniprot.tab,
subset = Entry %in% vt &
Cross.reference..GeneID. != "")
acc.order <- vt[which(vt %in% gi$Entry)]
choose <- subset.data.frame(gi, subset = Entry == acc.order[1])
gn <-
subset.data.frame(x = uniprot.tab,
subset = Entry %in% vt &
(Gene.names...primary.. != ""))
GS_ <-
paste(names(table(gn$Gene.names...primary..))[order(table(gn$Gene.names...primary..), decreasing = T)], collapse = ";")
EG_ <-
stri_extract_first(choose$Cross.reference..GeneID., regex = "[:alnum:]+")
if (length(GS_) == 0)
GS_ <- NA
if (length(EG_) == 0)
EG_ <- NA
eg.gs <- data.frame(EG = EG_,
GS = GS_,
stringsAsFactors = F)
return(eg.gs)
}
{
time0 <- Sys.time()
str <- pro_quant_df$Majority.protein.IDs
sp.anno <-
as.data.frame(t(
sapply(
X = str,
FUN = my_gene2,
simplify = T
)))
pro_quant_df$ENTREZID <- unlist(sp.anno$EG)
pro_quant_df$Alias <- unlist(sp.anno$GS)
Sys.time() - time0
}
}
data.df <- pro_quant_df
#### significant function define ####
sig.cutoff <-
function(data.df,
imp.rs = im_df.rs,
quant.type = quant.type,
...) {
ratio.cpr <-
names(data.df)[grep(x = names(data.df), pattern = "fc..")]
pv.cpr <-
names(data.df)[grep(x = names(data.df), pattern = "pv..")]
qv.cpr <-
names(data.df)[grep(x = names(data.df), pattern = "qv..")]
fdr.cpr <-
names(data.df)[grep(x = names(data.df), pattern = "fd..")]
unique.cpr <-
names(data.df)[grep(x = names(data.df), pattern = "^Unique.peptides")]
imputation.cpr <- names(imp.rs)
sig.cutoff.list <- list()
if (any(sample.list$replicates < 3)) {
sig.cutoff.df <- cbind.data.frame(imp.rs,
abs(log2(data.df[ratio.cpr])) > lr_co,
data.df[unique.cpr] >= unique_co)