#' customeTheme fucntion for ggplot
#'
#' takes in predited weights and true labels and determines performance characterisitcs
#' @param sizeStripFont font of size of facet labels
#' @param xAngle angle of x-axis labels
#' @param hjust horizontal justification 0-left, 0.5-center, 1-right
#' @param vjust vertical justification 0-low, 0.5-middle, 1-high
#' @param xSize font size of x-axis label
#' @param ySize font size of y-axis label
#' @param xAxisSize font size of x-axis label title
#' @param yAxisSize fotn size of y-axis label title
#' @export
customTheme = function(sizeStripFont, xAngle, hjust, vjust, xSize,
ySize, xAxisSize, yAxisSize) {
theme(strip.background = element_rect(colour = "black", fill = "white",
size = 1), strip.text.x = element_text(size = sizeStripFont),
strip.text.y = element_text(size = sizeStripFont), axis.text.x = element_text(angle = xAngle,
hjust = hjust, vjust = vjust, size = xSize, color = "black"),
axis.text.y = element_text(size = ySize, color = "black"),
axis.title.x = element_text(size = xAxisSize, color = "black"),
axis.title.y = element_text(size = yAxisSize, color = "black"),
panel.background = element_rect(fill = "white", color = "black"))
}
#' table of classification performances
#'
#' takes in predited weights and true labels and determines performance characterisitcs
#' @param weights are the predicted scores/probablities of test data
#' @param trubeLabels are the true labels associated with the test data
#' @param direction = "auto", ">", "<"
#' @export
normalizelibSum = function(genExp) {
lib.size <- colSums(genExp)
genExpNorm <- t(log2(t(genExp + 0.5)/(lib.size + 1) * 1e+06))
return(genExpNorm)
}
#' table of classification performances
#'
#' takes in predited weights and true labels and determines performance characterisitcs
#' @param weights are the predicted scores/probablities of test data
#' @param trubeLabels are the true labels associated with the test data
#' @param direction = "auto", ">", "<"
#' @export
plotSampleHist = function(data = data, main = NULL, xlim = NULL,
ylim = NULL) {
for (i in 1:ncol(data)) {
idx <- data[, i] > -1
shist(data[idx, i], unit = 0.25, col = i, plotHist = FALSE,
add = i != 1, main = main, ylim = ylim, xlim = xlim,
xlab = expression("log"[2] ~ "cpm"))
}
}
#' table of classification performances
#'
#' takes in predited weights and true labels and determines performance characterisitcs
#' @param weights are the predicted scores/probablities of test data
#' @param trubeLabels are the true labels associated with the test data
#' @param direction = "auto", ">", "<"
#' @export
annotateTranscripts = function(features, filter, mart) {
attr = c("description", "ucsc", "chromosome_name", "strand",
"hgnc_symbol", "refseq_mrna")
if (filter %in% c("ucsc", "trinity")) {
features = features
}
if (filter == "ensembl_gene_id") {
features <- unlist(lapply(strsplit(features, "\\."),
function(i) i[1]))
}
gene <- rep(NA, length(features))
if (filter %in% c("ucsc", "ensembl_gene_id")) {
hk.known <- getBM(attributes = attr, filters = filter,
values = features, mart = mart)$hgnc_symbol
gene <- unique(hk.known)
} else {
trinityMapFile <- read.delim("/Users/asingh/Documents/Asthma/biomarkerPanels/data/discovery/rnaseq/asthma.trinity.blastx.outfmt6.txt")
trinityMapFile$Contig <- unlist(lapply(strsplit(as.character(trinityMapFile$query_id),
"_"), function(i) paste(i[1], i[2], sep = "_")))
trinityMapFile$UniProt <- unlist(lapply(strsplit(unlist(lapply(strsplit(as.character(trinityMapFile$subject_id),
"\\|"), function(i) i[[2]])), split = "_"), function(x) x[1]))
trinityMapFile$GenSym <- unlist(lapply(strsplit(unlist(lapply(strsplit(as.character(trinityMapFile$subject_id),
"\\|"), function(i) i[[3]])), split = "_"), function(x) x[1]))
gene <- trinityMapFile$GenSym[trinityMapFile$query_id %in%
features]
}
gene
}
#' table of classification performances
#'
#' takes in predited weights and true labels and determines performance characterisitcs
#' @param weights are the predicted scores/probablities of test data
#' @param trubeLabels are the true labels associated with the test data
#' @param direction = "auto", ">", "<"
#' @export
multiplot <- function(..., plotlist = NULL, file, cols = 1, layout = NULL) {
library(grid)
# Make a list from the ... arguments and plotlist
plots <- c(list(...), plotlist)
numPlots = length(plots)
# If layout is NULL, then use 'cols' to determine layout
if (is.null(layout)) {
# Make the panel ncol: Number of columns of plots nrow:
# Number of rows needed, calculated from # of cols
layout <- matrix(seq(1, cols * ceiling(numPlots/cols)),
ncol = cols, nrow = ceiling(numPlots/cols))
}
if (numPlots == 1) {
print(plots[[1]])
} else {
# Set up the page
grid.newpage()
pushViewport(viewport(layout = grid.layout(nrow(layout),
ncol(layout))))
# Make each plot, in the correct location
for (i in 1:numPlots) {
# Get the i,j matrix positions of the regions that contain
# this subplot
matchidx <- as.data.frame(which(layout == i, arr.ind = TRUE))
print(plots[[i]], vp = viewport(layout.pos.row = matchidx$row,
layout.pos.col = matchidx$col))
}
}
}
#' table of classification performances
#'
#' takes in predited weights and true labels and determines performance characterisitcs
#' @param weights are the predicted scores/probablities of test data
#' @param trubeLabels are the true labels associated with the test data
#' @param direction = "auto", ">", "<"
#' @export
zip_nPure = function(.x, .fields = NULL, .simplify = FALSE) {
if (length(.x) == 0)
return(list())
if (is.null(.fields)) {
if (is.null(names(.x[[1]]))) {
.fields <- seq_along(.x[[1]])
} else {
.fields <- stats::setNames(names(.x[[1]]), names(.x[[1]]))
}
} else {
if (is.character(.fields) && is.null(names(.fields))) {
names(.fields) <- .fields
}
}
out <- lapply(.fields, function(i) lapply(.x, .subset2, i))
if (.simplify)
out <- lapply(out, simplify_if_possible)
out
}
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