sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
a55d6a5a70de93fa32559061c8f688e117a32fd3e38893f9fadcf91f2d137cec | R | 164 | 11 | hamming.window <- function (n)
{
if (n == 1)
c <- 1
else
{
n <- n-1
c <- 0.54 - 0.46*cos(2*pi*(0:n)/n)
}
return(c)
}
|
76e4493775efe71f9a3d174c7d8c2d3a62d61f90541c3ee2affe395c882fb72d | R | 198 | 8 | pdf("align_hum.pdf")
hum_align=read.table('hum_align',header=T,sep='\t')
summary(hum_align)
hist(hum_align$AlignLength,breaks=100)
plot(hum_align$SubjAccno)
plot(hum_align$QueryAccno)
q(save="no")
|
8f9ce518146c9fd6f1297b8ab921dabc9856811926b3103dcde25d3d388d4e1d | R | 207 | 11 | #' Pipe operator
#'
#' See \code{magrittr::\link[magrittr:pipe]{\%>\%}} for details.
#'
#' @name %>%
#' @rdname pipe
#' @keywords internal
#' @export
#' @importFrom magrittr %>%
#' @usage lhs \%>\% rhs
NULL
|
3cf3d6f1ed36d8b233ee0fc59d9752cc2a95494077f7cdf4fbba3b3b70acd2bb | R | 220 | 9 | # setting R_TESTS to empty string because of
# https://github.com/hadley/testthat/issues/144
# revert this when that issue in R is fixed.
Sys.setenv("R_TESTS" = "")
library(testthat)
library(fgsea)
test_check("fgsea")
|
62353672a640ff838b43afae6b6e6caac0c36095c17dd7b7e0af586cfb98bc53 | R | 222 | 13 | bincombinations <- function(p) {
retval <- matrix(0, nrow=2^p, ncol=p)
for(n in 1:p){
retval[,n] <- rep(c(rep(0, (2^p/2^n)), rep(1, (2^p/2^n))),
length=2^p)
}
retval
}
|
417255b9412a07da6550d956dc39f7233908998576a56a35a559b9e586638b89 | R | 227 | 8 | ## cmeans clustering should also work on data frames
library(e1071)
data(iris)
set.seed(123)
cm1 <- cmeans(iris[,1:4], 10)
bc1 <- bclust(iris[,1:4], 3, base.centers=20,iter.base=50,
base.method="cmeans")
|
ed72a0ff3345001087cbc9f001d045893c938c016a99c60b7c7b453d0627f5da | R | 229 | 8 | library(nlme)
fm1 <- lme(distance ~ age, Orthodont)
str(o1 <- getData(fm1))
df <- Orthodont # note that the name conflicts with df in the stats
fm2 <- lme(distance ~ age, df)
str(o2 <- getData(fm2))
stopifnot(identical(o1, o2))
|
5beeb0feea6ada29ee4e7ee18c0c343b276f29d3fe870d06a529f3943eb54e0e | R | 230 | 9 | #a=read.table('groel.msa.aln.out')
a=read.table('tseq.aln.out')
#png("plotcor.png")
#plot(a)
#dev.off()
b <- cor(a)
write.table( b , file = "groel_hydro_cor_mat.txt", sep = "\t",col.names
= FALSE, row.names = FALSE )
|
563f40ebb0755afc75afd6510429143a97eed0c1fa6a327e3f1b14ed33e643c4 | R | 239 | 7 | library(nlme)
data(Orthodont)
# add a column with an NA that is not used in the fit
Orthodont$Others = runif(nrow(Orthodont))
is.na(Orthodont$Others[3]) = TRUE
fm1 = lme(Orthodont, random = ~1)
augPred(fm1, length.out = 2, level = c(0,1))
|
5ab8fed40e3f12962a339087f80d8a4208e4bd80372bb72b0a3388b880a0e2d7 | R | 243 | 6 | gmt.file <- "./inst/extdata/mouse.reactome.gmt"
pathwayLines <- strsplit(readLines(gmt.file), "\t")
examplePathways <- lapply(pathwayLines, tail, -2)
names(examplePathways) <- sapply(pathwayLines, head, 1)
devtools::use_data(examplePathways)
|
80a8c7a00525c06740e3389eb9315c6c9a7b7f7a11ec84ee1e0727a3c8231982 | R | 248 | 6 | rnk.file <- "./inst/extdata/naive.vs.th1.rnk"
exampleRanks <- read.table(rnk.file,
header=TRUE, colClasses = c("character", "numeric"))
exampleRanks <- setNames(exampleRanks$t, exampleRanks$ID)
devtools::use_data(exampleRanks)
|
0177529708e457e751674b41538b0ff73bf06e4792ba84651770e8b0d303c748 | R | 253 | 5 | df <- read.csv("Extend Fig.2f.csv", row.names = 1)
ggplot(df, aes(x = prediction_score, group = age, color = age)) +
geom_density(aes(y = after_stat(scaled)), size = 1.2) +
theme_minimal() +
labs(x = "Prediction Score", y = "Scaled Density")
|
7ea4fe9c9cbe2db012e760f45768d0167971f899e3f121464ff001da7af03b09 | R | 279 | 5 | # code to prepare `exampleExpressionMatrix` dataset goes here
expressionMatrixFile <- "./inst/extdata/gse14308_expression_matrix.tsv"
exampleExpressionMatrix <- as.matrix(read.table(expressionMatrixFile, sep = "\t"))
usethis::use_data(exampleExpressionMatrix, overwrite = TRUE)
|
d0135f61cab68af646e2041527918d6ed8cb1f11102475597ace5f1d151f4697 | R | 294 | 8 | ## from PR#8905
library(nlme)
fm <- lme(distance ~ poly(age, 3) + Sex, data = Orthodont, random = ~ 1)
# data for predictions
Newdata <- head(Orthodont)
Newdata$Sex <- factor(Newdata$Sex, levels = levels(Orthodont$Sex))
(pr <- predict(fm, Newdata))
stopifnot(all.equal(c(pr), fitted(fm)[1:6]))
|
e66695c0a709a243e879f063f9d665aca41c7dcb8d56c142c94be48e7968d6ce | R | 302 | 6 | df <- read.csv("Extend Fig.2g.csv", row.names = 1)
ggplot(df, aes(x = prediction_score, color = Major, group = Major)) +
geom_density(aes(y = after_stat(scaled)), size = 1.2) +
facet_wrap(~group, scales = "free") +
theme_minimal() +
labs(x = "Prediction Score", y = "Scaled Density (0-1)") |
b2a8fbe65dc2ed9dfe0fa719ad9a7cc17b5b83ffafe9a030a41c24fe30727230 | R | 366 | 10 | options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
#
# A test to exercise the "infinity" check on 2 variables
#
test3 <- data.frame(futime=1:12, fustat=c(1,0,1,0,1,0,0,0,0,0,0,0),
x1=rep(0:1,6), x2=c(rep(0,6), rep(1,6)))... |
36e05c55f76ce29abc827ac17d4aa3d8b85be370e8eeb1a5fa7bc9fc5ecb8cad | R | 374 | 12 | # This file is part of the standard setup for testthat.
# It is recommended that you do not modify it.
#
# Where should you do additional test configuration?
# Learn more about the roles of various files in:
# * https://r-pkgs.org/tests.html
# * https://testthat.r-lib.org/reference/test_package.html#special-files
libr... |
339b494d77bc50e68c0259b7cf8ecc063a4d404b764d52d6dd987365a557d51e | R | 385 | 16 | #' MAsP Web Application
#'
#' This function initiates the R shiny app 'MAsP'.
#'
#'
#' @export
MAsPShiny <- function(){
options(shiny.maxRequestSize=100000*1024^2)
appDir <- system.file("shiny", "MAsP",package = "MAsP")
if (appDir == "") {
stop("Could not find UI directory. Try re-installing `MAsP`.", call. ... |
6965899ad91d8af3f9ce1e91029de96dc140d318621d20596d9ff52b96a4e1f7 | R | 388 | 17 | data=read.table('bac-2008-02-13.blast')
pdf("bacplot10.pdf")
hist(data$V3,breaks=10)
hist(data$V4,breaks=10)
hist(data$V5,breaks=10)
hist(data$V6,breaks=10)
hist(data$V7,breaks=10)
hist(data$V8,breaks=10)
hist(data$V9,breaks=10)
hist(data$V10,breaks=10)
hist(data$V11,breaks=10)
hist(data$V12,breaks=10)
plot(data$V12,da... |
7affabb8662cad4ca3e83d4dc171fd49ecb87b32b49311db9f5f55465de54047 | R | 395 | 13 | set.seed(1234)
library(knockoff)
library(corpcor)
library(RSpectra)
library(Rdsdp)
library(gtools)
for(i in 1:25) {
X<-read.table(paste("data/X_64_",as.character(i),".txt",sep=""))
X<-scale(X)
X1<-X[1:8000,] #exclude the test sets
X_k<-create.second_order(X1,shrink=T)
write.table(X_k,file=paste("dat... |
7b5d428caf9eb5e7587b277074a8a1286d193cdf7f48f61dfc8a3b9409ed50f3 | R | 403 | 14 | basic_test <- function() {
env1 <- new.env(hash=TRUE, parent=emptyenv())
env1$a <- 1:3
env1$b <- "foo"
env1[[".hidden"]] <- 3.1
z <- copyEnv(env1)
checkEquals(env1$a, z$a)
checkEquals(env1$b, z$b)
checkTrue(!exists(".hidden", z))
z <- copyEnv(env1, all.names=TRUE)
checkEquals(env... |
986859b29817a03fd66fc813b996cae94cfe560d2379c2aa3fb398595c31857e | R | 403 | 11 | #setwd("F:\\mgf\\")
#data<-scan("F://mgf//171010_Ip_Hela_ugi.raw.intensity0.charge0-comet-human.txt")
data<-read.table("F://mgf//171010_Ip_Hela_ugi.raw.intensity0.charge0-comet-human.txt",header=T,sep="\t",skip=1,row.names=NULL)
summary(data)
mzd=data$charge-data$exp_neutral_mass
hist(mzd)
aft<-fft(mzd)
aft<-fft... |
b7e959948179b3f420ec63511335c67d2d34de004407b8e25d534397df907105 | R | 450 | 38 | hamming.distance <- function(x,y){
z<-NULL
if(is.vector(x) && is.vector(y)){
z <- sum(as.logical(x) != as.logical(y))
}
else{
z <- matrix(0,nrow=nrow(x),ncol=nrow(x))
for(k in 1:(nrow(x)-1)){
for(l in (k+1):nrow(x)){
z[k,l] <- hamming.distance(x[k,], x[l,])
z[l,k] <- z[k,l]... |
f0f6e35ed5de7243aa463af177273c57bddd33f19b5a6706937bd492c0493a16 | R | 458 | 14 | ## tests of fix for PR#9831
library(nlme)
val <- c("10"=1.10,"14"=1.14)
vf <- varIdent(value=val, form=~1|age, fixed=c("12"=1.12))
vfi <- Initialize(vf,Orthodont)
str(vfi)
coef(vfi)
coef(vfi, unconstrained = FALSE, allCoef = TRUE)
vfiCopy <- vfi # copy of an initialized object
length(vfiCopy) # lengt... |
b9dd88e4dc0028c54d255e6dee4c66bed784fea1028d6e07cc78149fc51a2faa | R | 461 | 8 | df <- read.csv("Extend Fig.7c.csv", row.names = 1)
df$cluster <- rownames(df)
df$cluster <- factor(df$cluster, levels = df$cluster)
cluster_colors <- c("LRP1.0" = "#2EBAD1", "LRP2.1" = "#5BC2AE", "LRP2.2" = "#60BD6E")
ggplot(df, aes(x = cluster, y = Prop, fill = cluster)) +
geom_bar(stat = "identity") + scale_fi... |
1cfef547241ecd993ea1897673a73c3bd5dd4ccccdcd848ba21853952132ece7 | R | 474 | 5 | df <- read.csv("Extend Fig.4f.csv")
df$subclass_clean <- factor(df$subclass_clean, levels = c("Other", "053 Sst Gaba", "052 Pvalb Gaba"))
ggplot(df, aes(x = "PV Atlas", y = fraction, fill = subclass_clean)) + geom_bar(stat = "identity") +
scale_fill_manual(values = c("052 Pvalb Gaba" = "#F0E444", "053 Sst Gaba" = ... |
f6e422cc63b49e6093c7e06f1b8f2753677338a411f84777b8ccebfab9705142 | R | 487 | 29 |
## test some typical completion attempts
testLine <- function(line, cursor = nchar(line))
{
str(utils:::.win32consoleCompletion(line, cursor))
}
testLine("")
testLine("lib")
testLine("data(")
testLine("data(US")
testLine("data(US", 3)
testLine("?INS")
testLine("utils::data")
testLine("utils:::.show_help_on_to... |
b6de7b9f8f07868038bdaa6924d139fd19a71513780e72b7428037b2af17c176 | R | 499 | 13 | pdf("depth.pdf");
scf=read.table('Pwgs6dhmovlcod.posmap.frgscf.500.depth')
deg=read.table('Pwgs6dhmovlcod.posmap.frgdeg.500.depth')
hist(scf$V6,breaks=100,main="Scf")
hist(deg$V6,breaks=100000,main="Deg",xlim=range(0,40))
plot(density(abs(scf$V6)),col="blue",xlab="Depth",main="Scf Vs Deg",ylab="Density of Depth",xlim=r... |
70a31747e26bc4f07d42c9ada08415f954ada3e926496d2c0707beffb7a2453a | R | 519 | 18 | context("Pathways")
test_that("reactomePathways works", {
if (!requireNamespace("reactome.db")) {
skip("No reactome.db")
}
data(exampleRanks)
pathways <- reactomePathways(names(exampleRanks))
expect_true("12504" %in% pathways$`TCR signaling`)
expect_false(any(duplicated(names(pathways)... |
364a20d7186115e20d081e66127214e717137a96d2944d80bcb8043165303676 | R | 520 | 10 | a=read.table('rest_of_list.txt.aln.aln2hydro.txt')
a=read.table('groel.msa.aln.aln2hydro.txt')
b <- cor(a, method="pearson")
write.table( b , file = "groel.msa.aln.hydro.cor.txt", sep = "\t",col.names = FALSE, row.names = FALSE )
c<-symnum(clS <- cor(a, method = "spearman"))
write.table( c , file = "rest_of_list_hydr... |
685af3003f26ed204e053b905118054dbaa3a749ec3afc51199f55cacd9607cf | R | 523 | 12 | #for i in /cluster/work/users/ash022/veronica/*He*.d ; do echo $i ; timsconvert --chunk_size 5000000000 --verbose --input $i ; done
#sage sage.json -f human_crap.fasta --batch-size 40 /cluster/work/users/ash022/*.mzML
#cp lfq.tsv $HOME/PD/TIMSTOF/LARS/2024/240605_Veronica/HeLa/
inpF <-"TIMSTOF/LARS/2024/240605_Veronic... |
a2c3a2c99389c937147621e400675dda0c8000cfd09c32a974f7c4b658b69b30 | R | 526 | 35 | > newbler=read.table('NEWBLERAn')
> phrap=read.table('PHRAPAn')
> cap3=read.table('CAP3An')
> mira=read.table('MIRAAn')
summary(newbler$V2)
summary(newbler$V3)
summary(newbler$V6)
summary(newbler$V7)
summary(newbler$V8)
summary(mira$V2)
summary(mira$V3)
summary(mira$V6)
summary(mira$V7)
summary(mira$V8... |
d1cfd7338954908ef7c8ff9eec5f3b77d2e5140ecaa2977637292019f30f6733 | R | 543 | 23 | getWAIC <- function (stanfitsfile,waicfile,model2fit){
waicname = paste("M",model2fit,"WAIC",sep="")
if(!file.exists(waicfile)){
# calculate WAIC.
load(stanfitsfile)
f2@.MISC<-new.env(parent=globalenv())
modelWAIC <- list(waicname=waicname,waic=waic(f2),mstr=mstr)
rm(f2) # clear from ... |
4716d0da8ef316b03fff7a17eb6a0ef05d988cba0374cb539c92a7bbd396ba82 | R | 546 | 8 | df <- read.csv("Fig.4f.csv")
df <- df %>%
mutate(Supertype = factor(Supertype, levels = rev(sort(unique(Supertype)))), group = factor(group, levels = rev(sort(unique(group)))))
ggplot(df, aes(x = Supertype, y = relative_proportion_toP1, fill = group)) +
geom_bar(stat = "identity", position = position_dodge()) +... |
5a9b3c7dceac6f9080f9682c3f1d4314dad64c72c290a7e8e30651f5b1a701c9 | R | 549 | 10 | # Fig 1B ####
data <- read.csv('Fig.1b.csv')
ggplot(data, aes(x = reorder(Dataset, -SST_cells), y = SST_cells, fill = Dataset == "DS17")) +
geom_bar(stat = "identity") +
scale_fill_manual(values = c("FALSE" = "gray", "TRUE" = "skyblue")) +
geom_text(aes(label = SST_cells), vjust = -0.5, size = 3) +
geom_h... |
049c199c9bcece5e53df95a39931cb001fee40aa875c43a6e5f00cc95e43a563 | R | 560 | 7 | df <- read.csv("Extend Fig.2i.csv")
major_colors <- c("LRP" = "#1CB5BE", "Martinotti" = "#6770B2", "Non-Martinotti" = "#D9636E")
major_order <- c("LRP", "Martinotti", "Non-Martinotti")
df$Major <- factor(df$Major, levels = major_order)
df$group <- factor(df$group, levels = c("E12", "E16", "P1", "P5"))
ggplot(df, a... |
7dac78a7f4dada654afd278fcc2759f6c035065d10f7c9a60c5abfddc1a0ecef | R | 576 | 21 | # First terms of 1d Halton sequence https://en.wikipedia.org/wiki/Van_der_Corput_sequence
h <- c(1/2, 1/4, 3/4, 1/8, 5/8, 3/8, 7/8, 1/16)
n <- length(h)
test_that("the first terms of halton() are correct", {
expect_identical(sapply(seq_along(h), halton), h)
})
test_that("halton_sequence(n) equals h", {
expect_ide... |
5e7def9e993a977eb4668251f0321ede6ff4771d6f297eabd68c1c7bd300c9ae | R | 586 | 15 |
rm(list = ls()); gc()
setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/Documents_2020.07.04_1/real_data/rna2')
library(dplyr)
dvec = c(20, 30, 40, 50, 100, 200, 300, 400, 500)
out = matrix(NA, 100, length(dvec))
colnames(out) = paste0('d', dvec)
for (v in 1:length(dvec)) {
selected = read.csv(paste0... |
f4a2b8b97427973ec5c9a4aba62d10dab01fe1b0958db5a2d42049ab50cca8bc | R | 586 | 15 |
rm(list = ls()); gc()
setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/Documents_2020.07.04_1/real_data/rna1')
library(dplyr)
dvec = c(20, 30, 40, 50, 100, 200, 300, 400, 500)
out = matrix(NA, 100, length(dvec))
colnames(out) = paste0('d', dvec)
for (v in 1:length(dvec)) {
selected = read.csv(paste0... |
65c6b1b64cb0f73140b6081b13c78a92a67b87eadfce72865e5b41b730dbe22e | R | 607 | 17 | library(fgsea)
library(limma)
library(GEOquery)
es <- getGEO("GSE19429", getGPL = FALSE)[[1]]
exprs(es) <- normalizeBetweenArrays(log2(exprs(es)+1), method="quantile")
es <- es[!grepl("///", fData(es)$`Gene ID`), ]
es <- es[fData(es)$`Gene ID` != "", ]
es <- es[order(apply(exprs(es), 1, mean), decreasing=TRUE), ]
es <-... |
e1799bfe735f091a5bd1639a2fb88e2d50656e7df2eaae8d13c88be0b9bc34ef | R | 617 | 13 | jpeg("depth.jpg")
tech20map=read.table('ecoltech20pa.txt.depth.txt')
plot(density(tech20map$V2),col="black",xlab="Coverage",main="gsMapped Reads",ylab="Density",xlim=c(0,200))
allmap=read.table('allmapread.depth.txt')
lines(density(allmap$V2),col="red")
srmap=read.table('srmapread.depth.txt')
lines(density(srmap$V2),co... |
2f751723d1447677111058b6b6af37f19356f58f60904a35b6ce9f8f97e65b8c | R | 618 | 9 | data <- read.csv('Fig.2b_DS_anchors.csv')
ggplot(data, aes(x = reorder(dataset_id, -Anchors), y = Anchors)) +
stat_summary(fun = mean, geom = "bar", fill = "gray", width = 0.7) +
stat_summary(fun.data = mean_se, geom = "errorbar", width = 0.2, color = "black") +
geom_jitter(width = 0.2, alpha = 1, size = 2) +... |
e140c8adfd3377306a5ab74c159617b4204ed95a6ac087794d281c27743be172 | R | 622 | 13 | df <- read.csv("Fig.4e.csv")
supertype_order <- df %>%
filter(Major == "Martinotti") %>%
arrange(desc(proportion)) %>%
pull(Supertype_Other)
df$Supertype_Other <- factor(df$Supertype_Other, levels = supertype_order)
ggplot(df, aes(x = Supertype_Other, y = proportion, fill = Major)) +
geom_bar(stat = "... |
0ef74f89ae2a5621ee86fe0c80fcfd446c06e279533fdde324c0313a2d665978 | R | 626 | 6 | df <- read.csv("Fig.3d.csv")
subclass_colors <- c("045 OB-STR-CTX Inh IMN" = "#0E9E74","052 Pvalb Gaba" = "#F0E444", "053 Sst Gaba" = "#E6A126", "056 Sst Chodl Gaba" = "#5DB4E5", "Other" = "#E5E5E5")
df$Subclass <- factor(df$Subclass, levels = c("Other", "045 OB-STR-CTX Inh IMN","052 Pvalb Gaba", "056 Sst Chodl Gaba"... |
9bfdb7cbe23e7a070d62cda52d44e7b4f8b8b3937af512a2d94a5a48774d2a8b | R | 626 | 14 | library(Matrix)
library(imager)
library(raster)
library(rgdal)
source('~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/CSIDE/analysis/helper_functions/alzheimers_helper.R')
IM_DIR <- '~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/RCTD/data/Images/Pl... |
fb646421790c86e9a2fc7b185432b718c88bcebdf89105b85edb49dd5c663a40 | R | 634 | 8 | # Fig 1E ####
stacked_bar_data <- read_csv("Fig.1f_Stacked_Bar_Data.csv")
stacked_bar_data$subclass_name <- factor(stacked_bar_data$subclass_name, levels = c("052 Pvalb Gaba", "045 OB-STR-CTX Inh IMN", "056 Sst Chodl Gaba", "053 Sst Gaba"))
ggplot(stacked_bar_data, aes(x = "Atlas-v0", y = Fraction, fill = subclass_n... |
c5d01545b0d6db436c33a420afa62fc1ae4cf38d0306ef7ddeef7f4c80d2bfc1 | R | 640 | 23 | setwd("D:\\animesh\\projects\\research\\nonlinear")
'java weka.classifiers.meta.ClassificationViaRegression -t data4.arff -W weka.classifiers.trees.M5P -- -M 4.0]
data_tr$V1 <- data_xor$V1
data_tr$V2 <- data_xor$V2
data_tr$V3 <- data_xor$V3
data_xor <- read.table("xora.txt")
ftr = data.frame(data_xor$... |
a3d82af4b19b38d5b5b1c25b218425f8a5b510447500bb6022dd3b5a18421892 | R | 647 | 17 | #source http://al2na.github.io/genomation/
#install.packages( c("data.table","plyr","reshape2","ggplot2","gridBase","devtools"))
#install packages ("Rtools") from http://cran.r-project.org/bin/windows/Rtools/ and run find_rtools()
#had to restart R after Rtools installation
library(devtools)
find_rtools()
#source... |
01c1d244b8cefb869e9271136d4fd5d8ea9173f2b432b5c9c1274cf11128d979 | R | 653 | 14 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
datadir <- '../RCTD/data/SpatialRNA/Puck_210605/22'
myRCTD<- readRDS(file.path(datadir,'myRCTD_j20.rds'))
load(file = file.path(datadir,'exvar_thresh.RData')) # exvar, explanatory_variable
aggregate_cell_types(myRCTD, names(exvar))
cell_types <- c("... |
149922ada102d029422ba6f906e512daf9a891810cb43eb16d40928ec81541a8 | R | 653 | 14 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
datadir <- '../RCTD/data/SpatialRNA/Puck_210605/23'
myRCTD<- readRDS(file.path(datadir,'myRCTD_j20.rds'))
load(file = file.path(datadir,'exvar_thresh.RData')) # exvar, explanatory_variable
aggregate_cell_types(myRCTD, names(exvar))
cell_types <- c("... |
9888db7f41b9737903f71317b1880b0676b868f1b2a7aed2852433fded8d2b77 | R | 653 | 14 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
datadir <- '../RCTD/data/SpatialRNA/Puck_210605/24'
myRCTD<- readRDS(file.path(datadir,'myRCTD_j20.rds'))
load(file = file.path(datadir,'exvar_thresh.RData')) # exvar, explanatory_variable
aggregate_cell_types(myRCTD, names(exvar))
cell_types <- c("... |
b023e43feaa5a635928e563cd1c545b9769f93531ad7fb52c458d236ae4dd10c | R | 653 | 14 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
datadir <- '../RCTD/data/SpatialRNA/Puck_210605/21'
myRCTD<- readRDS(file.path(datadir,'myRCTD_j20.rds'))
load(file = file.path(datadir,'exvar_thresh.RData')) # exvar, explanatory_variable
aggregate_cell_types(myRCTD, names(exvar))
cell_types <- c("... |
bc69b50998f85fd8c742c6fa590dd2a39b845e27c01bf8db0318073c5003a775 | R | 658 | 27 | countpattern <- function(x, matching=FALSE)
{
nvar <- dim(x)[2]
n <- dim(x)[1]
## build matrix of all possible binary vectors
b <- matrix(0, 2^nvar, nvar)
for (i in 1:nvar)
b[, nvar+1-i] <- rep(rep(c(0,1),c(2^(i-1),2^(i-1))),2^(nvar-i))
namespat <- b[,1]
for (i i... |
7ec6f987b584c854c56fa0361edbb202e7f87cb4d827fe49b0c72f1e50bc8c95 | R | 659 | 15 | # "H:\mt_behavalluse\simpef.R"
simpef <- function(model, int, s1, s2) { # e.g. s1=action, s2=valence
flab = names(fixef(model))
cv = vector("numeric", length(fixef(model)))
cv[flab == int] = 1
cv[flab == s1] = 1
p1 = doBy::esticon(model, cv)
cat("simple ", s1, " effect for ", s2, " == 1: est = ", p1$Estima... |
e7046014a92d4b3611a787a3db0d1f70027948d2c1b3b35412ee78f87b8de31e | R | 671 | 26 | library(MASS)
PropCI <- function(x, n, conf = 0.95)
{
DF <- data.frame(y = x / n, weights = n)
mod <- glm(y ~ 1, weights = weights, family = binomial(), data = DF)
plogis(confint(mod, level = conf))
}
PropCI(14, 35)
## had scope error prior to 7.2-31
## single variable cases:
n.tot <- c(60,17,8,2,187,85,... |
bc02b795dedc54524f8c6ee7d23d309a8c75a8f0bedf4fc5e5bd971d4e334461 | R | 682 | 31 | g<-read.csv('g.csv')
s<-read.csv('s.csv')
gn<-read.csv('gn.csv')
sn<-read.csv('sn.csv')
t.test(s$PAIN.6HRS..VAS.,g$PAIN.6.HRS..VAS.)
t.test(s$PAIN.12.HRS..VAS.,g$PAIN.12.HRS..VAS.)
t.test(s$ANALGESIC.REQ..FIRST.24.Hrs.,g$ANALGESIC.REQ..FIRST.24.Hrs.)
t.test(s$Age,g$Age)
t.test(s$DURATION,g$DURATION)
jpeg(file="vas.jp... |
4f8e7c97cd002c3c67e6e5adea0a08b8dcc6d14cad6dc57695a6f4da5a644d20 | R | 687 | 10 | df <- readRDS("Extend Fig.11l.RDS")
df$orig.ident <- ifelse(grepl("HET", df$orig.ident), "P14 ctrl", "P14 Bax cKO")
df$supertype_name <- factor(df$supertype_name, levels = sort(unique(df$supertype_name)))
ggplot(df, aes(x = supertype_name, y = percentage, fill = orig.ident)) +
geom_bar(stat = "identity", position... |
37eb7eff4d0636738f437248f318be3091d9945fb53b5bb2dd82ac2e4099210f | R | 688 | 23 | #
# Data view : functions to visualize data transformation
#
#' Apply the data transformation function to a given region
#' @export
region.transform <- function(bw, chrom, start, end, strand, step = 50, bwmap = NULL) {
# collect reads
reads = step.bpQuery.bigWig(bw, chrom, start, end, step, abs.value = TRUE)
... |
e277cda8d82eb2b7987f81f3a68b84157ebc36f5be62ccb18311e157bbbce329 | R | 700 | 22 | #### -*- R -*-
require(stats)
Fr <- c(68,42,42,30, 37,52,24,43,
66,50,33,23, 47,55,23,47,
63,53,29,27, 57,49,19,29)
Temp <- gl(2, 2, 24, labels = c("Low", "High"))
Soft <- gl(3, 8, 24, labels = c("Hard","Medium","Soft"))
M.user <- gl(2, 4, 24, labels = c("N", "Y"))
Brand <- gl(2, 1, 24, labels = c("X", "M"))
detg <... |
6cbf55d413ab2c6b7d7ea94ed540602ece759e1a52c140ae4761a62af3d6905a | R | 702 | 17 |
rm(list = ls()); gc()
setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/Documents_2020.07.04_1/real_data/zeller/screen/')
library(dplyr)
out = matrix(NA, 100, 5)
colnames(out) = c('d20', 'd30', 'd40', 'd50', 'd100')
selected162 = read.csv(paste0('./v162/Zeller_raw_real_selected_v162.csv'), head = T)[,-1... |
84dd55cd4f0df95f0dacab2e651a681e47a1f3a3e1d0732aa510495f1e5fed04 | R | 703 | 8 | df <- read.csv("Extend Fig.2d.csv")
ggplot(df, aes(x = "DS17", y = mean_anchors)) +
stat_summary(geom = "boxplot",
fun.data = function(x) {
data.frame(ymin = min(x),lower = quantile(x, 0.25),middle = median(x),upper = quantile(x, 0.75),ymax = max(x))},
fill = ... |
096ed2efb1450274c3a32f37732ab34c270b838b5d8a89e7dffc59485997ade3 | R | 706 | 24 | #' @aliases shapviz-package
"_PACKAGE"
# Suppress R CMD check note
#' @importFrom xgboost xgb.train
globalVariables(c(
"from", "i", "id", "label", "to", "x", "shap", "SHAP",
"feature", "value", "color", "Var1", "Var2", "Var3", "S", "ind", "values"
))
.onLoad <- function(libname, pkgname) {
op <- options()
op... |
7b021e59c6990ea5c7f21f04611f5ec8491f9663cda759de5e5c86ba0b0ead20 | R | 712 | 20 | jpeg("sol-read-map-asm.jpg");
t=read.table('sr2cs.pairdist.txt',sep='\t')
summary(t)
t=t$V7[t$V7<10000];
plot(density(abs(t)),col="red",xlab="Distance in bp",main="gsMapped unique solexa reads",ylab="Density of Pairs",xlim=c(0,500))
t=read.table('sr2ns.pairdist.txt',sep='\t')
summary(t)
t=t$V7[t$V7<10000];
lines(densit... |
0ace294e2a0ebbcbfd000e2d8677a053255eab15ebc59843a40086b7afb03b49 | R | 729 | 44 | ---
#title: Distribution of Ions"
output: html_document
runtime: shiny
---
```{r data,echo=F}
lPep=100
nPep=100000
nTot=lPep*nPep
data<-runif(nTot)
#hist(sample(data,100000,replace=T),breaks=nPep)
sPep=rnorm(lPep)
n=1
i=1
while(n<=lPep){
print(n)
print(i)
sPep[c(seq(n,n+i-1))]=sample(data,i,replace=T)
n=n+i
... |
39e13c02155cf8aa14ab8dfacff6a6014cc3ab8b403b7b681632b3f7764ffe6a | R | 730 | 12 | df <- read.csv("Fig.2j.csv")
cell_colors <- c("LRP" = "#37C2D6", "MC" = "#4B2D83", "nMC" = "#E16037", "NA." = "#B4B4B4")
df_long <- df %>%
pivot_longer(cols = c(LRP, MC, nMC, NA.), names_to = "cell_type", values_to = "fraction") %>%
mutate(cell_type = factor(cell_type, levels = c("NA.", "nMC", "MC", "LRP")))
g... |
9028648f29831f3038e0b33ea6ba08a04d209bfc8974b40a51e0da50c1262975 | R | 730 | 19 |
jpeg("asmb-comp.jpg");
t=read.table('nre',sep='\t')
plot(abs(t$V4),col="blue",type="l",xlab="Match #",main="ATACked assemblies",ylab="Cumulated length",xlim=range(0,10000))
summary(t)
t=read.table('cre',sep='\t')
summary(t)
lines((abs(t$V4)),col="red")
t=read.table('crn',sep='\t')
lines((abs(t$V4)),col="orange")
t=rea... |
1cded46bf7d6832172e2cfe53382a3b8dda5c4b35300deb4f3a419c3ff629962 | R | 748 | 37 | res1 <- read.table("WT347356.txt")
resap <- read.table("ap.txt")
resapt <- read.table("apt.txt")
resdm <- read.table("dm.txt")
resfl <- read.table("fl.txt")
resm347 <- read.table("m347.txt")
resm356 <- read.table("m356.txt")
reswt <- read.table("wtt.txt")
df$ytrans <- df$conc/df$vel
resflc3 <- re... |
54c9395ccc994cbf18778124b59f50af333b29887870de4234a57feabdede2f8 | R | 753 | 20 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
datadir <- '../RCTD/data/SpatialRNA/Puck_210605/22'
reference <- readRDS('../RCTD/data/Reference/DropVizHC/scRefSubsampled1000.RDS')
nUMI <- reference@meta.data$nUMI
names(nUMI) <- rownames(reference@meta.data)
cell_type_names <- reference@meta.data... |
822fd45b944a138d80ef899c0fdc618a7a36d4893c3bfbde106b17f4c8bca9e3 | R | 753 | 20 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
datadir <- '../RCTD/data/SpatialRNA/Puck_210605/23'
reference <- readRDS('../RCTD/data/Reference/DropVizHC/scRefSubsampled1000.RDS')
nUMI <- reference@meta.data$nUMI
names(nUMI) <- rownames(reference@meta.data)
cell_type_names <- reference@meta.data... |
a3427765d658eedd16ef445e09f17a700b24002be89fd4447e6c8c5e50b41a5a | R | 753 | 20 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
datadir <- '../RCTD/data/SpatialRNA/Puck_210605/21'
reference <- readRDS('../RCTD/data/Reference/DropVizHC/scRefSubsampled1000.RDS')
nUMI <- reference@meta.data$nUMI
names(nUMI) <- rownames(reference@meta.data)
cell_type_names <- reference@meta.data... |
d768101e77b7c702653d6b4717ed426bb4022b34fcf2d0e6a828b71c2b538069 | R | 753 | 20 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
datadir <- '../RCTD/data/SpatialRNA/Puck_210605/24'
reference <- readRDS('../RCTD/data/Reference/DropVizHC/scRefSubsampled1000.RDS')
nUMI <- reference@meta.data$nUMI
names(nUMI) <- rownames(reference@meta.data)
cell_type_names <- reference@meta.data... |
b0f802850decac099c088f6d2979dece3bbeb468e619016418809586d3dc09fd | R | 755 | 22 | pdf("solexa-map-plot.pdf");
t=read.table('sr2cspp',sep='\t')
plot(density(t2$V7),col="blue",xlab="Distance in bp",main="Solexa read mapping",ylab="Density of Pairs")
t2=read.table('sr2nspp',sep='\t')
lines(density(t$V3),col="red")
legend(x="topright", legend=c("Celera","Newbler"),text.col=c("blue","red"))
dens=(density... |
4887ddba9514f57ff12106895e35e1875597ff5c980173f188117e3a42d331e5 | R | 761 | 21 | library(GEOquery)
library(limma)
# for collapseBy
source("https://raw.githubusercontent.com/assaron/r-utils/master/R/exprs.R")
gse14308 <- getGEO("GSE14308")[[1]]
pData(gse14308)$condition <- sub("-.*$", "", gse14308$title)
gse14308 <- gse14308[!rownames(gse14308) %in% c("NONE", "NA", "none", "NULL")]
exprs(gse14308... |
03a1b18a9d1e517995e9cc9cdea76128bd561686590a95c4af90f5076e4a7a33 | R | 763 | 28 | pdf("align_result.pdf")
fugu_align=read.table('fugu_align',header=T,sep='\t')
summary(fugu_align)
hist(fugu_align$AlignLength,breaks=100)
plot(fugu_align$SubjAccno)
medaka_align=read.table('medaka_align',header=T,sep='\t')
summary(medaka_align)
hist(medaka_align$AlignLength,breaks=100)
plot(medaka_align$SubjAccno)
s... |
d41935d34085629004b661c890b88ad0e0ca6c06a4de5a7c8757b2ccb6b3f903 | R | 769 | 20 | # Set environment language and working directory
Sys.setenv(LANG = "en")
setwd("D:/project/ndreamer_plot/")
# Read the dataframe
df <- read.csv("./AD_ADNC/down_significant_unparametric.csv", stringsAsFactors = FALSE)
# Filter to the top 100 rows (assuming rows represent the rank of significance)
df_top100 <- df[1:100... |
90b975fcfa14dc8461db6bc5cd7fd8d84b8cd6e84f42f4be71fb4e24db36b75c | R | 777 | 10 | NEWBLER = read.table('NEWBLER10x')
MIRA = read.table('MIRAAn')
PHRAP = read.table('PHRAPAn')
CAP3 = read.table('CAP3An')
par(mfrow = c(2,2))
hist(PHRAP$V3, xlab = "Contig Length",ylab = "Number of Contig (s)", col="cyan", border="black", main = "PHRAP", breaks=10,xlim=c(0,5000), ylim=c(0,5000))
hist(CAP3$V3, xlab... |
cbb364cc2b506d017b98883005038016e0f1bc988ea3d0b33a05076c74057fd7 | R | 783 | 23 | l250=read.table('l250.txt')
l250e=read.table('l250e.txt')
l500=read.table('l500.txt')
l500e=read.table('l500e.txt')
t.test(l250$V2,l250$V3,paired=TRUE)
t.test(l250$V4,l250$V5,paired=TRUE)
t.test(l250$V6,l250$V7,paired=TRUE)
t.test(l250$V8,l250$V9,paired=TRUE)
t.test(l250e$V2,l250e$V3,paired=TRUE)
t.test(l250e$V4,l250e$... |
c12d28986f4078e63efd620c6390828c224840a5ed8e70bc6480c2f83158834a | R | 806 | 9 | df <- read.csv("Extend Fig.5d,e.csv", row.names = 1)
df$dataset <- rownames(df)
df$color <- ifelse(grepl("S_", df$dataset), "#38B34A", "#1A6333")
base_dot <- function(x_var, x_lab) {
ggplot(df, aes(x = .data[[x_var]], y = mean_anchors, shape = dataset)) + geom_point(size = 3, stroke = 1.5, color = df$color) +
... |
5d4622917ad6c740f7007dbb36447e4a2e5c14d5ec1b3566ae7076ed92556508 | R | 810 | 25 | #' Location Formatting Function
#'
#' This function converts the location information to an coding-tractable format
#'
#' @param location the location information of each micro-slot
#' @return a new format that the codes are easy to deal with
#' @export
loc_convert <- function(location){
loc_vector <- base::unname(un... |
1098055c096701d41b2ba7c558cd863b1a5d26922a7804c6006ee1594cf6d805 | R | 829 | 36 | rm(list=ls())
library(tidyverse)
b1 = read_csv("batch1_pro.csv")
b2 = read_csv("batch2_pro.csv")
words = read_tsv("all_stories.tok")
b = bind_rows(b1, b2)
offset = 230
d = b %>%
filter(!(item == 3 & zone == offset + 1)) %>%
mutate(zone=if_else(item == 3 & zone > offset, zone - 3, zone - 2)) %>%
inner_jo... |
751f31417d6d524311056c33bf3d32b6d1449db0fb3fda1ced77a6bb93d86580 | R | 834 | 25 | pdf("solexa-read-plot.pdf");
t=read.table('sr2csp',sep='\t')
plot(density(abs(t$V7)),col="blue",xlab="Distance in bp",main="Solexa read mapping",ylab="Density of Pairs",xlim=range(0,1000))
t2=read.table('sr2nsp',sep='\t')
lines(density(abs(t2$V7)),col="red")
legend(x="topright", legend=c("Celera","Newbler"),text.col=c(... |
0e761beee2026a955e379b38bb79ddfdfca4cac8ce65d3feb9b25a06b41c733d | R | 836 | 27 | ## tests of offsets, and prediction from them.
data(anorexia, package = "MASS")
## via formula
fit1 <- lm(Postwt ~ Prewt + Treat + offset(Prewt), data = anorexia)
summary(fit1)
pred <- fitted(fit1)
stopifnot(all.equal(predict(fit1, anorexia), pred))
## via argument
fit2 <- lm(Postwt ~ Prewt + Treat, data = anorexia,... |
520e41992f1e0c70ee5ff39b8d892eea7a44d01268f04781ab417972aedfb1ff | R | 837 | 11 | df <- read.csv("Fig.2h.csv")
score_colors <- c("0" = "black", "1" = "#88191B", "2" = "#F79520", "3" = "#096B38")
criteria_info <- data.frame(DS = c("DS6", "DS7.1", "DS7.2", "DS16", "DS12.1", "DS12.2", "DS25.2","DS18", "DS19", "DS20", "DS21", "DS22", "DS23", "DS24", "DS25.1", "DS26","DS17.1", "DS17.2", "DS17.3", "DS17... |
eaa5a93d12b02f072007df60f96e5cd2800cad61e221c5dce4177351ce1a9a60 | R | 852 | 29 | library(cluster)
### clusplot() & pam() RESULT checking ...
## plotting votes.diss(dissimilarity) in a bivariate plot and
## partitioning into 2 clusters
data(votes.repub)
votes.diss <- daisy(votes.repub)
for(k in 2:4) {
votes.clus <- pam(votes.diss, k, diss = TRUE)$clustering
print(clusplot(votes.diss, votes... |
17658259d2ac01e56f62e17d0f0eed663b628fa0705eb090c3167d6f79faf5e2 | R | 858 | 12 | df <- read.csv("Extend Fig.13e.csv")
df$Group <- recode(df$Group, "Dach1+" = "LRP2.1", "Dach1-" = "LRP1")
df_summary <- df %>%
group_by(Age, Group) %>%
summarise(mean = mean(Proportion), se = sd(Proportion)/sqrt(dplyr::n()), .groups = "drop")
ggplot(df_summary, aes(x = Age, y = mean, fill = Group)) +
geom_b... |
fbefcfedc5dab5ac5117210a708eee62084f59368148959cdece456a9432eb64 | R | 860 | 18 | #https://stackoverflow.com/a/37487673
#data####
df<-read.table("proteinGroups.txt",header=T,sep="\t",row.names=NULL)
#group####
library("dplyr")
df$End<-as.Date(df$End,format="%m/%d/%Y")
df$Start<-as.Date(df$Start,format="%m/%d/%Y")
cumsum((df$Start)-lag((df$End), default=as.Date(1)) != 1)
cumsum(as.numeric(df$Start)-l... |
5c73376d231e7581a9c53cfcf4363fe262a49bb5aceb6fd76d6d895e2e96f8c4 | R | 872 | 28 | options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
#
# Simple tests of concordance. These numbers were derived in multiple
# codes.
#
aeq <- function(x,y, ...) all.equal(as.vector(x), as.vector(y), ...)
tdata <- aml[aml$x... |
e5e1f20bc8568207571cc0266d0e3a9d9b074d4ae828fb6b84f54f54ed50a6e6 | R | 884 | 39 | library(data.table)
library(mclust)
methods <- c("scVI", "saturn", "scanorama", "bbknn", "saturn")
output_dirs <- list(
scVI = "PATH_TO_OUTPUT_DIR/all/",
scANVI = "PATH_TO_OUTPUT_DIR/all/",
saturn = "PATH_TO_OUTPUT_DIR/all/",
scanorama = "PATH_TO_OUTPUT_DIR/all/",
bbknn = "PATH_TO_OUTPUT_DIR/... |
ea518a1ef63515ace5990dfede38ca431a1e95b81af1fbf93b2bb5deccab38a6 | R | 922 | 36 | resdataap <- read.table("ap.txt")
resdataapip <- read.table("apip.txt")
resdatafl <- read.table("fl.txt")
resdata <- read.table("data.txt")
resconc <- read.table("concval.txt")
t.test(resdataap$Ap,resdatafl$Fl)
t.test(resdataapip$Ap_Ip,resdatafl$Fl)
Welch Two Sample t-test
data: resdataap$A... |
ae7503c3dbd38de5a0fc3707c1323686744fd33efb56f4b6d94455acf3fb0379 | R | 951 | 25 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
library(devtools)
library(mgcv)
load_all()
pwd = getwd()
datadir <- paste0(pwd,'/data/tumor','/')
resultsdir <- paste0(pwd,'/results/ResultsTumorNonparam','/')
# Load RCTD which contains cropped puck, also load full puck
myRCTD = readRDS(paste0(dat... |
ff4a1feb8c1e8b586cd0e64db8b6b1811a2a7a96c70b741816872bd29b797275 | R | 958 | 19 | library(DropSeq.util)
refdir = "Data/Reference/DropVizCerAnnotated"
dge.path <- file.path(refdir,"F_GRCm38.81.P60Cerebellum_ALT.raw.dge.txt.gz")
class_path <- file.path(refdir,"F_GRCm38.81.P60Cerebellum_ALT.subcluster.assign.RDS")
dge <- loadSparseDge(dge.path)
cluster <- readRDS(class_path)
common_barcodes = intersect... |
416c101f72cc7ff7c60bd78b1eba3a019be9236a3f5d4108f02be43101b40c97 | R | 961 | 29 | library(GEOquery)
library(limma)
library(org.Mm.eg.db)
library(data.table)
# for collapseBy
source("https://raw.githubusercontent.com/assaron/r-utils/master/R/exprs.R")
gse14308 <- getGEO("GSE14308")[[1]]
pData(gse14308)$condition <- sub("-.*$", "", gse14308$title)
es <- collapseBy(gse14308, fData(gse14308)$ENTREZ_G... |
aa5e2ed8204cbf4d4b75823cb072f5ca5673d81e338bc97182844268b83d9746 | R | 966 | 30 | repliterate <- function(init_mean, likelihood_fn, epsilon = 1e-8, MAX.ITER = 20) {
mean_est_prev <- -100
posterior_mean <- init_mean
for(i in 1:MAX.ITER) {
ld <- likelihood_fn(posterior_mean)
samp_dist <- likelihood_to_dist(posterior_mean, ld$d1, ld$d2)
posterior_mean <- get_posterior_mean(samp_dist$m... |
e546b6084e70d10ed6b7c78e04a323f0ab07076b442ced38e768df4905b39bcb | R | 969 | 14 | df <- read.csv("Extend Fig.3b.csv", row.names = 1)
df$dataset <- rownames(df)
score_colors <- c("0" = "black", "1" = "#88191B", "2" = "#F79520", "3" = "#096B38")
criteria_map <- case_when(grepl("N1|N3", df$dataset) ~ "2", grepl("Af2", df$dataset) ~ "1", TRUE ~ "3")
df$criteria_score <- criteria_map
df_long <- df %... |
80ffc1f505e3b94941283e8ac1a3f06937103fb32c3007150375d502319ca1a5 | R | 981 | 25 | library(reactome.db)
library(org.Mm.eg.db)
library(data.table)
mouse.universe <- keys(org.Mm.eg.db, "ENTREZID")
# Selecting reactome gene sets
pathways <- na.omit(select(reactome.db, keys=mouse.universe, c("PATHID"),
keytype = 'ENTREZID'))
pathways <- split(pathways$ENTREZID, pathways$PATHI... |
ce8d1998af9c39d08d1e9d338453d038dcbc90fd64c7a685777b3385f8873367 | R | 993 | 30 | #!/usr/bin/env Rscript
rnk.file <- "./inst/extdata/naive.vs.th1.rnk"
gmt.file <- "./inst/extdata/mouse.reactome.gmt"
tag <- "naive_vs_th1"
res.dir <- "./inst/broad_gsea"
system.time(
system2("java",
c(
"-cp",
"~/Dropbox/gsea2-2.1.0.jar",
"-Xmx512m",
... |
4622c85baf73dc154f40c9c7f661f441ef2f9cf08887133e4e23ead69505a19a | R | 995 | 31 | #' Creates a piechart of number of isoforms needed to cross deltaPI threshold
#' @description A quick way to summarize DIE for a single comparison
#' @seealso \code{\link{DiffSplicingAnalysis}}
#' @param compDir output directory containing DiffSplicingAnalysis for a
#' specific comparison
#' @return Directory labelled ... |
b2b8e1cb29a960650607fd1eaf644b715edefc53f02b7e3df26f40abd163e152 | R | 1,002 | 39 | library(naniar)
library(ggplot2)
data <- read.csv("Z:/Download/clinical_data-20260608T085237Z-3-001/clinical_data/heart_disease-switzerland.csv")
View(data)
data[data==-9] <- NA
gg_miss_case(data)
gg_miss_var(data)
miss_var_summary(data)
ggplot(data = data,aes(x = trestbps,y = num)) + geom_miss_point()
miss_... |
c7867bd165e25485b9b685ad672b78eb38da09a5507f692d5e0b5a4607537280 | R | 1,009 | 18 | df <- read.csv("Extend Fig.4a,b.csv", row.names = 1)
df$dataset <- rownames(df)
df$dataset <- factor(df$dataset, levels = df$dataset)
df$color <- ifelse(df$dataset == "DS25.3", "#00AEEF", "#5A5B5B")
ggplot(df, aes(x = dataset, y = X.Cells, fill = color)) +
geom_bar(stat = "identity") + geom_hline(yintercept = 10... |
35c9cd42b8d67920c91db5badba835dd61198ffc87883b4525712cd4e646680c | R | 1,026 | 13 | df <- read.csv("Fig.2f,g.csv")
score_colors <- c("0" = "black", "1" = "#88191B", "2" = "#F79520", "3" = "#096B38")
plot_box <- function(data, y_var, y_label, y_lim) {
ggplot(data, aes(x = criteria_group, y = .data[[y_var]])) +
stat_summary(geom = "boxplot",
fun.data = function(x) {
... |
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