sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e3460cd880395a373a995d06406d102261c4123470b2d331b3eec55b3f7e9ce0 | R | 1,049 | 20 | library(RCTD)
library(readr)
slideseqdir = "Data/Slideseq/NewCerPuck_190926_08"
puck = readRDS(file.path(slideseqdir, "puckCropped.RDS"))
outdir = "NMFreg/DataCer"
nmf_weights <- as.data.frame(read_csv(file.path(outdir,"bead_deconv_df_norm.csv")))
rownames(nmf_weights) <- colnames(puck@counts)
nmf_weights <- nmf_weight... |
f4c5b6fdfaa0a9b7893e9045331e5a8abe0293528239fddaa147dc8e8491c605 | R | 1,050 | 21 | ####library####
#https://github.com/vdemichev/diann-rpackage
#install.packages("devtools")
#install.packages("rlang")
#install.packages("usethis")
#devtools::install_github("https://github.com/vdemichev/diann-rpackage")
library(diann)
####data####
fileName<-"C:/Users/animeshs/Downloads/SILACDIA2/report.tsv"
df <- diann... |
5df2404961305f76af3925bc8a5b7d66aa8339aa02b9f0215af5e5ac66c99061 | R | 1,055 | 26 | #' Filter unbarcoded reads
#'
#' @description This function filters out all the reads that don't have a barcode
#' and outputs them into a separate folder.
#' Option to concatenate output from several files if they belong to
#' the same sample.
#' @param outputFolder automatically sourced from GetBarcodes script
#' @pa... |
7765e693977c92c009c7e508d1b4d134818289bf0bcfc2fd286c0b21ec26de30 | R | 1,061 | 35 | require(datasets)
require(grDevices); require(graphics)
x <- 10*(1:nrow(volcano)); x.at <- seq(100, 800, by=100)
y <- 10*(1:ncol(volcano)); y.at <- seq(100, 600, by=100)
# Using Terrain Colors
image(x, y, volcano, col=terrain.colors(100),axes=FALSE)
contour(x, y, volcano, levels=seq(90, 200, by=5), add=TRUE, co... |
c7092e1fccca84dd64af1c86769680d664ad95d9c0f6d472ae898e04d3a414b7 | R | 1,069 | 37 | # load the gene expression matrix
chen <- get(load('GSE87544_1443737Cells.Expresssion.Matrix.log_tpm+1_.renamed.RData'))
head(chen)
dim(chen)
# load the labels of the cells
labels <- read.csv('GSE87544_1443737Cells.SVM.cluster.identity.renamed.csv')
head(labels)
str(labels)
# check if the orders of the cells in two d... |
f973b98b14cca7e820aa3a1b252d74daf6e03865fb99f8ae5b589582b4dd2f83 | R | 1,074 | 19 | df <- read.csv("Extend Fig.6d,e.csv")
summary_df <- df %>%
group_by(Family) %>%
summarise(n = dplyr::n(), mean_diag = mean(diag_delta), se_diag = sd(diag_delta) / sqrt(dplyr::n()), mean_ct = mean(ct_delta), se_ct = sd(ct_delta) / sqrt(dplyr::n()))
p1 <- ggplot(summary_df, aes(x = Family, y = mean_diag)) +
ge... |
fe67f4f8940fbda28d1ca4879ce6556783492ad4cd3f9c94f1e13d112d4ada8a | R | 1,076 | 40 |
rm(list = ls()); gc()
setwd('/home/yinfeiko/DeepLINK/newdata2')
dat1 = read.csv('yu_CRC_common.csv', head = T, stringsAsFactors = F)
nrep = 100
pe = rep(NA, nrep)
n = nrow(dat1); n.train = round(n*.5)
p = ncol(dat1) - 1
indmat.dist = matrix(NA, nrep, n.train)
top250 = matrix(NA, 250, nrep)
library("energy")
dist.m =... |
51cfdecb2722842466dd912ca769c20709c729fba79d035bb07b952f62b89fd2 | R | 1,100 | 37 | #pdf("gatc.pdf")
plot(1)
gi.id49175990.gatc=read.table('data/gi.id=49175990.gatc')
#summary(t(gi.id49175990.gatc))
#hist(t(gi.id49175990.gatc),breaks=100)
gi.id332341332.gatc=read.table('data/gi.id=332341332.gatc')
#summary(t(gi.id332341332.gatc))
#hist(t(gi.id332341332.gatc),breaks=100)
gi.id218693476.gatc=read.t... |
eb42f4f29dcd22cfcc985337faa48333ccc27242e2b66d90561984dd39d20cfe | R | 1,112 | 36 | library(clusterProfiler)
library(org.Hs.eg.db)
# Define the list of genes
gene_list <- c("ATP6", "COX2", "COX3", "CYTB", "GFAP", "GJA1", "LINC02742", "ND1", "ND2", "ND3", "ND4", "ND6", "PLCG2")
# Convert gene symbols to Entrez IDs
entrez_ids <- bitr(gene_list, fromType = "SYMBOL", toType = "ENTREZID", OrgDb = org.Hs.... |
b5725a8a4d8ab5548b8d0cbb59b10c6424991dcdf44962718679cdc55f2dfe85 | R | 1,113 | 34 | extractEstimates <- function(stanfitsfile,estimatefile,model2fit) {
# retrieve parameter estimates.
# ===================================================================.
# load parameter estimates.
load(stanfitsfile)
f2@.MISC<-new.env(parent=globalenv())
# retrieve estimates per parameter.
... |
757f3d2d14441f83f9ef9a322603764948b5ddfc69f8f6596535984d5018c096 | R | 1,134 | 38 | ---
title: "Untitled"
output: html_document
---
```{r setup, include=FALSE}
#https://cran.r-project.org/web/packages/tidyquant/vignettes/TQ04-charting-with-tidyquant.html
install.packages("tidyquant")
library("tidyquant")
``````
```{r cars}
c("YAR.OL", "GOOG", "TEL.OL") %>%
YAR<-tq_get(x='YAR.OL',get =... |
c74deae37b082a75fc279c004efc2cd02103670aedde8a6b55d552f06f51d40c | R | 1,138 | 31 | #' Creates a heatmap of DIE abundance across comparisons
#' @description A quick way to summarize DIE across multiple
#' pairwise comparisons
#' @seealso \code{\link{DiffSplicingAnalysis}}
#' @param treeDir output directory containing DiffSplicingAnalysis
#' @param comparisonList List of comparisons to be included in p... |
232e83c5e72e41e4e0d77c3e34acb8a76022aa7e15dd4e29a4e1a7553892cc43 | R | 1,170 | 42 | rm(list=ls())
setwd("/home/animeshs/Downloads/")
fileP<-"/home/animeshs/Downloads/"
fileN<-"diabetes.csv"
yy<-read.csv(paste0(fileP, fileN),header=T)
summary(yy[,9])
plot(yy,col=yy$Outcome,main="diabetes")
yyt=as.matrix(yy)
library(ggfortify)
log.yyt=log(yyt[,1:8]+1)
yyt.pca=prcomp(log.yyt,center=TRUE,scale.=TRUE)
au... |
63365a197c97394c8a7990268c82c81ceb18834268bdfb113c48326ed09eb001 | R | 1,195 | 14 | df <- read.csv("Extend Fig.5c.csv", row.names = 1)
df$dataset <- rownames(df)
score_colors <- c("2" = "#096B38", "3" = "#919495")
df$criteria_score <- ifelse(grepl("S$", df$dataset), "2", "3")
df_long <- df %>% pivot_longer(cols = starts_with("k"), names_to = "k", values_to = "fraction") %>% mutate(k = as.numeric(g... |
b03c3985b7b235243d3db763bced4d909d867f9397770dfc34659eaa16703bf4 | R | 1,246 | 27 | library(png)
source <- 'data/tumor/IDP 2020-02-27-002_mousemulti - cropping (1, 9451, 7028, 6223, 6386).png'
my_im <- readPNG(source, native = FALSE, info = FALSE)
library(geojsonR)
js_file_name <- "data/tumor/IDP 2020-02-27-002_mousemulti - cropping (1, 9451, 7028, 6223, 6386).geojson"
file_js = FROM_GeoJson(url_file_... |
76b76bb072f7c77f29ddbe3c9b1d33b4428b324e062784c255069cb358499690 | R | 1,265 | 46 | library("RSQLite")
DATA <- new.env(parent=emptyenv(), hash=TRUE)
.setUp <- function() {
DATA$p1 <- tempfile()
DATA$p2 <- tempfile()
db1 <- dbConnect(SQLite(), DATA$p1)
data(USArrests)
dbWriteTable(db1, "ua", USArrests, row.names=FALSE)
dbDisconnect(db1)
}
.tearDown <- function() {
file.re... |
765081eab8361f2fc96ecbc6d807338ba7448e2fb4f6e129d45d01efcba6ed99 | R | 1,266 | 47 | rm(list = ls()); gc()
dat1 = read.csv('human_rna.csv', head = T, stringsAsFactors = F)
nrep = 100
pe = rep(NA, nrep)
n = nrow(dat1); n.train = round(n*.5)
p = ncol(dat1) - 1
indmat.dist = matrix(NA, nrep, n.train)
top500 = matrix(NA, 500, nrep)
library("energy")
dist.m = function(mat) {
nr = nrow(mat)
smat <- app... |
5e27194807445951486166870645adb3b525a96862c73b505ccca84062f08626 | R | 1,267 | 47 | rm(list = ls()); gc()
dat1 = read.csv('murine_rna.csv', head = T, stringsAsFactors = F)
nrep = 100
pe = rep(NA, nrep)
n = nrow(dat1); n.train = round(n*.5)
p = ncol(dat1) - 1
indmat.dist = matrix(NA, nrep, n.train)
top500 = matrix(NA, 500, nrep)
library("energy")
dist.m = function(mat) {
nr = nrow(mat)
smat <- ap... |
3cfc97d4d0fe902183e8d7f259dfdec6a7d04a42bb00fb502254fc9b15de1228 | R | 1,270 | 15 | #check sema clinical trial paper and data, also the different doses and simulation code https://www.youtube.com/watch?v=zm9SAHc63_o
#Research Study Investigating How Well Semaglutide Works in People Suffering From Overweight or Obesity (STEP 1) https://clinicaltrials.gov/study/NCT03548935?tab=results#outcome-measures
... |
846c6af228831dc074e9da9562cfa512a402091c9eb5b9970099170d6965470e | R | 1,300 | 29 | #' Location Preview Plot
#'
#' Make a location preview plot for user to directly see the map of sample slots
#'
#' @param location the formatted location information
#' @return a location preview plot
#' @export
MyPreview <- function(location){
new_loc <- loc_convert(location)
new_loc$row <- factor(new_loc$row, l... |
aff51421af0bd4b3be2f071692a19e5c8151e17d5257e760444e018a0b5ce615 | R | 1,318 | 37 | options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
#
# Fit the models found in Wei et. al.
#
wfit <- coxph(Surv(stop, event) ~ (rx + size + number)* strata(enum) +
cluster(id), bladder, method='breslow')
wfit
# Check the ... |
e27804ddbbaee86354c032905d79d4f3ce95ff81ed21f939d22385b59552e25a | R | 1,331 | 27 | normalize_de_estimates <- function(myRCTD, normalize_expr, remove_junk = T, param_position = 2) {
if(remove_junk) {
all_genes <- rownames(myRCTD@de_results$gene_fits$con_mat)
junk_genes <- all_genes[c(grep("MT-",all_genes),grep("RPS",all_genes),
grep("RPL",all_genes))]
junk_g... |
759f2e113434613fc6c5ab9e51e97d6bcad508ebf0c8baf03845e37da7bd2816 | R | 1,336 | 36 | plotFits <- function(estimatefile,plotfitsfile,model2fit) {
# load parameter estimates.
load(estimatefile)
# print relevant stats/diagnostics.
# summary statistics.
print("means:")
print(apply(xbay, 2, mean)) # mean
print("SD:")
print(apply(xbay, 2, sd))# standard deviation
# PLOTTING.
... |
6f4b7cb5ed762f969db72ed9dcefd74ad7b700b1e5102b5ba85f6494f852719f | R | 1,339 | 38 | #' @useDynLib fgsea
#' @import Rcpp
NULL
#' Example vector of gene-level statistics obtained for Th1 polarization.
#'
#' The data were obtained by doing differential expression between
#' Naive and Th1-activated states for GEO dataset GSE14308. The
#' exact script is available as system.file("gen_gene_ranks.R", packag... |
44cec4675022dd18b1dc2baf9c8bfd659d52b00c4d535ff44e210c6c4876ccee | R | 1,349 | 45 | preparePathways <- function(pathways, universe, minSize, maxSize){
universeArg <- deparse(match.call()$universe)
# Error if pathways is not a list
if (!is.list(pathways)) {
stop("pathways should be a list with each element containing gene identifiers")
}
# Error if stats is not named
if... |
f9a9bbe1f3a1be6ac12f825c90be23577a52c209a5b70611dcaf4f773967eaf4 | R | 1,349 | 20 | #actin-actin dist ang histograms
work_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230516_new_analysis_for_paper/analysis_output/"
tomos = c("control_box046_09","control_box046_10","control_box056_01","control_box056_04","control_box056_06","control_box056_07","control_box056_12","control_box056_23","induced_bo... |
112dc791afd9af0c4e5c2883543a4d573aa51734a3361cd4cc1ef0018b22a704 | R | 1,401 | 30 | #prepares for NMFreg
library(readr)
config_data <- config::get(file = "conf/dataset.yml", use_parent = FALSE)
config <- config::get(file = paste0("conf/",config_data$config_mode,".yml"), use_parent = FALSE)
slideseqdir <- file.path("Data/Slideseq",config_data$slideseqfolder)
resultsdir = file.path(slideseqdir,"results"... |
703013959ab3fcbaceb66101a0ba11cf17b248a7fe770546095f1d40ab8cbac8 | R | 1,441 | 69 | # =========================
# Weight analysis
# =========================
library(rstudioapi)
dataFile <- selectDirectory(
caption = "Select Directory",
label = "Select",
path = getActiveProject()
)
setwd(dataFile)
getwd()
list.files()
library(readxl)
library(dplyr)
library(ordinal)
library(ggpl... |
1b7e3119e8326fd2e9e5fb14fa76131883f259296d38692dddcf621470b46893 | R | 1,445 | 49 | ## glm.nb with weights
library(MASS)
yeast <- data.frame(cbind(numbers = 0:5, fr = c(213, 128, 37, 18, 3, 1)))
attach(yeast)
n <- rep(numbers, fr)
## fitting using glm.nb with weights - wrong results in 7.2-18
yeast2.fit <- glm.nb(numbers~1, link = log, weights=fr)
summary(yeast2.fit)
## fitting extending the vector... |
450d277f5f1b50ee3e0e5c2a1074decf39b7126198db6dd3646d49d2caf3bccd | R | 1,454 | 52 | context("fora analysis")
test_that("fora works if pathways consists of one element", {
pthw <- c("1", "2", "10")
genes <- paste(1:5)
universe <- paste(1:10)
res <- fora(list("test_pathway" = pthw), genes, universe)
expect_true(nrow(res) == 1)
expect_equal(res$pval, phyper(1, 3, 7, 5, lower.ta... |
733e1aa8d80e22a41eb4983a5f2302ed3cab747e8ec39824a6b493978f6dcc20 | R | 1,454 | 22 | df <- read.csv("Fig.2c_combined_knn.csv")
score_colors <- c("0" = "black", "1" = "#88191B", "2" = "#F79520", "3" = "#096B38")
criteria_info <- data.frame(dataset = c("DS6", "DS7.1", "DS7.2", "DS16", "DS12.1", "DS12.2", "DS25.2","DS18", "DS19", "DS20", "DS21", "DS22", "DS23", "DS24", "DS25.1", "DS26"),
... |
4a04c3417d725230c6977162954bd717e460492058966ee3230df58c48941d24 | R | 1,467 | 32 | #' Conducts gene set enrichment analysis of significantly spliced genes
#' @description An easy way to look for enriched genes in the differential
#' splicing analysis. Uses the ClusterProfiler package to compare geneset
#' against the reference genome and outputs an .Robj file along with .pdf
#' network graphs
#' @see... |
25ef2e57b780ae112aa805547b18446a88fdc0cc62b8cae83d2dcbe34a5f3be8 | R | 1,475 | 33 | #puck 24
library(spacexr)
library(Matrix)
counts <- readr::read_csv('data/Reference/Testes/Testis_Ref_DGE.csv')
counts = tibble::column_to_rownames(counts, var = "X1")
metaData <- readr::read_csv('data/Reference/Testes/Major_Cluster_Ident.csv')
metaData = tibble::column_to_rownames(metaData, var = "barcode")
myCounts <... |
383b0e538fafcb4b5625350604daf9bbaf4915fa2e528728a7e2234229c58029 | R | 1,482 | 38 | ## human gene set profiles
data=read.csv("L:/Elite/gaute/test/SHBER.csv")
## mapping Uniprot to Gene Name
library(org.Hs.eg.db)
egmap <- revmap(org.Hs.egUNIPROT)[as.character(data$Entry)]
genelist <- as.vector(toTable(egmap)$gene_id)
##basic GO profile
library(goProfiles)
basicProfile(genelist, onto = "MF",... |
52669b58d5b46dd2a8db35b8e1c32b158d78cbee689b9a04509eb04247dbd2c3 | R | 1,504 | 43 |
rm(list = ls()); gc()
setwd('/Users/yikong/Dropbox (CSU Fullerton)/aResearch/DeepLINK/revision/common_feature/code1')
library(dplyr)
mean.se = function(x, ns=3) {
m = round(mean(x), ns); s = round(sd(x)/sqrt(length(x)), ns)
paste0(format(m, nsmall=ns), ' (', format(s, nsmall=ns), ')')
}
dvec = c(20, 30, 40, 50, ... |
e527d5c80bc88f137b4fecc3b536a4abad6194997e02a026bfe452b220cb2fe2 | R | 1,521 | 45 | ```{r data}
#data <- read.table("L://Elite/kamila/BT/datamwithoutsingletons.txt", header = T)
data <- read.table("L://Elite/kamila/BT/datam.txt", header = T)
```
```{r plot}
plot(data)
#install.packages('ggplot2')
#library('ggplot2')
g = ggplot(data,aes(hgb,time))
g+geom_point(aes(color=cd34))+geom_smooth(me... |
a10c418c664e0f2ab88158f1d2c0d308a93fa627fe298ea33a5ed5b8c77c903d | R | 1,523 | 30 | #used for decomposition experiment on known cell types
library(RCTD)
library(Matrix)
args = commandArgs(trailingOnly=TRUE)
if (length(args)==0) {
stop("callDoublets: At least one argument must be supplied: fold_index", call.=FALSE)
}
fold_index = as.integer(args[1])
print(paste0("callDoublets: loading fold index: ",f... |
4fb84f86e64a173ca2d46994e66764392197bdd2dada21b63224b26469f44cb0 | R | 1,524 | 48 | library(clusterProfiler)
library(org.Hs.eg.db)
library(ggplot2)
# Set environment language and working directory
Sys.setenv(LANG = "en")
setwd("D:/project/ndreamer_plot/")
# Define the function for GO enrichment analysis and plotting
perform_go_enrichment <- function(df) {
print(df)
# Iterate over columns from th... |
0f76170938fc6ab7097909dceb366c43478495ed3cbb00c1b78f3dec8aaf3cde | R | 1,525 | 52 | options(repos = c(CRAN = "https://cloud.r-project.org"))
suppressMessages(if (!requireNamespace("pacman", quietly = TRUE)) install.packages("pacman"))
pacman::p_load("optparse")
pacman::p_load("seqinr")
pacman::p_load("bit64")
pacman::p_load("ape")
pacman::p_load("tidyr")
option_list <- list(
make_option(c("-a", "-... |
79a55733d90b26dffe1f8fa1b5d3d4aa5a895ca69f6b1e1df781feceddabd3ca | R | 1,536 | 58 | ```{r}
#https://github.com/mlverse/torch
#install.packages("torch")
#install.packages("torchtext")
library(torch)
#library(torchvision)
x <- torch_tensor(1, requires_grad = TRUE)
w <- torch_tensor(2, requires_grad = TRUE)
b <- torch_tensor(3, requires_grad = TRUE)
y <- w * x + b
y$backward()
x$grad
#> torch_tensor
... |
2e62f0aaf528f54d24b29fd4c4a2594ebee2051f4c2d8876f544257dfea13a7e | R | 1,539 | 52 | library(fgsea)
library(data.table)
library(BiocParallel)
data(exampleRanks)
data(examplePathways)
ranks <- sort(exampleRanks, decreasing = TRUE)
ranks <- abs(floor(ranks * 0.5 + 0.5))
write.table(c(length(ranks), ranks), file="roundRanks.txt", row.names = FALSE, col.names = FALSE)
fgseaRes <- fgsea(examplePathw... |
24de05663a6a95f39e6505c954023348d68ae90394276c623d717123b30696fb | R | 1,540 | 65 | library(reshape)
library(ggplot2)
library(reshape)
library(car)
library(nlme)
require(multcomp)
library(rstudioapi)
dataFile <- selectDirectory(
caption = "Select Directory",
label = "Select",
path = getActiveProject()
)
setwd(dataFile)
a <- list.files(pattern='exp', ignore.case=T) #this should b... |
2d0a53e4f1b0d0582254482d8cb3455179d883c1552237c24abd36b794c64687 | R | 1,547 | 56 | # Functions that help to derive collection info for patchwork
# Same logic for axes and axis_titles
.collect_xy <- function(z) {
if (z$x && z$y) {
return("collect")
}
if (z$x) {
return("collect_x")
}
if (z$y) {
return("collect_y")
}
return("keep")
}
# Takes list of ggplots and determines col... |
3aba05679c9728b6d1505ee59bba8855ed08a5d861b66f8e2331ec8a6b59f201 | R | 1,551 | 38 | #' Align fastq.gz files with Minimap2
#' @aliases Minimap2
#' @description This function is a wrapper for Minimap2
#' with stringent parameters optimized for ONT/PacBio sequenced
#' data. It performs sam to bam conversion to reduce
#' space, and .bam output is necessary for downstream analysis
#' @seealso \code{\link{M... |
1c51f7352d562dffaf721b95f63419a3b57ef037ac89f55623d150866b137ee7 | R | 1,583 | 43 | df <- readRDS("Extend Fig.8b,c,d,e.RDS")
plots <- lapply(names(df), function(name) {
mat <- as.matrix(df[[name]])
self_vals <- diag(mat)
other_means <- sapply(1:nrow(mat), function(i) mean(mat[i, -i]))
df_long <- data.frame(
cluster = rownames(mat),
Self = self_vals,
Other = other_means
... |
682ea8c7cda8bb16af6aac8df1d1957d2c446c1569c7e7d181da197e14c5c169 | R | 1,588 | 36 | library(tidyverse)
library(ggpubr)
#read csv
work_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230527_new_analysis/" #directory where all the files should be read from
setwd(work_dir)
ctr_dist_ang <- read.csv(file = "control_memb_actin_dist_ang_info.csv")
ind_dist_ang <- read.csv(file = "induced_memb_actin_dis... |
61534d86217609401b215731a0377df3d9bcf47c336520c0348f67516ff162fd | R | 1,594 | 43 | # Set environment language
Sys.setenv(LANG = "en")
# Set working directory
setwd("D:/project/ndreamer_plot/")
library(ggplot2)
# Read the data
path <- c("./experimental/ablation.csv")
df <- read.csv(path)
# Rename columns
colnames(df) <- gsub("^X1\\.cLISI$", "cLISI", colnames(df))
colnames(df) <- gsub("^kBET\\.Accep... |
c8ca9ab815bd99e1756d6cc3d40b145e3ed7000ae8d2ac8dc62244d3e4c283c9 | R | 1,603 | 33 | #devtools::install_github('jdrudolph/PerseusR')
# All plugins can be split into 3 parts
# 1. Reading the command line arguments provided by Perseus and parsing the data.
# 2. Perform the desired functionality on the data.
# 3. Write the results to the expected locations in the Perseus formats.
# 1. Parse command line a... |
7b8cf87ade628b636e4f99f817cb981bf944e7372db1c4534f2b706453279fbb | R | 1,607 | 38 | #' Align fastq.gz files with STARlong
#'
#' @aliases STAR
#' @description This function is a wrapper for STARlong
#' with stringent parameters optimized for PacBio sequenced
#' data. It performs sam to bam conversion to reduce
#' space, and .bam output is necessary for downstream analysis
#' @seealso \code{\link{MapAnd... |
8f675cf9eb29cf35e09863ea4f045facbe3c2cad1b4dc946986075acf7fc9550 | R | 1,608 | 32 | #' Miami-Dade County House Prices
#'
#' @description
#' The dataset contains information on 13,932 single-family homes sold in
#' Miami-Dade County in 2016.
#' Besides publicly available information, the dataset creator Steven C. Bourassa has
#' added distance variables, aviation noise as well as latitude and longitude... |
b66bb56ecf728034af07927572811da6b9eee6480a6afa5c32ebc89d138a1d7e | R | 1,608 | 32 | library(ggpubr)
library(dplyr)
work_dir = "/Volumes/mahamid/Dorothy/Pipeline_after_CNN/20230527_new_analysis/analysis_output/"
setwd(work_dir)
ctr_data_read <- na.omit(read.csv(file = "memb_norm_control_memb_actin_dist_20_nm_bin_noupper.csv"))
ind_data_read <- na.omit(read.csv(file = "memb_norm_induced_memb_actin_dist... |
210b34ba5f5af5839d9716e90440275efd6944de6fd82d70725ecca7f9ac73da | R | 1,629 | 73 | # =========================
# Eye opening analysis
# =========================
library(rstudioapi)
dataFile <- selectDirectory(
caption = "Select Directory",
label = "Select",
path = getActiveProject()
)
setwd(dataFile)
getwd()
list.files()
library(readxl)
library(dplyr)
library(ordinal)
libra... |
b0011d3a7eef6ea6969f295d7f201bc439ac2a6ae1d8eae4584367e366c7c82f | R | 1,633 | 43 | options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
#
# Trivial test of stratified residuals
# Make a second strata = replicate of the first, and I should get the
# exact same answers
test1 <- data.frame(time= c(9, 3,1,1,... |
c217eeb53d23376d702986b598e5c8f5b164de4b943a4c0a1df21f3e9db5e99a | R | 1,634 | 60 | ## D:\\animesh\\booksNpapers\\lang\\statistics\\time_series\\timeseries_plots
## read cocoa price file, note that we use
## comma as decimal seperator and
## semicolon as field seperator.
a<-read.csv2("cocoa.csv")
## chop the year and avg columns
a<-a[,2:13]
## write table to vector b row by row.
## as.nu... |
4ebc432e163ad9c9a32b3e82415694132d8d5e7086042d712d904d0bdf775c29 | R | 1,650 | 60 | ica <- function(X, lrate, epochs=100, ncomp=dim(X)[2],
fun="negative")
{
if (!is.matrix(X))
{
if (is.data.frame(X))
X <- as.matrix(X)
else
stop("ica: X must be a matrix or a data frame")
}
if (!is.numeric(X))
stop("ica: X ... |
d76434cd96c5607a26ba8ecbb8701397353ef38e6e740823bed59dc766a04fb7 | R | 1,663 | 50 | #setup####
#https://www.r-bloggers.com/2021/04/random-forest-in-r/
#install.packages("randomForest")
#install.packages('caret')
#install.packages('ggplot2')
#data####
inpF="F:/OneDrive - NTNU/Aida/ML/dataTmmS42T.csv"
data=read.csv(inpF)
dim(data)
dGroup="Class"
print(dGroup)
data[,dGroup] <- as.factor(data[,dGroup])
pr... |
00a59cc24ed484b96a35e093f281e536f39fb0d0d56e0e9060d768001cf8b947 | R | 1,676 | 52 | rm(list = ls())
suppressPackageStartupMessages(require(rstan))
rootdir <- "F:/PIT/model/EEGmodelHC/HC"
datadir <- file.path(rootdir, "results")
resultsdir <- file.path(datadir, "stan")
models_to_check <- c(9, 10, 12, 13, 14, 15, 16, 19, 21) # Specify the models to inspect, e.g., c(9,10,12,14)
summ_list <- lis... |
b021e1610be0e40679076705f72b87aac2b69612cb2e04cc5b7109b6f827bc42 | R | 1,714 | 52 | # Run from project root, or set working directory accordingly.
# Exports harmony-cluster markers from the E2 Seurat object for SCSA input.
library(Seurat)
input_file <- "Data/E16_barcodes_E2.rds"
output_file <- "Automatic_annotation/seurat_E16_barcodes_E2_harmony_clusters.csv"
cat("Reading RDS file:", input_... |
a28d90f20c0aafa7fe65553c953d85d265b9d3de851491c0151abb36c70ba673 | R | 1,719 | 43 | #' Quantifies full-length isoforms in sample(s)
#'
#' @description Depending on whether you want to do downstream
#' differential expression analysis of full-length isoforms,
#' TSS, or PolyA sites, this function annotates and counts up
#' transcripts expressed in the given set of samples
#'
#' @param AllInfo Merged fi... |
ad3a367901e752f17270cc9d30cd85d68d8ae25724bf92f1877c8b1c9dee8e0b | R | 1,743 | 49 | #Rscript selectIDfromXlsx.r "L:\promec\Elite\LARS\2018\mai\Vibeke V\PDv2p5\Clots\suppltab2_v1.xlsx" "L:\promec\Elite\LARS\2018\mai\Vibeke V\PDv2p5\Clots\180507_VIBEKEV_C1-(12)Proteins.xlsx"
#install.packages(c("gglot2","svgite"))
args = commandArgs(trailingOnly=TRUE)
print(paste("supplied argument(s):", length(args)))
... |
920510b27750832232ac79a71b475bed9bb76fba09638b84c08a3dc67038872c | R | 1,750 | 43 | options(na.action=na.exclude) # preserve missings
options(contrasts=c('contr.treatment', 'contr.poly')) #ensure constrast type
library(survival)
# Create a "counting process" version of the simplest test data set
#
test1 <- data.frame(time= c(9, 3,1,1,6,6,8),
status=c(1,NA,1,0,1,1,0),
... |
3942982a8f38009a5b55ceed946e9f2998abb5280916c741bfc2eca3b762ee54 | R | 1,759 | 56 | #' @import dplyr
#' @importFrom magrittr %>%
#' @import ggplot2
#' @import cowplot
`%>%` <- magrittr::`%>%`
args <- commandArgs(trailingOnly = TRUE)
tree_dir <- args[1]
files = list.files(tree_dir,recursive = TRUE)[grep("results.csv",list.files(tree_dir,recursive = TRUE))]
cT <- unique(as.vector(read.table(args[2])$... |
9b26fb7c12936555e28130121509f926f2548af4223f114be1ad976250455223 | R | 1,761 | 31 | #Rscript evidenceQC.r "L:/promec/TIMSTOF/LARS/2024/240404_Mathilde/combined/txt/evidence.txt" "240404_Mathilde_TN_PDAC_P1_Slot2-4_1_7011 240404_Mathilde_TN_PDAC_P2_Slot2-5_1_7013 240404_Mathilde_TN_PDAC_P4_Slot2-7_1_7017"
#setup####
args = commandArgs(trailingOnly=TRUE)
print(paste("supplied argument(s):", length(args)... |
95bd752808e9f631e6cc21f3711a8d3de258fa5a582631e99cb5e8879a8cc493 | R | 1,770 | 78 | library(dplyr)
library(Seurat)
library(ggplot2)
library(patchwork)
library(SeuratData)
library(SeuratWrappers)
library(Azimuth)
unintegrated <- readRDS("PATH_TO_OUTPUT/unintegrated.rds")
runtime_RPCAv5 <- system.time({
integrated_RPCAv5 <- IntegrateLayers(
object = unintegrated,
method = RPCAIntegration,
... |
6b0e69013d92cbbc37b3a206601d7c08e8b5ca6feb8fde225b66da381bd13c6e | R | 1,781 | 15 | df <- read.csv("Extend Fig.6g.csv", row.names = 1)
cluster_order <- c("LRP1.0", "LRP2.1", "LRP2.2", "HIn1", "HIn2",
"MC1.1", "MC1.2", "MC1.3", "MC1.4", "MC1.5", "MC1.6", "MC1.7", "MC1.8", "MC1.9", "MC1.10", "MC1.11", "MC2.1", "MC2.2", "MC3.1", "MC3.2", "MC3.3", "MC3.4", "MC3.5",
... |
167e1d72416fb9e737a82b864943a9aa1c2f2b46eafd2d40f648e1f7751e6064 | R | 1,810 | 35 | read.Slideseq.mtx <- function(datadir, puck_no) {
mtx_file <- file.path(datadir, paste0(puck_no, '.matched.digital_expression_matrix.mtx'))
barcodes_file <- file.path(datadir, paste0(puck_no, '.matched.digital_expression_barcodes.tsv'))
coords_file <- file.path(datadir, paste0(puck_no, '_barcode_matching.txt' )... |
9b2ea70349f2a2fc13c2c6fff90420cdc08ff9acccd9dac5df4726d3b18a3d4d | R | 1,810 | 76 | library(dplyr)
library(Seurat)
library(ggplot2)
library(patchwork)
library(SeuratData)
library(SeuratWrappers)
library(Azimuth)
unintegrated <- readRDS("PATH_TO_OUTPUT/unintegrated.rds")
runtime_FastMNN <- system.time({
integrated_FastMNN <- IntegrateLayers(
object = unintegrated,
method = FastMNNIntegration... |
6f7984fb1188242cafc4e33c73acb7b2bc53c8d2045d26ab12a06d1ef2e1304c | R | 1,841 | 75 | library(dplyr)
library(Seurat)
library(ggplot2)
library(patchwork)
library(SeuratData)
library(SeuratWrappers)
library(Azimuth)
unintegrated <- readRDS("PATH_TO_OUTPUT/unintegrated.rds")
runtime_CCAv5 <- system.time({
integrated_CCAv5 <- IntegrateLayers(
object = unintegrated,
method = CCAIntegration,
ori... |
128e0169990623d5e142d5612e80434d6981c3e185d938cc8713777e66970d82 | R | 1,868 | 73 | library(dplyr)
library(Seurat)
library(ggplot2)
library(patchwork)
library(SeuratData)
library(SeuratWrappers)
library(Azimuth)
unintegrated <- readRDS("PATH_TO_OUTPUT/unintegrated.rds")
runtime_JointPCA <- system.time({
integrated_JointPCA <- IntegrateLayers(
object = unintegrated,
method = JointPCAIntegrat... |
41b43a5103eeecb84a8e5190e86a4b55067e16c52549f8cb853442fab9a10b1a | R | 1,881 | 71 | library(plyr)
library(ggplot2)
DATA <- read.csv("Fig.7e.csv")
# calculate density
mm1 <- DATA
mm1$density <- mm1$Vglut2spots / mm1$CellArea..um2.
# outlier check
boxplot(mm1$density ~ interaction(mm1$Age, mm1$Celltype),
main = "Outlier check per groep",
ylab = "density",
xlab = "... |
7a1ab5ed70a92cefca8f57cc6b075888ee508735bb415eac70655279a16e4949 | R | 1,891 | 75 | library(dplyr)
library(Seurat)
library(ggplot2)
library(patchwork)
library(SeuratData)
library(SeuratWrappers)
library(Azimuth)
unintegrated <- readRDS("PATH_TO_OUTPUT/unintegrated.rds")
runtime_HarmonyIntegration <- system.time({
integrated_Harmony <- IntegrateLayers(
object = unintegrated,
method = HarmonyI... |
e8574bfac46196237aaf8cb32bc1131e069de61ac18de58fc3aadfdc0939302e | R | 1,892 | 36 | #..\R-4.5.0\bin\Rscript.exe .\proteinGroupsNormGene.r "L:\promec\Animesh\Aida\txt207\proteinGroupsHCL.txt"
print("USAGE:Rscript proteinGroups.r <complete path to txt file>")
inpF<-file.path("L:/promec/Animesh/Aida/txt207/proteinGroupsHCL.txt")
selection=c(1:10)
id1="T..Gene.names"
id2="T..Protein.IDs"
selGene1="BRD4"
s... |
e79ae70fb9c9789b78d608efd6ab1e605053b0b5a8b7aea9ac8a76196b525303 | R | 1,898 | 45 | #!/usr/bin/env Rscript
library(optparse)
#Get the options
option_list = list(
make_option(c("--input"), type="character", help="This is a five column text file with the following fields\n
<pc bedGraph path>\t<pc type>\t<replicate name>\t<sample name>
First column is the pat... |
f53d9cb5376ceddca256203002a5f101075f61b76014b31f9fd2b66539512b00 | R | 1,912 | 20 | df <- read.csv("Extend Fig.3c,d,e.csv", row.names = 1)
df$dataset <- rownames(df)
df$criteria_score <- case_when(df$Stages %in% c("N1", "N3") ~ "2", df$Stages == "AF2" ~ "1", TRUE ~ "3")
score_colors <- c("0" = "black", "1" = "#88191B", "2" = "#F79520", "3" = "#096B38")
c <- ggplot(df, aes(x = Stages, y = mean_an... |
4cbeadb56c73cd2d30ef0c4b5a1f27ed4c6486ec3d958b4ad193fba0530bdb52 | R | 1,916 | 44 | #' Count exon inclusion and exclusion according to grouping factor
#'
#' @description This function groups reads by either the celltype
#' or cell-barcode to count inclusion and exclusion of exons. Inputs
#' to this function are based on previous preprocessing steps
#'
#' For ONT data we recomment \link[IsoQuant]{https... |
b4dd4409b558735ba6e5f1c433f16e868e83d615102accc9986bb70ccf247e5f | R | 1,936 | 45 | #' Spectral Clustering on filtered data
#'
#'
#'
#' @param df_filtered the filtered protein dataset
#' @return a PCA plot colored by label from spectral clustering, and a dataset containing cluster label.
#' @export
SClustering <- function(df_filtered){
withProgress(message = 'Spectral Clustering', value = 0, {
... |
849e75b9156084bcd5a9a58e853a9611f6c0c3c0e8345915de6768fa2e93e772 | R | 1,962 | 54 | context("Plots")
test_that("plotGseaTable works", {
data(examplePathways)
data(exampleRanks)
fgseaRes <- fgsea(examplePathways, exampleRanks,
minSize=15, maxSize=100, eps=0.0)
tf <- tempfile("plot", fileext = ".png")
topPathways <- fgseaRes[head(order(pval), n=15)][order(NES),... |
f5b15fb3ae59aa90d2d06f6a7c4328cc805b957416e3925c2d38a7027014b190 | R | 2,005 | 70 | library(plyr)
library(dplyr)
library(ggplot2)
#read in RT data from 2 separate files
b2 <- read.csv('batch2_pro.csv')
b1 <- read.csv('batch1_pro.csv')
d <- rbind(b1, b2)
##subtract 2 from zone to properly align region...should confirm with Hal that this is correct,
## but the RTs seem to line up correctly in plots
d$... |
a213a87d1b775216da24466c442398a68cba95fb254ae4d6677cabc714fbd0ae | R | 2,021 | 67 | interpolate <- function(x, a, adims=lapply(dimnames(a), as.numeric),
method="linear"){
if(is.vector(x)) x<- matrix(x, ncol=length(x))
if(!is.array(a))
stop("a is not an array")
ad <- length(dim(a))
method <- pmatch(method, c("linear", "constant"))
... |
807e523f000581f83bff6d37a37c98f369e0f1b07833479e5b9c9b0dec8e6ba1 | R | 2,023 | 39 | #' Make a "corrected" version of the All-Info file
#' after using IsoQuant to identify incorrect splice sites
#' @aliases bed2AllInfo
#' @description This function takes in a corrected bed file
#' that is generated after running IsoQuant, as well as the AllInfo
#' file that is the output of scisorseqr. It produces a ne... |
5f107f3ddf5e0cc3bdd51c41eb01990194fe2821c37cb3ccb493b371f63cd212 | R | 2,026 | 28 | # Fig 2 D, E #
df <- read.csv("Fig.2d,e_integration_metrics.csv")
score_colors <- c("0" = "black", "1" = "#88191B", "2" = "#F79520", "3" = "#096B38")
criteria_info <- data.frame(dataset_id = c("DS6", "DS7.1", "DS7.2", "DS16", "DS12.1", "DS12.2", "DS25.2", "DS18", "DS19", "DS20", "DS21", "DS22", "DS23", "DS24", "... |
f045ede50275ffd8a5505757afc2bec818f9f67c76ec779b8e77eb6b43c1fcaf | R | 2,043 | 49 | # WAIC...
# From Aki & Gelman 2014
# Little function to calculate posterior variances from simulation
colVars <- function (a){
diff <- a - matrix (colMeans(a), nrow(a), ncol(a), byrow=TRUE)
vars <- colMeans (diff^2)*nrow(a)/(nrow(a)-1)
return (vars)
}
log_sum_exp <- function(x){
x_max <- max(x)
x_max + log(su... |
3dcbb5fc91bb5c2378d69d6e8275bd4bce328170a3e04b0fdd419fc886aafba3 | R | 2,061 | 63 | rm(list = ls())
suppressPackageStartupMessages(library(rstan))
# ==========================================
# Standalone Script 2: Export raw Stan f2 draws to CSV
# ==========================================
# ---------- 1) Configuration ----------
# Define the export job list here
# Each row represents one export jo... |
67486f8c3ca3b3700f867a037c91592fb0a9d9a8efd794b0921b1086d3ddcfd3 | R | 2,070 | 56 | #4 different doses of carbamazepine alone (2, 4, 6, 8 mg/kg) and carbamazepine with tramadol (20mg/kg), flouxetine (20mg/kg) and melatonin (50mg/kg) given to mice
anticonvulsant <- data.frame(x = c (2,4,6,8), n = rep(8,4), y = c(0,3,6,8))
anticonvulsant$Ymat <- cbind(anticonvulsant$y, anticonvulsant$n - anticonvulsant... |
bf01a5504c1a6ba047531b5559b9f49afe1545162deef68516bdd8ebe2eccbf8 | R | 2,088 | 37 | fo='L:\\promec\\TIMSTOF\\LARS\\2026\\260518_Sonali\\DIANNv2P2.63.260612_140833.64.highacc\\Outcome_data_with_PRM_median_ratios.xlsx';
testtype='WSRT';
data=readxl::read_xlsx(fo,sheet=1);
print(dim(data))
dataSel1=t(data[data$"quality"=="HQ",])
print(dim(dataSel1))
dataSel2=t(data[data$"quality"=="LQ",])
print(dim(dataS... |
f65815f9c1fb6189839c43f7e73dc7607cc1ac469d939d46993a07e00ac24e96 | R | 2,090 | 70 | checkGesecaArgs <- function(E, pathways){
# Error if pathways is not a list
if (!is.list(pathways)) {
stop("pathways should be a list with each element containing names of the stats argument")
}
# Error if E matrix doesn't have rownames
if (is.null(rownames(E))) {
stop("E rows shoul... |
e5d377daa9fcb06428b5e2ebd61333a07d5530cac3030e3b8f6424873dec95f1 | R | 2,096 | 33 | #this script converts a DGE and coords matrix to a puck object saved as RDS
library(RCTD)
library(Matrix)
print("prepareCropper: begin")
slideseqdir <- "Data/Slideseq/Visium"
counts <- readMM(file.path(slideseqdir, 'matrix.mtx'))
barcodes <- read.table(file = file.path(slideseqdir, 'barcodes.tsv'), sep = '\t', header =... |
286faf4653bdab6a9a3adf9a66ae3b1d334e5e6a0381edc3f883ef54735a4378 | R | 2,102 | 34 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
#source('~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/RCTD/PersonalFiles/private_utils.R')
#source('~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/RCTD/de.R')
#source('~/Document... |
cfb777bb94c77554181405aa31a31d79e273a4e69ee9d5421c7d308eb7d4d55f | R | 2,102 | 34 | library(Matrix)
library(spacexr)
library(doParallel)
library(ggplot2)
#source('~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/RCTD/PersonalFiles/private_utils.R')
#source('~/Documents/MIT/Research/Rafalab/Projects/slideseq/Cell Demixing/ContentStructure/RCTD/de.R')
#source('~/Document... |
efa7a7017f92287c70d975970fcdd146f7078c51d70eded551c3952a369071d6 | R | 2,115 | 79 | ## By Anoushka Joglekar 2019. Edited 07/2020
args <- commandArgs(trailingOnly=TRUE)
current_dir <- args[1]
setwd(current_dir)
pairwise_dir <- args[2]
files = list.files(pairwise_dir,recursive = TRUE)[grep(
pattern = "results.csv",list.files(pairwise_dir,recursive = TRUE))]
mf <- list()
bp <- list()
cc <- list()
... |
86598ec11e3b40af3cba172e3e15a42e3381c06b4ad4bfc7fa4fef0a4658850f | R | 2,148 | 52 | # Function to generate a p-value for the spatial correlation between two parcellated cortical surface maps,
# using a set of spherical permutations of regions of interest (which can be generated using the function "rotate_parcellation").
# The function performs the permutation in both directions; i.e.: by permute both... |
8c40ba0e8f4dc184a92e061951dec237f9d0865b234247e9effcc0a8e86e36ea | R | 2,150 | 61 | ## some tests of inverse-gaussian GLMs based on a file suppled by
## David Firth, Feb 2009.
options(digits=5)
## Data from Whitmore, G A (1986), Inverse Gaussian Ratio Estimation.
## Applied Statistics 35(1), 8-15.
##
## A "real, but disguised" set of data (Whitmore, 1986, p8).
##
## For each of 20 products, x is... |
90976ec4fcc082c09abbb72f5916a80f63a9f433245dcd9659086f6ef908e9ae | R | 2,168 | 70 | # run_ace_model.R — AE model with rG + CI and nested test
run_ace_model <- function(roi, mzData, dzData, return_type = "AE") {
return_type <- match.arg(return_type, choices = c("AE", "ACE"))
# Ensure reproducibility and stable optimization
mxOption(NULL, "Default optimizer", "CSOLNP")
# 1) Fit full ACE
ACE ... |
5e81255ae1be1fb3f89ceaeb3fed3b0fad8e4cde794b410c1af25af735096e87 | R | 2,171 | 74 | #===========================================================================
# Helper functions to produce the beeswarm plots
#===========================================================================
# Example
# ggplot(iris, aes(Species, Sepal.Width)) +
# geom_point(position = position_bee(), aes(color = Species)... |
921e78df12322b13ffd1b1852926f57ac9fd08c4a5f86c84f81f56d152774ce8 | R | 2,197 | 78 | ## tests of the simulate.lm method, added Feb 2009
options(digits = 5)
# recommended packages should be present
if(!require("MASS")) q()
## cases should be named
fit1 <- lm(time ~ dist, data = hills)
set.seed(1)
simulate(fit1, nsim = 3)
## and weights should be takedn into account
fit2 <- lm(time ~ -1 + dist + clim... |
6781d0d5ab194454c02d9e9d406dbc9cc1d7e45561ce7a60983a9ee1cc89e6a4 | R | 2,217 | 56 | library(Seurat)
library(sctransform)
library(dplyr)
library(stringr)
library(patchwork)
library(ggplot2)
library(cowplot)
library(SoupX)
library(DropletUtils)
library(scCustomize)
library(scran)
source("Helpers/SoupXDoubletfinder.R")
# Comparing background ambient RNA Stating marker genes for diverse cell types in the... |
cf56f4950b321a6b28420e4a48dd66305aebd3282949f4bae049529a44712603 | R | 2,217 | 68 | # Nonlinear mixed-effects learning-curve models (p0, alpha) by group
# Fits separate models for trials 1-10 and 11-200
suppressPackageStartupMessages({
library(data.table)
library(nlme)
})
load("/Users/doq/Geode_Data/preprocessed_data.RData")
alldata <- as.data.table(alldata)
# Group definitions
alldata[, binar... |
4f608acc584019ec70f69b541116741fc2970761356c5ef0d63582fd30ba78bf | R | 2,223 | 73 | library(Seurat)
library(SeuratObject)
methods <- c("JointPCA", "Harmony", "FastMNN", "Conos", "Combat", "CCAv5", "RPCAv5")
base_dir <- "PATH_TO_INPUT_DIR_FOR_INTEGRATED_SEURAT_RDS/"
eval_base <- "PATH_TO_OUTPUT_DIR_FOR_EXPORTS/"
out_paths <- list(
"JointPCA" = file.path(eval_base, "JointPCA"),
"Harmony" = file.... |
a78a480fab9f092337f80a513124f2a95cbce02c5727b0408ac53fa4500e2377 | R | 2,238 | 45 | ```{r data, echo = FALSE}
inpD <-"C:/Users/animeshs/OneDrive - NTNU/Lisa/"
inpF<-paste0(inpD,"Log2FC monocytes all treatments trimmed and sorted final csv (Fig 2C).csv")
#inpF<-paste0(inpD,"Log2FC all nine cell lines trimmed and sorted final csv (Fig 2A).csv")
data <- read.delim(inpF,sep=";",dec = "," ,header = T,strin... |
f9bc69602a8451d270b77381468321d51c244677cdff5121c528963d2d6207c7 | R | 2,247 | 60 | ```{r TEiMeta, echo = FALSE}
#AEC: automated execution context / data provenance and its manifest - Salmon guy
#https://bioconductor.org/packages/release/bioc/vignettes/tximeta/inst/doc/tximeta.html
BiocManager::install("tximeta")
```
```{r VRS.ga4gh, echo = FALSE}
#https://github.com/ga4gh/vrs
BiocManager::install(c(... |
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