sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
80e4b6482e45f2f8fd93c0fc93ffe6f87bfe83601da2c6416f3a19b7f3fe2e02 | R | 1,327 | 38 | #' Reorder Matrices
#' @description Tries to find an order for matrix by different cluster methods.
#' @param x a matrix-like object.
#' @param is.cor logical value (defaults to TRUE) indicating wheater
#' \code{x} is a correlation matrix.
#' @param cluster.method a character string with the name of agglomeration meth... |
297738225ea9b9506cefc02b9dbef3b62d921449f102f9dbd26b9547d379ce89 | R | 1,329 | 33 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
suppressPackageStartupMessages({
library("dplyr")
library("tidyr")
library("readr")
library("tibble")
})
settings <- get_settings(omic = "phosphoproteomics", stage = "filter_STY", dataset_name = NULL) # dataset_name = "c9"
loc_p... |
2551cda166625ba4b9e9cbbe81304d45ae51eee3dec35f773643fd1bd97c8b8a | R | 1,330 | 56 | source(here::here("src/shared/R-helpers/memoise_decorator.R"))
suppressPackageStartupMessages({
requireNamespace("gprofiler2")
})
gost_memoise <- function(cache_dirname, ...) {
.gost_robust <- function(...) {
args <- list(...)
q <- args$query
if (length(q) > 0) {
Sys.sleep(1)
out <- gpro... |
9a0b7145b7149b44eba4441c142f33c7e799ef5e603a512396d8b9a662a6ef2d | R | 1,334 | 25 | plot_dendrogram_replicates <- function(matrix_proBatch_Tatjana,
sample_annotation = sample_annotation_PanCancer,
color_list = color_list_PanCancer,
biospecimen_id_col = 'patient_IDs',
sample_id_col ='sample... |
14d3a434ac4b985fd9ced56586f6f528e3ad6c86de5170f2f197eaaa1cd82ec2 | R | 1,346 | 32 | library(rstan)
library(ltjmm) # from https://bitbucket.org/mdonohue/ltjmm/src/master/
library(doParallel)
# rstan settings
options(mc.cores = parallel::detectCores())
options(auto_write = TRUE)
# Read data
# The data needs to be in long format with following columns: Patient_ID, Time.since.diag_scaled, Age_at_diagnos... |
eea621046f28c59483a051901ed9a8c05b8f1ca90b366ed5237c458b2c9626eb | R | 1,346 | 28 | # Laura Aarnos 211118
# Create new plate layout for meaRtools - Axion12
# Original code from meaRtools: Reading in MEA data
#library(IGM.MEA)
library(meaRtools) #attach meaRtools after IGM.MEA to get right parameter names
# name your plate layout - the name goes between ''
my.name <- 'Axion12' # e.g. '1-electrode'
... |
9cf71c296636d8d4e48c9446d8c0de96d0659c2c0438230acd45bfffa71ed9fa | R | 1,350 | 49 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/new_mito_unique.tsv", sep="\t", row.names="transcript_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.deseq2/ex... |
f22a997ad3f2740c25aabb7fb417719801680b803e3322a65c4703d469b83064 | R | 1,357 | 44 | #' Creates a \code{blots} argument
#'
#' This helper function creates a valid \code{blots} object. The
#' \code{blots} object is an argument to the \code{mice} function.
#' The name \code{blots} is a contraction of blocks-dots.
#' Through \code{blots}, the user can specify any additional
#' arguments that are specifica... |
97b987583861a5c01e26ecba5bf8a211cc160d9f63ea73d838f1eaeb435784d3 | R | 1,360 | 49 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/new_mito_full_length.tsv", sep="\t", row.names="transcript_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.dese... |
6c77c0fee6bda97e88a33cbbe38ddd06b8eeee119fcab954a23f4af9f8041167 | R | 1,371 | 41 |
```{r}
library("Seurat")
library("BayesSpace")
```
load spatialLIBD
```{r}
st_data_libd = read.csv("libd_df.csv",
header = T,row.names = 1)
st_data_coordinates = data.frame(read.csv("meta_data.csv",row.names =1))
st_df_libd = t(st_data_libd)
```
analysis
```{r}
library(SpatialExperiment)
... |
ea958827235caf41bf8d84108c55def5d3ef0b8904bb182f1d453b5facf572b0 | R | 1,371 | 41 | library(data.table)
D <- fread("C:/STUDIES/RESEARCH/neural_ODE/breast_cancer_data/clean_data/desmedt_11165genes_1sample_186T.csv")
top_row <- D[1, ]
time_row <- D[.N, ]
D <- D[-c(1, .N), ]
dim(D)
num_genes <- dim(D)[1]
### this part is just to see what's happening
idx = sample(1:500, 1)
mod_loess <- loess(as.numeric(... |
bdf7b63c6dd3ce35f8728132e80366c740bb74fe6a4400b1dc114ea72499459f | R | 1,372 | 17 | ###DTS script to check that all prerequisites are met and what kind of experiments have been conducted
#make sure all flies have the identical experimental design and find out which don't
xml_list = paste(dataset.path, unlist(do.call("rbind", lapply(dataset.data$resources, '[', 4))), sep = "/") #create list of all fil... |
8964d3186bf8828fb6fbacdd02c2bd3f9c3655e9ed508abd5d7862849f2a8384 | R | 1,379 | 34 | #' Subset of Irish wind speed data
#'
#' Subset of Irish wind speed data
#'
#' The original data set is much larger and was analyzed in detail by Haslett
#' and Raftery (1989). Van Buuren et al (2006) used this subset to investigate
#' the influence of extreme MAR mechanisms on the quality of imputation.
#'
#' @name wi... |
5091b6260f0cb30ef54a5b2362a2ce58fc232adeb99bf6a33187285018701474 | R | 1,386 | 49 | ```{r}
library(CARD)
library(MuSiC)
library(Seurat)
# library(data.table)
```
load sc_ref_data
```{r}
sc_data_1 = read.csv("sc_ref_hippo.csv",
header = T,row.names = 1)
sc_data_labels = read.csv("sc_label_hippo.csv",row.names = 1)
sc_df = t(sc_data_1)
```
load 5k
```{r}
st_data_1_5k = data.... |
77d8d59484da8f90fc1b6f15cbefcc8da0f2269cadb839db9ca5e9092e34b9bf | R | 1,386 | 36 | #!/usr/bin/env Rscript --vanilla
#load the libraries
library(readr)
library(tidyverse)
library(reshape2)
#load the annotation
sample_annotation_InterLab = read_csv('data_InterLab/3_data_for_plots/sample_annotation_InterLab.csv')
#load the spike-in concentrations
#load the list of spike-in peptides and corresponding... |
2e9188173e369f6781c39cc5f0513993978a93419aa56da06f810abad4158ca5 | R | 1,388 | 39 | library(ggplot2)
library(dplyr)
# read in results
xis.1 <- read.csv("juliacon/results/simulation1_case1.csv")
xis.2 <- read.csv("juliacon/results/simulation1_case2.csv")
xis.3 <- read.csv("juliacon/results/simulation1_case3.csv")
xis.4 <- read.csv("juliacon/results/simulation1_case4.csv")
xis.5 <- read.csv("juliacon/r... |
962a46a5372bfc6b3be1a5688d61dbc88c2c449e50e6c3c040f0795416971fda | R | 1,390 | 39 | # required libraries
library(clusterProfiler)
library(GSEAplot)
# set working directory
setwd("GSEA_Lam_et_al_2024") # locate directory containing archive files
# read in summary statistics for Lewy body differential expression
sumstats <- read.csv("LBpath_DEG_sumstats.csv")
# read in experimental gene set... |
3da06fb762eb37cc43a10a05bcffcd7e93f3f99b71666273c8643e3e29745bdb | R | 1,404 | 49 | #TODO: Check or refine this. Maybe change name
mutate.factR <- function(object, meta = "samples", data=NULL, ...){
# check for type of meta to modify
if(!meta %in% c("gene", "transcript", "AS", "samples")){
rlang::abort("Incorrect input for `meta`")
}
# get meta
if(meta == "samples"){
... |
74b36a0766298110590d3dc623acf550ab60ad5118ccd705b61c8e33f63aceea | R | 1,409 | 48 | ```{r}
library(CARD)
library(MuSiC)
library(Seurat)
library(spacexr)
```
load sc_ref_data
```{r}
sc_data_1 = read.csv("sc_ref_hippo.csv",
header = T,row.names = 1)
sc_data_labels = read.csv("sc_label_hippo.csv",row.names = 1)
sc_df = t(sc_data_1)
st_data_1_5k = read.csv("hippo_st_5k.csv",
... |
9ccf64e0e395f0c4f2a27a945d33cb44e9283d622783bca5410295392167a16d | R | 1,412 | 55 | source("common.R")
searchRepeats <- 1 # Number of times to continue search at each k
kValues <- c(10, 40, 3, 20, 6) # Concavity constants for implied weighting
timeout <- 240 # Minutes after which to terminate each search
hits <- 480 # Maximum times to hit a best score
ratchets <- 24 # Ratchet iterations
# Lo... |
56dc88a6a3f453891d91e53db4f204dbfc494be0fcdb4e05a5160b68327b791b | R | 1,417 | 38 | library(readxl)
library(boot)
#1. load excel or csv file containing n-NRF magnitudes "both_sites_magnitudes.xlxs"
both_sites_magnitudes <- read.csv("both_sites_magnitudes.csv")
#2. create dataframes for UCL and Exeter magnitudes separately
UCL_magnitudes <- both_sites_magnitudes[both_sites_magnitudes$Site == "UCL", ]... |
7df907d12c284daf418791d2a9524f275b04f2abe0719513767d4a09aa37ffac | R | 1,417 | 31 | mergeDGEList <- function(firstDgeList, secondDgeList,
DGEListLabels=NULL) {
stopifnot(is.null(DGEListLabels) || length(DGEListLabels)==2)
commonFeat <- intersect(rownames(firstDgeList$counts),
rownames(secondDgeList$counts))
commonSampleCols <- intersect(colnames... |
96ad8a380933a397cf2bef45dafce1019dcafd5143056237add55795a6214915 | R | 1,418 | 48 | suppressPackageStartupMessages(library(cellrangerRkit))
suppressPackageStartupMessages(library(ggplot2))
suppressPackageStartupMessages(library(reshape2))
#suppressPackageStartupMessages(library(cowplot))
suppressPackageStartupMessages(library(scales))
# Get paths.
args = commandArgs(TRUE)
cellrangerPath = as.characte... |
953e992eaabd09814c4ae7188aac03c03dfa05231ff9288f6b95dd6c5532cddf | R | 1,420 | 16 | ###DTS script to display a progressbar during the evaluation
if (exists("starttime")){iter_time = round((Sys.time()-starttime), 2)} else iter_time = 20 #calculates the iteration time
if (exists("Progressbar")){dev.off()} #deletes the previous plot. If not, this will generate an ever increasing number of plots in the e... |
a7b81996a821cd06fde1154edb8d76d05c46d7240e2d78177c4c149af48e13a3 | R | 1,428 | 27 | #!/usr/bin/env Rscript --vanilla
#load the libraries
library(readr)
library(proBatch)
library(RColorBrewer)
#load the data
sample_annotation_AgingMice = read_csv('data_AgingMice/3_data_for_plots/sample_annotation_AgingMice.csv')
#define the colors
color_list_AgingMice = sample_annotation_to_colors(sample_annotation_... |
72bbf1c27b234064b3c7193a293e71042fb761be01bb30d6b49244dc75846314 | R | 1,432 | 41 | #' Scale continuous with liner transform
#' @title Scale continuous
#' @param from input range.
#' @param to output range.
#' @return liner trans.
#' @importFrom scales trans_new
#' @rdname liner_trans
#' @author Houyun Huang, Lei Zhou, Jian Chen, Taiyun Wei
#' @export
liner_trans <- function (from, to)
{
force(from)... |
684e6a57074ef0b01cece7aaf9197303a6a1bf5adcb2274416e9611baf66ddff | R | 1,436 | 46 | parse.ums <- function(x, ums = NULL, umx = NULL, ...) {
if (is.null(ums)) {
stop("Unidentifiable model specification (ums) not found.")
}
if (!is.null(umx)) {
x <- base::cbind(x, umx)
}
## Unidentifiable part
# e.g. specified in blots as list(X = list(ums = "-3+2*bmi"))
mnar0 <- gsub("-", "+-", u... |
70263a76ccec49eb31d706bfc4232653133a7fd80c1d832e0f6dd3f7af61992e | R | 1,440 | 56 | #!/usr/bin/env Rscript
#############
## gene level
#############
# download metadata and kallisto summarized to the gene level
txi.gene = readRDS('checkpoints/kallisto_genes.RDS')
meta = readRDS('checkpoints/cayo_bulkbrain_combined_metadata.RDS')
txi.gene$counts = txi.gene$counts[,which(colnames(txi.gene$counts) %i... |
46b1067afed3d2390b490b93ffa2cd649c81eefbbda830f576e20dda46a64780 | R | 1,442 | 61 | #' Colour pal
#' @description Wrapper for the diverging palettes provided by \code{\link{RColorBrewer}}.
#' @param n number of different colors in the palette, minimum 3, maximum 11.
#' @return a palette.
#' @rdname colour-pal
#' @export
brown_blue <- function(n = 11) {
RColorBrewer::brewer.pal(n, "BrBG")
}
#' @rdnam... |
d64c12ffa4fedd38048eb277564315c83108f09ad679864c54126d0450390750 | R | 1,445 | 27 | # 程序功能：scRNA-seq 分析通用绘图参数与分组信息
# Functions: shared plotting themes, group orders and color palettes for figures 1-6
# 1. ggplot2 通用主题（类似 example 模板中的 main_theme）
main_theme = theme(panel.background = element_blank(),
panel.grid = element_blank(),
axis.line.x = element_line(size = ... |
d66ac3fc25bb2b00296d88de3657b2609e8b0762f17ca752306e3ad1b3e48243 | R | 1,451 | 60 | library(reticulate)
library(fda)
library(lme4)
np <- import("numpy")
#topfeat <- 5
#d = 1
#mat1 <- np$load("JPTA_exampleDataset1.npy") # Data of Shape: (N,P,T)
#y <- sample(0:1, dim(mat1)[1], replace = TRUE)
variables = list()
weeks = list()
class = list()
subject = list()
variable_values... |
3a3727cfba6e6e81bed1b269819a10ad0783dc80b69e4d2b0afa9bae50167a8c | R | 1,453 | 44 | ## This script generates median rank across N folds for each feature.
suppressPackageStartupMessages({
library(dplyr)
})
extract_rank <- function(tissue, feature){
new_feature <- paste0(tolower(feature), "s")
ml_file <- paste('../../..',tolower(tissue), '_m',
new_feature,'... |
c7ca8cba65cbe297f0eef739799c0125bdecb82294f61f26f22cdd24134c8388 | R | 1,453 | 42 | library(readxl)
library(meta)
#1. load excel or csv file containing mean and SD of n-NRF magnitudes at each site
metamean_bysite <- read.csv("metamean_bysite.csv")
# Oxford, UCL, Exeter. "metamean_bysite.xlxs"
meta_analysis <- metamean(n=n_hyp2,
mean = mean_hyp2,
... |
15db5cc0542e9873c76caafd28e6df9a8bc5ae0d28daacc0a90bf9086d8806b4 | R | 1,456 | 39 | ## Example sharing using posterior sampling.
library(mashr)
library(corrplot)
plot_heatmap <- function(feature, factor){
config <- list("0"="signOnly", "0.5"="general", "0.99"="strict")
fn <- paste("../../_m/mashr_meta_results.RData", sep="/")
load(fn)
pdf(file = paste0("sharing_heatmap_",config[[a... |
809a180b3d9c97c47c6f86f709ce4b1ddebbbe0455a9ddde92a54471b33f38ec | R | 1,456 | 39 | ## Example sharing using posterior sampling.
library(mashr)
library(corrplot)
plot_heatmap <- function(feature, factor){
config <- list("0"="signOnly", "0.5"="general", "0.99"="strict")
fn <- paste("../../_m/mashr_meta_results.RData", sep="/")
load(fn)
pdf(file = paste0("sharing_heatmap_",config[[a... |
b92b56364e3a7510a151511d7ca401a5ffd0a8f9055a181141318f21ef0eece4 | R | 1,456 | 44 | source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/atac.R')
source('~/scripts/perturbator/de.R')
library(Pando)
setwd('~/projects/cutntag/')
#### Read stuff ####
drugs_init <- read_rds('/links/groups/treutlein/USERS/fides/CutNTag_analysis/hashing/hashing/202205_drugs_all_combined/20220414_annotatio... |
e8c2fd6df9106723c8f2b98a58940f74397a347ffc3daa1650e9ddd82706f334 | R | 1,459 | 37 | library(tidyr)
library(reshape2)
library(plyr)
library(dplyr)
library(grid)
library(gridExtra)
library(Seurat)
library(textTinyR)
library(pbapply)
current = getwd()
folder = "1_calculate_pseudobulk"
setwd(current)
system(paste0("mkdir -p outputs/",folder,"/"))
## read in single cell data (raw UMI counts stored in RNA... |
a9c22062ce1724f298af05a20f0e2211f0f8225bab05a53bd804518bd8ecca34 | R | 1,460 | 39 | ## Example sharing using posterior sampling.
library(mashr)
library(corrplot)
plot_heatmap <- function(feature, factor){
config <- list("0"="signOnly", "0.5"="general", "0.99"="strict")
fn <- paste("../../_m/mashr_meta_results.RData", sep="/")
load(fn)
pdf(file = paste0("sharing_heatmap_",config[[a... |
dabcb556d92907437e8831b4c93ef55f22fab38f2c0123980a81e883d922d127 | R | 1,462 | 45 | #' Create the default ggcor theme
#' @details The theme_cor, with no axis title, no background, no grid,
#' made some adjustments to the x-axis label.
#' @param legend.position the position of legends ("none", "left", "right",
#' "bottom", "top", or two-element numeric vector).
#' @param ... extra params passin... |
e0c06f04aeab61e27b5229a0a61c29c68a628877a024e4caaae1373c8687f146 | R | 1,462 | 39 | ## Example sharing using posterior sampling.
library(mashr)
library(corrplot)
plot_heatmap <- function(feature, factor){
config <- list("0"="signOnly", "0.5"="general", "0.99"="strict")
fn <- paste("../../_m/mashr_meta_results.RData", sep="/")
load(fn)
pdf(file = paste0("sharing_heatmap_",config[[a... |
5002cef36012acee89b5cee56de62e219ad7708ddb97be29e2bd904fe5dbd7f4 | R | 1,467 | 33 | #!/usr/bin/env Rscript --vanilla
#load the libraries
library(readr)
library(aLFQ)
print(sessionInfo())
print(Sys.time())
#load the data
data.median <- import(ms_filenames= "data_InterLab/2_interim_data/all_sites_global_q_001_applied_to_local_global_medianCentered.tsv",
ms_filetype = "openswath"... |
8890966c70fe79e89086d52325c124c0ab44ca7875502de9b2e799c099180a8e | R | 1,471 | 73 | ## https://stackoverflow.com/questions/58266785/mice-2l-pan-multilevel-multiple-imputation-error-missing-values-in-pred-not-all?sem=2
skip_if_not_installed("pan")
library("pan")
# Not multilevel to illustrate need
set.seed(100)
data <- data.frame(
patid = rep(1:4, each = 5),
sex = rep(c(1, 2, 1, 2), each = 5),
... |
8367550b967087369fadeb7e4ea40c90e17a367a850b1e3660cd5a427ca309a5 | R | 1,472 | 56 | lmp <- function (modelobject) {
if (class(modelobject) != "lm") stop("Not an object of class 'lm' ")
f <- summary(modelobject)$fstatistic
p <- pf(f[1],f[2],f[3],lower.tail=F)
attributes(p) <- NULL
return(p)
}
coef.p.lm <- function(mod){
if (class(mod) != "lm") stop("Not an object of class 'lm' ")
coeffs=s... |
c07c12a1449998bd7a51a12857fb1471507c44b0fe9b7ee6d798f6c5d31302fa | R | 1,478 | 53 | ### Import data from salmon raw output into R
# Note: this script uses tximport as installed from the github repository:
# the bioconductor version for R3.5 has bugs
# Load packagess
library(magrittr)
library(tximeta)
library(tidyverse)
library(SummarizedExperiment)
library(DESeq2)
# Import metadata and add filenames... |
54ca312adfa59727cd50aab6783fe4f0bdf0a16dc0281a0f728f995a40b637e9 | R | 1,489 | 47 | ## This script generates a clustered dotplot for
## enrichment analysis (XCI)
library(dplyr)
library(ggplot2)
save_plot <- function(p, fn, w, h){
for(ext in c(".pdf", ".png", ".svg")){
ggsave(filename=paste0(fn,ext), plot=p, width=w, height=h)
}
}
#### Main
xci <- data.table::fread("../../_m/xci_enri... |
9b33276e1ae9a753ca67bdec3b813a45077790902b1736efecb4c3cfaf4b8900 | R | 1,490 | 28 | # Laura Aarnos 230718
# Create new plate layout for meaRtools
#library(IGM.MEA)
library(meaRtools) #attach meaRtools after IGM.MEA to get right parameter names
# name your plate layout - the name goes between ''
my.name <- 'Axion48' # e.g. '1-electrode'
# create plate layout for single electrode
new_platelayout = ... |
365fb9a2abd23e0e2cf0f685985edda6c34e6ac50f006b1a215619dda664f68d | R | 1,498 | 37 | #' @templateVar MODEL_FUNCTION task2AFC_sdt
#' @templateVar CONTRIBUTOR \href{https://github.com/heesunpark26/}{Heesun Park} <\email{heesunpark26@@gmail.com}>
#' @templateVar TASK_NAME 2-alternative forced choice task
#' @templateVar TASK_CODE task2AFC
#' @templateVar MODEL_NAME Signal detection theory model
#' @templa... |
515d081a81c42e2e598aded1473071c6e485cf467b6f1ed4dbab78ebea2e63fc | R | 1,500 | 44 | #5 SCENIC
# Setup---------------------------------------------------------------
rm(list = ls()) ; gc() ; gc()
set.seed(1234)
wdir <- 'set your working directory'
setwd(wdir)
save.path <- 'object folder path'
# load library
easypackages::libraries(c('Seurat','tidyverse','SCENIC','AUCell'))
# load pySCENIC data-----... |
19c632ffe309d25c7b1445d64107f43fc3e12d1a39a3d108cb082a1491742ac2 | R | 1,501 | 28 | # Laura Aarnos 230718
# Axion 48 plate layout for meaRtools
#library(IGM.MEA)
library(meaRtools) #attach meaRtools after IGM.MEA to get right parameter names
# name your plate layout - the name goes between ''
my.name <- 'Axion48' # e.g. '1-electrode'
# create plate layout for single electrode
new_platelayout = li... |
60b82af15aecb18544784a4705f42c6fdb59baa0b5152fdc16fcad2193c33b70 | R | 1,511 | 33 | #!/usr/bin/env Rscript --vanilla
#load the libraries
library(proBatch)
library(ggplot2)
library(dplyr)
library(readr)
proteome_df_AgingMice = read_csv("data_AgingMice/2_interim_data/raw_proteome_AgingMice.csv")
peptide_df_AgingMice = read_csv("data_AgingMice/1_original_data/peptide_annotation.csv")
proteome_df_Agin... |
f7b4ae23b0ae0ecff75cd8a3c223b911e66fed0a38c68c2a660ec79c02d4420a | R | 1,515 | 30 | ---
title: "Annotation report"
---
Typically in a sequencing experiment a lot of focus is directed towards ensuring that the quality control of fastq files is good. However, equally important is quality checking the mapping. Currently there are a number of cgat specific and external tools that have been developed to a... |
d1ab59043c975a15d1fa1a77caaedc9e9c845ccbca76b8fb95418ca8fcbdb146 | R | 1,516 | 39 | #### Extract colocaliztion results
library(here)
library(dplyr)
load_enloc_results <- function(tissue){
fn <- here("prep_eqtl_analysis", tolower(tissue),
"genes/interaction_model/dapg_finemap",
"fastENLOC/_m/pgc3.enloc.sig.out")
return(data.table::fread(fn) |> rename("RCP"=V6) |>
... |
f65af5804ba89a2f08a17be05885f8300fca96bbaa6c8673cedbdd1807d0bb0e | R | 1,521 | 56 | source('~/scripts/single_cell/atac.R')
source('~/scripts/single_cell/wrapper.R')
source('~/scripts/single_cell/markers.R')
source('~/scripts/single_cell/celltype.R')
source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/graphs.R')
source('~/scripts/grn/models.R')
library(ChIPseeker)
library(ArchR)
library... |
b2b9001be5086ad12c8730885fd69f042fddbd0d66e1e736cafdbb896afd87aa | R | 1,523 | 53 | # Check that mice.mids correctly appends the newdata to the
# existing mids object
init0 <- mice(nhanes, maxit = 0, m = 1, print = FALSE, seed = 1)
init1 <- mice(nhanes, maxit = 0, m = 1, print = FALSE)
init1$ignore <- rep(FALSE, nrow(nhanes))
init2 <- mice.mids(init0, newdata = nhanes, maxit = 0, print = FALSE)
tes... |
39811b34e6b35d6f7650315c151355a745458d143951ff74ec3d6aed70809942 | R | 1,524 | 37 | #'#################################################################################
#'#################################################################################
#' Process GSE169038 gene expression for deep learning
#'#################################################################################
#'#######... |
11db63964eafbe7b80118e0d0f9dc7ab848389a9873875a1458e1c33afebaa97 | R | 1,537 | 38 | library(readr)
library(ggplot2)
source('analysis_AgingMice/plot_split_violinplot.R')
source('../lib/plot_themes.R')
peptide_corr_raw <- read_csv('plots_AgingMice/interim_data_for_plots/peptide_cor_raw.csv')
print('file reading completed!')
Sys.time()
peptide_corr_batchCorr <- read_csv('plots_AgingMice/interim_data_for... |
72efcbf805b8a2ad6eaa54e259fa78e01f3923c0b5e8c03a3006705aa9cba61c | R | 1,541 | 39 | #'#################################################################################
#'#################################################################################
#' Download and preprocess GTEx data
#'#################################################################################
#'#########################... |
5c127cbda3c91b7ae65c371bb4ecf70c646d8df159160639712dc0ca7697748c | R | 1,546 | 33 | getwd()
df = read.csv("df_exp4_vdj1.csv")
head(df)
library(ggplot2)
library(ggsignif)
library(ggpubr)
df$Model=factor(df$Model,level=c("Without Heterogeneous GNN Module","With Heterogeneous GNN Module"))
df$Peptide=factor(df$Peptide,level=c("GTSGSPIVNR","FLKEKGGL","GTSGSPIINR","KLVALGINAV","GLCTLVAML","FPRPWLHGL","... |
c75ec66ca6db47146ec11817c7c89372564e3ebec188e4364efd99fbcd2ce999 | R | 1,554 | 37 | # set the random generator to V3.5.0 to ensure that this test
# passes in V3.6.0 and later
# see mail Kurt Hornik, dated 06mar19
suppressWarnings(RNGversion("3.5.0"))
imp <- mice(nhanes2, print = FALSE, maxit = 2, seed = 121, use.matcher = TRUE)
fit <- with(imp, lm(bmi ~ chl + age + hyp))
est <- pool(fit)
mn <- c(18.... |
392d923f0a12bb216cff5f3f0c45428b981cc5ab986813874d0c0d5e35096520 | R | 1,555 | 41 | library(MCMCpack)
library(dplyr)
make_bulk_sample <- function(seuratobj, celltype_ids, celltype_counts){
cell_ids = lapply(names(celltype_ids), function(celltype){
cell_count = celltype_counts[celltype]
sample(celltype_ids[[celltype]], size=cell_count, replace=TRUE)
})
bulk_sample = rowSums(seuratobj[,u... |
73c8ca06ebc6ce3906533adbfe79b054e340dfa12937986b9233e6f5e6f06975 | R | 1,564 | 48 | #!/usr/bin/env Rscript
# print usage
usage <- function() {
cat(
'usage: lib_stats.R <file>
lib_stats.R
author: Colby Chiang (colbychiang@wustl.edu)
description: Plot a library information for BAM file
positional arguments:
<input JSON> JSON file of library info, created by SVTyper
<output PDF> ... |
6e8c05634deb2d80c240a99ffaf6daa03dcf9797155d7b4e884c728e2b1321c9 | R | 1,566 | 35 | theme_publication <- function() {
new_theme = theme(plot.title = element_text(face = "bold",
size = rel(2), hjust = 0.5),
axis.title = element_text(face = "bold",size = rel(1.5)),
axis.title.y = element_text(angle=90,vjust =2),
... |
7c0e51ccc5291e109db58c8532715f960408dedb9d36d87f0eed8564f7861218 | R | 1,566 | 58 | rm(list=ls())
options(stringsAsFactors = F)
#setwd('')
meth.file=c('simulated_450k.rds')
impute.qc=read.csv('MeasuresTEST_EPICtoMM_v2_450k.csv')
######## load the functions
source("fns_ArrayConverterAlgorithm.R")
######## load the trained model
fitall = readRDS('fit_EPICtoMM_v2_EPIC450k.rds')
########## a quick look ... |
9f6d827230cda5eaab2c50cdc21d55705ba545db914376941e8f2a703dc32a37 | R | 1,567 | 33 | #!/usr/bin/env Rscript --vanilla
#load the libraries
library(proBatch)
library(ggplot2)
library(dplyr)
library(readr)
proteome_df_AgingMice = read_csv("data_AgingMice/3_data_for_plots/batchCorrected_proteome_AgingMice.csv")
peptide_df_AgingMice = read_csv("data_AgingMice/1_original_data/peptide_annotation.csv")
pr... |
d7a196bd68a0123c859c4f5f3ad99b844df0e2c1e75d49597b5f03e47525c5ee | R | 1,567 | 43 | #!/usr/bin/Rscript
library("optparse")
option_list = list(
make_option("--ld_ref_dir", action="store", default=NA, type='character',
help="Directory where FUSION LD reference files are stored [required]"),
make_option("--PLINK_prefix", action="store", default=NA, type='character',
help=... |
168003d6f8485a7236bc72a2d0f22c94161971b18efe828ceacc8a4e3dc52ad2 | R | 1,568 | 33 | getwd()
df = read.csv("fig5c.csv")
head(df)
library(ggplot2)
library(ggsignif)
library(ggpubr)
df$Model=factor(df$Model,level=c("Without Heterogeneous GNN Module","With Heterogeneous GNN Module"))
df$Peptide=factor(df$Peptide,level=c("GTSGSPIVNR","FLKEKGGL","GTSGSPIINR","KLVALGINAV","GLCTLVAML","FPRPWLHGL","GILGFVF... |
bb6539b3a41497cc7a0ab66b80f4c82b7e000367cbfcf7a7480a285c2e42efa4 | R | 1,569 | 34 | #'#################################################################################
#'#################################################################################
#'
#' Download and filter newer version of gene SYMBOL ID to update PROMOTE gene names
#'
#'##################... |
231964ef0bce44ec46943ebb1f68091ac39cfff2c432740824fe7f32e28432ef | R | 1,572 | 31 | library(tidyverse)
library(data.table)
library(R.utils)
library(here)
gtex <- fread("/data/GTEx_expression/GTEx_v8/GTEx_Analysis_2017-06-05_v8_RNASeQCv1.1.9_gene_tpm.gct.gz")
samples <- read_delim(file = "/data/GTEx_expression/GTEx_v8/GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt", delim = "\t")
pheno <- read_de... |
aec2fcb39875656c83f604a74f106433bb11bc4d46f9e94ab96a2b350781a679 | R | 1,574 | 38 | library(tidyverse)
# Fill this in with appropriate values
df <- data.frame(Balanced_Accuracy_Random = c(0.9,
0.9,
0.9,
0.9,
0.9,
... |
8feb6cea9db4774e40c94762d6e61b34246208825431b0e5597f96107d8271e3 | R | 1,578 | 31 | # Theme for graphs
theme <- theme(plot.title = element_text(vjust = 2), legend.key.height = unit(1.5, "lines"), panel.grid.minor = element_blank(), legend.position = "right") +
theme(panel.grid.major = element_blank(), panel.grid.minor = element_blank()) +
theme(axis.text.x = element_text(colour = "black", size = 1... |
ba7024569dc014d594dc2da7af17c81c6b9ba39aff4f707c578a04355bf07200 | R | 1,579 | 38 |
############################################################################
# R FUNCTIONS FOR THE INVERSE GAMMA DISTRIBUTION
#
# (by Jeffrey S. Rosenthal, probability.ca, 2007)
#
# The base package of the statistical software R does not seem to include
# built-in functions for the inverse gamma distr... |
cc98cc3df77ab22bfbc7e8b95b62e3d7bf8a651438192c812c5e800757403769 | R | 1,581 | 42 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
# Load data ---------------------------------------------------------------
parent_pseudo_homology <- read_tsv(here::here("data", "similarity.dat"), col_names = TRUE, show_col_types = FALSE)
# Main ---------... |
deb5384255b7b92138782e42ceb0fab3be5e46263f339e1efe3977d66d4319b8 | R | 1,586 | 43 | #'#################################################################################
#'#################################################################################
#' Prepare files to generate models with less or random gene sets
#'#################################################################################
#... |
3d08be4aa34affb51c14ad3e8b4521fcc43526142bb92c7d3be263a1688e7d4b | R | 1,587 | 34 | fit_plasticity <- function(df, weights = TRUE) {
df_means <- df %>% group_by(year_site.UID) %>%
summarise(year_site_mean = mean(BLUE))
df <- inner_join(df, df_means)
if(!weights) df$weights_vcov <- 1
df <- df %>% mutate(genotype.id = as.factor(genotype.id),
year_site.UID = as.factor(year_s... |
27bd55b96145cb581b0c6ca7805580020bf7b5033e1410ad0f9011f3a570d794 | R | 1,590 | 50 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
library("ggplot2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcript_counts_unfiltered.tsv", sep="\t", row.names="transcript_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed... |
46333201d91456e0c8ff0038e60afbddf3b7c4c7b95c52fe67e30feb189a952f | R | 1,596 | 50 | #' Plots individual posterior distributions using \pkg{bayesplot}.
#'
#' @param obj An output of hBayesDM. Its class should be \code{'hBayesDM'}.
#' @param pars Character vector of parameter names to plot.
#' @param show_density If \code{TRUE}, draws posterior densities via
#' \code{bayesplot::mcmc_areas}; otherwise ... |
8776ecbd3a118e8ff7120697a85d4a1cc10362db36efe8e4ad6de297f96ac029 | R | 1,597 | 69 | #' @rdname anno_link
#' @format NULL
#' @usage NULL
#' @export
add_link <- function(...) {
warning("`add_link()` is deprecated. ",
"Please see `?anno_link()` for more detail.", call. = FALSE)
NULL
}
#' @rdname anno_link
#' @format NULL
#' @usage NULL
#' @export
anno_link_label <- function(...) {
warnin... |
53646af1e279357aefa89f377e5f532a200eeda242532c9e2b912f78766fbbfa | R | 1,601 | 45 | guha_sim2_case2 <- function(nburn = 30000, nsamp = 20000)
{
rm(list=ls())
#if (!require(timeR)) install.packages('timeR')
sim2 <- read.csv("data/test/simulation2_case2.csv")
sim2_b <- read.csv("data/test/simulation2_case2_bs.csv")
y <- sim2$y
Xmat <- data.matrix(sim2[1:190])
source("additional_materia... |
be3b921dd0ff33eb8fa48ccd9a04b57ca53dafd88b9bdf5f41cb3fd5049d176e | R | 1,602 | 49 | ## Implementation of RRHO for sex-specific expression
library(RRHO2)
library(dplyr)
get_de <- function(){
err <- 1e-320
fn <- paste0("../../summary_table/_m/",
"differential_expression_analysis_4features_sex.txt.gz")
return(data.table::fread(fn) |>
filter(Type == "Gene") |>
... |
c3edb94ac41dcd1ec0c442cab241fcd98c0ebe9b1dd1faa37764792f71fe46d8 | R | 1,602 | 47 |
guha_sim2_case1 <- function(nburn=30000, nsamp=20000)
{
#rm(list=ls())
#if (!require(timeR)) install.packages('timeR')
sim2 <- read.csv("data/test/simulation2_case1.csv")
sim2_b <- read.csv("data/test/simulation2_case1_bs.csv")
y <- sim2$y
Xmat <- data.matrix(sim2[1:190])
source("additional_materia... |
cb7250884a4d2f2bc3e66f31bbe4104fd618d5c41f34fd57f415b6bbf38c387b | R | 1,603 | 35 | ##' @title Match samples in metadata and expression matrix
##' @description Check if samples in the metadata and expression data match
##' @param metadata metadata parsed from \code{\link{gdcParseMetadata}}
##' @param rna.expr \code{\link[limma]{voom}} transformed expression data
##' @return A logical value. If \code{T... |
9ab498a9984fa8d9ca62cf006394aeec61d6391b01d15ea6cea7446300a8d59d | R | 1,607 | 46 | # Load libraries ----------------------------------------------------------
library(tidyverse)
library(here)
# Load data ---------------------------------------------------------------
load(here::here("results", "pseudogenes", "pseudogene_annotated.rda"))
# Main -----------------------------------------------------... |
38f8f66c0aaeb33eccd7311f96b7f14f64118f47388d275e052362dc343e15c6 | R | 1,616 | 53 | ListPriors <- function(){
output <- data(package="PLIER")[[3]]
prior.index <- which(output[,"Title"]!="" & (!output[,"Title"] %in% c("Immune Markers (Clinical Pathways)", "Surrogate Proportion Variables","Vaccination Data" )))
print(output[prior.index,c("Item","Title")])
}#ListPriors
VarianceExplained <- funct... |
5ded1708c2bbbcf056920018ff055d1af29b11420d2e7c7ef7dc16d100720ff2 | R | 1,619 | 43 | test_that("COMPLETE-001: long and all produce same data", {
imp <- mice(nhanes, maxit = 1, m = 2, seed = 123, print = FALSE)
lng <- subset(complete(imp, "long"), .imp == 1, select = c(age, bmi, hyp, chl))
all <- complete(imp, "all")[[1]]
expect_equal(lng, all)
})
# preserve rownames type
nhanes3 <- mice::nhane... |
89095a80b9d9a811e678f1e51356ea772b0847da055ddfd1a56a103b67c5c877 | R | 1,619 | 51 | ## This script generates a clustered dotplot for
## enrichment analysis (XCI)
library(dplyr)
library(ggplot2)
save_plot <- function(p, fn, w, h){
for(ext in c(".pdf", ".png", ".svg")){
ggsave(filename=paste0(fn,ext), plot=p, width=w, height=h)
}
}
#### Main
xci <- data.table::fread("xci_enrichment_an... |
f0a46b21f95183ceaed53890f0176d191c076c2e78415d8149e21118f07d2222 | R | 1,622 | 54 | #### Visualization of GO plots
suppressPackageStartupMessages({
library(dplyr)
library(ggplot2)
})
save_plot <- function(p, fn, w, h){
for(ext in c('.svg', '.pdf')){
ggsave(file=paste0(fn,ext), plot=p, width=w, height=h)
}
}
get_top_GO <- function(tissue){
fname <- paste0(tolower(tissue),... |
b815da8f0e47a7efe22cc488abb5a88531c15f36b59711bff5fa1b620b5a4845 | R | 1,629 | 61 | library(paradox)
library(R6)
library(mlr3)
# Partial Least Squares Regression
LearnerRegrPls = R6Class("LearnerRegrPls",
inherit = LearnerRegr,
public = list(
#' @description
#' Creates a new instance of this [R6][R6::R6Class] class.
initialize = function() {
ps = ps(scale = p_lgl(tags = "train... |
c57765f405155203b21139f89d97a74e5308d3f2b9f00831f1d85427ab509944 | R | 1,629 | 63 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcript_counts_multiple_median_cpm_1.tsv", sep="\t", row.names="transcript_id"))
cts_med <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/transcr... |
da9fe798a228bbeca154a60ff1fca2d1f250ca596935a9717a3199542ee7813a | R | 1,630 | 43 | ---
title: "Untitled"
output: html_document
date: '2023-03-29'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(pacman)
p_load(tidyverse, ggsignif,multcomp, broom, emmeans, DescTools)
setwd("~/Documents/Systemic ESYT1 project locomotion, behavior, survival, anatomical data/Locomot... |
4b9e994a81c0447dcdada9f666028e5dfd2c3b34ee059880274c3beba0e94ed7 | R | 1,633 | 41 |
#load the libraries
library(readr)
library(tidyverse)
library(reshape2)
spike_ins_in_samples = read_csv('data_InterLab/2_interim_data/spike_ins_in_samples.csv')
#load the data raw (non-normalized) peptide data
peptide_df_raw = read_csv("data_InterLab/2_interim_data/peptide_df_raw.csv")
spike_in_quantities_raw = pep... |
3b488ebe0f4403b7cf9fd027d7deaff6e82fe73f4f04f1cbe46e9c6adc1a1f83 | R | 1,637 | 36 | cheminfo<-function (file, masterChemFile = masterChemFile)
#function from IGM.MEA package
{
masterChem = read.csv(masterChemFile)
masterCD <- as.data.frame(masterChem)
masterCD <- masterCD[, 1:9]
temp1 <- paste(masterCD$Project, masterCD$Experiment.Date,
masterCD$Plate.SN, sep = "_")
mas... |
6af61d055d3e6984ae4938278ee3ced245e9c5fd5df90cf3dd533b94fac107a2 | R | 1,637 | 65 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/gene_counts_unfiltered.tsv", sep="\t", row.names="gene_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.deseq2/experimental_design... |
2879f57534a4b0dc27607d6bfd1fcccf22be51b385ceb0e58effba464f5e8638 | R | 1,644 | 25 | jagsify <- function(d){
ACCN <- d %>% filter(condition=="N") %>% select(Participant, ACC) %>% unstack(form="ACC~Participant") %>% as.matrix
ACCA <- d %>% filter(condition=="A") %>% select(Participant, ACC) %>% unstack(form="ACC~Participant") %>% as.matrix
ACCB <- d %>% filter(condition=="B") %>% select(P... |
2a4960acc433f40bd9d5fb486752b0a29cd091fc6b133bc69c1cd56bd165277b | R | 1,644 | 61 | data('factRsample')
counts.path <- system.file("extdata/counts.txt", package = "factR2")
meta.path <- system.file("extdata/meta.txt", package = "factR2")
cts <- read.delim(counts.path)
cts <- as.matrix(cts)
meta <- read.delim(meta.path)
obj <- addTxCounts(factRsample, cts, meta, ~samples)
test_that("Test addCountsDa... |
e81a94cf228700cb4bd8549ecba468b4d50966e1c1f75dcb019cee7903696b82 | R | 1,644 | 44 | #!/usr/bin/Rscript
# Source: https://github.com/opain/Calculating-FUSION-TWAS-weights-pipeline/
library("optparse")
option_list = list(
make_option("--ld_ref_dir", action="store", default=NA, type='character',
help="Directory where FUSION LD reference files are stored [required]"),
make_option("--PLI... |
5653818aacd0bdfdd1bdd65c4b6b5ae14191a1bb28113b0c6c10ed655dc78d5a | R | 1,645 | 65 | #!/usr/bin/Rscript
## Import Libraries
library("DESeq2")
## Import counts matrix
cts <- as.matrix(read.csv("../../../data/bernardo/processed/04.deseq2/gene_counts_unfiltered.tsv", sep="\t", row.names="gene_id"))
## Import metadata
coldata <- read.csv("../../../data/bernardo/processed/04.deseq2/experimental_design... |
93b2810174831686d56fb0ad1ef74716f456228123c9bed77c101aec6642d3b2 | R | 1,647 | 50 | # Author:
#
# This snippet has been taken directly from here:
# http://www.cookbook-r.com/Graphs/Multiple_graphs_on_one_page_%28ggplot2%29/
# Multiple plot function
#
# ggplot objects can be passed in ..., or to plotlist (as a list of ggplot objects)
# - cols: Number of columns in layout
# - layout: A matrix speci... |
b89cb8fbfc2d037ac9e150dee78236603aba00dda9c392918447d522bb0af9a9 | R | 1,647 | 61 | #### module enrichment #####
#module load R/4.0.3-Python-3.8.5-Anaconda3-2020.11
# on the Kimel cluster
module load R/4.2.1
#install.packages("BiocManager")
#BiocManager::install("org.Hs.eg.db")
#install.packages('./HDO.db_0.99.1.tar.gz', repos = NULL, type="source")
#BiocManager::install("clusterProfiler")
#update.pac... |
a8c5fde438f975e954b7d9907d33879f72f8bc939bc8cd7bd135d9642f20ae0c | R | 1,653 | 56 | #### This script measures sematic similarity between GO annotations
suppressPackageStartupMessages({
library(dplyr)
library(GOSemSim)
library(corrplot)
})
load_go_data <- function(tissue, sex, ont){
fname <- paste0(tolower(sex), "/", tolower(tissue),
"_gsea_", ont, ".tsv")
retur... |
f1fa4f4ebd8e0cfc6a09bde1ec38157f81226e65a047648472b87b5fa4dc865d | R | 1,653 | 39 | #' Leiden 85+ study
#'
#' Subset of data from the Leiden 85+ study
#'
#' The data set concerns of subset of 956 members of a very old (85+) cohort in
#' Leiden.
#'
#' Multiple imputation of this data set has been described in Boshuizen et al
#' (1998), Van Buuren et al (1999) and Van Buuren (2012), chapter 7.
#'
#' The... |
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