sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
abedbdb2159689e3a5f6641bcb0652f7af9426ecf3b098e987c163806689bc47 | R | 2,939 | 91 | ---
title: "Supercluster Crossvalidation"
output: html_notebook
---
Version 1.0, July 2025, SA
For leave1out crossvalidation purposes, this script provides 212 clustering solutions leaving a subject out each time.
- Input: Hippo_BinxAxis_F_betas.txt and subj_list_n212.txt
- Output: clusters_leave1out.txt
- cluster... |
d9b14367c48bc606c844bc58c0421f2be1ae3779d2b3bd1d39236cb6d194d6be | R | 2,949 | 68 | # Load necessary libraries
library(lme4)
library(lmerTest)
library(emmeans)
library(ggplot2)
library(dplyr)
# -------------------------------------------------------------------------
# 1. Behavioral Analysis: US Expectancy Ratings
# -------------------------------------------------------------------------
# Analyzes ... |
a4741bc5cc740137667c090ad07bc55108e65ad12e8eba989c8ae8188c882456 | R | 2,957 | 99 | library(ggplot2)
library(rlang)
library(cowplot)
library(viridis)
library(tidyr)
library(dplyr)
setwd("~/Documents/pdbehavior/")
data <- readr::read_csv(here("Analysis_Files", "ASO","ASO Food Pellet - Buried_Food_Pellet.csv"))
data$SLC_Genotype <- factor(data$SLC_Genotype, levels=c("WT", "HET", "MUT"))
generate_boxpl... |
ed601b5bc556ee44f70cc863283b4976f73e932f355b563a4eee2180aae567b0 | R | 2,987 | 98 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\isttc\\results\\mice\\dataset\\cut_30... |
cd211272215674f3cbfcb4bc2ed7d052147497c8343687194759758620fb9fdf | R | 2,992 | 86 | #' This function trains, fits and evaluates a deep neural network (DNN) model.
#'
#' This function creates a DNN model from the reference dataset (scRNA-seq) by using the keras package.
#' @param out The output of the ds_split_data_encoder function. The input data used to train the DNN model.
#' @param hnodes The numbe... |
1556e3cb0de928a7a854925b43572c54bc2f926d7421754960308f18672da4e4 | R | 3,010 | 96 | # Concat.r
if (exists("Diff1_EM") && exists("Diff1_CC") && exists("Diff1_CCC")) {
# pdf("Concat.pdf")
# Set up the plot
par(las=1)
par(xpd=NA)
par(oma=c(4,0,2,0))
par(mfrow=c(1,1), mar=c(3,4,4,2)+.1)
LIM1 = -1.5
LIM2 = 4.5
if (SCALE==0) {
LIM1 = LIM1 * 5
LIM2 = LIM2 * 2.25
... |
f86801f2e82472ca635c543755edefb98cbfe78ff8095b3ea9c9f8ef0c1bbbd2 | R | 3,065 | 81 | library(Matrix)
library(monocle3)
library(cicero)
library(stringr)
library(argparse)
library(GenomicRanges)
library(BSgenome.Mfascicularis.NCBI.5.0)
library(ArchR)
library(parallel)
library(tidyverse)
parser <- ArgumentParser(description='extarct bedfiles from ArchR project')
#parser$add_argument('-I', '--input', help... |
db2f6097b1cd4d5b4b8efa21fd5342b686cc6def29374e902c44618d6c0de160 | R | 3,111 | 109 | #' Plotting item characteristics curves
#'
#' This function takes a fitted mirt-model and the underlying data and visualizes item characteristic curves.
#'
#'
#' @param model an object of class `SingleGroupClass` returned by the function `mirt()`.
#' @param items numerical vector indicating which items to plot (curr... |
7c79805cafc72ba7b19128c3ab732dceef09ed7c8832a33826eb4206f37adf52 | R | 3,120 | 68 | library(ggplot2)
library(cowplot)
library(tidyverse)
colors_to_use <- clusterExperiment::bigPalette
base_dir <- "/Users/sdaniell/Dropbox (Partners HealthCare)/Sara Danielli/Project/Ependymoma/6 - scRNAseq models"
metadata <- read_excel(file.path(base_dir, 'metadata.xlsx'))
FileName <- unique(metadata$FileName)
Pro... |
a1f4471841d6832c490d1a96867175e0a545fb24b4ff81b8c9ca1db233048329 | R | 3,135 | 77 | library(Matrix)
library(fitdistrplus)
library(ggplot2)
library(argparse)
parser <- ArgumentParser(description='fit model for filtering peak link')
parser$add_argument('-T', '--type', help='input subclass type')
parser$add_argument('-O', '--path', help='output directory path')
args <- parser$parse_args()
ctype <- args$... |
b6a0f195b3756ab03bfeccde94a2b514e157d46e7b24601ab81b7141f2b81194 | R | 3,136 | 100 | suppressPackageStartupMessages({
library(splatter)
library(scater)
})
library(stats)
library(rhdf5)
library(tidyr)
library(stringr)
# n_list <- c(100, 200)
# args <- commandArgs(trailingOnly = TRUE)
# n <- 200 #n_list[1+as.numeric(args[1])]
args = commandArgs(trailingOnly=TRUE)
if(length(args)==0){
ind <-... |
861a1b3dd98490978b82db8a4e9283b1d6387191d5cf5084bcc3bc6f3c28dae7 | R | 3,144 | 60 | # this file takes a single input, the sample_name (such as 'STEPN47_Region_2'). One can simply create a variable
# by running something like: "sample_name <- 'STEPN47_Region_2'", and run this script line by line, skipping the
# first part where the sample_name is read from supplied arguments, or can execute this scri... |
965dcb2b0b5169a49b93e66cee82382384a59ae1bdcb32086874acd847ffe882 | R | 3,160 | 81 | # install.packages("BiocManager")
# BiocManager::install("clusterProfiler")
# BiocManager::install("org.Hs.eg.db")
# BiocManager::install("AnnotationDbi")
# BiocManager::install("rrvgo")
library(clusterProfiler)
library(org.Hs.eg.db)
library(AnnotationDbi)
library(rrvgo)
library(purrr)
library(stringr)
library(ggplo... |
df3a4b472a08132738d86e23e9398000abf82d0cbc9adeb68f2efe4de3707d8a | R | 3,173 | 88 | #if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("limma")
#BiocManager::install("org.Hs.eg.db")
#BiocManager::install("DOSE")
BiocManager::install("clusterProfiler")
BiocManager::install("enrichplot")
#ÒýÓðü
library(limma)
library(org.Hs.eg.... |
f809def9fc43a1dc2a7104676c24353c25c9d01e15dc3a46e3c44f29f90e22b2 | R | 3,178 | 100 | ##' Line search for conjugate gradient
##' @param nlnet The nlnet
##' @param dw ..
##' @param e0 ..
##' @param ttGuess ..
##' @param trainIn Training data
##' @param trainOut Fitted data
##' @param verbose logical, print messages
##' @return ...
##' @author Henning Redestig, Matthias Scholz
lineSearch <- function(nlne... |
a847edc85213c0717d90f0e5f39bd7327f78e83a360594cee7f21541ad0b2e8c | R | 3,192 | 89 | merge_ieeg_and_behavioral_data <- function(roi, sub, distance_df, timelock_folder, keyword_include = "*", keyword_exclude = "9999"){
shift <- 0
hc_elecs <- list.files(path(here(), "data_mount", "remote", "preprocessing", sub, "ieeg", timelock_folder))
hc_elecs <- hc_elecs[grepl(keyword_include, hc_elecs) & !gr... |
1b92d36db35941aa241dc533820c7c355e53e36b14fb2e7a60f55b2ebe6f02cd | R | 3,204 | 84 | library(ArchR)
library(Seurat)
library(tidyverse)
library(presto)
library(parallel)
library(chromVARmotifs)
library("GenomicRanges")
library("BSgenome.Mfascicularis.NCBI.5.0")
gene_file="/cluster/share/atac_group/mafas5/ref/MJ_creat_Gene_Macaca_fascicularis_5.0.91.2.2.V20230616.txt"
exon_file="/cluster/share/a... |
ff7b5fc89307582b0465efef456da0c25ae06aed84a2cc221b3a696eb58ddf92 | R | 3,229 | 90 |
df<- read.table("mofafactor_most_cor_features.tsv", header = TRUE, sep = "\t" )
corr_coef <-cor.test(df$Factor2, df$Sarcosine, method = "pearson")$estimate
corr_p <- cor.test(df$Factor2, df$Sarcosine, method = "pearson")$p.value
p3<-ggplot(df, aes(x = Factor2, y = Sarcosine, color = group)) +
geom_point(size =2... |
be4f395797fa9584087e8075f04116028031d4d8e312dd899f93334c8e7d8809 | R | 3,240 | 87 | require(tidyverse)
require(keras3)
require(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras_model_sequential(input_shape = c(21L))
num_layers <- hp$Int("layers", min_value = 1... |
e4b11be7aea8ed5b20a6dc7a129346233ea8ba4ba183f7e055857b92378a0408 | R | 3,243 | 87 | require(tidyverse)
require(keras3)
require(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras_model_sequential(input_shape = c(1725L))
num_layers <- hp$Int("layers", min_value =... |
eff7bc89364a23d656a4e20a6af84cc55e6327ba7d5187f9384669ba50b63d7d | R | 3,275 | 102 | tryToNum <- function(x) {
x <- tryCatch(as.numeric(x), error = function(e) x, warning = function(w) x)
if (is.numeric(x)) {
return(x)
} else {
return(NULL)
}
}
isWholeNumber <- function(x) {
stopifnot(is.numeric(x))
sapply(x, function(y) y %% 1 == 0)
}
.message <- function(ve... |
111709729a665ac5bbe2b1ef6745b99369a84917f21dc012e220b0270659550d | R | 3,282 | 68 | library(dplyr)
library(Seurat)
library(ggplot2)
## load signatures for scoring
## Read ZFTA::RELA patient scores
NMFmarkers <- readRDS(file.path(resource_dir, "ZFTA.gene.set.merged.rds"))
## Combine neuronal signatures
NMFmarkers$Neurons <- c(NMFmarkers$ZFTA_NPC.like.1, NMFmarkers$ZFTA_NPC.like.3,NMFmarkers$ZFTA_NPC.... |
70fb0d211034a4843994d1905581b9d8c3fd30b4a952d1f313b0422b8344261b | R | 3,311 | 92 |
#' split a ggplot object into 2 ggplots; original plot minus legend and only the legend
#'
#' @param p ggplot object
#' @importFrom ggpubr get_legend as_ggplot
ggplot_split_legend <- function(p) {
return(list(plot=p + theme(legend.position = "none"),
legend=ggpubr::as_ggplot(ggpubr::get_legend(p))))
}
... |
6737c19d7f684af87ddb97ac17bfa4cac0d4f817572740c9a3252b54bad78ff1 | R | 3,316 | 91 | #if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("limma")
#if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("sva")
#ÒýÓðü
library(limma)
library(sva)
geneFile="interGenes.tx... |
d3feb12e584dd1579c7d24b175e5a0b6e76eb1a16747bc1769bd18fd8fc7ee41 | R | 3,318 | 79 |
#' peptide-level ExpressionSet from long-format peptide data
#'
#' @param peptides peptide tibble
#' @param proteins protein tibble
#' @param samples sample tibble
#' @importFrom Biobase pData fData exprs annotatedDataFrameFrom
#' @export
tibble_as_eset = function(peptides, proteins, samples) {
# append_log("groupin... |
9a6c3cddae36fb1223d2816aa568a87f386f801d679a047b862c3ae49c76186f | R | 3,347 | 93 | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
rotarod <- read.csv("Analysis_Files/ASO/ASO Rotarod - Rotarod.csv",header=TRUE)
rotarod$SLC_Genotype <- factor(rot... |
46ee17bf80de69ec238a18a42888834cc8d65fa8b36ca186842f5ae57b0a49d0 | R | 3,353 | 125 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%",
fig.retina = 2
)
```
<div style = "padding-top:1em; padding-bottom: 0... |
ca044d9b126175e4d0fc70403c1ad562dbb5613e602394966f02e5cce38d013a | R | 3,366 | 89 | compute_lfp_correlation <- function(sub_list, roi1, roi2, freq_df, tag){
# Loop over subjects #
for(sub in sub_list){
# filter to sub df
sub_df <- freq_df %>% filter(subject == sub)
# pull the first set of regions
roi1_elecs <- sub_df %>%
filter(region == roi1) %>%
pull(elec_s... |
910a436e0bb54f45c36418c2d35d49c1cd1625d61e006e730ce6e54eb920a835 | R | 3,372 | 92 | ### SOURCE:
# https://github.com/gcostaneto/KernelMethods/blob/master/Gaussian_Kernel.R
### Marginal likelihood for the bandwidth parameter of a Gaussian kernel following
### Perez-Elizalde et al. (2015) J. Agric. Biol. Environ. Stat.
### INPUTS
# theta = vector of h and phi
# y = vector of phenotypic data
# D = dist... |
c227dea9e10e41f3573e320d7515483d90596fac7dc1fd301cd2fedbac3411de | R | 3,387 | 120 | ---
title: "Correlation_Analysis"
output: html_document
date: "2025-06-09"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Configuration
## Load library
```{r}
library(data.table)
library(dplyr)
library(pheatmap)
library(ggplot2)
library(ggpubr)
library(cowplot)
library(ComplexHeatmap)
libr... |
ad6f6f2d229c9a7adbf3dc30a71996ecd7ad0fd4dd61b2dbcb6dd2330d318c4e | R | 3,407 | 97 | source('/cluster/home/chencheng/Mac_gaba/prepare.R')
library(ArchR)
library(parallel)
library(tidyverse)
library(GenomicRanges)
addArchRThreads(threads = 30)
library(argparse)
parser <- ArgumentParser(description='extarct bedfiles from ArchR project')
parser$add_argument('-G', '--group', help='specify a column in colD... |
9a94e554b2b50857b50b1912597911522e6f3aa8a1fbde39d5be3ce694bf492a | R | 3,441 | 101 | context("Testing buildNhoodGraph function")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(dplyr)
library(patchwork)
data("sim_trajectory", package = "miloR")
## Extract SingleCellExperiment object
traj_sce <- sim_trajectory[['SCE']... |
2f6707733ce96e789c72289ec711a6275490ae63a349b87637aeb1cddb9c8884 | R | 3,484 | 88 | source('/cluster/share/atac_group/mafas5/chen_ws/prepare.R')
library(ArchR)
library(parallel)
library(tidyverse)
library(GenomicRanges)
library(argparse)
addArchRThreads(threads = 16)
parser <- ArgumentParser(description='extarct bedfiles from ArchR project')
parser$add_argument('-I', '--input', help='input peak matr... |
f706ad23ca98ae3d8561b0838feb677ce81b0a878cd9761c99bb09aa5f905d06 | R | 3,497 | 129 | # ------------- Figure S7 --------------
#----Figure S7A----
library(Seurat)
library(ggplot2)
library(patchwork)
rm(list=ls());gc()
#1.ST validate prognostic marker----
visiumPath <- "./ST/P10-B1/"
visiumPath <- "./ST/P10-T1/"
visiumPath <- "./ST/P10-T2/"
visiumPath <- "./ST/P11-B1/"
visiumPath <- "./ST/P1... |
4f022e51eb2cb148b1508552cdc83a785b8490716fd3fb1850f2f0077ade3b8a | R | 3,501 | 103 | # Select.r
# Pick the right files to run (using cell database)
MEMORY = F
###########################################################################
options(stringsAsFactors = FALSE)
a = b = NULL
if (MONK=="zen" || MONK=="both") {
a = read.table(
paste0("/data/coord/zen/zenunits", ifelse(MEMORY, "_memory",
... |
7ee91889016034022b6cbd05ae3f5602eb702978a232c59330c2bedbf4f2fba3 | R | 3,518 | 93 | library(Rtsne)
library(here)
library(dplyr)
library(ggplot2)
library(cowplot)
### Compiling PFF data into one single sheet
## Rotarod --
data <- readr::read_csv(here("data", "PFF","PFF Rotarod - PFF_Rotarod_Analysis.csv"))
summary_rotarod <- data %>%
group_by(MouseID) %>%
summarise(mean_latency = mean(Average_La... |
816d70ba2ab7b59c6b4f33b19a8cb23ef8cb5dba3a1b4de5768cf601062fb33b | R | 3,522 | 88 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("src/kernel_functions.R")
source("src/scale2.R")
### The reference table has the pedigree and environment of each data record --
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
ug <- unique(ref$Pedigree)
ue <- un... |
7b6b6901eb281188ecd732dc1dbb2c7142427bfa0b2412d00ad9c9e3abf44f00 | R | 3,526 | 100 | require(tidyverse)
require(keras3)
require(kerastuneR)
require(abind)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
# The input shape comes from the concatenated outputs of the submodels
model... |
7a6211267aa0875164711c1cbd2885e5868ecf6f6f5708bb840ca2e4a0b7b1c3 | R | 3,532 | 52 | # devtools::load_all() # load our R package
#
# ## generate some test data
# npep = 4
# nsamp = 5
# nprot = 2
# samples = tibble(sample_id = paste0("s", 1:nsamp), group="grp1", exclude = F) %>% mutate(shortname=sample_id)
# peptides = tibble()
# for(j in sample(1:nprot, size = nprot)) { # randomize row order, to catch ... |
c2626a2241db7e273e082d7ddb5bdd36986f3554bc7fdc063620a4bd9d3ead3e | R | 3,554 | 97 | #' Average expression within neighbourhoods
#'
#' This function calculates the mean expression of each feature in the
#' Milo object stored in the assays slot. Neighbourhood expression data are
#' stored in a new slot \code{nhoodExpression}.
#'
#' @param x A \code{Milo} object with \code{nhoods} slot populated, alterna... |
8e151ad18758e6134e2661906c1a7229818840168385f8aa3a8f9f828388b248 | R | 3,585 | 96 | #Read project rds
projHeme=readRDS(proj_file)
projHeme$group=projHeme$SampleName
output_directory=getOutputDirectory(projHeme)
addArchRThreads(threads = 8)
options(future.globals.maxSize= 1000*1024^3)
projHeme <- addGroupCoverages(ArchRProj = projHeme, groupBy = "group",force = TRUE)
saveRDS(projHeme,file=past... |
8f468497b925b66534e2e53d1d1e899644834eaaaece290da11b32e559fee60d | R | 3,619 | 138 | #build a formula
form = function(y,x){
return(as.formula(paste0(y,'~',x)))
}
#get effects from linear models on biomarkers
get_effs = function(mod){
m = summary(mod)
res = data.frame(q=coef(mod)['(Intercept)'],k=coef(mod)[2],s=NA,r=NA)
if('glm' %in% attr(mod,'class')){
#sd of residuals is the RMSE
res$... |
ef24d47407c984b26a6a423518446e6fa85632db1f442f948bf7d6a83b84ed9b | R | 3,624 | 107 | #' Item difficulty distribution
#'
#' Visualizes the distribution of item difficulty (b) parameters from a fitted
#' mirt model. For dichotomous models (1PL, 2PL, 3PL), a single difficulty
#' parameter per item is plotted. For graded response models (GRM), all
#' threshold parameters are shown as separate colored point... |
398e09e19445b5c5f18ed81e1e43509834cc4775c2ef49179661e05573ac2a35 | R | 3,643 | 115 | .isMatrixBackedOrStop <- function(object, FUN) {
if (!.isMatrixBacked(object)) {
stop("'", FUN, "()' only supports matrix-backed minfi objects.",
call. = FALSE
)
}
}
.isMatrixBacked <- function(object) {
stopifnot(is(object, "SummarizedExperiment"))
all(vapply(assays(object)... |
4d5170403ff96c5b1ad008bd45983cefe7b2219be06e54aa25bb10b29c3c7734 | R | 3,646 | 127 | #builds a formula
surv_form = function(x){
return(as.formula(paste0("Surv(time,status)~", x)))
}
#' Projecting Phenotypic Age algorithm onto new data.
#'
#' @title phenoage_calc
#' @description Project Phenotypic Age algorithm onto new data.
#' @param data A projection dataset.
#' @param biomarkers A character ve... |
66a2ce33a3a7a17faab7b1450e894bf31c579bb1839b44882359076c48650b28 | R | 3,671 | 92 | #' Foo
#'
#' @param x foo
#'
#' @return fooo
#' @noRd
#'
my.foo=function(x){
cat("\n\nIMPORTANT NOTE!!!\nYou have run this analysis witht the option less=TRUE. This means that the analysis will try to reduce the number of modules detected, based on their expression pattern. If modules have very similar expression pro... |
810ea0b20e293001c2bd9ad5733bec6c6d21a2bc749960816a9019ab11c87e06 | R | 3,693 | 90 | #install.packages("reshape2")
#install.packages("ggpubr")
#install.packages("corrplot")
#ÒýÓðü
library(reshape2)
library(ggpubr)
library(corrplot)
inputFile="CIBERSORT-Results.txt" #ÃâÒßϸ°û½þÈóµÄ½á¹ûÎļþ
setwd("C:\\Users\\wx197\\Desktop\\×ÔѧÍø113»úÆ÷ѧϰ\\205.geoML×ÊÁÏ\\205.geoML×ÊÁÏ\\25.barplot") ... |
58416a755efb8522938154bd21172e235838dffecbb50aecd0896cb2d482c3b5 | R | 3,720 | 102 | #' Build a graph from an input adjacency matrix
#'
#' Construct a kNN-graph from an input adjacency matrix - either binary or distances between NNs.
#' @param x An n X n \code{matrix} of single-cells, where values represent edges between cells; 0 values
#' are taken to mean no edge between cells. If the matrix is not b... |
595517c745e4a4c5f11dc02ee391f2d55e45e30d9a8d0d6e5f4e933c077c0626 | R | 3,728 | 82 | #WholeBrain Analysis Pipeline - Coronal Sections
#
#Semi-Automated Pipeline for the Segmentation and Registration of Brain-Wide Labels
#
#Requires WholeBrain software package (http://www.wholebrainsoftware.org/cms/install/)
#Ilastik segmentation and further image preprocessing in ImageJ are recommended
#
#
#Removed nec... |
912a5907b081fdc9d48e68d57a32beadbd2702b61589a2ff64d7840f1cfdea07 | R | 3,745 | 102 | #' Count cells in neighbourhoods
#'
#' This function quantifies the number of cells in each neighbourhood according
#' to an input experimental design. This forms the basis for the differential
#' neighbourhood abundance testing.
#' @param x A \code{\linkS4class{Milo}} object with non-empty \code{graph} and
#' \code{nh... |
9df926d4178bd8fec5a419d8a50a6f69e5d2527e41fb8413369da2e9d386265d | R | 3,791 | 83 | #WholeBrain Analysis Pipeline - Coronal Sections
#
#Semi-Automated Pipeline for the Segmentation and Registration of Brain-Wide Labels
#
#Requires WholeBrain software package (http://www.wholebrainsoftware.org/cms/install/)
#Ilastik segmentation and further image preprocessing in ImageJ are recommended
#
#Pipeline modi... |
512e38dbefcfe77f9e96c7071e2311919c8190e852ac2dd63575b48630479d88 | R | 3,794 | 102 | ##' Scaling and centering a matrix.
##'
##' Does basically the same as \code{\link{scale}} but adds some
##' alternative scaling options and functionality for treating
##' pre-processing as part of a model.
##' @title Pre-process a matrix for PCA
##' @param object Numerical matrix (or an object coercible to such)
##' w... |
2591a7288b05efccdbfe36d49d5de0ca4a7192900fac8400107c6d59f21f14e5 | R | 3,798 | 118 | library(here)
library(ggplot2)
library(dplyr)
library(nlme)
library(cowplot)
library(tidyr)
## Environment --
here::i_am("Rscripts/Figure_3_Synucleinopathy.R")
generate_violinplots <- function(input_data, X, Y, min,max){
data<-as.data.frame(input_data)
ggplot(data=data,aes(x={{X}},y={{Y}}, fill={{X}})) +
geo... |
9dead5a7e781b908c8084308f91c1f5d5553ce603c73443f6415f7f8fd16c0e8 | R | 3,835 | 111 | #' Plotting conditional reliability
#'
#' This function takes a fitted mirt-model and visualizes a conditional reliability curve. Heavily inspired by code from Phil Chalmers (author or 'mirt')
#'
#'
#' @param model an object of class `SingleGroupClass` returned by the function `mirt()`.
#' @param theta_range range t... |
2ff75f9cc52741c97bd965ef59586a823219be0aed38cc1ce7de6fd1f4509bab | R | 3,836 | 102 | # doAll.r
OVERRIDE.OVERRIDE = T
options(warn=-1)
rm(FREQ.OVERRIDE,MEMORY.OVERRIDE,SACSPLIT.OVERRIDE,SPLITCODE.OVERRIDE,
SACONLY.OVERRIDE, CC_INACT.OVERRIDE,
ALIGN.OVERRIDE,NEAR.OVERRIDE,AREA.OVERRIDE,MONK.OVERRIDE,CLASS.OVERRIDE)
options(warn=0)
FREQ.OVERRIDE = "4:240:2" # ["4:240:2"] Overwritten for match... |
c780aa4f865f3d1a7560ec8c9cd6008a9f7a946862a32c93a7ab34f3cbbeaf6b | R | 3,842 | 101 | # load cleanActivityAndSleep data in data.frame
setwd("C:/Users/Masterthesis_Mayla/Step2_cleaning_ActivityandSleep/Result")
# install.packages("jsonlite")
library(jsonlite)
# path to csv directory
pfad <- "C:/Users/Masterthesis_Mayla/Step2_cleaning_ActivityandSleep/Result"
# list .csv data
paths <- list.files(path ... |
a588aa57ebe8a921a773cb443a3ea685aac2641af0744d9c1980a842929c47bb | R | 3,850 | 104 | # setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
### Modified from the original to account for missing pedigrees in the
### genotype data.
setwd("/homes/amkusmec/kernels_in_GP")
library(tidyverse)
library(RSpectra)
library(tensorEVD)
library(argparse)
### Process command-line arguments -----------------------... |
cd5875628d84c0e536cc09c0d2333029c4fe330d818af985cb6a5ebc7cc34240 | R | 3,867 | 94 | #' This function split the reference data in training and test set to use it as input for the ds_dnn_model function.
#'
#' @param scale.data scale.data A scaled/normalized matrix of gene expressions like in the `scale.data`
#' of the Seurat object. Rows are genes and columns are cells from the reference
#' dataset.
#' ... |
12c2048ce2326196153da30af18308cffc521f46af2b40fa3483fa7c42c5dc24 | R | 3,885 | 85 | ### Annotate neighbourhood results ###
#' Add annotations from colData to DA testing results
#'
#' This function assigns a categorical label to neighbourhoods in the differential abundance results
#' data.frame (output of \code{testNhoods}), based on the most frequent label among cells in each
#' neighbourhood. This c... |
71ae7c1f0f5dcd12c692f8c27117b9e7d775cb10fd7de8c9ca4e533c80a39278 | R | 3,892 | 105 | library(ggplot2)
library(here)
library(tidyr)
library(cowplot)
here::i_am("src/Figure_S2_Schizophrenia.R")
data<-read.csv("data/Q22/Startle_PPI_Analysis - Analysis.csv", header=TRUE)
data$SLC_Genotype<-data$SLC
data$SLC_Genotype <- factor(data$SLC_Genotype, levels=c("WT", "HET", "MUT"))
data$Q22 <- factor(data$Q22, l... |
7fe4444dc8c77dabcc3cd4faf25bd2bb76b8a8f417a5003c517089f71869cd80 | R | 3,893 | 117 | library(here)
library(tidyverse)
library(cowplot)
library(ggplot2)
### Establish location ---
here::i_am("src/PFF/PFF_Correlate_DAT_with _Rotarod.R")
### Read in input files ---
PFF_rotarod <- read.csv(here("data/PFF/PFF Rotarod - PFF_Rotarod_Analysis.csv"))
PFF_rotarod <- PFF_rotarod %>% filter(Day=="one") %>%
d... |
a2364acce1c419498bd731f58e4138b3e31bb6c89b4a37fc062577f71b0fc023 | R | 3,900 | 123 |
library(ggplot2)
library(rlang)
library(cowplot)
library(ggpubr)
library(viridis)
library(tidyr)
library(dplyr)
library(tidyverse)
setwd("~/Documents/slcspontaneous/Analysis_Files/")
data<-read.csv("SLC_Spontaneous_Open_Field_Analysis.csv", header=TRUE)
generate_boxplots <- function(input_data, X, Y, min,max){
da... |
79f49fd4b46034ef16a6f70f5baa7051ca9d88ed5f0cecbb1e756bb2d225244d | R | 3,903 | 109 | #if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("limma")
install.packages("pheatmap")
#install.packages("ggplot2")
#???ð?
library(limma)
library(dplyr)
library(pheatmap)
library(ggplot2)
logFCfilter=0.585 #logFC?Ĺ???????(logF... |
aa5e2102cf7b674d17debbc6eca20c59cfd82483f1b538ace92ac012b605d956 | R | 3,908 | 85 | library(Seurat)
library(dplyr)
library(data.table)
data_dir <- './data/'
gene_info <- fread('./1k1k_gene_GRCh37.txt')
# read cov
for(ct in c('CD4effCM','Bmem','CD4TGFbStim','CD8all','CD8eff','CD8unknown','DC','MonoNC','NKact','MonoC','CD4all','BimmNaive',
'NKmat','Plasma')){
obj <- readRDS(sprintf('%s/... |
e201532ad94eff16b6660827ef19c55b50fb584f8528e3bb49c256ed049a327e | R | 3,918 | 63 | # library(venn)
library(data.table)
library(dplyr)
# library(ggVennDiagram)
source('/home/panwei/lin00374/TWAS_helper.R')
wgtdir = '~/UKB_TWAS/WEIGHTS/'
resdir = '~/UKB_TWAS/result1/'
gwas_list = c('30000_irnt','30010_irnt','30020_irnt','30030_irnt','30040_irnt','30050_irnt','30060_irnt','30070_irnt','30080_irnt','300... |
de9e45c68c68d5ee32df6055f4973b8562df8fb75a30f295efdfbad70e4a5286 | R | 3,948 | 74 | library(dplyr)
library(Seurat)
library(ggplot2)
## Style plots
# Barplot function
theme_ggplot = theme(legend.position = "none",
plot.title = element_text(hjust=0.5, face="bold"),
panel.border = element_blank(),
plot.background = element_blank(),
... |
d38b228b8c09c2e5164f4d0d69429d360a24f4549dd2036b2818dbccf163cd97 | R | 3,956 | 143 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(ggeffects)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\isttc\\results\\synthetic\\results\\param_fr_alpha_tau_... |
ad0f34b104ec31c06649f7c657dec9228233bc84eba4fd297afd29ea144b5fa4 | R | 3,961 | 143 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(ggeffects)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\isttc\\results\\synthetic\\results\\param_fr_alpha_tau_... |
106dc7347bb728a4abebebdd3c258ce2414bf49aaef97d6fcef1ec88fba9ca61 | R | 3,962 | 136 | # =============================================================
# AGEING BIOMARKER CORRELATIONS
# =============================================================
# COHORT: Lothian Birth Cohort 1936 (LBC1936) Wave 2
# MODEL: Pearson's correlations
# =============================================================
# ----... |
9debb0e047ad6c784ebb24fe17d46917da999c690c4d51e14e0f9d3877a13d2c | R | 4,000 | 152 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\isttc\\results\\mice\\dataset\\tau_un... |
3af9a1c3596cfb4e1acbdc65e7ef8a7fd36978fba09a0498698a78568955fb4b | R | 4,002 | 107 | require(tidyverse)
require(keras3)
require(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras_model_sequential(input_shape = c(280L, 19L))
# inputs <- layer_input(shape = c(280L... |
493f4ff6c499988ebf7ebb9bf765bcb6081047173a26f47e23cbd4b522d56849 | R | 4,017 | 104 | #if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("limma")
#install.packages("dplyr")
#install.packages("ggplot2")
#install.packages("tidyverse")
#install.packages("devtools")
devtools::install_github("Hy4m/linkET", force = TRUE)
#ÒýÓðü
l... |
125718607003d24b0296ee864207d67cc8e4d50dbb0d06fa054c0e35cda13104 | R | 4,028 | 99 | # library(msEmpiRe)
# library(MSnbase)
#
# # load ExpressionSet we prepared earlier; LFQbench dataset processed by Spectronaut. we selected only peptides with good q-value and applied vsn normalization
# load("C:/temp/ExpressionSet_contrast A vs B.RData")
#
# # we stored our intensity data on log2 scale, transform back... |
4125e201206a85795d1734b89aeee59579ad79edd599804a158bb6e703933721 | R | 4,030 | 84 | #### public gist @ https://gist.github.com/ftwkoopmans/7c4413811f08162ab35814b8259b09db
# code snippet to demonstrate MS-EmpiRe reproducibility issues & resolve by setting set.seed()
# msEmpiRe package build used for this testcase: https://github.com/zimmerlab/MS-EmpiRe/tree/ae985a92dada3ea6bce9ee88e6eb674ffe975f41
#... |
5193e8669c913c7a381eaafb310e6a7f0368e0883d1ee81be6f5d17216d0ed1c | R | 4,035 | 115 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras_model_sequential(input_shape = c(21... |
37a5b1e41c3e1818daa1a27fb98410d32680728ea8cd2efe7093980abbf3dd88 | R | 4,039 | 115 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
require(tidyverse)
require(keras3)
require(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras_model_sequential(input_shape = c(17... |
0727f6f983fb33747650b1b60436ac867caedab8bd3652db1638c4f26d58eed9 | R | 4,048 | 107 | ##' Simulated data set looking like a helix
##'
##'
##' A matrix containing 1000 observations (rows) and three variables
##' (columns).
##' @title A helix structured toy data set
##' @name helix
##' @aliases helix
##' @usage data(helix)
##' @docType data
##' @references Matthias Scholz, Fatma Kaplan, Charles L. Guy, ... |
341449ee2480dce8e7b1c456f376fabfa28c9de9fade75e0a52907f31cd4eaed | R | 4,049 | 100 | #04_PRS_association.R
rm(list=ls())
#1.data preparations----
PRS=read.table('./01_taxa/all/Topic/v6.all.score',header = T)
datas=merge(PRS,meta,by=c('FID','IID'),sort = F)
datas=datas%>%mutate(
C1.sd = C1/sd(C1, na.rm = T),
C2.sd = C2/sd(C2, na.rm = T),
C3.sd = C3/sd(C3, na.rm = T),
C4.sd = C4/sd(C4, na.rm = ... |
6bc145d347b8f3f81b5815189dc99c18edb46a38265a87ea4b9cde1077667dd5 | R | 4,049 | 96 | #' Perform simulations to test deconvolution model with constructed bulk
#' tissue profiles.
#'
#' Function to implement the Houseman deconvolution method for a provided
#' set reference data and apply it to a series of bulk profiles
#' constructed from user-provided proportions. The function first selects
#' the sit... |
89830ace5c9af2ad2668dda549e84c939b131e7a4312a590607ed926a7a619c3 | R | 4,049 | 122 | #' Log function that takes any string, appends it to global log (log_ , a list) and prints to console
#'
#' @param x your message as a character string
#' @param type the type of message. options: info, progress, warning, error. For the latter, an R error is thrown after logging!
#'
#' @importFrom crayon red cyan blue ... |
4ad1d32e6bd8cb4c0e0afe400c6f121f1b877a6407e83200a35b2648c29d4ba5 | R | 4,066 | 81 | #Brain Analysis Pipeline - Coronal Sections
#
#Semi-Automated Pipeline for the Segmentation and Registration of Brain-Wide Labels
#
#Requires WholeBrain software package (http://www.wholebrainsoftware.org/cms/install/)
#Ilastik segmentation and further image preprocessing in ImageJ are recommended
#
#Read through comme... |
d03bc824c3729bf86d30515d14306c409f28090c9ab3b0c9b47b4d477434ecbd | R | 4,068 | 128 | # lineplot.r
LINES = T # Solid line to show mean
RIBBONS = T # Ribbon of +/- 1 SEM (can have both)
# Plots = list(24:32, 38:42, 90:110) # Name ALL frequencies; it'll handle it
Plots = list(24:36, 70:120)
PRINT_FIGURE_DATA = T
if (CC_INACT) stop("Not written\n")
# Pull out just the recorded frequencies
# Plot... |
00e0d29a8e75a4005b646a598741a99822bd06775a8c1bd696f3817fa34cf828 | R | 4,076 | 151 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\isttc\\results\\mice\\dataset\\tau_un... |
bed7d7864995c371d24f1a738f6c5ba2cbf98c33ccae99620a2318fb0f772a4a | R | 4,086 | 81 | #Brain Analysis Pipeline - Coronal Sections
#
#Semi-Automated Pipeline for the Segmentation and Registration of Brain-Wide Labels
#
#Requires WholeBrain software package (http://www.wholebrainsoftware.org/cms/install/)
#Ilastik segmentation and further image preprocessing in ImageJ are recommended
#
#Read through comme... |
78a67b3b1e0ab73c8da8eb39111c069a992db1a23efaa03e4fbae979928aea51 | R | 4,095 | 103 | nn.cre <- lapply(nn,\(x){read.table(paste0(path,x),sep='\t')})
nn.cre <- do.call(rbind,nn.cre)
nn.cre$name <- paste(nn.cre$V1,nn.cre$V2,nn.cre$V3,sep='_')
nn.cre <- nn.cre[!duplicated(nn.cre$name),]
gaba.cre <- lapply(gaba,\(x){read.table(paste0(path,x),sep='\t')})
gaba.cre <- do.call(rbind,gaba.cre)
gaba.cre$n... |
507c0a388446efa3160eff1c5e1cb4d05460163faaf4e6056eb024891a1f3399 | R | 4,098 | 118 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
... |
a00eddf42af686f131a4d018d5eb120f4f0d9d9f320de6c471c1137bfbeec073 | R | 4,100 | 161 | #!/usr/bin/Rscript
# check if necessary packages are installed
pkg.list <- installed.packages()[,"Package"]
if (!("argparse") %in% pkg.list) {
install.packages("argparse")
}
if (!("deconvSeq" %in% pkg.list)) {
if (!("devtools") %in% pkg.list) {
install.packages("devtools")
}
devtools::instal... |
d8dbf9e8ce2eb1321dc3426858394e28796150763618de282620c405d9eacb02 | R | 4,111 | 95 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(multcomp)
source("src/scales.R")
ref_pcs <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y <- scale(ref_pcs$GrainYield[ref_pcs$Set == "Train"])
yc <- attr(y, "scaled:center")
ys <- attr(y, "scaled:scale")
ref_pcs <... |
deb50676d55e75cf2274fe94940f122cb4a9a09d0d447abb3ab89dbf3f1dbd4a | R | 4,115 | 116 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
require(tidyverse)
require(keras3)
require(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras_model_sequential(input_shape = c(17... |
d92aabf7af0116a1d2ba44b3d264416bdc70f13b73f4e20a3be4d23d62b84286 | R | 4,121 | 116 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
require(tidyverse)
require(keras3)
require(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras_model_sequential(input_shape = c(17... |
a51d8a74ba053c8a0e6ce6fe723ef5b52b96f267c6cbdca045fb39ddce9a9074 | R | 4,126 | 116 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
require(tidyverse)
require(keras3)
require(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras_model_sequential(input_shape = c(17... |
592d92469d2ea25205eb224b42622f501d60f401ba7a5cc9b25f68added043ae | R | 4,132 | 116 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
require(tidyverse)
require(keras3)
require(kerastuneR)
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras_model_sequential(input_shape = c(17... |
535006f12adec394988c5dd7d8f8ec279bb391b60db218d5034f937ecfce9e53 | R | 4,139 | 87 | #' Filtering modules internally
#'
#' @param dynamicColors the dynamic colors
#' @param s.Wdata the single-cell Seurat object
#' @param datExpr the expression data used to calculate
#' @param geneTree the gene tree from the iteration we are filtering
#' @param my.power the power used to construct the trees
#' @importF... |
690b6a860a0d99d67c594a13bd2e10c7c9dad091fa5a7432b31fb17dc3024821 | R | 4,144 | 119 | # Load packages -----------------------------------
rm(list = ls())
library(dplyr)
library(Seurat)
library(ggplot2)
library(patchwork)
library(readxl)
library(data.table)
library(ComplexHeatmap)
library(circlize)
library(qs)
library(writexl)
# Organize environment -----------------------------------
base_dir <- "/Us... |
0431363244dab1fd086264ddb9d2f166750f1857675689a300e8ca5aa15e0e35 | R | 4,156 | 120 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(keras3)
library(kerastuneR)
# source("src/soil_tuner_classes.R")
HyperModel <- PyClass(
"HyperModel",
inherit = HyperModel_class(),
list(
build = function(self, hp) {
clear_session(free_memory = TRUE)
model <- keras... |
fb8cd9606f7a2226b90547c97b2008504ebb043e60428d154ea1a8a491c1e652 | R | 4,177 | 71 | #' This function trains, fits and evaluates n deep neural network (DNN) models, and select the best one according to the selected metric
#'
#' This function creates n DNN model from the reference dataset (scRNA-seq) by using the keras package,
#' and selects the best model based on the user selected metric.
#' @param n... |
e9b0f0c6c99f9aa0eca3e60619993b7ba4c290e699514d0bd1ab0f3a6c593e16 | R | 4,181 | 103 | library(Seurat)
library(tidyverse)
library(cluster)
#library(factoextra)
library(dendextend)
library(weights)
library(ggpubr)
library(matrixStats)
library(readxl)
base_dir <- "/n/scratch/users/s/sad167/EPN/scRNAseq"
resources_dir <- file.path(base_dir, 'scripts/resources')
source(file.path(resources_dir, "single_cell... |
3da3b86b3242d327e2a9fd5952c14e5d2086273a07cdaf9bf1dc00cd68a4b529 | R | 4,193 | 141 | #!/usr/bin/env Rscript
library('optparse')
library('MicrobiomeStat')
library('phyloseq')
library('tidyverse')
# arguments
# @dir_job, this is where job will be run, should include inputs and outputs folders
# @job_prefix, this is the string prefix for file inputs
# @job_suffix, this explains the type of form... |
2e84907440f36c91bf995cf6af95e1f2833cd926fdcacea85a9924f685c05515 | R | 4,223 | 125 | library(here)
library(tidyverse)
library(cowplot)
library(ggplot2)
### Establish location ---
here::i_am("src/ASO/ASO_Correlate_DAT_with_Rotarod.R")
### Read in input files ---
ASO_rotarod <- read.csv(here("data/ASO/ASO Rotarod - Rotarod.csv"))
ASO_rotarod <- ASO_rotarod %>% filter(Day=="One") %>%
filter(ASO_Tg==... |
4e57f3f28015bc7096f13fce413e05315372eee84d711fc67830f86ca0aa7b3b | R | 4,235 | 101 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(multcomp)
source("src/scales.R")
ref_pcs <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y <- scale(ref_pcs$GrainYield[ref_pcs$Set == "Train"])
yc <- attr(y, "scaled:center")
ys <- attr(y, "scaled:scale")
ref_pcs <... |
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