sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
cc3bc588a1267c720bfa2bc8eee349c2ebf7067cf44fec079f9e7072e2952c2d | R | 3,955 | 138 | # code Figure 2
# Optional generic preliminaries:
graphics.off() # This closes all of R's graphics windows.
rm(list=ls()) # Careful! This clears all of R's memory!
#-------------------------------------------------------------------------------
# Load rjags library and the wiener module
library(plyr)
library(dplyr... |
4edbb2c56ba83390079b4ac96cfaf4c6b6e734ecc249a14e9db6ec2cdd0b7892 | R | 3,961 | 104 | ---
title: "SupplFigure_AS24h"
author: "Fabienne Rössler"
date: '2023-06-08'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("../DLCAnalyzer_Functions_v4.R")
source("../UnsupervisedAnalysis_Functions.R")
library(s... |
84a0ac716bcea48c7b4a8aef0ac11a480b0fd658d1e9c040a04a572234bdbcbe | R | 3,978 | 69 | #' @title HIPvextract
#'
#' @description Extracts hippocampal vertex-wise surface-based measures for each subject in the 'HippUnfold' subjects directory, and stores it as a single .RDS file.
#' @details The function searches for the hippocampal surface data by listing out files with certain suffixes, extract the data f... |
2875da1e6563d8de686fc474841da0b42040643a103f618f7ad2dbcc0ea24eaf | R | 3,986 | 116 | #----- install and load packages -----#
# Execute just once
## Install bioconductor packages
#source("http://bioconductor.org/biocLite.R")
#biocLite("annotate")
#biocLite("AnnotationDbi")
#biocLite("GO.db")
#biocLite("org.Hs.eg.db")
#
## Install R packages
#install.packages('readr')
#install.packages('tmod')
#install.... |
c5ccb3dfbdbc6452de235eeeb40ccd835fb78c41773d7278a4d631f925ad19ea | R | 4,007 | 112 | ##-------------------------------------------
## Pull DNA methylation data from Synapse
## and prepare for analysis
##-------------------------------------------
library(data.table)
library(GEOquery)
library(synapser)
library(magrittr)
library(readxl)
# login to synapse
synapser::synLogin()
##----------------------... |
b175b79a170fea8e5123de4b0e4f1dd81ced859bd6f7823811d63ffb25e5524b | R | 4,011 | 89 | #Set the variables
path = "/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/data/mri/conn_matrix/rs"
beh_path = "/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/data/"
save_path = "/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/results/... |
e93543f447fe5d8a022a7b55daee105390b1a45a3e2cc52fbf9eaf870f1aabcc | R | 4,011 | 142 | #' @title Find an evenly spaced grid for x
#' @description
#' `makeraster()` fits the data to the specified raster.
#'
#' `fitraster()` tries different raster parameter and returns the raster that covers most of the
#' `x` values: The differences between the values of `x` are calculated (possible step
#' sizes). For ea... |
c8b5850b5185e4d02eefcf49181fc0ed032b016b021b01e9823ff2b42abd7e91 | R | 4,014 | 115 | #Plot violin plots instead of featureplots?
#Markers from Hodge et al Middle temporal gyrus
#Lake et al
#Bakken et al
# Load libraries
library(SingleCellExperiment)
library(Seurat)
library(Matrix)
library(scales)
library(tidyverse)
library(RCurl)
library(cowplot)
clusters_seurat_integrated <- readRDS("directory/clu... |
3b3bb75b95251e672a347c227153a564d28c3684bd850e7ce54b1b691547d86c | R | 4,015 | 119 | # Load necessary libraries
library(ggplot2)
library(clusterProfiler)
library(org.Mm.eg.db) # Mouse gene annotation
library(enrichplot)
library(DOSE)
file_path <- "E:/seq/Neuron/Neuron seq lipao.csv"
# Load the data
rna_data <- read.csv(file_path)
# Filter DEGs based on p-value threshold (p < 0.05)
deg ... |
421179207184d4936ff6572d0efc47c58108103ffc95799858f37f1db9c4ce76 | R | 4,017 | 84 | #----Figure_S06_E---------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure S06 Panel E for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#------------------------------------------... |
7141c6b0b5999742c1754ebc55868277782265ce0e628cb7bcfea2a2d753cdac | R | 4,017 | 116 | ---
title: "Graphical abstract"
author: "Guillaume A. Rousselet"
date: "`r Sys.Date()`"
output:
# pdf_document:
# fig_caption: no
# number_sections: no
# toc: yes
# toc_depth: 2
github_document:
html_preview: yes
toc: yes
toc_depth: 2
---
# Dependencies
```{r, message=FALSE}
library(g... |
1e261a0b4bf978f8dc6a3cb359e0c7ba4b63dbb23b15f3d76cc242bb4513863d | R | 4,023 | 139 | ---
title: "MBTY_Clustering"
author: "Lukas von Ziegler"
date: '2024-01-19'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer.R")
source("BFA_BFF.R")
library(sp) #tested with v1.3-2
library(i... |
f51d7def42cd06b85711d096504caf72935efcf9f5d9f8dbf87eafbd1f428097 | R | 4,024 | 128 | ---
title: "ClusteringAnalysis_IFS_25Clusters"
author: "Fabienne Rössler"
date: '2022-12-20'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load all libraries:
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer_Functions_v4.R")
source("Unsupervised... |
d3df6639755e20c15e2e539247be099439c92edf6833ca2e18bb8a9662bea7cb | R | 4,025 | 88 | #' Merge List of SE objects
#'
#' Merges a list of SE objects provided/ Combines assay, rowData, and colData data frames. Each
#' SE object must satisfy the following requirements:
#' \itemize{
#' \item{the name of the colData column containing sample identifiers must be the same across all SE objects}
#' \item{t... |
b7c7e6ff69f5748f21116a383f5c95ba935450a068cf022e3a210b2896788754 | R | 4,031 | 109 |
#' mochims__plot_additive_trait_3state
#'
#' Plot folding additive trait.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param input_dt data.table with model free energy estimates (required)
#' @param RT constant (default:0.001987*(273+24))
#' @param trait_names additive trait name (d... |
8b58f815ff3a718e9b7fe24cd58172c72b299a7995cb1e9e1c9b34b2e3bf8635 | R | 4,033 | 88 | #Set the variables
path = "/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/data/mri/conn_matrix/rs"
beh_path = "/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/data/"
save_path = "/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/results/... |
678e1d5f312f8a19b8db57fb2a1dbbb93f689991d1fa56d3b29a7a586b039a84 | R | 4,034 | 179 | library(data.table)
library(Matrix)
library(ggplot2)
library(tidyr)
library(tibble)
library(dplyr)
source("../utils/0-indices.R")
# Will be the dataset "wang" or "pbmc"
dataset <- commandArgs(trailingOnly = TRUE)[1]
qubic_path <- "~/QUBIC2/qubic"
tmp_path <- file.path("tmp", sprintf("%s-qubic2", dataset))
if (!dir.e... |
725515dd9cc2adc61ade3ed490dfaaca584b031f4742a8e13110b08f454a935f | R | 4,041 | 98 | ---
title: "VAME_Analysis_25Clusters"
author: "Lukas von Ziegler"
date: '2024-02-29'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer.R")
source("BFA_BFF.R")
library(sp) #tested with v1.3-2
li... |
4e9448b179c20438a2ffda7cf5ce18a08292c48d207d4760629bff2ce51dac1a | R | 4,043 | 158 | do not do that - but is in metadata
# Host_latin_facet_df <-
# get_ctg_data_geom_facet(explor_plot3d$data, "ctg_Host_latin", "Host_latin", "Host_latin_string")
# HERE ADDED for simple label panel - we do not do that
# labels_df <-
# explor_plot3d$data %>%
# dplyr::filter(isTip) %>%
# dplyr::select(label) %>%... |
af28b477d4ff7f7c37d7ef5af5390b66eefabfa9feabb5cd5012995be7a6d4e7 | R | 4,044 | 121 | ---
title: "Optic Nerve DEGlist creation"
author: "Jonathan Nelson and Katie Emberley"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
html_document:
toc: yes
toc_depth: 3
toc_float: true
number_sections: yes
theme: bootstrap
df_print: paged
code_folding: hide
highlight: pygments
-... |
7c43744ffa3bc1a1cb9756a7a4dfac6931309a431b639711da4a9220b095b0d0 | R | 4,046 | 90 | #Set the variables
path = "/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/data/mri/conn_matrix/rs"
beh_path = "/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/data/"
save_path = "/m/cs/scratch/networks-pm/effects_externalfactors_on_functionalconnectivity/results/... |
96422c95626f4565b845d1db6dcec8800bf25e4eb5c8a449e8bade3569bc2cca | R | 4,051 | 170 | ---
title: "Markos et al data integration"
author: "Jan Kubovciak, Institute of Molecular Genetics of the Czech Academy of Sciences"
output:
pdf_document:
number_sections: false
toc: true
df_print: paged
---
last updated: `r Sys.time()`
```{r setup_glob, include=FALSE}
knitr::opts_chunk$set(
echo = TR... |
2c84f6bc24bfe39fa21d5d78d833bd853569eef0a61993dab0d50f5733e8d531 | R | 4,053 | 118 |
###Script 2
#Normalize and Integrate Libraries
require(Seurat)
require(sctransform)
require(patchwork)
require(dplyr)
timestamp()
parallel::mclapply
#load data
neuM02 <- readRDS("datasets/neuM02.rds")
neuM03 <- readRDS("datasets/neuM03.rds")
neuM04 <- readRDS("datasets/neuM04.rds")
neuM05 <- readR... |
aa32faee98e501ab7c19162bc681106b73318f8f3da8f7471dc43f098d278917 | R | 4,066 | 130 | simThomas3d<-function(id, k, mu, s1, rg)
{
# simulate 3d Thomas process
gen0<-rpoispp3(k, domain = box3(c(rg[[1]],rg[[2]]),c(rg[[3]],rg[[4]]),c(rg[[5]],rg[[6]])), nsim = 1)
while(dim(gen0$data)[1]==0){
gen0<-rpoispp3(k, domain = box3(c(rg[[1]],rg[[2]]),c(rg[[3]],rg[[4]]),c(rg[[5]],rg[[6]])), ns... |
2d51e88012005eca997f85e6ce93c22c8c7176ff07f686e3ba8247ac4b270d3f | R | 4,069 | 119 | # Function -------------------------------------------------------------------
.all.equal <- function(target, current, ..., check.attributes = FALSE,
check.names = FALSE, check.column.order = FALSE,
check.label = FALSE,
tolerance = hy.getOption("tole... |
47d20a2343663410c6bfdfbac8963d289e96c6328de7e0a1c63ae6b1927e31fb | R | 4,071 | 72 | source('./scripts/01_Setup.R')
all2= readRDS("./data/processed/rds/SignificantCells.rds")
picocells_passing_mad_conf= all2$id
Pico_76 <- readRDS("./data/processed/rds/Pico_76_250kb_lift_SegFixed_cnv_stat_genome.rds")
Pico_76x= Pico_76[Pico_76$id%in%picocells_passing_mad_conf,]
Pico_76x= Pico_76x[!Pico_76x$chr%in%c('ch... |
96bff21da0eec800d497ead0fadcdbcbe5a03e543bd72ccd9503ab7bb2a89dcc | R | 4,071 | 142 | ---
title: "Clustering Yohimbine Data from Roche"
author: "Fabienne Rössler"
date: '2023-05-31'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer_Functions_v4.R")
source("UnsupervisedAnalysis_Functions.R... |
7beff5d159e1fb42d8728bed7110a4f9dae127155f4719b4bd536833a999e0f3 | R | 4,073 | 67 | #----Figure_S11_S12-------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure S11/ S12 for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#---------------------------------------------... |
4e6879af8584e52acd5ff374dcc8ce59e983a2bb3d9bc0c2f9e0171a40fcde8f | R | 4,080 | 62 | source('./scripts/01_Setup.R')
# A treemap R script produced by the Revigo server at http://revigo.irb.hr/
# If you found Revigo useful in your work, please cite the following reference:
# Supek F et al. "REVIGO summarizes and visualizes long lists of Gene Ontology
# terms" PLoS ONE 2011. doi:10.1371/journal.pone.0021... |
99e96792afd75864f3f33c52e6ae433dd24261e956aef270ba3c31c6831f11f6 | R | 4,094 | 99 | # SPACING ######################
###############################################
neurod2_peaks<-readRDS("~/neurod2/neurod2_peaks.Rds")
neurog2_peaks_motifs<-readRDS("~/neurog2/chipseq/neurog2_peaks_motifs.Rds")
library(dplyr)
# Compute which are the proportions of spacing between all E-boxes.
proportion_spacing<-fun... |
e4926caa70d08daab3f0ed782fb732849ebfb14a0f1fb661273890ca768c0a79 | R | 4,100 | 78 | # --------------------------------------------------------------------------------- #
# #
# Hippocampal volumes in UK Biobank are associated with APOE only in older adults #
# ... |
531301a83c6dbbfb7dae04194ecb579193198bb00fadd9c0a735d5c01a983919 | R | 4,104 | 158 | ---
title: "scCoAnnotate - Benchmarking"
output:
html_document:
df_print: paged
theme: flatly
toc: yes
toc_float: yes
toc_depth: 1
code_folding: hide
params:
tools: ''
min_agree: ''
ref_name: ''
pred_path: ''
fold: ''
accuracy_metric: ''
ontology_path: ''
onto... |
9e513851398b23fb1443e71fb86220d9863e272ab8a6c1d457d4750945f87a89 | R | 4,110 | 104 | #' @title fs_stats()
#'
#' @description Extracts descriptive statistics, for the whole-brain and subcortical region-of-interests (ROI), within a FreeSurfer subjects directory. It reads them from the aseg.stats file, as generated by the default FreeSurfer preprocessing pipeline.
#'
#' @param sdirpath A string object ind... |
1ec966467fafb1047a9761923e11f4a2f8ce28ec5c8f92efd580d52a85d81f10 | R | 4,115 | 114 |
library(psych)
library(tidyverse)
# load
baseline <- read.csv('/home/mgell/Work/reliability/text_files/abcd/baseline.csv')
followup <- read.csv('/home/mgell/Work/reliability/text_files/abcd/followup.csv')
#summary(baseline)
#summary(followup)
# remove vars with too many nans
behs <- c("src_subject_id","eventname",
... |
3a351e5931cec7e73af9c0fa4cea689a98259b5f7585fb63fed621cb0618cf1e | R | 4,124 | 110 | ##' An improved version for RRHO, which aims to correct the intepretation for top left region (up in x and down in y) and bottom right region (down in x and up in y).
##'
##' We improved the algorithm such that all four regions of RRHO plot are meaningful.
##' @title RRHO2
##' @param RRHO_obj RRHO object. See RRHO2_ini... |
2f941219cdc0362485aba5edf4cee1eb29dd15d1a0fda54888b4238508304a36 | R | 4,140 | 122 | ## -----------------------------------------------------------------------------
##
## Somatic variant detection in lesional focal epilepsy
## This script plots the meta-analysis cohort size and phenotypes
##
## Author: Dr. Christian Bosselmann
##
## Date Created: 2023-09-05
##
## Copyright (c) Christian Bosselmann, 20... |
4ee6a2267553a6075d918fb476db651ad90f7b94f1295ae1b94c6eb73b5566a7 | R | 4,141 | 139 |
# Generate a g-factor from the behavioral data
# Code adopted from Dubois et al. 2018: http://dx.doi.org/10.1098/rstb.2017.0284
# Original code for bi-factor analysis: https://github.com/adolphslab/HCP_MRI-behavior
# Generate a fluid intelligence factor (latent score)
# Generate a crystallized intelligence factor... |
b6fe8120bd7b5c2c6bf25052bfb61619d50b7866e402ebe8b9defd9ee3998365 | R | 4,145 | 161 | # Set generic ----------------------------------------------------------------
#' Functions `colSums()`, `colMeans()`, `rowSums()`, and `rowMeans()` for
#' `hyperSpec` objects
#'
#' `hyperSpec` objects can use the base functions [base::colMeans()],
#' [base::colSums()], [base::rowMeans()] and [base::rowSums()].
#'
#' ... |
91adcf397049626afee3f77de72950b9a5bf1dbd1c1886b304b6c2c362e4be08 | R | 4,160 | 121 |
# Modified script for IS-RSA with Movie as a Random Effect
library(tidyverse)
library(lme4)
library(broom.mixed)
# Load functions to import data
load_csvs_behavior <- function(path) {
file_list <- list.files(path, pattern = "df_full\\.csv", full.names = TRUE)
df_list <- map(file_list, ~{
df <- read_csv(.x)
... |
1a4b91efbd6597df968e3bf88dd7d89b7cfe7ff2b0ce72ded47deef8ba7ab87c | R | 4,167 | 72 | #----Figure_S13-----------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure S13 for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#--------------------------------------------------... |
a223b1b6f967fc2e4f250c415c8c5a36e961ac485f10b28e418612c0f9ce4e80 | R | 4,171 | 118 | ---
title: "T-Test"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## R Markdown
<!-- https://www.statisticssolutions.com/manova-analysis-paired-sample-t-test/ -->
<!-- We are interested be check the consistency of a DNN model before and after data transformation, and ana... |
dc066dfee4799953dace8facf7f591c327e24ea45510f48d179778af57f0e08e | R | 4,175 | 114 | ---
title: "SupplFigure_AS24h"
author: "Fabienne Rössler"
date: '2023-06-08'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer.R")
source("BFA_BFF.R")
library(sp) #tested with v1.3-2
library(im... |
9c288fb661202ff122e1d17ae5d03e3ec0403a4df6bc8f6ca40dccc330048748 | R | 4,220 | 95 | #########################################################################
#
# Gene expression across different MDD groups
# The expression of Schaefer 400 was obtained from python package abagen
#
#
# Liang Qunjun 2023-12-14
library(tidyverse)
library(ggeasy)
library(pls)
# load the CCA loading map
path_load <- "inpu... |
4d2ce55928d28bd7777a53a3d7dbdb96f1a4bc1dc1eab59565cb48f87efd306b | R | 4,221 | 101 | library(readxl)
library(ggseg)
library(ggsegSchaefer)
library(tidyverse)
library(ggplot2)
library(wesanderson)
library(dplyr)
setwd('/example/working/directory/')
# Labels data to load
# First column is an index not used
# Second column should contain the region name matching what ggseg expects for the atlas
# Third ... |
e450db78e1c4495778de618f3a3037d482f1ee5b4ec1d3bc4fc23cc4140cc6d7 | R | 4,236 | 77 | ---
title: "Interpolation of covariance matrices"
author: "Corson N. Areshenkoff"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
resource_files:
- images/interp_determinant.png
- images/interp_example.png
vignette: >
%\VignetteIndexEntry{Interpolation of covariance matrices}
%\VignetteEngine{knitr::rma... |
afef5492d283548c95ba2f38262d0258f32a52422c6289d990ba08e1d53ddc39 | R | 4,248 | 88 | library(stringr)
library(ggplot2)
library(data.table)
library(readxl)
#Fig5a, nearest TSS GO analysis
dat.plot<-data.frame(read_excel("plot_table_fig5.xlsx", sheet=1), stringsAsFactors = F) #,header=TRUE)
dat.plot<-dat.plot[order(dat.plot$pvalue),]
dat.plot$Description<-factor(dat.plot$Description,levels=rev(unique(da... |
6651717ac42a5f7825004c2ead336304e63c624d429bdfc7575806fba095001e | R | 4,255 | 117 | ## -----------------------------------------------------------------------------
##
## Somatic variant detection in lesional focal epilepsy
## This script plots the results of the dNdScv meta-analysis
##
## Author: Dr. Christian Bosselmann
##
## Date Created: 2023-09-05
##
## Copyright (c) Christian Bosselmann, 2023
#... |
a1294489540e2ddfb8bdb9bafdfc7ba5c8ebb70f616f64e0475d835809d8161d | R | 4,262 | 116 | ---
title: "panel_axon_AP_hyperpol"
author: "Franz Xaver Mittermaier"
date: "2024-04-05"
output: html_document
---
This script was added for the revision (Nat. commun. 2024)
This script plots the panels for the axonal AP shape changes in response to the 'hyperpol' subthreshold modulation paradigm. In brief, I ... |
e27134d7a25cb82735e433fb9d3e9031a3572d66336eae23cb81f943e821a658 | R | 4,269 | 103 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
stadia_EM_SP <- function(x, position, batch_vec, adj_mat_mnn, hyper, init, platform, d = 15L, K = 7L, adj_cutoff = 50., icm_maxiter = 10L, em_maxiter = 5L, verbose = TRUE, verbose_icm = FALSE, ... |
59f09a774c1607b32b2ead7e10834ddd27a612613581ee02f0db00f78cdd6469 | R | 4,274 | 131 |
tokenUI <- function(id) {
ns <- NS(id)
column(width = 6,
box(width = NULL,
title = "Reproducibility token",
textAreaInput(ns("repToken"), "Token", placeholder = "Your token will appear here."),
actionButton(ns("createToken"), "Create token")
# bsModalNoClose(id = ns("tokenModal")... |
803b85a1c29283351d37888055e2053dd1c8c67729d8485c10b8f6bb6017a700 | R | 4,277 | 169 |
# Function -------------------------------------------------------------------
#' @importFrom utils modifyList
.labels <- function(object, which = bquote(), drop = TRUE, ..., use.colnames = TRUE) {
validObject(object)
if (!missing(which) & !is.character(which)) {
warning(
"labels are not guaranteed to ... |
bf4026f7a289fe29223b4eb169455334cdbb596225e5ffa090299b5860bbeb99 | R | 4,279 | 90 | #' generate_type4_data
#'
#' This function generates data and plots for selected clusters.
#'
#' @param meta_data_file Path to the metadata file.
#' @param gene_count_file Path to the gene count file.
#' @param selected_clusters A named vector specifying the clusters to include.
#' @param Prefix Prefix for the output C... |
6331a707857c313f4b58166e4f9b5effeb513c2858c9c6d5e01e4977dac6cb3a | R | 4,280 | 168 | #' Merge additional extra data into an object
#'
#' Columns from `y` that are not in `x` are merged into `x`. Columns in `x` and
#' `y` are considered equal if they have the same name, the same content, and
#' the same label.
#'
#' Columns with different content than a column in `x` with the same name will
#' get `".y"... |
05484003ffdb03fb29d00e0bb7d1ee1ccbbe557453ae19c79808d4d2a35f8948 | R | 4,283 | 100 | library(lmerTest)
library(coefplot)
library(MuMIn)
library(qtl2)
library(sjPlot)
library(magrittr)
library(emmeans)
library(interactions)
################################################################################
# Description of the data
# `df_log` contains the behavioral data including the raw facs composite ... |
ba9d3d4fe2ccc174bda6baf653de860ba0d96f595801123f58ac7aea8ead62a5 | R | 4,289 | 102 | # docker run -ti --entrypoint /bin/bash -v /cis/home/ebridge2/Documents/research/graphstats_repos/causal_batch/:/causal_batch neurodata/cdcorr:0.0.1
# cd causal_batch/docs/causal_cdcorr_paper/Figure_reproduction/
require(causalBatch)
require(ggplot2)
require(tidyverse)
require(parallel)
source("../helpers/test_statisti... |
f1e83c5c2b83b24498dcbb453761d1f9d1efce34b30c20af569ecbca36727df1 | R | 4,294 | 144 | ---
title: "CRS1_LDB_Clustering"
author: "Lukas von Ziegler"
date: '2024-01-26'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer.R")
source("BFA_BFF.R")
library(sp) #tested with v1.3-2
libra... |
4ecbfbb6798d2f113cbde360657009ec8fe586cf694291ae144814ab812972a6 | R | 4,295 | 145 | #' Interactively select a polygon (grid graphics) and highlight points
#'
#' Click the points that should be connected as polygon. Input ends with right click (see
#' [grid::grid.locator()]). Polygon will be drawn closed.
#'
#' `map.sel.poly` is a convenience wrapper for [plotmap()], `sel.poly`,
#' and [sp::point.in.po... |
ac467aca2d7212e5e00debb3ee7d456eaca17ceb47e358e74e575ebb0547dcc5 | R | 4,296 | 167 | library(abind)
library(glmnet)
library(caret)
library(waveslim)
library(dplyr)
# Parse command-line arguments manually
args <- commandArgs(trailingOnly = TRUE)
# Initialize default values for arguments
train_observations <- 80
test_observations <- 10
align <- FALSE # Default to false (no alignment)
# Loop through a... |
553f814234c1a9874abccb8345b1e0218e0d94a1ab3b9569a8a06985df6aba0d | R | 4,301 | 110 | # RNA-seq reanalysis for Dr. Witt lab
# The input will be raw-count matrix
# The KO_3 sampels are hetero, will be removed for DE analysis
# The DESeq2 will be used for DE analysis, if you prefer Limma/ edgeR, I can re-run later
setwd("C:/Users/13831/OneDrive - LSU Health Shreveport/analysis for other labs/Siyuan an... |
9b48bbbb13c12951117b12f08937bb6a256f279e07c5b5326fb4878b991e0be5 | R | 4,301 | 108 | args <- commandArgs(trailingOnly = T)
filenamex= as.character(args[[1]])
standardize_name <- function(name) {
return(gsub('[^0-9a-zA-Z]+', '_', name))}
selectedCells = read.table(file.path('./data/processed/', filenamex, 'analysis'), header=TRUE)
delimiters = ".bowtie|.bt2"
samp <- readRDS("./data/processed/rds/Samp... |
583d402df205190740f6e2d1c2569b542d17ef93e446cd483d5e78257e51de41 | R | 4,303 | 126 | #BiocManager::install("rhdf5")
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
library(rhdf5)
library(Matrix)
library(tibble)
female_5_6 <- h5read("../raw_data_full/blum/GSM4911292_female5_6_25.h5", "matrix")
female_5_7 <- h5read("../raw_data_full/blum/GSM4911294_female5_7_30.h5", "matrix")
male_... |
2fdff2e5a3a409af71164c87d4e2f333c5494fcf59f47df7d8b8a8ccb41ba5f1 | R | 4,308 | 152 | #' @title Rubberband baseline correction
#'
#' @description
#' Baseline with support points determined from a convex hull of the spectrum.
#'
#' Use `debuglevel >= 1` to obtain debug plots, either directly via function
#' argument or by setting hyperSpec's `debuglevel` option.
#' @param spc `hyperSpec` object
#' @param... |
272880927329342c9d1fe38891519868ac440ff0ccc1c67458c09d1aaa5edcb5 | R | 4,313 | 146 | #========================================================================================#
# Author: James M Roe, Ph.D.
# Contributors: Didac Vidal-Piñeiro, Ph.D.
# Center for Lifespan Changes in Brain and Cognition, University of Oslo
#
# Purpose: Cross validation grid search for ML
# Script requires individu... |
6f9342e80078ab1a48c946da3a36155ae7f3aec695143a7e25708d97d6cf21bb | R | 4,315 | 89 | # Load and install missing packages ---------------------------------------
packages <- c("devtools", "Seurat")
package.check <- lapply(
packages,
FUN = function(x) {
if (!require(x, character.only = TRUE)) {
install.packages(x, dependencies = TRUE)
library(x, character.only = TR... |
2a1ee499f57802cfa1c4dac7b6a67b46dccca1b7afb06aa37695a1183ed5b607 | R | 4,316 | 96 | library("lmerTest")
library("multcomp")
library("emmeans")
library("readxl")
options(max.print=1000000)
X2P_TBI_data<- read_excel("GitHub/Bibineyshvili_2019/2P_TBI_data.xlsx",
col_types = c("numeric", "numeric", "numeric",
"... |
8824f12568a50c005f9981db03943fdde806b45a54faf81587711a41ae4ba8a4 | R | 4,316 | 99 | library(stats)
library(data.table)
library(readr)
library("scatterplot3d")
library(robust)
# receive cmd arguments
Args <- commandArgs(T)
wd <- Args[1]
dd <- Args[2]
para <- Args[3]
# # parameters for test only
# wd <- "/Users/siyuansmac/bioinfo/project/suzukii_WGS/genetic_diff/PCA/plot"
# dd <- "/Users/siyuansmac/bi... |
516e8ceec00426b88f4286ca8d34dd20f684adf4c1a4c946effe3559ab0ff7a1 | R | 4,320 | 121 | #' @details
#' Nicolet uses some more keywords in their header file.
#' They are interpreted as follows:
#' \tabular{ll}{
#' description \tab giving the position of the first spectrum \cr
#' z plot titles \tab wavelength and intensity axis units, comma separated \cr
#' pixel size \tab interpreted as x and y step s... |
c980c892c6772db10a8b72e402601eede672a9ee63905d3cfed2eb83a44b9da5 | R | 4,321 | 137 | #' Produce weighted bar plot of risk of bias assessment
#'
#' @description
#' Produce weighted bar plot of risk of bias assessment
#'
#' @param height An object of class \code{rob}.
#' @param overall A logical indicating whether to include a bar for
#' overall risk of bias in the figure.
#' @param weighted A logica... |
c9b4c4a751370260302d6915f117b35240c243074e0ab97a3e8158bca7dfd68e | R | 4,326 | 115 | #seurat analysis
library(Seurat)
library(tidyverse)
library(Matrix)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
counts <- readMM(file = "../raw_data_full/seurat_analysis_data/master_matrix.txt")
genes <- read.csv("../raw_data_full/seurat_analysis_data/master_genes.csv")
metadata <- read.csv... |
3cb19f033909610e6211396d101afe8829bffd95341e80c6993530d79477343b | R | 4,332 | 95 | run_RCTD <- function(st_counts_path, sc_counts_path, sc_labels_path, output_dir = ".", doublet_mode = "full", max_cores = 10){
library(spacexr)
library(Matrix)
library(peakRAM)
if (!dir.exists(output_dir)){
dir.create(output_dir, recursive = TRUE)
}
sc_counts <- read.csv(sc_counts_pat... |
6fbbcc0c0d541ea04f904c3cc19db8e9638ca23d2ca75c91fdde89b73143508b | R | 4,335 | 129 | ---
title: "Example analyses with VertexWiseR - Example 1"
author: "Charly Billaud, Junhong Yu"
date: "2025-07-16"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Example analyses with VertexWiseR - Example 1}
%\VignetteEngine{rmarkdown::render}
%\VignetteEncoding{UTF-8}
bibliography: references... |
49568ba9360155dd858ad3d1c0f69bb0a0c4121dd8105f6567b93b3b3dcf5919 | R | 4,336 | 97 | library(tidyr)
library(magrittr)
library(dplyr)
library(readr)
library(ggplot2)
library(reshape2)
#----- USER INPUT -----#
## specify the working directory ('wd')
wd="/Users/jshinb/Downloads/CellTypeThickness-master"
## specify an output directory ('od') in the working directory: default name: 'output'
od <- 'output'... |
4acb60fed41d8d0749399ff1914245cf66820469147d949e87dde84fc616017e | R | 4,362 | 95 | #' Center a collection of covariance matrices
#'
#' Center a dataset of covariance matrices by parallel transport
#'
#' @param d A dataframe. See details.
#' @param covmats An optional list of covariance matrices.
#' @param group_by Variable in d to group by.
#' @param from_constraints Logical expression indicating sub... |
2d87e91eb036d5dc1220c874561fb7fb9a1b9b4688d8c8d0a6a9234173153e2c | R | 4,363 | 152 | #### Markos et al 2024
#### Script used for analysis of 10X matrices
#### Zebrafish markers on Amphioxus data
# load prerequisities
library(Seurat)
library(patchwork)
library(ggplot2)
# load output of AmphiG4stage.R
AmphiG4stage<-readRDS("../timepoints_rds/Amp_G4.RDS")
# load output of AmphiN0stage.R
AmphiN0stage<-... |
83ee5328ae2acfb8c6986647f973afe0a964b5ec592d3cc21f8bec28f65d9a2e | R | 4,372 | 162 |
# Generate spectroscopic data for testing and exploration --------------------
#' @name generate_test_data
#'
#' @title Generate spectroscopic data
#'
#' @description
#' These functions generate hyper-spectral datasets that are mainly used for
#' exploring and testing functionality of \pkg{hyperSpec}.
#'
#' - `genera... |
b2860c8e59c3aaa5fd6a249003f1e824b256097477fa50a8b9dc46b9b5d5bf35 | R | 4,380 | 118 |
library(tidyverse)
# Load data
d = read.csv('/home/mgell/Work/reliability/res/ABCD_reliability_accuracy.csv')
# Plot Reliability and accuracy
plt = ggplot(data = d, aes(x = beh, y = reliability_r)) +
geom_bar(stat = "identity", width = .75) +
theme_classic() +
theme(axis.text.x = element_text(angle = 90),
... |
1ed27aa0955494de0c2bfae778f00dafcc36295793303eb8b2fd6f5224797320 | R | 4,399 | 132 | # Single cell RNA-Seq of Tlx3 WT vs KO samples
# Budhaditya Basu
library(Seurat)
library(tidyverse)
# Create control seurat object
control.data <- Read10X("JJ_2_count/filtered_feature_bc_matrix/")
control <- CreateSeuratObject(counts = control.data,
project = "Control",
... |
55c580dfe1ff4f3f73cef4d28a62765c01cc10385d45692b2e40eae5aa9a5acc | R | 4,400 | 161 | #author Justin Savage
#js664@duke.edu
#Version 1.0
#8/16/21
#This script is for combining the data from the coloc_results file output of the
# Syn_Bot ImageJ Macro. You should be able to simply select the entire script (Ctrl/Cmnd + A)
# and click the Run button in the top right corner. You will then select the same f... |
c9c3cda7b6cf40d28b563b17c85ed200a30825e379ed623cff07ecd572eb59be | R | 4,402 | 161 | # Set generic ----------------------------------------------------------------
# make generic functions without default
#' @noRd
setGeneric("mean_sd", function(x, na.rm = TRUE, ...) standardGeneric("mean_sd"))
#' @noRd
setGeneric("mean_pm_sd", function(x, na.rm = TRUE, ...) standardGeneric("mean_pm_sd"))
# Function... |
5cccc2763be3086b6b5291b7f343c9375ae9c87e902db6b0433938f7cd2cd250 | R | 4,412 | 125 |
library(tidyverse)
#net_cols <- c("#E69F00", "#56B4E9", "#009E73", "#F0E442", "#0072B2", "#D55E00", "#CC79A7") # original order
net_cols <- c("#0072B2", "#009E73", "#D55E00", "#56B4E9", "#E69F00", "#F0E442", "#CC79A7") # reordering for 3 colours only
plt_in = '/home/mgell/Work/reliability/input/'
plt_out = '/hom... |
ddd32ba9fbeb47fd49c3c3e39874faf085a54554a01ed4447b21dc098ae53082 | R | 4,414 | 191 | library(tibble)
library(dplyr)
library(ggplot2)
library(purrr)
library(tidyr)
library(data.table)
# Will be the dataset "wang" or "pbmc"
dataset <- commandArgs(trailingOnly = TRUE)[1]
output_path <- "output"
source("../utils/0-indices.R")
gene_indices <- read_gene_indices(sprintf("data/%s-gene-indices.txt", dataset)... |
f47abfc518679f91db5157f6ad7e300b050e24e561ed60ef667daf20a09deab6 | R | 4,440 | 79 | #----Figure_04D_S14-------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 04 Panel D and S14 for the connectivity analysis
# for Reinhard and Fukuda et al. 2024
#-----------------------------------... |
ec0b2abbee965b291d4f4a36bb37a20bca2a45e030203f2e470e26b093b48c8c | R | 4,445 | 87 | # Description: Extract highly variable genes of drosophila (DR) scRNA-seq data.
# Author: Jiaqi Zhang <jiaqi_zhang2@brown.edu>
# Reference:
# https://www.science.org/doi/10.1126/science.abn5800
# https://shendure-web.gs.washington.edu/content/members/DEAP_website/public/
library(Seurat)
library(stringr)
subsampleC... |
05710122c89afba67a05e31632832fcc0d26bd67b37cc040250127e44f200d5c | R | 4,463 | 142 | #### Notes ####
#### Libraries ####
library(GOstats)
#### Sourced functions ####
#### Functions ####
# Retrieve low, mid, and high variability genes
retrieve_variability_gene_lists <- function(jack.summary, ev.percentile=0.20, col="Mean_Local_Rank_Log2CV") {
bgd.genes <- jack.summary$GeneID
lv.genes <- jack.sum... |
23dbc43e5c1b67e78d2b560c7fea7f12be9ade3f7f68263d7f411008a5d712a1 | R | 4,483 | 160 | # load libraries and arguments
library(rBayesianOptimization)
library(tidyverse)
set.seed(1234)
initial.options = commandArgs(trailingOnly = FALSE)
file.arg.name = "--file="
script.name = sub(file.arg.name, "", initial.options[grep(file.arg.name, initial.options)])
source(paste0(dirname(dirname(script.name)), "/Fu... |
0fda80035d388f2d6e0b9eb7604a045e592cb0f5100e054b648a94fd62cb1d9a | R | 4,485 | 201 | ---
title: "Markos et al Pseudotime and Transitions"
author: "Jan Kubovciak, Institute of Molecular Genetics of the Czech Academy of Sciences"
output:
pdf_document:
number_sections: false
toc: true
df_print: paged
---
last updated: `r Sys.time()`
```{r setup_glob, include=FALSE}
knitr::opts_chunk$set(
... |
a5cd7bb210be322497108f31cbdf167a505b34967e6564efbb44da06c4759e2a | R | 4,491 | 123 | test_that("funnel back transforms supported proportion transformations", {
dat <- data.frame(event = c(2, 5, 8, 12, 18),
n = c(30, 40, 35, 50, 45))
pdf(tempfile())
on.exit(if (dev.cur() > 1) dev.off())
for (sm in c("PLOGIT", "PLN", "PAS", "PFT")) {
m <- metaprop(event, n, data = dat, ... |
4231e28d64266560e2d96599c84a9fe64f0713eff33b2c86fcdebea8f3fe1bb2 | R | 4,493 | 156 | #' Highlights and test the time-course of effects through mixed-effects models
#'
#' This function performs (g)lmer for each timepoint.
#' Data are first separated for each time-point, and a LMEM as specified by the 'formula'
#' parameter, which is passed to 'lmerTest::lmer', is performed.
#' The function returns t/z v... |
a39b23cc71118b01aa9f2e7c9e938873b526fa0e8801da4cbedb1edc974a807c | R | 4,493 | 88 | ## RMANOVA or RMANCOVA script (one-way or two-way) with pairwise comparisons that are corrected for multiple comparisons
library(stats)
library(psych)
library(rstudioapi)
library(readxl)
rm(list=ls())
#Print warnings as they come up for troubleshooting purposes
options(warn=1)
# Import Data in long format
data <- ... |
47bf994f86d0e111c2f3ddd49cb2ccb3f8faf35cb49b358f59ffaec6f0d79fc7 | R | 4,494 | 128 | # Script for converting nomenclature to TaxonomyNodes file, for use by EBI ontology and AIBS engineering teams
suppressPackageStartupMessages({
library(dplyr)
})
setwd("~/MOp_taxonomies_ontology-main/mouseM1_CCN202002013")
# Function to concert nomenclature table to taxonomy nodes CSV
nomtable_to_taxonomy_nodes <-... |
57979ba25ae11f6e531d02afd5dbac09a398da325f1dad9e9fa4b315c38ffd19 | R | 4,505 | 160 | ##########################################################
# Program to produce the combined ADNI and NACC dataset
##########################################################
library(tidyverse)
library(dplyr)
library(broom)
library(MASS)
library(gtsummary)
library(ggplot2)
setwd("/Users/elinorvelasquez/Desktop/adni_na... |
e7d8ed51fb5e1b27396fbc96509c796cf501e673929e87b666acdd211b6469f2 | R | 4,510 | 133 | test_that("metagen Fleiss1993bin example matches rma.uni", {
data(Fleiss1993bin)
ma_bin <- metabin(d.asp, n.asp, d.plac, n.plac, study,
data = Fleiss1993bin, sm = "RR", method = "I")
ma <- metagen(TE, seTE, studlab, n.e = n.e + n.c,
data = ma_bin, sm = "RR")
rma_c <- me... |
44ab49c317a3608e86cf8b93fb60057bf4e3d6259da0c596584c08ee44427d79 | R | 4,532 | 121 | ---
title: "AIPW Conditional ComBat"
author: "Eric W. Bridgeford"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{cb.correct.aipw_ccombat}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TR... |
c377655c2e6faceaeb3d97b361f3dfcbadace5dd606944630ed6a39ad25eeef7 | R | 4,534 | 129 | library(geodl)
setwd("Your Working Directory")
# Make terrain derivatives ------------------------------------------------
inDTM <- terra::rast("data/elev/dtm.tif")
terrOut <- makeTerrainDerivatives(dtm=inDTM,
res=2,
filename="data/elev/tstac... |
4f746ee3a0f21d74496c2fa4c9fbbcc9809ab799efa2d4a1e7c0698be515a914 | R | 4,535 | 125 | ## -----------------------------------------------------------------------------
##
## Somatic variant detection in lesional focal epilepsy
## This short script calculates the proportion of cases explained by each gene
##
## Author: Dr. Christian Bosselmann
##
## Date Created: 2023-09-05
##
## Copyright (c) Christian B... |
5032efed3a178ebcd3fd0a1397b91693174297da77935c3816002b5650c07327 | R | 4,535 | 83 | # CENTRAL DINUCLEOTIDE ######################
##############################################
###### NEUROD2
# Detect motifs in NEUROD2 peaks
neurod2_peaks<-read.table("e14.5.neurod2_highconfsubpeaks_summit.bed")
colnames(neurod2_peaks)<-c("chr","start","end","summit","peak_id")
neurod2_peaks<-find_motifs(neurod2_peaks... |
c09e2dbe0bcea599501fa17a84e0df9111370b4976b6f38dbde253cc855fd74e | R | 4,535 | 121 | library(forestplot)
#list.files(path = "/home/kau232/locuszoom_test/lincoln/Forest_plot/")
cil = function(beta, se) {
cil = exp(beta - 1.96 * se)
}
ciu = function(beta, se) {
ciu = exp(beta + 1.96 * se)
}
# NACC
# apoe33 -0.163 0.056
# apoe34 -0.248 0.055
# apoe44 -0.190112 0.103950
# apoe24 -0.56 0.246
# ap... |
8d2f31e58d2469f97dbb4a5647da7146d614ce6aa67a9ceccd396bd0a4619136 | R | 4,538 | 114 | # This is an example of how to load and display the functional (fMRI) and
# structural (sMRI) ICA maps, provided for the 2014 MLSP Competition, using
# R 3.1.0.
#
# It also includes an example of how to compute the correlation between the
# fMRI and sMRI maps, like was done in reference [2] below.
#
# This script requi... |
3f6cc091b4b46e8aed0f0d8f66816af4db5e786c28a6ac71e49d07988c33ab55 | R | 4,539 | 132 | ##################### ldsc for ABC promoter
## peak width= 1000
## 5'
## non 5'
Dist <- c('1k','2k')[1]
.libPaths(c(.libPaths(),'/sc/arion/projects/roussp01a/jaro/programs/R_libs_4_0_2/'))
primarySourceDir="/sc/arion/projects/roussp01a/jaro/atacseq_ad/NYGC_AD_R01/"
#primarySourceDir="/sc/arion/projects/roussp01a/li... |
530411f21c3242527ba435a153261415cd523e30520ba82f25ee8bf3b0d5404e | R | 4,542 | 177 |
#' mochims
#'
#' Main analysis script.
#'
#' @param startStage Start at a specified analysis stage (default:1)
#' @param stopStage Stop at a specified analysis stage (default:5)
#' @param base_dir Base directory for all input files (default:NB private CRG server path; change accordingly)
#' @param output_dir Output di... |
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