sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
4139f4009e8bb6652997a785411db2303a625158660e0d1f069d4af6813c4d57 | R | 7,049 | 176 | #Label Transfer
library(SingleCellExperiment)
library(Seurat)
library(tidyverse)
library(Matrix)
library(scales)
library(cowplot)
library(RCurl)
library(pryr)
library(RColorBrewer)
library(dplyr)
library(textclean)
clusters_seurat_integrated <- readRDS("directory/clustered_seu_obj.rds")
Allen_seurat <- readRDS("dire... |
7f6fca430fc7e3c27f7e9f782b2cb132ab901a2cbb75382d656eb033ed935351 | R | 7,061 | 249 | ---
title: "Nearest neighbour matrix"
output: html_notebook
---
## Purpose of script
**Input datasets:** - Protein to gene conversion excel sheet - shows what genes are associated to the shortlisted proteins - Unique gene excel sheet - gene list for which will be used to search on StringDB to find gene interactions
... |
43a53d295709f808df5895a6bfd02af9c3eaccd16e0f4c44839bcd0e3e256d71 | R | 7,068 | 170 | ---
title: "Example analyses with VertexWiseR - Example 2"
author: "Charly Billaud, Junhong Yu"
date: "2025-07-16"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Example analyses with VertexWiseR - Example 2}
%\VignetteEngine{rmarkdown::render}
%\VignetteEncoding{UTF-8}
bibliography: references... |
e45f8831aca6bc2a3ce1f6ffcf3ae4925f5039eae0f9747e94f04a3aabe0b108 | R | 7,082 | 207 | ---
title: "SupplFig_VAME_80Clusters"
author: "Fabienne Rössler"
date: '2023-03-01'
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")
li... |
8f07b20eb91a528dbad77495a3472c73ffae2fb9c92512fdcad180ab5b9c8e33 | R | 7,088 | 228 | # Creator: zchuri
# Date: 30 March 2015
# Function: iEff_w
# IEFF_W(CONNMX,TIPO)
# Computes given 'tipo' global or local efficiency given a connectivity
# matrix 'ConnMx'.
#
# The global efficiency is the average of inverse shortest path length,
# and is inversely related to the characteristic path length.
# The... |
4406562887d2850199d31f0391afa6bfbfc72db0e1d74ff463865569d471e8c6 | R | 7,104 | 157 | # --------------------------------------------------------------------------------- #
# #
# Hippocampal volumes in UK Biobank are associated with APOE only in older adults #
# ... |
6232d34b1a61b6f24ce63f7ff05274dd0d0ab1c31b4083d2b166a508e5a57c35 | R | 7,118 | 180 |
#-----Joint-Tissue-gene-expression-Imputation-(JTI)-----
cat('\n---Joint-Tissue-gene-expression-Imputation-(JTI)---\n')
library('glmnet')
library("optparse")
option_list = list(
make_option("--tissue", action="store", default=NA, type='character',
help="tissue name [required]"),
make_opti... |
f8c675747b59d7b4d71b4341dcfe202250789bca1a12b7f710c85b2db22a1f7b | R | 7,134 | 214 | ---
title: "Cell_type_staining"
output: html_document
---
```{r}
library(tidyverse)
library(ggforce)
library(ggsignif)
library(ComplexHeatmap)
library(shiny)
library(ggpubr)
library(RColorBrewer)
```
```{r}
data <- readr::read_csv("Z:\\16-Our Papers\\In Preparation\\PD_Diabetes_Alise\\Figures\\Figur... |
cdbbc1912989d1374b1e17dd5febe4665d64fd1f225eacdc8b4193c95f209886 | R | 7,141 | 194 |
#' mochims_thermo_model_results
#'
#' Evaluate thermo model results.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param temperature temperature in degrees celcuis (default:30)
#' @param literature_free_energies path to literature free energies (default:NA)
#' @param normalisation_di... |
6e4a69c6e5c06977c343daf6f9a9e78425ce16f4ab0d769a6ee89736574528ed | R | 7,158 | 134 | ## CellChat
library(dplyr)
library(tidyr)
library(stringr)
library(Seurat)
library(CellChat)
library(nichenetr)
library(UpSetR)
library(tibble)
library(patchwork)
library(openxlsx)
library(reshape2)
library(ggplot2)
library(wesanderson)
library(ggsci)
library(ggtree)
library(ggraph)
library(igraph)
library(tidyverse)
... |
bfd359377b93169922ce3b4c45b74669a7470a7c625d7e618902282ed2359738 | R | 7,198 | 183 | ---
title: "SupplFigure IFS (OFT1 and OFT3)"
author: "Fabienne Rössler"
date: '2023-05-02'
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_Function... |
67269697bf3b48225ff4646eb0a3fa4e1bce40de1e8c38945c7d9f35120028d1 | R | 7,231 | 212 | AnalyseExperiment<-function(dr, dataname, resdr, cropLim, nn){
# crop data as given in cropLim
# separate monomers from clusters based on k=th neares neighbour (given by nn)
# Model fit: for monomers: CSR and Thomas, for clusters: Thomas and double (nested) Thomas based on envelope tests and pair correlation functi... |
ca300722455da51c7c2838fde839d60eeed86fa8a2d43f5698a8841de473b881 | R | 7,232 | 190 | #' @title One step cluster
#' @description Creating poisson vector.
#' @details Input an integer and return the log density of a poisson distribution with lambda equals the input integer
#' @param norm.dat data input
#' @param select.cells data ref
#' @param counts meta input
#' @param method meta input
#' @par... |
feba955190b92cf258c56d2d416059437d1ebacff04aaf72938888c50717f265 | R | 7,232 | 183 | ---
title: "MBTY_Analysis"
author: "Lukas von Ziegler"
date: '2024-01-19'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer.R")
source("BFA_BFF.R")
library(sp) #tested with v1.3-2
library(imput... |
949aeffe675dff9543bd2fd4990554463fd7e8cbf0883641cab04cbb2cd3d35d | R | 7,237 | 162 |
library(tidyverse)
beh = 'Grip'
f = 'ridgeCV_zscore_averaged-source_Schaefer400x17_WM+CSF+GS_hcpaging_695_zscored-beh_HCP_A_motor_wnoise_all.csv'
plt_out = '/home/mgell/Work/reliability/plots/simulation_res/'
d = read_csv(paste0('/home/mgell/Work/reliability/input/',f))
d$test_MAE = d$test_MAE*-1
empirical = d[1,... |
128b96b30a281edc20d956cd72b595f835ecc7f7de1094d9e0882293d43c7cda | R | 7,250 | 176 | ### R-4.3.1
# library
library(TCGAbiolinks)
library(SummarizedExperiment)
library(limma)
library(edgeR)
library(biomaRt)
library(glmnet)
library(factoextra)
library(FactoMineR)
library(caret)
library(gplots)
library(RColorBrewer)
library(gProfileR)
library(genefilter)
library(ggpubr)
library(rstatix)
l... |
443984ce7596245947877a00761758722bb9d35341e0787e898a359411955416 | R | 7,252 | 127 | .libPaths(c(.libPaths(),'/sc/arion/projects/roussp01a/pengfei/tools/R_4_0_3'))
library(proActiv)
library(rtracklayer)
z=local({load('/sc/arion/projects/CommonMind/roussp01a/INGELHEIM/rnaseq/step2/files/step2Session_gtcheck.RData');environment()})
allInfo=z$allInfo
allInfo$path1=gsub('hydra','arion',gsub('.Log.fina... |
485862df3e0aee69cf64368ddd5c39113fd6e24c51f6bb71e17a5b67c9aef84f | R | 7,254 | 171 | #' assessPnts
#'
#' Assess semantic segmentation model using point locations
#'
#' This function generates a set of summary assessment metrics when provided
#' reference and predicted classes. Results are returned as a list object. For
#' multiclass assessment, the class names ($Classes), count of samples per class
#' ... |
00edc9db98d76cb7d0c69d481d23083851d2d605514129b2b68a04ff0bf3dac8 | R | 7,257 | 157 | library(Seurat)
library(dplyr)
library(ggplot2)
# Map clinical data to the metadata
merge_metadata <- function(sobj, metadata){
meta_data_sex <- inner_join(sobj@meta.data, metadata)
rownames(meta_data_sex) <- rownames(sobj@meta.data)
sobj@meta.data <- meta_data_sex
return(sobj)
}
# Assign the AD variable a... |
820283ab4193ac0f49b1bc82285617a48ac98d75f8dbbc1542797ecafb846936 | R | 7,259 | 259 | options(max.print=500)
options(stringsAsFactors=FALSE)
library(ggplot2)
library(reshape2)
suppressWarnings(library(patchwork))
## Args ================================
args <- commandArgs(trailingOnly = TRUE)
PRED_SUMM_PATH <- args[1]
CLF_NAME <- args[2]
PLOT_PATH <- args[3]
PLOT_WIDTH <- args[4]
PLOT_HEIGHT <- args... |
fbcf32fa89eeaf7ac3c2553101643cdd316ed1a353e7d24167d94ffab1192613 | R | 7,261 | 286 | #### Markos et al 2024
#### Script used for analysis of 10X matrices
#### Stage N5
# load prerequisities
library(Seurat)
library(patchwork)
library(ggplot2)
set.seed(10)
# Data load ####
AmphiN5stage.data <-Read10X(data.dir = "../10X_matrices/N5/filtered_feature_bc_matrix/")
# read gene id conversion table
gene_map ... |
8b64c079b9437f068490eda3891c62fadb33ac7f26a66de9857aead1958a338c | R | 7,265 | 220 | # File: compute_TdP_error.R
# Author: Kelly Chang
# Date: Oct 2017
# Version: 1.0
#
# Description: R script to perform Torsade de Pointes (TdP) risk
# classification using ordinal logistic regression and
# leave-one-out cross validation (LOOCV).
# F... |
42502382f83185e7357fa78f36a1efe6a1b82d14311faebe24a0b942f4b00b44 | R | 7,280 | 195 | ---
title: "leptin_arc_PT"
author: "Yuqi Tan"
date: "10/24/2021"
output: html_document
---
```{r, message=FALSE}
library(Seurat)
library(Matrix)
library(ggplot2)
library(dplyr)
library(patchwork)
library(viridis)
set.seed(123)
save_dir = "./data/"
```
| | ARC |
| ----------- | ----------- |
| all... |
84c65dee186c5e7d492838ffbb732759b305edf4bc96c5bd10b781e4eaa9d83e | R | 7,291 | 201 | #' Load Peaks from Data
#'
#' This function extracts peak information from a nested list and returns a structured data frame.
#'
#' @param data A nested list where the first level of names represent time windows, and the second level of names
#' represent modes. Each mode contains a list with electrode, peak_tim... |
efdc8b6cc2acdd42bf4d4a860ead0ef78bffde9d9156f2db2c6e8a2efc4c1ae2 | R | 7,302 | 180 | #' viewChips
#'
#' Plot a grid of image and/or mask chips
#'
#' This function generates a plot of image chips and/or image masks. It serves
#' as a means to visualize chips generated with the makeChips() or makeChipsMultiClass()
#' function. It can be used as a check to make sure chips were generated as expected.
#'
#'... |
f5dd3ad5d0f660962b9b8ebb3acf810ae0bb3dbf37a014f34b006134de288ec6 | R | 7,314 | 170 | #' Print results of a leave-one-out meta-analysis
#'
#' @description
#' Print results of a leave-one-out meta-analysis
#'
#' @aliases print.metainf
#'
#' @param x An object of class \code{\link{metainf}}.
#' @param prediction A logical indicating whether prediction
#' intervals should be printed.
#' @param overall... |
da3e8c32e3a442b7da145262d1ea6c1d3a5d4f9930965d30a44f58809e54655e | R | 7,319 | 169 | ## -----------------------------------------------------------------------------
##
## 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
#... |
2b0886dd6c4bcbc76d57580727d0ab1916eb9afd8dfa16171374e7e798431b35 | R | 7,326 | 153 | ---
title: "Convert to Allen Institute Cell Type Taxonomy Service (CTTS)"
output: html_notebook
---
This script takes standard output files form the Common Cell type Nomenclature (CCN) and converts them to the format required for input into the Allen Institute Cell Type Taxonomy Service (CTTS). It takes as input... |
673c87b629e5317c508c6dc699f4d49945a0e21d0110f5f6de07ed19cbf81b72 | R | 7,373 | 171 | library(dplyr)
library(readr)
library(lme4)
library(ggplot2)
library(effects)
library(patchwork)
# all_data_new <- list.files(path = "~/Desktop/accuracies", full.names = TRUE) %>%
# lapply(read_csv) %>%
# bind_rows
#scripts_dir = 'Z:/Desktop/MINDLAB2020_MEG-AuditoryPatternRecognition/scratch/working_me... |
fbb8bf5e7e61382a16aef651ff0de5f35e959ad3c9a871a05520ed2af764e797 | R | 7,373 | 220 | suppressPackageStartupMessages(library('randomForest'))
options(stringsAsFactors=F) # to avoid invalid factor level warning
## Command line args ================================
args <- commandArgs(TRUE)
CHORD_MODEL_PATH <- args[1]
MUTATION_CONTEXTS_PATH <- args[2]
OUTPUT_FILE_PATH <- args[3]
GLOBAL_LOG_LEVEL <- arg... |
050c18896bfa040713568c207819389dfa67e941de0aacee82ca9dad205629e7 | R | 7,384 | 291 | ---
title: "How to perform a meta-analysis with R: a practical tutorial"
author: "Sara Balduzzi, Gerta Rücker, Guido Schwarzer"
output:
rmarkdown::pdf_document:
number_sections: false
rmarkdown::html_vignette:
toc: false
number_sections: false
bibliography: references.bib
vignette: >
%\VignetteIndexEntr... |
557151496032c3984b9f0470392772a91c8d314afc4b18058423925331ae4456 | R | 7,384 | 189 | ---
title: "panel_hyperpol_diff_length"
author: "Franz Xaver Mittermaier"
date: "2024-06-14"
output: html_document
---
This script plots the graphs for the experiments, where different durations of hyperpolarizations after 800ms long depolarizations were induced.
```{r, message=FALSE, warning=FALSE}
#load l... |
4f31c6e2d60b3465d240a9e70c77c539dec9654e883a7dff43d2c5de9b9ccfe9 | R | 7,398 | 250 | # Copyright 2022 HYBRID Team INCIA, UMR5287, CNRS, Université de Bordeaux
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
# http://www.apache.org/licenses/LICENSE-2.0
# Unless required b... |
df24b645862da692dedead8fd0c86c9c830e285090d13af6840a6dd400c10aee | R | 7,407 | 191 | ---
title: "Correction_US_files"
author: "Fabienne Rössler"
date: '2023-07-13'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("../DLCAnalyzer_Functions_v4.R")
source("../UnsupervisedAnalysis_Functions.R")
library... |
3e2131996338212cac4eb765352df931b810d1943d05de01950326594f5bd4a7 | R | 7,419 | 227 | #' @name DEPRECATED-read.txt.Shimadzu
#' @concept moved to hySpc.read.txt
#'
#' @title (DEPRECATED)
#' Read Shimadzu GCxGC-qMS - spectra files (`.txt`)
#'
#' @description
#'
#' These data input functions are **deprecated** and they will be removed in
#' the next release of \pkg{hyperspec} package.
#' Now functio... |
6e9940924a13ed7fb6e9e6cd6dcc3b94429e2fc35606550bd3e276b0abf1b4ec | R | 7,461 | 147 | library(SingleCellExperiment)
library(SummarizedExperiment)
library(Seurat)
library(edgeR)
library(dplyr)
library(scater)
library(IHW)
library(limma)
library(org.Hs.eg.db)
library(ggrepel)
xx <- as.list(org.Hs.egENSEMBL2EG)
load(url("http://bioinf.wehi.edu.au/software/MSigDB/human_H_v5p2.rdata"))
de_res... |
24429b33f4eff2ae5df14c748d7b8762fcfb70d5dfa4b7b544d2355a16ecd2f4 | R | 7,471 | 199 |
# run the snapping for a single set of electrodes
doSnapDykstra <- function(X, V_all, adjacent, preSnapCloser = F, preSnapPial=F,
stopval=-Inf, maxeval=100, xtol_rel=0.01) {
# INPUTS: X - coordinates of electrodes (n x 3 matrix)
# V_all - coordinates of all good dura vertices (go... |
a908e7c5569474c5cff742ba92619462a69777fcd508a32c8a97d5b1db6f477c | R | 7,473 | 234 | ---
title: "PermutationTest"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## R Markdown
```{r}
install.packages("twot")
```
```{r}
library(coin)
library(twot)
```
```{r}
install.packages("readxl")
```
```{r}
library("readxl")
```
```{r}
mutantDataSet <- read_excel("C... |
fedc9f7ac651c844500d99b1bebba9a545c57ff2dcf1ccf1e4ab860d4b0f9c44 | R | 7,532 | 214 | #' Convert principal component decomposition or the like into a `hyperSpec` object
#'
#' Decomposition of the spectra matrix is a common procedure in chemometric
#' data analysis. `scores` and `loadings` convert the result matrices
#' into new `hyperSpec` objects.
#'
#' Multivariate data are frequently decomposed by me... |
5dc7eb6f4c3131000fbe470733ab77a2720f92e52d0cac5be3e25a149320b569 | R | 7,556 | 228 | ---
title: "KmeansClustering_CSI_70Clusters"
author: "Fabienne Rössler"
date: '2023-02-09'
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")
l... |
bfb690121a91fb3992f4c0addebad842eaabf05a55f4e1c8f032d249e43bb4ce | R | 7,565 | 164 | ---
title: "Convert to Allen Institute Cell Type Taxonomy Service (CTTS)"
output: html_notebook
---
This script takes standard output files form the Common Cell type Nomenclature (CCN) and converts them to the format required for input into the Allen Institute Cell Type Taxonomy Service (CTTS). It takes as input... |
af3f3e0fe49ddd182a4adafb6ec6b8c078b3962f90ba37277ea4f698b472d36e | R | 7,591 | 158 | ---
title: "Convert to Allen Institute Cell Type Taxonomy Service (CTTS)"
output: html_notebook
---
This script takes standard output files form the Common Cell type Nomenclature (CCN) and converts them to the format required for input into the Allen Institute Cell Type Taxonomy Service (CTTS). It takes as input the ... |
4aedc080d3dac68f1900905c4f031fd5aa23bcb9836a152f0f7dcc7db32fea43 | R | 7,594 | 219 | library(tidyverse)
library(lme4)
library(broom.mixed)
# Function to load all CSVs from a directory and add additional columns based on file name
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)
... |
7853245259d130a747e627069be3305eafb55f7e4434540da55fa2305e1928c3 | R | 7,622 | 198 | suppressPackageStartupMessages({
library(SingleCellExperiment)
library(scater)
library(scran)
library(DropletUtils)
library(argparse)
library(loomR)
library(Seurat)
library(purrr)
})
# create parser object
parser <- ArgumentParser()
# specify our desired options
# by default ArgumentParser will add an... |
2e69212d1b395134775d4c98dc8239cdca87cf0efbef6a3f4d2d70ae15bc061d | R | 7,630 | 119 | #----Figure_S01_preparations_for_nuclei------------------------------------------
#-------------------------------------------------------------------------------
# This is the code which has to be run in preparation for Figure S01
# for the connectivity analysis for Reinhard and Fukuda et al.
# nucleus segmentati... |
a77b3d65f6a0e57ac47233a425e843f8f532f3eb351d7e3dde0b3ef76889ba37 | R | 7,632 | 301 | # Auxiliary functions
#
# Package: meta
# Author: Guido Schwarzer <guido.schwarzer@uniklinik-freiburg.de>
# License: GPL (>= 2)
#
estimate.missing <- function(TE, TE.sum, type) {
#
# 1. Center around mean
#
TE.c <- TE - TE.sum
n <- length(TE.c)
#
# 2. Rank absolute values of centered values
#
r.star ... |
68ea9649dd221ba14d30eb9dd385c389a2f95a6dd1f726639f0576daa1214bcf | R | 7,640 | 140 | #----Figure_01_E----------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 01 Panel E for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#-------------------------------------------... |
2b7347e10a6f9a5aa3c75567665ef413d0d5ba7fd7dd12ae7683ed3a3e2d529a | R | 7,644 | 195 | #==========================================
# Produce Table S3: GWAS results summary
#==========================================
library(data.table)
library(magrittr)
library(rtracklayer)
library(flextable)
source("code/functions/find_closest_gene.R")
source("code/functions/make_or_95_ci.R")
bim <- fread("data/mega/m... |
3502b94775644518a994a748a5293af86a2c1b4211b2d18d3d32c0757a2223bd | R | 7,661 | 193 | #' Fit AIPW ComBat model for batch effect correction
#'
#' This function fits an Augmented Inverse Probability Weighting (AIPW) ComBat model
#' for batch effect correction using an empirical Bayes framework.
#'
#' Note: This function is experimental, and has not been tested on real data. It has only been tested with s... |
1c5496748578e76760bb47f2e7841f83841255d47e0c5bedf750e8277a976050 | R | 7,666 | 199 | library(magrittr)
library(survival)
library(dplyr)
library(ggplot2)
dat = t(assay(all_platforms))
# correlation to distance matrix
d = as.dist( 1-abs(cor(dat))) #exp(1-abs(cor(dat)) )-1
# min threshold for clusters
th_min = 2
# max threshold for clusters
th_max = 50
re<- seq(0.15,1, 0.05) %>% # L-W alphas
lappl... |
26159a0dd0e4b222b7398ddf8f10aa81f46a2cf141286b939f64d315df783a1c | R | 7,674 | 288 | # Summary -----------------------------------------------------------------
# Run parcel-wise repeated-measures ANOVAs on ISC between movies,
# after residualizing FisherZ for pairwise average framewise displacement
# Packages ----------------------------------------------------------------
library(tidyverse)
librar... |
6773efe563f04d5aa48ebc7e383d92f162e063ba4740bc8f69f64735842b2ddc | R | 7,674 | 200 | #### Notes ####
#### Libraries ####
library(ggplot2)
library(ggpubr)
#### Sourced functions ####
#### Functions ####
# Annotate boxplot with number of observations per grouping
annotate_n <- function(x) {
return(c(y = -0.01, label = length(x)))
}
# Annotate boxplot with median per grouping
annotate_median <- fun... |
25650300fb74f4d423394d18dde451a0b1d00b2eb5adc6e50327784fe7805cb3 | R | 7,694 | 214 | # Single cell RNA-Seq of Tlx3 WT vs KO samples
# Budhaditya Basu
library(Seurat)
library(tidyverse)
# Load RDS file
tlx3.combined <- readRDS("sc_PN5_tlx3.combined_integrated.rds")
DefaultAssay(tlx3.combined) <- "RNA"
# Check the orig.ident names
unique(tlx3.combined@meta.data$orig.ident)
# Calculate the ... |
15a1b5bfe23cb651e74b1e2947cdf926c65a31665e15707b43f5b99fc98eb27c | R | 7,701 | 248 |
# Summary -----------------------------------------------------------------
# Run a multivariate classification to differentiate movies based on ISC values
# Packages ----------------------------------------------------------------
library(tidyverse)
library(ggplot2)
library(glmnet)
library(caret)
# Prelude ------... |
ca7d1eaaa1f1441542ecb7db8ecb78becfb02db192d8404ddb5fa9c770fd9cb2 | R | 7,703 | 139 | ---
title: "Supplementary Figure 5"
author: "Greg Duncan, Katie Emberley and Jonathan Nelson"
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: py... |
c59f455b4ae7cd1d30c90bcb883fe3e7b888eb06bcb48480bd78e6d5ab25fc07 | R | 7,726 | 233 |
library(tidyverse)
### FIGURE 1A ###
# HCP A plot results for Age prediction - violin plot
beh = 'Age'
plt_out = '/home/mgell/Work/reliability/plots/Figures/'
f = 'ridgeCV_zscore_averaged-source_Schaefer400x17_WM+CSF+GS_hcpaging_695_zscored-beh_interview_age_wnoise_all.csv'
d = read_csv(paste0('/home/mgell/Wor... |
2323fdc2814e5ced42a9ec02ec5dc241b2d8b583921c8a21bfeaeaa0235df848 | R | 7,732 | 208 | library(ggpubr)
library(dendextend)
library(ComplexHeatmap)
library(Seurat)
library(SeuratDisk)
library(tidyverse)
library(SeuratWrappers)
library(RColorBrewer)
library(circlize)
library(UCell)
library(clusterProfiler)
library(monocle3)
source(file.path("R","functions.R"))
load(file.path("data", "v5_reference_mapped_... |
8b528d5904f6cc51609f01d61a881a647ef206e7a6b9146c171a51dbfe471a33 | R | 7,740 | 221 | ---
title: "edgewise_analysis"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
require(ggplot2)
require(energy)
require(parallel)
require(tidyverse)
require(mltools)
require(grid)
require(gridExtra)
ncores <- parallel::detectCores() - 1
alpha = .05
```
```{r}
met... |
6028cdf99be1cac6761e45a1eea47078b66aece0bd4cc2582efb1e7c879fcd61 | R | 7,751 | 175 | library(tidyverse) # for data manipulation and visualization
library(tidyr) # can use gather to convert wide to long format
library(dplyr) # can use pipe
FC <- read_csv("FC_PreRest_Learn.csv", col_names = FALSE) # This csv file was produced by FC_analysis_Fig4.m
newheaders <- c("PreRest_Ch2_9", "Learn_Ch2_9", "PreRest_... |
d7f3c44b60eeed427200c44f45706c6cf80ee9ad6101e65f78211a5749c71773 | R | 7,770 | 235 | ---
title: "Selection on organ-biased genes (*`r params$species.name`*)"
author: "Bucao, Christabel Floi"
output: html_document
date: "`r Sys.Date()`"
params:
input.selection.rds.path: "../../results/08_Selection"
input.tau.rds.path: "../../results/10_Expression_Specificity"
output.rds.path: "../../results/11_Sel... |
e73fa2b2287825ee1ce18b0d46115d59b459d4da181098a198b374c4a2821c07 | R | 7,832 | 187 | ###Spearman Scatter Plots
#### Coded: Benjamin Saef
#### Date as of commenting and documentation - 05/28/2024
#Scatter plots for best performing analytes from each platform (Benjamin) versus amyloid PET (x-axis):
# Yellowish green: amyloid PET negative, CDR >0
#Blue: amyloid PET negative, CDR 0
#Orange: amyloid PET ... |
9e97b190c72b91ca51bf9e2bad11f86116c2251759ed1e1061dbda4fc41594b4 | R | 7,846 | 188 | #' Import RevMan 4 data files (.mtv)
#'
#' @description
#' Reads a file created with RevMan 4 and creates a data frame from
#' it.
#'
#' @param file The name of a file to read data values from.
#'
#' @details
#' Reads a file created with RevMan 4 (Menu: "File" - "Export" -
#' "Analysis data file...") and creates a d... |
f8685bc34c4cb45f6f5dc7ff82d98baad6c2a7e9cf68e6aad9df0df6d8561044 | R | 7,897 | 291 | ---
title: "Extract supplementary tables"
author: "Bucao, Christabel Floi"
output: html_document
date: "`r Sys.Date()`"
params:
input.var.path: "../../results/03_Variability_Plots"
input.go.path: "../../results/07_GO_Enrichment"
input.camera.path: "../../results/07b_GO_cameraPR"
input.selectome.path: "../../res... |
cc84ddd7f01b1a8b055edf7894bf89269252bf3d49a0ee9fcb2c18b74e46667a | R | 7,907 | 221 | ---
title: "PREMS Analyse de données ANT"
author: "Christophe Pallier"
date: "`r Sys.Date()`"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, echo=FALSE, ouput="hide", message=FALSE, warning=FALSE}
require(readr)
require(stringr)
r... |
76c4a4ffa2f2f7c3c5f6fd72e16af50de7401a02d9ce02076600492ff1b5db4a | R | 7,956 | 250 | gm <- function(x, digits = 4, debug = FALSE) {
if (inherits(x, c("metabin", "metainc", "metaprop", "metarate"))) {
incr_not_0 <- sort(unique(x$incr[x$incr != 0]))
#
if (length(incr_not_0) == 1)
incr <- incr_not_0
else if (length(incr_not_0) == 0)
incr <- 0
else
incr <- paste0(... |
8b1eff8d532e5fd1c21b9902782df5ba96efb075862aaf85f1a4907906c8dbda | R | 7,962 | 193 | # docker run -ti --entrypoint /bin/bash -v /cis/project/ndmg/batch_effects/:/data -v /cis/home/ebridge2/Documents/research/graphstats_repos/causal_batch/:/base neurodata/causal_batch:0.0.1
# docker run -ti --entrypoint /bin/bash -v /mnt/nfs2/batch_effects/:/data -v /home/eric/Documents/research/graphstats-repos/causal_... |
6158724b6c9d26db73aaf7b214f5ba4628757948165d3bfb553047aea969d080 | R | 7,966 | 245 | #' @name DEPRECATED-read.txt.Renishaw
#' @concept moved to hySpc.read.txt
#'
#' @title (DEPRECATED)
#' Import Raman measurements from Renishaw ASCII-files
#'
#' @description
#'
#' These data input functions are **deprecated** and they will be removed in
#' the next release of \pkg{hyperspec} package.
#' Now func... |
aa59c46676d34d971ee5773a97f7cf07d10cbd72ce058113ed963686eb71a7f2 | R | 7,986 | 185 | #' Forest plot to display the result of a leave-one-out meta-analysis
#'
#' @description
#' Draws a forest plot showing the results of omitting each study in turn,
#' using the grid graphics system.
#'
#' @aliases forest.metainf
#'
#' @param x An object of class \code{\link{metainf}}.
#' @param layout A character st... |
4b7ae755b9d6a175b656101fa88642eec98207379f217c815b1f7c76a3872990 | R | 8,002 | 186 | #####################################################################
#
# This script is used to conduct post-CCA statistics, including
# 1. general CCA correlation between groups
# 2. Network-wise CCA contribution
# 3. Coupling between CCA map and time delay projection map
#
# Liang Qunjun
library(tidyverse)
library(... |
c54808daabe613d43610932061d027179e121eeaca458519e47c540d2e029eb0 | R | 8,019 | 187 | library(BayesSpace)
library(ggplot2)
library(SingleCellExperiment)
library(dplyr)
Juvenile15yr_DH1_1 <- readVisium("/scratch/mshruv003/visium_08_10_21/Hockman_Visium/X201SC21082979-Z01-F001_01/raw_data/run_spaceranger_count_DH1a/outs")
set.seed(102)
Juvenile15yr_DH1_1 <- spatialPreprocess(Juvenile15yr_DH1_1, platfor... |
c330447ecb065aebbcdd641fcaec9663f7df4455afb2d368fb7e0b464654700b | R | 8,038 | 204 | # Plot distribution of demographic variables in both discovery & replication samples
library(tidyverse)
library(ggplot2)
#library(ggthemes)
library(cowplot)
library(patchwork)
# Clear workspace
rm(list = ls())
# Load data from disovery/replication samples into dataframes
work_dir = '~/Code/multimodal_psychopathol... |
fa32f055ee4853f999f5cd7351b55030089b419be2a5d06a9cdbb1605508fc10 | R | 8,039 | 313 |
Notebook to test the preservedCoexp R package and add some plots to main figure 6. Showing how long it takes to generate co-expression matrices as the number of cells increases
Showing how long it takes to compute preserved co-expression scores of genes as the number of genes increases
Potentially run on my macboo... |
50b6ab789b6f77feed0f18f77064dfe14618cfbcc3ea5b112dcb69da695ad65a | R | 8,045 | 202 | ---
title: "Extract environmental variables"
author: "Siyuan Feng"
date: "12/6/2021"
output:
html_document:
fig_width: 15
fig_height: 15
---
## 0. setup paths and functions
```{r setup, include=T}
knitr::opts_chunk$set(
echo = TRUE,
message = FALSE,
warning = FALSE
)
knitr::opts_knit$set(echo = F,
... |
c36c443c546d0cf4116ece308e2ea07260a08321b23ce60650383516b72f688d | R | 8,046 | 191 | ---
title: "Human FI vs. Human MTG"
author: "Jeremy Miller"
date: "March 8, 2019"
output: html_notebook
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Overview
This script identifies common and distinct ET-associated genes in human middle temporal gyrus as compared with human f... |
c4d2e2d18f9781afce09647ab0d4161abd97b91a9ac17dba8816a3551fd37855 | R | 8,051 | 86 | 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... |
56189bc3b8b00eb7b1c9f6c82212e61baf1e1e7558ebc9e402f4a575f487035d | R | 8,060 | 228 | lat_esq<-rep("ESQ",100)
lat_dir<-rep("DIR",100)
mov_rep<-rep("REP",100)
mov_ped<-rep("PED",100)
olho_oa<-rep("OA",100)
olho_of<-rep("OF",100)
pos_c<-rep("C",100)
pos_f<-rep("F",100)
pos_p<-rep("P",100)
pos_o<-rep("O",100)
idd<-c("I1","I2","I3","I4","I5","I6","I7","I8","I12","I13","I14","I15","I17","I18","I20","I21","I2... |
24111811b4f93be19ebad47561c31f39cf4fc3b75e1f7c252fd06d1036cb4439 | R | 8,103 | 275 |
# Summary -----------------------------------------------------------------
# Run a whole-brain bootstrap test to detect differences in ISC between movies
# Packages ----------------------------------------------------------------
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(broom)
library(rstatix)
... |
d20ff396299a2ac59d6d9aeada186bb3daeece3521461ddf30bef1ed8e5f13fe | R | 8,111 | 192 | library(natverse);library(reticulate);library(neuprintr); library(hemibrainr); library(nat); library(natverse); library(nat.jrcbrains); library(fafbseg);
setwd(getSrcDirectory(function(){})[1])# set the working directory to where this code is
source("Connectomics_functions.R")#library(RConnectomicsAnalysis)
# ### pl... |
ddc81bcb3ff945acd78cc73612b9d1b26e4a6dee7d03591ac5e44614ddca493b | R | 8,143 | 168 | library(GenomicRanges)
library(data.table)
library(stringr)
library(RColorBrewer)
library(ggplot2)
library(ggpointdensity)
library(viridis)
library(readxl)
library(ggdist)
library(gghalves)
#Plot Fig3a, example STR expansions
dat.alleles<-data.frame(read_excel("plot_table_fig3.xlsx", sheet=1), stringsAsFactors = F) #... |
1fae2fb902e21ce48417651ff55c6c03eb812b49c180d24a50b7b5bff4b7fd75 | R | 8,148 | 191 | #' viewBatchPreds
#'
#' Generate image grid of mini-batch of image chips, masks, and predictions for all samples in a DataLoader mini-batch.
#'
#' The goal of this function is to provide a visual check of predictions for a mini-batch of data.
#'
#' @param dataLoader Instantiated instance of a DataLoader created using t... |
1bfee7e1319316fdcd4cd3e35ac26dd1d4db4d3f6f4443d1bc875dfb425106f7 | R | 8,257 | 200 | # Brain-Heart Arousal Movement analysis
# this script does an LMM analysis of the effects of arousal and movement on the
# brain-heart couplings within a ROI
# 2024 by Antonin Fourcade
# load packages -----------------------------------------------------------
library(ggdist)
library(gghalves)
library(raincloudplots... |
7c7034ec191d79939e644b5e6b2e81545c6b3b3d072c7b7eb718987107ae6fe6 | R | 8,262 | 217 | ### https://github.com/klarman-cell-observatory/ivPerturbSeq/blob/master/CellComposition/AdamsMethod.R
### only modified to remove cell types of small cell number
library(Seurat)
library(dplyr)
library(tidyr)
### redoing cell type composition collapsing germ layers
load("/media/Scratch_SSD_Voyager/sammi/RNA_editin... |
60b5255f16c0b8f8a656b98f1536c47b5baa0cf7515df7897de38e565e709bef | R | 8,266 | 226 | library(tidyverse)
library(lme4)
library(broom.mixed)
# Function to load all CSVs from a directory and add additional columns based on file name
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)
... |
6a7447a99f1272424cb8f967868f2f2ebeb3d95cafa26826a7b2f177010bba41 | R | 8,274 | 191 | if (!require(pacman)) {install.packages("pacman")}
pacman::p_load('this.path', 'arrow', 'progress', 'magrittr', 'tidyverse')
setwd(dirname(this.path())) # set working directory file location
# Global Options + Functions --------------------------------------------------
options(dplyr.summarise.inform = FALSE)
custom... |
ed3f7e56356ebf9fbab8c1b23b1a49be9302cb6cd82365de3eb42d14b458ebe4 | R | 8,274 | 142 |
###Script 7
#Heatmap of cluster metadata
library(dplyr)
library(viridis)
library(ComplexHeatmap)
#get metadata
md <- readRDS("output/atlas/metadata_integrated_neuronal_atlas.rds") %>%
select(percent.mt, percent.ribo, nFeature_SCT, Region, Treatment, scType_class, cluster_name, harmony_clusters, subcluste... |
9d5d58419092880691ba5388be4fd820431584bf73d6a7dc0bc2d81b876579db | R | 8,279 | 242 |
require("RColorBrewer")
require('directlabels')
library(reshape2)
library(knitr)
options(stringsAsFactors = F)
#plot mr result
df<-read.csv("~/../Dropbox/DansPaper/data/mr/mr.csv",header = T,stringsAsFactors = F)
df<-df[,c('geneid','sig_real_ci')]
#---load annotation---
gene_anno_pos<-read.table('~/../Dr... |
2ebb7ec8b1263230873c3e8e352ec74c50b5290817eadaefc7ea097b3737040a | R | 8,281 | 181 |
# Load and install missing packages ---------------------------------------
packages <- c("metrica", "ggplot2", "ggsci", "Hmisc", "pheatmap", "paletteer")
package.check <- lapply(
packages,
FUN = function(x) {
if (!require(x, character.only = TRUE)) {
install.packages(x, dependencies = TRU... |
c92b3578388a31a826b9608a110eb2aaab97b44694e6b68fa2b051807fe88ded | R | 8,297 | 188 | ---
title: "Overlaying plots and transparent thresholding"
author: "Charly H. A. Billaud"
date: "2026-03-05"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Overlaying plots and transparent thresholding}
%\VignetteEngine{rmarkdown::render}
%\VignetteEncoding{UTF-8}
bibliography: references.bib
-... |
d5fbd6ebc33a92da320a8c88e8371b0be200d0446cb3a7099fd5d4e351e02147 | R | 8,310 | 243 | ---
title: "Clustering_IFS_Shockbox"
author: "Lukas von Ziegler"
date: '2024-01-12'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Clustering the IFS shockbox sessions with kmeans.25
here we run a new kmeans 25 clustering for all IFS shockbox samples
`... |
a9132d0fd844ab561318d28b6542eeb23eaab52d74c2b19e0633a307b3307d3e | R | 8,312 | 191 | ############################################
# Install required packages
############################################
install.packages("data.table")
install.packages("dplyr")
install.packages("GenomicRanges")
install.packages("BSgenome")
BiocManager::install("BSgenome.Hsapiens.UCSC.hg38")
BiocManager::install("biomaRt"... |
904cd7b6994834c09c0d6740161269e29a95cdde5340045cab5562ba2e4736dd | R | 8,331 | 237 | lat_esq<-rep("ESQ",100)
lat_dir<-rep("DIR",100)
mov_rep<-rep("REP",100)
mov_ped<-rep("PED",100)
olho_oa<-rep("OA",100)
olho_of<-rep("OF",100)
pos_c<-rep("C",100)
pos_f<-rep("F",100)
pos_p<-rep("P",100)
pos_o<-rep("O",100)
idd<-c("I1","I2","I3","I4","I5","I6","I7","I8","I12","I13","I14","I15","I17","I18","I20","I21","I2... |
b3bf523597faeb183799a86448e22010945f43252436bedfd8e40145caa11c0e | R | 8,338 | 174 | #' 2D visualization plot of a neuroimaGene object
#'
#' Generates a 2D visualization plot of the neuroimaGene object. Neuroimaging
#' regions are defined by the atlas parameter and colored according to the
#' magnitude and direction of the aggregate effect from each gene in the
#' NeuroimaGene object. Colors can be def... |
4d35241b317a9c567ddbe7ad99d26bd29795c1ec25260fc2ebdfae2cf723e3bd | R | 8,355 | 216 | #' assessDL
#'
#' Assess semantic segmentation model using all samples in a torch DataLoader.
#'
#' This function generates a set of summary assessment metrics based on all samples
#' within a torch data loader. Results are returned as a list object. For
#' multiclass assessment, the class names ($Classes), count of sa... |
69af66ffa448a2f30fdc17b8d4ef04e53232c4c26e29dac211cca01d325ed158 | R | 8,362 | 219 | #' @include utils_internfunc.R
#'
NULL
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Functions
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' @title Functions for preprocessing data
#'
#' @importFrom stats lm qnorm rbinom varimax
#' @importFr... |
ba79843f790ce2015de3446ce0bd5f2ff786273f501893149eeaca52b3c501a2 | R | 8,403 | 237 | library(Seurat)
library(clustree)
library(dplyr)
library(plyr)
library(ggtree)
library(MetaNeighbor)
library(Matrix)
library(dendextend)
library(SingleCellExperiment)
flexsplit <- function(dat,char){
test=strsplit(as.character(dat),char,fixed=TRUE)
n.obs <- sapply(test, length)
seq.max <- seq_len(max(n.ob... |
f27241f328bb276592a05215d94a58506b195cae18f8e1c7c16d7f61fd97ccef | R | 8,414 | 255 | ---
title: "VAME_Clustering_AllData"
author: "Lukas von Ziegler"
date: '2024-02-23'
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
lib... |
fbb6c90c68dc11ac20b51c4bba80d9c0b762b30a415695d34c60d73bf95ca061 | R | 8,435 | 283 | #' *Deprecated* Highlights and test the time-course of effects through crossvalidation
#'
#' *Deprecated* - it's best to set-up yourn own. Variant of 'decode_signal()', in which different IDs (i.e., participants)
#' end up in different folds. In other words, data is not splitted by trials, but by
#' individuals. All th... |
e737bb24a8a3da017299aabc7c457e8f38b4672fc3841cb10600ab4171595615 | R | 8,453 | 109 | #' @title SURFvextract
#'
#' @description Extracts whole-brain vertex-wise surface-based measures for each subject in a 'FreeSurfer' output subjects directory, resamples the data to a common surface template, and stores it as a .rds file. This function requires the 'FreeSurfer' environment to be preset in the unix envi... |
ae4f36196ef1956c3eddd7b6b2655537e4dbe249860835201e6276bd4200413e | R | 8,465 | 158 | #----Figure_01_F----------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 01 Panel F for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#-------------------------------------------... |
51b405827549468e57085d2b0e86a3913225f5771e44263c6546a471993728f2 | R | 8,485 | 284 | #' Save a plot to a file
#'
#' Adapted from [ggplot2::ggsave()]. A general-purpose plot-saving function that
#' works with any R plotting system. Accepts `ggplot` objects, `grid` objects,
#' or any plotting call wrapped in a function. Unlike [ggplot2::ggsave()], which
#' only supports `ggplot` and `grid` objects, `save... |
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