sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1e5519e82b62b4dc2cbd41d939b41711cfc3cdec7c5be108fc5f6c73e427c3e7 | R | 19,878 | 711 | #' Compute OLS coefficients
#'
#' If the design matrix has full column-rank, then use the normal
#' least squares estimate. Otherwise, use the Moore-Penrose inverse
#' to compute the least squares estimate.
#'
#' @param y Target vector (n x 1)/matrix (n x m)
#' @param x Design matrix (n x p)
#'
#' @return Vector of OLS... |
1b1547de3f604ea2b4e7df474db93eae8070d0af91710edbfb9609dc0d2c8356 | R | 19,882 | 334 | # Load libraries
library(SingleCellExperiment)
library(Seurat)
library(tidyverse)
library(Matrix)
library(scales)
library(cowplot)
library(RCurl)
library(pryr)
library(RColorBrewer)
library(dplyr)
library(textclean)
#load filtered_seurat object
load("QC_directory/data/filtered_seurat_mt_genes_removed.R... |
f64058bcfe3e880037d139db858cc6ab90d80eb8d5f8c910fc4f3f60b85c787e | R | 19,887 | 602 | ---
title: "Figures"
output: html_document
---
### This script recreates and stores all figures from the manuscript (except Fig. S4A)
```{r Librairies}
library(tidyverse)
library(dplyr)
library(patchwork)
library(Seurat)
library(readr)
library(ggplot2)
library(circlize)
library(scales)
library(openxlsx)
```
### Load... |
988416397296f38e352f1a88e31867a1fc851cd8b5fa6b0e51f071efb413f88c | R | 19,989 | 362 | #----Figure_05------------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 05 Panel D for the connectivity analysis for
# Reinhard and Fukuda et al.
#-----------------------------------------------... |
6a8cb83e9389202f2a378aba96ef8b1b104db2156cc9a6cc0f6fd4e8a1b09676 | R | 20,165 | 428 | ---
title: "20240116_invitro_excitatory_plots"
output: html_document
date: "2024-01-16"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load libraries}
library(tidyverse)
library(ggplot2)
library(agricolae)
library(ggsignif)
library(multcomp)
library(nlme)
```
```{r load data}
puncta_a... |
973dd659ab0eebd225ce5eb473b27e2e1d430c2f1a493f3e9dcc660e763f41fe | R | 20,165 | 428 | ---
title: "20240116_invitro_excitatory_plots"
output: html_document
date: "2024-01-16"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load libraries}
library(tidyverse)
library(ggplot2)
library(agricolae)
library(ggsignif)
library(multcomp)
library(nlme)
```
```{r load data}
puncta_a... |
1af82b371a6540477e909b5061761d3f859e2ec455984fbcaeccd49d4f31d46e | R | 20,189 | 461 | ---
title: "Optic Nerve: Oligos"
author: "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
---
```{r setup, include=FALS... |
e6b57798b2f1769bb1400a1c2c82886f103c9ef8af18f201fc3e6b58c572b3f7 | R | 20,333 | 346 | setwd("/home/wangmg/Documents/deconvolution/Data_and_code_for_reproduction")
source("Code/utils/utils.R")
source("Code/utils/generate.R")
source("Code/utils/metrics.R")
source("Code/utils/plot.R")
#### Unpaired scenario
### The original data can be downloaded from https://singlecell.broadinstitute.org/single_cell/s... |
4ab7d3ad3a907f348a454e536aad0a90c86caf21d76b4247db6bd6a5d79b6a1f | R | 20,355 | 345 | ## =========================================
# evaluation scripts to:
# take in best performing model in Polarbear co-assay and Polarbear models
# calculate and plot:
# - gene-wise correlation
# - differentially expressed genes
# - peak-wise AUROC and AUPRnorm
# - FOSCTTM
# we evaluate on SNARE-seq study's cell type s... |
c47a531cda922b0d026ea4aef65f04ad08fc2696807e5338435bf2763c92f223 | R | 20,395 | 473 | #Secondary functions for conversions of surface data:
#- surf_to_atlas()
# Surface to atlas
#- atlas_to_surf()
# Atlas to surface
#- fs5_to_fs6()
# fsaverage5 to fsaverage6
#- fs6_to_fs5()
# fsaverage6 to fsaverage5
#- surf_to_vol()
# Surface to volume
#- fslr_to_fs5()
# fslr32k to fsaverage5
#- fs5_to_fslr()
# ... |
ee698722c43ea58281fb176b820156f25df16aff0f2efe37380efbb2bdcaabda | R | 20,436 | 436 | ---
title: "SensitivityAssays_AllData_25Clusters"
author: "Lukas von Ziegler"
date: '2022-11-29'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer_Functions_v4.R")
source("UnsupervisedAnalysis_Functions.... |
fb6f2e8053c0820ee61a740182b4cf6fa8da5f871e27d5d0bb23224550297846 | R | 20,563 | 576 | ---
title: "Pupilla::TOBII::Reduce_Features"
author: "Elvio Blini"
date: "January 2024"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Pupilla::TOBII::Reduce_Features}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
resource_files:
- images/load_ic1-1.png
- images/load_pc1-... |
cdb2eb6c703f90194fb49d500e1f166d4ecc2752bf1db43f87d6786d2b2468f0 | R | 20,568 | 564 | ---
title: "Analysis_diffClusters_MBT_LDB_IFS_Roche"
author: "Lukas von Ziegler"
date: '2024-02-22'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer.R")
source("BFA_BFF.R")
library(sp) #tested ... |
81ea783c6ded13439b8775d59c8baf6d28056a1e05d75bd5a9c19b3942d4abf4 | R | 20,634 | 325 | #-----------------------------------------------------------------------------------------------
# script for the data analysis of GWAS meta-analysis results using GenomicSEM
# written by Else Eising, 6 November 2020 - last updated by Gokberk Alagoz, 16 June 2023
# See https://github.com/MichelNivard/GenomicSEM/wiki/4.... |
a1d5a24ed5e402bfebcfe3647bfd442d22c7ec80a4a767e83e4ae46fbf21da27 | R | 20,876 | 520 | ---
title: "Figure6_TFS_Analysis"
author: "Fabienne Rössler"
date: '2023-04-18'
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(rlang)
library(sp) #tested with... |
3f6ae8537e34e83e95068ab9f248a364f15210ab874fdf02b1d3d2f31d621197 | R | 20,905 | 576 | # 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(runjags)
library(rjags)
library(loo)
... |
b208eaf290884f64bfa943c9d0528c4e6f45a464289e2885b463c3c2f92f0324 | R | 20,970 | 690 | #' Transform data from pairwise comparisons to long arm-based format
#'
#' @description
#' This function transforms data from pairwise comparisons to a long
#' arm-based format, i.e., two rows for a pairwise comparison.
#'
#' @param treat1 Either label for first treatment or a meta-analysis
#' or pairwise object (s... |
f2e7bdb2978c6737e0e239fbe37be8d24115f3f1637cef9b742a9d7cd07c7a8b | R | 21,043 | 559 | # significant gwas snp to target gene across neuropsychiatric disorders
library(stringr)
library(reshape2)
library(rtracklayer)
library(ComplexHeatmap)
library(circlize)
library(ggplot2)
library(cowplot)
library(data.table)
# # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # # #
# part1: get targets... |
4b97d8795b39f0bf3be8405d46d121bdbc1696651f173482c2612564235da179 | R | 21,090 | 679 | library(tidyverse)
library(janitor)
# library(gtsummary)
library(missForest)
`%nin%` = negate(`%in%`)
setwd("~/gitcode/CAIDE_APOE")
# NACC
## Import datasets
adrc.raw <- read_csv("resources/NACC/ADRC_all_9_16_2022.csv")
adgc.raw <- read_tsv('resources/NACC/ADGC_NACCIDs.txt', col_names = F) %>%
rename(NACCID = X1) ... |
c7357b87fff03ebc825201260244d68934c4b1f8362753eb41c0982eceb924f1 | R | 21,110 | 473 | ---
title: "Optic Nerve Integration and subsetting"
author: "Katie Emberley, Jonathan Nelson, Greg Duncan, and Jeremiah Reyes"
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
cod... |
c5a35f378cfb8de5c730999b14ab0c68d22b5a8eab254228d154e9960be893c3 | R | 21,148 | 423 | ## Function: enrichment analysis of genes
# -----------------------------------------
# Author: Cao Luolong, caoluolong@outlook.com
# Date created: 09-04-2021
# Date updated: 2022.07.05
# Warning:
# @HEU.@FUDAN.
############## load packages ###########
rm(list=ls())
# if (!require("BiocManager", quietly = TR... |
7db91d4e9728595cb4728303beee9eb0c4601919e328faa0a4cdeb9f0d54d13a | R | 21,317 | 681 | #' Import data of Cochrane intervention review
#'
#' @description
#' Reads Cochrane data package of a Cochrane intervention
#' review and creates a data frame from it.
#'
#' @aliases read.cdir
#'
#' @param file The name of a file to read data values from.
#' @param title Title of Cochrane review.
#' @param exdir The... |
9321de4639980181d875babe447e4f4e98117c3c673677458764dcd7ce94f6c2 | R | 21,351 | 361 | setwd("/home/wangmg/Documents/deconvolution/Data_and_code_for_reproduction")
source("Code/utils/utils.R")
source("Code/utils/generate.R")
source("Code/utils/metrics.R")
source("Code/utils/plot.R")
##### Paired scenario
#################### Preprocess data downloaded from TISCH
### The original data can be downloade... |
a79b0bbad2a43124c8005546e8c5473ea90dcfb869eb727f2fea04bcc00445a5 | R | 21,355 | 612 | ---
title: "Inferential - Session Models"
author: "<h3>by Michael Demidenko</h3>"
date: "`r format(Sys.time(), '%B %Y')`"
output:
html_document:
theme: united
highlight: tango
toc: yes
number_sections: yes
toc_depth: 2
toc_float:
collapsed: yes
smooth_scroll: yes
code_folding: ... |
e3511550a1dc26e1e51ec8fbb59b6dc3a227d363f4b2801a9e8453eb3dca19a3 | R | 21,465 | 455 | #' Sigmoidal Simulation
#'
#' @importFrom stats rbinom
#' @importFrom stats rnorm
#' @param n the number of samples. Defaults to \code{100}.
#' @param pi the balance between the classes, where samples will be from group 1
#' with probability \code{pi}, and group 2 with probability \code{1 - pi}. Defaults
#' to \code{0... |
882608ae2ed392554a2dbdb9b781199309987d0695425c53f9da76646e76d82d | R | 21,539 | 690 | ---
title: "Estimating onsets using cluster statistics: examples"
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
---
# Dependenci... |
842782c241426a536cd86f124e54181a365140a56b64d18dda08420a6afdae5a | R | 21,595 | 512 | ---
title: "S1F_Analysis_Script"
author: "Lena Gottschalk"
date: "2024-08-27"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Libraries
```{r}
library(tidyverse)
library(dplyr)
library(readr)
library(ggsignif)
library(forcats)
library(ggpubr)
library... |
e7bf2124b1badedafdc7495e5eab1ffd5c16049055e73e74861330ae1751aa75 | R | 21,608 | 562 | ---
title: "GAM_Figure5"
output:
word_document: default
html_document: default
pdf_document: default
date: '2023-09-20'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# tutorial https://noamross.github.io/gams-in-r-course/chapter1
```{r loaddata}
library(mgcv)
library(dplyr... |
53c2949b95ab59fda61bca28062c0fe4362cc426c22fffed904e5125c01fd11f | R | 21,738 | 447 |
###Script 21
#Identify vulnerability-correlated transcripts
library(Seurat)
library(sctransform)
library(patchwork)
library(dplyr)
library(viridis)
library(ggplot2)
library(ComplexHeatmap)
library(enrichR)
library(RColorBrewer)
library(MAST)
library(ggrastr)
library(ggrepel)
library(fgsea)
md <- re... |
1a1a8ee35f9132985112fbe05d641c4ea1f105d595a98370dadc6bf0d5131243 | R | 21,783 | 512 | .libPaths(c("/data/tu_hardikar/R/x86_64-pc-linux-gnu-library/4.0", "/afs/cbs.mpg.de/software/r/4.0.2/ubuntu-bionic-amd64/lib/R/library"))
#setwd("W:/MPI/Daydreaming/LSD3/")
setwd("/data/p_02260/all_lsd/gradients/")
# load necessary packages -------------------------------------------------
library(FactoMineR)
librar... |
c60a9cc0b9c452c1dc64aa0ee09ce9757e18f54e0a4e3449923f3ba556e708ad | R | 21,837 | 496 | # FNIH Project 2
# Function to make AUC Forest plots
#### Coded: Kellen Petersen
#### Date as of commenting and documentation - 05/31/2024
getAUCs_withLabels <- function(T,breaks1,indicatorline,rev_axis=FALSE,y1,y2){
df <- T %>%
select(company,AUC,AUC_Lower,AUC_Upper,analyte)
df$AUC <- as.numeric(df$AUC)
... |
ef2f02ef9c30d338f3f075a6ed034049b8fa64129711b56c91765fc951570195 | R | 21,994 | 549 | #Differential expression analysis for Paediatric vs Adult
#Based off HBC tutorial https://github.com/hbctraining/scRNA-seq_online/blob/master/lessons/pseudobulk_DESeq2_scrnaseq.md
#See DESeq2 notes on Wald test https://bioconductor.org/packages/devel/bioc/manuals/DESeq2/man/DESeq2.pdf
library(scater)
library(Seur... |
815dd7f23db9d0059a21ef5b1d4aa9339d265d2ac97f1b4ac1a118a4be1aea14 | R | 22,093 | 335 |
###Script 9
#Other plots full atlas
library(dplyr)
library(ggplot2)
library(viridis)
library(ComplexHeatmap)
library(ggrastr)
library(ggrepel)
#################
###Violin plot###
dt.vln <- readRDS("output/marker_plots/violinplt_data.rds")
dt.vln <- dt.vln %>%
rename(Final_Cluster_Assignment = cellty... |
aa8aea739d38c04a73930bfdbef18ba7b906acd4fbe6de88485293b4fa594885 | R | 22,146 | 526 | #### Garner, Kind et al. 2024 ####
## preprocessing ##
# This script performs a couple of preprocessing steps and saves the output
# to disk. Only required to run once if the output of this script is missing
# or whenever there are changes to this script. "Main.R" loads the output of
# this script for further analysis... |
4ec14d0a665f42065f2cef44bece96ae1e26f8a86acbed12ccc79b192a61f8f9 | R | 22,148 | 657 |
#-------- THEME --------------------------------------------
umap_theme = theme(
aspect.ratio = 1,
text = element_text(size = 10),
axis.title = element_blank(),
axis.text = element_blank(),
axis.ticks = element_blank(),
axis.line = element_blank(),
panel.bord... |
11ebdd5a9bd59978d7e95150aae982affade632123a92f9159eaa1f37197ff92 | R | 22,179 | 679 | options(max.print=500)
options(stringsAsFactors=FALSE)
## Args ================================
args <- commandArgs(trailingOnly = TRUE)
VIS_DATA_PATH <- args[1]
PLOT_PATH <- args[2]
## Dependencies ================================
load_packages <- function(){
required_packages <- c("ggplot2", "ggh4x", "stringr",... |
c415235fcd9233acc7bbfaddf4687b18fe682e10599c5786ad4133e6eb9ebe63 | R | 22,230 | 425 | ### Newest GSVA pipeline requires R > 4.3.2 for gsvaParam to work
### installing R=4.3.2 in local home directory and will run everything in this script command line
# /media/Home_Raid1_Voyager/sammi/R/4.2.3/bin/R
# install.packages("remotes")
# library(remotes)
# install_github("rcastelo/GSVA")
# test code below work... |
41b103b6ea7ca730b1ea62f53d9b6a70d2d6d74a6eac2d7dee53f8a47df009cc | R | 22,363 | 490 | ---
title: "TEBC pre-discharge faecal samples: correlations between principal coordinates and the abundance of species and gut-brain/gut-metabolic modules from shotgun metagenomic sequencing"
author:
- "Kadi Vaher"
- "PCo-s calculated in the 16S_ordination script"
date: '`r Sys.Date()`'
output: html_document
---
... |
7977e855c8c9af27768a4966c9fda7561f6e37ec68d22bd45de65ee2dc5c94fa | R | 22,363 | 767 | #' Combine and summarize meta-analysis objects
#'
#' @description
#' This function can be used to combine meta-analysis objects and is,
#' for example, useful to summarize results of various meta-analysis
#' methods or to generate a forest plot with results of several
#' subgroup analyses.
#'
#' @param ... Any number... |
5b5a52f42e3b13bbb883b5d8a59bd2efefd4e0f74316781dd980614857462436 | R | 22,522 | 1,031 | ---
title: "SEFL1c1d_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::opts_chunk$se... |
2f0864be5adc6ddb7809f430c3978c957f078bf7566ca6b0ffb473592349a80e | R | 22,558 | 719 | library(shiny)
library(dplyr)
library(msiImporter)
library(xml2)
library(stringr)
library(Cardinal)
library(shinyjs)
library(EBImage)
customCSS <- "
body {
background-image: url('background.png');
background-size: cover;
color: #333;
font-family: 'Arial', sans-serif;
}
.h4, .h3, .h2, .h1, h1, h2, h3, h4 {... |
14f97c4b20baf5ca37b88320dd61f206a44e219753ecdc0b00d0f5d5fc892cf8 | R | 22,581 | 621 |
cat('-----PrediXcan-R----- \n-----2021-07-27---- \n')
library(optparse)
library(glmnet)
library(RSQLite)
library(data.table) #new added
library(foreach) #new added
library(doParallel) #new added
option_list = list(
#general
make_option("--main_dir", action="store", default=NA, type='character'),... |
60eb8e7f85cb239bb425ce778732f48a1ac2a649f74d334894e8cb280bebb285 | R | 22,614 | 1,051 | ---
title: "SEFL10g2g3_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::opts_chunk$... |
74d83f3f9902293c7a45288bc686a6c7e558d327c2b7298543773f4818e96fbe | R | 22,633 | 588 | ---
title: "README"
author: "Lukas von Ziegler"
date: '2023-08-29'
output:
html_document:
toc: true
---
# Behavior Flow
This package enables efficient meta-analyses of unsupervised behavior analysis results. It builds a data object containing label data from multiple recordings/samples, with l... |
871b646444142b1464e14a4794a3ab825f20a01825876e0deb052e2e04348b92 | R | 22,721 | 572 | library(clusterProfiler)
library(DOSE)
library(org.Hs.eg.db)
library(enrichplot)
library(tidyverse)
library(cowplot)
library(venn)
set.seed(1)
sig_path <- "new-analysis/Astrocytes/Ast_M_F_difference.csv"
out_path <- "new-analysis/Astrocytes/"
global_gender_spec_genes <- read.csv(sig_path)
gene.df <- bitr(globa... |
3c47c744d79a1ed155854c37eaa13a3cdff4f388119a589c01395a0d8d7659b7 | R | 23,038 | 439 | setwd("/home/wangmg/Documents/deconvolution/Data_and_code_for_reproduction")
source("Code/utils/utils.R")
source("Code/utils/plot.R")
library(Seurat)
library(ggplot2)
library(ggpubr)
library(ggprism)
library(ggrepel)
#################### Preprocess the original scRNA-seq data
### The original scRNA-seq dataset is co... |
bbdcc066e36e4c1a696869d010a41c65557f183419a9de7c5d5fa66f87a0ab1b | R | 23,156 | 648 |
#' Function for getting the plotting genes (to avoid typing it out a bunch of times)
#'
#' @return gene vector
#'
#' @export
formatPlotGenes <-function(plotGenes){
for (k in -sort(-grep("-",topGenesExc))){
plotGenes=c(plotGenes[1:k],gsub("-","\\.",plotGenes[k]),plotGenes[(k+1):length(plotGenes)]) # Convert - to ... |
d7737f29704315193b531ab9dec9ae55f834fe0ca3658baef9bfe522335708fe | R | 23,170 | 478 | #' Simulate Binary Covariates
#'
#' @importFrom stats rbinom
#' @param groups group assignments, as an \code{[n]} vector, which take values from \code{0} to \code{K} for \code{K} groups.
#' @param balance a parameter that governs the similarity between the binary covariate distributions for the \code{0} group
#' agains... |
2d66bb7dcbb974dc43f7a9fd30c65dfe593d5987d6492e09fb4ba4d1ef34bb4c | R | 23,175 | 662 | ---
title: "Code for plotting Figure 1"
author: "Amy Tresenrider"
date: "2024-03-18"
output: html_document
---
```{r}
suppressPackageStartupMessages({
library(tidyverse)
library(ggplot2)
library(tidyr)
library(viridis)
library(ggridges)
library(purrr)
library(monocle3)
library(RColorBrewer)
library... |
80852c865f4f2c188a197844cb228c5cc2b5fd88c41eb74da6e8e7ce24b99d2f | R | 23,200 | 642 | ---
title: "Estimating onsets using cluster statistics: simulation"
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
---
The cluste... |
49774d512da71f7c22dbf15d7a2320efaa90462f1f77a1a34947a22be5061b65 | R | 23,279 | 617 | #========================================================================================#
# Written by James M Roe, Ph.D.
# Center for Lifespan Changes in Brain and Cognition, Department of Psychology
# University of Oslo, Norway
#========================================================================================... |
ad1b24af580a0bff80add587a04a010ce8d1f65a48a062d2b1f768b8c74f3a87 | R | 23,344 | 675 | #' Forest plot to display the result of a meta-analysis
#'
#' @description
#' Draws a forest plot in the active graphics window (using grid
#' graphics system).
#'
#' @aliases forest.metabind
#'
#' @param x An object of class \code{\link{metabind}}.
#' @param layout A character string specifying the layout of the
#'... |
7518efe02766d75bbce8e402f54a315d09994c6d21b80530dc23c21dd9887212 | R | 23,427 | 785 | #' Calculate the risk difference from meta-analysis results
#'
#' @description
#' Calculate the risk difference from the risk ratio, odds ratio, or hazard
#' ratio, and a baseline probability, i.e., control group event probability for
#' binary outcomes or survival probability for hazard ratios. Results are
#' reporte... |
3949789339065b55cc90443d59be8b027ce25d6650cfdfa3b071b9def2e34564 | R | 23,612 | 555 |
```{r, setup, include=FALSE}
knitr::opts_knit$set(root.dir = '/home/werner/projects/meta_qc_organoid/code')
```
Starting to explore organoid datasets
```{r}
library(Seurat)
library(data.table)
library(ggplot2)
library(MetaMarkers)
library(dplyr)
library(MetBrewer)
library(tidyr)
library(tibble)
library(parallel)
li... |
aac14596783cdef3167fe0c4f9b394db6a502b72d44e18e8e2d986cc745df56e | R | 23,672 | 662 |
#Find Tlx3 levels in different cell types of cerebellum at different dev stages
#Data is from : https://doi.org/10.1016/j.cub.2018.07.062
#Analysis by Rahul Jose
#
library(tidyverse)
library(gdata)
library(Seurat)
# Move and rename raw files -----------------------------------------------
#file lists
mtx... |
c2a68333666d4b205b362fb574098fec47925cd50f4af7a60dd8c943505de7a5 | R | 23,684 | 371 | library(data.table)
library(readxl)
library(ROCR)
library(Matrix)
library(scran)
library(ggplot2)
library(ggallin)
calculate_cor <- function(true_profile_mat, pred_profile_mat, snareseq_rna_mat_libsize, cor_method="pearson", norm_pred=FALSE){
# calculate correlation: overall, by gene, and by cell
# cor_method: "p... |
93fad702a33ba739b117b098bd7cb3a068374d1ac4ed3088bfd263a96a9e3a88 | R | 23,876 | 596 | rm(list=ls())
library(tidyverse)
library(foreign)
library(haven)
library(pROC)
library(splines)
library(growthrates)
library(parallel)
library(doParallel)
library(SuperLearner)
library(gbm)
library(rms)
library(tableone)
library(missRanger)
library(shapviz)
library(data.table)
library(cutpointr)
librar... |
7753a8a00e77cd96cb4e6728877d6b08d6130b715ad5bd0608bbe51cbca30eec | R | 24,002 | 1,133 | ---
title: "SEFL1F_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::opts_chunk$set(... |
28a121ad796baeadb4143b260bcb4fd828df4ed2833d79c78c28784ba944d226 | R | 24,011 | 513 |
###Script 18
#DE plots
library(dplyr)
library(snow)
library(ggplot2)
library(ComplexHeatmap)
library(RColorBrewer)
library(ggrepel)
library(ggrastr)
library(viridis)
library(enrichR)
library(future)
########################################
###Volcano plot DE genes each cluster###
ident.de <- functio... |
0ca2793a52f41d0c8622cd64103345db18f194f7c06054af6bca33df131dcc64 | R | 24,122 | 319 | ---
title: "neuroimaGene"
output: rmarkdown::html_vignette
resource_files:
- inst/extdata/vis_abstract.png
vignette: >
%\VignetteIndexEntry{neuroimaGene}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
library(knitr)
knitr::opts_chunk$set(
collapse = TRUE,
co... |
ebffb7bf2aff74b7c6d47d29feef4dbdee6a96d539ad736376244c73344cebe6 | R | 24,242 | 486 | # To run this code, first run the main script `main_results.R` (at least to the data load)
# ··············································································
## Extra Analyses --------------------------------------------------------------
# ·······························································... |
f9278eeb7186f886bf93b7415391c7e60001b4e938eeb1d668810aca714d4b66 | R | 24,648 | 732 | ---
title: "FADS2-by-breastfeeding interaction in UK Biobank"
author: "Giulio Centorame"
date: "`r Sys.Date()`"
output:
rmdformats::downcute:
toc: 6
number_sections: yes
---
This notebook contains the code for the main text interaction plots
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
li... |
0bb2219108ece081cbc2af40a7fe23acd6861d2d02d6e630882771ea0eb43152 | R | 24,696 | 626 | ---
title: "Weak and strong FWER correction"
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
---
Extension of the `fdr_demo.Rmd` n... |
c26b83a50a236310c7fbb37fcecb79ed0ad860dd25d28390659c8e5a3559d554 | R | 25,023 | 738 | ---
title: "Inferential - Run Models"
author: "<h3>by Michael Demidenko</h3>"
date: "`r format(Sys.time(), '%B %Y')`"
output:
html_document:
theme: united
highlight: tango
toc: yes
number_sections: yes
toc_depth: 2
toc_float:
collapsed: yes
smooth_scroll: yes
code_folding: hide... |
bfe02ee622b6a81fbeb0cee9d26df96a4539a51883aab59f34e3642e8e818143 | R | 25,128 | 818 | #' Calculate the number needed to treat (NNT)
#'
#' @description
#' Calculate the number needed to treat (NNT) from the estimated risk
#' difference, risk ratio, odds ratio, or hazard ratio, and a baseline
#' probability, i.e., control group event probability for binary outcomes or
#' survival probability for hazard r... |
8747fd204ff16157629361a8c9488fc1c5a5561d7997ccf650c9942dfeacff3e | R | 25,292 | 666 | #### Notes ####
#### Libraries ####
library(ggplot2)
library(viridis)
library(ggpubr)
#### Sourced functions ####
source("../functions/annotate_boxplots.R")
#### Functions ####
set_facet_vars <- function(df) {
if ("Sex" %in% colnames(df)) {
facet.vars <- vars(Tissue, Sex)
} else { facet.vars <- vars(Tissue)... |
5e7d021ecbe763c21fb7da7dffabb008395c518312eee45c2da7cda4a2166295 | R | 25,457 | 570 | # fine mapping SNP To target gene using ABC (bip)
# ABC-MAX
library(RColorBrewer)
library(dplyr)
library(stringr)
library(reshape2)
library(rtracklayer)
library(ComplexHeatmap)
library(circlize)
library(data.table)
load('/sc/arion/projects/CommonMind/roussp01a/INGELHEIM/atacseq/step3_neuron_width500/files/abc_piso/fi... |
c66a5ccc64c5c6638215e1dc6513c484db1493a2cc1de31e762aef119ba63979 | R | 25,806 | 591 | ### Fisher Kernel: elife 2024 Statistics and figures
##### Preparation #####
# install and load relevant packages
if (!require("pacman")) install.packages("pacman")
pacman::p_load(correctR, ggplot2, Cairo, ggpubr, lawstat, forcats, ggstatsplot)
resultsdir <- "/path/to/results" # this should point to the directory whe... |
6e46c77587a669c0ed0d38ae20a5b87aafac0b52100dff56bc873588e711c36a | R | 25,895 | 507 | #' Description of R object of class "meta"
#'
#' @description
#' Detailed description of R objects of class "meta".
#'
#' @details
#'
#' The following R functions create an object of class \code{"meta"}:
#' \itemize{
#' \item \code{\link{metabin}}, \code{\link{metacont}},
#' \code{\link{metacor}}, \code{\link{metag... |
d393615f1d6daf80addf42ee35ccf13a4b0fddb32cc23996bed9a03e35aa16d3 | R | 25,929 | 992 | library(ggplot2)
library(tibble)
library(tidyr)
library(dplyr)
library(scregclust)
library(Seurat)
library(purrr)
library(vctrs)
library(glmmTMB)
if (!dir.exists("data")) {
dir.create("data")
}
if (!dir.exists("output")) {
dir.create("output")
}
path <- "data/pbmc_granulocyte_sorted_3k_filtered_feature_bc_matrix... |
401e8faff6dda84e46f123f8733da5de834ce95242a2782a75516a82cde84bc4 | R | 25,996 | 725 |
library("stats")
library("vegan")
library("effects")
library("stats")
library("ggplot2")
library("dplyr")
library(car)
library(lme4)
library(lmerTest)
library(effects)
cbbPalette <- c("#FFC20A", "#D35FB7", "#4B0092","#E66100", "#CC79A7", "#000000", "#F0E442", "#D55E00")
micro=read.csv("slide_measurements.csv")
hea... |
84ae6b82efd963c270bb91a45bb4da373d05ed59de77b36d6a80a3fd41848850 | R | 26,184 | 638 |
Exploring the preservation of co-expression for cell-type specific gene markers, at a higher level of granularity than the broad cell-types used in the rest of the paper
Redo the analysis getting preserved co-expression scores for each gene in each dataset, this time save all the data, not just the mean value
```{... |
6ff440c7f82236eb7ca490d3b96dd898cd86077487a0d99952c1ce08ade756b5 | R | 26,262 | 792 | ### --------------------------------------------------------------------------------------------------
###
### plotspc - Plots spectra of hyperSpec object
###
### convenient plot interface for plotting spectra
###
#' Plotting spectra
#'
#' Plot the spectra of a `hyperSpec` object, i.e. intensity over
#' wavelength... |
b265f340437794714f7df95bedb20c95554b316f03e566d939f1a4bb2086833f | R | 26,457 | 656 | #' luz_metric_recall
#'
#' luz_metric function to calculate macro-averaged, class aggregated recall
#'
#' Calculates recall based on luz_metric() for use within training and validation
#' loops.
#'
#' @importFrom luz luz_metric
#'
#' @param nCls Number of classes being differentiated.
#' @param cropFactorMsk Number of ... |
abd30fdafb5449a521f99293e9ee8a6882d595dd2862ad0b716f402588cdf4ee | R | 26,532 | 418 |
load("/media/Scratch_SSD_Voyager/sammi/RNA_editing/Figure2_TimeSeries_R1/TS_all_RNA_logNorm_int_annot_v1_col_v1_20230124.rda")
aei_table_df <- read.table('/media/Scratch_SSD_Voyager/sammi/RNA_editing/Figure2_TimeSeries_R1/TS_allRNA_AEI_byCT_byExp_20230510.txt', header = T, sep =',')
adar_tpm <- read.table('/media/Scra... |
5e6d9f8f9efa7d8df595c3c50e584e9f6d203f67a7f9d7b1f50039f9641c9ab6 | R | 26,545 | 789 | #' Extract results from meta-analysis object
#'
#' @description
#' Extract study and meta-analysis results from meta-analysis object
#' which can be stored in an Excel file.
#'
#' @param x A meta-analysis object of class \code{meta}.
#' @param sortvar An optional vector used to sort the individual
#' studies (must ... |
ceffcffa41a716a55a248d130447c637a0330918f865e74e86f077c21e642691 | R | 26,627 | 819 | #' Drapery plot
#'
#' @description
#' Draw a drapery plot with (scaled) p-value curves for individual
#' studies and meta-analysis estimates.
#'
#' @aliases drapery
#'
#' @param x An object of class \code{meta}.
#' @param type A character string indicating whether to plot test
#' statistics (\code{"zvalue"}) or p-... |
a9ebfe85a02405cf7ba7db3a594e837fae99b912e7dc1f5bfca8be1956ad8173 | R | 26,630 | 849 | #' Plot density of prediction distribution highlighting areas of clinically
#' important benefit or harm
#'
#' @description
#' Plot density of prediction distribution highlighting areas of clinically
#' important benefit or harm
#'
#' @param x An object of class \code{cidprop}.
#' @param cid A numeric value or vector s... |
be3d32555c2d0b7bf19f216ecc6f0ace0e85f1724e87ce333fa043f688c6c011 | R | 26,810 | 838 | #' L'Abbé plot for meta-analysis with binary outcomes
#'
#' @description
#' Draw a L'Abbé plot for meta-analysis with binary outcomes.
#'
#' @aliases labbe labbe.metabin labbe.default
#'
#' @param x An object of class \code{metabin}. Alternatively, the x
#' coordinates of points of the L'Abbé plot.
#' @param y The... |
0185ce88b6b138b9895b3baccb6bb29977e8b84ef6132ac3cfe354eeff5bb657 | R | 27,013 | 892 | #' Risk of bias assessment
#'
#' @description
#' Create table with risk of bias assessment or add table to existing
#' meta-analysis
#'
#' @param item1 Risk of bias item 1 or a meta-analysis object of class
#' \code{meta} with information on risk of bias assessment.
#' @param item2 Risk of bias item 2.
#' @param it... |
70804e055e4e080986083c73e7a36273f75cae73f752c8c2b9921bf3e7c2269a | R | 27,178 | 994 |
local({
# the requested version of renv
version <- "1.0.7"
# the project directory
project <- getwd()
# figure out whether the autoloader is enabled
enabled <- local({
# first, check config option
override <- getOption("renv.config.autoloader.enabled")
if (!is.null(override))
return(o... |
524cc5b200916fadfbcf8fae2413491cc05687d17c2e6fd2d8e92c376a98111d | R | 27,251 | 607 | ---
title: "Extended Data Figure 2 EFGH, Extended Data Figure 4 B"
author:
- name: "Emir Turkes and Stephanie Fowler"
date: '`r strftime(Sys.time(), format = "%B %d, %Y")`'
link-citations: true
output:
html_document:
code_folding: show
number_sections: true
theme: lumen
highlight: haddock
toc: t... |
31cc22f5720a56266fe585bead62e21438504e5712d2a276f4684cd1ecdb08a0 | R | 27,357 | 624 | #' @title 3D Surface plotter
#'
#' @description Plots 3D cortical surfaces
#'
#' @param surf_data A numeric vector (length of V), where V is the number of vertices. It can be the output from SURFvextract(), CAT12vextract(), FSLRvextract(), HIPvextract(), SCMvextract().
#' @param surf_color color of the cortical surfa... |
bb168e74fa0dbbdbb60db3110a2f94448817b7b60f90572939f00b1350d88df9 | R | 27,449 | 639 | system("
cd ~/p/openchr/noack
#resizear los summits todos igual, para que no pasen cosas raras. y a parte centrimo lo hace asi tambien
#de momento hago grande la ventana. luego ya mirare a ver como filtro eso
#tambien poner un ID
cat Proneural_correlation_sorted_summits.bed | awk '{print $1,$2-750,$... |
688a2787c3f6711813bcac312b3514eee0e22a1955792cd3b3f660b9cb8519d5 | R | 27,606 | 802 | # Core function to create forest plot
#
# Package: meta
# Author: Guido Schwarzer <guido.schwarzer@uniklinik-freiburg.de>
# License: GPL (>= 2)
#
calc_pooled_prefix_extra <- function(lines, pooled.cols, do.calc,
leftcols, width.left.cols,
colgap... |
41c1323770580cd074effc4b00df6851dbc9acd5480eeae8f58cae6a9a66f3e6 | R | 27,740 | 474 | setwd("/home/wangmg/Documents/deconvolution/Data_and_code_for_reproduction")
source("Code/utils/metrics.R")
library(ggplot2)
library(reshape2)
n_archetypes_list <- c(5, 10, 15, 20)
sigma_list <- c(0.1, 0.2, 0.3, 0.4, 0.5)
#################### Paired
### PANDA package can be installed by install.packages("Code/PA... |
fda8e5bf5c319576db0aa256670cd401202a2b1c6bd7fbdc3a278982cd5c4b72 | R | 27,916 | 753 | # 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(runjags)
library(rjags)
library(loo)
... |
f994c89be1fc283c8100f5198920c0398425dffe27b8e483d9dfdd5e212909e1 | R | 27,992 | 1,032 |
local({
# the requested version of renv
version <- "0.17.3"
# the project directory
project <- getwd()
# figure out whether the autoloader is enabled
enabled <- local({
# first, check config option
override <- getOption("renv.config.autoloader.enabled")
if (!is.null(override))
return(... |
61e284f28718ba80bcc9bd960c38e90101413cd76262366cb15c921911f7fe72 | R | 28,063 | 1,389 | ---
title: "SEFL10N5b_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::opts_chunk$s... |
a89844ac66b5a01210c3b63d2bb61ab66e74340d4a859be592217b76d757f55d | R | 28,099 | 898 | ---
title: "DPX-EEG: LMM Analysis behavioural data"
author: "José C. García Alanis"
date: "`r Sys.Date()`"
output:
html_document:
theme: lumen
highlight: pygments
css: "styler.css"
---
Created with `r R.version.string`
```{r, setup, include = FALSE}
knitr::opts_chunk$set(class.source = "customCode",
... |
a090cf321ae6e83825802c340b7802784ef2019b38fa92afbe1c8e4b2d6fe8a2 | R | 28,134 | 597 | ---
title: "Cross-species analysis"
author: "Jeremy Miller"
date: "March 27, 2019"
output: html_notebook
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Overview
This script performs alignment of deep excitatory neurons in human frontoinsula, human middle temporal gyrus, mouse p... |
ffa9c27c20e4498ba6a984c164d2795d249c3cb2ead9b3683b63ef33be735cc0 | R | 28,370 | 461 | ---
title: "SensitivityAnalysis_AllData"
author: "Fabienne Rössler"
date: '2023-07-13'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("../DLCAnalyzer_Functions_v4.R")
source("../UnsupervisedAnalysis_Functions.R")
... |
32d94c15e16e34631bf6cd8da03d539a1086246e65eb4381edb59c22a1790449 | R | 28,387 | 507 | ## NEUROG2 / NEUROD2 OVERLAP ########
#############################################
library(eulerr)
library(bedr)
library(dplyr)
neurog2_peaks<-read.table("~/neurog2/chipseq/e14.5.neurog2_peaks.subpeaks.bed")
neurod2_peaks<-read.table("~/neurod2/chipseq/e14.5.neurod2_highconfsubpeaks_summit.bed")
join<-bedr.join.region... |
ef627cd2e79db0140046f87939eb691df1ea418dd2c872372ab57e857923b458 | R | 28,458 | 570 | ---
title: "20240116_invivo_a2d1_plots"
output: html_document
date: "2024-01-16"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load libraries}
library(tidyverse)
library(ggplot2)
library(agricolae)
library(ggsignif)
library(multcomp)
library(nlme)
```
```{r load data}
#manual_synbot_... |
67332bb5b9dd3b6c46150699de6237ccbb9c23b7e57b97a90c6f5508ae20bf4c | R | 28,459 | 624 | # fine mapping SNP To target gene using ABC (SCZ)
# ABC-MAX
library(RColorBrewer)
library(dplyr)
library(stringr)
library(reshape2)
library(rtracklayer)
library(ComplexHeatmap)
library(circlize)
library(data.table)
load('/sc/arion/projects/CommonMind/roussp01a/INGELHEIM/atacseq/step3_neuron_width500/files/abc_piso/fi... |
6011338ca764d99bc6f78e77321988d53a62668948c67a710285ccc8d8b122ad | R | 28,616 | 887 | #' Forest plot to display the result of a cumulative meta-analysis
#'
#' @description
#' Draws a forest plot showing the results as studies are cumulatively added,
#' using the grid graphics system.
#'
#' @aliases forest.metacum
#'
#' @param x An object of class \code{\link{metacum}}.
#' @param layout A character st... |
b8a230e50505913ed6492cee4de575c14c90d6f260564bf3cd280314159cb4c6 | R | 28,630 | 754 | ---
title: "DAOY-CBO_Seurat_EXPLORE1_50K"
author: "JJ"
date: "12/08/2022"
output: html_document
---
```{r graphical_output}
options(bitmapType='cairo-png') # this line of code needed BEFORE knitr code in chunk below
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(dev = "png",
dpi = 300,
... |
26cd00d9f3b0c46c8eaab11c590dfe7580971260ca2afbb05c9bf5e93a8a6aea | R | 28,678 | 789 | # FNIH Project 2
# make AUC tables
#### Coded: Kellen Petersen
#### Date as of commenting and documentation - 05/28/2024
# Load libraries
library(tidyverse)
library(data.table)
library(RVAideMemoire)
library(pROC)
library(RColorBrewer)
library(MuMIn)
library(here)
library(cowplot)
library(ggeasy)
#here::i_am("make_AU... |
23eed70d83feef119e2c94c67cd76c12abea46f718f50cd9e94b3315a53e4b35 | R | 28,940 | 844 | ---
title: "M3C Workflow"
author: "James Lucas Cairns"
date: "2024-07-15"
output:
html_document:
df_print: paged
---
## Loading Packages
```{r}
library(Cardinal)
library(rayshader)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(M3C)
library(spatstat.geom)
library(RColorBrewer)
library(keras)
library... |
e84f17009fa7fcc3876fb5d1514b0e1f6f9749bdb9e40ab3bec8892fd8afbb41 | R | 29,015 | 863 | #' Trim-and-fill method to adjust for bias in meta-analysis
#'
#' @description
#' Trim-and-fill method for estimating and adjusting for the number
#' and outcomes of missing studies in a meta-analysis.
#'
#' @aliases trimfill trimfill.meta trimfill.default
#'
#' @param x An object of class \code{meta}, or estimated ... |
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