sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d53c534d4fa9569c45bce6056a37d6700a0bfb0eab326628972ee7b3220e0a18 | R | 10,190 | 245 | #-----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_option... |
000fd6ff959cee13cf65c48b4b7a20dc7c4d8a04773ce76dc22c7a953f5d6f31 | R | 10,215 | 160 | ##############################################################################
#
# Subject selection
#
# In this script, we selected the data of the subjects who is suitable in
# this study.
#
# Liang Qunjun 2023/11/13
library(tidyverse)
library(NbClust)
library(ggiraphExtra)
library(ggsci)
library(ggeasy)
library(ti... |
fc919a459d5a5b825f16e5742f5fa085d8f7a578f44a6de65d13202e01eacda3 | R | 10,242 | 212 | ### determine which guide the cells in ADAR-KO teratoma carries
library(dplyr)
library(stringr)
# working path
setwd('/media/Scratch_SSD_Voyager/sammi/RNA_editing/')
input_dir = '/media/NAS1/Sammi_NAS1/Cellranger_Output/ADAR_20220614/dual_ref/'
ter_list <- c("ter1","ter2","ter3","ter4")
#Load dataset
ter_KO <- list(... |
c3d25cefce3ca3e68979e29f8ffa9b73aa014fd576a56260f9fd5a3740154e11 | R | 10,248 | 349 | ---
title: "Astrocyte Subcluster Correlation based on High Variable Genes"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load required packages
```{r}
require(dendextend)
require(SNFtool)
require(data.table)
require(tidyverse)
require(pbapply)
require(ComplexHeatmap)
```... |
b63c9fadc8469941c11cf2672768ba0dad5159cc4751ea779c3c17fe46573cf2 | R | 10,256 | 213 | library(dplyr)
library(Seurat)
library(tidyverse)
library(patchwork)
library(ggplot2)
library(scCustomize)
# load snRNA-seq data
# construct a data pathway table first(including the pathway and data type)
data.pathway <- read.csv("../data.detail.csv")
data.list <- list()
data.pathway.multiomes <- subset(data.pathway,d... |
0a6abe23283f161960ad6c091290892650dda96f64d535eb4021b8913a2428d8 | R | 10,314 | 379 | ---
title: "InferCNV_NGCHM_DAOYcoculture.Rmd"
author: "JJ"
date: "2024-08-24"
output: html_document
---
```{r graphical_output}
options(bitmapType='cairo-png') # this line of code needed BEFORE knitr code in chunk below
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(dev = "png",
dpi = 300... |
7b9aa4f64654111a482f2c7c371184f304af5a893ebe45d9a0e66ee890faf601 | R | 10,367 | 203 | library(dplyr)
library(tidyr)
library(stringr)
library(ComplexHeatmap)
library(ggplot2)
library(tibble)
library(reshape2)
library(openxlsx)
library(ggpubr)
library(gridExtra)
library(cowplot)
library(ggprism)
MD <- read.csv('Spots_metadata.csv', stringsAsFactors=FALSE)
MD$Sample2 <- paste(MD$Patient,MD$Line, MD$Mouse... |
7b80eaf05d1bf16d6e232cac0a58d7cc82e0ae98f04eee6123ffb50cc515713a | R | 10,378 | 316 | ---
title: "InferCNV_NGCHM_ONS76coculture.Rmd"
author: "JJ"
date: "2024-08-24"
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$set(dev = "png",
dpi = 30... |
422942d0377dd2370afcb35949be9376215162c390dac942f29d947852f2f116 | R | 10,437 | 148 |
#' mochims__plot_model_performance
#'
#' Plot model performance.
#'
#' @param input_dt data.table with model free energy estimates (required)
#' @param report_outpath output path for scatterplots (required)
#' @param suffix suffix for output files (required)
#' @param highlight_colour colour for highlights (default:re... |
ee9e70c6923639b4c490336af7564355fb185891b6076a693a06317f622bc7f0 | R | 10,479 | 194 | ---
title: "Figure 2"
author: "Greg Duncan, Katie Emberley and Jonathan Nelson"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: yes
toc_depth: 3
toc_float: true
number_sections: yes
theme: bootstrap
df_print: paged
code_folding: hide
highlight: pygments
---
```... |
a5083dd5b2518d1638e61a23d254819a50a0fe0f9a316be03713d3842ea94a0d | R | 10,515 | 366 | # Fun: show ------------------------------------------------------------------
.show <- function(object) {
print(object)
invisible(NULL)
}
#' @name show
#' @rdname show
#' @aliases show show,hyperSpec-method
#'
#' @title Show brief summary of `hyperSpec` object
#'
#' @description
#' Functions `show()`, `print()`, ... |
42fcc33bc32ea83fae812ab7f945e0371d7c5e5afad8ba101f63dde8f064f29a | R | 10,529 | 176 | rm(list=ls(all=TRUE))
path='C:/Users/Christoph/Documents/ETH/git/dslab/summaries/20181120_22-29-55_aug10bincont__fold'
data=data.frame()
for (fold in 0:9){
data=rbind(data,read.csv(paste(path,fold,'.csv',sep="")))
}
data$squared=(data$label-data$prediction)^2
human=read.csv('C:/Users/Christoph/Documents/ETH/git/dsla... |
3cf1eafc457c0a82d2efc9f72ef27434e2f6aff22c12abc37137c10dba54d321 | R | 10,530 | 319 | #### This is Alex and Ben :)
require(tidyverse)
library(pheatmap)
library(Hmisc)
library(RCy3)
library(igraph)
library(plyr)
library("dplyr")
library("psych")
library(maplet)
# Commit number 1030613ae4bc88ec4d98a466fe2251b70f60a5e8 10/10/20
if (!exists('qmdiab')){
base::load(data.makepath("QMDiab/qmdiab_2019_03_13.r... |
f8405518d0f1731306ec761e1560a1a7987a9b1285a6f433ae9c572d4b0e5032 | R | 10,539 | 186 | setwd("/home/wangmg/Documents/deconvolution/Data_and_code_for_reproduction")
source("Code/utils/metrics.R")
source("Code/utils/plot.R")
library(ggplot2)
##### MERFISH
### The original MERFISH data can be downloaded from https://alleninstitute.github.io/abc_atlas_access/descriptions/Zhuang-ABCA-3.html.
### The par... |
9d4d3238740ee467259e227a205a4c8dbedbb066a51f30e3cb40c587dca8363f | R | 10,568 | 245 | ---
title: "panel_plasticity_dAP5"
author: "Franz Xaver Mittermaier"
date: "2023-10-12"
output: html_document
---
This script will plot the panel for the plasticity experiments with dAP5. For description of the stimulation paradigms, see "panel_plasticity_500ms_train.Rmd".
```{r, message=FALSE, warning=FALSE... |
9420b0d19227f97d92874c00680708aa3ad1f24e9cf263de43cf72d6667c1cbf | R | 10,595 | 291 | ---
title: "Variability of organ-biased genes (observed vs. randomized) (*`r params$species.name`*)"
author: "Bucao, Christabel Floi"
output: html_document
date: "`r Sys.Date()`"
params:
input.rds.path: "../../results/12_Organ_Bias"
output.rds.path: "../../results/14_Organ_Bias_Observed_vs_Random"
species: "DRE"
... |
11993c2f4621ca724ec0f829a935d78d806b1ad26c9ff4e723cbd48b12b94e82 | R | 10,617 | 190 | #----Figure_04_I----------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 04 Panel I for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
# The first run might take long as all syna... |
f6145fdc61cd119c3a86504a8c596449f748e8d932db52c399dea9c17645375f | R | 10,682 | 99 | 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.002... |
6a02a3c6e0969ecc6e8d6d6861d6100b2f195a68df5619c1b92dec3aa25b219f | R | 10,692 | 229 | suppressPackageStartupMessages(require(tidyverse))
suppressPackageStartupMessages(require(magrittr))
server <- function(input, output) {
y_axis <- list(
title = "",
showline = TRUE,
showticklabels = F,
mirror="ticks",
showgrid = F,
zeroline = F,
linecolor = plotly::toRGB("black"),
li... |
c1d7aeb467c4e63c0b49e1da3535b306c18f1b810bf347c54cad7c751c0fd5c6 | R | 10,733 | 458 | #' Auxiliary functions for (back) transformations
#'
#' @description
#' Auxiliary functions to (back) transform effect estimates or
#' confidence / prediction interval limit(s).
#'
#' @details
#' Often in a meta-analysis, effect estimates are transformed before
#' calculating a weighted average. For example, the log ... |
e023d901e0c70b7c70f5a6059cf2ce7ab8a172bfd1910500a2d9243b5053ce4e | R | 10,766 | 246 | library(DESeq2)
library(RITAN)
library(RITANdata)
library(ggplot2)
library(stringr)
library(biomaRt)
# Set working directory
setwd("/Users/derek/Desktop/Katie/")
#read in raw count data and metadata with columns of first matching rows of second
countdata<-read.csv("Liver_gene_expression_rawcounts.csv", row.names = "e... |
2ac0702cb6d18b7c5d6a8804d14112b577cde18ccbe3141999b4ee0d61314e14 | R | 10,771 | 297 | #' Conversion from log odds ratio to standardised mean difference
#'
#' @description
#' Conversion from log odds ratio to standardised mean difference
#' using method by Hasselblad & Hedges (1995) or Cox (1970).
#'
#' @param lnOR Log odds ratio(s) or meta-analysis object.
#' @param selnOR Standard error(s) of log odd... |
20ad867327c9df71dd3561c12a9258c48355d161f287ed21af1b039706047057 | R | 10,779 | 207 | library(ggplot2)
library(reshape2)
library(RColorBrewer)
###################################################################################################################
# PCA MDS
################RNA###########################################################
setwd('/sc/arion/projects/CommonMind/roussp01a/INGE... |
a61a8f7987a5433edae18e5f2a01a0c9da6815d4d62e35b259087af633607d16 | R | 10,802 | 324 | ---
title: "Variability rank correlation across conditions (*`r params$species.name`*)"
author: "Bucao, Christabel Floi"
output: html_document
date: "`r Sys.Date()`"
params:
input.variability.rds.path: "../../results/03_Variability_Plots"
output.rds.path: "../../results/04_Correlation_Across_Conditions"
species: ... |
fe337397a57126284de38268d9ddb9ad68696b0a644d8682d8475ad504b0a334 | R | 10,894 | 308 | #### Notes ####
# Functions to filter samples for quality control
#### Libraries ####
library(edgeR)
library(matrixStats)
library(sva)
#### Sourced functions ####
source("../functions/unit_tests.R")
source("../functions/helper_functions.R")
#### Functions ####
# Remove barcodes not included in metadata and map to s... |
c78537d88f2fe958b10a27ca729b5ab7a5d6b49c8de3a1b6cde43a123b30f155 | R | 10,933 | 346 | # Summary -----------------------------------------------------------------
# Run a parcel-wise ANOVA to detect differences in ISC between movies
# for the Emofilm dataset
# Packages ----------------------------------------------------------------
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(broom)
li... |
9b3036e996dde150b588f4ab285dac97b023f8024de2a0d202e4f0faa79427c9 | R | 10,971 | 319 |
# Summary -----------------------------------------------------------------
# Run a parcel-wise ANOVA to detect differences in ISC between movies
# Packages ----------------------------------------------------------------
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(broom)
library(rstatix)
# Prelud... |
e997236b7973c1c069a8544311ae09f415bc6413cc20e5d837877e3c1fc80ec8 | R | 11,010 | 319 |
# Summary -----------------------------------------------------------------
# Run a parcel-wise ANOVA to detect differences in ISC between movies
# Packages ----------------------------------------------------------------
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(broom)
library(rstatix)
# Prelud... |
da233e8ccb2437d475410e91a7a6e72759c149ea8bc9e31fc97915a9a09a17ff | R | 11,026 | 277 | #Alicia Petrany, draft as of 4.20.2023
#Contains all helper function for DEGage
#'@title parse.input
#'@description
#'parses input of the user based on the data type of the input
#'
#'@param counts user input for 'counts'
#'@returns dataframe containing counts
parse.input <- function(counts){
dtype <- class(counts)
... |
8c683c73ad355148402e0412e9feeb548a169f6e6e752d1737bce266e86b729e | R | 11,029 | 152 | ```{r load-config, incluse=FALSE}
source("config.R", local = knitr::knit_global())
```
```{r load-csv}
# Load filtered dataset
df_2sc = readRDS("parts/patient_sc2.Rda") # Data of patients with 2 SC MRI
df_sc_mri = readRDS("parts/sc_all.Rda") # All SC MRI data
```
``` {r load-treatment}
# Import treatment data
df_trea... |
860a5dbf9d2eb9490b3a4fec28625beac1b7c22d8115f5cd189308cc3fb281a0 | R | 11,033 | 307 | # Copyright 2021 The On Combining Bags to Better Learn from Label Proportions Authors. All Rights Reserved.
#
# 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/lice... |
e3ca9ddf34395f3c56341d55fef4b14e428c6a5862ac08791aa892a8fef7cb57 | R | 11,033 | 306 | # Copyright 2021 The On Combining Bags to Better Learn from Label Proportions Authors. All Rights Reserved.
#
# 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/lice... |
146b3b7c18327bb8523acbb23e6e70a056d5aef421cc51509e7e1a493982ddd8 | R | 11,034 | 306 | # Copyright 2021 The On Combining Bags to Better Learn from Label Proportions Authors. All Rights Reserved.
#
# 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/lice... |
24ae1c8191d28e20849e820e6f50a1739620d97e0f598cb5eed77bedc9787630 | R | 11,034 | 306 | # Copyright 2021 The On Combining Bags to Better Learn from Label Proportions Authors. All Rights Reserved.
#
# 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/lice... |
40ccbfd8ffe6caf920fb8e77958bc17fe80e65f7baceb604ace90c64aff35c5d | R | 11,034 | 306 | # Copyright 2021 The On Combining Bags to Better Learn from Label Proportions Authors. All Rights Reserved.
#
# 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/lice... |
a4c11025d4b8aa34c241eccaaec2d33f810e29f7b946283b85fbc4095d45220a | R | 11,034 | 306 | # Copyright 2021 The On Combining Bags to Better Learn from Label Proportions Authors. All Rights Reserved.
#
# 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/lice... |
fdbc7d6c6144897eb63b1ea81096e09e1a8f419014e75e867f5675d71a5f57ed | R | 11,035 | 306 | # Copyright 2021 The On Combining Bags to Better Learn from Label Proportions Authors. All Rights Reserved.
#
# 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/lice... |
6e429821ddd7f7991d0d3fc3d908d50b3a6e784053b11ae4fda9faefc55456e8 | R | 11,045 | 371 | ### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
###
### initialize -- initialization, called by new("hyperSpec", ...)
###
### C. Beleites
###
### ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# Function ------------------------------------------------------... |
1977595123d3640b3246e926606b565c631e557dd1d75a553fb62c785acc5524 | R | 11,086 | 271 | ---
title: "Quality control assessment"
author: "Jeremy Miller"
date: "March 8, 2019"
output: html_notebook
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Overview
This script reads in the data output to the feather file and uses it to calculate and plot some QC information for... |
ae5472899eff84ab9742476ffe22b6c4716b770fd150b5707bc39a3ab6758d79 | R | 11,098 | 328 |
# Summary -----------------------------------------------------------------
# Run a parcel-wise ANOVA to detect differences in ISC between movies
# Packages ----------------------------------------------------------------
library(tidyverse)
library(ggplot2)
library(ggpubr)
library(broom)
library(rstatix)
# Prelud... |
07e049aa6278d51e926ae588bd5278b71ea04af76802994e9d4695e0e272a54e | R | 11,121 | 303 | #' Conversion from standardised mean difference to log odds ratio
#'
#' @description
#' Conversion from standardised mean difference to log odds ratio
#' using method by Hasselblad & Hedges (1995) or Cox (1970).
#'
#' @param smd Standardised mean difference(s) (SMD) or meta-analysis
#' object.
#' @param se.smd Stan... |
753aac7f412dc818863d25a7f02d5edc3e28b2ead0b73c7c99f0bfe10ab675ea | R | 11,133 | 281 | #==========================================
# Produce Table 2: GWAS results summary
#==========================================
library(data.table)
library(magrittr)
library(rtracklayer)
library(flextable)
source("code/functions/find_closest_gene.R")
source("code/functions/make_or_95_ci.R")
bim <- fread("data/mega/me... |
ffdb0eccdd0fbf829e75b5c30182e0f8026f4188574f22793128cc7f7441b4e4 | R | 11,134 | 341 | # Optional generic preliminaries:
graphics.off() # This closes all of R's graphics windows.
rm(list=ls()) # Careful! This clears all of R's memory!
#-------------------------------------------------------------------------------
# Load rjags library and the wiener module
library(rstatix)
library(runjags)
library(rja... |
ee722647250b0cacd28d48e327c5c6d5918cbb76eb373b77a9c493353a5f2ef8 | R | 11,157 | 214 | ## ----echo=FALSE---------------------------------------------------------------
knitr::opts_chunk$set(crop = NULL)
## ----message=FALSE------------------------------------------------------------
library(CTSclocks)
data(MurphyGSE88890)
agePred.v = CTSclockAge(beta.m, CTSclock = 'Neu-In', dataType = 'bulk', CTF.m = N... |
5a21570d997c4e4e4e6e498746a2b5092ca5463762dd7fb6056bbe4663fd524b | R | 11,165 | 277 | # Install optparse package to parse the parameters
if (!require("optparse", character.only = TRUE)) {
install.packages("optparse", dependencies = TRUE)
library("optparse", character.only = TRUE)
}
# Parameters definiton
option_list <- list(
make_option(c("--inputPath"), type="character", default = NULL,
... |
66f07b85105c8091e3f5af7cddc09e728eb37b8c9d1fc94bb3fdc67bd388cc11 | R | 11,165 | 345 | ## Backend Modules ###
suppressPackageStartupMessages(library(dendextend))
suppressPackageStartupMessages(library(magrittr))
suppressPackageStartupMessages(library(parallel))
suppressPackageStartupMessages(library(foreach))
suppressPackageStartupMessages(library(cluster))
suppressPackageStartupMessages(library(Summari... |
dc9a0fa7c1a70edc28c4fe1764191fc75eae2dfc1af99038737553b8a3f98136 | R | 11,169 | 296 | ---
title: "RRHO2 Tutorial"
author: "Caleb Huo"
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
html_document:
toc: true
toc_depth: 2
number_sections: true
vignette: >
%\VignetteEngine{knitr::rmarkdown}
%\VignetteIndexEntry{RRHO2}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::op... |
c1a66e0c0a196512961aa6ac68b435e17c5f522ef0a80eb77e7d46ef5e227816 | R | 11,249 | 335 | #' @name DEPRECATED-read.txt.Witec
#' @concept moved to hySpc.read.txt
#'
#' @title (DEPRECATED)
#' Import Raman spectra from Witec files
#'
#' @description
#'
#' These data input functions are **deprecated** and they will be removed in
#' the next release of \pkg{hyperspec} package.
#' Now functions in package ... |
c79eec64ed1691b77aa60c0d028e080956f435562fb971ca913ee191b3364c33 | R | 11,262 | 529 | ---
title: "SEFL10e_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$set... |
058ef8ba603e051c7ea77330f699f0cfcc28d0687a916968ce3f4af5b3533f3e | R | 11,277 | 352 | #Load libraries
library(geodl)
library(dplyr)
library(torch)
library(luz)
library(ggplot2)
setwd("Your Working Directory")
#Set device to GPU
device <- torch_device("cuda")
#=================Data==========================================================
#Create training chips Data Frame
trainDF <- ma... |
26df6f3675261a5094f00b64c29d31ae6dea0093dc480a71b2ea5100119345e6 | R | 11,282 | 323 | ## Collection of custom functions for working with flywire data.
# vec_norm() function -----------------------------------------------------
## Caluctates for a given vector (x) the vector magnitude and returns the
## the normalized vector / unit vector.
vec_norm <- function(x){
vec_mag = apply(x, 1, function (x... |
817d5105526f253d5c5b57ba3dce35e993d6dd5922858eb283364ab9fc6bfbc3 | R | 11,302 | 286 | ---
title: "KmeansClustering_AllData_25Clusters"
author: "Lukas von Ziegler"
date: '2022-05-31'
output:
html_document:
toc: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer_Functions_v4.R")
source("Unsupervised... |
df057f03eb369ca5c8b73484d7b53a8f3c9d691e44713cb5d193e12dc7e78f05 | R | 11,311 | 351 |
options(stringsAsFactors = F)
library(RSQLite)
library(LSD)
#---------------------------------------
#---UTMOST---original---Whole_Blood
tissue='Whole_Blood'
model='ut_original'
#gtex
con <- dbConnect(RSQLite::SQLite(), dbname=paste0('~/../Dropbox/DansPaper/data/db/',model,'_',tissue,'.db')) #... |
75f24fc6e4a67d7e4e4583c9a977fa9811605861b19fdf98ad52c0fcaaf54ae6 | R | 11,331 | 441 | # naming scheme for the internal functions:
# .<generic>_<x><y> with <x> and <y> the first and second parameter/operand:
# h ... hyperSpec object
# n ... numeric (scalar, vector, or matrix)
# m ... matrix
# _ ... missing
# Function -------------------------------------------------------------------
#' @include expan... |
dc5b4656de728e3e34ad4b62fbdc981d470573bcae9c9ff698a185069eaca1e1 | R | 11,333 | 343 | # Multiclass example ------------------------------------------------------
# Preparation -------------------------------------------------------------
#Load libraries
library(geodl)
library(dplyr)
library(torch)
library(luz)
library(ggplot2)
setwd("Your Working Directory")
#Get device
device <- torch... |
6359394a08c32368262d0793fca070c6ba5444114b05e5ba49e90cfc95966dd3 | R | 11,347 | 227 | ##' An improved version for RRHO, which aims to correct the intepretation for top left region (up in x and down in y) nad bottom right region.
##'
##' We improved the algorithm such that all four regions of RRHO plot are meaningful
##' @title RRHO2
##' @param list1 data.frame. First column is the element (possibly gene... |
606464b2b60c079bea52bfd50fdb8eef82721390cc59a985730e1b62f8a4b2fc | R | 11,366 | 141 | #############################################################################
#
# Temporal feature abnormality for Agitated MDD
#
# For this pipeline, we tested the temporal structure among agitated MDD, HC
# and non-agitated MDD.
#
# The alternative features include:
# 1. Peak duration (ETS)
# 2. Time delay in ... |
e0c44ab03d2c1a2b96bb4a4f8cb001d91583a5105e3facec145e08aafb835c2d | R | 11,417 | 377 | #rm(list=ls())
#make source data file
# options(bitmapType = "cairo")
# resdir="/cluster/p/p274/cluster/projects/p040-ad_change/ADchangeRisk/reproduce/results"
# setwd(resdir)
library("here")
# load packages ----
library("magrittr")
library("tidyverse")
library("sgof")
library("openxlsx")
# ROIs ----
roinames=c(... |
88bda3342d3dc6ed1058de656e704acb8985ccb4f4213f4a6e0531b97eebf76a | R | 11,439 | 319 | #######################################################
### Deep Brain Stimulation: STN vs GPi.
### Functions
### 2022 Katrin Petermann
#######################################################
create_variables <- function(data){
## date variables
data$date_dbs <- as.POSIXct(data$date_dbs, format = "%Y-%m-%d")
dat... |
432420e2a6fb0e144baed0e968557d22ded642fa868c08751bb91a1029565a09 | R | 11,443 | 230 | #-------------------------------------------------------------------------------#
#
# Input parameters to set (n=6)
# 1. hemisphereParameter: 'lh' (left hemisphere) or 'rh' (right hemisphere),
# 2. ReferenceGeneListFile: Path to the file contatining Gene Symbols, correlation
# between Brain... |
e6ba8caa182be405b9496c7a589924cd67cb14fe558b7a69a7333b7139ea8324 | R | 11,449 | 328 | ---
title: "`r subdir_name`"
author: "Kadi Vaher"
date: "`r Sys.time()`"
output: html_document
---
```{r setup, include=T, message=F}
subdir_name <- "microbiota_mri_demo_paper"
# load packages
library(tidyverse)
library(here)
library(magrittr)
library(glue)
library(ggpubr)
library(knitr)
library(readxl)
#library(MVN)... |
408ae85c9321ebdd15ea97c90f0e44d5369fb99de8b89a567254e3aa52de0cd3 | R | 11,523 | 406 | #' Polynomial baseline fitting
#'
#' These functions fit polynomial baselines.
#'
#' @details
#' Both functions fit polynomials to be used as baselines. If `apply.to`
#' is `NULL`, a `hyperSpec` object with the polynomial coefficients
#' is returned, otherwise the polynomials are evaluated on the spectral range
#' of `... |
0f56a8b58a01a3cada7a15db1b4ccdc408ff0339e76b74ae17ea793b31605ba6 | R | 11,544 | 457 | ---
title: "20240408_synbot_puncta_processor"
author: "Justin Savage"
date: "`r Sys.Date()`"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load libraries}
#author Justin Savage
#js664@duke.edu
#Version 1.0
#8/16/21
#This script is for combining the data from the ... |
f5459767ae37064f3820a001981ef1f5ea28d184ffc4bcd66f5946991c6b4374 | R | 11,552 | 432 | # Function -------------------------------------------------------------------
#' Binding `hyperSpec` objects
#'
#' Functions to bind `hyperSpec` objects.
#'
#'
#' The former difficulties with binding S4 objects are resolved since R version
#' 3.2.0 and `cbind()` and `rbind()` now work as intended and expected for
#' ... |
69345c4138c854912325a7155853d5b2e364f49f349da283523c866a93651a0e | R | 11,560 | 205 | require("svMisc")
require("ggplot2")
require("dplyr")
require("tidyr")
require("reticulate")
library("stringr")
library("jsonlite")
library("RColorBrewer")
library("knitr")
library("doParallel")
library("foreach")
library("gtsummary")
library("labelled")
options(warn=-1)
# Only show code in HTML output, not in word do... |
54e6a34db0800dbec62b8f9c19ebd95f2de73d652ff0cd88c0cd17771281a70b | R | 11,576 | 182 | source('./scripts/01_Setup.R')
SampleInfo2= readRDS('./data/processed/rds/SampleInfo.rds')
SampleInfo2$Amplification.Method= gsub("PicoPlex v2|PicoPlex v3", 'PicoPlex', SampleInfo2$Amplification.Method)
delimiters = ".bowtie|.bt2"
aa2= strsplit(SampleInfo2$File_name, delimiters, perl = TRUE)
aa3= sappl... |
5ba207380dd99ffb8dcea4cabcdad80a2d3bca71fed770c0b678fc0a0815c4c4 | R | 11,586 | 338 | #-----MR-JTI-----
cat('-----MR-JTI----- \n')
options(digits = 4)
library("optparse")
library("glmnet")
library('HDCI')
option_list = list(
make_option("--df_path", action="store", default=NA, type='character',
help="Path to dataframe of gwas and eQTL summary statistics [required]"),
... |
1c5fc2d0372d4bcce37708dab4dc2f2a11d925e11389eec5c9c0a45e029a4b28 | R | 11,601 | 212 | library(dplyr)
library(tidyr)
library(stringr)
library(ComplexHeatmap)
library(ggplot2)
library(tibble)
library(reshape2)
library(openxlsx)
library(graphics)
library(vcd)
library(ggpubr)
library(gridExtra)
library(cowplot)
library(ggprism)
MD <- read.csv('Spots_metadata.csv', stringsAsFactors=FALSE)
MD$Sample2 <- pas... |
6083f6b075c6c667b48b1d7bf4e1d599940904653308030a25c11b578c75c3a0 | R | 11,648 | 241 | #### MOUSE ######
a=read.delim("all_crossvalidated_predictions_mESC.txt")
b=read.delim("all_crossvalidated_predictions_mouse.txt")
colnames(a)[2:3]=c("mESCPred","mESCActual")
a=merge(a,b,by=1)
c=read.delim("ensembl2geneName_v90_mm10.txt")
colnames(c)[2]="geneName"
a=merge(a,c,by=1,all.x=T)
a[a=='']="NA"
summary(lm(mE... |
f3a02e7d00df4164ed43d2c21d6f45cdd78c357163d6bb1ef4816c7334e83749 | R | 11,648 | 363 | ---
title: "Pupilla::EyeLink::Read_and_preprocess"
author: "Elvio Blini"
date: "January 2024"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Pupilla::EyeLink::Read_and_preprocess}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
resource_files:
- images/trial_13-1.png
- imag... |
cd89064ba3aa680eea1ac4e1894b495884bf2f84c6a2412dc1cfdcd6ead38af5 | R | 11,670 | 304 | #' Adjust for batch effects using an empirical Bayes framework
#'
#' ComBat allows users to adjust for batch effects in datasets where the batch covariate is known, using methodology
#' described in Johnson et al. 2007. It uses either parametric or non-parametric empirical Bayes frameworks for adjusting data for
#' bat... |
b6713cb485254a25a8af31f3a8f0d308a0365226e7405d77445c963709344bce | R | 11,676 | 323 |
library(psych)
library(ggplot2)
library(dplyr)
library(tidyr)
library(faux)
# create perfectly correlated vectors with a given vector (i.e. sample cor.)
# Credits to:
# https://stats.stackexchange.com/questions/15011/generate-a-random-variable-with-a-defined-correlation-to-an-existing-variables
### SET UP ###
#s... |
8e9ae788013cafe67012aefaa9d5fedf95c41f7ede75fe48402f0dc1ca207d52 | R | 11,694 | 344 | ## -----------------------------------------------------------------------------
##
## Somatic variant detection in lesional focal epilepsy
## Depmap functional analysis
## ref: Gatto et al. 2023; Shimada et al. 2021
##
## Author: Dr. Christian Bosselmann
##
## Date Created: 2023-09-05
##
## Copyright (c) Christian Bos... |
bdd44f048847ca4db31330b4f4fbf05129770db409db9e09bd8d282733616c7d | R | 11,699 | 252 | ##############################################################################
#
# Canonical correlation analysis
#
# This script is used to obtain the correlation between time delay using
# canonical correlation analysis.
#
# We obtained the correlation value and the CCA loading of each region.
#
# Note. The CCA used ... |
76a0f38b31e237157a379bc343658c40b94c2d427e3897a257c614d507b8563a | R | 11,753 | 240 | #' @title Get cell type name with annotation of major class, subclass and top 3 markers
#' @description Get cell type name with annotation of major class, subclass and top 3 markers.
#' @param input_data Data input, single cell expression matrix or pseudo cell matrix.
#' @param cell_type_file Annotation of cell typ... |
6b3a7a5a035800e608c9a217d01b6cb61cb987055cab2e6a4ad9db291493a53c | R | 11,793 | 318 | ### --------------------------------------------------------------------------~
###
### .extract - internal function doing the work for extracting with [] and [[]]
###
### --------------------------------------------------------------------------~
# Function ------------------------------------------------------------... |
ac726f9bc0f1c089b49b24a940e9edf4b7d26bb98b6f5d8cae7e1b5583a67d65 | R | 11,833 | 254 |
###Script 8
#Plot cell composition
library(dplyr)
library(ggplot2)
library(ComplexHeatmap)
###Make files for scCODA analysis (in python)
#cca
tmp <- unique(md[,c("orig.ident", "Region")])
readRDS("output/atlas/metadata_integrated_neuronal_atlas.rds") %>%
select(orig.ident, Region, cca_clusters) %>% ... |
b83be8d510a18cbeb0850294cd7901c01bdab09ce37a151d8fa04c93549671cf | R | 11,853 | 286 | # edit to your filepath containing csv-files from "refine_FI_analyses.Rmd"
wd <- ""
setwd(wd)
library(dplyr)
library(survival)
library(ggplot2)
library(ggsignif)
library(survminer)
library(coxphw)
library(lubridate)
library(ADNIMERGE)
##read the ADNI dataset with the FI-variables, the recent adverse events log and t... |
18104d871a2140f98418abeb72dc533221102a8f6444769fb794d0a109746e0a | R | 11,861 | 292 |
library(dplyr)
library(babelgene)
library(ComplexHeatmap)
library(RColorBrewer)
library(viridis)
library(enrichR)
library(ggplot2)
################
###Grubman AD###
################
res <- read.csv("results/grubman_MAST_vulnerability_results.csv", row.names = 1) %>%
filter(FDR < 0.05)
tmp <- ortho... |
f363cfa2e508824a0015153b071fd021ec826775e22852b218cd3519e74e256c | R | 11,911 | 255 | ---
title: "Pairwise Detection Figure"
output: html_document
date: "2024-01-28"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
require(tidyverse)
require(grid)
require(dplyr)
require(gridExtra)
require(ggpubr)
require(scales)
```
```{r}
select <- dplyr::select; mutate <- dplyr::mutate;... |
1d2997b29aba05def2a8568a1b3f9283643e3f91e019c9a581ce05cc0c660475 | R | 11,919 | 275 | ---
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... |
c4edf02abf529d89b5267ad47de1aea91726d4b730e6b388a10420e9459b50a9 | R | 11,926 | 373 | #' @name DEPRECATED-read.jdx
#' @concept moved to hySpc.read.jdx
#'
#' @title (DEPRECATED)
#' JCAMP-DX import for Shimadzu library spectra
#'
#' @description
#' This function is **deprecated** and will be removed in the next release
#' of \pkg{hyperSpec} package.
#' Please use function `hySpc.read.jdx::read_jd... |
8001304b6975783f235cf196fc1b1c54cd3c89b03fb0ba0d0868b807dc1156cb | R | 11,930 | 179 |
###Script 11
#Normalize and Integrate Libraries (neurons only)
library(Seurat)
library(SeuratWrappers)
library(sctransform)
library(dplyr)
#load data
neuM02 <- readRDS("raw_matrices/neuM02.rds") %>% CreateSeuratObject(project = "neuM02")
neuM03 <- readRDS("raw_matrices/neuM03.rds") %>% CreateSeuratObject(... |
b674a8ab45b15bf34bb0005250c6f2f0da22545664de6e71815627a0c15a009e | R | 11,939 | 291 | source(codes.makepath("autofocus/Frontend_Modules.R"))
library(networkD3)
library(htmlwidgets)
suppressPackageStartupMessages(library(MetBrewer))
suppressPackageStartupMessages(library(plotly))
suppressPackageStartupMessages(library(shiny))
suppressPackageStartupMessages(library(igraph))
suppressPackageStartupMessages(... |
fdec0a7ad0ba6338f359bdd5b40a11fa9cd63c44dd1239de7af3c12cf21ef74b | R | 11,939 | 327 | ---
title: "R Notebook"
output:
html_document:
self_contained: no
params:
seed: 42
nPerms: 10000
editor_options:
chunk_output_type: console
---
```{r}
library(tidyverse)
library(ggbeeswarm)
library(ptestr)
library(ggpubr)
library(rstatix)
library(eegUtils)
```
# LOADING FILES
```{r eval=FALSE, include... |
4f2d258828ecc5709c0d7bb88f90d886eaaa9e8b76996c1c91fdecab2439f251 | R | 11,941 | 529 | ---
title: "SEFL10e3_Retraining_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::op... |
933cbd524a862494e42288ef4aa90ae3d046a3fa969c41fbd11ec4ff8c135797 | R | 12,000 | 347 | #========================================================================================#
# Author: James M Roe, Ph.D.
# Center for Lifespan Changes in Brain and Cognition, University of Oslo
#
# Purpose: prepare individual-specific slopes in longitudinal AD-control data (ADNI) for ML
# Script requires indivi... |
63c6d47cb17a9babcc78828608a8d27def20b3482dbb4549d960488a4cd9800e | R | 12,079 | 324 | # Auteur : Rihab AZMANI
# script Zhang data (R)
rm(list=ls())
library(tidyverse)
library(dplyr)
library(stats)
library(ggplot2)
library(RColorBrewer)
library(S4Vectors)
setwd("/home/orlab/Desktop/Rihab/DatasetZhangEtal/")
Mydata <- read.table("listOligo.csv" , header = T , sep <- "," , dec = ".")
str... |
5de549acb29b9811f8a402d500359749c6ed60ea9d0ad6bac8f2a2c195aab6f1 | R | 12,083 | 292 | #' Generating_simulation_data
#'
#' A function that generates simulated data. This function is used to generate simulated experimental data and
#' save the results to a file.And the directory path for the output result is the current folder
#'
#' @param spot_spatial_count A vector representing the UMI count of each cel... |
bd9818bd564b9e415aadb8e411fe0f4856eec87ec805f4d1f7f93fe88b3d95d8 | R | 12,096 | 294 | ---
title: "TEBC pre-discharge faecal samples: ordination (PCoA of bray-curtis dissimilarity)"
author:
- "Kadi Vaher"
- "Code to derive principal coordinates of microbiota community composition from 16S rRNA gene sequencing data"
- "This tutorial was used as an example:"
- "https://ourcodingclub.github.io/tuto... |
7d235aae72b6bdaceb387c4c5350671aa7eb410c476dedb32151240dc0d65f51 | R | 12,158 | 380 | #' @name DEPRECATED-wlconv
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Convert different wavelength units
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and ... |
a2dc9dfc5b71c012e4debf27c4efbc59d4cd6705c64150f7937d467491f52cb0 | R | 12,175 | 237 | ---
title: "Example analyses with VertexWiseR - Example 3"
author: "Charly H. A. Billaud"
date: "2026-10-19"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Example analyses with VertexWiseR - Example 3}
%\VignetteEngine{rmarkdown::render}
%\VignetteEncoding{UTF-8}
bibliography: references.bib
-... |
53f04c344f90897dc2491f2c16b29e5535dc2539695e20df5e7bcd79adbfd91b | R | 12,177 | 337 | #' Read data from a directory of feather files. From library(scrattch.io)
#'
#' @param feather_dir Directory containing feather files.
#' @param genes Genes to read
#' @param group_by desc_bases to use for grouping samples
#' @param group_ids ID values to use for filtering
#'
#' @export
get_feather_data <- function(fea... |
7eabc280bf9eb50bcc24ca6f68dd73fedc6a10ac3a0afb3d886f91b900b51740 | R | 12,184 | 329 | library(clusterProfiler)
library(tidyverse)
library(assertthat)
library(SeuratDisk)
library(Seurat)
library(tidyquant)
library(extrafont)
load(file.path("data", "v5_reference_mapped_all_filtered.RData"))
all$group[all$group == "MF"] <- "BMDM"
all$group <- factor(all$group, levels = c("PBS","T","NK","BMDM"))
##subset ... |
0687f0408c133a1ec301aceb458b797a5db6c19ac87643c7626e07720f244480 | R | 12,221 | 352 | # 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)
... |
8d8ed344533e7fad590c8aa120279b7092c54f243e071759fa0a85ad4f45b94e | R | 12,234 | 240 | boxQuants <- c(0.01,0.25,0.5,0.75,0.99)
quantQuants <- c(0.01,0.1,0.2,0.3,0.4)
plotModelComp <- function(sigDat, quantDat, boxDat, eventDat,
baseDat, TitleDat, modelDat, altModelDat=NULL,
numModelVals=20){
pseudoQuants <- seq(1/numModelVals,1-(1/numModelVals),
... |
7e3ab5d33ea75a720188703fb6cd8fc6a30864d4fd0925593df2433ef44f73ba | R | 12,257 | 206 | #Convert h5ad object to a Seurat object
Convert("/path/to/file/adata_merged_raw.h5ad", dest = "h5seurat", overwrite = TRUE)
LongCovid_seu <- LoadH5Seurat("/path/to/file/adata_merged_raw.h5seurat", meta.data = FALSE, misc = FALSE)
LongCovid_seu
nCount = colSums(x = LongCovid_seu, slot = "counts") # nCount_RNA
nFeature... |
2a0a0ef1203bbbfec4180d260c24de0b0ef2b6a29f82aa05dda53d537784e5d9 | R | 12,273 | 248 | #' @title Decode surface data
#'
#' @description Correlates the significant clusters of an earlier vertex-wise analysis with a database of task-based fMRI and voxel-based morphometric statistical maps and associate them with relevant key words. Decoding currently works with surfaces in fsaverage5 space only."
#'
#' @de... |
534127a8696f25450f1a464e855f0838163dc7101d83c1c897c6599482e4bf25 | R | 12,275 | 403 | ---
# For vignette ---------------------------------------------------------------
title: Unstable Laser Emission
subtitle: "Example Workflow for Time-Series Spectra Dataset `laser`"
description: "laser: Example workflow for time-series spectra dataset `laser`."
# Authors --------------------------------------... |
b1d258f8089f9002e55e7b7b8c845814114047c3b9ff0d6ec39658e42b7e3165 | R | 12,294 | 356 | #' Get node label
#'
#' Gets the label of a node. The label is the molecule name if the node is a leaf. The label is
#' the cluster number, BIC and p-value if the node is internal.
#'
#' @param R R struct object.
#' @param i The index of the module to be labeled.
#'
#' @return Label of node i.
#'
#' @noRd
get_node_labe... |
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