sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
7b10a620437245a24705c705b769c6dcdf82046e69e4d711e4c9d1e44a918d5c | R | 44,287 | 871 | #---------- Package Loading ----------
library(data.table)
library(TwoSampleMR)
library(dplyr)
library(tidyr)
library(ieugwasr)
library(ggplot2)
library(patchwork)
library(htmlwidgets)
library(plotly)
library(cowplot)
library(MRPRESSO)
#---------- Custom Function ----------
# Path concatenation operator ... |
a393148d85d9de25f62545770f4e87882196b7aaa62d8623a2114e6d6b0846f2 | R | 44,361 | 1,282 |
#Useful functions for sc analysis
kk2foldchange <- function(kk){
GO <- as.data.frame(kk)
GeneRatio <- strsplit(GO$GeneRatio,"/")
GRdf <- data.frame()
for (i in seq_along(GeneRatio)){
GRdf[i,1] <- GeneRatio[[i]][1]
GRdf[i,2] <- GeneRatio[[i]][2]
}
GRdf$V1 <- as.numeric(GRdf$V1)
GRdf$V2 <- as.num... |
a006eae760fc5e0f02948f50f0f06c0c58a598d7b63628c4b155f00f42b7d1e7 | R | 44,766 | 1,165 | # Siwei 09 Aug 2023
# Siwei 05 Jul 2023
# plot 1MB proximal region of rs1532278 (CLU)
# chr8:27608798
# init #####
{
library(Gviz)
library(rtracklayer)
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg38)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(ensembldb)
library(org.Hs.eg.db)
library(gr... |
99c3f1da70d1b05983259705b3f6134c3ac033d26f1e44a83a1b0f33edc6ef40 | R | 45,759 | 1,021 | ---
title: "Dunnart peak characterisation"
author: "lecook"
date: "2022-02-23"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
This analysis looks... |
18076e72571f41fbdcf803c2ebea6811e0eb26d49a8dd8ecb6a67fce385f46c7 | R | 46,080 | 1,055 |
library("tidyverse")
library("SingleCellExperiment")
library("ggrepel")
library("here")
library("sessioninfo")
library("here")
library("broom")
library("patchwork")
library("Metrics")
library("DeconvoBuddies")
#### Set-up ####
plot_dir <- here("plots", "03_HALO", "08_explore_proportions")
if (!dir.exists(plot_dir)) d... |
a1f610e360dcf4d03c2f0c068b3bfc36541a6885eea2998a3d590edc1e82f73b | R | 49,379 | 1,761 | # make figures for Alena PICALM paper
# Siwei 18 Apr 2024
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
}
## Fig_Ex_7g ####
df_raw <-
read_excel("tables_4_plot_v3.xlsx",
sheet = 1)
df_2_plo... |
8c8409cab0e1e2d795194ad228ad732e3b6830de785404be600812de7e18009a | R | 49,848 | 1,543 | library(Matrix)
library(dplyr)
setwd("/home/Fatemeh/0--ThirdProject/")
##### Load Meta Data #####
load(file='DATA/Gastric/meta.Rdata')
ls()
head(meta)
dim(meta)
#write.csv(meta, file = "/home/fatemeh/Fatemeh/0--ThirdProject/GastricTME-GastricTME/input_data/cell_metadata_with_stage.csv", row.name... |
b9c0a0222bd4e5cbd003385d9bf79ce6ace741f42b2cb5a4c16c6318d2c0f36c | R | 52,396 | 973 | # This vignette shows how to apply CellChat to identify major signaling changes as well as conserved and context-specific signaling by joint manifCSF learning and quantitative contrasts of multiple cell-cell communication networks. We showcase CellChat’s diverse functionalities by applying it to a scRNA-seq data on cel... |
c17f0c18359c672e33e6d6ae100485b02366b8a8ab132801a4936d319561abb8 | R | 52,662 | 979 | # This vignette shows how to apply CellChat to identify major signaling changes as well as conserved and context-specific signaling by joint manifCSF learning and quantitative contrasts of multiple cell-cell communication networks. We showcase CellChat’s diverse functionalities by applying it to a scRNA-seq data on cel... |
c4d1c0df8df81c808bc2a5bcdd31ab1030aa0d2638738dd0b0e3afef6a75c660 | R | 53,781 | 1,352 | ## This script was used to produce Figure 6.
salloc -A def-sfarhan --time=0-8 -c 1 --mem=40g
module load StdEnv/2020
module load r/4.2.2
R
library(Seurat)
library(ggpubr, lib="/lustre03/project/6070393/COMMON/Dark_Genome/R/x86_64-pc-linux-gnu-library/4.2")
library(ggplot2)
library(tidyr)
library(stringr)
library(d... |
3d87d18d60a55041037e907f064b18c92843e7aeb32cda3c226bc56672c28613 | R | 54,546 | 774 | ---
output:
html_document: default
pdf_document:
latex_engine: xelatex
editor_options:
markdown:
wrap: sentence
---
########################################################################################################
# PBMC Vignette
```{r, eval=FALSE, include = FALSE}
# Note: This document is the... |
30e38c16f992d0b7152bb8dd3f58e789147eb4b2522c25235a5b469cbf2e98f3 | R | 54,553 | 1,311 | ---
title: "S1: Group comparisons and behavioural analysis with brms"
author: "I. S. Plank"
date: "`r Sys.Date()`"
output: pdf_document
---
```{r settings, include=FALSE}
knitr::opts_chunk$set(echo = T, warning = F, message = F, fig.align = 'center', fig.width = 9)
ls.packages = c("knitr",# kable
"ggplot2", ... |
36506b363ff2f0c494721267bf4bc0fbb2d2c09d4e4df18f2ab91b3aa8530628 | R | 54,585 | 1,087 | ---
title: "DEMO script for Childhood gut microbiome is linked to mental health at school age via the functional connectome"
author: "Fran Querdasi"
post date: "2024-11-18"
output: html_document
---
This script uses a simluated dataset (data_simulated.csv) to demo the code used in the manuscript Querdasi, Uy et al. "Ch... |
5717d3ad17b3373b2e68b5aa484efc8b80cb8bcd0c4c8287d0ddb5ff54f5b301 | R | 54,697 | 1,466 | ---
title: "Results reproduce"
output: html_document
date: "2025-08-25"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
---
title: "Final_review"
output: html_document
date: "2025-08-19"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r setup}
# install.packages("/U... |
5a6a550acd02468ef87b58065eee701fc294801927effe889f1c1bf2e323e410 | R | 54,838 | 1,094 | # Author: Lauren Rylaarsdam, PhD
# 2024-2025
############################################################################################################################
#' @title sampleComp
#'
#' @description Visualize the distribution of a metadata variable across groups of cells
#'
#' @param obj The amethyst object... |
83eeed770b8df7ce70ae2e0660eec9ec90ff38f9514d80bbc0484736ad62c67d | R | 55,394 | 1,152 | ### MNT 10x snRNA-seq workflow: step 04 - downstream comparisons
### **Pan-brain analyses**
### - n=24 samples from 5 regions
### * Cross-region analysis/correlation and comp. to other datasets
#####################################################################
library(SingleCellExperiment)
library(EnsDb.Hsa... |
9ef6fd1a9b911c5e6068d7116a151887dc829d830068365a05723336ee26c749 | R | 56,636 | 1,242 | Sys.setenv("VROOM_CONNECTION_SIZE" = 5000000)
require(optparse)
require(tidyverse)
require(ggpubr)
require(cowplot)
require(scattermore)
require(extrafont)
require(ggrepel)
require(clusterProfiler)
require(scattermore)
require(ComplexHeatmap)
require(ggplotify)
require(ggvenn)
# variables
THRESH_FDR = 0.05
LABS_BULK =... |
d61143ade68168b32d412a3d660da975ce06ac02dfeae6cc37040ebd3eaff301 | R | 56,941 | 815 | ---
output:
html_document: default
pdf_document:
latex_engine: xelatex
editor_options:
markdown:
wrap: sentence
---
########################################################################################################
# Brain Vignette
```{r, eval=FALSE, include = FALSE}
# Note: This document is th... |
614500590a2c97a13bde3b27cabe8de858f4c032397f437f481e7c649fb43a68 | R | 58,291 | 1,299 | ### MNT 10x snRNA-seq workflow: (step 04?:)
### Miscellaneous lookings-into / finalizing graphics for manuscript
### - Brief neuron-specific clustering for DLPFC (Maynard-Collado-Torres et al.)
### - 10x pilot snRNA-seq paper (Tran-Maynard et al.)
####################################################################... |
4493457da08fdacffbf04ac7bbb497a4a8b11a782d002ccdc76d669bea2d3b65 | R | 59,879 | 1,717 | # 13 Jun 2022 Siwei
# Evaluate WASP calibration results
# init
library(readr)
library(plyr)
library(dplyr)
library(stringr)
library(Rfast)
library(ggplot2)
library(RColorBrewer)
# load data
# ASoC_df_raw <-
# read_delim("~/Data/FASTQ/Duan_Project_024/hybrid_output/bam_dump_4_fasta_01Jun2022/WASP_to_calibrate_b... |
dd8ae2e5366a800ecd8b3310df41876d217cd621bd865b1de0d29609d184ce09 | R | 60,268 | 1,142 | ##File to analyze differentially expressed genes between neuronal types of the CA3 Class.
library(dplyr)
library(stringr)
results_list = readRDS("Output_Types.RDS")
results_list = unlist(results_list,recursive = FALSE)
p_ajust = function(x){
p_val_raw = as.numeric(unlist(x$p_val))
p_val_bh = p.adjust(p =... |
2d4c03ed813f44bad347e5d5d952570769c85acb11185aab2cf35399a4ba29ea | R | 62,047 | 1,287 | ---
title: "R Notebook"
output: html_notebook
---
Load standard packages
```{r, message=FALSE, warning=FALSE, include=FALSE}
rm(list = ls())
library(tidyverse)
library(scales)
library(broom)
library(tidyheatmaps)
library(clusterProfiler)
library(org.Hs.eg.db)
library(scales)
library(ggdendro)
library(ggrepel)
library(l... |
9a8d1e70c507f97d93a4f94d3e646089cd89678ff84f3cb1bd906a69b80f3f44 | R | 62,295 | 1,856 | ---
title: "1000_Upload_figures"
author: "Matteo Gasparotto"
date: "2025/08/15"
description: ""
output:
bookdown::html_document2:
code_folding: hide
fig_caption: true
toc: yes
toc_depth: 4
toc_float:
collapsed: yes
link-citations: yes
editor_options:
markdown:
wrap: 72
---
This cod... |
91ac5eb4689c7dbd3b83ee5bd7aaa2508caeaf58849c9096e36ad3da8b5c3858 | R | 62,527 | 1,042 | expression_matrix <- read.table('G://Cones_GAMM//GSM4271906_WT_d170-3.dge.txt', , header = TRUE, row.names = 1)
expr1 <- CreateSeuratObject(counts = expression_matrix, project = '1')
s.genes <- cc.genes.updated.2019$s.genes
g2m.genes <- cc.genes.updated.2019$g2m.genes
ProcessSeu <- function(Seurat){
Seurat <- Norma... |
8d835116fae3f847dac218f785365b3d84c4e2e8611b1e4a2999815a2ff8e40e | R | 62,577 | 1,978 | ---
title: "CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project - Clusters - Harmony - EC & HIP"
author: "Isabel Castanho"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
---
```{bash, eval=FALSE, engine="sh"}
# Run the script... |
88a22d3e5bbf83edb6bfbdf747be8c7b0e115bbf60d0589f256babef4f2cf7d8 | R | 65,715 | 1,355 | ---
title: "Childhood gut microbiome is linked to mental health at school age via the functional connectome"
author: "Fran Querdasi"
post date: "2024-11-18"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Setup
## Set seed
```{r}
main.seed = 6024
```
## Load libraries
`... |
e58b2d794e839ea825c0af5cc8491fcb2f29b2cd386e5388b9e5abdc1d254b8b | R | 68,312 | 1,760 | #####################################################################################################
# #
# Amethyst metacells: utilities to create and work with metacell-level Amethyst objects. #
# Includes:... |
89fb28d58cc8de0b586da042a73f1c79ebc511978e1171690e3d2c4819127562 | R | 69,318 | 2,017 | # --- Dependencies ---
library(dabestr)
library(ggplot2)
library(cowplot)
# --- Safe null default operator ---
`%||%` <- function(a, b) if (!is.null(a)) a else b
# --- Custom version of add_swarm_bars_to_raw_plot using geom_point instead of geom_rect ---
custom_add_swarm_bars_to_raw_plot <- function(dabest_effectsize... |
94301b15a3742f8af40b8c4541d26dad8648adaa428dae6eebf1f6cb615a9360 | R | 70,353 | 1,922 | # 13 Jun 2022 Siwei
# Evaluate WASP calibration results
# init
library(readr)
library(plyr)
library(dplyr)
library(stringr)
library(Rfast)
library(ggplot2)
library(RColorBrewer)
# load data
# ASoC_df_raw <-
# read_delim("~/Data/FASTQ/Duan_Project_024/hybrid_output/bam_dump_4_fasta_01Jun2022/WASP_to_calibrate_b... |
2bab717f9fa340419572d83ea55fa9e25911c29d3db019379d6bacb697600ec7 | R | 72,801 | 1,883 | ---
title: "Integrated_scRNAseq_revised"
author: "Carl Manner"
date: "2025-05-23"
output: html_document
---
#setup
load libraries. not all of these were used in the analysis, presented in the paper, but all were active in my environment. Many of these were for exploratory analyses that didnt make it into the final pape... |
73683375bf9cf51b05d8183c047c3e5523bf92332aceda4833ad13484f40a0cb | R | 73,438 | 1,604 | setwd('') # Set working dir
library(tidyverse)
# Related constants functions -------------------------------------------------------
N_G_POWER = 921 # Required sample size estimated through Gpower analysis
DESCRIPTION_ALL_PHE <<- read_csv('description/descriptions_all_phenotypes.csv')
PATH.GGSEG.HO <- 'description/ggse... |
3e8f62039cf65574fe7b7efa47d37d9e3c4291d6b2d4a05f93608d568d26afa1 | R | 77,695 | 1,416 | #!/usr/bin/env Rscript
# print warnings as they happen instead of collecting them for after a loop ends
options(warn=1)
# define valid parameters
parameters <- list(
fusions=list("fusionsFile", "file", "fusions.tsv", T),
annotation=list("exonsFile", "file", "annotation.gtf", T),
output=list("outputFile", "string",... |
39c2b0708f2822cbea64e1fbae1cb76eaeffcbd09400e7ebc39c5cc84fc0f884 | R | 78,420 | 1,886 | #---------------------------------------------- Lipidomics experiment class ----
Lips_exp = R6::R6Class(
"Lips_exp",
public = list(
initialize = function(name, id = NA, slot = NA, preloaded = F, data_file, experiment_id){
self$name = name
self$id = id
self$slot = slot
self$preloaded_data... |
c44db5eee8914b7f8bf9317f38ce18bc3991e02036695f735274a16390d2c599 | R | 78,892 | 1,948 | #functions and related helper code for x_0092 analysis
################################################################################
################################################################################
################################################################################
# Helper function... |
b32d7f0513a6b842dc18be2be8cad9e8c7bf4dfd81b5f458536d793f2eb263f8 | R | 81,993 | 1,769 | ---
title: "Analysis pipeline for article: In silico Modelling links microbiome-derived metabolites to risk factors for Alzheimer’s disease"
output:
html_document:
df_print: paged
---
Before running this script, make sure that following files are in the *inputs* folder:
All inputs with an (\*) are not included ... |
b4e9b507d2e00c28833c40a4dec9aff399a20527e1e8ea57a261bfd2076f2eba | R | 86,617 | 2,035 | # WELCOME TO THE BEHAV3D TUMOR PROFILER (R script edition) Here you can freely
# and in your own environment execute the desired functions of the BEHAV3D Tumor
# Profiler pipeline. Remember to change any desired parameters to your own
# preferences Please set the working directories where required
# You must execu... |
4fbad1cbb6d971a9ff486346cee2a9bd7cd804163eb17df25a7aed14aa18b4e3 | R | 86,760 | 2,563 | # ================================
# Load or Install All Libraries
# ================================
packages <- c(
"readxl","conflicted", "readr", "tidyr", "tibble", "pheatmap", "ggbreak",
"imputeLCMD", "openxlsx", "limma", "writexl", "showtext", "jsonlite", "curl",
"ggplot2", "scales", "ggrepel", "sva", "bioma... |
082b6355a9528674edb73ed1cea7d22f816bec13f11fdbcc11eb9d708a680bc5 | R | 90,006 | 1,891 | #' @title Boxplot of normalized expression stratified by genotypes for eQTL.
#' @param variantName (character) name of variant, dbsnp ID and variant id is supported, eg. "rs138420351" and "chr17_7796745_C_T_b38".
#' @param gene (character) gene symbol or gencode id (versioned or unversioned are both supported).
#' @par... |
f6084c1d799da7aa3544f96f0b6ac6c5a004fbe829e63851366c88e13dedcbbb | R | 90,842 | 2,101 | library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(cowplot)
library(xlsx)
library(enrichR)
library(readxl)
library(ggrepel)
library(ggpubr)
library(ggsignif)
library(tidyverse)
library(sctransform)
library(scCustomize)
library(pheatmap)
library(openxlsx)
library(tidyr)
install.p... |
8a4aaac077d0a6f9d94915acb89b924237ba15ebf918cf9d246bada1460046d1 | R | 92,466 | 2,038 | #' @title Query basic information for genes, including name, symbol, position and description
#' @param genes A charater vector or a string of gene symbol, gencode id (versioned or unversioned), or a charater string of gene type.
#' \itemize{
#' \item \strong{gene symbol (Default)}.
#'
#' A character string or a ch... |
53ee9111a76103a6308c6104cf78a58be1c186e80d5d7838cfbf193768a51833 | R | 92,725 | 888 | ---
title: "Cross-species peak level comparisons"
author: "lecook"
date: "2022-02-23"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
knitr::opts_chunk$set(echo = TRUE)
library(data.table)
library(kn... |
7776b803fcd5ad2adfbf33a252403c0653290a6ae2b93254bda41aaf149fa594 | R | 94,614 | 2,217 | #------------------------------------------------------- Class distribution ----
class_distribution_generate = function(r6, colour_list, dimensions_obj, input) {
print_tm(r6$name, "Class distribution: generating plot.")
if (input$class_distribution_plotbox$maximized){
width = dimensions_obj$xpx_total * dimens... |
095edad36b964f9ef2468b4e02d224945d90acf9b5f0195d2ca2ce4463e4fa95 | R | 95,521 | 1,957 | #' @title Download normalized gene expression at the sample level for a specified tissue.
#' @param genes (character string or a character vector) gene symbols or gencode ids (versioned or unversioned are both supported).
#' @param geneType (character) options: "auto","geneSymbol" or "gencodeId". Default: "auto".
#' @p... |
b8bd33aa43c1f36988dc77161648786bac51baeb5ef7069dd97a3f215e1e7660 | R | 97,854 | 2,622 | ---
title: "BEHAV3D Tumor Profiler - Guided Tutorial"
output:
html_document:
theme: united
df_print: kable
toc: true
toc_depth: 1
toc_float: true
css: styles.css
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
editor_options:
markdown:
wrap: 72
---
```{r setup, inc... |
d2a3834c400c61e93ac9b95888894732798c86aae4e976dbd438f31f99fad579 | R | 103,390 | 2,586 | ---
title: "Data loading, merging and annotation
"
output: html_document
---
```{r}
library(Seurat)
library(cowplot)
library(patchwork)
library(tidyverse)
library(ggpubr)
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(EnhancedVolcano)
library(viridis)
library(dplyr)
library(scales)
```
#############... |
6c7b86c45cf7a34ba4aa726d6ace1c7448b054b25027aece152af1312dcbbe33 | R | 111,066 | 3,211 | # Utility functions
#--------------------------------------------------------- Color functions ----
colors_switch = function(selection) {
switch(EXPR = selection,
'Blues' = 9,
'BuGn' = 9,
'BuPu' = 9,
'GnBu' = 9,
'Greens' = 9,
'Greys' = 9,
'Oranges' = 9,
... |
2a9306b130cdf7758d555357120adb8523b11ec80a19bf26b09b9018dbc4656a | R | 111,586 | 3,103 | ---
title: "CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project - QC"
author: "Isabel Castanho"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
---
```{r setup, include=FALSE}
# Load packages
library(tidyverse)
# Update libr... |
0c8d3128ecffddf23d992f97865c9f7f5f95f45d234c858f648a2758f8b7d18b | R | 113,099 | 3,918 | # make figures for Alena PICALM paper
# Siwei 19 Mar 2024
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(lme4)
library(lmerTest)
}
# library(seqLogo)
## Fig_1e ####
df_raw <-
read_excel("ta... |
f7aaf82c385e1248342d2a3440b0aa03baf3d3b7e7f1a9b04ef0650e10df9751 | R | 115,621 | 2,788 | ---
title: "Data loading, merging and annotation
"
output: html_document
---
```{r}
library(Seurat)
library(cowplot)
library(patchwork)
library(tidyverse)
library(ggpubr)
library(ggplot2)
library(pheatmap)
library(RColorBrewer)
library(EnhancedVolcano)
library(viridis)
library(dplyr)
library(scales)
... |
d14fdb0ba8500c4fffd68289e8f7fa7d98633092bc27a967769771e246331fda | R | 117,253 | 2,890 | #!/usr/bin/env Rscript
### Code from inferCNV for making plots
# Returns the color palette for contigs.
#
# Returns:
# Color Palette
get_group_color_palette <- function(){
return(colorRampPalette(RColorBrewer::brewer.pal(12,"Set3")))
}
#' @description Formats the data and sends it for plotting.
#'
#' @title Plo... |
1d930c1e0ac9f48a43469f52aaa5692402472af226fb0bb6b092f8435872945e | R | 119,335 | 3,171 | ---
title: "MRI Network Analysis"
output: html_notebook
editor_options:
chunk_output_type: console
---
Graph Network Analysis of Mouse MRI Data
This R Notebook contains all code used to reproduce the analyses and figures
presented in our iScience paper on graph network analysis of TetTag-DREADD mouse fM... |
0bcde1c384ba0970a1a2a6ccfb958e8902d0cc726d4bcfec0259a6e6be044694 | R | 140,833 | 3,301 | ## This script was used to produce Figure 3.
salloc -A def-sfarhan --time=0-8 -c 6 --mem=40g
module load StdEnv/2020
module load r/4.2.2
R
library(Seurat)
library(ggplot2)
library(tidyr)
library(stringr)
library(dplyr)
library(ggrepel)
library(rstatix)
library(inflection, lib="/lustre03/project/6070393/COMMON/Dark_... |
94bb8fd11908acfb9e9f3adf839087118c4c0675674d1adf1f67cfed084da2e9 | R | 160,110 | 2,436 | ### Single-cell DNA methylation analysis tool Amethyst resolves distinct non-CG methylation patterns in human astrocytes and oligodendrocytes ###
# Author: Lauren Rylaarsdam, PhD
# Date: July 2023 - August 2025
# ***Please cite our manuscript if you use Amethyst or any of the following code.***
# This script encompas... |
11be0c22694104a03b2f1cf5aeca40dcee5537117d0e55adbbf674f6f5df4d94 | R | 176,020 | 3,518 | ---
title: "Childhood gut microbiome is linked to internalizing symptoms at school age via the functional connectome"
author: "Fran Querdasi"
post date: "2024-11-18"
output: html_document
---
This version has been edited to add additional analyses for Nature Communications Reviews
Line 306 reads in brain and cbcl sPLS... |
0ea6dcc38d0a54f60116251005abbbe9759ad8d09d5052d8527547a052969b56 | R | 186,251 | 4,120 | ---
title: "ROSMAP 3rd revision"
output: html_notebook
editor_options:
chunk_output_type: console
---
#loading library
```{r setup, include=FALSE}
library(dplyr)
library(agricolae)
library(mixOmics)
library(ggplot2)
library(scales)
library(tibble)
library(tidyr)
library(tidyverse)
library(BiocGenerics)
library(Rt... |
8173a3752b1a159840740503d0ca8daeed04245b4040045ca15d0bef2a3c892e | R | 200,000 | 5,431 | ---
title: "amygdala_nuclei"
author: "Amar Ojha"
date: "2025-04-29"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(pacman)
p_load(tidyverse, ggthemes, ggpubr, devtools, remotes, mgcv, broom, glue, gamm4, parameters, hrbrthemes, rstatix, neuroCombat, cAIC4, interactions... |
2ddd74a9a1e34eeec427080465826e41cabd4f4969cbecc979236bdd6cb4c54c | R | 200,010 | 3,504 | ---
title: "Metagenomics of Parkinson’s disease implicates the gut microbiome in multiple disease mechanisms"
author:
- Zachary D Wallen
- Ayse Demirkan
- Guy Twa
- Gwendolyn Cohen
- Marissa N Dean
- David G Standaert
- Timothy Sampson
- Haydeh Payami
output:
pdf_document:
toc: yes
toc_depth:... |
cb43525c2cdf8d8f9a3b63abb450be53df30fcf9d47eabbea73efc15e579d1af | Shell | 21 | 2 | #!/bin/sh
popscle $1
|
169fff337fc5fc43fec69d47a9a5d4ee92e8489e09dd1586ebedab47ae511007 | Shell | 30 | 3 | #!/bin/bash
conda deactivate
|
ffa1a17f8f768b26558869d1f32646901766bb43f2472dcd006896c0042a8831 | Shell | 30 | 2 | make html
cp -a build/html/* . |
948fea812307f6a330072cab02138f011ad345111c2acd533c1e676bea9ffba0 | Shell | 31 | 1 | # This file intentionally empty |
958d4123b9be5abcd6303ab941258cf54e0a552302e7cc3c3cb8f8f98796efec | Shell | 34 | 2 | #!/bin/bash
echo job01 > job01.txt |
39a83f8dbbb6a01d1639ea3af1170db61b568e3ec79e6edb8c89b275ffb8ae08 | Shell | 35 | 2 | #!/bin/bash
echo job03 > job03.txt |
69da354faaba8d77c2e6ddae555408d71e326b00cbdf8763fc10296488f813fc | Shell | 35 | 1 | sphinx-apidoc -F -o . ../scrubber/
|
706533d15ab2a4032d8f429fe8c900c84c020e93eb316e5f6232f4cbd45a7fc8 | Shell | 35 | 2 | #!/bin/bash
echo job02 > job02.txt |
ff99ab3014e55ae37a4d5545a0a0a954cb9987de34cd5199ec0a6b9bed2bf7e0 | Shell | 35 | 2 | #!/bin/bash
echo job04 > job04.txt |
06a13712398bfbed277001a8dfcb633434a88c0565733ae18f8a9e5c536e49b9 | Shell | 36 | 2 | source env.sh
flask slide-server-run |
33410eb11b3e32ff06441f95f80e8ef0516783ebccad579643648a897be0e32f | Shell | 36 | 3 | #!/bin/bash
ENV_NAME=openfold_venv
|
c40502f7b257d814f48b61c50f8a05f94760362615f0e13248cce8369039bbfe | Shell | 39 | 4 | #!/bin/sh
rm -r results
rm -r figures
|
7332b9e505bb4d86ee8a9e69caf130c7411348342de30c69ec3548f557f2a153 | Shell | 40 | 2 | #!/bin/bash
sudo veyon-cli service stop
|
33141b524b566cb11a13a4c2bdf54da04795308534cad6d33d430f7631f8cd17 | Shell | 41 | 1 | pyinstaller --noconfirm anylabeling.spec
|
951db277d5fc8828141c9c0a86eced01878c0b0f88aee25c875f7af0536b07d7 | Shell | 41 | 2 | #!/bin/bash
sudo veyon-cli service start
|
d98837e3f2ee62c6dc38e2bd3485aaf16ec3cfcfb0e128d879ed592f5accfe62 | Shell | 43 | 2 | #!/bin/bash
sudo veyon-cli service restart
|
1d7dc52b4c3d53b10728838b46a8d7ad70449b6d6b09eb153c9a01a9617a8260 | Shell | 48 | 3 | set -ex
pip install visdom
pip install dominate
|
e74fd1893f2de2f9f8e90be1880a0217ea696881691e9ff451bda0fc91b2930d | Shell | 51 | 3 | #!/bin/bash
# properties = {properties}
{exec_job}
|
652f444a1b4ae5161eac0f42117af4b0893952d0a8b5ddce9aa6b68a7056715d | Shell | 56 | 2 | IMAGE_NAME="structure_gen"
docker build -t $IMAGE_NAME . |
092ee40bd71a9319331cd0cdfb943c728be745af34c111c33ace8f3f425e9604 | Shell | 57 | 2 | python coverage_panel.py
python coverage_panel_hetero.py
|
6e0fa09dd163531fd3b8cd70c7ade2e38fcad28ca8ba04cdda0cb2bd3ddf9046 | Shell | 57 | 7 | #!/bin/sh
cd 3rdparty/x11vnc && \
rm -rf \
m4 \
misc
|
a702373fbfc56d76156d7297695c9c3ecb539c62a805979bb73eb0c4bde8aa4b | Shell | 57 | 2 | #!/usr/bin/env bash
python3 setup.py build_ext --inplace
|
3493ac54dcbbcb8051aae2c54763ba52f8333e709479d4fc359529e6dee3d2dc | Shell | 58 | 1 | mpirun -np $NBEADS $LMP -in in.lmp -partition ${NBEADS}x1
|
2e9671967d15012e5c11819c7732ccefdbec8de4d62a99d70da65547f90fcc7b | Shell | 60 | 1 | mpirun -np 4 $LMP -in in.lmp -p 4x1 -log log -screen screen
|
91e6a8332368df3c8084256168484737d95ea5900d1d8a85011d50c94345cfd0 | Shell | 60 | 1 | mpirun -np 2 $LMP -in in.lmp -p 2x1 -log log -screen screen
|
c76946590517c52e1a66245998b772accb0b3d09a29e8ff895eae7b23cd1d7ce | Shell | 61 | 3 | #!/bin/bash
DIR=$(cd "$(dirname "$0")"; pwd)
$DIR/aydin/aydin |
a3fab2b9c772c2c8093e6d02ae154d55a4a810496e1dd67ddcfaa31811e507fc | Shell | 63 | 3 | #!/bin/sh
git log --date=short --pretty='format: %cd [%h] %s'
|
25af652ce8d97b64cc8fb7656ce69a63c22653d62a53d051e85f91267fc5ab77 | Shell | 64 | 1 | docker build -t hydrogym-sindy:latest -f ./Dockerfile-hydrogym . |
3e94db17cd21d5b14eb3339fdb95d211a837c29e1ef79b5be43a2ae66af900f7 | Shell | 64 | 2 | #!/bin/bash
coverage run -m pytest && coverage html -d coverage
|
41b23715d9e5bd542d0bf6e0ddd9fab569e813341cd86335b2b7a80dfe75e3e1 | Shell | 64 | 1 | eval "$(/opt/conda/bin/conda 'shell.bash' 'hook' 2> /dev/null)"
|
5f6ce4212a8afa780a8bf3c04018446d0ed8c47b81611af6747237d13ee4a238 | Shell | 64 | 5 | #!/bin/bash
ARENA_PATH=$1
$ARENA_PATH/Arena $SAMPLS_PATH $2 $3 |
7294d9768f4cb8d5b421dc7eb107c35af5b852a68ecf188141da3f7927137072 | Shell | 64 | 6 | for EACHFILE in *.bed
do
echo $EACHFILE
wc -l $EACHFILE
done
|
ab4753a5ca5f4608add27975fed315d311b46590780a73a847cfadca0adb44a9 | Shell | 65 | 2 | python setup.py clean
python setup.py build_ext --inplace --force |
8154672ffd61cebc96a44f88ae975c51b9c1ac58e4c7b02f289291facf287275 | Shell | 66 | 2 | curl -LsSf https://astral.sh/uv/install.sh | sh
brew install node
|
e0e241278d78d3b890bfb57387840ffd660f88dd3cb501a7db5ad5de83c4a928 | Shell | 67 | 7 | #!/bin/sh
cd 3rdparty/kldap && \
rm -rf \
autotests \
kioslave
|
e5e2da836ed843cc7c1c8e73b9ad763edaf2ea739ea79cd561d6d4392f08b379 | Shell | 68 | 1 | $PYTHON setup.py install # Python command to install the script. |
2b5292f40694d7eb9ac932fd5ddd8770625841c4dc3386ca1d6f8bd2330c84bd | Shell | 69 | 1 | # > logs_st/${DATE}_${TEACHER}_${STUDENT}_kd_a:${A}_b:${B}.log & |
79c2c2445e000729166855dd62ee5f60a8ccb9dede1fcf923d8159398a012b9f | Shell | 74 | 4 | cd graph_engine/backend
pip install --user .
cd ../..
pip install --user . |
ac784679b3118debf99c088f0ee54cbca823342cab72654de0777f45d4c320e5 | Shell | 74 | 3 | eval "$(/opt/elix-inc/py-runtime/setup)"
enable-conda
conda activate kmol
|
992884963b2f043eb2124a075bff521ce43992fd70e2691059e4a852a51cbb8f | Shell | 75 | 3 | #!/bin/sh
exec uvicorn app.main:app --host 0.0.0.0 --port "${PORT:-8081}"
|
ecb52d20ae44197fe7229632a8b21156ab1134a0f014ede3e7b866c83d686dd6 | Shell | 75 | 7 | #!/bin/sh
cd 3rdparty/kitemmodels && \
rm -rf autotests \
docs \
tests
|
64789fa437297d8a72951bd5f258f3362c19ebdb14f2a76cfb1c0882a9b8edbb | Shell | 76 | 4 | #!/bin/bash
rm -r src/accelerometer.egg-info build dist
rm -r conda-recipe
|
851bbcaf6d46d27887eb8a582ef37f873fece0d15916e0d6461a011749913917 | Shell | 76 | 1 | mpirun -np 8 lmp_mpi -partition 8x1 -in in.scp -log log.lammps -screen none
|
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