sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
fd1c2fa0651f58aef871b12f2ef001b701ec61e07ab3c79589c285cb794b606a | R | 5,467 | 146 | #' Joint Principal Trend Analysis (JPTA) of two high-dimensional longitudinal datasets.
#' @param x A N * P * T longitudinal data tensor. N is the number of subjects, P is the number of features, T is the number of time points.
#' @param y A N * Q * T longitudinal data tensor. Q is the number of features.
#' @param ni... |
9b390ea9dbbb20d51f8280d76df4cd62474bcde7699375211a4b1ebaf8f0c42f | R | 5,477 | 146 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
library("GeneTonic")
library("topGO")
library(DESeq2)
library(pcaExplorer)
library(dplyr)
library("ggplot2")
run_gene_tonic = function(de_tab, female_dds, cat, fname, organism, foldchange, padj) {
rownames(de_tab) = de_tab$gene_id
... |
0f331a9cf6d6205ca016cc9babf6206c3767bf5967b4e4605edcb3f3d87f9f72 | R | 5,479 | 77 |
| | SCD (N=125) | Control (N=197) | p value|
|:---------------------------|:-------------:|:---------------:|-------:|
|**lh_parahippocampal** | | | 0.664|
| N-Miss | 0 | 1 | |
| Mea... |
67b77557a7158d71797aea064e97bc810ca71d605ee420c6d1eaa5b556095e1a | R | 5,491 | 76 | #' @title Hierarchical Bayesian Modeling of Decision-Making Tasks
#'
#' @name hBayesDM-package
#' @aliases hBayesDM hBayesDM-package
#'
#' @import methods
#'
#' @description
#' Fit an array of decision-making tasks with computational models in a hierarchical Bayesian framework. Can perform hierarchical Bayesian analysi... |
dec6c50a94d82fe43cd60f4667163d552b0d17e9466ebfd095dcb649dd848204 | R | 5,491 | 149 | ##' @title Parse metadata
##' @description Parse metadata either by providing the \emph{.json}
##' file that is downloaded from GDC cart or by parse metadata
##' automatically by providing the projct id and data type
##' @param metafile metadata file in \code{.json} format download
##' from GDC cart. If ... |
66ec7d0780fd8b9f7038b0ad8515ad48af8efbc69b99cfaedc44007fdccb3d5d | R | 5,505 | 204 | .libPaths(c("/maps/projects/scarball/people/chg306/R_libs",
"/opt/software/R/4.2.2/lib64/R/library",
"/maps/projects/scarball/apps/Modules/software/R-packages/4.2.2"))
library(limma)
library(edgeR)
library(DESeq2)
library(magrittr)
library(tidyverse)
library(Cairo)
library(MetabolAnalyze)
libra... |
a96bcfc8171fcd5770231f06d6f7aba9d6c31efe1967c753e62316bb5e26e868 | R | 5,513 | 132 | source('~/scripts/single_cell/atac.R')
source('~/scripts/single_cell/wrapper.R')
source('~/scripts/single_cell/markers.R')
source('~/scripts/single_cell/celltype.R')
source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/graphs.R')
source('~/scripts/grn/models.R')
setwd('~/projects/cutntag/')
marks <- re... |
89dfa1e5cc51e5fc411f3a38307755f85268a3227acf317c785c0aa0be27accc | R | 5,516 | 144 | #'#################################################################################
#'#################################################################################
#' Evaluate correlation between GSE179848 measurements and scores
#'#################################################################################
#'... |
77e6866db76f6e3dee58b753a5c026e7728581267ff88b87607c753eebab2466 | R | 5,528 | 133 | library(lme4)
library(multcomp)
library(BayesFactor)
library(lmeresampler)
library(svglite)
library(ggplot2)
library(readxl)
# -----------------------------------------------------
# Load in the data
# -----------------------------------------------------
data <- read_excel("/Users/jkosnoff/Library/CloudStorage/Box-Bo... |
c0dd31b0bba0c59c821a69e4792277b1fd8a1d9c4ad4fa427c621fe8eaa84455 | R | 5,535 | 161 | # setup GRanges
gr1 <- GenomicRanges::GRanges(
seqnames = "1", strand = rep("+", 2),
ranges = IRanges::IRanges(
start = c(1, 501),
end = c(100, 600)
)
)
gr2 <- GenomicRanges::GRanges(
seqnames = "1", strand = rep("+", 3),
ranges = IRanges::IRanges(
start = c(1, 201, 501),
... |
e1065555b7df6678ee4cff9157018229e6a3984fa7108ff22d3730b530a9e44b | R | 5,540 | 163 | #' Imputation of quadratic terms
#'
#' Imputes incomplete variable that appears as both
#' main effect and quadratic effect in the complete-data model.
#'
#' @aliases mice.impute.quadratic quadratic
#' @inheritParams mice.impute.pmm
#' @param quad.outcome The name of the outcome in the quadratic analysis as a
#' charac... |
75732cbcf7e3020ca142a44e6ee385795b584132b7fc064cbea79fee6953bed8 | R | 5,545 | 95 | #!/usr/bin/Rscript
library(tidyr)
library(dplyr)
library(readr)
library(purrr)
library(tibble)
library(stringr)
library(forcats)
library(ggplot2)
gtf_new_from_known_start <- read_tsv("../../data/processed/other/meme_annotation/new_exon_START_boundaries_from_new_transcripts_in_known_genes.tsv", col_types = "cccddccci... |
48167a69c25642ac98e9bb078488eb6456a863b367704c981588ba3683b7c521 | R | 5,549 | 148 | #'#################################################################################
#'#################################################################################
#' Representativity of GSAS
#'#################################################################################
#'##################################... |
2339576355f1b77310c77b0d5b8cc5af44e920a78f1e642970eda47e797b8781 | R | 5,567 | 130 | ##' @title Kaplan Meier plot
##' @description Plot Kaplan Meier survival curve
##' @param gene an Ensembl gene id
##' @param rna.expr \code{\link[limma]{voom}} transformed expression data
##' @param metadata metadata parsed from \code{\link{gdcParseMetadata}}
##' @param sep a character string specifying which point sho... |
f0630382aa57fc7f80551b81d3c36ef0cacf5b960aa5a082e3be6f2330969063 | R | 5,574 | 192 | #' Imputation by a two-level normal model
#'
#' Imputes univariate missing data using a two-level normal model
#'
#' Implements the Gibbs sampler for the linear multilevel model with
#' heterogeneous with-class variance (Kasim and Raudenbush, 1998). Imputations
#' are drawn as an extra step to the algorithm. For simula... |
25aae8b1e092bc99870fdfc5c93e2e35526fd8a1931fcc2c59a6b114b1a7945c | R | 5,604 | 188 | source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/atac.R')
source('~/scripts/perturbator/de.R')
source('~/scripts/perturbator/enrichment.R')
library(Pando)
library(SeuratWrappers)
library(harmony)
library(future)
plan('multiprocess', workers=36)
filter <- dplyr::filter
select <- dplyr::select
setwd... |
9b3dd87f27d518cb80bd14edfe9ceccee182ddbfe8462e76f4e012a60da1ad33 | R | 5,617 | 204 | #' Check whether object is a data tree
#'
#' @param x Object to test for tree structure
#'
#' @return (logical scalar) TRUE or FALSE
isTree <- function(x){
any(class(x) == "Node") & any(class(x) == "R6")
}
parse_abi_hierarchy <-
function(abi){
abi_list <- fromJSON(file = abi)$msg[[1]]
tree <- recursive_b... |
053f882f10c7f4ead0f2a4522f10498715a6b89866bcba92d04746538d25e624 | R | 5,629 | 123 | #!/usr/bin/env Rscript
library('clusterProfiler')
library('ggplot2')
library(tibble)
############ PREPARATIONS ############
source(here::here("src/shared/R-helpers/get_settings.R"))
source(here::here("src/shared/R-helpers/memoise_decorator.R"))
source(here::here("src/shared/R-helpers/enrichGO_memoise.R"))
settings <... |
f7243c138e22f001e3e0e35cddc1fae95a1e1e17df8dad24635862e619d8cb30 | R | 5,634 | 162 |
# package -----------------------------------------------------------------
library(dplyr)
library(lme4)
library(MuMIn)
library(GWmodel)
library(car)
# data set ----------------------------------------------------------------
data_output_2 = data_output %>% filter(!is.na(Volume))
# corr --------------... |
2fedbd499606afd248e89348c9c8ac66364b8625f93ec321ce6b6daa8d5c262c | R | 5,642 | 135 | ## This script calculates model statistics across N folds.
suppressPackageStartupMessages({
library(ggpubr)
library(dplyr)
})
save_plot <- function(p, fn, w, h){
for(ext in c(".pdf", ".png", ".svg")){
ggsave(filename=paste0(fn,ext), plot=p, width=w, height=h)
}
}
extract_rank <- function(... |
1af5dcaac588b4560ba9e4542e6bd198237657d1515fd9e92a4d1078ef263f38 | R | 5,662 | 152 | ---
title: "3CT"
output:
word_document: default
html_document: default
pdf_document: default
date: '2022-12-02'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load data
```{r}
setwd("~/Documents/Systemic ESYT1 project locomotion, behavior, survival, anatomical data/Behavioral paradigms d... |
7a01a6ae2c59feb28c3548daa0450dc8c8f3d4efceb0eecd60019f6e9f9a6eb4 | R | 5,663 | 152 | ---
title: "3CT"
output:
word_document: default
html_document: default
pdf_document: default
date: '2022-12-02'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load data
```{r}
setwd("~/Documents/Systemic ESYT1 project locomotion, behavior, survival, anatomical data/Behavioral paradigms ... |
99370f264cb1a1c30fc2838de76720f2a58521762b9a2ae668de2c3737006064 | R | 5,668 | 135 | ########################
# FindAllMarkers + ClusterGVis visualization + GO enrichment
########################
library(Seurat)
library(dplyr)
library(ggplot2)
suppressPackageStartupMessages(library(ClusterGVis))
if (!require("ComplexHeatmap", quietly = TRUE)) BiocManager::install("ComplexHeatmap")
library(ComplexHeat... |
c706ef0219ce7d57cac9edd314aa2131469fb79013a6fb65f52b12e0a070bdc2 | R | 5,672 | 166 | # GTEx analysis
# setup--------------------------------------------------------------------------------
rm(list = ls()) ; gc() ; gc()
set.seed(1234)
wdir <- 'set your working directory'
setwd(wdir)
save.path <- 'object folder path'
# load library
easypackages::libraries(c('recount3','edgeR','tidyverse','org.Hs.eg.db... |
ea60851b60c8319bda7edb435fb2ec50de1c4f88668d3998179c695a7eb39531 | R | 5,673 | 126 | #!/usr/bin/env Rscript
###############################
## TO BE RUN AFTER emma_model.R
###############################
##load data
e.keep = readRDS('filtered_expression_matrix.rds')
keep.genes = readRDS('keep_genes.rds')
meta = readRDS('cayo_bulkbrain_combined_metadata.rds')
meta = meta[order(match(meta$LID, colnam... |
93bcfac4febd1331d23be7fcd24d09f4d92202b5e30120222bef671472535c09 | R | 5,710 | 170 | #' Imputation by random forests
#'
#' Imputes univariate missing data using random forests.
#'
#' @aliases mice.impute.rf
#' @inheritParams mice.impute.pmm
#' @param ntree The number of trees to grow. The default is 10.
#' @param rfPackage A single string specifying the backend for estimating the
#' random forest. The ... |
383380b48525699b3e76f36aec965d4b9f2af8a844bf0020554f59b9bc9785c4 | R | 5,711 | 183 | ### modified from ComplexHeatmap packages
### see https://github.com/jokergoo/ComplexHeatmap/blob/master/R/grid.dendrogram.R for details.
#' @importFrom stats as.dendrogram as.hclust is.leaf nobs order.dendrogram
#' @noRd
dend_tbl <- function(dend,
bcols = NULL,
direction = "to... |
4424641e5eb314bdebe3460cd440552f37be1bc5aeb00a2c8ad0786ea9877b30 | R | 5,717 | 138 | #4 select ROIs
# Setup---------------------------------------------------------------
rm(list = ls()) ; gc() ; gc()
set.seed(1234)
wdir <- 'set your working directory'
setwd(wdir)
save.path <- 'object folder path'
# load library
easypackages::libraries(c('Seurat','tidyverse','SPATA2'))
# create SPATA object--------... |
a526665caa90eba5ecb951407d9c2145b4be6b5a4ebef2d2c7eab317688dc623 | R | 5,718 | 155 | #! /usr/local/bin/Rscript
#'#################################################################################
#'#################################################################################
#' Run methylation QC data
#' This code performs quality control to methylation data.
#' Input:
#' - path to config file wi... |
2b4695571128ef8f4245a66277c4da208ff920cbcbac8d15877fe91aef32c2b7 | R | 5,742 | 132 | #### Code for plotting volcano plot for Lam, Ndayisaba et al 2024 Neuron FIGURE 8C showing depletion of essential genes in 3K-sfGFP sample
#### Note that the 3 genotypes used in the U2OS CRISPR/Cas9 genome-wide screen were: SNCA-3K-sfGFP (referred to as "E3K" here), SNCA-WT-sfGFP (referred to as "WT"), and sfGFP contro... |
eca80f7746adcfe52ae05d124faa402baa16d1596339c14ac160f1674829aa0b | R | 5,743 | 167 | ###################################################
# ~~~ FramebyFrame package ~~~
# functions to import RAWs.csv
# Francois Kroll 2022
# francois@kroll.be
###################################################
# function averageFramerate(...) ------------------------------------------
#' Calculates average framerate
... |
318f6c8a50c77e3fc821d209f795413e91ad2d0bb24dcf1abe96a29bd2c02ecf | R | 5,749 | 177 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
source(here::here("src/shared/R-helpers/gseGO_memoise.R"))
source(here::here("src/shared/R-helpers/gseKEGG_memoise.R"))
suppressPackageStartupMessages({
library("AnnotationDbi")
library("dplyr")
library("purrr")
library("ggplot2... |
a6d9760958309b730be34677128caf14a89ea58ac4681c26384b2707e33dc6cd | R | 5,765 | 139 | #'#################################################################################
#'#################################################################################
#' Explore gene sets included in the model
#'#################################################################################
#'###################... |
315efe382d028b1ffd1ab7623ea5ef154a6163bdd40d4e7f86da3756493ae271 | R | 5,780 | 91 | library(ggpubr)
library(ggplot2)
library(ggsci)
library(ggsignif)
library(gghalves)
library(tidyverse)
# Donor1 new clone fraction
pred_donor1 = read.table('donor1/pred.tsv', header=TRUE, sep='\t')
test_donor1 = read.table('donor1/test.tsv', header=TRUE, sep='\t')
Hetero_donor1 = left_join(test_donor1, pred_donor1, by... |
a83d9869dc3f2a47daba58f911258a92935d923dcb090a2df1cbddb7d1bc4c82 | R | 5,794 | 152 | ---
title: "Compare Enrichement Results"
author: "Emma Jones"
date: "2023-12-13"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Compare Enrichment results across DGE, DTE, and DTU.
The purpose of this script is to get real statistics and values for directly comparing ... |
f3a1a00be08beec2804002078b426d5ac1084b326120b6c313745823e039fc2e | R | 5,798 | 151 | options(warn=-1)
suppressPackageStartupMessages({
library(tidyverse)
library(RColorBrewer)
library(optparse)
})
option_list = list(
make_option(c("-a", "--abundance"), type="character", default=NULL, help="input abundance file: gene_normalised_abundance.txt or filteres_transcripts.txt [REQUIRED] [default= %de... |
c297e101206062b43d1e01ad4c5a087a6e3dfd5caf0049d64d8afdacb7c016a1 | R | 5,813 | 88 | #### Code for plotting graph in Lam, Ndayisaba et al 2024 Neuron, Figure S7F
#### Plotting total neuritic inclusion length per well (microns) across 4 conditions:
#### (1) pi-N A53T-sfGFP (no PFFs)
#### (2) pi-N A53T-sfGFP (with PFFs)
#### (3) pi-N deltaNAC-sfGFP (no PFFs) ("NoNAC)
#### (4) pi-N deltaNAC-sfGFP (with P... |
00f97c2a04072125bc7ddb99a8023c1862c3ab3497a464a5e065e05f926f6db4 | R | 5,817 | 165 | ## Differential expression with limma (permutation, subsampling)
suppressPackageStartupMessages({
library(SummarizedExperiment)
library(optparse)
library(dplyr)
library(limma)
library(edgeR)
library(here)
library(sva)
})
## Functions
extract_de <- function(contrast, label, efit, seed_int)... |
bae7a624c111c2aa5f9eab4d16172f2cb14b4b35a1c2270f3a986e2d99d2e8b9 | R | 5,822 | 132 | #!/usr/bin/env Rscript
# ---------------------------------------------------------------
# 差异基因 TPM 矩阵，分肠段 edgeR 差异分析
# 比较组: (1) D30C vs D0 (2) D30T vs D30C
# 统计各肠段上/下调基因数，绘制双向柱形图：
# - x 轴上方 = 上调，下方 = 下调
# - 颜色区分比较组（红 = D30C vs D0，蓝 = D30T vs D30C）
# - 上方 = 上调，下方 = 下调；坐标轴置于底层，无柱边框
# 输出:
# 1) DEG_summary_by_se... |
3a697143fc322747b07671b52e216447ce4e35ab96f1f0afa42bd179dc693981 | R | 5,823 | 165 | ## Differential expression with limma (permutation, subsampling)
suppressPackageStartupMessages({
library(SummarizedExperiment)
library(optparse)
library(dplyr)
library(limma)
library(edgeR)
library(here)
library(sva)
})
## Functions
extract_de <- function(contrast, label, efit, seed_int)... |
cbf7104141d45c7d0cf2c1a017aed71b78fee2079191995ff4db180d9a435c06 | R | 5,823 | 165 | ## Differential expression with limma (permutation, subsampling)
suppressPackageStartupMessages({
library(SummarizedExperiment)
library(optparse)
library(dplyr)
library(limma)
library(edgeR)
library(here)
library(sva)
})
## Functions
extract_de <- function(contrast, label, efit, seed_int)... |
81bfb412b52ea8eebfb44c9605ff9f529c9e9f2365ea459503c384fcca596765 | R | 5,826 | 178 | # set working directory
setwd("~/Desktop")
maindir <- getwd()
datadir <- file.path("~/Desktop/")
# load packages
library("readxl")
library("ggplot2")
library("ggpubr")
library("reshape2")
library("emmeans")
library("hrbrthemes")
library("umx")
library("interactions")
library("car")
library("ggscatter")
# import data:... |
2e1acb40e6a4c4ef57c02cb0dff790b0499ba9298fb8f648f9b1e10aac754849 | R | 5,850 | 177 | install.packages("gdata")
install.packages("lme4")
install.packages("ggplot2")
install.packages("reshape2")
install.packages("LMERConvenienceFunctions")
install.packages("parallel")
install.packages("viridis")
install.packages("readr")
install.packages("lmerTest")
install.packages("multcomp")
install.packages("tidyr")
... |
e025c9a0d3ea6c1cf05ca775a2167f39e5b738e1ab213092c0099254de9adb2a | R | 5,882 | 143 | #' Quick selection of predictors from the data
#'
#' Selects predictors according to simple statistics
#'
#' This function creates a predictor matrix using the variable selection
#' procedure described in Van Buuren et al.~(1999, p.~687--688). The function is
#' designed to aid in setting up a good imputation model for... |
4901a756a5d15a678a365af02153c614a3536a7c18bef88c419c272a2953d4fe | R | 5,896 | 169 | #' Plot Correlation Matrix Quickly
#' @description quickcor is convenient wrapper for creating a number of different types
#' of correlation matrix plots because of adding some extra settings by default.
#' @param x,y matrix, data frame or quickcor object in \code{print()}.
#' @param mapping NULL (default) or a list of... |
06485ce91d5a307c16139cd0e777b7364cde5e2ee17c6f625700a9a058a71496 | R | 5,897 | 146 | ##' @title Plots for enrichment analysis
##' @description Bar plot and bubble plot for GO, KEGG,
##' and DO functional enrichment analysis
##' @param enrichment a dataframe generated from
##' \code{\link{gdcEnrichAnalysis}}
##' @param type type of the plot, should be one of \code{'bar'}
##' and \code{'bu... |
12d9c8e0b4e8ad2534150d1cb87583e0fbd826d08cdcd8bd35b35933361cf3c0 | R | 5,905 | 231 |
# Load libraries ----------------------------------------------------------
# as ggtranscript is in development during the creation of this script
# i have included the code to install the exact version using (0.99.2)
# based on the commit SHA
# devtools::install_github("dzhang32/ggtranscript@88d23b43b42de3e49a67dc81... |
02738c1b2319cb52cb639098c327fa488f8ffa0c3eaedeb142b3872e3e5e3abf | R | 5,909 | 155 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
Bisection <- function(egvalues, q, xmin, xmax) {
.Call(`_STAAR_Bisection`, egvalues, q, xmin, xmax)
}
Bisection_Binary_SPA <- function(muhat, G, q, xmin, xmax, tol) {
.Call(`_STAAR_Bis... |
149ca896a09f634af42006fcbf4ed69087e8c1b92264c06cbc953d199b16342d | R | 5,925 | 168 | #' Matrix of Correlations, P-values and confidence intervals
#' @description \code{correlate} uses \code{cor} to find the correlations and use \code{cor.test} to find
#' the p values, confidence intervals for all possible pairs of columns ofmatrix.
#' @param x,y a matrix object or NULL.
#' @param cor.test logical, if T... |
415a2065fad3b7cbab5e77429ca39f5da2e29c88bd71c8efec8e29b176bfd496 | R | 5,930 | 216 | #' \code{mipo}: Multiple imputation pooled object
#'
#' The \code{mipo} object contains the results of the pooling step.
#' The function \code{\link{pool}} generates an object of class \code{mipo}.
#'
#' @param x An object of class \code{mipo}
#' @param object An object of class \code{mipo}
#' @param mira.obj An object... |
8d7ecefad6276cdda4306db64cfa07663d90c20414255af8f5a9fb6699366d43 | R | 5,943 | 236 |
# Load libraries ----------------------------------------------------------
# as ggtranscript is in development during the creation of this script
# i have included the code to install the exact version using (0.99.2)
# based on the commit SHA
# devtools::install_github("dzhang32/ggtranscript@88d23b43b42de3e49a67dc81... |
4b2d3491b9306ed17607bc34caee7670b99974f469d214b25ff57b3a1a713b27 | R | 5,954 | 156 | ---
title: "NOR"
output:
word_document: default
html_document: default
pdf_document: default
date: '2022-12-05'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load data
```{r}
setwd("~/Documents/Systemic ESYT1 project locomotion, behavior, survival, anatomical data/Behavioral paradigms d... |
e1bccfafa1d8e2a02c441236d8caeb6a4804ecb610de1ba055007c54e1a4a336 | R | 5,955 | 156 | ---
title: "NOR"
output:
word_document: default
html_document: default
pdf_document: default
date: '2022-12-05'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load data
```{r}
setwd("~/Documents/Systemic ESYT1 project locomotion, behavior, survival, anatomical data/Behavioral paradigms d... |
5c0dffa596f5e503ad968eecaefe1432bf1d91b0fa04c4a70a9c359844876f55 | R | 5,956 | 165 | ##Bulk rnaseq data propcessing
##ROSMAP
setwd('F:/deconbench/rosmap/raw/bulk')
load('ROSMAP_rawcounts_38704genes.RData')##raw count matrix and metadata
tmp<-read.table('rosmap_sn_ihc_sample.txt',header=T,sep='\t')
mrna<-bulk[,bulkmeta$projid%in%tmp$projID]
mrmeta<-bulkmeta[bulkmeta$projid%in%tmp$projID,]
... |
f0241b6bd5b69b58b834a072e50159c35594bcb97eaf1d6161dd2c72efcf0ecf | R | 5,973 | 172 | ## This script analyzsis gene set enrichment with permutation
suppressPackageStartupMessages({
library(DOSE)
library(here)
library(dplyr)
library(biomaRt)
library(org.Hs.eg.db)
library(clusterProfiler)
})
get_biomart <- function(){
ensembl <- useMart("ensembl", dataset = "hsapiens_gene_ense... |
38cdede659ec9ae5c19a7a696d0aa876a99beb7d15af31d6f9dc13a366b5bbd9 | R | 5,984 | 153 | library(limma)
library(edgeR)
library(tidyr)
library(reshape2)
library(plyr)
library(dplyr)
library(grid)
library(gridExtra)
current = getwd()
folder = "2_calculate_residuals_and_DE_analyses"
setwd(current)
system(paste0("mkdir -p inputs/",folder,"/corrected_expression/"))
system(paste0("mkdir -p outputs/",folder,"/2_... |
c2dae5fa7c5d27e855fa04e8aed3ab7fef1898a7ec9211c1882a1d172818d8c2 | R | 6,010 | 230 | #' Multivariate Imputation by Chained Equations (Iteration Step)
#'
#' Takes a \code{mids} object, performs \code{maxit} iterations and
#' produces a new object of class \code{"mids"}.
#'
#' @details
#' This function enables the user to split up the computations of the Gibbs
#' sampler into smaller parts. This is usefu... |
5a992aacda8427d87fd60739fa16b9e0b0a3a3b8fd175544c16dfbd2a7e05e5d | R | 6,021 | 241 | ---
title: "DESEq2 within brain region by sex"
author: "Emma Jones"
date: "2023-05-30"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## DESeq across and within brain region DE across sexes
The purpose of this script is to compare expression at the gene and transcript le... |
efb855be3e63965cedc581867aa1514d9956e7fe52178791df2a51e5ff45d8a3 | R | 6,050 | 152 | #!/usr/bin/env Rscript
source(here::here("src/shared/R-helpers/get_settings.R"))
suppressPackageStartupMessages({
library("dplyr")
library("tidyr")
library("readr")
library("tibble")
library("ggplot2")
requireNamespace("forcats")
library("preprocessCore")
library("protti") # framework used for QC plot... |
11daa99dd678854ab9fcf9afea39faaee07eda4855a6ccf4baa05051e42c185f | R | 6,058 | 139 | ---
title: "Figure 5. DEG counts across intestinal segments (bulk RNA-seq, edgeR)."
date: "2026/9/16"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# Clean workspace
rm(list=ls())
# Load setting and functions
source("../script/stat_plot_functions.R")
# Set output directory
ou... |
385a29186ca461571cf6675111cc5e55752ef5f438bfacfe0c1d1df6cf4886a1 | R | 6,078 | 127 | #####################################################################################################################################
################## R-script to read YAML DTS dataset files, visualize and statistically evaluate data. Reports in HTML ##############
####################################################... |
f8cca72d6a86332be2dd7f0768c815f8b1082f809657c252874d29230bdf0094 | R | 6,091 | 204 | #' Box-and-whisker plot of amputed and non-amputed data
#'
#' Plotting method to investigate the relation between the data variables and
#' the amputed data. The function shows how the amputed values are related
#' to the variable values.
#'
#' @method bwplot mads
#' @param x A \code{mads} (\code{\link{mads}}) object, ... |
819df06214328cdfc62dfd9b1a6e803e681effdf8c94c292fc50b6a6ef22018f | R | 6,108 | 187 | ---
title: "Reprocessing and demultiplexing of G1-G13"
author: "Luis Montano"
date: '2023-07-19'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Processing of samples G14-G27 on cellranger
These samples from 10X Genomics were obtained in 2020. They are subject to a comp... |
2b9e782c51a41203447279b77dfcceadd636d6a35915f86d66e4c5efcc77d9ae | R | 6,122 | 118 | library(proBatch)
library(readr)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(png)
source('../lib/plot_themes.R')
#load the data:
sample_annotation_AgingMice = read_csv("data_AgingMice/3_data_for_plots/sample_annotation_AgingMice.csv")
normalized_df_AgingMice = read_csv(file = "data_AgingMice/3_data_for_plo... |
34c8656d3c4a53203f3a583d79d289d8cf562a0b9b4c5de64384f37e5474087e | R | 6,127 | 170 | rm( list=ls(all=TRUE ) )
# Load and install necessary packages
load_required_packages <- function(packages){
new.packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
invisible(lapply(packages, library, character.only = TRUE))
}
requ... |
566945134a0d58ece12994f0fc8f08b59419a7f24f754046af45c5eb85bb887b | R | 6,130 | 170 | rm( list=ls(all=TRUE ) )
# Load and install necessary packages
load_required_packages <- function(packages){
new.packages <- packages[!(packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
invisible(lapply(packages, library, character.only = TRUE))
}
requ... |
732f6aaada02b7344eba321c3566db470f52c15f89841a698bdc282f5d6e472a | R | 6,137 | 159 | ##' @title Univariate survival analysis of multiple genes
##' @description
##' Univariate Cox Proportional-Hazards and Kaplan Meier
##' survival analysis of a vector of genes
##' @param gene a vector of Ensembl gene ids
##' @param rna.expr \code{\link[limma]{voom}} transformed expression data
##' @param metad... |
1ec40b9bdfd09156af9d9d27bbb770002b16de7375f06c9bbece714106f9a0c2 | R | 6,146 | 142 | #Deantoni et al. 2024
#Circadian rapid eye movement sleep expression is associated with brain microstructural integrity in older adults
#https://doi.org/10.1038/s42003-024-06415-y
#Load packages
library(dplyr)
library(tidyr)
library(ggplot2)
library(fitdistrplus)
library(glmmTMB)
# Load data files ----
data_SE <- rea... |
8c29526c41301664e7d087aad4f6fae48d3d3bf852f4f4078e4774ffdf582e1a | R | 6,148 | 167 | source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/atac.R')
source('~/scripts/perturbator/de.R')
library(Pando)
filter <- dplyr::filter
select <- dplyr::select
setwd('~/projects/cutntag/')
#### Read data ####
marks <- read_rds('data05/all_marks_list_v3.4lines.rds')
rna <- read_rds('data05/RNA/RNA_a... |
25d554abad913f251cd108d981f958ea0b4b2c5486e3793d19a5fc97ec9b21d8 | R | 6,159 | 169 | options(warn=-1)
suppressPackageStartupMessages({
library(tidyverse)
library(optparse)
})
option_list = list(
make_option(c("-a", "--abundance"), type="character", default=NULL, help="input NORMALISED abundance file: gene_normalised_abundance.txt [REQUIRED] [default= %default]", metavar="character"),
make_opt... |
305938f368f3bf343411d4f11fa5f723d896dbf5cb98a2e4e071db611ae2f7e8 | R | 6,164 | 142 | ###########################################################################
## Author: Youtao Lu <luyoutao@sas.upenn.edu>
##
## Copyright (c) 2021-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2021-2023, James Eberwine, Perelman School of Medicine, University of ... |
946082cf9a74ad80efa60c6463827db60cbefd425a956c65f122a56e47344a14 | R | 6,168 | 188 | #----load scripts and libraries----
#load libraries
liblist = c('parallel', 'doSNOW', 'stringr','MASS',
"nleqslv", "data.table", "deSolve")
sapply(liblist, library, character.only = TRUE)
setwd("C:/STUDIES/RESEARCH/neural_ODE/ground_truth_simulator/code")
#import simulator code
source('Classes.R')
source('... |
8f00a63f1eaf90180401893e09b73e7ad7e9252a274540c22c3efd3d2dc07e68 | R | 6,175 | 145 | ---
title: "Figure 2. Marker genes and GO enrichment visualization per cell type (ClusterGVis)."
date: "2026/9/16"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# Clean workspace
rm(list=ls())
# Load setting and functions
source("../script/stat_plot_functions.R")
# Set output... |
3f2a5feb62654f58357875b7094f55f14fc4f84dcdf3471a03ea44e10613a028 | R | 6,183 | 109 | setwd("~/Documents/Microarray Gen/Angela_HRLR_EE_Stress/NAcc_Remove2outliers/11_FGSEA/UpdatedGMT_20210325/GMTs_toMakeRat")
GMTfiles_ToMakeRat<-list.files()
#Original read-in code - it turned out that this was actually problematic (see notes below):
# c2.all.v7.3.symbols<-read.delim("c2.all.v7.3.symbols.gmt.txt", sep=... |
f994d0262de83f07c5632a2969de8e67a9e312a22a102a2c4ea0e86ddd54ab6c | R | 6,199 | 220 | ---
title: "RR3_brain_ST_data_clustering_analysis"
output: html_document
date: '2022-12-17'
---
## Loading packages, creating seurat object
```{r, echo=FALSE, include=FALSE}
library(Matrix)
library(magrittr)
library(dplyr)
library(ggplot2)
library(Seurat)
library(patchwork)
library(STutility)
library(Rcpp)
library(phe... |
9ee63d808ac65a86632cd7fb0eceb8a53ce8457d8bb3702e593e23b4f7e34e25 | R | 6,202 | 189 | ---
title: 'Saldana, Montano, et al. -- ATAC-seq: Annotation with NB super-enhancers'
author: 'Florian Halbritter'
date: '`r format(Sys.time(), "%B %d, %Y %H:%M:%S %Z")`'
output:
html_document:
toc: yes
toc_float: yes
code_folding: show
highlight: pygments
df_print: paged
---
# Intro
In this ... |
93e6516e2cf24701508bee6b52ccc5ab9b55b3db149ec79f6ed19d5989ac58b7 | R | 6,220 | 142 | ###########################################################################
## Author: Youtao Lu <luyoutao@sas.upenn.edu>
##
## Copyright (c) 2021-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2021-2023, James Eberwine, Perelman School of Medicine, University of ... |
1e7594482382d860e65ae344e184319389081371e7b6aceec277f5c888d755a9 | R | 6,247 | 168 | .addExonstoTx <- function(x, y, drop.unmodified = FALSE,
allow.external.exons = FALSE) {
# check for length of x and y
if (length(x) != length(y)){
rlang::abort("x and y are not of the same length")
}
# drop all metadata first
S4Vectors::mcols(x, level = "within") <... |
d85271a502d7d2d2fd55098c2e87387445f47efc54675ec71a6c0c6356548d92 | R | 6,258 | 184 | #' Extracts the completed data from a \code{mids} object
#'
#' Takes an object of class \code{mids}, fills in the missing data, and returns
#' the completed data in a specified format.
#'
#' @aliases complete
#' @param data An object of class \code{mids} as created by the function
#' \code{mice()}.
#' @param action A n... |
0d7c1f1e93b815bb6f0f3fb039b20e79c1c0b21026625daf6483975316326417 | R | 6,318 | 149 | source('~/scripts/single_cell/de.R')
source('~/scripts/single_cell/atac.R')
source('~/scripts/perturbator/de.R')
library(Pando)
filter <- dplyr::filter
select <- dplyr::select
dist <- stats::dist
setwd('~/projects/cutntag/')
#### Read data ####
marks <- read_rds('data/CT/all_marks_list_v3.3motifs.rds')
rna <- read... |
cb12278f086430a20d7ae2713611d3c77bbf05a9915dbdd0f0b58b4473ea22ae | R | 6,324 | 197 | #' Imputation at level 2 by predictive mean matching
#'
#' Imputes univariate missing data at level 2 using predictive mean matching.
#' Variables are level 1 are aggregated at level 2. The group identifier at
#' level 2 must be indicated by \code{type = -2} in the \code{predictorMatrix}.
#'
#' @aliases 2lonly.pmm
#' @... |
259e39c8635432ce9eab82ce41b02a57e142ec597161dc8899dd4370f3ef0ced | R | 6,327 | 177 | library(ggplot2)
library(scales)
library(ggthemes)
library(patchwork)
library(kableExtra)
library(data.table)
source('utils.r')
# fig 1
results_fnames <- list.files('results/', full.names = T)
results_fnames <- results_fnames[grepl('ys.csv', results_fnames, fixed = T)]
results_dummy <- results_fnames[grepl('dummy',... |
10f7a322a138fcdc5012c60e0b4c6627b355c86b8d2d2d8379a9ba253ce54ec5 | R | 6,334 | 192 | #' Creates a \code{blocks} argument
#'
#' The `make.blocks()` helper function generates a suitable
#' \code{blocks} argument for the \code{[=mice]{mice}} function.
#'
#' @param x A `data.frame`, character vector with
#' variable names, or `list` with variable names.
#' @param partition A character vector of length 1 us... |
933db897761efc9c7995cda7a038a06a29dbcc8af84a13036dc2179dbfe0ada9 | R | 6,359 | 153 | ##-------------------------------------------------------
## LOAD MODULES AND LIBRARIES
#-------------------------------------------------------
# module purge && module load shared slurm python/3.7.x-anaconda
# module load gcc/8.3.0
# module load hdf5_18/1.8.17
# module load R/4.1.1-gccmkl
rm(list = ls())
suppressP... |
225083f4a0621b040abf9f5599c8c265e1d31dfae3ff432583641b3244cc9e43 | R | 6,375 | 98 | library(TCGAbiolinks)
library(SummarizedExperiment)
#mRNA
mRNA_query <- GDCquery(project = "TCGA-BRCA",
data.category = "Transcriptome Profiling",
data.type = "Gene Expression Quantification",
workflow.type = "STAR - Counts")
GDCdownload(mRNA_query, method = "api",d... |
a8931ca08ac6e51f5b4badbc252e48c12b2dc422aef6a94997665e446d7816de | R | 6,378 | 194 | ---
title: "Supplementary Information for: Organ systems of a Cambrian euarthropod larva"
author: 'Martin R. Smith\*, Emma J. Long\*, Alavya Dhungana, Katherine J. Dobson, Jie Yang, Xiguang Zhang'
date: "`r Sys.Date()`"
output:
word_document:
reference_docx: "Character_report_template.docx"
toc_depth: ... |
fe186536cae227445b33335379dde490aa5a8fc9504da836ff36accbd56409f3 | R | 6,388 | 147 | ##### Code to look for coordinates of gene hits in FIGURE 8D plot from Lam, Ndayisaba et al 2024 Neuron.
##### Labels were then added manually to the plot.
#7/12/22: Add labels manually to volcano plots: 3K-WT differential log2 fold change
load("/Users/isabel/Dropbox (Partners HealthCare)/Isabel-Lindquist:Khurana:Vi... |
d834ea8ec737f18f154e4c49d52df4eafa457f420ac0b01149b4ef05d2ec797a | R | 6,400 | 215 | # This script reads in the seurat object, checks normalization and makes summary HC and PCA plots
suppressPackageStartupMessages({library(org.Hs.eg.db)
library(tidyverse)
library(Seurat)
library(clusterProfiler)
library(Libra)
library(DESeq2)
library(magrittr)
library(ggrepel)
library("pheatmap")
lib... |
ea583997a4ba29a6f7880a60df71757b73c557d100d65905ae630187877aac48 | R | 6,405 | 194 | ---
title: "Supplementary Information for: Digestive, vascular and nervous systems of a Cambrian euarthropod larva"
author: 'Martin R. Smith\*, Emma J. Long\*, Alavya Dhungana, Katherine J. Dobson, Jie Yang, Xiguang Zhang'
date: "`r Sys.Date()`"
output:
word_document:
reference_docx: "Character_report_templ... |
bbc09db6c62d9182320b1b26447647544e8e1361f894411e2e4ef52272251778 | R | 6,412 | 152 | #!/bin/R
library(data.table)
library(optparse)
option_list <- list(
make_option(c("-g", "--gitdir"), help="Absolute path to motrpac-atac-seq-pipeline repository, e.g. '~/ATAC_PIPELINE/motrpac-atac-seq-pipeline'"),
make_option(c("-j", "--json"), help="Global JSON parameters"),
make_option(c("-m", "--dmaqc"), hel... |
380d8a1c49aa57943a15b10f8bd22e6eb48ca6c9da9df926f8390f79e5b82480 | R | 6,416 | 205 | #' Imputation by a two-level normal model using \code{lmer}
#'
#' Imputes univariate systematically and sporadically missing data using a
#' two-level normal model using \code{lme4::lmer()}.
#'
#' Data are missing systematically if they have not been measured, e.g., in the
#' case where we combine data from different s... |
858e0fd57deed41f9f392fa440807e548b123fd7e9e2d02f947dd13b5987766e | R | 6,418 | 183 | suppressPackageStartupMessages({
library(clusterProfiler)
library(enrichplot)
library(DOSE)
library(org.Mm.eg.db)
library(org.Hs.eg.db)
library(ggplot2)
library(pathview)
library(tidyverse)
})
make_go_enr = function(genelist, prefix, folder, orgDB, pvalueCutoff, qvalueCutoff,
pAdjus... |
6e4bb6e16d67e556798be30cdcb4fd775a05ac106e912d781ae59134328b8cce | R | 6,458 | 154 | ##-------------------------------------------------------
## LOAD MODULES AND LIBRARIES
#-------------------------------------------------------
# module purge && module load shared slurm python/3.7.x-anaconda
# module load gcc/8.3.0
# module load hdf5_18/1.8.17
# module load R/4.1.1-gccmkl
rm(list = ls())
suppressP... |
21537d733ba8a456ea43862b4ad1e8a3815b62596baef606a4b0f0a2d991a69d | R | 6,461 | 216 | ## Compare expression between control and treatment across all non-placenta tissues
library(limma)
library(edgeR)
library(DESeq2)
library(magrittr)
library(tidyverse)
experiment_data <-
snakemake@input[['expression_results']] %>%
readRDS() %>%
{DGEList(
counts = assays(.) %>% extract2("counts"),
sample... |
a4b9a0e1c075268af0580de811df53c413f654e4ea6ab3efd35b2190213bc847 | R | 6,480 | 178 | ## Create a CWAS gene set: Step 2
rm(list = ls())
library(tidyverse)
gm0 = read.delim('~/Dropbox/gene-mat-recipe_v2/my_gene_matrix_231123.txt')
head(gm0)
gm0 = gm0 %>%
dplyr::select(gene_id, gene_name, ProteinCoding,
lincRNA = Long.ncRNA)
gm0$tmp_id = do.call(rbind.data.frame, strsplit(x = gm0$gene... |
d3b6b2816086c4236f45575295d71ddb6a54cb91ce097c409daa2904f5f520b7 | R | 6,505 | 197 | #' Converts an imputed dataset (long format) into a \code{mids} object
#'
#' This function converts imputed data stored in long format into
#' an object of class \code{mids}. The original incomplete dataset
#' needs to be available so that we know where the missing data are.
#' The function is useful to convert back op... |
07e04bd7dceb34bfb59d4434f703dc3f1276c9ebc59d9938fdf06a613ed7af24 | R | 6,517 | 178 | ---
title"BrainAge_VidalPineiro_Tests of equivalence (superiority) (early life v. aging) (XGB & LASSO)"
author: "dvp"
date: "11/2/2020"
output:
html_document: default
pdf_document: default
---
# Setup
```{r setup, include=FALSE}
#install.packages("~/R/imported_packages/3.6.0/itsadug_2.3.tar.gz", repos = NULL, type... |
96120e2aa0315c3f28e7e5fe0a14029ed4d87ebd71dc83bebfab3d9ca0ab7a9b | R | 6,518 | 162 | #'#################################################################################
#'#################################################################################
#' Select GO and KEGG features for model
#'#################################################################################
#'#########################... |
1dea72dfe8f1083c92d79a4078d4d44424b32af6500444f147e370094fa68980 | R | 6,520 | 186 | #' Wrapper function that runs MICE in parallel
#'
#' This function is included for backward compatibility. The function
#' is superseded by \code{\link{futuremice}}.
#'
#' This function relies on package \code{\link{parallel}}, which is a base
#' package for R versions 2.14.0 and later. We have chosen to use parallel f... |
83925bfe2b479cfabdb59940d523c70adf41bdf9694021336de0eb69b3b59c8b | R | 6,541 | 131 | #Notes for when I come back to this:
#Reformat Dropviz (+Zeisel), StressGenes, and Psychiatric genes into .gmt format and combine them.
DropViz_HC_Mouse_Temp<-list()
DropViz_HC_Rat_Temp<-list()
DropViz_HC_Human_Temp<-list()
names(table(DropViz_HC_CellTypeClusterMarkers_Concise_RatOrthologs$CellType))
for(i in c(1:... |
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