sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
b66edc20bc8a13862898236c6fb9a5853e33ad53284c1ebeaf971e53684ccf3b | R | 5,652 | 197 | ---
title: "Mixed effect model for batch correction"
author: "Joyce Hsiao"
date: 2016-01-12
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
```
## Objective
Update the batch-corrected counts... |
22a62120db903361fbab23f5c5b0378d4724c2517a52f4bf0ab9bef24bcd2f0a | R | 5,666 | 168 | # Created on Thu Jul 13 17:50:17 2021
# Name: gradients_correlation_ranking.R
# Purpose: Selects the top n (number of synapses) post for a given pre and
# evaluates the Pearson's correlation coefficient between all possible
# pairs.
# Author: Andrea Vaccari (avaccari@middlebury.edu)
#
# Generates:... |
cdb8b546ead9cdfbace7b2a47a8a2b8ce4aacfe8a5ab3fcd1a9e59ef41f6e007 | R | 5,670 | 175 | ---
title: "ERCC counts"
author: "PoYuan Tung"
date: 2015-07-07
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
library("edgeR")
library("ggplot2")
theme_set(theme_bw(base_size = 14))
```
## I... |
38c19ba3553786a1903d93ab88ccabf2f03df8bfc5d0c620e3a2687bd2ed0d69 | R | 5,699 | 117 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
60cd68ede3b59a2ac35c852c33d2215b424b39087384e534c35bb9f006055f49 | R | 5,707 | 127 | #Code for sup figure 4
#Panel A: HD encoding accuracy for both cues
initial_offset_precision_data <- read.csv("C:/Users/mbasn/Dropbox/Manuscript-Basnak/Nat Neurosci Aug 2024 revision/zenodo/Ext_data_fig_4_analysis/initial_offset_precision_data.csv")
initial_offset_precision_data <-
initial_offset_precision_da... |
4c082fd21f42274fe815f9e55b38aa542a065f00c74278a40d590c61d4ecd8d2 | R | 5,731 | 186 | ---
title: "model coeff."
author: "SRomano"
output: html_document
editor_options:
chunk_output_type: console
---
# Load packages
```{r setup}
library(phyloseq)
library(vegan)
library(dplyr)
library(tidyverse)
library(SIAMCAT)
# load my function to run siamcat
source("/Scripts/Model_coefficient_extraction.r")
set.... |
38798a0430d024efdb4c36a8f6d787a6bda8adfd75ea66d5cc9036ca666f2683 | R | 5,734 | 111 | #There are two replicates.Replicate1 contains five plates (P0_CD133_RGC_Plate1-5) and replicate2 contains three plates (P0_CD133_RGC_Plate6-8).Here P0_CD133_RGC_Plate1 is used as an example.
#1 Building the genome index of mm10
STAR --runThreadN 20 --runMode genomeGenerate \
--genomeDir STAR_index/mm10/ \
--genomeFast... |
5672f3ab09e3155e1150d7ab982e6158cc6eb11d6643c0a8dd7d028732595b1d | R | 5,748 | 171 | # R Corbett 2024
#
# Generate somalier principal component Kaplan-Meier survival curves and forest plots for PBTA ancestry cohort
# Load packages
library(tidyverse)
library(survival)
library(patchwork)
library(colorblindr)
# Define directory paths
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
analysis... |
fc23da3a7b01f28e645af9826e5a61090a0bd47d9ef3b76ca7d657143248bf15 | R | 5,750 | 131 | ##############################################
# smFISHHMRF Benchmarking - Kidney 10x Visium
# Authors: Mauminah Raina
##############################################
library(Giotto)
library(smfishHmrf)
# Embeddings - Sample SSL57
counts <-read.csv(paste0("/N/slate/mraina/REGNN_final/REGNN_SSL/result/Embeddings... |
c8b7aee30e9e9c93b9cddec05f99ebe88f3b6a3fe192e54cefd0dccd1bb32a78 | R | 5,774 | 143 | ---
title: 'Merge PBTA ancestry and histology data'
output:
html_document:
toc: TRUE
toc_float: TRUE
author: Ryan Corbett
date: "2023"
---
Load libraries and set directories
```{r load libraries and set directories}
library(data.table)
library(tidyverse)
root_dir <- rprojroot::find_root(rprojroot::has_dir("... |
c807e242b25f70ef19b5ad76a0f2ff34189b504f5931b761f4b36fa800570142 | R | 5,783 | 214 | ---
title: "RNAscope pseudobulk shuffle final iteration, cohort1"
author: "Sean Maden"
date: "2023-09-03"
output:
pdf_document: default
html_document: default
---
This notebook shows a pseudobulk shuffle experiment in which $S_{deconvolution}$ is shuffled.
# Variables
Descriptions of the variables used. Find ref... |
00c78291bc6003da3e02b52e48532ad32f02041eb87100b8be57c8c93fd9675f | R | 5,796 | 161 | rm(list = ls())
library(ggplot2)
library(cluster)
library(ggdendro)
library(dendextend)
library(factoextra)
Project_path = '/Users/panguoqing/Desktop/help/project'
Save_path = paste(Project_path, 'Data/CaseControl', sep = '/')
data_FieldID = read.csv(paste(Project_path, 'Data/CaseControl/data_FieldID.csv', sep = '/')... |
7ff71c33b4139a3dd55e6ecb650c91569cce520f43d9bec7c4314b0d8ab349d5 | R | 5,798 | 198 | ---
title: "Potential batch effect in the ordering effect of the capture sites"
author: "Joyce Hsiao"
date: 2015-10-02
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
```
## Objective
We inves... |
39841b5f13bfa4e406873b552875cd6e8497719c97133b2d9f7773bdc80dbd4b | R | 5,813 | 193 | #' Fit (generalised) linear random effects model robustly, checking for convergence
#'
#' This function fits a (G)LME. If the fit does not converge, it will try different
#' optimisers, and if still not converging will remove the integration over random effects.
#'
#' @param mdl_formula Complete model formula
#' @param... |
acf910dcd5e84a726288b7443c34db802abe31a58e15f9d9527fead8791fb003 | R | 5,829 | 170 | # R Corbett 2023
#
# Generate Kaplan-Meier survival curves and forest plots for PBTA ancestry cohort
# Load packages
library(tidyverse)
library(survival)
library(patchwork)
library(colorblindr)
# Define directory paths
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
analysis_dir <- file.path(root_dir, "a... |
0df612abe913efebcabbaf4518393544ec7ff51011c2645f83f72d45daaab16b | R | 5,834 | 190 | ---
title: "Subsample high coverage single cells"
date: 2015-07-13
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
```
```{r message=FALSE}
library("dplyr")
library("ggplot2")
library("cowplot"... |
0a63823fb8dd39fd53abcb61249576606895e53e2fc960ea08a660a4d5952e54 | R | 5,839 | 227 | ---
title: "01_run"
author: "Sean Maden"
date: "2023-10-17"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
libv <- c("ggplot2", "gridExtra")
sapply(libv, library, character.only = TRUE)
knitr::opts_chunk$set(echo = FALSE)
setwd("..")
setwd("..")
load("./env/01_pseudobulk/01_re... |
775858f45cabb7f9a6be42e4c1452cc0b0a7dc80d310221aa852e498ba240526 | R | 5,846 | 188 |
numCorGenes <- function(corr, corr_t, corr_min){
corr_thres = abs(corr) > corr_t
length(which(colSums(corr_thres) > corr_min))
}
getCorThreshold <- function(corr, init_corr_t=.2, numgenes_target=1500, corr_min=3, numcores=3){
corr_t <- init_corr_t
while( numCorGenes(corr, corr_t, corr_min=corr_min) >= ... |
2bae4843b349c743f53d3d21290391f56d43bc4f6352fdcd0a5813a3c27134ec | R | 5,862 | 180 | ---
title: "Coverage of endogenous genes - bulk reads"
date: 2015-02-23
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(cache = TRUE)
```
The sequencing coverage from the UMI pro... |
376388f46e65649a56d8a423e22cb3fff9adc23dcf9caf3cb6d1b9c92615dcbc | R | 5,873 | 206 | #' VR Threat ggplot theme
#'#'
#' @param base_size Base font size
#' @param base_family Base font familt
#'
#' @return A ggplot theme object.
#' @export
#'
#' @examples
theme_vrthreat <- function(base_size=14, base_family="sans") {
ggplot2::theme_classic(base_size = base_size, base_family = base_family)
ggplot2::th... |
f62a502e7c932d31c58a0e5efc4c6dfed6efbba71d3e6da1be7b8ca6ecb88c3a | R | 5,880 | 186 | ---
title: "Coverage of endogenous genes"
date: 2015-02-22
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(cache = TRUE)
```
The sequencing coverage from the UMI protocol should ... |
9ad8f8fd6a7e2fc09bb317ccf725cf5932c8f158315865a184aee2e82805db90 | R | 5,889 | 187 | ---
title: "Coverage of endogenous genes - single cell reads"
date: 2015-02-23
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(cache = TRUE)
```
The sequencing coverage from the ... |
f96dfab826287c16bb8cb316e5a12340039d82df8a31affb0a2d5ee3969dbe71 | R | 5,890 | 209 | ---
title: "Process sequence data - LCLs"
author: "John Blischak"
date: 2015-04-11
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
PoYuan performed the original troubleshooting of the UMI protocol with LCLs from individual NA19239.
One flow cell worked wel... |
13c1ca807b674abc5906bebb9bb9489a7c86d6d34409d2a926720300eb56c419 | R | 5,900 | 152 | #!/usr/bin/env Rscript
# Command line argument processing
args = commandArgs(trailingOnly=TRUE)
if (length(args) < 5) {
stop("Usage: dupRadar.r <input.bam> <annotation.gtf> <strandDirection:0=unstranded/1=forward/2=reverse> <paired/single> <nbThreads> <R-package-location (optional)>", call.=FALSE)
}
input_bam <- arg... |
567e77f3c95390e92a952e094738687abb8ffdbce848d0147220f0a107522b8e | R | 5,918 | 219 |
normalize_feature_values <- function(feature_values) {
unique_values <- sort(unique(feature_values))
if (length(unique_values) == 1) {
return(rep(1, length(feature_values)))
}
if (is.numeric(unique_values)) {
low <- min(unique_values)
high <- max(unique_values)
return(unlist... |
911ebd8806e2cd38aef61a25b413fecd32e026651df074e05b4716026aa973db | R | 5,943 | 184 | library(ArchR)
library(stringr)
library(ggplot2)
library(dplyr)
library(glue)
library(parallel)
library(Seurat)
library(GenomicRanges)
library(HDF5Array)
library(SummarizedExperiment)
library("tidyr")
library(argparse)
# Fig. 5c-f and ED Fig. 10c-h
args = commandArgs(trailingOnly=TRUE)
addArchRThreads(threads = 20)... |
fca56909fba54c6649193d072235e23795da584be3bd90146f5370574757a175 | R | 5,963 | 143 | # LRT for Timecourse FOXA2 OE RNA-Seq DE Analysis
# Fig. 2A
library(DESeq2)
library(tidyverse)
library(RColorBrewer)
library(ComplexHeatmap)
set.seed(7712)
library(clusterProfiler)
## Read in counts and sample table ----------------------------------------------------------------------------------------------------
... |
615388aecae4e5b5e40537cb259583a99ae757ecd13f51fed31a0b1825d62828 | R | 5,971 | 222 | ---
title: "Plate reader growth curves of GAT201 mutants in RPMI media combining NaHCO3 and cAMP"
author: "Liz Hughes & Edward Wallace"
date: "05/03/2024"
output:
html_document:
toc: yes
toc_depth: 2
---
```{r call_shared_functions_formatting, child = "shared_functions_formatting.Rmd"}
```
# Summary
This e... |
6350243626e90e619bb2915b3bbb5c1c3b80eed44338424bb573275b3c181b80 | R | 5,977 | 170 | ---
title: "Standardize with counts per million"
date: 2015-09-30
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(fig.width = 8, fig.height = 8)
```
```{r packages, message=FALSE... |
2a86526e1a28ada323f36a08e9ce3049b9f7cf89350bb4232b416136af4d317d | R | 5,993 | 131 | #This is some code adapted from steps 8.1 and 8.2 of the 2024 protocol:
#2024 protocol:https://www.protocols.io/view/brain-data-alchemy-project-meta-analysis-of-re-ana-yxmvmejkng3p/v1?step=8.2
#Megan Hagenauer, July 20, 2026
############################################
#This part is a little different:
#Starting wit... |
acb16ec4e890291d24814fd8c73b8bb09b339021e87c584b8a1a25a39ada00bf | R | 6,000 | 93 | ---
title: "Plot MCluster Methylation"
---
```{r Libraries, warning=FALSE, echo=FALSE}
require(ggplot2)
library(tidyverse)
library(RColorBrewer)
library(data.table)
library(pheatmap)
```
# Visualize Methylation Clusters
the clustering of regions by methylation values was done on accre using the script hclust.R. The fil... |
99ec1d4d7040fc560aced9199d94f73dd6a22f46a7575491b90e021dc081d86f | R | 6,012 | 194 | ---
output:
html_document:
toc: true
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
At lower number of sampled cells, reads appear to perform better with lower sequencing.
On the other hand, the molecules look as expected, higher sequencing depth le... |
a6b643c5c799f6b52aa7b769fb5dbed7c51f5062175673b33ba302b0f1cdc212 | R | 6,029 | 195 | ---
output:
html_document:
toc: true
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
At lower number of sampled cells, reads appear to perform better with lower sequencing.
On the other hand, the molecules look as expected, higher sequencing depth le... |
53cfec9868dec64571ede70b6c0014da3a7241f2ccec75fcf3c453dcaa86b3e5 | R | 6,069 | 159 | #!/usr/bin/env Rscript
"Compare the variance estimates of subsamples to all cells.
Usage:
subsample-variance.R [options] [--quantiles=<q>...] <num_cells> <seed> <exp>
Options:
-h --help Show this screen.
--individual=<ind> Only use data from ind, e.g. 19098
--min_count=<x> The minimum c... |
fe7ce00cd5d61d9c130902078abec30618b1ad9482a5a14af85901022543a88c | R | 6,101 | 97 | library(lme4)
library(sjPlot) #for plotting lmer and glmer mods
library(tidyverse)
# Analysis of mean global efficiency after random and targeted attacks
# 1. Mean was taken over sequential removal of random/targeted nodes
# 2. AUC was taken over selected density thresholds (0.3-1)
# 3. Here we perform
# - A. a mixed ... |
e6146eb0bfab0d433d4b34b2fbfbe0fdb21e3463c3899be05c0c0db6cbdd8762 | R | 6,102 | 169 | ---
title: "Account for technical noise measured with ERCC controls using Poisson GLM transformation"
date: 2015-12-22
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(fig.width ... |
52902ce6c91d89aa211536cbcdcd7d03d20d54b7e9984200078040ae76f95492 | R | 6,124 | 150 | #Code for aligning limma results from different datasets
#Megan Hagenauer
#July 27, 2023
###############################
#I tweaked this code from last year to align using NCBIid within species
AligningRatDatasets<-function(ListOfRatDEResults){
Rat_MetaAnalysis_FoldChange_Dfs<-list()
for(i in c(1:length(Li... |
60453bbb7faa91ac38a1baa359e375149acd328522d3696b67276843e54df4a3 | R | 6,131 | 158 | ##############################################
# BayesSpace Benchmarking - Kidney 10x Visium
# Authors: Chandrima Modak, Mauminah Raina
##############################################
#Load required packages
library(Matrix)
library(BayesSpace)
library(Seurat)
library(SingleCellExperiment)
library(ggplot2)
li... |
226a4d23919bb5fb38a71460275ce5626f64c68a437d985579f320688238a4c1 | R | 6,132 | 150 | library(ggplot2)
theme_Publication <- function(base_size = 14) {
library(grid)
library(ggthemes)
(theme_foundation(base_size = base_size)
+ theme(
plot.title = element_text(
face = "bold",
size = rel(1.2), hjust = 0.5
),
text = element... |
9c7f73c2628193129ddb6c47346db02bb9e916e40b94833bb54d2cb810e69b50 | R | 6,162 | 237 | ---
title: "MDACC RNASeq QC for Anders lab MelBrM"
author: "Marissa Lee, DCI Bioinformatics"
date: '`r format(Sys.Date(), "%B %d, %Y")`'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
code_folding: hide
number_sections: true
editor_options:
chunk_output_type: console
---
```{r ... |
963f041c8f2b9205f9c9be1ad789929de95856fff9e95ebe9b718f9d1ce5d01d | R | 6,176 | 126 | rm(list = ls())
library(ggplot2)
library(ggrepel)
library(ggbreak)
require(dplyr)
require(tidyr)
require(RColorBrewer)
Project_path = '/Users/panguoqing/Desktop/help/project'
Save_path = paste(Project_path, 'Data/CaseControl', sep = '/')
Threshold = 0.05
Threshold_1 = 0.01
Threshold_2 = 0.005
colors <- c('Blood count'... |
dcc3e88df8e669b8abe982bddbf27bb3431326d92dc3eedca497f897e4bc23b9 | R | 6,178 | 134 | ### Tripsacum greenhouse experiment
### Phenotypic analysis
### Code by Joel Swift 04-2024
library(tidyverse)
library(ggpubr)
library(lme4)
library(lmerTest)
library(emmeans)
# Theme set and Color Palettes
theme_set(theme_bw())
treatment_palette <- c("Waterlogged 24" = "#26008E",
"Waterlogged 4... |
f130fc459b6c367273fa078868822934cf338a51b8f89ce3515ee42eee834cdd | R | 6,184 | 195 | ---
title: "Create gene-x-sample count matrices and annotation file"
date: 2015-09-30
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(fig.width = 8, fig.height = 8)
```
```{r pac... |
f6e1b8776151dc1abd0d430e283b0528884205b0e0cb826ea980ab7c9bf6a1fd | R | 6,199 | 125 | library(tidyverse)
library(Signac)
library(Seurat)
library(patchwork)
set.seed(1234)
library(scCustomize)
library(EnsDb.Hsapiens.v86)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(BSgenome.Hsapiens.UCSC.hg38)
d14 <- readRDS("../single/simple_d14.RDS")
d2 <- readRDS("../single_d2/simple_d2.RDS")
d21 <- readRDS("..... |
accaffacc9d60de5fab7b34cfab0aadec0504301c80f7ef37fe3340073d4bc14 | R | 6,204 | 244 | # the whole structure
newargs <- commandArgs(T)
#1. input 1mb interaction frequency
#2. backbone structure
#3. 5kb result output directory
#4. 5kb pairwise distance matrix
#5. output result name
input_if <- read.table(newargs[1])
#load("/mnt/ufs18/rs-027/compbio/wanglab/haowang/Proj10_recon_3D/output/chr21_... |
a0ce316b49719afd636077f8a1824b9bed12d86f87e2594864c5dcd55bee8371 | R | 6,210 | 47 | ---
bibliography: refs.bib
---
```{r chunk-options, include=FALSE}
source("chunk-options-paper.R")
```
## Discussion
### Study design and sample size for scRNA-seq
Our nested study design allowed us to explicitly estimate technical batch effects associated with single cell sample processing on the C1 platform.
We f... |
912b98399dabd0cfec5cbca5445a3792001616a72bd5e64ea60c778098f3617e | R | 6,232 | 116 | #Adding RNA-Seq Library Size to the summarized experiment for a dataset
#Megan Hagenauer
#July 20, 2023
#Set working directory:
setwd("/Users/hagenaue/Dropbox (University of Michigan)/LaptopBackup_20221123/Teaching/BrainDataAlchemy")
#load useful code packages
library(SummarizedExperiment)
library(gemma.R)
library(pl... |
83e9dc6a852708d80e66e4be21bc8b616578f470a9e4248a83a61a9de51c61d8 | R | 6,241 | 192 | ---
title: "Using ngsplot to calculate coverage over ERCC genes"
date: 2015-03-15
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
opts_chunk$set(cache = FALSE)
```
Here I use [ngsplot][] to cal... |
230a5d33ddce0b133b76d3a38f0e8e54fb7ea777943c23fb71fad84c34712b28 | R | 6,242 | 175 | # creates final plots for the Simulations of the PRS GXE project - Rev1
# grab command line arguments
args = commandArgs(trailingOnly = TRUE)
print("Arguments received:")
print(args)
if(length(args) < 2) {stop("not enough Arguments received")}
baseLoc = args[1]
outputLoc= args[2]
plotName= args[3]
# Debug vars
# b... |
432da736d93e9100b7cde79ba22467de46a51fa46c806b847330fcfe838fec43 | R | 6,252 | 176 | ---
title: "Coverage of ERCC spike-ins"
date: 2015-02-22
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
```
This is a companion to the analysis of the [coverage of endogenous genes][endo].
It ... |
636268995e9a3eea47f0ddd26859d75d7a224206d3a6de44bc82c1555f52d8b9 | R | 6,281 | 144 | # LuNE RNA-Seq
library(tidyverse)
library(DESeq2)
library(ComplexHeatmap)
set.seed(7712)
# CCS1477 Treatment DEGs Fig 7F -----------------
# mr399 to mr 404
ff <- list.files(path = "/projects/b1126/Viriya/rna-seq/mr399_mr404/", pattern = "*ReadsPerGene.out.tab$", full.names = TRUE)
lune_counts.files <- lapply(ff, read... |
aa9faff6ca8bbef9c5bf4bb25a4f46c48859674eff47220b10903c68b5ba4951 | R | 6,290 | 180 | #!/usr/bin/env R
# Author: Sean Maden
#' musicParam-class
#'
#' Applies the MuSiC::music.basic() implementation of the MuSiC deconvolution
#' algorithm.
#'
#' @include lute_generics.R
#' @include referencebasedParam-class.R
#'
#' @details Main constructor for class \linkS4class{musicParam}.
#' @rdname musicParam-c... |
03f560cc706bf122ecae390a75d8e876d91e074340826f690fa93ef68d0d9da7 | R | 6,291 | 213 | ---
title: "Ordering effect of the capture sites"
author: "Joyce Hsiao & Po-Yuang Tung"
date: 2015-09-26
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
```
## Goal
We investigated potential o... |
2692b198feb02fbd36b746509f38eafa5046d4ced595481907634458cd5acc54 | R | 6,304 | 110 |
# Environment setup section
set.seed(0); rm(list = ls()); gc()
load_pkg <- rlang::quos(tidyverse, tibble, readxl, dplyr, tidyr, reshape, patchwork,
Seurat, ggcorrplot, ggplot2, rstatix, org.Mm.eg.db, org.Hs.eg.db,
rjson, purrr, grid, pheatmap, sargent, biomaRt, enrichp... |
045df089dfcb07e0d28d27c11fe7f8ad16a4f04915610bc06b2ed4d581e5a242 | R | 6,331 | 166 | rm(list = ls())
library(ggplot2)
library(lme4)
library(lmerTest)
library(dplyr)
library(reshape)
library(nlme)
library(permuco)
library(ggseg)
library(stringr)
library(mediation)
library(lm.beta)
code_location <- dirname(sys.frame(1)$ofile)
source(paste0(code_location, '/model_funs.R'))
source(paste0(code_location, '/... |
b34d7e7d28e25d252bef624da1302a943d993e479f9580682db86e04547638b2 | R | 6,342 | 181 | ---
title: "Per gene statistical comparison of CVs"
author: "Joyce Hsiao"
date: 2015-11-12
---
**Last updated:** `r Sys.Date()`
**Code version:** `r system("git log -1 --format='%H'", intern = TRUE)`
```{r chunk-options, include=FALSE}
source("chunk-options.R")
library(knitr)
opts_chunk$set(message = FALSE, warning... |
e399c5fea077baee8a34b1b0077d2728829277871239fd195c0b3e3d28889fa6 | R | 6,369 | 189 | #'@export
#'@title PTMwRapper
#'@description a function that takes in input a report from spectronaut,
#'candidates table, and condition setup. Run the PTMwRapper, and create
#'a MultipleExperiment out of it. The wrapper summarize PTM site by
#'their median abundance and run limma analysis out of it.
#'to process the... |
713c1fd3f1e727a6963d789077df0fd84d131051ff7b91aa580c0c49d6a7e10c | R | 6,373 | 171 | library(dplyr)
library(readxl)
library(ggplot2)
library(ggrepel)
library(enrichplot)
library(clusterProfiler)
library(org.Mm.eg.db)
setwd("")
# Figure_S14c, bar plot showing the protein numbers of quantified and identified proteins
{
rm(list=ls())
obj_list = readRDS(file = "ct_dataset_02.rds")
temp.df = ... |
7747ade4ea88a13d382557f86dd02a2faa8454018019e97dd38af076c473f34b | R | 6,373 | 124 | #Manuscript Figure 4c - phee syllabe duration or length plots and stats
phee_duration<- phee.analysis%>%
dplyr::mutate(subject= factor(subject))%>%
dplyr::rename(duration=`Delta Time (s)`)%>%
dplyr::filter(!(syllable %in% c("end", "start", "")))%>%
... |
7baef03c2c88f693d46b9d48c8b3d10f8176f430efbff6f50e1d62bda6dd9573 | R | 6,392 | 114 | ---
title: Heatmap for Motif Enrichment Across time
---
```{r, load libraries, warning=FALSE, message=FALSE}
library(tidyverse)
library(data.table)
library(RColorBrewer)
library(pheatmap)
```
Read in files with and without background
```{r , message = FALSE}
# (1) Get a list of files to iterate over
setwd("/Users/linds... |
d9b2ab75f97381098fe4ec53b62ca516fa5eaa9a6109440014332ddd538846e2 | R | 6,395 | 156 |
# Specify which metric to run this for (need to run twice to reproduce all plots)
#metric = "r"
metric = "rho"
get_mylist = function(subset,metric) {
# subset - list of TRUE/FALSE
# metric - "r" or "rho"
mylist = c(); pos = 0; at = c()
tests = c("Seq_Enc","LossFn","Optimizer","LRScheduler","Model")
for (tes... |
fb96b42d3d635014500a3fb6bb1ae19d5b673bd230ff9054a244090a75a8104d | R | 6,403 | 150 | #STReEF05_Rmodel
# This R code is developed for the manuscript 'Structural and
# Effective brain connectivity in focal epilepsy' by Jelsma et al.
# author: Susanne Jelsma
# date: October 2021
# calculate the jaccard index, construct a linear multilevel model
----------------------------------------------------------... |
0ac87a47c6354af7e6a8e2ef10af1d1a61ac5e4a9f9e86a78bf02da4aa429d7d | R | 6,435 | 134 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
4dde73a81355da5de3e1aff67166d74ae0e8b0c87e67b24f400f04cc133b7d2b | R | 6,435 | 134 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
428f3571301b2c63779d7ae9b60c5d97556e324adecdf57274c6e0445ec22194 | R | 6,440 | 131 | rm(list = ls())
Project_path = '/Users/panguoqing/Desktop/help/project'
Save_path = paste(Project_path, 'Data/CaseControl', sep = '/')
library(ggplot2)
library(reshape2)
library(dplyr)
library(cluster)
library(NbClust)
library(ggrepel)
loess_predict_all = read.csv(paste(Project_path, "Data/CaseControl/S5_loess_predict_... |
d12dedfeb136ae2cc64650b87a99f9b324228e2ae715785eeb1943b9bbd81b89 | R | 6,441 | 132 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
972225a934180235d284ab38f1945da6d8ef7ac546c22e9a66c11eb509527469 | R | 6,448 | 132 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
9961e1fea5f2faaa1668a86bc1ba8195a33c47b0566d004671f247e42540dbfa | R | 6,448 | 132 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
7ffd5635e6b03694d0315bb66e57cca30e4257e858378c1feda13de464307281 | R | 6,451 | 132 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
3bcae1d6a10aa0119c81d77d3473bbf08f54657d039563399502ecee55c54f3d | R | 6,454 | 134 |
## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
aed93ea9390590722117002ca4ba23770f7a1b779f9a7a48fe99fba704596d36 | R | 6,454 | 134 |
## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
4bd2f655828ae774b532c760c374e59579636ddbffc6e6ffc8cb973b97c98b3e | R | 6,459 | 135 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
712c414558f257bc6b7578a49fe9f24b5bd52ea19d38a9a7ca96719230c98cfc | R | 6,459 | 135 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
b0d56bb1e9ae1a898062f29ed4530cf7b1524e9b472c306a05c5cebcd78a2752 | R | 6,459 | 135 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
b5fb1fa31a3506354204645a7feec1cb8910698804ce2af2f6c1dba19c13d58d | R | 6,459 | 135 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
fa0a822a70ebc15287f33716d9cd4e5462d52ae9811770994598bab9e47cebf4 | R | 6,459 | 135 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
297925c718ecd1f399f50cfbc85b2c474b6a1fdc0f2e7118e75474b7c161420c | R | 6,460 | 207 | ---
title: "KO Coeff"
author: "SRomano"
output: html_document
editor_options:
chunk_output_type: console
---
# Load packages.
```{r setup}
library(phyloseq)
library(dplyr)
library(SIAMCAT)
library(tidyverse)
source("/Scripts/Model_coefficient_extraction.r")
set.seed(56987)
```
# KO
## Load metadata
```{r}
meta<... |
6f3c1c73e18a667211d75763c3c02b17d6fbcc1d7254fee262dfce4f7fb32cee | R | 6,463 | 133 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
7c299ce4af195abe3661be69650721235fc156b9c680d626a738c3c44d1ea67f | R | 6,467 | 133 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
f4ceba2265e970117f52d67e47542901e3d3e9f87540931bff4222197601e233 | R | 6,475 | 133 |
## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
713c531766fad003dd9c0c146a24c103a0dffc7b6331721591addd22700ff311 | R | 6,476 | 134 |
## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
7a29b0b033388a50a577a4244de73147429bae88cf7d609e79eb17010d8d2816 | R | 6,476 | 133 |
## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
53cf0d5cefc501b7556d2676e3688b0de233399718658be05c13ead5f745744f | R | 6,477 | 134 |
## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
dce2f51904580d2d9fbb50aaf3526825f57cdfdec2f82eaa1a8a58be9ef5723b | R | 6,477 | 134 |
## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
f7872bd870c13260dfbe2fd0033bb6d4a0be1424b6d1d0a82ddefd960ab15490 | R | 6,477 | 134 |
## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
de068dc1b56740c63f429030219f8e5dc4c02ebf59b181d539be347f35f05538 | R | 6,485 | 186 | # sif=/mnt/data1/public/singularity/dcibioinformaticsR-v2.1.sif
# wd=/mnt/data1/workspace/Anders/MelBrM
# rtmp=/mnt/data1/workspace/Methods/Containers/MissingPackages/v2.1:/rtmp
# singularity exec \
# --containall \
# --no-home \
# --bind ${wd}:${wd}:rw \
# --bind ${rtmp} \
# ${sif} \
# R
library(tidyverse)
library(op... |
a4870b96ac4caea0614f13e8a987e82f041775a4e69532a6b90f303a48c76195 | R | 6,511 | 175 | # IT neuron subtypes projecting to medial subnetwork
# Figure 2E 3D soma distribution of subtypes in medial pathway ----
Label_Subtype_IT_Medial = c(6,7,8,9,10,11,29,31)
Index_IT_medial = Index_IT[Label_Subtype_PFC[Index_IT] %in% Label_Subtype_IT_Medial]
#
# tmpColor = c("6"="#e41a1c", "7"="#377eb8", "8"="#4daf4a", ... |
e63b6eb829df418cb6e3b8b9389fac39b439efc40d26aac98c8bf135f1480511 | R | 6,531 | 252 | ---
title: "Plate reader growth curves of GAT201 mutants in RPMI and CO2-independent media"
author: "Liz Hughes & Edward Wallace"
date: "23/05/2023"
output:
html_document:
toc: yes
toc_depth: 2
---
```{r call_shared_functions_formatting, child = "shared_functions_formatting.Rmd"}
```
# Summary
Plate reader... |
810ca8e902b80cc4d915837de3630118c87dbb13aedd49d877c65b4997ac0354 | R | 6,577 | 354 | args <- commandArgs(T)
#1. fraction of basis to use
#2. penalty
#3 distance threshold
#4 input pairwise distance matrix
#5 input interaction frequency matrix
#6 output file name
#7 output file dir
library(parallel)
library(mgcv)
library(Matrix)
#given a pairwise distance matrix, calculate gram matrix... |
c3aa7352dc8355f3eef58d4f6a42442c5b65331b19fa9e77d055b1cec667a739 | R | 6,582 | 128 | #Manuscript Figure 4d - phee syllabe RMS amplitude plots and stats
phee_amp_1<- phee.analysis%>%
dplyr::mutate(subject= factor(subject))%>%
dplyr::rename(duration=`Delta Time (s)`,
rms_amp =`RMS Amp (U)`)%>%
dplyr::filter(!(syllable %in... |
6a1c2c333bbdf83d3a570d6165d84fd878196e1088318cceb937633c5ece16cb | R | 6,583 | 89 | #' flamingo.main_func
#'
#' Main function of FLAMINGO, a wraper for all steps
#' @param hic_data_low Input Hi-C data in .hic format, .mcool format or sparse matrix format.
#' @param file_format foramt of the input hic data. Could be .hic, .mcool or sparse matrix format.
#' @param domain_res Size of the domains in bps,... |
4dc9fd73ffbe817571f8362a31d9026437e1122b7c40a64cf7fc2c6e1cb9ab43 | R | 6,627 | 177 | ---
title: "Batch Importing ASC Files"
author: "Austin Hurst"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Batch Importing ASC Files}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r results="hide", message=FALSE}
# Import libraries required for t... |
073735d3ab48309a6edb13f1614e2250e91e648decf1fef05aac844ab8299146 | R | 6,654 | 206 | ## Testing ___________________ import_segmentation_ij __________________
## Testing slice objects
test_that("reading segmentation info from a slice object works", {
skip_on_cran()
skip_on_ci()
s <- slice(slice_ID = "1_4",
slice_directory = test_object_path("slice"))
expect_message(
s <- impor... |
a922255cb6eedf490d95ab28301b67fbe1ee783557d7694073efc66011f584ad | R | 6,719 | 140 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
559b33b8837bb86ce39df534cf6537ed7a4dbf90c60ded3cea290771675679c1 | R | 6,720 | 140 | ## ----setup
list.of.packages <- c("dplyr", "vegan", "BiocManager", "mlr3")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages,
lib = .libPaths()[2],
... |
449bf23a312a62c66167f0273265495568d0524384ce65918f4e1a664d67c56b | R | 6,735 | 263 | ---
title: "Making a grid of kmers"
output: html_notebook
---
To make an image that summarize the distribution of k-mers for a sample, we will need to assign each kmer to a pixel. We used tSNE to place kmers in a 2D plane based on their sequence similarity. Here we will move kmers around as needed to assign each one u... |
e81e553944c9edc3653fa0230afbf084b0dfb328784c0c22efb3e77d1645031f | R | 6,746 | 139 | #This code document includes the code for a function that is designed to run a basic meta-analysis of Log2FC and sampling variance values using our previously generated objects MetaAnalysis_FoldChanges & MetaAnalysis_SV
#Megan Hagenauer
#July 25 2024
######################
#Installing and loading relevant code packag... |
587b308764938bcd5eb39b5f38dde371c337a527809fc46320cc2295d9f9b58b | R | 6,781 | 117 | library(tidyverse)
library(Seurat)
library(patchwork)
set.seed(1234)
options(scipen = 999)
stepped <- DiscretePalette_scCustomize(num_colors = 24, palette = "stepped")
stepped <- stepped[c(7,6,5, 16,15,14, 12,11,10,9, 24, 24)]
source("../../../../../multiome2/atac_integration/epi_loose/meta_highlight_manual.R")
sourc... |
efac92829a54b2c77ad16b350b8823c8171bda262f98a2571846fca48d6e9c4b | R | 6,810 | 222 | test_that("add_noise returns an n x p array", {
g <- add_noise(p = 10, 1000)
expect_equal(dim(g), c(1000, 10))
})
test_that("add_noise returns a matrix", {
g <- add_noise()
expect_true(is.matrix(g))
})
test_that("add_noise returns genotypes with MAF in maf_range", {
tol = 0.02
maf_min = 0.05
maf_max =... |
322360d00985d690aaa1991e0f7cd806b79e86bb87dd7f51464e6db0cef6e833 | R | 6,832 | 135 | #' Fit the null logistic mixed model and estimate the variance ratio by a set of randomly selected variants
#'
#' @param plinkFile character. Path to plink file to be used for calculating elements of the genetic relationship matrix (GRM)
#' @param phenoFile character. Path to the phenotype file
#' @param phenoCol char... |
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