sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1214d50e18913037731fa5c6ffb3fa5d8309357bb94ca2fc25dbfbf6f94dd989 | R | 19,446 | 746 | # =========================== #
# 3. APOE interactions: NSHD #
# =========================== #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(mice)
library(purrr)
library(tidyr)
## Read in data ----
# Principal components
pcs <- read... |
9b561dd50865cba4b646bbc3c08162f22fc3ab36f6bc56a1e5871656b8262821 | R | 19,529 | 442 | ### LC snRNA-seq analysis
### Build SCE from raw count matrices; perform nuclei calling
### qrsh -l bluejay,mf=32G,h_vmem=34G -pe local 2
# (which points to this script with `R CMD BATCH`)
### Adopted from https://github.com/LieberInstitute/DLPFC_snRNAseq/blob/main/code/03_build_sce/build_basic_sce.R
### Initiated ... |
c4377ee2c2b9c48a5e9c019d61aa814d0998947f55aa345d254dae80b88c796a | R | 19,533 | 476 | ############################################
### Additional analysis taking 100 random
### multiplications of the volumetric data,
### establishing a measure of robustness of
### our main conclusions in light of the
### great variability that might be possible.
### This volumetric variability might come from
### unacc... |
bd348f7e6f42eed38be71b948367705f67f2e1ed751e8e05ecba604ec37a9cfb | R | 19,549 | 450 | library(GSVA)
# library(singleCellTK)
# source("sergio_analysis_functions.R")
library(BiocParallel)
library(Seurat)
# library(SeuratData)
library(dplyr)
library(ggrepel)
library(ggplot2)
# to be bench marked
library(gsdensity)
library(CelliD)
# library(escape)
library(VAM)
library(DescTools)
library(GSEABase)
library(A... |
dbbd44b900569501f60a880d5008734afb8626d0ae36d2ef9947f5257d87dbb0 | R | 19,670 | 543 |
############################################
### Analysis carried out in response
### to Reviewer #3. Specifically, these
### analyses seek to explore strepsirrhine
### versus haplorrhine scaling and how these
### groups interact with the main PGLS by
### dummy coding these groups.
####################################... |
4003137fdbf7f7860b495d90e27a73ec3387e4b6ddd02241461f05d35626036e | R | 19,694 | 444 | # import libraries
library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)
library(ggpubr)
library(clustree)
library(dendextend)
library(randomForest)
library(biomaRt)
library(circlize)
library(rjson)
library(ComplexHeatmap)
library(readxl)
library(biomaRt)
# import integrated and subclustere... |
1710a26955da2330302bf35cc527782db9001a7446cda15e7557ea227cfe877e | R | 19,752 | 522 | ---
title: "Compiling Expression for SVM"
author: "Stephen P Plassmeyer"
date: "8/6/2020"
output: html_document
---
Update 10-26-2020
* Compiled expression by AS2, aggregated plasmid count, normalization.
Updated 07-19-2021
* Use LMM BC random effects to adjust elementwise expression measurements
```{r setup, includ... |
b39cc4293b759e436f555384a8b436606b606532e24d37aa806abb8df7044239 | R | 19,788 | 675 | # ========================== #
# 2. APOE interactions: UKB #
# ========================== #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(mice)
library(purrr)
## Read in data ----
# Serostatus
serology_dat_genqc <-
readRDS("data_d... |
93ce7995d4f25f619acf485590287fab05620a74281a001655f516dc212c691e | R | 19,820 | 585 | arr <- function(from, to=0) {
if (to==0) {
for (n in tect.neighbours[from,]) {
if (n != 0) {
to = n
arrows(tect.positions[from,1], tect.positions[from,2], tect.positions[to,1], tect.positions[to,2],length=0.1)
points(tect.positions[from,1], tect.positions[from,2], col="Blue", cex = 2... |
1691a7da6bf468f877d162a7e959e5992b74611b08bded281afea183caf386c1 | R | 19,861 | 668 | # This code is to plot Fig.S7
# WANG. 20-Feb-2024
# Remove all objects created before to prevent clusing
rm(list=ls())
# Load required packages
library(dplyr)
library(ggseg)
library(ggplot2)
library(tidyr)
library(patchwork)
library(readr)
library(reshape2)
# Set the working directory to the path where your file... |
9dc0377bdbe11009dc95dbc18b49f350d6a2b27baac1997e7e2cfc07e15cba68 | R | 20,036 | 596 | ---
title: "LDSC"
author: "Amy Hicks"
date: "05/08/2022"
output:
html_document:
code_folding: hide
toc: true
toc_float: true
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(corrplot)
library(devtools)
library(DT)
library(here)
library(ggplot2)
library(GeneOv... |
324fd9f76382717595fbb7580a34d6e2d375033abba84d2d7135e237084a796b | R | 20,085 | 433 | #!/usr/bin/env Rscript
# version: 1.4.0
script_version <- "1.4.0"
cat(paste("Tractor Script Version :", script_version), "\n")
# Changelog
## v1.4.0 - 2024-05-10 (Enhanced File Handling, Performance Improvement)
### - Added support for compressed (gz) hapcount/dosage and phenotype files.
### - Improved file readin... |
b52bb687c3ffb656ad378139bbbc1c7286968d1c071be18a39ac2d2c3fd04b63 | R | 20,089 | 721 | # This code is to
# 1. load the data and add more variables
# 2. calculate and plot the average ordered
# 3. calculate the percentage change of the thickness
# 4. linear regression to time effects
# WANG. updated 09-Aug-2024
# WANG. 04-March-2024
# Remove all objects created before to prevent clash
rm(list = ls())
l... |
006587a17b5837969da77295efcd264ab9f0d54f1884b31e2a871875d03c4f84 | R | 20,098 | 652 | ########################################################################
# LC snRNA-seq analyses: cluster identification using known marker genes
# Lukas Weber, Jun 2023
# including code from Matthew N Tran
########################################################################
library(here)
library(SingleCellExperi... |
4de5e427528beedd4e3ea290a1cc808029ef7f9016b28f800a5ac7245885c645 | R | 20,194 | 359 | ---
title: "Figure 6"
author: "Stephen P. Plassmeyer"
date: "5/05/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../../../code/mpra_tools/mpra_tools.r')
library(ggpubr)
library(MASS)
library(ggbeeswarm)
theme_set(theme_bw(base_size = 6))
```
... |
08c551640730ae0accc0691d85f58c9b54d53efa7bdbd068cba2aa4979cb0dca | R | 20,202 | 454 | ---
title: "Filtering Method Development"
author: "Stephen P Plassmeyer"
date: "7/16/2020"
output: html_document
---
* Updated 08-05-2020, adding grid search
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../mpra_tools/mpra_tools.R')
library(progress)
```
### Load Data
```{r}
compiled_cou... |
8c9e912f5d770b6873a775dd8939158ed59a21509a03def2ae29bdc89d7ad723 | R | 20,347 | 320 | ---
title: "Deconvolution of bulk RNA-seq data with granulator"
output:
BiocStyle::html_document
author:
- name: Vincent Kuettel
affiliation: Autoimmunity Transplantation and Inflammation Bioinformatics, Novartis Institutes for BioMedical Research, Novartis Campus, Basel 4056, Switzerland.
- name: Sabina Pfister
... |
30b4db5e8890f10c45afefe8bc6b6d7fc18a3c1f0471220049d40727fdc8a717 | R | 20,354 | 561 | #!/usr/bin/env Rscript
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
library(gridExtra)
#increase RAM usage so the futurized Seurat functions...
#can access larger global variables
options(future.globals.maxSize = 4000 * 1024^2)
#set all directory paths
outdir <- paste0("/gladstone/bioinfo... |
487734601da01edfb509a3fdb8e8e5f9333ab6a6266430f3cb44a980022ca44b | R | 20,364 | 438 | ---
title: "Ranges of Expression, Translation Efficiency, and Effect Sizes"
author: "Stephen P Plassmeyer"
date: "7/29/2020"
output: html_document
---
To Do's:
* Figure out the common set of elements for which a result can be calculated in each condition and use that set of of elements across all conditions! (some ele... |
f50e1f63741a3ceecf25435b79658a397f70d37219eddf0d00846f3f67ae854a | R | 20,385 | 472 | #run locally on Ayushi's laptop
#load required packages
library(Seurat)
library(dplyr)
library(ggplot2)
library(EnhancedVolcano)
library(scales)
library(tools)
#set all directory paths
basedir <- paste0("~/Dropbox (Gladstone)/YH_NK01-Results/",
"10_HMGB1i_snRNAseq_results/06_seurat_analysis_batch1_b... |
d9edd51aa924c07366fb1a4f5a958885a7b94f1a9b75754a919edfb703449ee8 | R | 20,491 | 360 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/mm10/hic/config.R'))
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,... |
21740fbddc1541aab8fd2b79f5ac518b6a0832617fa21aa693c5c1f1d78d1571 | R | 20,598 | 272 | suppressPackageStartupMessages(library("dplyr"))
suppressPackageStartupMessages(library("patchwork"))
suppressPackageStartupMessages(library("ggplot2"))
suppressPackageStartupMessages(library("RColorBrewer"))
suppressPackageStartupMessages(library("cowplot"))
suppressPackageStartupMessages(library("reshape2"))
suppress... |
ad6950be00c99ae0fddab29d66d0bfce7cb2876a2372c29731ec6f295c6c5cb8 | R | 20,662 | 476 | ---
title: "Primary_GCN_ukbec"
author: "Amy Hicks"
date: "15/12/2021"
output:
html_document:
code_folding: hide
toc: true
toc_float: true
df_print: paged
---
```{r setup, include=FALSE, results = FALSE}
library(CoExpNets)
library(CoExp10UKBEC)
library(biomaRt)
library(tidyverse)
library(knitr)
librar... |
46a582dd28281ce8a0a42052b586591012c1ff48476d66fa4dbf1e892dadce63 | R | 20,670 | 710 | # This code is to
# 1. load data and add more factors
# 2. calculate the percentage change
# 3. linear regression, meta, and equivalent
# WANG. 04-April-2024
#updated 26-Aug-2024
# Remove all objects created before to prevent clash
rm(list = ls())
library(dplyr)
library(ggseg)
library(ggplot2)
library(tidyr)
library... |
0e81ed2314c5dd92eb8ae8b22895473c9ff998c9e079d893552c14b9a3225379 | R | 20,701 | 392 | ### Load libraries
library(clusterProfiler)
library(enrichplot)
library(ggplot2)
library(gprofiler2)
library(org.Mm.eg.db)
library(GOSemSim)
library(compEpiTools)
BiocManager::install("compEpiTools")
BiocManager::install("DOSE")
BiocManager::install("org.Mm.eg.db")
### define result paths
# Create desir... |
e7499073e167473e2c4da51e74637bfd4789e7976abd33f9081c834b1e3f936c | R | 20,842 | 402 | ### Load dependencies------------------------------------------------------------------------
library(tidyverse)
library(caret)
library(ggplot2)
library(ggpubr)
library(ggpattern)
library(corrplot)
library(reshape2)
library(AnnotationDbi)
library(limma)
library(rstatix)
library(dorothea)
minNrOfGenes = 5
dorotheaData =... |
adcfef601bc0a7e9a16681c7e792f83843e9a1ebbbfa5b0ee6e40309007628f5 | R | 20,853 | 452 | # R version 4.1.2 (2021-11-01)
# R skript to reproduce the results and graphs for manuscript: Mueller et al,
# Soundscapes and artificial intelligence provide powerful tools to track biodiversity recovery in tropical forests
#
# data are soundscapes collected on 43 plots in Ecuador along a recovery gradient in t... |
a0d1a1d442f2ef61303b71fd00b50c198060a3eb9beb06b561ebd6d227d91baf | R | 20,879 | 437 | ############################################
### Distribution of scaling in brain volumes
### and all phenotypes of interest (incl. ratios)
### per sex (and also age for humans). Although
### these analyses are definitely liited by
### the small sample size, they may show whether
### in humans and chimpanzees, ratios ... |
eee5ea1ecbd6a2e31fc0e8ad2a9dd6bb73898c41ec6e4c6afb6e3e1ac343fd16 | R | 20,967 | 297 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
caacad48f040c6715a9565e83971d782b1ad18ea21139852194dff05b4191748 | R | 20,989 | 480 | ---
title: "Calling Hits for 410 Data"
author: "Stephen P Plassmeyer"
date: "4/22/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../code/mpra_tools/mpra_tools.R')
library(ggsignif)
library(ggthemes)
library(ggpubr)
```
### Load Data
```{r}
compiled_counts <- ... |
2cafffd1cc97afbb0bc692de13b0106be689d68153ef11d8ddadc86e2533f9d5 | R | 21,012 | 541 | ############################################
### Analysis carried out in response
### to Reviewer #1. Specifically, these
### analyses seek to explore whether the main
### result holds when we remove specimens that
### may have suspiciously low or high cerebellar
### volumes.
###
### We scanned the literature specific... |
a00f508c97712c401e200d3d188a654fd3cad6c63413cc5f12d8f08bc5201ddd | R | 21,142 | 312 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
5a5291588cb8a6c82adf55f7627f189197bbd1a26afe955367f7169a0a105bf4 | R | 21,144 | 301 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
e61ad220c7bb81a93fb81edd84b8fd8b1fd7d6a26909b636c06748275ddb4c8b | R | 21,145 | 767 | # This code is to
# 1. load the data and add more variables
# 2. calculate and plot the average ordered
# 3. calculate the percentage change of the volume
# 4. linear regression, meta, and equivalent
# WANG. updated 24-July-2024
# WANG. 04-March-2024
# Remove all objects created before to prevent clash
rm(list = ls()... |
58748be00413a5a5735f95978d16b613ad592b87f490355c6ffd5ab909c3541d | R | 21,413 | 434 | ---
title: "Ranges of Expression, Translation Efficiency, and Effect Sizes"
author: "Stephen P Plassmeyer"
date: "11/10/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
library(ggsignif)
library(patchwork)
```
# Overview
... |
eb9f91857b7e34501cece0b8e77ff9ece69749be03645eee336826913b68f30d | R | 21,443 | 319 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
d46783aacacbb6fb9867b80112c071b0ff1410a56053ad3fd74b48e0a67fa8a1 | R | 21,510 | 492 | ---
title: "Comparing Modelling Approaches"
author: "Stephen P. Plassmeyer"
date: "10/31/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../../code/mpra_tools/mpra_tools.r')
library(ggpubr)
library(MASS)
library(ggrepel)
library(preprocessCore)
theme_set(theme... |
e223a3e7ff301698a59996b0620458462defa594ffeb309e0bae8f3a118bc6a4 | R | 21,514 | 471 | ---
title: "Primary_GCN_gtex"
author: "Amy Hicks"
date: "15/12/2021"
output:
html_document:
code_folding: hide
toc: true
toc_float: true
df_print: paged
---
```{r setup, include = FALSE, results = FALSE}
library(CoExpNets)
library(CoExpGTEx)
library(CoExp10UKBEC)
library(biomaRt)
library(tidyverse)
l... |
61a278acb7d8ef256b74712b26d2ad1723e00c4eddeb113c9027f36bb39e5f2c | R | 21,532 | 397 | library(tidyverse)
library(ggplot2)
library(ggpubr)
library(ggridges)
library(rstatix)
library(ggrepel)
# Analysis for classification of protection-------------
all_selected <- data.frame()
for (i in 1:10){
fold_lrt_res <- data.table::fread(paste0('LRT_results_cpa/LRT_latent_embeddings_global',i,'_after_results.csv'... |
33d98cd166eef58cd22198d76b065bde68bcd8a5286019f5ec82a2e6fca1f946 | R | 21,630 | 372 | ---
title: "Calling Hits on N2a 410 Data"
author: "Stephen P Plassmeyer"
date: "Last Updated: 11/18/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
```
Updates 08-05-2020:
* Moved threshold selection to the qc markdown... |
389c90e41b9996ec655e625173e2ddf9581ad16bee01b306778ac9961a93431c | R | 21,679 | 832 | ---
title: "LC analysis pipeline: segmented regions"
author: "Lukas Weber"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
number_sections: true
toc_depth: 3
toc_float:
collapsed: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, cache = TRUE)
```
# Introduction... |
d294ac0cb5c4b5537ca9c5740eec3e5e89e0393a00fcca8e152c90ad677aca82 | R | 22,021 | 470 | ---
title: "Comparison of Cross Experiment Reproducibility"
author: "Stephen P Plassmeyer"
date: "04/01/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source("../../code/mpra_tools/mpra_tools.r")
```
# Verify if Re-consolidated data agrees with old consolidation of MPRA... |
9eec90013441beeb7cac185b058216e7f68a6bdac224b31a9ad422d424808a4d | R | 22,139 | 443 | library("gamlss")
pop_norms <- read.csv("~/r_files/pop_norms_demos.csv")
########### BOYS @@@@@
boys_white = subset(pop_norms, pop_norms$Race == 'White' & pop_norms$Gender == 1)
boys_asian = subset(pop_norms, pop_norms$Race == 'Asian' & pop_norms$Gender == 1 )
boys_black = subset(pop_norms, pop_norms$Race == 'Blac... |
1c45dcf1eaf589c62a24f78258a8571be18713c493a1c7244025a6ecd70cf590 | R | 22,164 | 894 | # =================== #
# 1. Analysis: SABRE #
# =================== #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(mice)
library(purrr)
## Read in data ----
source("scripts/functions.R")
# Serostatus
dat_serostatus <- readRDS("da... |
b4422c366833224d890f044409f3fb7041098038f2576cb7595b5aa5dab460ec | R | 22,176 | 542 | plot_time_series <- function(input, klass, koos, plot_nr, max_y_value) {
# Change linecolor in case of dark mode
if (input$dark) {
line_color="grey80"
} else if (input$dark==FALSE) {
line_color="black"
}
############## Adjust X-scaling if necessary ##########
#Adju... |
238e5324afedd90f5f609daf80aa0d1c490cf8c488f4fda660559af2a0ea7ce4 | R | 22,183 | 666 |
## pulled from here, and then tweaked slightly: http://www.biostars.org/p/18211/
# CODE
heatmap.3 <- function(x,
Rowv = TRUE, Colv = if (symm) "Rowv" else TRUE,
distfun = dist,
hclustfun = hclust,
dendrogram = c("both","row", "... |
9622f104d870790f9e1faf50b251b9d6a903435df11aca34c3c40b53d298cfaf | R | 22,206 | 490 | ---
title: "Format and Compile Counts"
author: "Stephen P Plassmeyer"
date: "Updated 02/15/2022"
output: html_document
---
Updated compilation of counts, same data as of 03-21-2020, but with updated counts which were prepared with the updated barcode counting script from 05-10-2020. The updated script first per... |
867487e3b429d1ca21869f17fd6593c2271123b7ef24eeded699ebc6a1b4272b | R | 22,359 | 943 | # ================== #
# 1. Analysis: NSHD #
# ================== #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(mice)
library(purrr)
## Read in data ----
source("scripts/functions.R")
# Serostatus
dat_serostatus <- readRDS("data_... |
5923d4f35acdda7dbe5f746be09ec3ee54e53503a08bfa330e5436047c5f9fe4 | R | 22,648 | 478 | ---
title: "Figure 3"
author: "Joe Raymond"
date: "April 5, 2021"
output: pdf_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(tidy = TRUE)
knitr::opts_chunk$set(eval = TRUE)
knitr::opts_chunk$set(fig.width = 8)
knitr::opts_chunk$set(fig.height = 4)
knitr::opts_chunk$se... |
e517dce7d02fc5129adeac2353255319d75ff8fe8f94c33dd6dbf17a63d40fdd | R | 22,656 | 409 | ##############################
# Load relevant Libraries ----
##############################
require(ggplot2)
require(stringr)
require(data.table)
require(readr)
require(mgcv)
require(gratia)
require(parallel)
require(visreg)
#######################
## Read in HCPD data ##
#######################
# hcpd_data <- read.c... |
e9c35f45c72c9a75c8085b803199ebe6903eafb7c32e7f37c76374c3a3329923 | R | 22,664 | 633 | #' Run the DAFi algorithm
#'
#' Method to run general DAFi workflow.
#' Will transform the data via FCSTrans and scale each channel to the range 0-4096 by default.
#'
#' @param rawfcs_path path to raw fcs file
#' @param config_path path to DAFi cell gating hierarchy configuration file (inclusion gates)
#' @param re... |
6cf0cb5cb21341de2cdcd3cd981bf5ef349d12218771f6d7e5948df3b0faf96f | R | 22,670 | 828 | # ================= #
# 1. Analysis: UKB #
# ================= #
## Set up ----
filepath <- ""
setwd(filepath)
## Load libraries ----
library(data.table)
library(dplyr)
library(magrittr)
library(mice)
library(purrr)
## Read in data ----
source("scripts/functions.R")
# Serostatus
dat_serostatus <- readRDS("data_der... |
7411700cb70dc58642ce103fdeb8ab6cad85f34474077e5041815a3eb997f82f | R | 22,672 | 466 | ### ----------------------------------------------------- ###
# Ancestral state reconstructions
# Roberto Toro, Katja Heuer 2018
# Script adapted by N. Magielse
# for inclusion of cerebellar and ansiform area data - 2022
### ----------------------------------------------------- ###
#-----------------------------... |
11508e5904dcf50f04252570cd2de041433346bff7d2b0a93e6fa88962ca132d | R | 22,731 | 473 | library(tidyverse)
library(ggplot2)
library(ggsignif)
library(ggpubr)
## Load all cmap data---------
genespace <- 'best_infered plus landmarks'
cmap <- data.table::fread('../preprocessing/preprocessed_data/cmap_all_genes_q1_tas03.csv',header = T) %>% column_to_rownames('V1')
cmap <- t(cmap)
geneInfo <- read.delim('..... |
be2ae42de0ac00628984c292bb2e210d834bb67a6c40ade8d018500c8623395f | R | 22,747 | 364 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
357ece2c5a4848b086d4a666a15136beabdd9284f4b7f584b81a2d31fbabdb61 | R | 22,787 | 387 | # This code is to plot the Fig. 3
# WANG. 28-April-2023
# Updated 28-Aug-2023
# Set the working directory to the path where your files are located
setwd("/Users/wang/Desktop/Research_projects/BBSC/MRS/Output_v2_4/QuantifyResults")
# Remove all objects created before to prevent clusing
rm()
# Load required packages
... |
6ec4c73951a423bc05b949fae8080ce1e516409b6b35565e18036b80de980fb0 | R | 22,902 | 566 | ## packages for data table processing
library(here)
library(tidyverse)
library(rlang)
library(writexl)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
## main differential gene expression package
library(SingleCellExperiment)
library(DelayedArray)
library(HDF5Array)
libr... |
dcaed74049c3fb7e4a42911657bb1bbcbe51eb31d2d00f3e76ed67121fc69c47 | R | 22,968 | 544 | ##### load libraries #####
library(data.table)
library(kimisc)
library(GenomicRanges)
start_time <- Sys.time()
##### get commnad line arguments #####
args <- commandArgs(TRUE)
filedir <- args[1]
if(grepl("\\/$", filedir)==F){
filedir <- paste0(filedir, '/')
}
##### get config parameters #####
curfile <- thisfile()
... |
0ba11fc3f7c1a8f22c61f0bb4b1772ea75b63707d6b81e0ef6954f0da4430c62 | R | 23,453 | 720 |
setwd("/nfs4/chaozhang/proj/Neuron/Aritra/merFISH/script/")
#devtools::install_github("tractatus/wholebrain")
library(wholebrain)
match_CCF = function(slice,
slice.col = 1,
ABA_coronal = 50,
ABA_color = "left",
x.scale = 8.5, y.sc... |
8d546234cfca316597a9e4c63a2893d38e6b8e200cb1e7b2e1f0eb152870e27e | R | 23,509 | 447 | ---
title: "Ranges of Expression, Translation Efficiency, and Effect Sizes"
author: "Stephen P Plassmeyer"
date: "11/10/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
library(ggsignif)
library(patchwork)
```
# Overview
... |
cf244c62140ba0e68d0a9005ecacec4132a6e4ad73aa88ccb25ba7c5ab62582b | R | 23,608 | 643 |
#This script uses a filtered version of the patch seq data set from the following publication. We compare this data to our own, and use it for classification.
# Nathan W. Gouwens, Staci A. Sorensen, Fahimeh Baftizadeh, Agata Budzillo, Brian R. Lee, Tim Jarsky, Lauren Alfiler,
#Katherine Baker, Eliza Barkan, Kyla B... |
fca4e4325b5f09e58f9c18316b5b54f397f61c71988aec8010217d577afd1876 | R | 23,697 | 441 | ---
title: "Ranges of Expression, Translation Efficiency, and Effect Sizes with LMM"
author: "Stephen P Plassmeyer"
date: "Updated 07-11-2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
library(ggsignif)
library(patchwork)
... |
eda9009a40b624e327051c80a783d9c5f9c85f0fdbab8ab132d676e0ace0e992 | R | 23,713 | 230 | '''
# Citation: Under Revision
# Email: qususu@cibr.ac.cn
# Date: 2023-05-11
# Brief introduction:
Multiple-level analyses were conducted after getting the primary clusters as shown in the Methods. Main steps were processed by using R v4.1.0,
such as DEG analysis, re-cluster of some clusters and so on.... |
fc8152cb43decb53c0b92c2305fd21ae499582ceefdfbe0e31715434a34235b9 | R | 23,799 | 598 | ---
title: "Tony's Barcode Effects Simluation"
author: "Anthony Fischer"
date: "3/22/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r Calling Packages, include=FALSE}
packages <- c("data.table","tidyverse","plyr","progress","parallel","doParallel","f... |
24d0f28077a09ad876c0df13920b39419408acc947e86c114883c8ba69b84ca9 | R | 23,902 | 498 | library(tidyverse)
library(gg3D)
library(ggpubr)
library(patchwork)
library(ggplot2)
library(colorblindr)
library(viridis)
library(RColorBrewer)
library(lsa)
#Process function to add condition ids and duplicate ids
pca_visualize <- function(embbedings,
dim=2,
scale... |
67d5ff3fe9eaf7dc6889df7231a92bfbe85c42670afac08e3a2b4bb3a17c3429 | R | 23,905 | 503 | library(tidyverse)
library(ggplot2)
library(ggsignif)
library(ggpubr)
### Performance of different size inputs-----------------
data <- data.table::fread('../results/MI_results/diffsize_allgenes_10foldvalidation_withCPA_1000ep512bs_a375_ht29.csv')
colnames(data)[1] <- 'fold'
results <- data[,c(1,4,5,6,7,8,9)]
data_... |
812b6b64f904f192bbaa57adfd985ce9590b6daa82cea18059ec770c1b65cb04 | R | 23,955 | 646 |
#This script computes and save DE genes between groups for later reference
## ----receive input from nextflow----------------------------------------------
#Get parameters
args <- commandArgs(TRUE)
path<-args[1]
file<-args[2]
print(path)
timepoints<-substr(file,12,nchar(file)-17)
#Load standard package list
sourc... |
e192c402eeb2c2c2d8c0ef97095934d70951d9faacabc08612b390b367d769e5 | R | 23,995 | 526 | ### LC snRNA-seq analysis
### Marker detection with t-test
### qsub -l bluejay,mf=76G,h_vmem=80G
### Initiated: MNT 07Apr2022
library(SingleCellExperiment)
library(scater)
library(scran)
library(scry)
library(batchelor)
library(BiocParallel)
library(jaffelab)
library(gridExtra)
library(here)
library(sessioninfo)
... |
3b4d1557a3f7eff63e900fc1c42fb227550c37c1ca49be449459e618c2e8ee29 | R | 24,128 | 743 | ########################################################################
# LC snRNA-seq analyses: cluster identification using known marker genes
# Lukas Weber, Oct 2022
# using code by Matthew N Tran
########################################################################
library(here)
library(SingleCellExperiment)
... |
d55b287f5f39463eb561983d23b8a4be8df31f5ed8a17cc57cd07b9dbf4a234f | R | 24,161 | 572 | ############################################
### Analyses carried out in response
### to Reviewer #3. Specifically, these
### analyses seek to explore ape versus non-
### ape scaling and how these groupings
### interact with the main PGLS by
### dummy coding these groups.
############################################
#... |
bf3a5a0aa623dd6e2dcc3d14e58296d7f5bfb70c3d5ca1b073ec4219c5b98585 | R | 24,220 | 492 | ---
title: "455 IVT Timecourse Analysis"
author: "Stephen P Plassmeyer"
date: "9/16/2020"
output: html_document
---
To Do:
* Check how sensitive the half-life measurements are to the library half life estimate
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../code/mpra_tools/mpra_tools.R'... |
39794279c7bcee19b2d1391c04be8f9a799d47ffa81ea8240584bb27c630ac51 | R | 24,257 | 535 | # import libraries
library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)
library(ggpubr)
library(clustree)
library(dendextend)
library(randomForest)
library(biomaRt)
library(circlize)
library(rjson)
library(ComplexHeatmap)
library(readxl)
library(biomaRt)
library(factoextra)
library(cluster)... |
ede75db5ebb976bb2506931a63f4317e8726597283b13552a09788a3cece709c | R | 24,310 | 688 | # Libraries
snapshot <- "/lab-share/RC-Data-Science-e2/Public/DST_software/pipeline_R_snapshot/r4.1-20220803-bch-dst-v1/"
.libPaths(snapshot)
library(Seurat)
library(SeuratDisk)
library(ggplot2)
library(Nebulosa)
library(patchwork)
library(pbapply)
library(ggrepel)
library(harmony)
tpColors <- tenColors <- c('#a9d6e5'... |
3bdd0a96e7a86b3bae8e2388b01256ea1339e03b3dcb4310bd027b3dae315a29 | R | 24,358 | 327 | library(ggplot2)
library(ggalluvial)
library(ggpubr)
library(ggtern)
library(tidyverse)
library(gridExtra)
pops<-c("CLM","MXL","PEL","PUR","ACB","ASW")
anc1_prop<-c(82,43,32,138,881,765)
anc2_prop<-c(277,500,761,150,5,41)
pulses_false_true<-c("false","true")
##functions
plotsfunction <- function(run){
#####ANCEST... |
598cd31648b7b9da037b3ad30283e4e6c4f65fac52a4f1eba6adf34caf0f7d19 | R | 24,694 | 520 | ---
title: "N2a 410 MPRA QC"
author: "Stephen P Plassmeyer"
date: "10/27/2020"
output: html_document
---
* Update 10-27-2020: Re-ran with AS2 aggregated plasmid count normalization for transcript abundance and ribosomal occupancy
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/m... |
8dfeb6723d6e8edd5ec09e3ba31162dae6f5b03a043056a3d4f2cebaaa1face7 | R | 24,771 | 480 | #### Set working dir and source some functions ####
setwd("/Users/derekvantilborg/Dropbox/PycharmProjects/MoleculeACE")
source('Experiments/figures/R_scripts/vis_utils.R')
#### Data prep ####
# Import data
benchmark = read_csv('/Users/derekvantilborg/Dropbox/PycharmProjects/MoleculeACE/MoleculeACE/Data/results/Molec... |
d6c91baf111aeed06220ae9ccc30d4b9a801edba632b982e083681260d1cc8e5 | R | 24,809 | 438 | rm(list=ls())
gc()
####################
## Load libraries ##
install.packages('pheatmap')
install.packages("lmerTest")
install.packages("robustHD")
library(pheatmap)
library(lmerTest)
library(gamm4)
library(nlme)
library(psych)
library(mice)
library(ggplot2)
library(data.table)
library(dplyr)
library(readxl)
library(p... |
985461e4e9b451e576a3aaf3239d3a116224b9f58c7e0ba21043ec9ac244bfe9 | R | 24,833 | 498 | #This script is testing the development of white matter covariance networks in terms of Fiber density and cross-section (FDC) metric
#using Generelized Additive Models (GAMs). All GAMs include sex, mean DWI framewise displacement and number of DWI bad slices
#(index of scan quality) as covariates. Included at the end ... |
5669a4e0e95acdf45e0517f22cb8bb1cffc27505bcf10c3808d63b5a9bebf691 | R | 25,016 | 442 | # This script is investigating whether white matter microstructure (FDC) underlies differences in executive function
# using Generelized Additive Models (GAMs). We used the Executive Efficiency factor to describe executive function performance.
# All GAMs include sex, mean DWI framewise displacement and number of
# D... |
684a65abba31198f3ddc833879bf18ac027cdf22f824d49c95b0124d323b503b | R | 25,502 | 546 | #TODO: grey underplotting for faceting does not work on "cluster" (monocle default),
#but works on anything in pData
#TODO: This is always underplotting. Probably makes figures memory twice as large
monocle_theme_opts <- function()
{
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
the... |
a5fd32022c9d030e2c91c8ff00e7c6cd16104898f9786e20a64aa39d30f13937 | R | 25,549 | 500 | rm(list=ls())
gc()
####################
## Load libraries ##
install.packages('pheatmap')
install.packages("lmerTest")
install.packages("robustHD")
library(data.table)
library(dplyr)
library(readxl)
library(pheatmap)
library(lmerTest)
library(robustHD)
library(RColorBrewer)
library(MuMIn)
library(lme4)
library(sjstats... |
2bb7aecddceadd7bf2bb2f008b08efd56a453d8f3ff74b250a0209793a9dc152 | R | 25,576 | 575 | ####################################################################################################
### Set Up for PAVMOD fmri
# last modified on June 2023
####################################################################################################
#------------------------ SETUP PATH -----------------... |
8ca0ec6cb7c0bed934b41b260bf9828c79a506ee5401260b86886bb0be3f8cdc | R | 25,583 | 640 | setwd(file.path(path.expand(dirname("~")), "Downloads"))
library(tidyverse)
library(lme4)
library(car)
library(zoo)
library(ggplot2)
library(qqplotr)
library(patchwork)
library(interactions)
library(effects)
library(MuMIn) # for effect size
library(sjPlot)
library(dplyr)
library(performance)
library(lmtest)
library(cor... |
e24e1292d61f566606017b4c4cdeb58d6ddd2820eff45ee9ac4179dad4a713e3 | R | 25,677 | 534 | ### MAGMA with locus coeruleus (LC) snRNA-seq populations
# - Compile gene set analyses (step3) results
# - Explore gene-level stats for provided summary stats
# MNT 09Jun2022
library(rtracklayer)
library(GenomicRanges)
library(jaffelab)
library(SingleCellExperiment)
library(fields)
library(RColorBrewer)
libra... |
fb53c0328136ec3531c4c4899b0b3681fb4359c17a6fad573fb9828aa6b48425 | R | 25,831 | 463 | library(Seurat)
library(SeuratData)
library(dplyr)
library(qpcR)
library(scRNAseq)
library(stringr)
sparsity <- function(matrix){
sparsity <- sum(matrix == 0)/(dim(matrix)[1] * dim(matrix)[2])
return(sparsity)
}
pang.markers <- as.data.frame(read.delim("PanglaoDB_markers_27_Mar_2020.tsv"))
cm2 <- read.csv("Cell_mar... |
21fcadd39b55f89a827b35e5033101d5eb63389d8c81106290e75b9316131bdc | R | 25,843 | 486 | # Loading necessary libraries
# Load required libraries
# Seurat: For single-cell RNA sequencing (scRNA-seq) data analysis and visualization
# ggplot2: For creating elegant data visualizations
# dplyr: For data manipulation and transformation
library(Seurat)
library(ggplot2)
library(dplyr)
# Merge all datasets into a ... |
848bfbfce4e7c5544bc492c543291064d8d86eab1429ed3b049b299c802d8353 | R | 25,894 | 625 | ---
title: 'MES Timecourse JS: original + resequencing'
author: "Jared Slosberg"
date: "8/17/2020"
output:
html_document: default
pdf_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r init, echo = T}
.libPaths("/home/jared/R/x86_64-pc-linux-gnu-library/4.0/")
#source('... |
950bcaabb796dc73f7789cd110c42340289f746c943d76839ff56c2a60c585a3 | R | 25,987 | 398 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
88534c26710d1f806da702ba3c899ee9d2a7e05444f75f12faeca01bbdea89a2 | R | 26,082 | 757 |
#' Co-plot version of gisticChromPlot()
#'
#' @description Use two GISTIC object or/and two MAF objects to view a vertical arranged version of
#' Gistic Chromosome plot results on the Amp or Del G-scores.
#'
#' @author bio_sun - https://github.com/biosunsci
#'
#' @param gistic1 first \code{GISTIC} object
#' @param g... |
64ddc5cf6348a55263a2bf27a91973927d8f70c4e159c66683261b87e578f02c | R | 26,127 | 404 | #!/usr/bin/env Rscript
# =============================================================================
# BCR repertoire analysis (Immcantation framework)
# Meningeal (dura mater) B-cell responses during chronic Trypanosoma brucei
# infection: heavy-chain immunoglobulin repertoire of spleen and dura mater from
# naive a... |
91ac04799520b8a4e90c9b5060fc8f5780fe8123008a7a9d5538fbc10122750c | R | 26,347 | 378 | source("src/lib/Genome.R")
library("ensembldb")
library("EnsDb.Hsapiens.v86")
library("EnsDb.Mmusculus.v79")
## Genome assembly defaults to hg38 (human), mm10 (mouse) and rn6 (rat)
.RERUN <- FALSE
if (.RERUN) { warning("Rerunning everything...") }
## EnsDb.Rnorvegicus.v79 is rn5, so we cannot use what bioconductor pr... |
208b251e6d0cb60361022aac53326e5d0a01ee7eecba560d11054704c9fa27b8 | R | 26,382 | 1,141 | ```{r}
source("../../src/setup.R", echo = FALSE)
```
<!------------------------------------------------------------------------------>
<!------------------------------------------------------------------------------>
# I. Data
***
```{r}
(Casp_P8_data <- load_casp_data() |> map(\(x) filter(x, Stage == "P8")))
Casp_P... |
40e4ff8c1b1a2fc3c0413cbfc502639104f25f2e61170c7c1679d1a538b5e47f | R | 26,382 | 1,141 | ```{r}
source("../../src/setup.R", echo = FALSE)
```
<!------------------------------------------------------------------------------>
<!------------------------------------------------------------------------------>
# I. Data
***
```{r}
(Casp_P4_data <- load_casp_data() |> map(\(x) filter(x, Stage == "P4")))
Casp_P... |
e6f82def291e6050b7bd25e1092c46edc3fc99b0ab76d4bba343d1e4f90d3928 | R | 26,470 | 706 | # import libraries
library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)
library(ggpubr)
library(clustree)
library(dendextend)
library(randomForest)
library(biomaRt)
library(circlize)
library(rjson)
library(ComplexHeatmap)
library(readxl)
library(biomaRt)
library(igraph)
# import integra... |
9a5f1ce1b27bfa8be3ab9011f50d696d03a03f7f7ce176990e2e48f21c092dd8 | R | 26,589 | 365 | ## Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2022, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2022, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to the terms of... |
6d6591270d6041619a8658c92281675b55c8e4dacdcfbf018fb5c59b4246faec | R | 26,630 | 434 | ---
title: "Associations_PFN_Cognition"
author: "Arielle Keller"
date: "3/21/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
options(width=250)
library(tidyr)
library(dplyr)
library(ggplot2)
library(lsr)
library(lme4)
library(lmerTest)
library(mediation)
library(ggcorrplo... |
b570ab9ff79ef73e1f39f82e858d6c8e3136303b3c40dc902c7c089bb85c55d5 | R | 26,654 | 759 | # This function gets ancestral DNA sequences and calculates the number of convergent
# and divergent changes for each pair of species (from baseml model)
getChanges_tips_baseml <- function(res) {
tr <- res@phylo
idx <- combn(tr$tip, m=2)
changes <- sapply(1:ncol(idx), function(x) {
#x=1
nodeID <- getMRCA(tr, id... |
d14f3df591f2d7bc10f5e02e9e6b65927e72579227d41b42ffd10941d757306a | R | 26,748 | 730 | ###############################################################################
# Neural substrates of parallel devaluation-sensitive and
# devaluation-insensitive Pavlovian learning in humans
# fMRI analysis
# last modified on MAY 2023
##################################... |
5a1a7e2b1eabb3ae341bc913926f263f0208440d5129d97dee4e9fc271fa048f | R | 26,756 | 460 | source('scripts/config.R')
source('scripts/aux_functions.R')
source('scripts/plot_functions.R')
source(paste0(main_dir,'results/hg38/hic/config.R'))
source(paste0(main_f,'scripts/main_functions.R'))
source(paste0(main_f,'scripts/aux_functions.R'))
source(paste0(main_f,'scripts/plot_functions.R'))
source(paste0(main_f,... |
68940b69491b1b663376786d39a38cf568c6136fb990bf7920b2c71e8325ddd4 | R | 27,389 | 1,028 |
### retrieve all genes/entries for a particular annotation type ###
#' @title FUNCTION_TITLE
#' @description FUNCTION_DESCRIPTION
#' @param keytype PARAM_DESCRIPTION
#' @return OUTPUT_DESCRIPTION
#' @details DETAILS
#' @examples
#' \dontrun{
#' if(interactive()){
#' #EXAMPLE1
#' }
#' }
#' @seealso
#' \code{\link[... |
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