sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
ae69349eb0fce365a18471d75ab82406fdaf5345a420bb9b3071edfd8d6605f7 | R | 5,146 | 88 | library(data.table)
library(dplyr)
library(moloc)
library(foreach)
library(doParallel)
moloc <- function(i){
Phe1_moloc_dat <- Phe1_dat %>% filter(Phenotype == moloc_pairs[i, exposure])
Phe2_moloc_dat <- Phe2_dat %>% filter(Phenotype == moloc_pairs[i, outcome])
com_SNP <- Reduce(intersect, list(Phe1_moloc_dat$SNP, ... |
46d8ebc584c400a17a58ec07d4675c3f9b7e002d1748b331887085ac03cb303c | R | 5,147 | 88 | library(data.table)
library(dplyr)
library(moloc)
library(foreach)
library(doParallel)
moloc <- function(i){
Phe1_moloc_dat <- Phe1_dat %>% filter(Phenotype == moloc_pairs[i, exposure])
Phe2_moloc_dat <- Phe2_dat %>% filter(Phenotype == moloc_pairs[i, outcome])
com_SNP <- Reduce(intersect, list(Phe1_moloc_dat$SNP, ... |
8d8cf194ac02661b459b0919702632e4481819bae5a67c100f2834cbabbb077b | R | 5,154 | 135 | #!/usr/bin/R
# ------------------------------------------------------------
# Make a figure to show our cohort along multimodal reference.
# and add ABC scores to it as well
# Updated 11/29/2023
# ----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste... |
d284b7d67fe97002582956a83469b91bfd50d66694f9d96da8ddb037e2172869 | R | 5,155 | 107 | ## ---------- Script -----------------
##
## Purpose: perform differential isoform usage analysis on mouse rTg4510 ONT and Iso-Seq targeted datasets
## Adapted tappAS DIU analysis scripts
## https://github.com/ConesaLab/tappAS/blob/master/scripts/DIU.R
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
## --------... |
cbfff22115ae617b5c6d1c1d275d11df0bd48aaf3563e8027467d07c21e9980b | R | 5,167 | 120 | library(Seurat)
library(ggplot2)
library(sctransform)
library(ggpubr)
library(DoubletFinder)
library(loomR)
library(patchwork)
library(SeuratData)
library(SeuratDisk)
data <- Read10X(data.dir = "/data/breadhea/qwang178/NOMIS/scRNA/Drop8/cellranger_outputs/sample_1/outs/filtered_feature_bc_matrix/")
data <- CreateSeura... |
928c1a8f6ca43676c670594d01e493a1c5c4b3b61087d7be59176a547c032b2a | R | 5,172 | 168 | #!/usr/bin/R
# -------------------------------------------------------------------
# Compute pathway enrichments for differences subtypes in a celltype:
# Updated 12/02/21
# -------------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'lo... |
22c4f65f5f2217ee9bd05b4c42b5194175408f5d82440351d562bd7fd438d16a | R | 5,175 | 150 | # common functions for reproducible reporting
# - Bayesian framework R/brms interface to Stan
# - editable vector figures in PowerPoint
# - estimates P-values with emmeans
#source("/opt/fix-rstudio.R")
#Sys.setenv(TZ="CET")
require("tidyverse")
require(readxl) # normal Excel input
require("openxlsx") # for normal w... |
9c4795d03701f316be5065901058f47c068c60739af33e455105b96a6340bd11 | R | 5,182 | 129 | library("Seurat")
library("tidyverse")
library("ggpubr")
library("purrr")
library("biomaRt")
############# step1: load the Single cell experiment objects #############
setwd("./")
Seurat.SNC.Single.Double <- readRDS("Step1.DataIntegration/Seurat.SNC.Single.Double.rds")
setwd("./Step2.QC")
## QC for doublet identific... |
91247728bb21d5cc035d270785510c257ab061776a4a5f264b8aa897b5b8111f | R | 5,184 | 154 | #!/usr/bin/R
# ---------------------------------------------------
# Plot composition differences in a heatmap for Exc:
# Updated 03/16/2022
# ---------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
library(ggplo... |
abf3f6e35d7544076816c46be2ee1f643689a7fb35c92ffd7dbfa22702ffd15b | R | 5,186 | 159 | #!/usr/bin/R
# ---------------------------------------------------------
# Calculate and plot the module enrichments for covariates:
# Updated 11/28/2021
# ---------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, '... |
724d1a9d8d8e2aa84c26d009bf9929c36d643a2c473bdb22ce85c39ec1e0b262 | R | 5,188 | 175 | #' Expand scalar, vector, matrix or similarly shaped `hyperSpec` object to matrix
#'
#' Helper function for `hyperSpec` arithmetics.
#'
#' A row- or column vector or
#' a matrix of size 1 x n or n x 1 or
#' a hyperSpec object with 1 row or 1 wavelength will be expanded
#' along the size-1-dimension.
#'
#' Dimensions th... |
d6e34ceb7ce12d56796c4a7569fb1dd1804071b1d1e30fd19fd2b791b3c2c279 | R | 5,193 | 180 | #!/usr/bin/R
# ---------------------------------------------------
# Plot selected points on the glial (astrocyte) umap;
# Also plot module expression for these points
# Updated 01/25/2023
# ---------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta"))... |
89a99da32f2d3e315520c32e493e8287b65db153399e5df1c2776c6049e820ae | R | 5,196 | 134 | # ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
# Linnarsson lab Database analysis
# Patrick S. Hosford, December 2023
# ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++
#Required R packages
require(Seurat)
require(tidyverse)
require(umap)
require(ggplot2)
require(loomR)
requi... |
54f565db87eb0edbd768074f93442544de425ae6d9c3a163e1d84d1619e15418 | R | 5,200 | 173 |
#' helper function to create/update project configuration json file in gpa output directory
#'
#' @param gpa a glm_pipeline_arguments object
#' @param job_sequence a list with named logicals indicating whether
#' that part of the pipeline was submitted or not
#' (currently fi... |
65cc815865c256643f5e0fe32d219d12018800b09034eb034f38c9c8e570e772 | R | 5,200 | 164 | #!/usr/bin/R
# -----------------------------------------------------
# Look at DEGs for fatty acid metab genes for LA expts.
# Updated: 09/18/23
# -----------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidy... |
344eb4fb10704c7acf9db69e6fd13a21a4ed7a4e94b0f3838dad230046e63a02 | R | 5,206 | 103 | library(Seurat)
library(dplyr)
library(patchwork)
setwd('/scRNA')
#####
# Integrated analysis
#####
data.dirs = c('CN6_E10-5A/mm10_cur','CN6_E11-5A/mm10_cur',
'scRNA_CN7_e10-5/rep1_6-12-17/negOnlyCells',
'scRNA_CN7_e10-5/rep1_6-12-17/posOnlyCells',
'scRNA_CN7_e10-5/rep3_9-1... |
bbb664a309b4ed15ab6a6615f9e9772d5040d1b450d606ae6b72a40a8ed7aa4e | R | 5,209 | 140 |
################################################################################
# SCRIPT TO CALCULATE THE AVERAGE OF THE DIFFERENT BRAIN VOLUME REGIONS BETWEEN
# BOTH HEMISPHERES, LEFT AND RIGHT (LH & RH), FROM A .CSV FILE.
################################################################################
# Co... |
142e1c641ea781ac8533a823053195c839aeead0c5d082e04a5b6c4f4ea12c88 | R | 5,214 | 140 |
#' archstabms_ddPCA_singlebackground_linear_model
#'
#' Linear model from single mutant effects (in WT background) from shallow ddPCA data.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param ddpca_outpath path to ddPCA data (required)
#' @param position_offset residue position offse... |
5fd9d7daef353d31a3be26f791c97df092401ffbb4896cb76614f4019eb52140 | R | 5,220 | 176 | #' @name DEPRECATED-read.spc.Kaiser
#' @concept moved to hySpc.read.spc
#'
#' @title (DEPRECATED)
#' Import functions for Kaiser Optical Systems `.spc` files
#'
#' @description
#'
#' These data input functions are **deprecated** and they will be removed in
#' the next release of \pkg{hyperspcc} package.
#' Now f... |
1c555ff1bf0787a355ff4cbd3b8a10d68627c0a69cb1af43d768cf319095ca81 | R | 5,221 | 109 | cct_combine_pvals = function(test_results, cct_vtypes = c("missense", "spliceai")) {
cct_combined_pvals = tibble()
for (vtype in cct_vtypes) {
this_cols = grep(paste0(vtype, "-"), names(test_results), value = TRUE)
# use score pvalue to checkt if the p-value from the combined vartype + plof test... |
3da554bd20e0ff66610f0432299cb5231913265067c4a0fede30a0c6410ff3af | R | 5,243 | 175 | ### --------------------------------------------------------------------------
###
### plot_c - plot timeseries, concentration, ...
###
### C. Beleites
#' Calibration plots, timeseries plots, and depth-profiles
#'
#' Calibration- and timeseries plots, depth-profiles and the like
#' `plot_c` plots intensities of a ... |
dec2ca6d8fd3e4113bd980b81551bdfbe85a2a7950d6adf5ee6b276e688c17f2 | R | 5,255 | 105 |
# check samples where DMP is upregulated
p1 <- RRBS_completebetas %>% filter(position == "chr18:32431597") %>%
reshape2::melt() %>%
left_join(., RRBS_Phenotype[,c("Sample_ID", "Genotype", "Age_months")], by = c("variable" = "Sample_ID")) %>%
filter(Age_months == "8") %>%
mutate(value = ifelse(is.na(value),... |
5d68b3068a730122c5ba45944c90b1c5987fa4fdcef7a982e74c42cc2f898acf | R | 5,258 | 174 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
print("Libraries loaded, starting now")
# Get the passed parameters
option_list = list(
make_option(c("-m", "--modelNotes"), type="character",
# default="_fix_Anatomical_Site_rand_Donor_HM10UMI=100_mito=10Scrub=0.2noPackerMNN=sampleNameK=... |
d85b24725cf364315ccad3545412c03d439d92f0b94d639822362207747bd067 | R | 5,260 | 120 | ## infercnv
## get the genome position of each gene from gtf file
wget https://github.com/broadinstitute/infercnv/blob/master/scripts/gtf_to_position_file.py ./
###################################################### Run_infercnv.R begin
rm(list = ls())
library(Seurat)
library(infercnv)
setwd("Couturier_scRNA/GBM_res")
... |
e24ad12252ba264fb51c2c52fbf35a569355377b9e89028da669474d123360f1 | R | 5,261 | 136 |
#' archstabms_ddPCA_linear_model
#'
#' Linear model from from shallow ddPCA data.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param ddpca_outpath path to ddPCA data (required)
#' @param position_offset residue position offset (required)
#' @param load_mochi_data python (required)
#... |
e8b919c2b100d8eb421d7d209903226b9ce4aff06a803949ce4560dc81f32d67 | R | 5,265 | 170 | #!/usr/bin/R
# ------------------------------------------------------
# Run Nebula on e4 interaction with selected genes only:
# - Used for Oli-M7 + Opc-M9 test
# Updated: 06/27/23
# ------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_m... |
36fc274ce72ca12f89f12690ecad6f6d17a813ae6e1567d03ebaf302e4a4b72b | R | 5,277 | 110 | # STAT_MODEL_TYPE2.R
# This is the main code that runs the second type of model, namely to compare the
# cortical activity during a freezing event to a voluntary stop and to a successful
# event of the same type as the freezing event (e.g., turning freeze vs stop vs successful turn).
# INPUT:
# - data_FOG.csv (create... |
06186543bf9fd3ecf26940773b15a5fa413e4c4ab05f0238529739b9b1867585 | R | 5,278 | 146 | #!/usr/bin/env Rscript
# create OPNMF brain parcellations by projecting W matrix onto volume space #
args = commandArgs(trailingOnly=TRUE)
### Arguments ####
# 1: Species of OPNMF output - "chimip" or "human" #
# 2: relative path and name of GM mask used in creating OPNMF data matrix and OPNMF solutions #
# Utilisi... |
a6f69992d612a28e64029d7244497d1d39cff2e6aac1e893969099da0e97c03e | R | 5,278 | 175 | #!/usr/bin/R
# --------------------------------------------------------------
# Score cells in all regions/EC for now using the internal DEGs:
# Updated: 12/31/2021
# --------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.... |
e94921e41e82ea4cef0eada841d1f92081438db36c7cae4ccc1df0a6c78d5ebe | R | 5,282 | 169 | #!/usr/bin/R
# ---------------------------------------------------------
# Auxiliary functions for cross-module matrices / networks:
# Updated 11/30/2021
# ---------------------------------------------------------
library(cbrbase)
library(tidyr)
library(viridis)
library(ComplexHeatmap)
library(circlize)
library(igraph... |
2fa6075ee0ddc4d231c21ac77ea64a5f64ac6e9081872920078b18b499da012d | R | 5,287 | 138 | #' plot
#'
#' @description This function plots a summary of the results of the model.
#'
#' @importFrom dplyr filter select
#' @importFrom magrittr equals
#'
#' @param x A tibble including a cell_group name column | sample name column | read counts column | factor columns | Pvalue column | a significance column
#' @par... |
ba3e0b5c0b326e752245171d8064af7b975c3db34237384ff7f74106e4d1256e | R | 5,309 | 132 | setwd("/Volumes/projects_herting/LABDOCS/Personnel/Devyn/ABCD-AIR_RSI/OHBM 2023/analyses/")
library(tidyverse)
library(readr)
library(dplyr)
library(psych)
library(skimr)
long_df <- read_csv("/Volumes/projects_herting/LABDOCS/Personnel/Devyn/ABCD-AIR_RSI/OHBM 2023/dataset/20230428_long_df.csv")
long_df <- long_df %>%... |
c5def56dfe4d564faf3dce2d6dc2818ad746f11502fec6c782d3604a29b8e9f2 | R | 5,312 | 192 | library(jsonlite)
library(lme4)
library(lattice)
library(lmerTest)
library(MuMIn)
library(performance)
library(rstudioapi)
library(xtable)
library(emmeans)
library(car)
library(tibble)
library(dplyr)
library(ggplot2)
# ----------------------------------------------------------------------
# Configuration and data loa... |
fe2d30977dae5e53beeb1a269158e4fee7e325cad9d93ccebfef63f0c31f6a05 | R | 5,314 | 129 |
#' archstabms__saturation_library_design
#'
#' Saturation combinatorial mutagenesis library design.
#'
#' @param pdb_file path to PDB file (required)
#' @param pdb_chain_query query chain id (default:A)
#' @param pdb_chain_target target chain id (default:B)
#' @param colour_scheme colour scheme file (required)
#' @par... |
e592e2b5e69c362cb80f4620dd034b0803a6b4e92dfc23173fd6d0b76e2c8f86 | R | 5,319 | 98 | ## ---------- Script -----------------
##
## Purpose: project-related functions for characterising ONT mouse targeted transcriptome datasets
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
## ----------Plot colours-----------------
label_colour <- function(genotype){
if(genotype == "WT"){colour = wes_palette(... |
7b6ccd9f1021bd6e10f88b9486993737d7af8d325790709e340fa87424a0a6e0 | R | 5,325 | 140 | #!/usr/bin/R
# ----------------------------------------------------------
# Why does AG have more up DEGs than other regions (4a?)
# Updated: 10/25/23
# ----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
l... |
d31d7b64de082ad904dfd14fe2a8baca8ebb171cc3da75658aaff570045856d3 | R | 5,331 | 165 | #Second Script to Launch
load("sctransv2_pd_cond.Rdata")#output of "1_raw_PD_processing.R"
#install.packages("HGNChelper")
lapply(c("dplyr","Seurat","HGNChelper"), library, character.only = T)
system('wget https://raw.githubusercontent.com/IanevskiAleksandr/sc-type/master/R/gene_sets_prepare.R')
system('wget https://... |
fa57953fa0cfb195d5b48b1bc47aa7c2c4e3599d15d40da4e2617dd6017b777a | R | 5,339 | 88 | library(ggplot2)
library(ggseg)
library(ggpubr)
library(extrafont)
thickness_plot_0_1_data = read.csv('[your directory]/thickness_0_1_t.csv', check.names = FALSE)
thickness_plot_0_1_data = thickness_plot_0_1_data[thickness_plot_0_1_data[, 'p-value'] < 0.05, ]
thickness_plot_0_1_data$importance = -log10(thickness_plot_... |
b634ec31122b423330f4376cbb3e8e201c4e93429021cb1e02f03d2d80461667 | R | 5,360 | 159 |
#-----------------------------Identification of tissue-specific and developmental stage-specific expressed genes---------------------------------------
#
#1. It was performed in separate periods to screen out tissue-specific expressed genes
setwd("F:\\ATAC\\processFiles\\RNA-tissue_specific0711")
library(dplyr)
... |
5aec43fe35d640bfaebcb9d43d2426bd662d0147d986913b91582bb6d168b2de | R | 5,361 | 133 | setwd("./Projects/SNCSynuclein")
library("Seurat")
library("tidyverse")
# load the Seurat object
Seurat.SNC.final <- readRDS("Step5.FinalClustering/Seurat.SNC.final.rds")
# normalize the data before plotting
DefaultAssay(Seurat.SNC.final) <- "RNA"
Seurat.SNC.final <- NormalizeData(Seurat.SNC.final, assay = 'RNA')
##... |
3d56969f33019dde81398f451cdb1951967357243705d0655edcb2ef3ba9772a | R | 5,364 | 142 | ## HiC-Pro
## Copyleft 2015 Institut Curie
## Author(s): Nicolas Servant, Eric Viara
## Contact: nicolas.servant@curie.fr
## This software is distributed without any guarantee under the terms of the GNU General
## Public License, either Version 2, June 1991 or Version 3, June 2007.
##
## Plot R1/R2 Pairing results
##
... |
ee799ad3aa3040645148053bee0859de84ff1a3da37df9d5d69f616cd093648c | R | 5,370 | 147 | #!/usr/bin/R
# -------------------------------------------------------------------------
# Determine consistent up / down patterns for each covariate
# Determine unique subtypes of DEGs for each pathology / AD measurement
# Plot coeff. + clusters of these DEGs across EC neurons
# Updated: 12/06/2021
# -----------------... |
bb91453faf0d3356698a8424f74f22b8d65b830de8bbad0850f55a6f95dddff1 | R | 5,371 | 156 | # For the expanded sample, conduct EFA and CFA and retrieve scores and loadings.
# Adapts code from:
# https://github.com/adolphslab/HCP_MRI-behavior/blob/master/intelligence.ipynb
# Output:
# dubois_efa_load.csv EFA loadings.
# dubous_efa_scores.csv EFA scores.
# dubois_cfa_load.csv CFA loadings.
# dubous_cfa_scores.c... |
8ff69d034fd8c5cbbc7a7cf1944ef419451489cd812784e980dfa4d473605c82 | R | 5,388 | 142 | #!/usr/bin/env Rscript
# Helper script to clean VCF stats output from collectQC.sh
###Set master parameters
options(stringsAsFactors=F)
###Load optparse
require(optparse)
###List of command-line options
option_list <- list(
make_option(c("-N", "--nsamp"), type="integer", default=NULL,
help="number o... |
964f8456ce44c12efda1b28a2cea03ec4c3e5fcd798f11d7cf3c51a27625d41c | R | 5,390 | 168 | #' Impute missing data points
#'
#' Replace `NA`s in the spectra matrix by interpolation. With
#' less than 4 points available linear interpolation of the 2 neighbor points
#' is used. For larger numbers of neighbor points, smoothing interpolation is
#' performed by [stats::smooth.spline()].
#'
#' @param spc hyperSpec... |
5b98d7ee95a584b4b4b837d24e77b8f261b574557d9cdda5b72ccbbf11daa309 | R | 5,398 | 141 | #!/usr/bin/R
# -----------------------------------------------------------
# Plot top DEGs that are consistent or unique across regions:
# Updated: 02/17/22
# -----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.... |
c30540dfabc5a440cf86ead992a4d80b74c513b21459ad95e6a16a2a91a6787b | R | 5,400 | 106 |
formulateStats <- function(dat, gene){
df <- dat[["norm_counts"]] %>%
filter(isoform %in% dat[["anno_res"]][["isoform"]]) %>%
group_by(isoform, associated_gene, time, group) %>%
dplyr::summarize(mean_norm_counts = mean(normalised_counts)) %>%
mutate(group = ifelse(group == "CONTROL","WT","TG"), ... |
559484c9e88a8971649ed920d337ac74cd4ce47d72766015990143148496a7cc | R | 5,401 | 141 | #!/usr/bin/R
# --------------------------------------------------------
# Plot the aggregated DE results across different methods:
# - Adding DESeq2 results, + make aggregate datasets
# Updated: 11/02/23
# --------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
sourc... |
87b3139dce24624efa6c4fade8e2078b730ad5fcfa47804ba248fbe7535af148 | R | 5,401 | 174 | # split_train_test <- function(data_list, train_prop = 0.9) {
# if (train_prop == 1) {
# return(list(train = data_list, test = NULL))
# }
#
# n <- nrow(data_list[[1]])
# train_index <- sample(1:n, floor(n * train_prop))
# test_index <- (1:n)[-train_index]
#
# train_list <- lapply(data_list, functi... |
5c4747bb004621ccd9123837d0daedb459957de9e417d2cf098ef6f28a3a98ac | R | 5,409 | 90 | #!/usr/bin/env Rscript
## ----------Script-----------------
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
## proteogenonomics analysis following paper review
## process merged (ONT + Iso-Seq) targeted data after collapsing by protein ORF following G.Shenkyman pipeline
## re-determined reference isoform usi... |
b534df356a6f40e94b7114b0a9f75bc5a5f82b4e537eb263c60275fcb0c9dc6b | R | 5,415 | 147 | ### --------------------------------------------------------------------------~
###
### read.txt.wide
###
### Format:
### x y ... int (wl1) int (wl2) ... int (wl p) z ...
###
#' @rdname DEPRECATED-textio
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Import `hyperSpec` objects from ASCII (text) files
#... |
da637c6b3407a49a3980b5a27ca168a0398d54a3e9de27f64f8c61afcf5a5907 | R | 5,431 | 84 | `%!in%` = Negate(`%in%`)
segregate_tappasresults <- function(output_tappas, type){
# tappas ouput with p values for each regression coefficient
dat = output_tappas
# apply Rsquared threshold at 0.5
dat = dat %>% filter(`p-value` < 0.05) %>% filter(`R-squared` > 0.5)
if(type == "IsoSeq"){
### model... |
1777e3bfdd8f4f9781a3276a858bd1da0e0070a3e12cecbf842d124de4f6ea68 | R | 5,432 | 138 | #!/usr/bin/env Rscript
# args = commandArgs(trailingOnly=TRUE)
print("Starting Now")
library("SymSim")
source("/net/trapnell/vol1/home/readdf/trapLabDir/fibrosis/results/2020_12_29_SymSim_Power_Work/simFuncsDavidMod.R")
library(distr)
source("/net/trapnell/vol1/home/readdf/trapLabDir/fibrosis/results/HeirarchalDE_Meth... |
8666b84e2b09a4995c1b81fc1b62795f127c51ddd1b30984672250793e31adfb | R | 5,454 | 188 | ---
title: "Subclustering astro"
author: "Shahzad"
date: '2023-11-01'
output: html_document
---
```{r}
library(dplyr)
library(Seurat)
library(patchwork)
```
```{r}
#Create astrocyte object (astro) for subcluster analysis
astro <- (subset(gswt, idents =c("Astrocyte")))
astro
```
```{r}
#Visualize astro object featur... |
7678caebb03cd1fcf17d13397847f6c2ee03203707d6045565b807e14675414d | R | 5,456 | 133 | test_that("input and output dirs are fine", {
path <- tempdir()
on.exit(unlink(path))
expect_true(check_directories(input_dir = path,
output_dir = paste0(path, "/result")))
})
test_that("result dir already exists", {
path <- tempdir()
on.exit(unlink(path))
withr::local_te... |
7b5aa4ff6a820534f99f1ed6b60ab18f4cbc15aecaf3a4e563da57803813c632 | R | 5,479 | 120 | library(dplyr)
library(pheatmap)
library(dtwclust)
Tis_spe <- read.csv("C:\\Users\\qjp\\Desktop\\RNA-Tissue_specific.genes.csv", header = T)
##RNA-seq data
Exp <- read.csv("F:\\RNA\\RawTables\\OriginalRef\\GeneCountMatrix.tpmUnit.csv", header = T, row.names = 1) %>% select(., contains("70"))
Exp_Mean <- data... |
65812c2657744228e7dedff2e36bf603da4779d1c8f0812a38ecac3973b7e87d | R | 5,483 | 144 | #This script integrate E6.5-8.5 mouse embryo dataset (Pijuan-sala et al., 2019) with human gastruloids (Moris et al., 2020)
#Link to Pijuan datasets are provided in https://shendure-web.gs.washington.edu/content/members/hGastruloid_website/public/
#Human gastrloid datasets from this paper are provided at National Cente... |
309b1ea578944e08f694ce8957037081d9c1b24c046b7337f7e1cd6ba2d06a12 | R | 5,495 | 198 | library(jsonlite)
library(lme4)
library(lattice)
library(lmerTest)
library(MuMIn)
library(performance)
library(rstudioapi)
library(xtable)
library(emmeans)
library(car)
library(tibble)
library(dplyr)
library(ggplot2)
# ----------------------------------------------------------------------
# Configuration and data loa... |
d98c5a2d2559f809aefc08c9fcc6f7f47d8820a0da036581726741b88482f423 | R | 5,501 | 112 | # test output directory selection for sub/ses paths
test_that("get_output_directory uses software-specific sub/ses directories", {
gpa <- create_mock_gpa()
gpa$output_locations$feat_sub_directory <- file.path("{gpa$output_directory}", "feat_l1", "sub-{id}")
gpa$output_locations$feat_ses_directory <- gpa$output_l... |
ed40105d3cd820d0fd036a5ce851e7f036c99391eed87a5bcd365654daef7fed | R | 5,507 | 119 | # Load info table
label_df = read.csv('[your directory]/cluster_label.csv', check.names = FALSE)
dk_label = read.csv('[your directory]/dk.csv', check.names = FALSE) # brain region dictionary
dk_label = dk_label[complete.cases(dk_label), ]
dk_label$label = gsub('lh_', '', dk_label$label)
dk_label$label = gsub('rh_', ''... |
d0fc5130f56f76f3008b1b429153f83e68b76b139d02c0cb057c0b539acf4cb2 | R | 5,512 | 190 | ### ---------------------------------------------------------------------------
# FIXME: update examples
#' Export `hyperSpec` objects to ASCII (text) files
#'
#' These functions write `hyperSpec` objects to text files.
#'
#' @rdname write_txt
#' @aliases write_txt_long
#'
#' @param file Filename or connection to writ... |
9f4b4586c2ca6e660eca0220d367bcc47c5e25c8c8e0f8479a6426931bd2dd2a | R | 5,520 | 136 | ####################################################################################################
###
### File: 01_obtaining_coordinate_data.R
### Purpose: Obtaining the features related to the coordinates.
### Authors: Gabriel Rodrigues Palma, Rafael Moral
### Date: 31/03/23
###
##############################... |
73402bd4ac2d4893e3b7e3e09618114358010b829d8e47cfd1eed68f597c4cc4 | R | 5,528 | 193 |
#### Write MSI data file(s) ####
## -----------------------------
writeMSIData <- function(object, file, ...)
{
ext <- tools::file_ext(file)
if ( tolower(ext) %in% c("ibd", "imzml") ) {
writeImzML(object, file, ...)
} else if ( tolower(ext) %in% c("img", "hdr", "t2m") ) {
writeAnalyze(object, file, ...)
} els... |
071601815b96ba515c6d8c6344e75ae7c8e12aebcab5d6023b4f5e4916bf5487 | R | 5,530 | 129 | #' Calculate Residuals Between Observed and Predicted Proportions
#'
#' @description
#' `sccomp_calculate_residuals` computes the residuals between observed cell group proportions and the predicted proportions from a fitted `sccomp` model. This function is useful for assessing model fit and identifying cell groups or s... |
a3a7a2499560035cda46ab5013034c678ad9f196a542de126805beff5914337f | R | 5,542 | 114 | library(tidyverse)
library(yardstick)
metrics_data = read_tsv("eLife_Metrics.tsv") %>%
filter(!is_duplicate)
###############################################
# Compare the metrics against the curated
# labels.
###############################################
curated_data_training = read_tsv("Image_Curation_1-5000.ts... |
f21985abc9a99cb8c0da9fc6e70f31227af28e30c0890902965d089c78fe433c | R | 5,545 | 175 | #!/usr/bin/env Rscript
# Tim.load(lassosum)
library(lassosum)
args <- lassosum:::parseargs(include.others = FALSE)
opts <- args
#### Options specific to standalone version ####
standalone.opts <- c("nthreads", "data", "n", "pval", "beta", "OR", "pheno", "covar",
"out", "lassosum.pipeline", "pseu... |
4b8e5d0dafb70f13c3c3722df7b8296266a402bbb477ab3ea7874efb247bd5d6 | R | 5,550 | 132 |
library(Rcpp)
library(stringr)
library(arrow)
library(yardstick)
library(ggplot2)
library(gdata)
library(dplyr)
library(tidyr)
library(logger)
FitLinearModel <- function(data_list, all_x_cols, model_name = "") {
log_info("Fitting model")
model_formula = paste0("y ~ ", paste(all_x_cols, collapse = " + "))
... |
4367e0096ffd2a31a59851c3229f24302d54b2e8504d281e9640304aa011e2e1 | R | 5,557 | 137 | #!/usr/bin/env Rscript
# Talkowski SV pipeline downstream analysis helper script
# Make list of all nonredundant pairs of batches from an input list of batches
###Set global parameters
options(stringsAsFactors=F,scipen=1000)
###################
###HELPER FUNCTIONS
###################
#For any two batches, find mo... |
d1d4c5b1719e464a63696722176374deb2c8ba15a352ca29e3b21d2d71a4feff | R | 5,558 | 127 | # Title: Nested active zone density analysis
#
# Description:
# This script performs a nested active zone density analysis.
# It calculates the area of active zones within each nested bouton.
#
# Author: Daniel Parthier / Felicitas Brüntgens
# Date: 05.10.2023
# Last modified: 19.04.2024
# Version: 1.0
# Load librar... |
7b34880bcfcdc30ee79aca380d960da0f647f4f70716dee843fbb8ec8bcc43d2 | R | 5,574 | 144 |
redoProcessing <- function(inputCDS, useMNN=TRUE, mnnCategory="sample"){
set.seed(7)
inputCDS <- monocle3::estimate_size_factors(inputCDS)
inputCDS <- preprocess_cds(inputCDS)
if (useMNN){
inputCDS = align_cds(inputCDS, alignment_group = mnnCategory)
}
inputCDS = reduce_dimension(inputCDS)
re... |
b056fbce8afd68d7642320123575e36ccee6d9701263827c4e62b84cf96492ef | R | 5,577 | 204 | #!/usr/bin/R
# ------------------------------------------------
# Classify DEGs as individual vs. region-specific:
# Updated: 03/29/22
# ------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
library(ggpl... |
f8f6ed3fa2e75bc70140979c1c6585cef8b49e62176e48c8651d9628db970657 | R | 5,585 | 184 | #' @name spc-bin
#' @title Wavelength binning
#'
#' @description
#' In order to reduce the spectral resolution and thus gain signal to noise
#' ratio or to reduce the dimensionality of the spectral data set, the
#' spectral resolution can be reduced.
#'
#' @details
#' The mean of every `by` data points in the spectra i... |
cf099ff763cc73a2fbdab977f2468f60ed8a1210d8ec9d247a9b93cedd83ae93 | R | 5,596 | 143 | #!/usr/bin/R
# --------------------------------------
# Aggregate the DE results for selgenes:
# Updated: 02/24/22
# --------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
print(version)
options(width=175)
# Direc... |
b8b0da1741c1cced46ae7fa19f5e3f77c22c75a140b6639bac4a872800deebdc | R | 5,603 | 104 | # *************
# 1b-celltype-annotation.r
# Note: these codes are for exploratory data analysis -- you can ignore this script.
source("Analysis/util.r")
library(Seurat)
library(dplyr)
library(ggplot2)
library(magrittr)
library(data.table)
file.dir = 'Results/Initial/Initial integration/'
# Load result of integratin... |
5786d8416c322a67802cd3ffb9eade8d97f34195a8ce431aed13a4cd2942b2df | R | 5,614 | 113 | # RUN_MODEL.R
# This function runs the actual bayesian model for the given data with or without averaging it over channels belonging to the same ROI
# INPUT:
# - data_input: subset of data for a given condition (see: stat_modeltype1/2.R)
# - brm_form: formula for the Bayesian analysis (see brmsformula)
# - ROI_hemi: RO... |
5fca61e0a7219fab899a73bd3cdac339b74c096c64cd03df79ab71917d770e12 | R | 5,630 | 127 | # boxplots for weight changes
# Jenn Kim
# load libraries
library(tidyverse)
library(cowplot)
theme_set(theme_cowplot())
library(nlme)
library(lsmeans)
library(plotrix)
# load data
data <- read.csv("BehaviorCohort_BodyWeights.csv")
data <- as.data.frame(data)
data$LPS.Treatment <- factor(data$LPS.Treatment, levels=c... |
f3b9bcc6da0e5f3fa1b3c52936c127d436e91496336333bbfafe28f5e44e453a | R | 5,637 | 154 | #' Get the index of equations in a string vector
#'
#' @param eq_begin the beginning index of an equation
#' @param eq_end the end index of an equation
#'
#' @return a index vector
get_eqloc <- function(eq_begin, eq_end){
eq <- eq_begin[1]:eq_end[1]
if(length(eq_begin) > 1) {
for(i in 2:length(eq_begin)) eq <- ... |
0fd29f951b479797810d872585e10438553888a596c6215ef3233acfb87b1c94 | R | 5,639 | 159 | #!/usr/bin/R
# ------------------------------------------
# Aggregate the DEGs runs for multiple runs:
# Updated: 01/04/22
# ------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
library(ggplot2)
library(ggpub... |
a58690663bca096f63a24e0df00f9e5cc0fe595b69e41010fdb945a46ed3c6ea | R | 5,644 | 168 | ---
title: "R interface"
output:
html_document:
toc: true
toc_depth: 3
toc_float:
collapsed: false
css: custom.css
---
[**pmlbr**](https://github.com/EpistasisLab/pmlb) provides the following user-facing functions and variables in the R environment.
For installation instructions or a high-level... |
3ce6924f69d2d8956e5b2412feb1fd6c446ba3350784754cbdbd3ee64aa88f48 | R | 5,655 | 168 | library(ROGUE)
library(Seurat)
library(tidyverse)
library(ggplot2)
library(clusterProfiler)
fig.path <- "/home/pauling/projects/04_SEmodel/07_NC_revision/02.figures/10.brain"
my.co <- c("#FF8C00", "#D02090", "#FFE7BA", "#00B2EE","#00C5CD", "#B0E2FF", "#FF83FA","#9FB6CD", "#AB82FF", "#FFC1C1","#00868B","#FF4040", "#3C... |
d86da58fac4602c3e3880b1b6141df306a1697cc68e738f2140daadb4ae7d23d | R | 5,663 | 149 | # Tests for the feat_killed trap logic generated by run_feat_sepjobs.
#
# The trap must:
# 1. Iterate all_odirs (every output directory for the job)
# 2. Skip directories that already have .feat_complete
# 3. Write .feat_fail only to directories that are still in-flight or not started
if (identical(.Platform$OS.... |
fee49db43cdc3997cf260274bdcaebc5c41775a2bf8006c9b830f205ea548e8b | R | 5,667 | 179 | # For the specified k, for pairs of summary scores relating to g or processing
# speed, conduct bivariate normality tests, plot scatterplots, and plot QQ plots.
# Output:
# binormtest_p.csv Bivariate normality test p-values for variable pairs.
# binormtest_pthres.csv Bivariate normality test p-values for significant l... |
de9f403404c31a9dcba9753951b8d765bc33ad0e1c511870ebe23eb15f898845 | R | 5,674 | 131 | ---
title: "m6a UMAP plots"
output:
html_document:
df_print: paged
---
In this document we integrate the RNA abundance and methylation data obtained from bullseye in single-cell DART data.
```{r echo = FALSE, message = FALSE, warning = FALSE, eval = FALSE}
library(tidyverse)
# Mutant reads
mut_filename <- "/G... |
8e099ce942589140e781e5729069a73c48246cfd68640d96173cc7fac45f1463 | R | 5,677 | 188 |
# Faux cell data generation function -----------------------------------------
#' @rdname faux_cell
#' @aliases faux_cell generate_faux_cell
#' @docType data
#'
#' @title Faux cell dataset for testing & demonstration
#'
#' @description
#' This is a synthetic data set intended for testing and demonstration.
#' Functio... |
552fdeb8dfc577c581c8343319b2f5b8f673169b0b9fc488dd9832ba16b9d806 | R | 5,688 | 168 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(tidyr)
library(tidyverse)
library(RColorBrewer)
print("Libraries loaded, starting now")
monocle_theme_opts <- function()
{
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border =... |
a5a58f9d35e92e6152cf371fc2ef242ebff1d49829f8daee3948612625198287 | R | 5,688 | 139 | #'
#' Creates a yaml file storing the parameters that are used for the analysis of the NOSA software results
#' and returns these parameters.
#'
#' @param yc A yaml class object created by YAMLClass$new().
#' @param dirs List with the paths to the input and result directories and the sheet names to be read in.
#' @para... |
3e2ac5b8a72600b24e4c58d5bcccdf25a00bc3f8cce6423ce8d6f06999f26231 | R | 5,691 | 176 | #!/usr/bin/R
# ----------------------------------
# Run Nebula on selected genes only:
# Updated: 02/23/22
# ----------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
library(Matrix)
# For nebula:
library(nebula)
library(DESeq2)
l... |
4e41e87432e0cf7705c45fadfc03766739617bfa04ae7a5c6a126257c69f1f13 | R | 5,693 | 129 | # load the reshape2 package
library(reshape2)
library(dplyr)
setwd("D:/home_wkstn/F/Brain_region_project/LIANA/Analysis_aggregate_rank_magnitude_all_projid/Sign_interaction_table")
table=read.csv("Sign_interaction_table.csv",row.names=1)
#########################################################################... |
39ab95d410a9e5c113af66a685f027eeac479398f840642f6d12c143c56a9e9f | R | 5,714 | 168 | #!/usr/bin/R
# ------------------------------------------------
# Plot DEG overlap with modules for different vars
# Updated 11/04/2023
# ------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
libra... |
38946261639f821d22c537347c37618e0c326824da9714a0ce34220c3446a5b0 | R | 5,716 | 110 | ## ---------- Script -----------------
##
## Purpose: input variables for ONT targeted mouse transcriptome datasets
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
suppressMessages(library("dplyr"))
suppressMessages(library("stringr"))
## ---------- Directory and input files -----------------
dirnames <- list... |
d463c4010e1df042f9cbae40fe260647d229afb8a1f82c272ed45e94e86e0e1f | R | 5,720 | 156 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(msigdbr)
library(fgsea)
library(tidyr)
library(tidyverse)
print("Libraries loaded, starting now")
# Get the passed parameters
option_list = list(
make_option(c("-m", "--modelNotes"), type="character",
# default="_fix_... |
b2623835ab4c50bb728d9abd365225121135e0d2726039a0c55f39b9ea725a01 | R | 5,754 | 156 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(msigdbr)
library(fgsea)
library(tidyr)
library(tidyverse)
print("Libraries loaded, starting now")
# Get the passed parameters
option_list = list(
make_option(c("-m", "--modelNotes"), type="character",
# default="_fix_... |
157131a647a68f85813a159145a2cde4f136550a241ce250ed056a40e3abe97d | R | 5,756 | 181 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
print("Libraries loaded, starting now")
# Get the passed parameters
option_list = list(
make_option(c("-m", "--modelNotes"), type="character",
# default="_fix_Anatomical_Site_rand_Donor_HM10UMI=100_mito=10Scrub=0.2noPackerMNN=sampleNameK=... |
4f6717955b899a86276032a77004633024072c01843effc59d55b16b871ae6cb | R | 5,757 | 171 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(msigdbr)
library(fgsea)
library(tidyr)
library(tidyverse)
print("Libraries loaded, starting now")
# Get the passed parameters
option_list = list(
make_option(c("-m", "--modelNotes"), type="character",
# default="_fix_Anatomical_Site... |
d8a39c112b9cb2c1d02e5ba590b20d701e3ebe896ebb939ab018c93930b18a36 | R | 5,759 | 126 | library(ggplot2)
library(Seurat)
setwd("D:/Mouse_data_integration")
pbmc=readRDS(file="Astrocytes_integrated_all.rds")
#write.csv(pbmc@meta.data,file="Astrocytes_integrated_all_metadata.csv")
data=read.csv("Astrocytes_integrated_all_metadata_addition.csv",row.names=1)
pbmc=AddMetaData(pbmc,data)
#saveRDS... |
6c9422f2e1a3b58cb3a24c172ceadb4492560071881d93085e12828d470121b1 | R | 5,762 | 171 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(msigdbr)
library(fgsea)
library(tidyr)
library(tidyverse)
print("Libraries loaded, starting now")
# Get the passed parameters
option_list = list(
make_option(c("-m", "--modelNotes"), type="character",
# default="_fix_Anatomical_Site... |
a446f98cce12fff1baab58895205960653800e17915a3a788f4a3f1265030102 | R | 5,798 | 191 | # Function -------------------------------------------------------------------
#' Create label from units for wavelength axis
#'
#' This function takes the name of units for wavelength axis and creates an
#' R expression which can be used as a label for `@wavelength` axis for
#' `hyperSpec` object.
#'
#' @param unit (... |
b29cf2b9fa38a8b74cc1b8edeeee6b18241428e3ed6cc61d42339f59cfaaf9fc | R | 5,802 | 170 | #' Loads predictions from univariate and ML models
#' from directories containing parquet files
#' and adds some useful columns
#' @param cases_per_dataset The number of cases per dataset (internal/eicu) to display
#' @return A beautiful dataframe containing forecasts from different models.
.load_predictions <- functio... |
650c37ccd9413ace225dc8378c27b0dddfa873f30ccd7de70b46e09ef08c03f2 | R | 5,804 | 149 |
#' archstabms_ddPCA_biophysical_model
#'
#' Biophysical model from shallow ddPCA data.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param temperature temperature in degrees celcuis (default:30)
#' @param ddpca_outpath path to ddPCA data (required)
#' @param position_offset residue p... |
31121abf6dd1cbb9d9cab9872c5acafc9aa1992c7479d7081c134da3cfa4d045 | R | 5,806 | 163 | #!/usr/bin/R
# ---------------------------------------------------------------
# Plot volcano plots for extended data for aggregated DE results:
# Updated: 12/10/21
# ---------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/lo... |
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