sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
478d1a4c815e0f1691bdcc16c25e6a42021876d478861c209c117890845ea4b3 | R | 7,329 | 234 | longitudinal_l2_scopes <- function() {
c("id_session", "id")
}
longitudinal_l3_input_modes <- function() {
c(
"per_session",
"pooled_sessions_subject_ev",
"subject_rows",
"3dlmer"
)
}
normalize_l3_input_mode <- function(x, default = "per_session") {
allowed <- longitudinal_l3_input_modes()
... |
400e7988960f7dc3c15033b53b4def3ee328579c09356b32772ee63d5fe661bd | R | 7,337 | 218 | # get_all_coef_df <- function(dir, pattern=".*rt_rs.*RT", emt_number=1, trend_match="trend") {
# coef_files <- list.files(dir, pattern=pattern, full.names = TRUE)
# all_coef <- lapply(coef_files, function(x) {
# df <- readRDS(x)
# data <- df$emtrends_list[[paste0("emt_", emt_number)]]
# dupe_names <-... |
a3ee80a993d49364f9b76cbba077f1df4a894d9ecd85b7cd722bb16caa005210 | R | 7,340 | 125 | #!/usr/bin/env Rscript
# This script is intended for the generation of test data required for the mrcepid-runassociationtesting test suite.
# Load necessary modules
library(data.table)
library(tidyverse)
# Make sure we get reproducible results
set.seed(1234)
# Now we generate a set of files:
# 1. A base coviarate ... |
9572acf709c504e3734719ec7e97831ed2a6d501940e5215ca36c674e0616bf8 | R | 7,362 | 257 | library(jsonlite)
library(lme4)
library(lattice)
library(lmerTest)
library(MuMIn)
library(performance)
library(rstudioapi)
library(xtable)
library(emmeans)
library(car)
library(tibble)
library(dplyr)
library(ggplot2)
# ----------------------------------------------------------------------
# Configuration and data loa... |
f8fcd5234b16148e7ef6e2c753f2e2c42c1f24a3613eeafa6bcb17249cad5a6e | R | 7,386 | 197 | #' This function generates syntax for FSL Feat .fsf files for the EVs tab of a first-level fMRI analysis.
#' It accepts a numeric design matrix whose colum names correspond to individual EVs in the model
#'
#' This allows you to generate a design matrix dynamically based on a numeric matrix that has been
#' setup in R ... |
7764cdbb53f6f19caaa7987549acf10e643eb36e76e06eb9938178a4db2d1ed0 | R | 7,392 | 203 | ---
output: pdf_document
params:
ncells: 50
seed: 1337
nullsim: TRUE
---
```{r}
library(dplyr)
library(spASE)
set.seed(params$seed)
expit <- function(x) {
return(exp(x)/(1+exp(x)))
}
```
```{r}
# Slide-seq data
# puck 4 needs to be rotated and shifted
load('pucks.3-4.spatial.RData')
puck3.spatial <- puck3.sp... |
5b6fe801db9b1a4c54dd5622448dfffc9f9d06821f18074d62119b2b0c9b8805 | R | 7,403 | 130 | ## ---------- Script -----------------
##
## Purpose: perform differential analysis on mouse rTg4510 ONT and Iso-Seq targeted datasets using linear regression
## Transcript level separate analysis
## Gene level separate analysis
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
# https://hbctraining.github.io/DGE_wo... |
d4f4b793a81c0d3d2ad6a58994118c541036cabd95b9e22e13b0bd34c41c5777 | R | 7,411 | 181 | #!/usr/bin/R
# --------------------------------------------------------
# Calculate composition differences using binomial emmeans
# Run contrasts on 2-level categorical vars, plot heatmap
# of effect sizes (log2 OR) and adjusted p-values
#
# Here, we compare region differences in major cell types
# Updated 12/20/2021 ... |
e055d624dd16680bed6fb52a01958ec7c02b6106e5a5ad007950102b9d451fe1 | R | 7,417 | 174 |
hardAssignDonorAges <- function(inputCDS){
colData(inputCDS)$Age = 0
colData(inputCDS)$Age =ifelse(colData(inputCDS)$Donor == "W134", 43, colData(inputCDS)$Age)
colData(inputCDS)$Age =ifelse(colData(inputCDS)$Donor == "W135", 60, colData(inputCDS)$Age)
colData(inputCDS)$Age =ifelse(colData(inputCDS)$Donor == "... |
e6240649629b753929d302f821a45765e9780bf3e0f57ab3360b63437e4f3c0e | R | 7,434 | 214 | #!/usr/bin/R
# -------------------------------------------------
# Plot stats on DEGs with shared/specific profiles:
# Updated: 06/23/23
# -------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
library(C... |
0f6b75311dae37c98f40e971997c40fae3b1d035398f9c81a026ad1383b5724a | R | 7,452 | 234 | ---
title: "006_KO_gastruloid"
author: "Nobuhiko Hamazaki"
date: "7/27/2022"
output: html_document
---
```{r}
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(stringr)
library(RColorBrewer)
library(tidyseurat)
library(cowplot)
library(viridis)
library(ggthemes)
set.seed(seed = 0)
my_theme_f... |
5445344032e7ee10b8a5703ba00a07a1cc579f429bb9139f07b00d5490ae886e | R | 7,465 | 197 | ---
title: "Basic Guide for NosaAnalysis"
author: "Julia Thüringer"
date: '`r Sys.Date()`'
output:
html_document: default
pdf_document: null
vignette: >
%\VignetteIndexEntry{Basic Guide for NosaAnalysis}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
### Introduction
Because all settings... |
56b38fc3e6ad79792027f52c42fd52151e645a84db0e45baa7aa27b480d0852c | R | 7,470 | 179 |
library(Libra)
data <- qs::qread("C:/SynologyDrive/Projects/scRNAseqR/results/sakshi_4/NSM-NS-NC-M/NSM-NS-NC-M.qs")
# SeuratObject::DefaultAssay(data) <- "RNA"
SeuratObject::DefaultAssay(data) <- "RNA"
comp_data <- data[, data$orig.ident %in% c("NS", "NC")]
comp_data <- comp_data[, comp_data$mapmycells_supercluster ... |
4a909926eda977e303c16b5cd7a7e0b4da538f6a30b0f13dc2224f3df92b9baf | R | 7,492 | 251 | test_that("deconvolve_reglin recovers a continuous latent signal", {
set.seed(1001)
kernel <- c(0, 0.25, 0.5, 0.2, 0.05)
n_time <- 80L
n_latent <- n_time + length(kernel) - 1L
latent <- as.numeric(stats::arima.sim(model = list(ar = 0.6), n = n_latent))
bold <- fmri.pipeline:::decon_convolution_matrix(n_time... |
3e35764281e8fb5082f94097ca9ba796e20906e20980e65be899d339c66ca4f7 | R | 7,495 | 229 | # This shiny app displays forecasts produced by the code in this repo
# Work in progress, especially the data loading part is not very efficient
library(shiny)
library(ggplot2)
library(cowplot)
# Set the working directory to the root directory of the project
setwd("../..")
source("R/setup.R")
source("R/shinyforecasts... |
47d3992b065074e1101ad5e029608ca21d7e071fd856f6e8f70bd2eab80e5584 | R | 7,503 | 269 | # Function -------------------------------------------------------------------
.sample_h <- function(x, size, replace = FALSE, prob = NULL, index = FALSE) {
validObject(x)
if (missing(size)) size <- nrow(x) # normal default does not work!
rows_i <-
sample.int(nrow(x@data), size = size, replace = replace, p... |
806adca6f708d6303992f94f0f043162059993d85705f899add360a35c0ce22b | R | 7,511 | 246 | ---
title: "Correlation MPA and SF, W, CI Ctrl NTI Naloxone"
author: "Yaromir Kobikov <kobikov.yaromir@gmail.com>"
date: "Date: `r format(Sys.time(), '%d/%m/%Y')`"
output:
word_document: default
html_document:
toc: true
toc_float:
collapsed: false
editor_options:
chunk_output_type: inline
---
```{... |
7f1aed7acd89b492913b027bdacce808042a9db9c1588b87156c3ace44d32044 | R | 7,514 | 236 | #!/usr/bin/R
# --------------------------------------------------
# Second part of metabolic modules figures
# - Enrichments for modules
# - Module scores heatmap
# - Boxplots vs. e4
# Updated 04/04/2022
# --------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists... |
c8714b4e1db642c5e1e9c6627f7f404f9a8b15493dd60a3b93839fe5bad3aeb1 | R | 7,522 | 173 | #' @title Function to obtain LASSO estimates of a regression problem given summary statistics
#' and a reference panel
#'
#' @details A function to find the minimum of \eqn{\beta} in
#' \deqn{f(\beta)=\beta'R\beta - 2\beta'r + 2\lambda||\beta||_1}
#' where
#' \deqn{R=(1-s)X'X/n + sI}
#' is a shrunken correlation ma... |
8b3891b95bd4239c13663f98bed1509bbd313cc3603614fa97927f76e23438b0 | R | 7,525 | 180 | #' sccomp_predict
#'
#' @description This function replicates counts from a real-world dataset.
#'
#' @references
#' S. Mangiola, A.J. Roth-Schulze, M. Trussart, E. Zozaya-Valdés, M. Ma, Z. Gao, A.F. Rubin, T.P. Speed, H. Shim, & A.T. Papenfuss, sccomp: Robust differential composition and variability analysis for singl... |
4b7366a2d0cfd54c7a615a81749ebc414e41367eec595f8fb0d0abcd06e7b3a9 | R | 7,528 | 211 | ---
title: "Ptgds analysis"
output: html_document
---
This vignette replicates our results for Figure 4 (\textit{Ptgds} analysis)
```{r}
library(ggplot2)
library(tidyr)
library(GenomicRanges)
library(rtracklayer)
library(reshape2)
library(motifmatchr)
library(GenomicFeatures)
library(BSgenome.Mmusculus.UCSC.mm10)
lib... |
af152611757d58ed89119ceb43de51a011eb02348a1bd53da4b12d701224838d | R | 7,529 | 186 | library(data.table)
library(stringr)
library(boot)
library(DescTools)
set.seed(1)
prs_file <- c("../../PRS-CS_KoGES_target/score_sum_ukb_excluded_indiv.profile.reformat",
"../../PRS-CS_KoGES_target/individual_phenotypes/score_sum_indiv.profile.BMI.reformat",
"../../PRS-CS_KoGES_target/indiv... |
d111bc473e2b1f9eed7deb61dcfe63794a5eadb8361ff62b99ad3c4953429812 | R | 7,550 | 237 | #!/usr/bin/R
# ---------------------------------------------------
# Calculate enrichments for the final aggregated DEGs
# vs. the final modules (+ save resources for plots).
# Updated 03/18/2021 to add plots for each module
# ---------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'mu... |
513a00eaa2b4edf476e32efaf653261289587fd5049adf554a5e6f0c564a2de7 | R | 7,562 | 213 | #' Convert principal component decomposition or the like into a `hyperSpec` object
#'
#' Decomposition of the spectra matrix is a common procedure in chemometric
#' data analysis. `scores` and `loadings` convert the result matrices
#' into new `hyperSpec` objects.
#'
#' Multivariate data are frequently decomposed by me... |
7af7a2621a26bdfac8138c185c55d3a9e765a55c7052b37417f64d16f51e7396 | R | 7,580 | 174 | test_that("BibTeX file is read in correctly", {
path <- "zotero_export.bib"
name <- "zotero_export.bib"
df1 <- load_multi_search(path, name, "bib")
df2 <- load_search(path, "bib")
# Check columns
expect_true(all(c("author", "year", "journal", "doi", "title", "pages", "volume", "number", "abstract", "record... |
9fa7c5c91c8c97dfe7fe2bbfacb7d730b92c0fee994a5dc5db1f502c8ccd7e0d | R | 7,582 | 219 | #!/usr/bin/env Rscript
# Performs ROC curve analysis to determine optimal GQ threshold
###Set master parameters
options(stringsAsFactors=F,scipen=1000)
svtypes <- c("DEL","DUP","INS","INV","CPX","BND")
###########################
###GENERAL HELPER FUNCTIONS
###########################
#Read data & compute median a... |
9c77b191974e54f89cab57cc5942ba64c589c3b4278af231dfa93d2da3f5cb97 | R | 7,593 | 176 | library(dplyr)
setwd("F:\\RNA_20221101\\02_ModifiedGenome\\C_POEgeneSelection\\figures_tables\\02_POE基因位置分布")
input=c("F:\\RNA_20221101\\02_ModifiedGenome\\C_POEgeneSelection\\figures_tables\\02_POE基因位置分布\\data\\")
##pig chromosome length
chr_bed <- read.table(paste0(input,"pig_karyotype.txt"), header = 1)
#... |
68692c03d0777d3b9d039720f3e34c67fd49110bf6a78c68bc86a408d5bdaea6 | R | 7,598 | 251 | #!/usr/bin/R
# ---------------------------------------------------
# Get the metadata enrichments for external datasets:
# Updated 12/01/2023
# ---------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))... |
e08a7eb53bd14fb9a1c5e1dc9cb7f80809a9b75982421968c28b633d06c36dbf | R | 7,625 | 317 | # Function -------------------------------------------------------------------
#' Convert `hyperSpec` object into a data frame or matrix
#'
#' @description
#' [as.data.frame()] returns `x@data` (as data.frame).
#'
#' [as.matrix()] returns the spectra matrix `x@data$spc` as matrix.
#'
#' @name as.data.frame
#' @rdname ... |
ffd420c40e2b084e103209715dbd33ae925fc185fedaa038bff4d1f158870179 | R | 7,625 | 223 |
library(Seurat)
library(dplyr)
library(ggplot2)
library(limma)
library(muscat)
library(purrr)
library(scater)
##########################################################################################################
# MCT
setwd("G:/PFC_429_final_Spring2021/Data")
pbmc=readRDS("Exc_merge_40_perc... |
09c31768b68b13df443d7b8f9baf6035a6fec59036d103c77ae435175edc401a | R | 7,652 | 184 |
#' ---
#' title: "Extraction of Barcodes and gene fragments"
#' author: "Tomas Bjorklund"
#' output:
#' pdf_document:
#' highlight: tango
#' geometry: margin=0.7in
#' ---
#' This workflow extracts barcodes and the gene fragments synthesized with the CustomArray using bbmap2. The fragments are then suitable for a... |
48df3fde06877f163554f726fbc76e1c431b4874eb8bba43aa4a37aced1980d4 | R | 7,652 | 241 | #!/usr/bin/R
# -----------------------------------------------
# Plot some example genes with regional patterns:
# Updated 06/21/2023
# -----------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
source(p... |
1cd53d517eb645ecbc10dc8048f976868ebdc124ea10e140c0bd8dc1c06916d5 | R | 7,661 | 200 | ---
title: "Hippocampus-V2 variants"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Here we make some plots of the C>U variants found in the hippocampus sample. For obtaining these variants we have:
In each replicate:
- Kept only variants with FDR... |
1859a9cbefa9570ade0ed2d13d1ea7afad046859f58559c8cdf785347ad05710 | R | 7,664 | 238 | #' @name DEPRECATED-read.txt.Shimadzu
#' @concept moved to hySpc.read.txt
#'
#' @title (DEPRECATED)
#' Read Shimadzu GCxGC-qMS - spectra files (`.txt`)
#'
#' @description
#'
#' These data input functions are **deprecated** and they will be removed in
#' the next release of \pkg{hyperspec} package.
#' Now functio... |
e39fdc264815fa9f12768ecb22da1150284f1d8bf09f9b100d2abba2a2936c2c | R | 7,690 | 150 |
## implemented and merged with sample_analysis
NSM.data <- Seurat::Read10X(data.dir = "C:/SynologyDrive/Projects/scRNAseqR/data/samples/sakshi NGN2 microglia/NSM/filtered_feature_bc_matrix")
M.data <- Seurat::Read10X(data.dir = "C:/SynologyDrive/Projects/scRNAseqR/data/samples/sakshi NGN2 microglia/M/filtered_feature... |
be92837cf51138466e373f5f19e0480112c1fb8d7adc80f35c960a5f2a62ece4 | R | 7,697 | 215 | library(lmerTest)
# library(sjPlot)
library(glmmTMB)
library(ggplot2)
library(dplyr)
library(ggeffects) #ayestestR, parameters, pROC, sjstats, epiDisplay
library(effects)
library(readr)
library(ggChernoff)
library(merTools)
library(lattice)
# library(epiR)
# library(epicalc)
# library(pROC)
# library(lattice)
library(... |
ed5d1033f35aaa24a3685ce74f07026179c17f8cdd7a5e67493d19e9f3e11c92 | R | 7,711 | 198 | # Load the required libraries
library(ggplot2)
library(dplyr)
# Read the CSV file
data <- read.csv("tsnrvals.csv")
data_stan <- read.csv("tsnrvals.csv")
# Read the file containing the list of subjects for the new flip angle
new_flip_angle_subs <- read.csv("newflip.csv", header = TRUE)
# Create a new column in data i... |
b418f12fd847a73d522b1c3f44c5e105ac1edd900b3e48550bc9e064149dc61f | R | 7,721 | 205 | #!/usr/bin/env Rscript
# Talkowski SV pipeline downstream analysis helper script
# Identify variants with unstable frequencies before/after minGQ
### Set global parameters
options(stringsAsFactors=F, scipen=10)
### Import pre-minGQ frequency data
load.preMinGQ.freqs.PCRMINUS_only <- function(pre.freqs.PCRMINUS.i... |
e13f404219d41347b34e95ea034ed6bceffae9e90493683cb64eb367488478b6 | R | 7,721 | 262 | ---
title: "dartseq replicates of mice hippocampus"
output:
html_document:
self_contained: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
In this document we show the variant count per sample, and the variant relationship between dart-seq replicates of mice hippocampus.
#### WT, ... |
c2b945df98fdc83196dfe4e2d03ff0c363a66573e5fd25d3dfafa64e173790c4 | R | 7,750 | 133 | #functions for processing data after RCTD is fit to the data
# Collects RCTD results
gather_results <- function(RCTD, results) {
cell_type_names = RCTD@cell_type_info$renorm[[2]]
barcodes <- colnames(RCTD@spatialRNA@counts)
N <- length(results)
weights = Matrix(0, nrow = N, ncol = length(cell_type_names))
we... |
a800ffd691dda86ce07661723a00d584da371f45e8a6502534e0ef8640e48760 | R | 7,758 | 208 |
########################
######### Input ########
########################
# *obj: Refers to Seurat object
# *gene_list: A list with multiple gene sets to run GSVA algorithm on
# *split_loc: Boolean which refers whether the algorithm will run on 2 levels of pseudo-bulking
# ie (pseudo-bulking cells from the same donor... |
21a0502aa88a5787917001e2fcd4a34c45e89eaa34e115c7548c943f1e8dd112 | R | 7,772 | 198 | # ************
# 2d-roundtwo.r
# Another round of integrating data, following removal of contaminating cell clusters
source("Analysis/util.r")
library(Seurat)
library(data.table)
library(magrittr)
library(dplyr)
library(ggplot2)
# -------------- Removal of contaming subclusters -----------------
#re <- readRDS('Result... |
b14cb0fca324f51aca094b2c5a0156ffef411effef434ac224eccd343a9d9517 | R | 7,779 | 211 | is_l2_passthrough_estimable <- function(dmat, cmat, tol = sqrt(.Machine$double.eps)) {
if (checkmate::test_data_frame(dmat)) dmat <- as.matrix(dmat)
if (checkmate::test_data_frame(cmat)) cmat <- as.matrix(cmat)
if (!is.null(cmat) && !is.matrix(cmat)) {
cmat <- matrix(as.numeric(cmat), nrow = 1L)
}
is.mat... |
6bf285ea4b23bf0db38dfbd2fb6e3545073db208febe113186559899cff2214d | R | 7,784 | 198 | require(arrow)
require(plyr)
require(dplyr)
require(stringr)
require(tidyr)
require(data.table)
phenotype_suffix = snakemake@params[["phenotype_suffix"]] #TODO delete this later
code_dir = snakemake@params[["code_dir"]]
out_path = snakemake@output[["out_path"]]
query_phenotypes = snakemake@params[["phenotypes"]]
corr... |
ef2bcc76b01c46c2f92a9238d518522fd9a461641e8d5e9fd887f880ffe489d3 | R | 7,795 | 180 | #relationship between methylation and expression of tissues-specific gene
library(dplyr)
library(ggplot2)
library(magrittr)
library(ggtext)
library(ggpubr)
dataExp <- read.csv("F:\\RNA\\RawTables\\OriginalRef\\GeneCountMatrix.tpmUnit.csv", header=T, row.names=1) %>% select(., contains("70"))
feature <- c("... |
a286c8c8b9219038f90b3a6045e4bc1413dedbf0a065470d36d6ee18f616c739 | R | 7,805 | 205 | library(tibble)
#### Phenotype Names ####
monti_staar_name_dict = c('new_phenotypes' = '_new_phenotypes',
'all_phenotypes' = '_all_phenotypes',
'training_phenotypes' = '_training_phenotypes')
phenotype_renamer = c('WHR Body mass index BMI corrected' = 'WHR',
... |
e7bd197c925d9e98a0a13ec96d4dee00aee4db36a8c58d9157255613ee70793f | R | 7,805 | 228 | ---
title: "005_LDN"
author: "Nobuhiko Hamazaki"
date: "7/15/2022"
output: html_document
---
```{r}
library(dplyr)
library(Seurat)
library(patchwork)
library(ggplot2)
library(stringr)
library(RColorBrewer)
library(tidyseurat)
library(cowplot)
library(Signac)
library(viridis)
library(ggthemes)
library(networkD3)
set.s... |
1ed52817276ac82397f47588572efbe3ee9ab6d879a9a7675f142638a51be19d | R | 7,809 | 223 | #!/usr/bin/R
# --------------------------------------------------------------
# Nebula as in minimal example, but using the precomp. matrices:
# Updated: 10/31/23 to add interaction
# --------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir... |
3e389c1ca9fdaec9dd861d0a0bca4b97cad804ccf6aa5b1f20335b7753f410bc | R | 7,812 | 308 |
# Function -------------------------------------------------------------------
#' Get index
#'
#' `.getindex()` does the actual work of looking up the index for `wl2i()`,
#' `.extract()` and `.replace()`.
#'
#' @param x `hyperSpec` object.
#' @param wavelength Wavelength values.
#' @param extrapolate (logical):
#' -... |
baeb9a148fe594906165fed4892c8a6732eb5f6f50d123b702b957681a321fe4 | R | 7,822 | 245 | #!/usr/bin/R
# -----------------------
# Nebula minimal example:
# -----------------------
library(cbrbase)
set_proj('DEVTRAJ')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
library(Matrix)
# For nebula:
library(nebula)
library(DESeq2)
library(RUVSeq)
library(qvalue)
# For plotting
library(gg... |
6383704eca7c0f2ea0e0c838d0c0036090459d9fdb5835c8d8a6e78e15322b94 | R | 7,832 | 275 |
#### Projection to latent structures ####
## --------------------------------------
setMethod("PLS", "ANY",
function(x, y, ncomp = 3,
method = c("nipals", "simpls", "kernel1", "kernel2"),
center = TRUE, scale = FALSE, bags = NULL,
verbose = getCardinalVerbose(), chunkopts = list(),
BPPARAM = getCardinalBPPA... |
293c2bec5a1039233181ba0bd28a9390d00c85336c3a922aaa03537ebf609daa | R | 7,837 | 245 | #load the packages
library(edgeR)
library(DESeq2)
library(limma)
library(org.Hs.eg.db)
library(tximport)
library(csaw)
library(readr)
library(ggplot2)
library(ggrepel)
library(WGCNA)
library(ggpubr)
library(bestNormalize)
library(ComplexHeatmap)
library(reshape2)
library(tidyverse)
library(caret)
library(MASS)
library(... |
7db21e75ce3995191dfbadb3677c00c7e973ed3d4019e688850843ecbf75aa84 | R | 7,850 | 197 | require(arrow)
require(plyr)
require(dplyr)
require(stringr)
require(tidyr)
require(ggplot2)
require(data.table)
phenotype_suffix = snakemake@params[["phenotype_suffix"]] #TODO delete this later
masks = snakemake@params[["masks"]]
code_dir = snakemake@params[["code_dir"]]
out_path = snakemake@output[["out_path"]]
qu... |
2300328c07f552f4bbd08c821265d2cef8c83f6dae1591d78a1b22db3b512cb2 | R | 7,869 | 239 | # Preprocessing and QC of DS2 raw data
# input :
# data_raw/02_DS2_raw_ssce.Rdata
# output:
# data/02_DS2_DblFinder.Rdata : computed scDblFinder doublet detection results
# data/02_DS2.ssce.Rdata : QC filtered, logNormalized dataset
# data/02_DS2.ssce.MKNN40.Rdata : igraph k=40 MKNN graph
# data/... |
62caad5eaf0faa518d6f24f563bca3f4ecfd261f1fbe660fca343d23e7f8e612 | R | 7,875 | 230 | #!/usr/bin/R
# --------------------------------------
# Comparison of published with own DEGs:
# - regional DEGs in this analysis
# Updated: 07/27/23
# --------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, 'multiRegion/load_metadata.R'))
library(tidyr)
library... |
23f8b2a8aeb8d292b1be14edd51241c98634dc8709b17ea994291602ae37f4bd | R | 7,892 | 230 | library(Seurat)
library(dplyr)
library(ggplot2)
library(limma)
library(muscat)
library(purrr)
library(scater)
##########################################################################################################
# MCT
setwd("G:/PFC_429_final_Spring2021/Data")
pbmc=readRDS("Exc_merge_40_percent_fi... |
19e734f2dd878165f0ced807d7d850747b449ac417dbc43367ac412337b1badc | R | 7,893 | 170 | #' Identify the last valid volume acquired in a given run.
#' Subjects often exhibit head movement after run ends (MATLAB closes), but scan hasn't stopped
#' This occurs because the MB raw transfer of the prior run is occurring, but does not finish before the current run
#' Thus, truncate mr files to be 12 seconds afte... |
6d58e903337530b2bf6e78c42f0729b86fa8f81cd94ad82d420f478f34c0a98b | R | 7,902 | 239 | #' Sets Precomputed Probabiliites as Global Variable
#'
#' Given a matrix, \code{Q_mat}, or log P(y|x), under the Poisson-Lognormal model.
#' Sets this as a global variable for fast computations in the future.
#'
#' @param Q_mat_loc Matrix of precomputed probabiliites, as previously computed by \code{\link{get_Q_mat}}
... |
864e30b3ae01a3ea3d32f0e60531cea7a181e9d5e91a6240dabb20f6e6c085ed | R | 7,943 | 169 | #' Generate a ggplot2 heatmap with row and column dendrograms
#'
#' @param dataMatrix A data.frame containing the input data.
#' @param orderCol Reorder the columns (default=T)
#' @param orderRow Reorder the rows (default=T)
#' @param dendroLineSize Size of the dendrogram lines (default=0.5)
#' @param fontsize Font siz... |
7ba038aae3950838256764b6a6630e97c377a008a41d22f64779ca54a114f216 | R | 7,955 | 212 |
library(tidyr)
library(ggplot2)
library(glmnet)
library(ROCR)
library(data.table)
library(monocle3)
library(gridExtra)
library(RColorBrewer)
# Get shared functions between this and runRegressionModel.R
source("./utilityFunctions.R")
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleC... |
84b3ec050d49d1d01a59275182233ff0ffb7f77f920d9dea30ac2d25456f000a | R | 7,957 | 210 | #!/usr/bin/R
# ---------------------------------------------------------------
# Score cells in the entorhinal cortex using the consistent DEGs:
# Updated: 12/06/2021
# ---------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metada... |
04d2bf7a3e265e79bed94ed76ea366e24e1bb6b3b504216065c487dcdca43c02 | R | 7,978 | 198 | #!/usr/bin/R
# --------------------------------------------------
# Aggregate the DE results across different methods:
# Methods: Nebula, MAST, Wilcoxon (cell + ind-level)
# Updated: 11/15/23
# --------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(bindir, '... |
665869137b699b77cf81c15701413e7924e92141c04e437a1ae9d0277e877fc0 | R | 7,980 | 251 |
# Get functions
library(monocle3)
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
# modStatus <- loadMonoclePackages()
library(tidyr)
library(ggplot2)
# Note: Need bedtools loaded for this! bedtools/2.29.2
library(cicero)
library("optparse")
# Get the passed paramet... |
3d1edd8b90c0e0785c9d60317d25455d222996ee4a3c4cb9b479b8ed0b00dba1 | R | 7,981 | 186 | ---
title: "Penn Machine Learning Benchmarks"
---
Penn Machine Learning Benchmarks (PMLB) is a large collection of curated benchmark datasets for evaluating and comparing supervised machine learning algorithms.
These datasets cover a broad range of applications including binary/multi-class classification and regressi... |
91784d9e75f537efbd59ae408fc1bd325ecfb2f4546e7d6b7e27b1eefb6ee118 | R | 8,009 | 220 | #!/usr/bin/R
# ------------------------------------------
# Load metadata for the multiregion project:
# ------------------------------------------
library(cbrbase)
library(RColorBrewer)
library(tidyr)
set_proj('DEVTRAJ')
# Arguments:
args = commandArgs()
if (length(args) > 0){
project = args[1]
} else {
proj... |
1c048a3bc61ca9673271a14b48420b9fd89938a9e486af1f330c6f13c397a92e | R | 8,012 | 280 |
#### SpectraArrays ####
## --------------------
# Similar to Assays class from SummarizedExperiment
# but allows 1-dimensional array and list elements
# and requires spectra to have unique array names
.valid_SpectraArrays <- function(object)
{
errors <- NULL
arrays <- try(as(object, "SimpleList"), silent=TRUE)
if... |
0fcfd7911189f9eaf1eaf7a2f4ba5d6ff17ce0183d7452da2cf054337f2ab988 | R | 8,020 | 214 | #!/usr/bin/R
# ---------------------------------------------
# Functions to load data at a pseudobulk level:
# Averaged at level of subtype + individual + region
# Updated 12/01/21
# ---------------------------------------------
library(cbrbase)
library(Matrix)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")... |
b1c458c254708116ecf5b289f605f00dc7b77bf69d955425b4e3e04f3492076e | R | 8,027 | 275 | #!/usr/bin/R
# ----------------------------------------------------------
# Calculate the cross-ct module correlations and thresholds:
# Updated 11/30/2021
# ----------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir... |
8515ad43075b7b4a1e78b54b8fd8a9df0fa54fcafb1c6018ad7a706cdef98124 | R | 8,030 | 178 | #' R6 class for running 3dFWHMx on a group of input files using a scheduler/cluster
#'
#' @importFrom R6 R6Class
#' @export
afni_3dfwhmx_list <- R6::R6Class("afni_3dfwhmx_list",
private = list(
fwhmx_batch = NULL, # batch object
wall_time = "10:00:00", # 10 hours default for all jobs
fwhmx_objs = list(),
... |
e0b0ab972ab054f24abac56905969146db278b03414a1b5edbab08b099e1094b | R | 8,035 | 203 | wd <- "~/projects/chimp_human_opnmf/"
setwd(wd)
# load in libraries needed for script #
# need to install pacman if not already installed
if (!require("pacman")) install.packages("pacman")
pacman::p_load(fs, ggplot2, dplyr, tidyr, reshape2, readr,
broom, tibble, MASS, sfsmisc, cocor, oro.nifti, neuroba... |
7f62e6648cb2fa90b19f7e59a7fe1c5e09a9427148cf964e876537655c605a8e | R | 8,048 | 217 | ---
title: "01_data_cleaning"
author: "Veronica Mandelli"
date: "2/3/2022"
output: html_document
---
# set paths
```{r}
work_dir = "..." # main folder
codepath = file.path(work_dir,"code")
datapath = file.path(work_dir,"results","data_wrangling_output") # path of data already extracted from NDA database
resultpath = f... |
0b5412be2c05a596d7ae8ce912e4df9b5fdca830dbcf572ced362924f931fb99 | R | 8,052 | 228 | #!/usr/bin/R
# ---------------------------------------------------------
# Replot the DEG enrichment table from the modules package:
# Updated 11/27/2021
# ---------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, '... |
27dd52af564b23d5464022f45b4cb461d96e7cff8ef852db4311947a0d2bae68 | R | 8,067 | 305 | ---
title: "Correlation MPA and SF, W, CI"
author: "Yaromir Kobikov <kobikov.yaromir@gmail.com>"
date: "Date: `r format(Sys.time(), '%d/%m/%Y')`"
output:
word_document: default
html_document:
toc: true
toc_float:
collapsed: false
editor_options:
chunk_output_type: inline
---
```{r setup, include=F... |
010295162d6297ea3d002cf2383161dafdd268f89163c25bc26687eefcf539f2 | R | 8,073 | 171 | Auc_Analyser = setRefClass(
"Auc_Analyser",
contains = "Analyser",
methods = list(initialize = function()
{
callSuper(
description = "For each time point (stimulus) and sample, the time of the
maximum value is found within a PeakSearchWindow in order to subsequently
calculate the area unde... |
fd98cee88c21aecbedf12f745802fc9461e3251df9dc9aad7d4aa496736381b1 | R | 8,090 | 155 | ## ---------- Script -----------------
##
## Purpose: perform differential analysis on mouse rTg4510 ONT targeted datasets using linear regression
## Transcript level - Iso-Seq, RNA-Seq (hybrid, already aligned to Iso-Seq)
## Gene level - Iso-Seq, RNA-Seq
## 1/ run DESeq2 (Wald and LRT)
## 2/ annotate results using cla... |
7a5c3ba18489b6208548d244cba2b872d3d8c9f6571d3b744179ca448b05d6ab | R | 8,094 | 252 |
#### Spectral binning ####
## --------------------------
# MSImagingExperiment
setMethod("bin", "MSImagingExperiment",
function(x, ref,
spectra = "intensity", index = "mz",
method = c("sum", "mean", "max", "min",
"linear", "cubic", "gaussian", "lanczos"),
resolution = NA, tolerance = NA, units = c("ppm", "... |
7423314e4d4df6b14432f47901c365d5a5436b92ba0ed672c04735ca40f03ac6 | R | 8,099 | 180 | get_means_sds <- function(cell_type, gene, de_results_list, params_to_test) {
de_results <- de_results_list[[1]]
ct_ind <- which(colnames(de_results$gene_fits$mean_val) == cell_type)
L <- dim(de_results$gene_fits$s_mat)[2] / dim(de_results$gene_fits$mean_val)[2]
ct_ind <- L*(ct_ind - 1) + params_to_test
means... |
f6aecbeb7388dcc31d356e94574720bd852c4de935d1a5ce69ccdcd832db54bf | R | 8,126 | 162 |
hardAssignAnatomicalSites <- function(inputCDS){
colData(inputCDS)$Anatomical_Site = "Not_Specified"
colData(inputCDS)$Anatomical_Site = ifelse(grepl("Apex", colData(inputCDS)$sample),
"Apex", colData(inputCDS)$Anatomical_Site)
colData(inputCDS)$Anatomical_Site = ifelse(grepl("Septum", colData(inpu... |
e4ad8def51481284555ad315cdb07827ff6c9ba23a8db68a423306a994cbd86d | R | 8,127 | 248 | fsl_do_convolve_reference <- function(input, kernel, phase = 0L, renorm = TRUE) {
n_input <- length(input)
n_kernel <- length(kernel)
output <- rep(0, n_input)
for (t in seq.int(0L, n_input - 1L)) {
kernel_norm <- 0
i_start <- max(0L, 1L + t + phase - n_input)
i_end <- min(n_kernel, t + phase + 1L)... |
0806a187909b8795279fd94a15a9e6def6d381fcbe28d7c6cfc449b8f403e363 | R | 8,139 | 190 |
#' archstabms__window_library_design
#'
#' Saturation combinatorial mutagenesis library design.
#'
#' @param fitness_abundance_file path to abundance fitness data (required)
#' @param pdb_file path to PDB file (required)
#' @param pdb_chain_query query chain id (default:A)
#' @param pdb_chain_target target chain id (d... |
cfe9bbbea4d376bce806853d42aded8eeb0a14aba776957796c3d9926916ab2b | R | 8,171 | 189 | library(data.table)
library(dplyr)
library(survival)
library(survminer)
library(broom)
source("00_read_prs.R")
dir_out.coxph.tab <- "./coxph_PRS_table/"
dir.create(file.path(dir_out.coxph.tab), showWarnings = FALSE)
#########
file.df_merged <- "dataframe.coxph_survival_input.rds"
if (file.exists(file.df_merged)){
... |
590f0ecb07a1858dcf990f75c94804368efdd83d340bf5ab30e043b4020ef90b | R | 8,181 | 212 | ---
title: "Bin1 Integration"
author: "Szi Kay Leung"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float: true
code_folding: hide
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
# libraries
suppressMessages(library("ggrepel"))
# output directory
output <- "/gpfs/mrc0... |
3ab3fa4efc534fd714158e028d3cf06303286cb9b13efe7bdfa3923a35baf4e1 | R | 8,190 | 228 |
library(tidyr)
library(ggplot2)
library(glmnet)
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-p", "--pValFIMOcutoff"), type="numeric",
default=1e-5,
help="Max FIMO p value to retain match", metavar="character"),
make_option(c("-f", "--feature... |
bcd6550b51f954678d0cb4039dfa0962220029c090d77624d33e655b1c3b3434 | R | 8,203 | 238 |
# Get functions
library(monocle3)
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
# modStatus <- loadMonoclePackages()
library(tidyr)
library(ggplot2)
# Note: Need bedtools loaded for this! bedtools/2.29.2
library(cicero)
library("optparse")
# Get the passed paramet... |
4f0c5967e98ffdf1c60a131df00288a14062190c037b319b21a84a34331780c4 | R | 8,207 | 238 |
# Get functions
library(monocle3)
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
# modStatus <- loadMonoclePackages()
library(tidyr)
library(ggplot2)
# Note: Need bedtools loaded for this! bedtools/2.29.2
library(cicero)
library("optparse")
# Get the passed paramet... |
c4267490e2e80c678b6349a4c3153fbe8af872da6e3ff977a17bb5ece523639a | R | 8,217 | 180 | #' Populates parcel-wise statistics from an atlas or set of clusters back into NIfTIs in the original space
#'
#' @param atlas_nifti The filename of the NIfTI image containing atlas values to match against \code{stat_dt}
#' @param stat_dt a \code{data.table} object containing the statistics to write to parcels in the ... |
50bf02f6369ef5daa024f1eccd5ce4893d11e763bb3e1a9605f4032654eaf44c | R | 8,243 | 200 | ---
title: "1-4xLPS_MicrogliaMorphology"
author: "Jenn Kim"
date: "06/11/2023"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# load packages and set seed
```{r}
library(MicrogliaMorphologyR)
library(factoextra)
library(ppclust)
set.seed(1)
```
# read in data
```{r}
dat... |
77e4ff4c585259bec90a94d502f9cb9eaa68ca212444377dd9386ea6405b548e | R | 8,264 | 213 | # Relative phase data and analyses
library(ggplot2)
library(ggpubr)
library(bpnreg)
library(openxlsx)
library(emmeans)
# Read in experimental data
dat = data.table::fread("your path/All_Hand_Position_Data.csv")
dat2 = dat[!dat$id %in% c('S010', 'S011','S012', 'S018', 'S019'),] # Removing subjects who did ... |
827e15655b3844d6614363f74165035247945d6314340cd36f84eb86962245b6 | R | 8,268 | 154 |
#' archstabms__plot_validation_scatter
#'
#' Plot correlation with validation data.
#'
#' @param input_dt data.table with model free energy estimates (required)
#' @param lit_inpath path to literature free energies (required)
#' @param report_outpath output path for scatterplots (required)
#' @param highlight_colour c... |
f8b216de69b645a4451d7ada1514a5772fc8affbff5dd8d994b53fde65bd1578 | R | 8,276 | 215 | #!/usr/bin/env Rscript
# Talkowski SV pipeline downstream analysis helper script
# Identify variants with unstable frequencies before/after minGQ
### Set global parameters
options(stringsAsFactors=F, scipen=10)
### Import pre-minGQ frequency data
load.preMinGQ.freqs <- function(pre.freqs.PCRPLUS.in, pre.freqs.PCR... |
d0278ddb47e2f1caa6a81d709810d695e2f2751b7a071b9fc7a468f2d3b39712 | R | 8,303 | 180 | #' spacexr: an R package for assigning cell types and cell type specific differential expression to spatial transcriptomics data.
#'
#' @section Running RCTD:
#'
#' To get started, create a \code{\linkS4class{SpatialRNA}} object (called \code{puck} here) for the
#' spatial transcriptomics data and a \code{\linkS4class{... |
9522cfb024a870dcf61e98fb40a4ab3111c28f4ae512d11e3a52cfd991ffb1d8 | R | 8,336 | 181 | #' This function reads an SPM.mat file and generate contrasts based on the design matrix specification
#'
#' @param output_dir location for SPM outputs and scripts for estimating contrasts. Must contain SPM.mat already
#' @param condition_contrasts see generate_spm_mat
#' @param unit_contrasts see generate_spm_mat
#' @... |
142ef2254cc9ee79f2179b3de178dcbe4791a094a6b72f8edd35c9a3d6bf3c01 | R | 8,339 | 165 |
library(dplyr)
library(pbapply)
library(data.table)
library(plyr)
library(broom)
Mathys_DA <- read.table(file = "Mathys_DA_path.csv", sep = ",", header = TRUE)
Mathys_DA$CD83_status <- ifelse(test = Mathys_DA$DA4 > 0, yes = "CD83_pos", no = "CD83_neg")
Fujita_DA <- read.table(file = "Fujita_DA_path.csv", sep = ",", ... |
92982169153418a7230068860ed6808a6af699790ff28098336c684a2ad794ed | R | 8,341 | 207 | wd <- "~/projects/chimp_human_opnmf/"
setwd(wd)
# load in libraries needed for script #
# need to install pacman if not already installed
if (!require("pacman")) install.packages("pacman")
pacman::p_load(fs, ggplot2, dplyr, tidyr, reshape2, readr,
broom, tibble, MASS, sfsmisc, cocor, oro.nifti, neuroba... |
2130de7cdd166f12b0d3fb7e99653d741406292d0c21de70b359e6f7237ca441 | R | 8,343 | 207 | wd <- "~/projects/chimp_human_opnmf/"
setwd(wd)
# load in libraries needed for script #
# need to install pacman if not already installed
if (!require("pacman")) install.packages("pacman")
pacman::p_load(fs, ggplot2, dplyr, tidyr, reshape2, readr,
broom, tibble, MASS, sfsmisc, cocor, oro.nifti, neuroba... |
c07831a19c9c17e1e8a2404983de21db32f38767e7d44c908ac63c2bc01d1ce5 | R | 8,355 | 223 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(tidyr)
library(tidyverse)
print("Libraries loaded, starting now")
monocle_theme_opts <- function()
{
theme(strip.background = element_rect(colour = 'white', fill = 'white')) +
theme(panel.border = element_blank()) +
... |
9bb30107d6da095a5ca3f4e5d905e836f0d6dc3a543ffcac31cf62efd7fa4de2 | R | 8,369 | 188 | validate.lassosum.pipeline <- function(ls.pipeline, test.bfile=NULL,
keep=NULL, remove=NULL,
pheno=NULL, covar=NULL,
validate.function=function(x, y)
cor(x, y, use="complete"),
... |
66d6a210083212ae17c1007291e1524fc2eb9fafd688dba17227bec5e6c86463 | R | 8,371 | 223 | library(testthat)
test_that("afni_3dlmer_setup builds 3dLMEr tables from refit model covariates", {
tmp_dir <- tempfile("afni_3dlmer_setup_")
dir.create(tmp_dir, recursive = TRUE)
mask_file <- file.path(tmp_dir, "mask.nii.gz")
file.create(mask_file)
harvested <- list(
l3_model1 = list(
contrast1 ... |
07d93c277864a001454c73c03fddb8207ee12aba47525c9c7c66ee4c1ef807e6 | R | 8,394 | 168 |
library(ggplot2)
library(dplyr)
library(readr)
library(stringr)
library(viridis)
library(tidyr)
library(ggrepel)
options(stringsAsFactors=FALSE)
# 9-13-21 David: Adding command line parser so I can specify the hyperparameter set used and use that to define input files/output names
library(optparse)
# Get the passed p... |
49d3522c7299a97b36bcc49a15f1633bfc3edf4f747d78e62c77b38532b2c5f2 | R | 8,400 | 284 | # For the specified k, for pairs of cognitive and network reconfiguration variables,
# 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 la... |
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