sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f575f2a9b2a9da4078ea96970dfe696fe53dc718e13adf12c383f122677bef35 | R | 1,098 | 35 | library(LSD)
library(data.table)
#############################
##Species-specific analysis##
#############################
pdf("Fig2B.pdf")
a=read.delim("pM10Kb_1KTest/predictions.txt")
actual=read.delim("57epigenomes.median_expr.txt",F)
colnames(actual)=c("Gene","UnscaledExpr")
actual$UnscaledExpr=log10(actual$Unsca... |
a0a5a224ea3a338b2f90b07899341a0fd48b491771078e9ce2b6b17a3656ce3c | R | 1,101 | 39 | ---
title: "Upload a dataset"
#runtime: shiny_prerendered
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
#library(shiny)
```
### Example
A dataset with two conditions and three replicates each would look like this:
```{r tmp, echo = F}
m <- matrix(rnorm(60, 200, 50), 6, 6)
r... |
8fce114e24da5265b90712de2fccc1e2b8f1265e871508d992639c4b65b37791 | R | 1,105 | 38 | #Convert seu object to anndata object
library(withr)
library(Seurat)
library(reticulate)
library(SCP) #Not with conda env activated
seu <- readRDS("directory/Consensus_broad_high_res_annotated_seu.rds")
Idents(seu) <- seu$Biological_replicate
min <- min(table(seu$Biological_replicate))
#Min = 4671
#Downsample func... |
0e1499219275ccffd5d793f4e1590ef344469eb0f2871b556e7d0b65c9bd253a | R | 1,109 | 32 |
#======================================================
# annote a variant to the nearest gene
#======================================================
find_closest_gene <- function(chr, bp, g,
chrom_col="chr",
pos_col="value",
... |
ba860a6bceb23c8831bf8d9f8c573170f08f9cf4b457df6dfbcbff145bef5dfb | R | 1,109 | 25 | # File: stepprotocol.R
# Author: Zhihua Li
# Date: 2016
# Version: 1.0
#
# Description: Helper R function to define voltage step protocols.
#
stepprotocol<-function(holdv, holdt,conv,cont,testv,testt,gapt){
#generic step protocol consisting a holding step, a conditioning step, and a te... |
8a921f14d22811bf4398b1b6dbaeb3fd46c353ddb3712909747ffd3cfd5d0d22 | R | 1,116 | 32 | #' Translation of a covariance matrix
#'
#' Translates a covariance matrix from one basepoint to another, Formally,
#' projects a covariance matrix onto the tangent space at one basepoint,
#' transports to the tangent space at the second basepoint, and projects back onto
#' the space of SPD marices.
#'
#' @param x A sy... |
24d714ffaa4da9dcc413c44d774bcba1b757a6d3f1ce83f326d6e90e245bfe77 | R | 1,117 | 38 |
#======================================================
# annote a variant to the nearest gene
#======================================================
library(data.table)
library(argparse)
parser <- ArgumentParser()
parser$add_argument("-v", "--variant_file",
help = "path to file with variant p... |
fd0bc76b90d140f8d975fd1350e6ec55680fb5ba99f55400597bae43fbefa273 | R | 1,119 | 42 | #' Aspirin after Myocardial Infarction
#'
#' @description
#' Meta-analysis on aspirin in preventing death after myocardial
#' infarction.
#'
#' Data example in Fleiss (1993) for meta-analysis with binary
#' outcomes.
#'
#' @name Fleiss1993bin
#' @aliases Fleiss93
#'
#' @docType data
#'
#' @format
#' A data frame w... |
9829299839bc31fe8adc3f2c2b7ad49739fc09dbf096972678ab4ed6e0049fc2 | R | 1,127 | 57 | library(lme4)
library(sjPlot)
library(performance)
library(ggplot2)
library(lmerTest)
library(multcomp)
library(effects)
library(report) # Load the package every time you start R
library(palmerpenguins)
library(car)
library(rcompanion)
df = read.csv("\\data\\behavior\\repeated_wordsfinal.csv")
colnames(df)... |
0528a58d91adb14f73eb4728b47d0174b4cd4d2afe6ea813038b21ea3cec0e24 | R | 1,134 | 43 | #############################################
#
# These functions are used to estimate the
# duration between peak and the hight after
# ETS rss estimation
ETSmetrices <- function(rss_rile){
sbj <- stringr::str_extract(rss_rile, pattern = 'sub-[0-9]*')
rss_raw <- readr::read_csv(rss_rile, col_nam... |
8d7159e17c086011f410c57b9f594b8ca23380920c0a14dbd2d511a6a199ee0f | R | 1,135 | 29 | library(ggplot2)
library(ggeasy)
# 设置频率和时间范围
frequency1 <- 0.1 # 0.1 Hz
frequency2 <- 0.03 # 0.03 Hz
time <- seq(0, 4 / frequency1, length.out = 1000) # 两个周期，时间从0到4/frequency1
# 计算两个余弦曲线的值
cosine_values1 <- cos(2 * pi * frequency1 * time)
cosine_values2 <- cos(2 * pi * frequency2 * time)
# 创建数据框
df <... |
deb0b9f45cdc17eda4ab70978a8cee78d2ed5e1a476b2d7db117e803b2522d3d | R | 1,136 | 51 | library(ppcor)
library(progressr)
library(foreach)
library(fastcluster)
# Will be the dataset "wang" or "pbmc"
dataset <- commandArgs(trailingOnly = TRUE)[1]
output_path <- "output"
library(data.table)
source("../utils/0-indices.R")
expression <- fread(file = sprintf("data/%s-pearson-resid.csv", dataset))
genesymbo... |
19d0ac1d08d36f61c887774437caafb5b5c886385a9a59c2df8b34cd6ced5a2e | R | 1,141 | 39 | library(dplyr)
library(ggplot2)
## set _DEBUG_BANDWIDTH = True (or _DEBUG_START_BANDWIDTH) in resquiggle.py
## tombo resquiggle bandwidth_test_reads/ genome.fasta --processes 8 > band_boundary_tuning.txt
dat <- read.table('band_boundary_tuning.txt', header=TRUE)
std_bw <- names(sort(table(dat$bandwidth), decreasi... |
c9310dbc88d966573849aa3d363acd0fce74b3c9ad14accf21ad70c62afbf54c | R | 1,141 | 43 | setwd("E:\\platform-paper\\fig2modules_figs3neurites_1224\\")
print(getwd())
#import df
df = read.csv("middle_files\\df_183brain_316region_density.csv")
#create corr
b_num = dim(df)[[1]]
r_num = dim(df)[[2]]-1
corr = data.frame(matrix(ncol = b_num, nrow = b_num))
colnames(corr) = colnames(df)[1:r_num+1]
ro... |
06c2a59584485c2be7ca840aa2b28680a7854eb67946beb078236336fbab53dd | R | 1,143 | 25 | library(ggplot2)
library(dplyr)
# def a function to format decimals in ggplot
fmt_dcimals <- function(decimals=2){
function(x) format(x, nsmall = decimals,scientific = FALSE)
}
# read the table including divergence
setwd("/Users/siyuansmac/Desktop/PhD research/Suzukii WGS analysis/dataset.nosync/annotation/Refseq/r... |
1c2047371e98a96f3edf4dbd94f0ff6603dc81d43dd729abdcafe6c42bc61f62 | R | 1,145 | 29 | # loading libraries
library(Seurat) # v5.0.3
library(SeuratWrappers)
library(patchwork)
library(dplyr)
library(ggplot2)
library(sctransform)
library(glmGamPoi)
# Load merged object after preprocessing
stress.merged <- readRDS("~/Downloads/scRNA/stress_merged.rds")
# Visualize transcripts per cell
VlnPlot(stress.merge... |
924246d1ae3c55ae56b19ff2047f97593974f1874d1474d397b61318ef91002c | R | 1,147 | 34 | #===============================================
# Perform PCAiR
#===============================================
# mostly taken from here:
# https://www.bioconductor.org/packages/release/bioc/vignettes/GENESIS/inst/doc/pcair.html
library(pacman)
p_load(data.table, GENESIS, SNPRelate)
cargs <- commandArgs(trailingOnl... |
698d0be41e8c709c3b2b1255d7e258111ab7a548d3245cc4b2391472c372f860 | R | 1,148 | 24 | #' NIDP annotation data
#'
#' annotation data for all NIDPs taken from the UKbiobank
#'
#' @docType data
#' @name anno
#' @format A data.table with 10 columns and 3935 rows:
#' \describe{
#' \item{gwas_phenotype}{character: UKB-derived neuroimaging derived phenotype (NIDP)}
#' \item{modality}{character: MRI neuroim... |
80df5a7698cea301e684e3792e86ff5938c62c1d8a2f34e16597940e1b7e4c9c | R | 1,149 | 49 | library(openbabelR)
convertTest <- function(){
smi = "cc1cccc1c"
inStr = openbabelR:::istreamFromString(smi)
outStr = openbabelR:::ostreamToString()
conv = openbabelR:::OBConversion(inStr,outStr)
message("setting formats")
openbabelR:::OBConversion_SetInAndOutFormats(conv,"SMI","SDF")
message("converting")
... |
c26607ecb17b64daa0243852bf287c2aada33b26ff7d754db7756a0d8ac52e0b | R | 1,152 | 41 | rm(list = ls())
setwd("~/Documents/Analyses/Data/Memory")
# Choose data
myData = read.csv(file="PatientData.csv" ) # PD patients
# Check data
head(myData, 10)
# LF group
myDataLF <- subset(myData, Randomisation.order==1)
# Create delta
myDataLF$DV <- myDataLF$Memory.task.2-myDataLF$Memory.task.1
# rank data
myDat... |
b153a70506681debb74fd60ff991c390d76a03b418bd4b88115eab9fbf41f466 | R | 1,154 | 42 | spd.dimred.congedo <- function(X, k = 2, ...){
n <- length(X)
S <- matrix(0, nrow = nrow(X[[1]]), ncol = ncol(X[[1]]))
W <- matrix(1/(n^2 - n), n, n)
Xisqrt <- lapply(X, isqrtm2)
for (j in 2:n) {
for (i in 1:(j-1)) {
L <- logm2(Xisqrt[[i]] %*% X[[j]] %*% Xisqrt[[i]])
... |
4eff5d4555bc80ef503d46ba2cec8062cb4c07b35f8f60bb9d4341ab2ebc78ed | R | 1,159 | 27 |
#' mochims__get_reference_id
#'
#' Get reference id with absolute amino acid residue position.
#'
#' @param input_dt data.table with id and mut_order columns (required)
#' @param position_offset residue position offset (default:0)
#'
#' @return Reference id with absolute amino acid residue position
#' @export
#' @impo... |
704eae05d82f270b32290fcff4e7b60dc8ebce74e42025f7d097b1aa8579281e | R | 1,167 | 50 | spd.mean.p.est <- function(x, p = .5, tol = .01, max.iter = 20){
n <- length(x)
k <- dim(x[[1]])[1]
phi <- .375 / abs(p)
# Initialize mean to mutually commutative case
X <- Reduce(`+`, lapply(x, mat.frac.pow, p)) / n
X <- mat.frac.pow(X, 1/p)
# Input matrices, depending on sign(p)
... |
e6a8d81440b5eb7221467a5a8338f772b8fe1f4b342091015d7d423fd8dbd149 | R | 1,169 | 33 | #!/usr/bin/env Rscript
# Load necessary libraries
library(argparse)
library(data.table)
# Setting up Argument Parser
parser <- ArgumentParser()
# Adding arguments
parser$add_argument("-i", "--input", type = "character", help = "Input CSV file name")
parser$add_argument("-s", "--snp_column", type = "character", defau... |
6ddb46599e8ec33f5dfa6d308596671c893599c05a9b650655ee3db49ec378bf | R | 1,170 | 34 | library(data.table)
library(latticeExtra)
getbest <- function(dir){
files = list.files(path=dir, pattern='.txt', full.names=T)
sbtable <- as.data.frame(do.call("rbind", lapply(files, FUN=function(file){
cmd = paste("tail -2", file, "| perl -ne \'@a=split /= /; print $a[1];\'")
tmp = t(read.table(textConnection(s... |
0e1b0e79772e30ed1c38824254092ea7b1e926083d17d2ff7f9e8dbc27c7cee3 | R | 1,186 | 42 | #============================
# Make covariate file
#============================
library(data.table)
library(readxl)
library(stringi)
rosmap <- as.data.table(read_excel(
"/data_global/ROSMAP/greg_20200109/dataset_843_basic_01-09-2020.xlsx"))
load('data/rosmap/mypcair.Rdata')
n_pcs <- 10
pcs <- as.data.table(mypca... |
207a2771716ecef6a80a96108ac5ac26eb4eb436efda1da0c97b1b0b7823e7de | R | 1,189 | 39 | NonIter1GFit<-function(x,y){
# non-iterative estimation, ised for parameter initialisation
x <- x[!is.nan(y)]; y <- y[!is.nan(y)];
x <- x[is.finite(y)]; y <- y[is.finite(y)];
x <- x^2;
y <- y-1;
n <- length(x);
# minimize absolute deviation
S = rep(0,n);
S[1]= 0;
S[2:n] = 1/2*(y[2:n]+y[1:n-1])*(x[2:n]-x... |
35032cc53ee2e0c71ec94a7070ee5bdf12b741b2f464a42735a5837229d2b393 | R | 1,190 | 30 | # loading libraries
library(Seurat) # v5.0.3
library(SeuratWrappers)
library(patchwork)
library(dplyr)
library(ggplot2)
library(speckle) # v1.2.0
library(sctransform)
library(glmGamPoi)
# Loading Seurat object
stress.merged.filt.sct.cca.umap <- readRDS("~/Downloads/scRNA/stress_merged_filt_sct_cca_umap.rds")
# Creati... |
0b80281d8d5a25bcee23272c115098a0a117491c23326134bae0389967c0b934 | R | 1,195 | 44 |
library(tidyverse)
set.seed(1234)
args = commandArgs(trailingOnly = TRUE)
out = args[1]
reference_name = args[2]
lab_path = args[3]
ontology_path = args[4]
ontology_columns = strsplit(args[5],... |
e46604e852a4fbb84b8c64d638b5a7ad35562e2b12c6ff1840be1e0bc37db49a | R | 1,196 | 48 | ---
title: "gctb_QC"
author: "Gokberk Alagoz, L&G Dept., MPI for Psycholinguistics"
date: "`r Sys.Date()`"
output: rmarkdown::github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(data.table)
```
##
```{r cars}
genSEM4gctb = fread("/data/clusterfs/lag/users/gokala/beat-dyslexiae... |
0cf34c4f5afa6819954958b49ab2b53494bfbbf2f4c43590d2c1e042eec295b1 | R | 1,197 | 50 | library(data.table)
library(dtplyr)
library(dplyr, warn.conflicts = FALSE)
library(readr)
save.image("debug.RData")
merge_data <- function(phenotypes_path,
SBP_path,
DBP_path,
variants_path,
output_path) {
# Read data in
p... |
37a08f1ab6979ef756bd186fe8cd19f7401b08c8ca5c3ae6c3ed0a0e3fd61ec0 | R | 1,197 | 42 |
library(data.table)
get_results <- function(files) {
nfiles <- length(files)
file_list <- vector(mode = "list", length = nfiles)
for (i in 1:nfiles) {
file_list[[i]] <- fread(files[i])
}
results <- rbindlist(file_list)
return(results)
}
get_file_paths <- function(prefix, pattern) {
paste0(prefi... |
625d2f72e00dfac31cc274c13d67bf5d450fbdd549b1569db1bda7fc0e774753 | R | 1,197 | 62 | library(Seurat)
##samples so that evenly distributed between batches
batchSample<-function(seur,batchs="batch",total=100,perturbation="perturbation",maxPerc=.3)
{
seur<-SubsetData(seur,names(seur@ident)[!is.na(seur@meta.data[,perturbation])])
seur<-SetAllIdent(seur,perturbation)
seur2<-seur
ret... |
21394e069c3ff39a00e25d75e124b410a52be1fab8ee333160a7f634f3be0990 | R | 1,202 | 53 | # HACK use conda env if/when this gets fixed: https://github.com/conda-forge/r-arrow-feedstock/issues/56
renv::activate()
library(ukbtools)
library(dplyr)
# Save image for debugging
save.image("debug.RData")
extract_ukb <- function(basket_path,
phenotypes_file,
basket_n... |
44453dda4df385933af40e2332c6dd40dd9072263712eb54221b8c7dd6bb6c19 | R | 1,203 | 40 |
#' mochims_position_class_violins_datasets
#'
#' Coupling scatterplots for multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param annotations Annotations directory (required)
#' @param base_dir Base directory (required)
#' @param output_dir Output directory (required)
#' @... |
2a2e086fdf14545d540deabb28a27f42cf4c971538a01159b80974bec0835569 | R | 1,208 | 38 | ---
title: "topoplots"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(tidyverse)
library(akima)
library(scales)
library(mgcv)
library(gridExtra)
library(png)
library(grid)
```
```{r}
df %>%
dplyr::group_by(Eyes, State, Electrode, Side) %>%
dplyr::sum... |
af59c7f913e46f9d011bbd8d71e9da3faf838b56c43e28323896266f28d353d4 | R | 1,216 | 40 | #' Barbiturates spectra from `.spc` example files
#'
#' A time series of mass spectra in a list of `hyperSpec` objects.
#'
#'
#' @name barbiturates
#' @docType data
#' @format The data sets consists of a list of 5 hyperSpec objects with a single
#' spectrum each. They are the first five spectra of the BARBITUATES.SPC... |
8e0cfd57bd322513c36ae5ef8ee4582c005e84be987f41e0697b022166043e36 | R | 1,231 | 52 | generate_new_splits <- function(is_regulator) {
# Split genes into three group. Group 3 will be used in both runs and
# group 1 and 2 in one run each. This way we can evaluate clustering
# performance on group 3.
shuffle_idx <- sample(
which(!is_regulator), sum(!is_regulator), replace = FALSE
)
grp_idx ... |
b7e8059a27a14fe85a56327d07acc19942d7b6a692e97f2428f4736694385a33 | R | 1,233 | 36 | library(Seurat)
library(dplyr)
library(rhdf5)
library(Matrix)
# Read in the data
h5_file = h5read("./mouse_leukocyte.h5", name="dge")
sample.data = t(h5_file$block0_values)
colnames(sample.data) = h5_file$axis0
rownames(sample.data) = h5_file$axis1
sample.data = as.data.frame(sample.data)
# Make seurat object
sample ... |
c493c85c246cab87f4acaa3286fcc0a8a9dd50cb9dc1c442416df809b59ff5e4 | R | 1,233 | 26 | options(stringsAsFactors = FALSE)
list.of.packages <- c("dplyr")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Package"])]
if(length(new.packages)) install.packages(new.packages)
library(dplyr)
argv <- commandArgs(TRUE)
sample.1000g = read.table("/data_global/1000g/integrated_call_sa... |
0dee47d2ef3192cdcbbac1ff2a3266c625d4cdb94ec030c80f89166374cc3f91 | R | 1,236 | 35 | #' @name hyperSpec-package
#' @title Package "hyperSpec": interface for hyperspectral datasets
#' @description
#' This package gives an interface to handle hyperspectral data sets in R.
#' Hyperspectral data are spatially or time-resolved spectra, or spectra with
#' any other kind of information associated with the spe... |
91f917b41da6f95f8c7a2786045947bcbfd1274b5c427ddb82bf7beca14ee0a9 | R | 1,242 | 40 | ## -----------------------------------------------------------------------------
##
## Somatic variant detection in lesional focal epilepsy
## Dictionary of constants and strings
##
## Author: Dr. Christian Bosselmann
##
## Date Created: 2023-09-05
##
## Copyright (c) Christian Bosselmann, 2023
## Email: bosselc@ccf.or... |
b41fc60392d39e2330af9aa39ed8afad56f7d7f2ea4028d080859df4d44285d3 | R | 1,243 | 48 | ## this is a script for applying PEER on gene expression data
## more detailed info can be found: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3398141/
# load library and data
# columns = genes, rows = samples
library(peer)
l <- read.csv("DA_30_norm.csv")
l[1:3, 1:3]
l2 <- t(l[-c(1)])
l2[1:3, 1:3]
colnames(l2) <- l$... |
34ca391a018ec1bc76744d379389cc13ab9cace155a260158fbfa5b3e8a933a8 | R | 1,244 | 66 | ---
title: "Behavioral analyises"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook,
#load libraries
```{r}
library("dplyr")
library('lme4')
library('lmerTest')
library('arrow')
require('flexplot')
library("tidyverse")
library('stats')... |
70d0d4b1b306e710eb5db5040ad0d3a570f5f42b612aa804f511155040017dc6 | R | 1,254 | 27 | minDPScore <- -250
plotDP <- function(dpDat, tbDat){
for(reg in unique(dpDat$Region)){
regDat <- dpDat[dpDat$Region == reg,]
regDat$Score[regDat$Score < minDPScore] <- minDPScore
mp <- mean(range(regDat$Score[grepl(reg, regDat$Region)]))
if (grepl('fwd', reg)){
tbReg <- ... |
6f75c7b3c9da3fa93b50dd288e70239285f503ad8911774497202729d5415618 | R | 1,256 | 27 | #----Figure_06_A----------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 06 Panel A for the connectivity analysis for
# Reinhard and Fukuda et al. 2024
#-------------------------------------------... |
d529a33727e90855f6d02211c4c660f6725105bad46003955266cd76e2faf86b | R | 1,260 | 66 | ---
title: "Behavioral analyises"
output: html_notebook
---
This is an [R Markdown](http://rmarkdown.rstudio.com) Notebook. When you execute code within the notebook,
#load libraries
```{r}
library("dplyr")
library('lme4')
library('lmerTest')
library('arrow')
require('flexplot')
library("tidyverse")
library('stats')... |
1dc9c2d914e511576605915f7777fb93e4c57d3ecbdabdce77f4027e8f5abac2 | R | 1,266 | 16 | #' Sentiment analysis with included 3-dimensional affect space consiting of rationality, social impact, and valence.
#'
#' @description Calls the sentiment function using the built-in dictionary. Note that this function will be slow on its first call, because the dictionary is only loaded if needed. This dictionary cov... |
44606321e1dfb62d8e425ed9c558112dc7989ae8b81088112a88ae03982ecb48 | R | 1,266 | 42 | #' Check if a matrix is symmetric and positive definite
#' @param ... One or more numeric matrices
#' @param tol Numerical tolerance for checking symmetry and positive definiteness
#' @details Function checks whether the matrix \code{x} is symmetric and positive
#' definite. Symmetry is evaluated up to an entrywise to... |
87c1b41eb2520777805281e9e912661bf3f5774fefe8794612a7de23cf34d4fd | R | 1,266 | 29 | source('./scripts/01_Setup.R')
setwd("./data/processed/rds")
aa= grep("_male_median.rds",list.files(), val=T)
aa2= aa
for (i in 1:length(aa2)){
fname = file.path('./data/processed/rds/',aa2[i])
readRDS(fname)}
all = lapply(aa2, function(x) readRDS(x))
all2= data.table::rbindlist(all)
all2= data.frame(all2)
delimx... |
58012304f3a0356e3ef0216983083a5fd71b739fb6a6f1bddeafe522e683912a | R | 1,272 | 24 | args <- commandArgs(trailingOnly = T)
filenamex=as.character(args[[1]])
turan_SegNorm = read.table(file.path('./data/processed/', filenamex, 'SegFixed'), header=TRUE, sep="\t")
turan_SegNorm$CHR= as.character(turan_SegNorm$CHR)
turan_ploidy <- readRDS(file.path('./data/processed/rds', paste0('cell_cn_', filenamex, '.r... |
9fa4312b9f8eac8601d88fac20497039070589613f0fc9d3be3674eaccc60c61 | R | 1,276 | 43 | ---
title: "Sequential Knit of DEG files"
author: "Jonathan Nelson and Katie Emberley"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
html_document:
toc: yes
toc_depth: 3
toc_float: true
number_sections: yes
theme: bootstrap
df_print: paged
code_folding: hide
highlight: pygments
-... |
3e35b0aff12c41a6a7268184b085bb5276fa75492841600e7b64c521eed6d960 | R | 1,277 | 36 | nlshrink_analytical <- function(x) {
n <- nrow(x)
p <- ncol(x)
if (n < 12)
stop('Sample size must be at least 12 for nonlinear shrinkage')
sample <- cov(x) * ((n-1)/n)
eig <- eigen(sample)
u <- eig$vectors
u <- u[,rev(1:ncol(u))]
lambda <- rev(eig$values)
lambda <- lambda[ma... |
914d18b921b85f02847c83ee41457d353ad43c6afb4e3d1e6ef62937adfb0a43 | R | 1,282 | 59 | #### Output Modules #####
#### XML export ####
dend_to_text_wrapper <- function(
R,
i,
filename
){
fileConn<-file(filename)
writeLines(paste("<root>",dend_to_text(R, i),"</root>"), fileConn)
close(fileConn)
}
dend_to_text <- function(
R,
i
){
me <- R$HCL$merge
#Leaf case
if(i <0){
index <-... |
9577fd70b91841b6a9175a71d8006cdaed14cc8fbce78403346de0744f5e5643 | R | 1,282 | 45 | library(data.table)
library(ordinal)
coloc <- fread("data/coloc_results.txt")
coloc <- coloc[!duplicated(coloc)]
coloc <- coloc[QTL_Source == "ROSMAP"]
coloc80 <- coloc[QTL_Type == "mQTL" & PPH4 >= 0.8]
npe <- coloc80[, names(table(GWAS_Phenotype))]
load("data/cpg.RData")
np_cpgs <- coloc80[, QTL_Phenotype]
cpg_cols <-... |
04c19bf42f20e55c72561ea6faa11e6b682b2ef99ea5a26f73aa3a7ac7d981ad | R | 1,286 | 30 | library(RColorBrewer)
library(zoo)
folder = args[1]
species = args[2]
for (type in c('gradinput','intgrad')){ #'saliency', 'elrp', 'deeplift'
b=list()
for(num in 1:10){ #10 folds of CV
say(type, num)
a=fastread(paste("zcat ", folder, type, '.', num, ".txt.gz",sep=''))
a$V2=(a$V2-min(a$V2))/(max(a$V2)-... |
e8408b6b612be05abd1aadfbdbe88d7baccff81a947d928371ccf1b5ba2feee7 | R | 1,290 | 31 | library(data.table)
library(latticeExtra)
cv_folder = args[1]
predfolder = args[2]
outfile = args[3]
files = list.files(path=cv_folder, pattern='.txt', full.names=T)
table <- do.call("rbind", lapply(files, FUN=function(file){
cmd = paste("tail -2", file, "| perl -ne \'@a=split /= /; print $a[1];\'")
tmp = t(read.t... |
c74e785614f6b433f5255d30b230197023941a769672c9525b694f7418ce3b9f | R | 1,292 | 30 | library(data.table)
library(ordinal)
library(mediation)
library(MASS)
library(readxl)
rosmap_clin <- setDT(read_xlsx("/data_global/ROSMAP/greg_20200109/dataset_843_basic_01-09-2020.xlsx"))
rna <- fread("../np_phewas/data/rnaseq/rnaseq.txt.gz")
grn_col <- colnames(rna)[grep("ENSG00000030582", colnames(rna))]
rna_cols <-... |
c2d5103262c836041d551b188ff0d31fec089b2084d7e1d3967befa0014130ce | R | 1,296 | 46 | ---
title: "Pathways_normalized"
output: html_document
---
```{r}
library(tidyverse)
#library(ComplexHeatmap)
library(pheatmap)
library(RColorBrewer)
#library(reshape2)
#library(janitor)
library(gapminder)
library(viridis)
```
```{r}
setwd("P:/PD_Diabetes/Manuscript_1/RNAseq/TPM_data/modeling")
data... |
fa31ee8ca22ebfacaaeb7ce407bad5be234d5aacca6b5b9e9f912209d0d09f8a | R | 1,300 | 43 | ---
title: "Sequential Knit of Microglia Subclustering COde
author: "Jonathan Nelson and Katie Emberley"
date: "`r format(Sys.time(), '%d %B %Y')`"
output:
html_document:
toc: yes
toc_depth: 3
toc_float: true
number_sections: yes
theme: bootstrap
df_print: paged
code_folding: hide
hig... |
34693d0058ebe76345fb005908289135864ef886f727538eed5ec82441a4b7be | R | 1,304 | 36 | #' string_levels_var
#'
#' HELPER
#' This function allows returning the unique levels of a factor into a new column and concatenating as a string for plotting.
#' This is useful when the factor/categorical variable levels/values are nested in the first element of dataframe.
#'
#' @param data A dataframe containing the ... |
6d9e9e3e6f744518f5e37350542ef27762230e0210a6cf429b470cf7390e320d | R | 1,309 | 42 | #' @describeIn DEPRECATED-read.ENVI
#' @include DEPRECATED-read.ENVI.R
#' @export
read.ENVI.HySpex <- function(file = stop("read.ENVI.HySpex: file name needed"),
headerfile = NULL, header = list(), keys.hdr2data = NULL, ...) {
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... |
91c81fceb9989858a2742d6c936010d4ffaf4e35c5749ddf689d151ff49d0c07 | R | 1,319 | 37 |
library(data.table)
library(rtracklayer)
library(stringi)
genes <- rtracklayer::import('/home/lmsh224/downloads/gencode.v40.annotation.gtf.gz')
genes <- as.data.table(genes)
genes[, gene_id_trunc := stri_replace_all_regex(gene_id, '\\..*', '')]
coloc_dir <- 'output/coloc/mega/'
coloc_files <- list.files(coloc_dir)
g... |
a891cfde1bdee5de9bd99635e5ee102abd0535c0b34602b4de9af0177bc2c18b | R | 1,319 | 42 |
#' mochims_thermo_model_results_datasets
#'
#' Evaluate thermo model results of multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param literature_free_energies path to literature free energies (required)
#' @param base_dir Base directory (required)
#' @param output_dir Out... |
5b795356f0f6c34b818d798d3e3761c8a2642c14244a52666ce6cbd776e5abad | R | 1,320 | 68 | ---
title: "Prepare Gene marker data"
output: html_document
---
This script is used to process the marker genes results of each brin region, to be used as input for the correlation analysis of astrocyte subclusters.
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load packages
```{r}
library(data.t... |
1ead6ba08abbbde6ff120c462e5b96f20d4a91e1b20415bd7b2d3bfe5c637cb4 | R | 1,324 | 54 | # prep POLMM results for METAL meta-analysis
library(data.table)
library(argparse)
source("code/functions/strip_alleles.R")
source("code/functions/make_or_95_ci.R")
parser = ArgumentParser()
parser$add_argument("-r", "--results")
parser$add_argument("--bim")
parser$add_argument("-n", "--null_model")
parser$add_argume... |
baa62be1d5e677c53454bb4d283f1ca221b95c335634fd9f69f5609fb9b44c94 | R | 1,326 | 45 | # # ---------------------------------------------------------------------------
# # Title: Combine RT data files
# # Contains: Code to combine individual RT data files into a big dataframe
# # Project: DPX-EEG
# # Author: Jose C. Garcia Alanis (alanis.jcg@gmail.com)
# # Date: Mon Sep 18 12:06:31 2023
# # ----... |
0f0331ab543cfb9d40437df44b6e26955152921409b1a424356a7bca52b6a530 | R | 1,329 | 48 | #' Empty `hyperSpec` object
#'
#' Empty produces an `hyperSpec` object with the same columns and wavelengths
#' as `x`. The new object will either contain no rows at all (default), or the
#' given number of rows with all data initialized to `spc` and `extra`,
#' respectively.
#'
#' @aliases empty
#' @author C. Beleites... |
0560b0b307751f0b3c74cb8f376e165c8c0d7bbbc9d10e19dfaa20705fff8323 | R | 1,342 | 32 |
###Script 10
#Export raw gene expressions for neurons only
library(Seurat)
library(dplyr)
library(SoupX)
#get non_neuronal subclass IDs
tmp <- readxl::read_excel("output/mapmycells/Allen_metadata.xlsx", sheet = "subclass_annotation")
non_neuronal <- tmp[c(grep("NN",tmp$subclass_id_label),grep("IMN",tmp$sub... |
56f9960e0e3c0f7e21e40d93942fbed6690d102e1aa383f897a09e8906bcdd2c | R | 1,346 | 46 | # Initialize subtype parameter c in function ParameterInitialize()
#' @importFrom mclust Mclust mclustBIC
.InitialC <- function(Y, q = 7) {
init <- list()
clust_result <- mclust::Mclust(data = Y, G = q, modelNames = "EEE", verbose = FALSE)
init$c <- clust_result$classification
init$mu <- clust_result$parameters... |
db37450df972fa6ba8415b4f97931f50f838e8f9baabac09c432732bbe8162f4 | R | 1,347 | 36 |
#' mochims__predict_fitness_3state
#'
#' Predict fitness from folding and binding energies for 3-state model.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param phenotype_name phenotype name (required)
#' @param folding_energy folding energies for 3-state model (required)
#' @param ... |
315d2d3122c2d7fea3ccfa24f234e18f9a035f0800f411247df3d79fe4b1b4e5 | R | 1,348 | 33 | #' Interpolation between two symmetric, positive-definite matrices
#'
#' Function smoothly interpolates between two SPD matrices \code{x} and \code{y}.
#' The interpolation is parametrized by \code{t}, where \code{t=0} returns \code{x},
#' \code{t=1} returns \code{y}, and \code{t=.5} returns the mean of \code{x} and \c... |
5bf66e33409f1549217cc1021b93109c56caf7159b4e65e1361e114b9929a311 | R | 1,352 | 53 | # Function -------------------------------------------------------------------
.droplevels <- function(x, ...) {
x@data <- droplevels(x@data, ...)
x
}
#' Droplevels for `hyperSpec` objects
#'
#' Calls [base::droplevels()] on the data.frame in `spc@data`.
#'
#' @param x hyperSpec object
#' @param ... handed to [b... |
7911d21cbe34e09d5c45c66bf9efead97b179f8e1b4c04f9f9e3ef0a3cbf5299 | R | 1,354 | 39 | ---
title: "Upload annotation data"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Expected format
Uploading annotation data is optional and not necessary to run the analysis. In the annotation file, the first column should include the name of each sample, as they are... |
bc1d50f2e6ae421055df6ff90e2b91f197a8aae45a97200df7c29ab3e58b9d94 | R | 1,355 | 52 |
#' mochims__distance_matrix_from_PDB
#'
#' Residue distance matrix from PDB.
#'
#' @param input_file path to PDB file (required)
#' @param residue_subset subset of residues (default:all)
#' @param query_chain query chain (default:A)
#' @param metric distance metric (default:'scHA')
#'
#' @return data.table with minimu... |
57d3a688d1e58dbed01b51b58170727cbaa2461568e1032ef115cff3fd69f5a5 | R | 1,363 | 66 | extrVec <- function(x, varname, levs, first = FALSE) {
res <- sapply(x, "[", varname)
#
if (max(sapply(res, length)) == 0)
return(NULL)
#
if (first)
res <- lapply(res, first)
#
if (length(res[[1]]) == 1) {
res <- unlist(res)
names(res) <- levs
return(res)
}
#
print(res)
stop("L... |
9fa5f44dbde963284b9a2915a3670a0f817f6e48a7e498e9efc32419fccd102e | R | 1,363 | 60 | #bootstrap of the correlation coefficient between the two versions of the measures
library(ggplot2)
#library(CarletonStats)
#library(boot)
library(bootcorci)
#test normality
shapiro.test(ukb_1000$proxyBMD)
ggplot(ukb_1000, aes(proxyBMD)) +
geom_histogram()
#######bootstrap
set.seed(123)
b_BMD <- bootCor(ukb_300... |
cdd9ba339e93278848726ed27ebf4467f12f0012a1835b3ccc9d6a8ed0fc6a30 | R | 1,364 | 44 |
ora <- function(gene,
database = NULL,
pAdjMethod = "BH") {
database <- fread(file.path(database), sep = '\t', header = T)
gene <- gene[gene %in% database$UniprotID]
if(length(gene) == 0) {
return (NULL)
} else {
# Genes and their counts in input and backg... |
c48130b1c6ca8a39f63355cb32e8ecabdc13d7a7582b7f973c160f644ceac39e | R | 1,365 | 51 | #' @name DEPRECATED-fix_spc_colnames
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Ensure that the spectra matrix has the wavelengths in column names
#'
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are p... |
e43b3669da3a49c638d0b231b7e0f7c65ffc101333c2d49c1293e3d3e6446f1a | R | 1,365 | 48 | # The contents of "read.txt.PerkinElmer.R"
read.txt.PerkinElmer <- function(files = stop("filenames needed"), ..., label = list()) {
## set default labels
label <- modifyList(
list(
.wavelength = expression(lambda / nm),
spc = expression(I[fl] / "a.u.")
),
label
)
if (length(files) ==... |
2d661f0e06aa2d527cf488aad07cd3e0e2cc2089a6336dbbf3570c6498a4b44e | R | 1,376 | 38 | library(zoo)
library(GenomicRanges)
library(mixtools)
file = args[1]
fold = args[2]
species = args[3]
if(species == 'human') expr=read.delim("all_crossvalidated_predictions.txt") else expr=read.delim("all_crossvalidated_predictions_mouse.txt")
kmerlen = 1
a=fastread(paste("zcat ", file, sep=''))
ids=a[,1]
preds = e... |
47f64cac2a05fe32c3374d962c7b6cd8b6147eea4c86982565ccfcd602da851a | R | 1,377 | 43 |
#quantile function for fitdist objects
quantile.fitdist <- function(x, probs = seq(0.1, 0.9, by=0.1), ...)
{
if (!inherits(x, "fitdist"))
stop("Use only with 'fitdist' objects")
myquantiles.fitdist(f = x, probs = probs, cens = FALSE)
}
#internal quantile function for fitdist
myquantiles.fitdist <- fun... |
6148173f0a1b7770bc2047b94dd9f4aba76b5b35a5300e8dd600e5e89ae60310 | R | 1,377 | 25 | #----02_update_root_ids---------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the file for updating root ids of clk neurons and NSC used in the
# connectivity analysis for Reinhard and Fukuda et al. 2024
# This script ha... |
86a0e6a9b16554d0a757c3e2d98ee1540e23e086299fa68ed012d0d482042274 | R | 1,379 | 35 | test_that("update.meta resets tau methods when adding cluster", {
dat <- data.frame(events = 1:5, N = 5:1 * 100, grp = c(1, 1, 2, 2, 3))
ma1 <- metaprop(events, N, data = dat)
ma2 <- metaprop(events, N, data = dat, cluster = grp)
ma3 <- update(ma1, cluster = grp)
expect_false(ma1$three.level)
expect_true(... |
fb92f00f925f9e797b8bd012cdf39e793d7cd3a7d48aa8213da6406f59615a7d | R | 1,381 | 38 |
#=====================================================================
# Intake a SAIGE null model and output residuals as phenotype in new
# phenotype-covariate data table
#=====================================================================
library(data.table)
library(argparse)
parser <- ArgumentParser()
parser$a... |
745c7ec80198dced1c58935dce01ffb1ab6d73b16517f09b851671f3c96a1645 | R | 1,387 | 40 | args <- commandArgs(trailingOnly = T)
filenamex=as.character(args[[1]])
selectedCells = read.table(file.path('./data/processed/', filenamex, 'analysis'), header=TRUE)
analysisType = "mad"
analysisID <- "analysis"
loc= readRDS(file.path('./data/processed/rds', paste0('location_', filenamex, '.rds')))
loca= loc[loc$CH... |
0765b1f7c3549460ca0c13e1a596f71ac684fd7bff0410bf6decaf7f56ddc846 | R | 1,388 | 46 |
#====================================================
# Get SAIGE results files by chromosom and rbindlist
#====================================================
library(data.table)
library(argparse)
library(stringi)
parser <- ArgumentParser()
parser$add_argument("-p", "--prefix",
help = paste0("... |
78204f8a38e51b7de4ac3b83f0284bf14d2a01f5acd9b7a71b4f0475895ee45e | R | 1,390 | 42 | # Load the stats package (if not already loaded)
library(stats)
#EA
# Define variables
N <- 20000 # Total number of genes in the background set
K <- 1642 # Number of genes in the gene set of interest
n <- 149 # Number of genes in the sample being tested
k <- 20 # Number of overlapping genes between the gene... |
3d06ac772c0c40d119a3eaa15ab5501ac41a6a0e2baffa7cf4d89e82659feba9 | R | 1,392 | 45 | library(diffusr)
library(matrixcalc)
library(MASS)
library(Matrix)
dir_data <- '../data'
dir_sparse_index <- paste(dir_data, 'rwr_mat.txt', sep = '/')
adjacency_mat_sparse_index <- read.table(dir_sparse_index, header = TRUE, sep ='\t')
row_index <- as.matrix(adjacency_mat_sparse_index$row_idx)
col_index <- as.matr... |
f3ef25596cc36f4bf2d917c29518ddd451f0a059efe02acdeabd93b7d7622ea5 | R | 1,393 | 46 |
library(diffusr)
library(matrixcalc)
library(MASS)
library(Matrix)
dir_data <- '../data'
dir_sparse_index <- paste(dir_data, 'rwr_mat.txt', sep = '/')
adjacency_mat_sparse_index <- read.table(dir_sparse_index, header = TRUE, sep ='\t')
row_index <- as.matrix(adjacency_mat_sparse_index$row_idx)
col_index <- as.mat... |
4640672ac5bafac9b96e3d5a506782a21f3fde8193d375a238bc78b675b7259c | R | 1,400 | 42 | #! /usr/bin/Rscript --vanilla
#==============================================================
# perform PC-AiR
#==============================================================
library(data.table)
library(GENESIS)
library(SNPRelate)
library(argparse)
parser <- ArgumentParser()
parser$add_argument("-b", "--bfile",
... |
77f9da574496f314d908496d1318b94c334c2a8d53cc7c0bc0a191611e0f3512 | R | 1,408 | 64 | # Calcuate meta.
# args[1]: The input matlab .mat file. Has: 'TVal','N1','N2','nTests'
# args[2]: The name for the output matlab .mat file.
# install the following R packages:
#
# install.packages("metansue")
# install.packages("R.matlab")
#
# More details about 'metansue' R package can be found at
# https://www.met... |
6764c33a5c8888dfd9addfd316decd7ce69e772c33913a83abf73308ceeb58e9 | R | 1,410 | 41 | #' @name extract_diffusion
#' @title Extract latitudinal diffusion of a crop
#'
#' @description `extract_diffusion()` extracts the diffusion of a crop by latitude.
#'
#' @param database A data.frame containing a crop database.
#' @param direction A string stating the direction of diffusion (i.e., north).
#' @return A t... |
b73b1945250839536ff445f3985d1532d143c0eec40ee182e35b6060ea675c3b | R | 1,410 | 46 |
#-------------------------------------------------------------------------------
# ggplot themes for figures
#-------------------------------------------------------------------------------
#' theme with border
#' used for volcano plots
my_border_theme = function(){
theme(
axis.line.x = element_line(color = "b... |
df3cebab4cf8e1715acf867dd73ff2722640fe3f5dcf9bf1a66b74cb83e2711e | R | 1,410 | 49 | ---
title: "Pupilla"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Pupilla}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#"
)
```
An R package for the processing and analysis of eye-track... |
e25d0adbe1b69adfa3b6ad595371c02dbb380e8a4416818fe5707e2137175fcc | R | 1,412 | 53 | test_that("metacont gives no weight to non-positive or missing sample sizes", {
n.e <- c(10, 0, NA, 20)
n.c <- c(10, 10, 10, 20)
mean.e <- c(2, 3, 5, 6)
mean.c <- c(1, 1, 1, 2)
sd.e <- rep(1, 4)
sd.c <- rep(1, 4)
expect_warning(
metacont(n.e, mean.e, sd.e, n.c, mean.c, sd.c),
"non-positive values... |
e63f887250c2347e0bdbb4c3fa503f26ea8f908f639e5a7c0764309bff896fc4 | R | 1,413 | 51 | ## -----------------------------------------------------------------------------
##
## Somatic variant detection in lesional focal epilepsy
## This script collects plots and finalizes them for publication
##
## Author: Dr. Christian Bosselmann
##
## Date Created: 2023-09-05
##
## Copyright (c) Christian Bosselmann, 202... |
ef12916cbcf4588aee0b1975e47f091f8d7bdea94b1c6b6261ff1e1aadca6387 | R | 1,413 | 57 | #### Notes ####
# Functions for computing expression specificity
#### Usage ####
# Step 1: Compute mean expression per tissue (if multiple samples per tissue)
## mean.expr <- mean_expr_per_tissue(expr.matrix, metadata)
# Step 2: Compute tau from mean expression
## tau <- compute_tau(mean.expr)
#### Sourced functions... |
f9eb4fb94dbb0759c4994f5dd3b56a0e4af8f0068914707411669ce0d5c07ed1 | R | 1,417 | 35 | # Write a celltype specific version of the count table with plasmid and each celltype
options(stringsAsFactors = FALSE)
args = commandArgs(trailingOnly=TRUE)
cond.data <- args[1] ## Tab delimited file with celltype in first column and number of replicates in the second. Should include DNA/plasmid
count.table <- args[... |
9db6df10d894091d232f073661bf722ebb2a8debc8768453910abacbdf98e4e2 | R | 1,428 | 36 | library(data.table)
library(rtracklayer)
library(stringi)
# colocalization results
pheno <- fread("shared/nacc_rosmap_act_np.pheno")
phenos <- colnames(pheno)[3:ncol(pheno)]
coloc_list <- vector(mode = "list", length = length(phenos))
names(coloc_list) <- phenos
for (i in 1:length(phenos)) {
p <- phenos[i]
coloc_pa... |
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