sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
16f1555ec55aa00346e15a462539387d5b02bad357862297fb7fa6bd81127706 | R | 886 | 28 | args <- commandArgs(TRUE)
if (length(args)==0) {
stop("At least one argument [input file] must be supplied!", call.=TRUE)
} else if (length(args)==1) {
inputfile = args[1]
file_ext = substr(inputfile,nchar(inputfile)-2,nchar(inputfile))
if (file_ext == '.am'){
outputfile = paste(substr(inputfile,1,nchar(... |
fba159a0c624ef0f9a40b4a6a92b7ae4673a4d1141884669efe6805e50b794e4 | R | 896 | 27 | # Merge all permutation files into one file
library(tidyverse)
library(glue)
# load permutation data
path <- "bids/derivatives/meg-derivatives/grand_ave/brain-space/visual_model/permutations/"
path_save <- "bids/derivatives/meg-derivatives/grand_ave/brain-space/visual_model/permutations/all_perm_merged.csv"
path_sa... |
76ce0189a59a1de370c774b1f56dcf205031c238601517ade17df6d77da33067 | R | 902 | 30 | require(gierer)
rgc.file <- system.file('examples/Gierer2D-500-WT-retinal-gradients.txt', package='gierer')
sc.file <- system.file('examples/Gierer2D-500-WT-SC-gradients.txt', package='gierer')
op.file <- "/tmp/wts.dat"
rgc.dat <- as.matrix(read.table(rgc.file))
tec.dat <- as.matrix(read.table(sc.file))
a <- gierer.f... |
433fc334f0bbe9d70217d34fb926123056d1231470932bf2df1c7703e8297332 | R | 904 | 35 | ## Code to run and deploy Shiny app
## Code provided by Leonardo Collado-Torres
## Available at:
## https://github.com/LieberInstitute/Visium_IF_AD/tree/master/code/05_deploy_app
library(rsconnect)
library(here)
## Or go to your shinyapps.io account and copy this
## Here we do this to keep our information hidden
lo... |
bf2c4448f8aa3f3ddb81f56581f6b6c09783d3e5608293329ba86f80f8f11ccc | R | 909 | 30 | library(tidyverse)
d<-read_tsv('SuppData2.txt')
d2 <-d %>%
group_by(CRE) %>%
summarise(ntags=n()) %>%
filter(ntags>3) %>%
inner_join(d)
d2 <- d2 %>% mutate(log2expr = log2(d2$Exp_Mean))
d3 <- d2 %>% group_by(CRE) %>%
summarise(mean_log2expr = mean(log2expr)) %>%
inner_join(d2) %>%
muta... |
7b51ef031473074b03c14a75182316472c2cc6b3fc58a3068364d8127d0d87d0 | R | 910 | 42 | library("SingleCellExperiment")
library("lobstr")
library("here")
library("scuttle")
library("sessioninfo")
load(here("processed_data", "SCE", "sce_updated_LC.rda"), verbose = TRUE)
lobstr::obj_size(sce.lc) / 1024 ^ 3
# 5.413584 B
assays(sce.lc)$counts <- NULL
lobstr::obj_size(sce.lc) / 1024 ^ 3
# 4.861437 B
assays(... |
7a5ab07fb0ddfcb9a38a61d8dd221e5900cc50d55c9b1813f79a1976736a697e | R | 916 | 27 | ## Generic functions for package scalop
#' @export
setGeneric("rowFtests", function(x, fac, var.equal=TRUE)
standardGeneric("rowFtests"))
#' @export
setGeneric("colFtests", function(x, fac, var.equal=TRUE)
standardGeneric("colFtests"))
#' @export
setGeneric("rowttests", function(x,
... |
a1cfd1393391d010a39b2d218ac8660d0be240e1bf0b6b4c644b8097c96610de | R | 917 | 22 | #' @title Keep rows whose names match a pattern
#' @description row_subset() is a wrapper around m[str_detect(rownames(m), pattern), ]
#' @param m Input matrix or data frame.
#' @param pattern Pattern to look for. The default interpretation is a regular expression, as described in stringi::stringi-search-regex. Control... |
55d28faeeb3c1c6a9d509461b8476d0aef8a10ce54adf0fd1d002c1359600527 | R | 924 | 43 | library(Signac)
library(Seurat)
library(argparse)
library(BSgenome.Rnorvegicus.UCSC.rn6)
parser <- ArgumentParser(description = "process input arguments")
parser$add_argument("--rds", action = "store",
help = "path to rds object to compute gene activity for")
parser$add_argument("--out", action = "store",
help = "w... |
f74203e69bb6754171aabc9641f1412e1b3cdbb34671bfbd53df3317fd0def75 | R | 926 | 22 | #' @title Keep columns whose names match a pattern
#' @description col_subset() is a wrapper around m[, str_detect(colnames(m), pattern)]
#' @param m Input matrix or data frame.
#' @param pattern Pattern to look for. The default interpretation is a regular expression, as described in stringi::stringi-search-regex. Con... |
161c3d03b2f3b9d1970ae7df73e403bfcc23c5e520bb98554d3604db7aa87733 | R | 927 | 23 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' Compute the Local Inverse Simpson Index (LISI)
#'
#' @param D Distance matrix of K nearest neighbors.
#' @param knn_idx Adjacency matrix of K nearest neighbors.
#' @param batch_labels A cate... |
41d69e6b34ecb5b47bc2d9e1b9ccc6b8f9e6548f86037461ca330ddada78a210 | R | 928 | 28 | #' @title Turn a list into a dataframe
#' @param L a list
#' @rdname ldcast
#' @return a dataframe with as many rows as the longest vector in the list
#' @export
ldcast = function(L, na.rm = FALSE, na.value = NULL) {
if (!is.list(L)) L = list(L)
Class = unique(sapply(L, class, simplify = F))
if (Class %in... |
0da1bd2986dd51ac267e16477c6753378de005ed3b6ded0090b8746a21d0a545 | R | 929 | 21 | args = commandArgs(trailingOnly = TRUE)
paintor <- read.table(args[1], header = T)
causal <- read.table(args[2])
snps <- dim(paintor)[1]
paintor$Posterior_Prob <- paintor$Posterior_Prob/sum(paintor$Posterior_Prob)
x = max(paintor$Posterior_Prob)
summ = x
set <- paintor[which(paintor$Posterior_Prob == x)[1],"pos"]
pain... |
7a0f3afe72231f2e909b6237dec08f023e2198f54959bead3ad1d7eab0b80ff9 | R | 930 | 31 |
library("ctc");
library("ape");
get_cluster_info = function( Rdata_file ) {
load(Rdata_file);
ordered_genes_file = paste(Rdata_file, ".ordered_gene_matrix", sep='');
ordered_genes = hc_genes$data;
write.table(ordered_genes, file=ordered_genes_file, quote=F, sep="\t");
gene_tree = hc2Newick(... |
cd2bd62d8fa1d365dd17d165071c7393336110a297ffa90f0f0bae3a5f41a27e | R | 931 | 27 | # SingleR spot annotation wrapper script.
library(SingleR)
singleR <- function(st_expr_df, sc_expr_df, sc_labels,
n_centers, de_n, de_method) {
##### Runs SingleR spot annotation #######
st_expr <- as.matrix( st_expr_df )
sc_expr <- as.matrix( sc_expr_df )
##### Subsetting to genes in com... |
b96cc1ca6d79a6c127c4434f280d807b1972039aee1101c13aa24820401db583 | R | 932 | 29 | library(SeuratDisk)
library(Seurat)
library(tidyverse)
library(here)
## load in the filtered Seurat object
obj_merged = here('data/tidy_data/Seurat_projects',
"BU_OUD_Striatum_refined_all_SeuratObj_N22.h5Seurat") %>%
LoadH5Seurat()
DefaultAssay(object = obj_merged) = 'RNA'
names(obj_merged[[]])
... |
e002126d8a73355875c0c0299e1a095e773e7986503dcfba0da36639b12137b9 | R | 940 | 43 | ## test that the transformation interface works in R.
library(SimpleITK)
translationTest <- function()
{
fname <- deparse(sys.call(0)[[1]])
dimension <- 3
tx <- c(1,2,3)
translation <- TranslationTransform(dimension, tx)
offset <- translation$GetOffset()
if (! (tx[1] == offset[1] && tx[2] == offset[2] &&... |
945ac3f85d10fedbf4315bd650bc0be3c32a84b155e69be0c67e50b57fd4bb51 | R | 948 | 29 | ###################################################################################################
#run BayesTraits
#Run "Instructions_BayesTraits_CommandLine_VarRates Tree.docx"
#Open PP_Output.txt and save rescaled Branch lengths in "PP_Output_..._rBL.txt"
#Note to delete the first row of branch lenghts of the... |
9b59a4c157e914784aa7d2277b4f52dd615e560e52572a6b26108311047c5933 | R | 948 | 22 |
#' @title Flip the (nested) elements of a character vector (or list) with its names
#' @description A convenience function to switch the names and elements of a named character vector or a named list of character vectors.
#' @param X A named character vector or a named list of character vectors.
#' @return A named cha... |
39aea59053a6f7964fcf3c60cd0cfdc2dce0aef9805a76f8f51135985e5d9190 | R | 953 | 25 | ################################################################################################
# Generate Supplementary Figure 12, Supplementary Figure 13, Supplementary Figure 14
################################################################################################
setwd("D:\\PhD\\Lab\\Project_3_SVG\\... |
db5dad1945b9115e3c0fbb67a67aa699b455f1406250874762807c8a0d065382 | R | 953 | 25 | ################################################################################################
# Generate Supplementary Figure 16, Supplementary Figure 17, Supplementary Figure 18
################################################################################################
setwd("D:\\PhD\\Lab\\Project_3_SVG\\... |
4fe94cf89510c8e6cec9d0b5a8abdcdb0505e7335f79278f08b918b5dc4a5481 | R | 960 | 37 | data <- read.csv("E:/VANS/data/total_mmse_genecounts_normalized.csv")
coeff <- c()
pv <- c()
mmse <- as.numeric(data[1,2:length(data)])
for (i in 2:length(data$X))
{
test <- cor.test(mmse, as.numeric(data[i,2:length(data)]),
method="spearman",
alternative="two.sided")
coeff... |
b86d90ee1bea2cc43a822a60c8f7de9b0fdaa513bc0ba1d0576509953be620b9 | R | 960 | 22 | ########################################
# Below are the variables that will be used in the model
########################################
##### Therapeutic variables #########
# Estimated reduction in annual rate of repeat expansion for cells transduced with therapeutic. Example: "4" will yield a four fold lower rat... |
d1620222125283a1364a6668d04fed76913b98ac4441fb71ef5940a5cf75d4c0 | R | 973 | 31 | suppressMessages(library(Seurat))
suppressMessages(library(Signac))
library(ggplot2)
library(argparse)
parser <- ArgumentParser(description = "process input arguments")
parser$add_argument("--rna", action = "store", type = "character",
help = "path to snRNA-seq rds file")
parser$add_argument("--out", action = "store"... |
00cb4ea641b090a5a256b424f48cdcaca28cda7cc74026c35e8fac2e2e3500aa | R | 977 | 32 | ---
title: "Supplemental Table N"
author: "Anthony Fischer"
date: '2022-06-14'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
source('../../../../code/mpra_tools/mpra_tools.r')
library(ggpubr)
library(MASS)
theme_set(theme_bw(base_size = 8))
```
##This markdo... |
ff3b7a56633f3ac363019aa1c32818bec51e549997d3eba1cf55e7ca91ec6c02 | R | 980 | 21 |
#' @title Center matrix rows by subsets of columns
#' @description
#' @param m a matrix
#' @param groups list of character vectors specifying the column ids to center by. Default: split_by_sample_names(colnames(m))
#' @param by how centering should be performed. One of 'mean' or 'median'. Default: 'mean'
#' @retu... |
d9404a8b71f1c88799d08323004a3b753ee179e1030329d7295bd0e1133db306 | R | 982 | 36 | library('org.Mm.eg.db')
##########preso da sclvm on GITHUB
getEnsembl <- function(term, species = 'mMus'){
if(!(species %in%c('mMus','Hs'))){stop("'species' needs to be either 'mMus' or 'Hs'")}
if(species=='mMus'){
if(require(org.Mm.eg.db)){
xxGO <- AnnotationDbi::as.list(org.Mm.egGO2EG)
#org.Mm.egENSEMBL
x <- org.Mm.... |
ffefb1406864d5ebf34bf0927fde5e59bf7d588b6894f8064f7d4a86212a976b | R | 987 | 41 | # distance based on positive TCRs from single cell data
HLA_filepath = "../data/HLA_I_pseudo_40.csv"
df_HLA = read.csv(HLA_filepath, header = TRUE)
pos_filename = "st4_HLA_I_zheng_2021_ensemble_reshape_20.csv"
df_pos = read.csv(paste0("../results/", pos_filename), header = TRUE)
dim(df_pos)
head(df_pos)
stopifnot(d... |
28f0516f2739a93505feffb7ff26f2304b6d9d8bd5aaa8a10f05b1dd5e350838 | R | 988 | 40 | #' Save as single-line fasta
#'
#' @export
#'
writefasta <- function(x, outpath=NULL, ext=".fa") {
if (!is.null(names(unlist(x)))) {
for (i in seq_along(x)) {
cat(paste0(names(x[[i]]), "\n", x[[i]]), sep="\n", file=paste0(outpath, "/", names(x)[i], ext))
}
} else {
stop("Need names")
}
}
# Write single f... |
a50d2b49e5cb2c3c0e75949fc368fa7624f3bdc2b4967819d024f1e01824573b | R | 997 | 27 | #' Summary statistics of MAF
#' @description Summarizes genes and samples irrespective of the type of alteration. This is different from \code{\link{getSampleSummary}} and \code{\link{getGeneSummary}} which returns summaries of only non-synonymous variants.
#' @details This function takes MAF object as input and return... |
bff5808b64b745ceb5039c0e2624808d441018f415ee7fefe6c3b69bd3585482 | R | 999 | 34 | #' Convert multi-line to single-line fastas
#'
#' @param fastapath path to FASTA files
#' @param outpath output path
#'
#' @export
#'
# fasta <- files[1]
multi2single <- function(fasta, fastapath=NULL, outpath=NULL) {
# lots of files
if (!is.null(fastapath)) {
files <- list.files(fastapath, pattern=".fa$", full=... |
67820949430f1145be79a3f09902f87eded44f79629c6d390e179a07465793e0 | R | 1,004 | 51 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(finalfit)
library(mice)
```
# Load Data
```{r}
cog.data = read.csv("data/cog.data.csv")
```
# Prep Cog Data
```{r}
cog.data.ordered = cog.data[order(cog.data$f... |
acdbcd0b31f514c2f0143288464a88f07fc2b24766fe224d44dea7a8fc1ef009 | R | 1,008 | 35 | #' @title fold changes
#' @description fold changes
#' @param x matrix or character vector with matrix column names
#' @param y matrix to compare against or matrix with x and y column names
#' @param ... is.log and cutoff
#' @return fold changes
#' @rdname foldchange
#' @export
foldchange = function(x, y, ...) {
U... |
199b5d65221dd4038093ecedf1a3b6bb895fe8dd4bee0e678ddf59571c2895c6 | R | 1,009 | 35 | library(Seurat)
options(Seurat.object.assay.version = "v5")
library(stringr)
library(purrr)
Sys.setenv("_R_USE_PIPEBIND_" = TRUE)
projdir <- here::here()
rdir <- file.path(projdir, "package/R")
import::from(.from = "cembav2env.R", .directory = rdir,
Sa2Integration)
import::from(.from = "utils.R", .directory = rdir,
... |
5e31ac4796e228f7f47f860630d2adc40dd34c5428fa0650b1575705e9d4ac35 | R | 1,015 | 22 | #############################################################
# Generate 3D simulations with dropout issues
#############################################################
InputDir <- "..\\Data\\3D_Standard_Sim\\"
OutputDir <- "..\\Data\\3D_Standard_Sim_Dropout\\"
InputFiles <- list.files(InputDir)
for(InputFil... |
81b068a4ffbde344c9ecbf6378c914738d1ea2910af2d2ee2adaccf6e0516c88 | R | 1,016 | 35 |
# compute the sequence score matrix based on blosum62 using python
# load the results and compute distance based on the formula in
# NetMHCpan paper
HLA_filepath = "../data/HLA_I_pseudo_40.csv"
df_HLA = read.csv(HLA_filepath, header = TRUE)
df_blosum62_X_score =
read.csv("../results/st5_blosum62_X_seq_align_scor... |
fc381b3117e6ab347a4d0c61757f92843efe7773798333a287982b05486efedf | R | 1,016 | 28 | ## conda activate r4
## packages for data table processing
library(tidyverse)
library(data.table)
library(here)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
options(repr.plot.width=11, repr.plot.height=8.5)
DATADIR='data/tidy_... |
ecad97cf654143502130c000776c9eaf6e167f7ee25e08b8986672c3ddcf63f9 | R | 1,017 | 33 | library(dplyr)
library(RColorBrewer)
library(gplots)
load("cluster_1_2_3.RData")
load("bulk_dev_RNAseq.RData")
rownames(bulk_RNAseq) = bulk_RNAseq$WBGene
bulk_RNAseq = select(bulk_RNAseq,-c("WBGene","max"))
my_palette <- colorRampPalette(c("lightgreen", "black", "red"))(n = 299)
col_breaks = c(seq(-6,-2.01,le... |
c4355bdf81de35b1406c70fd49f7b3bb3ef652b5f013934b3c611d3342272471 | R | 1,019 | 41 | #######################
##### Kimura fits #####
#######################
# Last edited: 31/03/23 by LVB
# Description: Fitting the Kimura distribution to single-cell data.
#---- Packages
library("tidyverse")
library("kimura")
library("parallel")
#---- Data
load("data/parsed/01-parsed-data.RData")
#---- Kimura fits
... |
b4ef49b1155d1a2af7e22a3d14d9b3c8f5b7f2d224707604af6e3aed7911b87f | R | 1,021 | 32 | #get libraries
basic_libraries <- c("tidyverse",
"devtools")
for (lib in basic_libraries) {
if (require(package = lib, character.only = TRUE)) {
print("Successful")
} else {
print("Installing")
install.packages(lib, repos="http://cran.r-project.org")
library(lib, character.... |
d09c0fb9fb3a61ce1a0cb4a34c536edabdc7514494cd2988f8ba6df0062be863 | R | 1,033 | 22 | #############################################################
# Generate ablation test for single granlarity
# Note: BSP uses paired granularities
#############################################################
InputPath <- "../Data/Single_Granular_Test/Var/"
InputFiles <- list.files(InputPath)
OutputPath <- "../... |
1ceef49fc99041a62810584856e1b0960187113165f01954e10f374dade02728 | R | 1,034 | 37 | library(ape)
library(geiger)
library(phytools)
library(nlme)
setwd("your directory")
##Import tree and data
mammals<-read.table("final_compilation with percentage values.R1.csv",header=T,sep=",",row.names=1)
tree<-read.tree("4705sp_mean_selected.tree")
tree<-treedata(tree,mammals,sort=T,warnings=T)$... |
111cacf198c4e497153b3391233a2013e8f86f8c82fddee23f68aaeb3cdfbb10 | R | 1,038 | 42 | # this code computes distance matrices for HLAs, based on predicted scores
# from Glazer 2022 model on server on the pairs between Zheng 2021 tcrs and
# HLAs
# distance based on positive TCRs from single cell data
pos_filename = "st8_Glazer_2022_server_on_zheng_2021_pos_extended_hlas_146.csv"
df_pos = read.csv(past... |
7107355dede43259f1a93c9ea73697cdda3975998d9381f1e50c231af13155c4 | R | 1,043 | 34 | set.seed(123)
x <- mockSC(nc = 50, nt = 3)
y <- mockSP(x)
z <- getMGS(x)
res <- SPOTlight(
x,
y,
groups = x$type,
mgs = z,
group_id = "type",
verbose = FALSE)
test_that("plotTopicProfiles common", {
p <- plotTopicProfiles(x = res[[3]], y = x$type, facet = FALSE, min_prop = 0.1)
expect_i... |
6f3d795997c29cd23e13fe8c7bba82bc2c026153d5e3c5dfb9b2cd86e2425db5 | R | 1,045 | 35 |
#' @title FUNCTION_TITLE
#' @description FUNCTION_DESCRIPTION
#' @param formula PARAM_DESCRIPTION
#' @param data PARAM_DESCRIPTION
#' @param mod PARAM_DESCRIPTION, Default: loess
#' @param n.sd PARAM_DESCRIPTION, Default: 1.5
#' @param ... PARAM_DESCRIPTION
#' @return OUTPUT_DESCRIPTION
#' @details DETAILS
#' @example... |
a119e75a10f615a432d86bdbd5718c1267ea61fe33971a0431cb7eda4dba4c92 | R | 1,049 | 32 | ##########################################################################################################################
# Analysis using SPARKX in the comparison
#
# Ref: https://xzhoulab.github.io/SPARK/
# https://xzhoulab.github.io/SPARK/02_SPARK_Example/
#
# Usage:
# Rscript sparkx.r /home/wangjue/svgGranularity/... |
83719a63895208078ffbf2e1bc95799fed9551ee5c57cda6bf4d3ad473bc96e1 | R | 1,058 | 36 | ## library(R.utils)
## library(purrr)
args <- commandArgs(trailingOnly = TRUE)
rpdpeakDir <- args[1]
outdir <- args[2]
# debug
## rpdpeakDir <- "/oasis/tscc/scratch/szu/projects/CEMBA2/18.snap2_peakcalling/out/tscc/rpdpeak"
## outdir <- "."
recordSummitFiles <- file.path(outdir, "mba.whole.naiveSummitList.list")
## ... |
c114b7918f43db1f1cabfb5d5b71e3351a2b24a2d1f030428761e367b76c87c0 | R | 1,060 | 32 | # Merge all permutation files into one file
library(tidyverse)
library(glue)
path <- "bids/derivatives/meg-derivatives/grand_ave/brain-space/visual_model/permutations/temporal-cross/{level}/"
path_save <- "bids/derivatives/meg-derivatives/grand_ave/brain-space/visual_model/permutations/temporal-cross/{level}_tc_perm_a... |
7e61607bef1d3cdd508c50be5a00ea1bb777769e19b0eda6530c5ddd1fd7e01e | R | 1,068 | 31 | # This model is written so that errors are appropriately trapped and
#handled when running
# using a shared memory multiprocessor
library(lme4)
# Note that lme4 functions do not report p-values. This package incorporates p-values into the output of the functions lmer and anova.
library(lmerTest)
processVoxel <-functi... |
da14af550f741f2aea419387d634cc0d163cb8cfaefae031cee15a3f011565f2 | R | 1,071 | 27 | # This model is written so that errors are appropriately trapped and
#handled when running
# using a shared memory multiprocessor
library(nlme)
processVoxel <-function(v) {
Y <- voxeldat[,v]
e <- try(mod <- lme(Y ~ High+Low, random=~1|subject, method=c("ML"), na.action=na.omit, corr=corAR1(form=~1|subject), co... |
ea7cb5b3ade7dcba39bef38a8c3019e38ebd9a93f779984e77a1a995a8878f72 | R | 1,073 | 42 | #' Obtain the raw query from a \code{cAnalysis} object.
#'
#' @name extract
#' @rdname extract-methods
#' @param object of class \code{cAnalysis}, \code{molecularcAnalysis},
#' \code{molecularComorbidity} or \code{comorbidity}
#' @return A \code{data.frame} containing the raw result from the initial data
#' (comorbid... |
3485e7edd573a43662e4fc06800b10d4dd8d29b702e1da50b88ec0c85bea86ba | R | 1,079 | 48 | library(Signac)
library(Seurat)
library(argparse)
library(BSgenome.Rnorvegicus.UCSC.rn6)
parser <- ArgumentParser(description = "process input arguments")
parser$add_argument("--rds", action = "store",
help = "path to rds object to compute gene activity for")
parser$add_argument("--celltype", action = "store",
help... |
d9a6bfe5dc353c6a28e5b3ee50d9c13d79aafdffcb676700ab28bdee33e8c0a2 | R | 1,084 | 35 | # Merge permutations
library(tidyverse)
library(glue)
library(stringr)
path <- "bids/derivatives/meg-derivatives/{subject}/brain-space/visual_model/perm/merged/{level}/"
# merge permutations per subject
for (subject in c(1:19)) {
print(subject)
all_perm <- c()
subject <- str_pad(subject, 2, pad = "0")
subje... |
2b47c476753d0409092dfbc370dfd2895c4295986ae10f921b1fe8d7f5237541 | R | 1,085 | 20 | library(data.table)
library(plyr)
library(tidyverse)
#Extra code to apply RBP gene names to IDs
neg_IDs <- fread("Human_RBP_Motifs_Enriched_in_High_Excitatory_Expression.tsv")
pos_IDs <- fread("Human_RBP_Motifs_Enriched_in_High_Inhibitory_Expression.tsv")
ID_list <- fread("ID_to_protein.txt")
setnames(ID_list,... |
b2bf0bf18f48bd9c55de0b73fb85ffbcb04e36eb8044f987c4ef1fcdfbdcf357 | R | 1,087 | 26 | library(tidyverse)
library(ggplot2)
library(gridExtra)
OR_OR.con<-read_delim(file="/media/storageE/lisa_dipc/contact-densities/ors_ors_unknown_allele_contacts_densities.txt", delim = "\t", col_names = c("cell","sex","OR_OR"))
density_table<-OR_OR.con %>%
separate(cell,into = c("zone","num"), sep="OMP", remove=FAL... |
019da52909cda0cfb90de9aa9cc984506bbcad173664b9b348c1f42f42440582 | R | 1,089 | 19 | #set custom colour palette
smoothBlue <- c('#142459', '#176BA0', '#19AADE', '#1AC9E6', '#1bd4d4', '#1DE4BD', '#60F0D2')
smoothPink <- c('#29066B', '#7D3AC1', '#AF4BCE', '#DB4CB2', '#EB548C', '#EA7369', '#F0A58F')
smoothOrange <- c('#820401', '#C02323', '#DE542C', '#EF7E32', '#EE9A3A', '#EABD38', '#E7E34E')
customCol <... |
990ac8449d905e1eb862db2c9c6e5f13ddbe57c52ee9499e167efbc646a37536 | R | 1,091 | 36 | #' @title wilcoxon-tests
#' @description wilcoxon-tests
#' @param x matrix or character vector with matrix column names
#' @param y matrix to compare against or matrix with x and y column names
#' @param ... adjust.method and cutoff
#' @return wilcoxon-tests
#' @rdname wilcoxtest
#' @export
wilcoxtest = function(x, y,... |
8d0b197af1a8bcc0c8270fc7df36e281b68a9b777ee8be4aff612ae918e6ebcf | R | 1,092 | 42 | #' Obtain the data from a \code{comorbidity}, \code{molecularComorbidity},
#' \code{cAnalysis} or \code{molecularcAnalysis} object.
#'
#' @name extract
#' @rdname extract-methods
#' @param object Object of class \code{comorbidity}, \code{molecularComorbidity},
#' \code{cAnalysis} or \code{molecularcAnalysis} object.
... |
53480d0a52b8d660335b48c9512ff7804ad6dc44b05d801103b2884a5bcbed84 | R | 1,095 | 40 |
library(igraph)
####GRAPH filename
filename='../OscopeData/FirstData/WaterfallbootstrapSequential_CPUDPSF_TF_2000Boot_data_s45res_C28G8442_edgeNetwork.csv'
genedet='../OscopeData/GenesLabels/Mapping/Water45.txt'
data=read.csv(file=filename, header= TRUE,row.names=1)
G=graph_from_data_frame(data,directed=FALSE)
ww=get... |
9db5099176b9eecd929d91bca009491280f10dd8475b307ebc98374e192da269 | R | 1,097 | 40 | require(scales)
library(flipMultivariates)
library(caTools)
# dont' forget to set the working directory
# setwd("wherever your data is")
dset = read.csv('lda.csv')
print(str(dset))
print(summary(dset))
# clean up the dataset
dset$ap_ar[is.na(dset$ap_ar)] = 0
dset$bovera[dset$bovera < 0] = 0
set.seed(1)
row.number =... |
3f8b40b693bfefc56d90ca5461040ed51b1afa553590c1ab34a632539a774cd4 | R | 1,103 | 26 | #' @title Get Genomic Information
#' @param symbols character vector of HGNC symbols
#' @export
#' @rdname GeneInfo
GeneInfo = function(symbols) {
# see https://davetang.org/muse/2013/12/16/bioconductor-annotation-packages/
columns = c('ENTREZID', 'GENENAME')
info = AnnotationDbi::select(org.Hs.eg.db::org.Hs.eg... |
a1c3e0c315da5d2dd165616b599b616f19f2b39b2f47149eb934df6d80347a1c | R | 1,118 | 37 | context("Load data")
test_that("Load data", {
# load data
load_ABIS()
# test sigMatrix_ABIS_S0
expect_equal(class(sigMatrix_ABIS_S0)[1],"matrix")
expect_equal(class(plot_similarity(sigMatrix_ABIS_S0))[1],"gg")
# test sigMatrix_ABIS_S1
expect_equal(class(sigMatrix_ABIS_S1)[1],"matrix")
... |
1c1f860b33b1cab1a829dca56a0d783eb14941b12c17da7a19a33829200e9550 | R | 1,122 | 30 | #############################################################
# Generate simulation data
#############################################################
InputData <- openxlsx::read.xlsx("mmc6.xlsx",
colNames = FALSE)
InputData_exp <- as.numeric(unlist(InputData[,-1]))
Num_genes <- c(2... |
345d5663203f193211a658cf3f9ec209d6a396a46d10d113b5dd5985c1605e02 | R | 1,134 | 30 | #!/usr/bin/env Rscript
suppressMessages(library(argparse))
suppressMessages(library(devtools))
parser = ArgumentParser(description='Calculate CDR3 summaries and add them as columns to the given data frame.')
parser$add_argument('in_csv', help='A CSV with an amino_acid column with CDR3 sequence.')
parser$add_argument(... |
3ab9813377de94d692508b860db3e05ddb06c8c1db99de38ed6b4067e9a9cd93 | R | 1,152 | 29 | suppressMessages(library(GenomicRanges))
suppressMessages(library(dplyr))
suppressMessages(library(rtracklayer))
suppressMessages(library(Signac))
library(GenomeInfoDb)
library(argparse)
parser <- ArgumentParser(description = "process input arguments")
parser$add_argument("--dapeaks", action = "store", type = "charact... |
ecda5a7b7ee485f638ff100bf19d8000ff9acb0970bfe426e4d55008282f888b | R | 1,157 | 29 | #05/24/2023 Fengyao Yan
#Plot the confusion matrix to show the performance of DL model when predicting single tissue.
library(cvms)
library(data.table)
library(ggplot2)
pred_dat <- fread('../../data/single_t/pred.csv', data.table = FALSE)
true_dat <- fread('../../data/single_t/true.csv', data.table = FALSE)
dat <- c... |
0e738d899117859c0d4c5440d14a350461e81139ff54313dcb3a436566c12533 | R | 1,171 | 54 | ####╔═════ ══════╗####
####💠Project Config💠####
####╚═════ ══════╝####
cli_h2("┗ [CONFIG] Loading external {.file _configs.yml} file")
configs <- load_yml(here("_configs.yml"))
options(
scipen = 999L,
digits = 4L,
mc.cores = max(1L, parallel::detectCores(logical = TRUE)),
glmmTMB.cores = max(1L, par... |
9dc1dec5c232224e53081e0b769fd3c0fb7f046e28b206554be109300f7951be | R | 1,175 | 31 |
#### PCA With DeX proteins: coming from toplot!
#Sanity check and just doing a rowmatch leads to 128 unique across all comparisons!
###Creating new cleanDat
#Kmeans_Expr_Matrix <- backg_nano_kmeans[backg_nano_kmeans$X %in% match_diff_ex_kmeans$V1,]
write.csv(cleanDat, file = "ALL_cleanDat_used4PCA.csv")
write.cs... |
f4ef8ba402e4dd1f9025e3455513cd207817702206b275aa1f5aa60bad4f8e07 | R | 1,176 | 31 |
# Dark-rearing analysis
require(dplyr)
require(tidyr)
require(ggplot2)
# --- Escape probability
d <- read.csv('./_Science/Xenopus-Behavior-git/02_Collision_Avoidance/data/dark_rearing_successes.csv')
d <- d %>% mutate(Successes = escapes/n)
d <- d %>% rename(Group=group)
names(d)
ds <- d %>% group_by(Group) %>% su... |
c20108adc262409f9332469019fd3c3703eb65acc051c717ad86aac728937d62 | R | 1,191 | 24 | #This script is a sanity check that you have the right folders in the right places. it'll tell you if you're missing directories you should have
if(!dir.exists(paste0(path,'/output'))){
warning("You don't have an output folder in your pipeline directory! Creating folder...")
dir.create(paste0(path,'/output'))
di... |
8058933ab819f45e26c9ec694b4123a0fed2b593052f9f36e705fd473a8abc65 | R | 1,201 | 34 | theme_figure <- function(){
font <- "" #assign font family up front
theme_bw() %+replace% #replace elements we want to change
theme(
# #grid elements
# panel.grid.major = element_blank(), #strip major gridlines
# panel.grid.minor = element_blank(), #strip minor gridline... |
7526c9fc7f69924d4d109cef120966d06d1adfa266ea9554a142193cf0bbe6a1 | R | 1,208 | 27 |
#' @title List combinations
#' @description Combine all pairs of list elements
#' @param ... Unnamed list(s). If only one list is provided, it will be combined (pairwise) with itself.
#' @param by Function to apply. Default: c("intersect", "union", "c")
#' @return List with combined elements
#' @seealso
#' \code{\l... |
d740e1fb6e2b0829bf051182bdcc86ce22b5541b1fe05bb78daee3889e94fe3e | R | 1,208 | 32 | ##########################################################################################################################
# Analysis using SpatialDE in the comparison
#
# http://bioconductor.org/packages/release/bioc/vignettes/spatialDE/inst/doc/spatialDE.html
# Changed for only performance usage and record
# Ref: htt... |
9c3b6197f052b0f52c3a0e0d7794b4ca38796d3d122b8db64543e2b8af5c6dd4 | R | 1,209 | 38 | library(tidyverse)
projdir <- here::here()
rpack <- file.path(projdir, "package/R")
import::from(.from = "cembav2env.R", .directory = rpack,
Sa2PeakCalling, cembav2env)
globalPeaks <- Sa2PeakCalling$readBed4File(
file.path(projdir, "20.nmf", "out",
"peaks.from.global.modules.bed")
)
pdc <- data.table::fread(
... |
245d46981913397fc68e778591ed75c2d4b128942e78ec07f4a5035a4032e0f9 | R | 1,213 | 41 | # use neighborhoods from step1 to identify neighborhood-specific degs
#!/usr/bin/env Rscript
# to run locally:
# SCRATCH1=/mnt/g/scratch
# docker run -it --rm \
# --workdir $HOME \
# -v /mnt/g/ckd:$HOME/ckd \
# -v $HOME:$HOME \
# -v $SCRATCH1:$SCRATCH1 \
# -e SCRATCH1="/mnt/g/scratch" \
# p4rkerw/sctools:R4.1.3 R
lib... |
6e0c912c9b8b90b12a56f93110c4f4ce4eb0d90e298b94222bcb3d8afdd63d41 | R | 1,218 | 30 |
#' Write the output statistic files
#'
#' This function takes an argument structure that specifies all the parameters
#' that should be input to npointillist and returns an array of voxel data and
#' a design matrix.
#' @param prefix Prefix for output, to be prepended to outputs
#' @param results Data returned from th... |
76a0eb0649382a2d2759573f8f1bf525de5467f2a6a17a07166f27de1b0c87c4 | R | 1,220 | 34 | ---
title: "Selecting Variants for Sanger Sequencing"
author: "Stephen P Plassmeyer"
date: "7/24/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
library(tidyverse)
library(data.table)
library(patchwork)
library_annotations <- fread('../../../library_design/mr_library_fu... |
23769749267f61d3751b238589cd05b5721eaeae50750187f5d556b097228ea0 | R | 1,226 | 38 | ############################
##### Install packages #####
############################
# Last edited: 11/04/23 by LVB
# Description: Installs and updates necessary packages. Note these scripts were
# written and executed in R v.4.2.3. While code should have reasonable
# back-compatibility, you may need to update R. T... |
658b80073f19f014075094d6d8b8b8b154f9bc0bcdb34be9b255ec2b7cf07c76 | R | 1,226 | 34 | #-----------------------------------------------
# LICENSE
#-----------------------------------------------
# Copyright 2019 Novartis Institutes for BioMedical Research Inc.
# Licensed under the GNU General Public License, Version 3 (the "License");
# you may not use this file except in compliance with ... |
10e3c128f7db36590fef1a82b80a6a407d164300f4b7920d53bcbe8a78340df5 | R | 1,228 | 65 | ########################################
# LC snRNA-seq analyses: remove doublets
# Lukas Weber, Oct 2022
########################################
# run in interactive session on JHPCE
# screen -S LC
# qrsh -l mem_free=20G,h_vmem=22G,h_fsize=200G -now n
# cd /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/code/anal... |
e50f5ef3ae63ccf2d9b9ca63b2f9666f3b0b3ba9aa24763b7e13f204531b6d8c | R | 1,228 | 37 | # R script that runs the LR GO analysis #
library(clusterProfiler)
library(org.Mm.eg.db)
library(org.Hs.eg.db)
#library(enrichplot)
#library(ggplot2)
GO_analyse <- function(genes, bg_genes, species,
p_cutoff, q_cutoff, onts) {
#p_cutoff=.01, q_cutoff=0.5, onts='BP') {
#... |
db6ed501b827e52638193e30285abd25c8af596b9d040aea7ed2a23cf1830938 | R | 1,232 | 46 | library(Seurat)
options(Seurat.object.assay.version = "v5")
library(stringr)
library(purrr)
Sys.setenv("_R_USE_PIPEBIND_" = TRUE)
projdir <- here::here()
rdir <- file.path(projdir, "package/R")
import::from(.from = "cembav2env.R", .directory = rdir,
Sa2Integration)
import::from(.from = "utils.R", .directory = rdir,
... |
d09260790b551060b06debd3374613121b2e093f1a51ebaa92766e919ef71d7d | R | 1,250 | 33 | #TimesVector
#
#Created by Inuk Jung on 2016-08-3.
#Copyright (c) 2016 Inuk Jung. All rights reserved.
#
#This program is free software: you can redistribute it and/or modify
#it under the terms of the GNU General Public License as published by
#the Free Software Foundation, either version 3 of the License, or
#(at you... |
f8ed05e4a7e39c1b92061640cddc720156b4288cc9e730ae1e368820d41a2e7c | R | 1,255 | 49 | library(Seurat)
library(dplyr)
library(patchwork)
library(R.utils)
library(rhdf5)
library(hdf5r)
library(Matrix)
library(igraph)
library(SeuratObject)
library(RcppCNPy)
library(reticulate)
np <- import("numpy")
method <-'Seurat'
t<-c('DPT','YAN','Deng')
for ( i in 1:length(t)) {
print(t[i])
name<-paste0(t[i])
h1=H5... |
a902364e3fbf7af1a62ff29c8faa81b0ef0e5423de1fd9295cb22fc930dc2f70 | R | 1,262 | 32 | library(data.table)
library(kimisc)
args <- commandArgs(TRUE)
filedir <- args[1]
if(grepl("\\/$", filedir)==F){
filedir <- paste(filedir, '/', sep="")
}
curfile <- thisfile()
source(paste(dirname(curfile), '/ConfigParser.R', sep=""))
config <- ConfigParser(file=paste(dirname(curfile),'/app.config', sep=""))
ENSG <- ... |
3f580ff6b24155b90e78af1ac4d75feb5df385bdf68f604c8faff6c1e38453b8 | R | 1,263 | 31 | #############################################################
# Calculate spatial stratified heterogeneity
#
# Ref: Wang, Jin-Feng, Tong-Lin Zhang, and Bo-Jie Fu. "A measure of spatial stratified heterogeneity." Ecological indicators 67 (2016): 250-256
#############################################################
... |
41b87abf433bf236b553cd28e65ed9b0cb536a51da251cdf8040dccc9dc61908 | R | 1,266 | 53 | ---
title: "cellScaleFactors"
author: "Sean Maden"
date: "2023-09-07"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Introduction
The `cellScaleFactors` package provides access to cell size scale factor values
from the literature. These are useful for cell type refere... |
8143ec579b68ee0ab8fb127d364f03b39fe05e74dcd2c927daf08f45d09d6097 | R | 1,274 | 49 | #Visualize UMAP colored by cell type
#visualize UMAP colored by batch
library(dplyr)
library(monocle3)
library(here)
library(ggplot2)
library(scales)
source(here("scripts/accessory_functions/monocle_mods.R"))
cds <- readRDS(here("6month_LMMP.rds"))
#by cell type
pdf(here("plots/supp_figures/UMAP_by_celltype.pdf"))
... |
713c7d29f5e28d53153eb58f5f752f0358686a2cdf2d6bdc1653e57427bdb972 | R | 1,277 | 36 | ####################################################################################
# this script subsets by rat but iterates through all celltypes
####################################################################################
library(argparse)
library(Signac)
library(Seurat)
library(stringr)
parser <- Argument... |
54d6e583a3fe0735e6fe6ef2c07defe61ba4954243bc84f68b381d3ba886030d | R | 1,278 | 42 | # 05/19/2023 Fengyao Yan
# This file is written to create the dataset of mean gene expression values of all genes in each tissue type
library(tidyverse)
library(data.table)
data_folder <- '../../data/raw'
tissue_list=c('Brain', 'Breast', 'Colon', 'Kidney', 'Liver', 'Lung', 'Ovary', 'Pancreas', 'Prostate', 'Skin', '... |
0168d57d15045fbef50800421adedd3ab161a0cba16abd1d0e122d47708c0657 | R | 1,281 | 53 |
library("edgeR")
target_list_file_to_DGEList = function(targetsFile, minSumTagCounts = 5) {
# format should be like so, including header:
# files group description
# fileA.txt grpA my grpA replicate
# fileB.txt grpB my grpB replicate
# ...
targets = read.delim(file=targetsFile, stringsAsFactors =... |
33d7c07dd6c5fbe7d7f871fd973f347f6b083d417193bc5939122c3028f4790d | R | 1,289 | 34 | library(data.table)
library(kimisc)
args <- commandArgs(TRUE)
filedir <- args[1]
if(grepl("\\/$", filedir)==F){
filedir <- paste(filedir, '/', sep="")
}
ensg_v <- args[2]
curfile <- thisfile()
source(paste(dirname(curfile), '/ConfigParser.R', sep=""))
config <- ConfigParser(file=paste(dirname(curfile),'/app.config',... |
77f6a7499cf89f393a95d290e343cd770dcf8db4faf3b78ff1327e67196d7e15 | R | 1,294 | 21 | #' Draw an elbow plot of cophenetic correlation metric.
#' @details This function draws an elbow plot of cophenetic correlation metric.
#' @param res output from \code{\link{estimateSignatures}}
#' @param bestFit rank to highlight. Default NULL
#' @seealso \code{\link{estimateSignatures}} \code{\link{plotCophenetic}}
#... |
cb6a9434baf4e4c0ec39b75c326b8c3c14b820d9d3e12821090c0511a8bf41ff | R | 1,306 | 40 | ---
title: "R Notebook"
output: html_notebook
---
```{r}
library(tidyverse)
suppressPackageStartupMessages(library(openxlsx))
library(readxl)
library(limma)
library(edgeR)
suppressPackageStartupMessages(library(clusterProfiler))
suppressPackageStartupMessages(library(org.Hs.eg.db))
library(lubridate)
library(extrafont)... |
c303bb41a9f6dd12d0f905b9a94d01199181efa6e7818907e5fd191a0c59c763 | R | 1,307 | 37 | ### MNT 29Nov2021 =======
# Check Read 1 files for discrepant read lengths, as seen before in Tran-Maynard 2021 project:
# Should be exactly 28bp = 16 [BC] + 12 [UMI]
library(ShortRead)
library(jaffelab)
FASTQ.dir <- "/dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/fastq/snRNA-seq/"
### Read in abridged sample... |
6d3bb1c89be245aea3c8c1adcef25df433e12564d478df48795e34059d24dcbb | R | 1,321 | 32 | library(ggplot2)
library(tidyverse)
library(lawstat)
library(psych)
Setting <- list(theme_classic(),
theme(plot.margin = unit(c(1, 1, 1, 1), "lines")),
theme(axis.title.x = element_text(size = 15, vjust = -0.5, color = "black")),
theme(axis.title.y = element_t... |
c808b5415ffaa76ac0e3fb82b30ebacb72d25313538a11b9c9301aef161f2cd5 | R | 1,323 | 49 | #=========================================================================
#
# Copyright NumFOCUS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/license... |
fc1e03321df5586d024368dbf2adb61b529ab472f9432a518445f71f0b6300b7 | R | 1,327 | 60 | ### Libraries
library(plyr)
library(dplyr)
library(tidyr)
library(matrixStats)
library(fst)
library(misha)
library(data.table)
library(circlize)
library(ComplexHeatmap)
library(ggplot2)
library(cowplot)
library(patchwork)
library(scales)
library(GenomicRanges)
library(ggpubr)
library(LSD)
library(pals)
library(Hmisc)... |
6303fa82c18f62db0fce2b651c36b5ea57933ac3f249b088cea499df2c2711d5 | R | 1,332 | 49 | library("splatter")
library(rhdf5)
set.seed(0)
# ----------------Generate the simulation data using Splatter package------------------
run<-function(cells,genes){
params = newSplatParams()
d=5
params = setParams(params, list(batchCells = cells,
nGenes = genes,
... |
9d83d236a5acf318122f660fe35eada6a25f3838f25a8227488937f2fcb663e4 | R | 1,336 | 47 |
library(spatialLIBD)
sce_path_zip <- fetch_data("spatialDLPFC_snRNAseq")
sce_path <- unzip(sce_path_zip, exdir = tempdir())
sce <- HDF5Array::loadHDF5SummarizedExperiment(
file.path(tempdir(), "sce_DLPFC_annotated"))
sce
# class: SingleCellExperiment
# dim: 36601 77604
# metadata(3): Samples cell_type_colors cel... |
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