sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7b04816abc8e647aae0c9200d349c6999a401c6f63f19586628a3cea7a349cc6 | R | 1,740 | 56 | #=========================================================================
#
# 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... |
87ac76a1e466697e3257ce0916ea0e995c03e662cded4bd03b0bcbf6af170ae2 | R | 1,740 | 58 | library(BayesSpace)
library(ggplot2)
data_path <- "/home/xuchang/Project/DeepST/code_20220617/All_methods/data"
save_path <- "/home/xuchang/Project/DeepST/code_20220617/All_methods/Results"
# sample <- c('Section1_Posterior', 'Section1_Anterior', 'Section_Coronal', "Human_breast_FFPE")
# sample <- c('15150... |
1ad6ca7d6c11baf1dc49091a0dba3d7987b27cf1765b24e08b7f273ff9058699 | R | 1,742 | 43 | # this script will run epianeufinder with 1MB windows on the multiome atac fragments files
# SCRATCH1=/mnt/g/scratch
# docker run -it \
# --workdir $HOME \
# -v /mnt/g/ckd:$HOME/project \
# -v /mnt/g/cellranger_atac_counts:$HOME/cellranger_atac_counts \
# -v /mnt/g/cellranger_multi_counts:$HOME/cellranger_multi_counts... |
f996195f2be7c8c16ea5b2f6308591e013233f80fe49823a4b2899649a0e0f5e | R | 1,742 | 76 |
library(GO.db)
######################
library(biomaRt)
library(limma)
Dirlist=c('../Communities/Networks02/','../Communities/NetworksPiDiv9/','../Communities/FirstData/')
#file='W45fastgreedy.txt'
file='Safastgreedy.txt'
thresh=8
for (h in c(1:3)){
Dircom= Dirlist[h]
#Dircom='../Communities/Networks02/'
filecomm=p... |
48cf2792f36d85658dd0ec047cab39f5b1edad0d0de71139fafa8307ee9379f4 | R | 1,750 | 50 | library(SeuratDisk)
library(Seurat)
library(tidyverse)
library(here)
## load in the filtered Seurat object
obj_merged = here('data/tidy_data/Seurat_projects',
"OUD_Striatum_refined_all_SeuratObj_N16.h5Seurat") %>%
LoadH5Seurat()
DefaultAssay(object = obj_merged) = 'RNA'
head(obj_merged[[]])
## ... |
73013b790898cc8a976640a9b3c5ea88bef3cc3b34c3b6abc30b17b907f959e6 | R | 1,752 | 47 |
#' @title A plot-friendly output for the Barnes-Hut implementation of t-SNE.
#' @description A plot-friendly output for the Barnes-Hut implementation of t-SNE: test different perplexities and dimensions. See Rtsne::Rtsne for details on the algorithm.
#' @param m matrix; each row is an observation (eg. cell); each colu... |
d5348f67ecf1ed68c18ce45e985ff929f0c2e49e38da8c99c89b59c3266fa5bc | R | 1,758 | 53 | # density plots for thresholds
library(dplyr)
library(ggplot2)
library(here)
library(gridExtra)
atac_frags <- readRDS(here("ckd","atac_aggr_prep","atac_frags.rds"))
# LOY per library by sequencing depth in PT
# pull meta data with number of counts per cell
meta <- readRDS(here("ckd","atac_aggr_prep","step6_meta.rd... |
16ce5071ac60acd354df4ee77e7f209c0d4514fbb80ae4282f7e46f37f6d9e20 | R | 1,767 | 52 |
# EXAMPLE USAGE
source("heatmap.3.R")
# example of colsidecolors rowsidecolors (single column, single row)
mat <- matrix(1:100, byrow=T, nrow=10)
column_annotation <- sample(c("red", "blue", "green"), 10, replace=T)
column_annotation <- matrix(column_annotation, nrow=1)
rownames(column_annotation) <- c("Variable X"... |
bdd5ba857ebfb49dabce53707d1716100e7e578d97f9446d8763a95a1f34b2d8 | R | 1,767 | 58 | # Prepare data for nmf.
# TODO: This script can be generalised.
# Currently, I use it case by case.
library(data.table)
library(hdf5r)
packdir <- file.path(here::here(), "package", "R")
import::from(.from = "cembav2env.R", .directory = packdir,
cembav2env)
# * configs
outdir <- "data"
outh5 <- file.path(outdir, "cp... |
ea3b0ad494b21234175d7130b9a8e0274366f32921ba5b7698d2e2235768f265 | R | 1,776 | 58 | library(dplyr)
library(stats)
load("bulk_dev_RNAseq.RData")
load("cluster_1_2_3.RData")
zscore = function(x){
x = (x - mean(x))/sd(x)
}
for(i in 1:nrow(bulk_RNAseq)){
bulk_RNAseq[i,"max"] = max(as.numeric(bulk_RNAseq[i,2:49]))
}
bulk_RNAseq2 = filter(bulk_RNAseq,max>10)
rownames(bulk_RNAseq2) = bul... |
43c93bb42f832e25639960ace4114f31288d06dc2bdad2490b5edd1883b4292e | R | 1,778 | 45 |
#' @title Apply a Function to All Pairs of Elements in 'x' or Between 'x' and 'y'
#' @description Apply a Function to All Pairs of Elements in 'x' or Between 'x' and 'y'
#' @param x a vector or a list of vectors
#' @param y a vector or a list of vectors. If y is NULL, y will be set to x, such that if x is a list, th... |
4171545ae07a285de09c5e4ef9d84dd4c6c6ecac4895902aa4b14c485e78a593 | R | 1,788 | 73 | library(nnls)
library(data.table)
library(pbapply)
library(glue)
f_name_ref <- "../../data/raw/selected_gtex_top_1_NT_1_fc_3_data.csv"
dat <- fread(f_name_ref, data.table = FALSE)
dat <- dat[, -1]
exclude <- c("Adrenal Gland", "Fallopian Tube", "Vagina", "Salivary Gland")
dat <- dat[!dat$tissue %in% exclude, ]
ogs ... |
2e9c822938365f930265926019f305f4f781067fd6e0aa9baafda95a3604d36f | R | 1,789 | 53 | #' @title Combine ggplot grobs or plots
#' @description combine ggplot grobs or plots
#' @param ... plots/grobs to combine. A mix of the two is fine.
#' @param by axis to combine plots by. Default: 'x'
#' @param size which plot's dimensions to prioritise. One of 'first' or 'last'. Default: 'last'
#' @param draw logical... |
d2a65310cf75cf51f2ba4c6127508c7fa34fdf39a5a00733726cec3bbcbd7df4 | R | 1,790 | 43 | ####################################################################################
# this script is flexible for getting barcodes by rat, by celltype, or by both
####################################################################################
library(argparse)
library(Signac)
library(Seurat)
library(stringr)
p... |
8cddedbbf36aee7b999613d1b782b2edd7cf6da9bd89380ec96233d63c2f3a7b | R | 1,804 | 66 | #' @title FUNCTION_TITLE
#' @description FUNCTION_DESCRIPTION
#' @param category PARAM_DESCRIPTION, Default: c("H", "C2", "C5")
#' @param subcategory PARAM_DESCRIPTION, Default: NULL
#' @param exclude.subcategory PARAM_DESCRIPTION, Default: 'HPO'
#' @param split.by.subcategory PARAM_DESCRIPTION, Default: F
#' @param sp... |
3948489e71986ef6eed170d7c52c84d4092857d128fdcc8daafee5f49c98aef8 | R | 1,806 | 54 | #-----------------------------------------------
# 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 ... |
798c9b3f133a5fb3673b0c9101c351a9fba0383dab53d7d5086649f7c09e9740 | R | 1,806 | 46 | library(data.table)
#Read in the latest 455 N2A NovaSeq data
counts <- fread("compiled_counts_455_10182021.csv")
#Reformat to Counts Per Million, ugly but here goes
for(i in 3:14){
counts[[i]] <- round((counts[[i]]/sum(counts[[i]]))*1e6,0)
}
#We sum the DNA counts and the RNA for this
fwrite(data.ta... |
838e04569e223ff49d7b17b73ebffd1fab05b9f7141564c664e0f63a9e490253 | R | 1,814 | 49 | # this code draws barplot for the p-values from MiRKAT-S on Chowell 2018
# subjects after filtering out subjects with HLA-Is that cannot be matched
# to any of the 85 ones from Emerson
library(ggplot2)
library(ggpubr)
result_dir = ("../results")
MiRKAT_S_2018_table = "st12_chowell_2018_MiRKAT-S_pvalues.csv"
df_mir... |
cccb51f7172961432f0febd2e64668c2b665ca2548d078e9dd532e4babaecd15 | R | 1,818 | 72 |
# This script uses iterative heirarchical clustering to group cells,
# checking at each stage for DEgenes to define groups as distinct
## ----receive input from nextflow----------------------------------------------
args <- commandArgs(TRUE)
path<-args[1]
file<-args[2]
print(path)
set.seed(123)
timepoints<-substr(... |
812b14dfbd9d813eb78c9c2a5f731b0fc24061dc7ac269ca4e8b88fc00dcc63e | R | 1,820 | 52 | ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFactors = F)
## packages for data table processing/plotting
library(tidyverse)
library(rcartocolor)
library(data.table)
library(ggsci)
library(KEGGREST)
library(here)
DATADIR='data/tidy_data'
##... |
e279f8b58dd8af54d2e4f4b0685d5b25903e6cd6917a8006d5093b47639e8cbd | R | 1,820 | 58 | # this code gets the side-by-side violin plots based on the scores on
# test positive pairs, test negative pairs, Szeto 2021 43 pairs
library(ggplot2)
library(ggpubr)
library(pROC)
theme_set(theme_classic())
results_dir = "../results/"
pos_random_forest_file = "st3_test_pos_random_forest.csv"
neg_random_forest_fil... |
94509fd8f6f94de6ab706dbe1e2d2d9d13712d15964334e03cafbf55e6ba8573 | R | 1,821 | 59 | ######create a summary network with nodes given by the communities
####find intra-cluster density:
library(igraph)
karate <- graph.famous("Zachary")
wckarate <- walktrap.community(karate) #any algorithm
comm=wckarate
Net=karate
comm=wt
Net=G
######compute the number of edges within each community
intra=sapply(unique(... |
95e53f5b1fdf8f1482e3f658099973fd2e7cff55ac86de3035c4e0c9cbeead6a | R | 1,824 | 83 | ##############################################################################
# LC snRNA-seq analyses: normalization, feature selection, dimension reduction
# Lukas Weber, Oct 2022
##############################################################################
library(here)
library(SingleCellExperiment)
library(scate... |
bd9991c46cca474908e1b643e943a1e13d0a10410369ab3087070976127e47eb | R | 1,825 | 49 | library(ggplot2)
library(lmtest)
setEPS()
for (i in 1:10){
latent_embeddings<-read.csv(paste0("results_intermediate_encoders/embs/combined_10fold/latent_embeddings_global_",i,".csv"))
data<-latent_embeddings[,c(1:(dim(latent_embeddings)[2]-3))]
outcomes<-latent_embeddings[,c((dim(latent_embeddings)[2]-2):dim(late... |
635bb4b52f04b158c90f81192c9425269dc83b2afc5f4b05c4c782475bfecb1c | R | 1,828 | 75 | library(SPOTlight)
library(SingleCellExperiment)
# library(RcppML)
set.seed(321)
# mock up some single-cell, mixture & marker data
sce <- mockSC(ng = 200, nc = 10, nt = 3)
spe <- mockSP(sce)
mgs <- getMGS(sce)
# Function to run the checks
.checks <- function(decon, sce) {
mtr <- decon[[1]]
rss <- decon[[2]]
... |
7e81bca6b6a0efe9d9eddf3d158af8015134be2b593cb35a60ad4c7f402f3ec7 | R | 1,829 | 43 | "pwr.f2.test" <-
function (u = NULL, v = NULL, f2 = NULL, sig.level = 0.05, power = NULL)
{
if (sum(sapply(list(u,v, f2, power, sig.level), is.null)) !=
1)
stop("exactly one of u, v, f2, power, and sig.level must be NULL")
if (!is.null(f2)){
if (is.character(f2))
f2 <- cohen.... |
151dfb4a008d2caaedb1305b031c056b61b39c2a1933b25aa6e0da255fb65d8e | R | 1,837 | 82 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(lavaan)
library(semPlot)
library(factoextra)
source('functions/explained_variance.R')
source('functions/heatmaps.custom.R')
```
# Load Data
```{r}
vrf.cog.heart... |
0f8aa89681116a73420adb7555ef80a18c380a1e0c460bb539a14ce6a77e4324 | R | 1,839 | 50 | dirichletClusters = function(tsb.dat){
#Initiate mcmc parameters
nburn = 1000
nsave = 10000
nskip = 20
ndisplay = 1000
mcmc = list(nburn=nburn,nsave=nsave,nskip=nskip,ndisplay=ndisplay)
#Hyper parameters
prior1 = list(alpha=0.05,m1=rep(0,1),psiinv1=diag(0.5,1),nu1=6,tau1=0.2,tau2=1000)
#Run main funct... |
2f88f6f375ee07b666da2c9004e5ddf8773b7a072089bc76365fc30f3f137efe | R | 1,848 | 60 | # roc curve for DePTH trained on Emerson 2017 and testted on Emerson 2017
library(ggplot2)
library(pROC)
# load scores for hla_i
pos_i_file = "HLA_I_all_match_test_pos_20.csv"
neg_i_file = "HLA_I_all_match_test_neg_20.csv"
df_pos_i = read.csv(paste0("../results/ensemble_scores/HLA_I_all_match/", pos_i_file), heade... |
76fc17f6eb437efadd46e8e710506ad69913f701d01de10f4f3b3405746ffa16 | R | 1,854 | 44 | #!/usr/bin/env Rscript
# Here's some R I used to process the aggregated results of this command:
#
# df = read.csv(paste(dir, 'loss_regression.agg.csv', sep=''))
# df = df[, colnames(df)!='X.Intercept.']
# df = df[, colnames(df)!='beta']
#
# ggplot(
# drop_rows_with_na(melt(df, c('sample', 'model'))),
# ... |
3daa427fa455b9169990e52aa6d7306380ae96775d5db8a6799172b3d88e183a | R | 1,856 | 56 | #-----------------------------------------------
# LICENSE
#-----------------------------------------------
# Copyright 2018 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 ... |
a5b521cc93d9a50631edaaa7c51e38aceb661be5c1989fd99da64b6da946aa87 | R | 1,864 | 55 | ---
title: "Formating VCF for DeepSEA Models"
author: "Stephen P Plassmeyer"
date: "12/9/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(tidyverse)
library(data.table)
```
This notebook takes in the library design csv file and formats a tab-separated file follow... |
5ee26dca7dbd6132a9cc6ab792aa62635d243c4a8fb2627e04689e605670978a | R | 1,865 | 72 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(finalfit)
library(mice)
```
# Load Data
```{r}
heart.radiomics = read.csv("data/heart.radiomics.csv")
heart.traditional = read.csv("data/cardio.data.csv")
```
... |
a5323378f1214986bd750cb3093f02666e0ea7977f62040ca63ab482fc4dc77a | R | 1,865 | 38 | mouse#library(AnnotationDbi)
library(tidyverse)
#library(biomaRt)
### Load lookup table
homolog_table <- read.delim('HOM_MouseHumanSequence.rpt.txt')
## Load human and mouse genes
mouse_map <- data.table::fread('data/mouse_map_genes.csv') %>% select(-V1)
human_map <- data.table::fread('data/human_map_genes.csv')%>% s... |
7d79d1c24cd4acc89b20b06493d7f85bad768f2346ee86ea00d0097e9831abd6 | R | 1,868 | 47 | #' Write an output SGE submit script
#'
#' Generate an SGE submit script for the given workflow
#' @param prefix Prefix for output, to be prepended to outputs
#' @param resultnames List of names for the expected outputs
#' @param modelfile Name of the model file that contains the processVoxel command
#' @param designma... |
415c272149753aa3bd6868eb52dfb515f4a6102a8f595f46d16a7afefddf0426 | R | 1,874 | 37 | #
# correlation with bulk RNAseq
bulk_expr = read.table("/nfs4/chaozhang/proj/Neuron/Aritra/bulk/stringtie/merge.TPM.txt", row.names = 1, header = T)
merFISH_integrated = readRDS(file = "./RDS/merFISH_final.0827.RDS")
merFISH_avg_expr = AverageExpression(merFISH_integrated, group.by = "orig.ident", slot = "counts")
m... |
757195aa6eea6475549bec79f7ef1b3949bac1c06cd23faf532cfc3052c690ca | R | 1,878 | 57 | #=========================================================================
#
# 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... |
4710c4586e4ff3d113c0603ccfaf57dc32c9a2fa5bac5a87f6ff46359cfc5904 | R | 1,883 | 58 |
#' @title Convert a list to a jaccard matrix and plot with `scalop::gmap`
#' @param L list of character vectors to compute jaccard indexes between.
#' @param ratio aspect ratio. Default: 1
#' @param limits colour key limits. Default: c(0, 1)
#' @param tile.col draw lines between tiles. Default: 'black'
#' @param tile... |
6e9a93e694c0d970123d10bd94691564c535e506f915767b5905ce8cf81d8a81 | R | 1,886 | 58 | #05/23/2023 Fengyao Yan
#plot dot plot of mse change for CMP dataset comparing NNLS vs DL
library(ggplot2)
library(tidyverse)
mse_dots <- data.frame(matrix(ncol=4, nrow=0))
colnames(mse_dots) <- c('tissue', 'mse', 'n_gene', 'model')
mse_nnls <- read.csv('../../data/gtex/mse_all_gtex_nnls.csv', row.names = 1)
mse_dee... |
db54ef9bd8b204d544bf24a68f99eac68f248dcdfa23f2c3b358bb6f6af5e2e2 | R | 1,887 | 56 | project = function(data.matrix, latent.matrix) {
latent.unit = latent.matrix / norm(latent.matrix, 'f')
proj.scores = t(data.matrix) %*% latent.unit
data.projected = data.frame(outer(latent.unit,t(proj.scores)))
return(data.projected)
}
evr_ = function(data.matrix, latent.matrix) {
data.projected = project(d... |
91b14bdc822aba4340c53f6ae146a93df8d33733482b805a0b5dba756297afc1 | R | 1,892 | 42 | # A function I use to do my own windowing to compare results with sequence data to normal plp models.
timeWindowing <- function(plpData, windows){
maxTimeBeforeIndex <- max(abs(windows))
covariates <- plpData$covariateData$covariates %>% collect()
cohorts <- plpData$cohorts %>% collect()
joined <- covariates %>% in... |
6be5a830a0975c37582a5148be9c3c963fd6bc291a3c753e66ee1dc8f62389db | R | 1,898 | 80 | ---
title: "R Notebook"
output: html_notebook
code_folding: show
---
```{r interactive_check}
if(interactive()){
print(date())
print("This was run interactively. Please run with rmarkdown::render")
}else{
print(paste0("This notebook was compiled on ", date()))
}
```
```{r, message = F, results = 'h... |
a56375ada081696b3693288199a7a9a6bdb6d1aad785e204702e08a02e47c3d5 | R | 1,899 | 60 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(finalfit)
library(mice)
library(purrr)
library(dplyr)
```
# Load Data
```{r}
vrf.latents = read.csv('data_derived/dim_red/latents_vrf.csv')
cog.latents = read.c... |
a9c1bfac3fda02686bb8a2ea4d99a8246457ac2698c6f912025d9ae1b82e2303 | R | 1,905 | 44 | "pwr.anova.test" <-
function (k = NULL, n = NULL, f = NULL, sig.level = 0.05, power = NULL)
{
if (sum(sapply(list(k, n, f, power, sig.level), is.null)) !=
1)
stop("exactly one of k, n, f, power, and sig.level must be NULL")
if (!is.null(f)){
if (is.character(f))
f <- cohen.ES... |
61f9ebfacd1e87243759a4bd591ee9df90d7eed0222bdea91ed143012c528c06 | R | 1,906 | 63 | source("paper.R")
scnmMap <- cembav2env$load.sa2.subclass.names()
rownames(scnmMap) <- scnmMap$subclass_id_label
allenSubclass2Region <- Sa2Integration$getAllenSubclass2Region()
rownames(allenSubclass2Region) <- allenSubclass2Region$subclass_id_label
mrMeta <- list(
Telencephalon = c("Isocortex", "HPF", "OLF", "AMY"... |
84144a38a0ed656de793c46ff23f55a98a56fd62d6398792fbecd86d2783ac3c | R | 1,906 | 68 | ####╔══════ ══════╗####
####💠 Project Setup 💠####
####╚══════ ══════╝####
load_package_if_installed <- function(pkg) {
suppressPackageStartupMessages({ require(pkg, character.only = TRUE, quietly = TRUE, warn.conflicts = FALSE) })
}
install_if_missing_then_load <- function(pkgs) {
for (pkg in pkgs) {
... |
cd695a9ea9774301a8987f4be9810039320358a517eef9f83a43cdd01eb5d870 | R | 1,908 | 56 | library(Seurat)
library(SeuratDisk)
library(tidyverse)
library(RColorBrewer)
library(data.table)
library(here)
library(rcartocolor)
PLOTDIR='figures/exploratory/preprocess_snRNA-seq'
here(PLOTDIR, c('plots', 'tables', 'rdas')) %>% sapply(dir.create, showWarnings = F)
###################################
# 0) pre-set c... |
8d9b86fc045f8d36add8aebb350f0a2dec4c5d78604daeeff54986fc8a0c124e | R | 1,916 | 61 | #=========================================================================
#
# 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... |
7d0fe7b931bf605114189576b5c5f642765e4b3026594ce00d912f85a8593963 | R | 1,920 | 57 | #05/19/2023 Fengyao Yan
#plot TSG genes distribution in 15 tissue types
library(ggplot2)
library(ggpubr)
library(data.table)
convert_dat <- fread('../../data/raw/martquery.txt', data.table=FALSE)
data0 <- fread('../../data/raw/selected_gtex_top_1_NT_1_fc_3_data.csv', data.table=FALSE)
data0 <- data0[,-1]
data0 <- da... |
e6de199d8170a1c334457089bdfe026069a153b3e9c44e2c69992d5d45d17523 | R | 1,921 | 69 | library(data.table)
library(optparse)
packdir <- file.path(here::here(), "package/R")
import::from(.from = "utils.R", .directory = packdir,
checkArgsExistOrStop, prepareOutdir, checkFileExistOrStop)
import::from(.from = "peak.R", .directory = packdir,
loadStatPeak.NMF)
import::from(.from = "cembav2env.R", .director... |
5db2aa2e09c9b2d1c0f92c104c0776b2bc8b8faf7008520d49b4c5b68f8b3927 | R | 1,923 | 59 | suppressMessages(library(Signac))
# suppressMessages(library(Seurat))
library(GenomicRanges)
library(Matrix)
suppressMessages(library(HDF5Array))
library(argparse)
parser <- ArgumentParser(description = "process input arguments")
parser$add_argument("--fragments", action = "store", type = "character",
help = "path to... |
115111f2f9263e64b59a974f3d6165069ce2a3822091e30d35f7ff0bf63b3fa1 | R | 1,935 | 102 |
get_Poisson_conf_intervals = function(seq_range, quantile_vec = c(0.05,0.95), plot=T) {
num_rows = length(seq_range)
num_cols = length(quantile_vec)
m = matrix(nrow=num_rows, ncol=num_cols)
colnames(m) = quantile_vec
rownames(m) = seq_range
row_count = 0
for (i in seq_range) {
q = qpois(quantile_vec,... |
5e9f6bafb0a59150cf72a592af90d73bbad20e17a0f3c539fd9c3b4586440837 | R | 1,943 | 42 | add_oncoprint = function(type, x, y, width, height, variant.classes, type_col) {
for (i in 1:length(variant.classes)) {
if (any(type %in% variant.classes[i])) {
grid::grid.rect(x, y, width - unit(0.5, "mm"), height -
grid::unit(1, "mm"), gp = grid::gpar(col = NA, fill = type_col[var... |
667f9bf65545597bd1a30db2059e66396b53fec1f630198488e3978456932636 | R | 1,946 | 69 | library(data.table)
library(optparse)
packdir <- file.path(here::here(), "package/R")
import::from(.from = "utils.R", .directory = packdir,
checkArgsExistOrStop, prepareOutdir, checkFileExistOrStop)
import::from(.from = "peak.R", .directory = packdir,
loadStatPeak.NMF)
import::from(.from = "cembav2env.R", .director... |
996299b56d5ce48494bfb7abcbfd6141ddd8437dc52492285da0cb185a14ee78 | R | 1,951 | 62 |
library(Matrix)
library(monocle3)
library(cicero)
#CellType = "ABC_NN"
args <- commandArgs(trailingOnly = TRUE)
CellType <- args[1]
print(CellType)
path = "/oasis/tscc/scratch/smamde/mouse_atlas/mtx_files/"
# Read in matrix data using the Matrix package
indata <- Matrix::readMM(paste0(path,CellType,".mtx"))
# Binari... |
70d03d952d415cbd5ab3427972c48f9a81f8ae29516a6d966af17d15b47ab09b | R | 1,953 | 35 | library(tidyverse)
library(reshape2)
library(ggsignif)
library(ggpubr)
### Assign labels and specific cell-types in validation data-------------
alldata <-data.table::fread('../../../Fibrosis Species Translation/human lung fibrosis/all_mouse_lung.csv')
allmeatadata <- data.table::fread('../../../Fibrosis Species Trans... |
3e3e6bd79ad916c1dff72ef350e89de99522d7daad77018e9e05347679fc6b5b | R | 1,959 | 60 |
.fitBimodalBySampling = function(x,
sample.size = 100,
tries = 2000,
force.tries = FALSE,
prob = 0.95,
coverage = 0.8,
si... |
dc992459f429edb6a6e0cfb5a24616e33332e07f8a764a1e2939bc2fd9afb4e0 | R | 1,963 | 56 | #-----------------------------------------------
# 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 ... |
9b1ba2f925ad4edd80b0f4eba563db5b06ac861adf8b158363d8cbe583fc09bd | R | 1,972 | 61 | library(dplyr)
load("cell_emb_late.RData")
load("cell_L4.RData")
load("sc_exp.RData")
load("celltype.RData")
load("cluster_1_2_3.RData")
zscore = function(x){
x = (x - mean(x))/sd(x)
}
###all cells at 440 mins enrichment
min440 = filter(emb_late,Start_time < 440 & End_time >= 440)
names(min440)[names(m... |
f8e2c466207f2a0497da7b7cd75be2a1ae87c63030a5ed89a638deda57b7ad99 | R | 1,980 | 76 | library(tidyverse)
library(reticulate)
library(gamlss.dist)
library(gamlss)
library(fitdistrplus)
usePython <- "/home/szu/mambaforge/envs/sa2/bin/python"
reticulate::use_python(usePython)
pd <- reticulate::import("pandas", convert = FALSE)
projdir <- here::here()
workdir <- file.path(projdir, "18.snap2_peakcalling")
... |
aa4bb7c7e04a710705733dc5c47e49ade923f598d2876ff42b84b2e408e33f04 | R | 1,983 | 61 | # Gregory Way 2017
# PanCancer Classifier
# scripts/viz/nf1_summary_figures.R
#
# Visualize summary for NF1 classification scores
#
# Usage: Run in command line
#
# Rscript --vanilla scripts/viz/nf1_summary_figures.R
#
# Output:
# Heatmap for included cancer-types in NF1 classifier
library(dplyr)
library(pheatmap)... |
abef25d5d15bac216a0ae9b745bb5728d89bbccb8137401268ad5b37841ba4d2 | R | 1,983 | 49 | #' Writes maf summaries to output tab-delimited text files.
#' @details Writes MAF and related summaries to output files.
#'
#' @param maf an \code{\link{MAF}} object generated by \code{\link{read.maf}}
#' @param basename basename for output file to be written.
#' @param compress If `TRUE` files will be gz compressed. ... |
baf7610ccffa054e6155e5a8e7ec5f77357521a04440d9fd9058885d5c24079e | R | 1,983 | 40 |
#' @title State Fractions (by group)
#' @description Get the fraction of cells in each state, by group if <groups> provided. Here, the rows in <scores> are interpreted as cells and the columns as their state scores, but more generally the function assigns each row to its maximum column (state) and subsequently count... |
cf3f3f2afb7d615fce7e3d5ec0abac4e4807acb70d0bd23d6185411d2827bf5d | R | 1,986 | 37 | #' @title
#' Introduction to file2meco package (\href{https://github.com/ChiLiubio/file2meco}{https://github.com/ChiLiubio/file2meco})
#'
#' @description
#' For the detailed tutorial on the file2meco package, please follow the tutorial link in the github repository
#' (\href{https://github.com/ChiLiubio/file2meco}{htt... |
2087cb127a9f61151387bebb48a9dba7d2a4c7cf7e025e1f4215e81225ef693b | R | 1,994 | 60 | # cell chat
library(Seurat)
library(CellChat)
library(patchwork)
options(stringsAsFactors = FALSE)
library(Matrix)
library(dplyr)
library(ComplexHeatmap)
# construct data
tnbc1.tu.obj <- readRDS("tnbc1.tu.obj.rds")
tnbc1.normal <- readRDS("tnbc1.normal.rds")
ar.p <- subset(tnbc1.tu.obj, subset = HALLMARK_G2M_CHECKPOIN... |
5f459011af38e4d8dbf5a19663641458f80c7b8db443250cb464dfdb6ed05958 | R | 1,997 | 59 | library(Seurat)
library(dplyr)
library(ggplot2)
# If your expression matrix contains a mix of different pre-determined cell clusters or labels, it may be wise to run OscoNet seperately for each cell type.
# To do so specify the cell label you would like to isolate and run below. Remember to modify your output file nam... |
afb773d7ecbf8dd2d49b683d92508792419f2ff5a021703f3500f33d5f9b8671 | R | 1,999 | 64 | # compare expression between two randomly selected tags
# from each of the CRE elements before and after normalization
args<-commandArgs(trailingOnly=T)
suppressMessages(library('ggpubr'))
suppressMessages(library('ggplot2'))
suppressMessages(library('gridExtra'))
workingdir<-args[1]
tag_cnt_cutoff<-as.numeric(args... |
0b2e8ad18450e1012c9ad532bd1798683ab03cd063076657aaff128ffcc932c3 | R | 2,000 | 50 | library(tidyverse)
library(readxl)
### Load data
human_hiv <- data.table::fread('data/Rv144_Systems_Serology.csv')
colnames(human_hiv)[1] <- 'id'
human_hiv <- human_hiv %>% mutate(trt=ifelse(trt=='PLACEBO',0,1))
human_hiv <- human_hiv %>% mutate(infect=ifelse(infect=='Yes',0,1))
human_expr <- as.matrix(human_hiv[,12:... |
af782c1d96d230e1f9c85b39e29acdf64354d741e000aad1da74ce1736e952f3 | R | 2,006 | 62 | #' Aggregate cancerhotspots reports
#' @description Takes tsv files generated by \code{\link{cancerhotspots}} and aggregates them into an MAF for downstream analysis
#' @param tsvs TSV files generated by \code{\link{cancerhotspots}}
#' @param minVaf Min. VAF threshold. Default 0.02
#' @param minDepth Min. depth of cove... |
aef935b24710adae13c830a0787a732362ce65772949e6234d7e59ad65092938 | R | 2,010 | 82 | # this file is the third part for getting p-value cutoff for FDR 0.05
# for each of the 20 permutations, get the number of false positives at each true data
# p value cutoff.
# first, sort the p values; O(nlogn)
# second, for each cutoff, do binary search. O(nlogn)
# this part is be done in the format of 20 job subm... |
047e8ceac1c3925efc9abfc946b49d4f56ab140148ec5d237f76f3c83fde5681 | R | 2,016 | 55 | library(stats)
library(dplyr)
library(tidyr)
load("Cel_dup_N4Cel.RData")
load("celltype.RData")
load("sc_exp.RData")
for(i in 1:ncol(sc_exp)){
cur = filter(celltype, index == colnames(sc_exp)[i])
colnames(sc_exp)[i] = cur$celltype2
}
for(i in 1:nrow(sc_exp)){
sc_exp[i,"max_cell"] = colnames(sc_exp)... |
d1b3d1226021a60e19d7a0b2b2fddb75d5da842b21b495c67dc1629b8a057b95 | R | 2,016 | 60 | #-----------------------------------------------
# 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 ... |
64778126995bc52d0336297abb1ce03c9b67e155aa1e83a0eb67bcb437daa219 | R | 2,019 | 68 | ####╔═══ ══╗####
####💠 Utils 💠####
####╚═══ ══╝####
cli_h2("┗ [SCRIPTS] Loading helper functions")
#--------------#
####🔺Pipes ####
#--------------#
"%ni%" <- Negate("%in%")
"%s+%" <- \(lhs, rhs) paste0(lhs, rhs)
"%ne%" <- \(lhs, rhs) if(is.null(lhs) || rlang::is_empty(lhs) || (length(lhs) == 1 && lhs == ... |
7649cd9d41ac286cf202bf87d43b7075d6bb70834f9c7bf385211585638cbe5b | R | 2,028 | 47 |
library(gifti)
library(R.matlab)
library(RNifti)
library(ciftiTools)
ciftiTools.setOption('wb_path', '/Users/askeller/Documents/workbench/')
library(ggplot2)
PFNs_hardparcel<-read_cifti('/Users/askeller/Documents/Kellernet_PrelimAnalysis/hardparcel_group.dscalar.nii')
map_predacc_PC1_lh <- PFNs_hardparcel$data$corte... |
94caa5eae011969698efef1a9a677ff04375b0f1c2dcee3e980a9743cbcd9d29 | R | 2,029 | 52 | # RCTD deconvolution wrapper script
library(RCTD)
library(data.table)
rctd <- function(st_counts, st_coords, sc_counts, sc_labels,
doublet_mode, min_cells, n_cores) {
###### Performs RCTD deconvolution of the st data from sc data #######
# Making sure correct namings #
colnames(st_counts) <- r... |
7b25e9b829e48b9a4e398db54782e9dcaa60d6df7e0ae1102bbe46fd4c056e74 | R | 2,037 | 80 | # load packages
suppressPackageStartupMessages({
library(caret)
library(rhdf5)
library(HDF5Array)
})
# split train and test on 50% training set, 50% test set
# input "rna" can be sce, seurat, or matrix, "cty" is a vector of cell type
train_test_split <- function(rna, cty, seedInput = 1234){
set.seed(seedInpu... |
515a2000bda8e613bba87bc4bed73fb2837e3197c3ddc8d26ac2fa91833b5384 | R | 2,042 | 54 | #! /software/crgadm/software/R/4.2.0-foss-2021b/bin/Rscript
### USAGE CNAG cluster
## module purge; module load R/4.2.0-foss-2021b;
## Usage: cis_pairs_dist.v03.R <infile.pairs> <out_basename>
## example: cis_pairs_dist.v03.R mapped.mq40.Capra_hircus.pairs mRupRup
# Name: cis_pairs_dist.v03.R
# date: 2025-10-16
... |
456b2108c843a398d70b5d6875919c78fd216346e6750d05d4d67644e9298694 | R | 2,049 | 56 | # Since ggplot2 4.0, geom_text() and geom_label() take default aesthetics such
# as family, size, and colour from the theme. The repel geoms should match them.
#
# ggplot(...) + geom_text_repel(...) + theme_gray(base_family = "serif")
#
context("theme defaults")
custom_theme <- theme_gray(
base_size = 16, base_fam... |
0fa1b238c574ade909e5aa8d008c5dc1a94cc9992a33e9fcba308c70fccf48f2 | R | 2,052 | 80 | ---
title: "R Notebook"
output: html_notebook
---
# Set Up
```{r setup}
knitr::opts_knit$set(root.dir = normalizePath("../.."))
```
# Imports
```{r}
library(finalfit)
library(mice)
library(purrr)
library(dplyr)
```
# Load Data
```{r}
cardio.data = read.csv("data/cardio.data.csv")
brain.confounders = read.csv('data/b... |
0759fa26b11cf4d196b76fc60eb99d5bde30b634d71ac4ef2cd72f269bc9d616 | R | 2,063 | 59 | # quantile normalization for dyn data
library(preprocessCore)
library(Seurat)
library(dplyr)
design <- "5000.gene.4ct"
n.cells <- 4000
n.cells.per.type <- 1000
n.genes <- 5000
version.n <- 1
output_dyn_data <- function(design, version.n){
gen <- read.csv(paste0(design, ".dyn.v", version.n, ".S.clean.csv"), header ... |
daee542f7d42dc70b88279a6ca4ce578b1afd4e2dec17db5e747b0e240cf2455 | R | 2,071 | 66 | # summarise tr and cumulative permutations and merge + tstat
# subject, level, perm
library(tidyverse)
library(glue)
args <- commandArgs(trailingOnly = TRUE)
subject <- args[[1]]
level <- args[[2]]
perm <- args[[3]]
subject <- str_pad(subject, 2, pad = "0")
subject = glue("sub-{subject}")
path <- glue("bids/derivat... |
07bad99c98387d62325c90fd043ad88675687609edc9f6b21d0b57ff58e4185d | R | 2,076 | 37 | library(tidyverse)
library(ggplot2)
library(gridExtra)
#####################################
# boxplot of ChIP signal (Figure 2B)
#####################################
# zonal OR color palette
zonal_fine_colors_fish_classIfirst<-c("#990000","#FF0000","#FFCC00","#33CC33","#0099FF","#0000FF")
zonal_fine_colors_dark_fi... |
20dc699cdfcfb5d702a57904570d148ea1f547605e81804dcd2000c7800dd56e | R | 2,077 | 62 | #-----------------------------------------------
# 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 ... |
7e04bd3a9fdf8ca0d57f137fc83b85d1e2c674a7258a00ba1ceb1762bb87b652 | R | 2,078 | 90 | #' Results of ConNetGNN() for scATAC-seq data from SNARE-seq dataset
#'
#' @description A list to store the gene association network of scATAC-seq data.
#' Case data from the SNARE-seq dataset.
#'
#' @format a list of three adjacency matrices.
#' @examples
#' data(ATAC_net)
"ATAC_net"
#' Results of ConNe... |
0576b709608f1cff6149a3179a730ec732955655f39ac5d413f602a095f68562 | R | 2,081 | 65 | # compare expression between two randomly selected tags
# from each of the CRE elements before and after normalization
args<-commandArgs(trailingOnly=T)
suppressMessages(library('ggpubr'))
suppressMessages(library('ggplot2'))
suppressMessages(library('gridExtra'))
workingdir<-args[1]
tag_cnt_cutoff<-as.numeric(args... |
9a428e626e1716289fc6b6b059df70d45143bbe5e7762dabe9a468b42ca909aa | R | 2,086 | 68 | library(purrr)
Sys.setenv("_R_USE_PIPEBIND_" = TRUE)
library(stringr)
projdir <- here::here()
resourcedir <- "../resource"
rdir <- file.path(projdir, "package/R")
import::from(.from = "cembav2env.R", .directory = rdir,
Sa2Integration)
# * simply extract allen's cluster marker genes
annotAllen <- Sa2Integration$loadA... |
864f05487fcd670e0a88768f4f554ff2eb8a97dfc1f6c5aae0890200bb76266e | R | 2,092 | 50 | rm(list=ls())
gc()
####################
## Load libraries ##
install.packages('pheatmap')
install.packages("lmerTest")
install.packages("robustHD")
library(data.table)
library(dplyr)
library(readxl)
library(pheatmap)
library(lmerTest)
library(robustHD)
setwd('/share/inspurStorage/home1/zixing/ABCDR/Project_1')
datase... |
e4193aede8459c4cd05b730df3ba4f1694e631a0af02a71cbcf45aca847403f8 | R | 2,094 | 71 | # suggested comp env - mem_free=150G thread 4
## conda environment can be created from https://github.com/brianherb/HumanHypoDev2Adult/blob/main/Hypo_R_Env.yml
library(CoGAPS)
library(Seurat)
flexsplit <- function(dat,char){
test=strsplit(as.character(dat),char,fixed=TRUE)
n.obs <- sapply(test, length)
seq.max <- se... |
17fe6995e182f1ea52d09f12a5d520ca7a95b752fd15e64b00c9a301f42c443c | R | 2,095 | 80 |
#This scripts calls a validation function to check cluster assignments by random forest models trained on DE genes
## ----receive input from nextflow----------------------------------------------
args <- commandArgs(TRUE)
path<-args[1]
file<-args[2]
dendrogram<-args[3]
print(path)
set.seed(123)
timepoints<-substr(f... |
2d0bc9029d7e72de98e04603bd11501dfea300e6969eb365049c10232f21a118 | R | 2,095 | 59 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
library(stringr)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_me... |
bc96f5c97d3c4007aa2ef9bcff4275b63b48be0014f465dd33004f42f55e8450 | R | 2,103 | 61 | ---
title: "Proteomics"
output: html_document
date: "2023-07-19"
---
```{r}
library(ggplot2)
```
```{r}
volcano <- function(dea, th.logFC=log2(1.5), th.FDR=0.05, hl=NULL, sxlim=NULL){
dea$logFC <- dea$diff
dea$significant <- as.integer(dea$FDR <= th.FDR & abs(dea$logFC) >= th.logFC) * sign(dea$logFC)
if(isTRUE... |
678c9cbf1f43d43aa5bfb176c61fe5bab65effc98140ce4e69cf69678cd35728 | R | 2,104 | 88 | # this file is the fourth part for getting pvalue cutoff for FDR 0.05
# calculate
# \hat{q}(p_(n)) = E(# false positives with pvalue <= p_(n))/n; O(1)
# for i = n-1, n-2, ..., 1, calculate
# \hat{q}(p_(i)) =
# min(E(# false positives with pvalue <= p_(i))/i, \hat{q}(p_(i+1)))); O(n)
# load the p values o... |
4c7ba3ee9af09c3fbb545ff10615cacd3438074161e2a404f87b0b02027c883e | R | 2,105 | 49 |
library("gamlss")
pop_norms <- read.csv("~/r_files/pop_norms.csv")
boys = subset(pop_norms, pop_norms$Gender == 1)
boys[boys==0] <- NA
boys1<-na.omit(boys)
xsteps = (35 - 4)/31
xlim1 = 4 - xsteps
xlim2 = 35 + xsteps
ysteps = (25 - 5)/20
ylim1 = 5 - ysteps
ylim2 = 25 + ysteps
#*************************************... |
d613013fd3c0c0f1a90d123c817d9e3809bfdd386cd3368f0e1c34158a95211f | R | 2,106 | 63 | #-----------------------------------------------
# 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 ... |
5f696ef55d6be0a2e9232ea659f5fccdee06aba65a10755488794042362c9ad5 | R | 2,108 | 54 | ## packages for data table processing
library(tidyverse)
library(igraph)
library(network)
library(sna)
library(stringr)
library(RColorBrewer)
library(data.table)
library(here)
library(leiden)
ss <- function(x, pattern, slot = 1, ...) {
sapply(strsplit(x = x, split = pattern, ...), '[', slot) }
options(stringsAsFac... |
1f79f24d67d6d08e3ef63610e19b11ff56f43f210507194f304fb6152b3bd2f0 | R | 2,111 | 58 | snapshot <- "/lab-share/RC-Data-Science-e2/Public/DST_software/pipeline_R_snapshot/r4.1-20220803-bch-dst-v1/"
.libPaths(snapshot)
library(Seurat)
library(ggplot2)
library(ggrepel)
setwd('/lab-share/RC-Data-Science-e2/Public/Daniel/p147-scRNAseq-Koehler/')
# Loading data
load('/lab-share/RC-Data-Science-e2/Public/Danie... |
0e5a249823d2e25572c5800707f3f411f05413cf82d2ebc465a10908e473823e | R | 2,114 | 60 | require(symphony)
require(Seurat)
snapshot <- "/lab-share/RC-Data-Science-e2/Public/DST_software/pipeline_R_snapshot/r4.1-20220803-bch-dst-v1/"
.libPaths(snapshot)
source('/lab-share/RC-Data-Science-e2/Public/Daniel/rFunctions.R')
setwd('/lab-share/RC-Data-Science-e2/Public/Daniel/p147-scRNAseq-Koehler')
# Loading Da... |
6f9f67582f66207f1811cd191addc285160d35969451d882aec5321190e8c85d | R | 2,116 | 53 | library(NASTIseq)
## generation of test set
# data(WholeRoot)
# WholeRoot$genepos$feature <- 'gene'
#
# set_attri <- function(attri){
# attri = paste('gene_id ', '"', attri, '"', ';', sep = '')
# return(attri)
# }
#
# WholeRoot$genepos$attributes = as.character(lapply(as.character(WholeRoot$genepos$attributes), se... |
abf30298163b50916f8000a1ad875e6a56b8d37b8179d4a3054affa47af2ab04 | R | 2,119 | 63 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
exp_mean <- function(x, p, i, ncol, nrow, groups, group_sizes) {
.Call(`_symphony_exp_mean`, x, p, i, ncol, nrow, groups, group_sizes)
}
log_vmr <- function(x, p, i, ncol, nrow, means, gro... |
8b7a166f16234d7723abfe29060e69e0e32b35ca1b6be0711ec87a0a5026453b | R | 2,121 | 66 | library(Matrix)
library(monocle3)
library(cicero)
#CellType = "ABC_NN"
args <- commandArgs(trailingOnly = TRUE)
CellType <- args[1]
print(CellType)
path = "/oasis/tscc/scratch/smamde/mouse_atlas/mtx_files/"
path_save = "/oasis/tscc/scratch/smamde/mouse_atlas/shuffle_cicero/cicero_shuffle_results/"
# Read in matrix dat... |
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