sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
12d6e15124493883fca1fa9809c900bba316589d5b8fd1b32db8959b772cf811 | R | 2,196 | 51 | ## import package
.libPaths("~/R/x86_64-pc-linux-gnu-library/4.2")
library(Seurat)
library(ggplot2)
library(ggpubr)
library(sctransform)
library(harmony)
library(dplyr)
library(plyr)
## load all the objects
Hypo_integrated <- readRDS("~/Yi_Huang/objects/Hypo_integrated.rds")
AgRP_subclusters <- readRDS("~/Yi_Huang/obj... |
36364c191ec0e16aab1a2210c55440ccab5cf9c8e5ec2534e2bf5bfd4bfec28f | R | 2,203 | 56 | rm(list = ls())
options(stringsAsFactors = F)
library(umap)
library(ggplot2)
library(ggrepel)
library(dplyr)
library(scatterplot3d)
options(rgl.useNULL=TRUE)
library(rgl)
rgl::setupKnitr(autoprint = TRUE)
setwd("~/Documents/Virginia Tech/Project_4_VCU_postmortem_SCZ/RNA_DEseq/UCSD_gtf/glia/")
df = read.csv('All_SCZ_Con... |
0d3e59b2bd4e764f594993da824ce58ebe5609746a08506b97fa34fc5425d2ef | R | 2,218 | 48 | #!/usr/bin/env Rscript
#this script gets blastn results as an input, and outputs the coordinates of regions with high intrachromosomal similarity per respective basepairs
require(tidyr)
require(dplyr)
options(scipen=999)
args <- commandArgs(trailingOnly = TRUE)
file <- args[1]
print(file)
plotRectangle<-function(spe... |
115bfbc48da380c063e866e999397db17c151d167006f8937c3b60a4e87115de | R | 2,218 | 70 | library("stars")
library("xgboost")
library("ALEPlot")
set.seed(1)
outdir = file.path("data", "rds")
if (!dir.exists(outdir)) dir.create(outdir)
names = c("elevation", "slope", "stdev", "multitpi", "convexity",
"entropy", "openness", "median500", "median1000")
reference = list.files("data/raster_maps", pat... |
5e320e9f122bae0ac27c352c2b7734cb65306f173a77ca2950df659a4547f0d8 | R | 2,224 | 56 | library(dplyr)
library(reshape2)
library(ggplot2)
res = readLines("benchmark_res.txt")
header = c("Size", "Dataset", "F77time", "F77err", "F77nops", "Cpptime", "Cpperr", "Cppnops")
## Benchmark result for symmetric solver
sym_start = grep("eigs_sym", res) + 4
sym_end = grep("========", res)[2] - 1
sym_dat = strsp... |
00e5a5a709e490f92096b0da31d3d6297172d6c8fa8cb241716f39990b8f2cea | R | 2,226 | 95 | ---
title: "Compare firing properties across firing modes classified at the single-trial level"
author: "Matt Nolan"
date: "01/11/2023"
output: html_document
---
# Setup
```{r}
library(tidyverse)
library(lme4)
library(emmeans)
```
# load data
```{r}
# cells_data <- readRDS(file = "cells_df.Rda")
cells_data <- readRD... |
5f6da600c873c3516773dd17fcbf174483a3e730b4d9987844f8d2a37283323a | R | 2,234 | 65 | # Load necessary libraries
library(dplyr)
library(tidyr)
library(ggpubr)
library(rstatix)
library(pheatmap)
library(RColorBrewer)
# Set working directory to Interaction Score files
setwd('Interaction_score/')
# Read interaction scores and annotations
file_names <- list.files(pattern = ".*SUM")
interaction_scores <- d... |
be477f4b483df09665ef807d01cac23ffbd339ba812b0d15ab559c425b66651f | R | 2,247 | 82 | library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
library(patchwork)
library(rtracklayer)
library(cowplot)
sObj_Filtered <- readRDS("rObjects/sObj_cluster.rds")
sampleInfo <- readRDS("rObjects/SampleInfo.rds")
names(sObj_Filtered) <- sampleInfo$ID
## re... |
76ef12362a1d2282714faee8281fea4c376f8a0af165319a6bfe6a765701c732 | R | 2,249 | 59 | library(Seurat)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggpubr)
library(pROC)
library(cowplot)
# Load data
df <- read.delim2('per_cell_type_annotation.tsv')
dat0 <- readRDS("PlateletsOnly_cluster_Anno.rds")
# Preprocessing for ROC analysis
df <- distinct(df, Sample_ID, Outcome, Severity)
dat01 <- dat0@... |
b869f8b53c9b7bda58762a68f4f47d16ade38afdec5f593b3b412aecbb71e591 | R | 2,257 | 77 | ## ---Libraries ------
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
library(patchwork)
library(rtracklayer)
library(cowplot)
library(BSgenome.Hsapiens.UCSC.hg38)
library(JASPAR2020)
library(TFBSTools)
library(patchwork)
##--- Functions --------
howMa... |
9db5e493be339d836f3e9faccf9f21339c7f502d264bfb5df1d0c1e608fa9b1f | R | 2,286 | 45 | #dev.off()
#rm(list=ls())
library(ggplot2)
library(tidyr)
library(gridExtra)
library(ggthemes)
library(qqman)
library(dplyr)
Data <- read.table("FSTscan_GATK_20vs20_5kb2.5_IT.windowed.weir.fst", header = T) #the same was performed for the SA lineage contrast (input file FSTscan_GATK_10vs10_5kb2.5_SA.windowed.weir.fst)... |
02b7154f1a9ab5b96e4403ab7b22711237419f4a7204b1e408836c8e7a8b38c7 | R | 2,302 | 98 | library(tidyr)
library(dplyr)
library(base)
library(stats)
library(r2symbols)
setwd('Desktop/Proteomics polyGR/')
quant<-readRDS('quant/MSstats_ProtQuantity_Summarization.rds')
quant2<-quant$ProteinLevelData
quantTNPO1<-quant2%>%filter(Protein=='Q92973')
EGFP<-quantTNPO1%>%filter(GROUP=="EGFP")
GR<-quantTNPO1%>%filt... |
53e0c78f21df5a7bee4c6731266133137d700cef1c1760c8ddf4836160951028 | R | 2,310 | 83 | ## Libraries
library(here)
library(dplyr)
library(parallel)
library(here)
library(tidyverse)
library(DT)
pname <- "AMC"
cname <- "mTOR"
#################################@
## Compare cohorts
## FCD2a-FCD2b, FCD2a-TSC, FCD2b-TSC
## get data
samples <- listSamples(projects = pname , condition = c("mTOR_control")) %>%
... |
e69ea6fa3c073f17515cb4378aef64bb34bb258d388e4c3b90205de5096ac526 | R | 2,320 | 51 | rm(list=ls())
dev.off()
library("genoPlotR")
library(gridExtra)
setwd("C:/Users/Nathalie/Dropbox/WallLizard_NanoporeAssemblies/")
############### Fig. 2D - gene annotations in candidate region (exon level)
data <- read.csv("CandidateRegion_Genes_Location_Spa_exons.csv", header = T)
dna_data <- dna_seg(... |
3a730e940b862091ad459684c3caabc684c9e9be9b8342415ff57e10e4176458 | R | 2,332 | 105 | master <- read.csv("MasterFrequencies_PCA.csv", row.names = 1)
masterscaled <- scale(master)
pca <- prcomp(t(masterscaled))
pcs <- data.frame(pca$x)
### Export the PCs to use in graphpad
write.csv(pcs, "pcs.csv")
plot(pcs$PC1, pcs$PC3)
a1 <- data.frame(pcs$PC1[1:5], pcs$PC3[1:5])
a2 <- data.frame(pcs$PC1[6:13]... |
b3a2a3a85df79b7beeeb656a33335840fbbb68c51190881dbaf0f220c9aa2290 | R | 2,348 | 40 | #' pGLS model comparison using phylogenetic analysis of covariance (gls.ancova)
#'
#' Computes F-ratio test of GLS models that include different sets of indicator variables.
#' @param form Model formula.
#' @param data a data frame containing the variables in the model.
#' @param Sigma Matrix describing the covariance ... |
c0fd69b0b49f9686cfed7d74763020450090bd0df97e9869fa549a3d319dafd6 | R | 2,355 | 90 | # perform T-SNE
"tsne" <- function(X, initial_config = NULL, k = 2, max_iter = 1000, min_cost = 0, epoch = 100) {
cat("Performing t-SNE.\n")
momentum = 0.8
final_momentum = 0.8
mom_switch_iter = 250
epsilon = 500
min_gain = 0.01
initial_P_gain = 4
n = nrow(X)
eps = .... |
2678f5d421a7ffad411df7e6d7bb35de9697600c2b35e94ed8100f7232afa779 | R | 2,362 | 61 | # Load necessary libraries
library(dplyr)
library(SeuratDisk)
library(Seurat)
library(clusterProfiler)
library(org.Hs.eg.db)
library(ggplot2)
library(RColorBrewer)
# Set the working directory to where DEG files are located
setwd("~/DEG/Platelets")
# Function to process DEG files
process_DEG_file <- function(file_name... |
b130489b831a3cdd9c40d4d7ba60ecdade6dcae18cd9343fe6f99b809bfc95ef | R | 2,394 | 80 | # -----------------------
# UMI-BC report
# -----------------------
library(ggplot2)
library(dplyr)
library(ggseqlogo)
library(grid)
library(gridExtra)
library(stringr)
args <- commandArgs(trailingOnly = TRUE)
input.csv <- args[1]
report.file <- paste(input.csv, '_report.pdf', sep='')
x <- read.table(input.csv, sep... |
653d269c0ae266747b7e2afebb820b1a5ef715537ae58de01623f1dfebac22f0 | R | 2,401 | 124 | ---
title: "Retina Clusters Eye Samples"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(Seurat)
library(cowplot)
library(SCFunctionsV3)
library(dplyr)
library(tidyr)
library(stringr... |
9f491b66bf50d2f727e401911721c7b7613fc7f1d689e18dc6df2f7fc53c2b04 | R | 2,405 | 85 | library("stars")
library("corrr")
source("code/misc/getRegion.R")
tab = read.csv("data/classification_table.csv")
files = list.files("data/digital_maps", recursive = TRUE,
pattern = "OK_POWIERZCHNIE_PODSTAWOWE+\\.shp$",
full.names = TRUE)
res = c(30, 30)
if (!dir.exists("data/ras... |
ac28b6c52b82c455f02cf7b6bcb4d4adf8ea38c81ffe2eeceb118f407eab6097 | R | 2,422 | 78 | ## input data
dataDir <- file.path(allData, sampleName)
## output data
sce_output <- paste0(outputDir, sampleName, "_sce.rds")
batch_corrected_seurat_output <- paste0(outputDir, sampleName, "_seurat.rds")
## samples
filepath <- list.files(dataDir)
id <- filepath %>% gsub("results_", "", .) %>% gsub("_Results", "", .... |
85c254498fc11c0525c5ab2789fb09dc831406a33cd06880a06e81d74f8492c9 | R | 2,444 | 50 | ---
title: "PyPALM Comparison"
output: html_notebook
---
Load libraries
```{r}
library('permuco')
library('dplyr')
library('nptest')
```
Load data
```{r}
roi_vols = read.csv('~/FUSE-HWNI/dlurie/Projects/timescale_hierarchy/data/resources/BNA_ROI_volumes.txt', header = FALSE, col.names = "Z")
ts_data = read.csv('~/FUSE... |
2b51250c9552424772f863ebea67bd510c1f57018f33c7b8a7f56621cc99484a | R | 2,445 | 43 | ---
title: "Spin Test Parcel Rotation Matrix"
author: "Audrey Luo"
date: "2023-12-01"
output: html_document
---
This Rmd file using rotate_parcellation to generate 10k rotated permutations of a parcellation, given the coordinates of left and right hemispheres of this parcellation on the sphere.
The output is a vector... |
5fafb61963a744b0b18e0e1ae157cd7678793fb16b3d22745a7a582a144e0e52 | R | 2,449 | 79 | sampleName <- paste0(tissue, "_D", day)
## input data
dataDir <- file.path(allData, sampleName)
## output data
sce_output <- paste0(outputDir, sampleName, "_sce.rds")
batch_corrected_seurat_output <- paste0(outputDir, sampleName, "_seurat.rds")
## samples
filepath <- list.files(dataDir)
id <- filepath %>% gsub("resu... |
6269dad0b1db82be613706d8b5235b6e7487ddb5b056f07729d8359f8e77b556 | R | 2,451 | 44 | # Code for adding metadata by pseudotime and then doing GO or GSEA
library(naniar)
library(gprofiler2)
Feature3D(data.combined, "Pseudotime")
UMAP3D(data.combined)
# make cutoffs 1-7.5
# Also there are some weird cluster pseudotime issues between 1.1 and 1.5 so i will just remove those clusters as well
# Ori... |
95495311a1265a70a92af3d6ee49854d67a71f599555f14ce225c5fde5f196d1 | R | 2,454 | 39 | # Run with Rscript generate_reference.R /tmp/organage-sources OUTPUT.csv
# The scoring expressions are read verbatim from the pinned authors' script,
# so this oracle does not reimplement the Python port.
args <- commandArgs(trailingOnly=TRUE)
root <- args[1]
output <- args[2]
script <- readLines(file.path(root, "scrip... |
a66c2edd57cba7feb87c784a2f1c4738a51c725915fd9621f6e5e97d1963b03c | R | 2,456 | 52 | ### DATA
load("bipolar_data_Cell2016.Rdata")
# Remove libraries that contain more than 10% mitochondrially derived transcripts
mt.genes = grep("mt-", rownames(bipolar_dge), value = TRUE)
cells.use = colnames(bipolar_dge)[colSums(bipolar_dge[mt.genes, ])/colSums(bipolar_dge) < 0.1]
bipolar_dge = bipolar_dge[, cells.use]... |
abc3d4caebc21313fbf13829fdacdb458f69049c92d1102ba77860d1af6b38f4 | R | 2,459 | 57 | library(DESeq2)
library(ggplot2)
SampleName = list.files(path = "star/",pattern = "R.bw")
SampleName = gsub("_sorted_R.bw","",SampleName)
Group = gsub("[0-9]$","",SampleName)
sample.meta = cbind.data.frame(SampleName,Group)
sample.meta = apply(sample.meta, 2, as.factor)
mtx = read.table("genes_expression_expected_count... |
065bdcaecb4f3bfd622e46a4ce549d7fbbb7b18a3f5bbc5f9b8efb8f8ea4a333 | R | 2,461 | 40 | # Run with original source directories and an output directory. No aggregator inputs.
# Rscript generate_reference.R <prcPhenoAge checkout> <IntrinClock original directory> <output>
args <- commandArgs(trailingOnly=TRUE)
prc_source <- args[1]
intrin_source <- args[2]
out <- args[3]
dir.create(out, recursive=TRUE, showW... |
30e31e53be92a8e012655909583cd02b129a556cbbd1c5ea27c5cd9cbf536d5b | R | 2,467 | 76 | library(dplyr)
library(Seurat)
library(ComplexHeatmap)
library(GSVA)
library(GSEABase)
library(limma)
library(ggplot2)
library(ggpubr)
library(tidyr)
library(rstatix)
# Load gene sets
genesets <- getGmt("h.all.v7.5.1.symbols.gmt")
# Load data and prepare
dat0 <- readRDS("Platelets_cluster_Anno.rds")
DefaultAssay(dat0... |
c340ce22941097f462037fc3352b7ecaadbe5fdc63e4c8acb307c138e4b4ed68 | R | 2,477 | 47 | ### Prepare pathology datasets for analyses
savedir <- paste0(params$opdir, 'processed/')
dir.create(savedir,recursive = T)
# load and format pathology dataset
parent_tau <- read.csv(paste0(basedir,"data/TauMLi2_Parent_RemovedFT.csv"), row.names = 1,) # sublaminar pathology (360 regions per hemisphere)
parent_tau <- ... |
6db4391253d24c015605b7e6135ed0b6421a9aeaa55bfc3caea2d4dedbbf7fcf | R | 2,484 | 62 | #!/usr/bin/env Rscript
library(plyr)
library(tidyverse)
library(readr)
library(here)
args = commandArgs(trailingOnly=TRUE)
data_path = args[[1]]
data <- read_rds(here(data_path))
data_filt <- data %>% filter(native %in% c("ENG", "English", "ENGLISH", "english")) #I peeked at what people put that semantically maps t... |
f624b4050894cd57bb0b70c247acc366aa586510fed472e62f3a3c70b0050ed4 | R | 2,501 | 80 | R (v4.2.1)
#https://www.nature.com/articles/s41593-020-00787-0
#https://research.libd.org/spatialLIBD/articles/spatialLIBD.html
library("spatialLIBD")
packageVersion("spatialLIBD")
#‘1.13.4’
## Connect to ExperimentHub
###############this code buck was used to delete the old cache (As I downloaded them in R4.... |
3fcde8dbadf94953f34fbba7d965e91a21b79f417d46b4c23a2c9f77709a4ed2 | R | 2,507 | 63 | # make_counts() and sig() come from helper-counts.R
test_that("ScoreSignatures_UCell accepts count matrix", {
m <- make_counts()
ref <- ScoreSignatures_UCell(m, features = sig(m), ncores = 1)
expect_true(is.matrix(ref))
expect_identical(rownames(ref), colnames(m))
expect_equal(
ScoreSignatures_UCell(Matr... |
8cbbfab0ea44ababb669a57a396a9dfc7d2c2e53e0c365d229a366655d3c9dd5 | R | 2,514 | 84 | library("lightgbm")
library("yardstick")
set.seed(1)
df = readRDS("data/dataset.rds")
n = round(0.7 * nrow(df))
trainIndex = sample(nrow(df), size = n)
train = df[trainIndex, ]
test = df[-trainIndex, ]
rm(df, trainIndex)
# class must be numeric, start with 0 and numbering must be continuous
train$class = as.numeric(t... |
b95e6bdb7ca79fb2db86f8392755cf96a66548a9643a18cb5238a482d2c30674 | R | 2,548 | 70 | #### USAGE ####
# This file makes correlation plots of simulations based on agrn setting files.
# In case you are using Rstudio, just click on "Source" button in the topright corner of this pane!
# From command line invoke: `Rscript src/plot.R` or `R -f src/plot.R`
#### Installation of neccessary packages ####
if(!r... |
a25ec02d1b9468d197d30694f50dd2bf6ffa3971cf206fbc37fc92967d23746d | R | 2,564 | 111 | ---
title: "Individual Clustering"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(Seurat)
library(cowplot)
library(SCFunctionsV3)
library(dplyr)
library(tidyr)
library(stringr)
libr... |
5766fd4908d9f1ab0fdf26dbf3796e134ba0838bad073ff663f62057a898f02d | R | 2,574 | 66 | #' To calculate cell-to-cell similarity matrix on gene (spatially auto-coorelated genes) expression based on Euclidean distance; to build up a K-nearest neighbors (KNN) graph based on the cell-to-cell similarity matrix; then to convert the KNN neighbor relation matrix into an adjacency matrix
#'
#' @param nk Number of ... |
1d235a2e704d9d11dda154b7c809cffbb3b4d1b82ba187e246ada89024fee6b7 | R | 2,582 | 59 | # Internal helpers, tested directly
test_that("check_signature_names fills in missing and duplicate names", {
f <- list(1, 2, 3)
expect_equal(names(UCell:::check_signature_names(f)),
c("signature_1", "signature_2", "signature_3"))
names(f) <- c("a", "", "a")
# the empty name and the duplicate a... |
a314f00cc5f5fbd058cd0e279a7859797f02fa8fb01f6876ee53180dff095971 | R | 2,583 | 75 | source("/cbica/projects/network_replication/manuscript/code/scripts/sensitivity_analyses/computeConnectivityMetrics_restOnly.R")
##################
# Set Variables
##################
args <- commandArgs(trailingOnly = TRUE)
dataset = args[1]
##################
# Set Directories
##################
config_data <... |
2bc024fea0cb4a94282cecebcce7f1f816372439bc7469e69bbba8b9b6a50e3a | R | 2,585 | 59 | library(dplyr)
library(tidyr)
library(ggpubr)
library(rstatix)
file_names <- list.files(pattern = ".*T_cell_interaction_score.txt")
data <- do.call("rbind",lapply(file_names,FUN=function(file_names){read.delim2(file_names)}))
plot_interaction_scores <- function(data, group_by_var, file_name) {
# Prepare data
dat... |
bfeea98af855edc181f3e8ca1013f68f9e87e93a31d1348976a948173d0623b4 | R | 2,585 | 78 | #!/usr/bin/env Rscript
options(width=200)
#########################
#
# Loading Data and Libraries
#
#########################
args <- commandArgs(trailingOnly=TRUE)
cliargs <- args[-(1:2)]
options('warn'=1) #report non-convergences, etc
library(mgcv)
# library(languageR)
library(optimx)
library(ggplot2)
#The below... |
3045754d5d706b73a9a6aff79eb341863b4aba702f69d125f5f4172fcd857a57 | R | 2,591 | 68 | # Group Comparison 4-Fold Bilateral EC
library(ggplot2)
library(gridExtra)
library(ggprism)
library(ggpubr)
library(lsr)
# Load the Df
#SC----
SC_EC_Subj<-read.csv('/Volumes/ROBBOT/Projects/GridMagination/Tables/02_QuadratureFilter/SC_CombinedROI_Subj.csv')
SC_EC_Subj$SubjectID<-factor(SC_EC_Subj$SubjectID)
SC_EC_... |
f84f69debfaddd0aeacd0d8393a965b7d164192b3d8771867c89945cb8d96ef9 | R | 2,596 | 50 | npr <- c('Oxtr', 'Avpr1a', 'Avpr1b', 'Avpr2',
'Tacr1', 'Mc1r', 'Mc3r', 'Mc4r', 'Oprl1', 'Tacr3',
'Gpr83', 'Galr1', 'Npy1r', 'Npy2r', 'Npy4r', 'Npy4r2', 'Npy5r',
'Sstr1', 'Sstr2', 'Sstr3', 'Mchr1', 'Oprd1', 'Oprk1', 'Oprm1',
'Trhr', 'Hcrtr1', 'Hcrtr2', 'Qrfpr', 'Npffr1', 'Npffr2'... |
f6ea4f2e5f2170f5e51ca309cf3af49ae4610b032e09902799a875a2698829f8 | R | 2,599 | 97 | #' Start monitor ram usage in linux
#'
#' Create a log file in the working directory that then is elaborated by get_max_ram_used
#'
#' @param log_name Character string that will be given to the log file saved on the disk
#' @return Log file on the disk with the ram usage
#' @export
start_monitor_ram=function(log_name){... |
e43055602567eeb0d57c43708950b9a0db9da4ffd4acf16393e48bd7d03e7dae | R | 2,603 | 62 | suppressMessages(source("../scripts/helper.R"))
library("zoo")
# list of species to compare
com_list = list( c("Xboc","Isopu"), c("Xboc","Spur"), c("Xboc","Hmia"), c("Xboc","Nvec") )
order_xboc = c("Xboc_GUT","Xboc_EPIDERMIS","Xboc_CILIATED_CELLS","Xboc_PHARYNGEAL","Xboc_PIGMENT","Xboc_GLAND1","Xboc_GLAND2","Xboc_GLA... |
c6cccf98e3817feb1d2b5c08dc9440ded4437280ebd3634d066bd4d58bd706f9 | R | 2,615 | 66 | ### DATA
# Human cerebral organoids + fetal neocortex
# SMARTer
# Camp JG, Badsha F, Florio M, Kanton S et al. Human cerebral organoids recapitulate gene expression programs of fetal neocortex development. Proc Natl Acad Sci U S A 2015 Dec 22;112(51):15672-7. PMID: 26644564
# GSE75140
ann <- read.table("Camp_Neurons_S... |
8a2356e321e2ae3bd0db330d8a206a0aabc3e62dc0cfb5b94ff536c9d8c38a2b | R | 2,619 | 47 | rm(list=setdiff(ls(),c('params')))
basedir <- params$basedir # make this whatever you like, end with /
setwd(basedir)
source('code/aba/aba_fxns.R')
source('code/misc/miscfxns.R')
source('code/misc/plottingfxns.R')
abadir <- paste0(basedir,'data/aba/')
dir.create(paste0(abadir,'atlas'),recursive=TRUE)
load(file=paste0(a... |
3ece848417a195f96f3cd2fe7a2a93f651961eed52c28fe674861ebc1742f9ed | R | 2,621 | 133 | ---
title: "QC - ATACseq"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
li... |
9db3cb57967268991107fc63e6e1601e33a1e87d34d551726ae104e66cbf742a | R | 2,623 | 71 | library(tidyverse)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggpubr)
library(rstatix)
dat01 <- read.delim2('per_cell_type_annotation.tsv')
dat <- dat01 %>%
filter(label != "")
dim(dat)
n_cells_01 <- dat %>%
dplyr::group_by(Sample_ID, label) %>%
dplyr::count(label) %>%
tidyr::spread(label, n)
#... |
2c14203073d8f749a836884aad0095f62bb8e90ef9e9dd4515c12d239efc92a5 | R | 2,648 | 95 | library(Seurat)
library(harmony)
library(ggplot2)
library(dplyr)
### Functions
howManyCells <- function(sObj){dim(sObj)[2]}
## downsample to 4000
downsample <- function(sObj, no_cells = 4000){
if (howManyCells(sObj) > no_cells){
set.seed(1234)
s <- sample(1:howManyCells(sObj), no_cells, replace = FALSE)
... |
5c70064e2bb6a4acf57e43407f87e4f473dc4d9a89258eee426948ff6875da41 | R | 2,650 | 121 | ### THIS SCRIPT REQUIRES SAGA GIS AND GDAL TO BE INSTALLED ###
## the extent of original DEM is much greater than the borders of Poland
## DEM must be in "tmp" subfolder (it will be cut to the borders later)
output = file.path("data", "variables", "tmp")
DEM = file.path(output, "01_ELEVATION.tif")
## SLOPE
system(
... |
c69ac03131f963d38193c757782062f381975f296ba28861ec0246cd08732231 | R | 2,658 | 74 |
# Load required libraries
library(SeuratDisk)
library(dplyr)
library(Seurat)
library(ggplot2)
library(stringr)
library(GO.db)
library(org.Hs.eg.db)
library(KEGGREST)
library(HGNChelper)
library(tidyr)
library(ggpubr)
library(rstatix)
# Load dataset
df <- readRDS("Platelets.rds")
DefaultAssay(dat0) <- "RNA"
# Functio... |
5e52249f600f70c448da516f7ec889d7955a496d09ff57e17f8ad035cfd99add | R | 2,668 | 54 | ################################################################################
# affectDefaultValuesSwath.R 1.0.0 #
# Authors: Alexandre Sta (Institut Curie) #
# Contact: myproms@curie.fr ... |
0e240589efb8821d9b539904194342f26dd080632300e7bd94ec8e7ddcbaaa19 | R | 2,697 | 67 | # Load required libraries
library(readxl)
library(dplyr)
library(VennDiagram)
# Read the data from Excel file
data <- read_excel("/data/mayerlab/Github/Dvoretskova_et_al_2023-DATASETS/Table_S4_E16_clusters_DE.xlsx")
# Get column titles of the data
column_titles <- colnames(data)
# Assuming your data frame is named "... |
adf651fe906807d171f9fff486ff2505678df3ba4b0378410e828d13a6c3f597 | R | 2,702 | 68 |
# Plot regression models with clinical scores
library(ggplot2)
# Load models
setwd("/Users/huser/Documents/analysis")
load(file="updrsmods.RData")
a.labels <- c("Rate", "Length", "Interval", "Amplitude",
"1/f offset", "1/f exponent", "Alpha power", "Alpha centre freq.", "Beta power", "Beta centre fr... |
a4bcb212841dfbf866c0dca3136a6d75b52eceb52b151cf32e5f1adf0941fb4f | R | 2,706 | 47 | crossbait_contamination = function() {
samples_df = tlx_read_samples("~/Workspace/Datasets/HTGTS/samples/All_samples.tsv", "~/Workspace/Datasets/HTGTS/TLX") %>%
dplyr::filter(grepl("concentration", experiment)) %>%
dplyr::mutate(run=dplyr::case_when(
grepl("PW103|PW104|PW105|PW106|PW107|PW108|PW109|PW11... |
9029816ddf7e1fd1879efdd64b43ca2d3eaa1ea0ffbed850dd29bc936aaefa20 | R | 2,717 | 124 | # A is the similarity matrix by SIMLR
# X is the data of size nxp
"SIMLR_Feature_Ranking" <- function( A, X ) {
# traspose X
X = t(X)
# start the computation
res = lapply(1:100,FUN=function( x ) {
cat(x,"\n")
index = sample(1:nrow(A))
index = index[1:round(nrow(A)*0.9)]... |
8d25a54c3ef331fe3ac4ea21cbdb7df34e6aa24c877b9fcd1bc1fed26369aff8 | R | 2,718 | 88 | ---
title: "figure 5"
author: "Pavitra Roychoudhury"
date: "2023-07-16"
output: html_document
---
Panels for figure 5
```{r}
rm(list=ls())
library(Seurat)
library(tidyverse)
options(dplyr.width=Inf)
library(cowplot); options(scipen = 999)
all3<-readRDS('./seurat_analysis_all3.rds')
```
TSNE showing LAT expression
``... |
7671f047113aad87f131961d7f73680bf60a708c4b04e577ea0a985e0917de8b | R | 2,726 | 62 | # Load required library
library(data.table)
library(ggplot2)
library(gridExtra)
# Create an empty list to store the data.frames
data_list <- list()
# Get a list of all directories in the specified path
path_to_directories <- "/Users/elleke/SURFdrive/Projects/MOSTest/Results/Discovery/cFDR/pleioprs"
dirs <- list.dirs(... |
cd5eb10116cb4a36f14ec15e7b20e18e6adc96cfa0dba349a2fa7cf5a80d9243 | R | 2,730 | 82 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
library(harmony)
require(data.table)
require(SingleCellExperiment)
library(cowplot)
library(rsvd)
library(Rt... |
7f5619c7709973521f0b9a4c954bfc7958e8e54747c0d60341f0cd59b7fa12e4 | R | 2,739 | 49 | #' Selecting tuning parameters for sparse CCA
#'
#' @references We adapted and modified the code from the CCA function in the PMA package to be compatible and scalable with single-cell multimodal integration through parallel computing.
#'
#' @param x Expression-driven adjacency matrix
#' @param z Spatial-driven adjacen... |
bcdf4210e794ff1dc97a3d9b936c3d997cd248750474acc6de796de484f51891 | R | 2,744 | 76 | #' @title Wrapper for clUMAP with Thresholding and Plotting
#'
#' @description This function applies thresholding to granule scores within a specified object and column,
#' inverts the thresholding if a specific miscellaneous name is provided, and then calls clUMAP
#' for plotting based on the thresholded values.
#'
#'... |
3b3cb79f22db83a644f83215fe963f8f4bca47a1724992f9d74a126fb32d0c46 | R | 2,753 | 66 | library(SingleCellExperiment)
library(scater)
create_sce_from_counts <- function(counts, colData, rowData = NULL) {
if(is.null(rowData)) {
sceset <- SingleCellExperiment(assays = list(counts = as.matrix(counts)),
colData = colData)
} else {
sceset <- Sing... |
a1772345e1610c9d4f7829b46d2234f83e3deea6fd97533a9cc5c78efcd96ef6 | R | 2,768 | 55 | ### This code creates Fig. 3A and fig. S5
dev.off()
rm(list=ls())
library(ggplot2)
library(tidyr)
library(gridExtra)
library(dplyr)
Data_depth_ind <- read.table("Read_Depth_Per_Ind", header = T, sep="\t") #can also be done with Read_Depth_Per_Ind_post
Data_depth_ind[,c(2:150)] <- sapply(Data_depth_ind[,c(2:150)], as.... |
24239e902619878dee935a36b1d6eed0324408f93225ea5cc16a7b75040e4c83 | R | 2,790 | 59 | #################
### Load data ###
#################
rm(list=setdiff(ls(),c('params','grp','treatment','injection.site')))
print(grp)
print(treatment)
basedir <- params$basedir
setwd(basedir)
savedir <- paste0(params$opdir,'modelcomparison/bootstrap/',paste0(injection.site,collapse='-'),'/')
dir.create(savedir,recur... |
ead6b68dfccea2bb71f5735ea033acfe0ec08654e1e86f0a3bde10e3f84f0780 | R | 2,799 | 79 |
#SR_TAU_CELL
.libPaths("/data/user/jfisher7/.conda/envs/SR_TAU_CELL/lib/R/library")
#make sure the environment is clean
rm(list=ls())
args <- commandArgs(trailingOnly = TRUE)
num1<- args[1]
num2<- args[2]
library(MASS)
library(parallel)
library(signatureSearch)
library(tidyverse)
library(HDF5Array)
library(Summar... |
725ed39b1c2c6041bfc250a4e0d72b6c489f06002a605277656f95902fbb55b0 | R | 2,800 | 76 | #SR_TAU_CELL
.libPaths("/data/user/jfisher7/.conda/envs/SR_TAU_CELL/lib/R/library")
#make sure the environment is clean
rm(list=ls())
args <- commandArgs(trailingOnly = TRUE)
num1<- args[1]
num2<- args[2]
library(MASS)
library(parallel)
library(signatureSearch)
library(tidyverse)
library(HDF5Array)
library(Summari... |
91f5feab37a781511c449bdf57bada072e4937b62b556c17ba9c5e2868258b7b | R | 2,800 | 76 | #SR_TAU_CELL
.libPaths("/data/user/jfisher7/.conda/envs/SR_TAU_CELL/lib/R/library")
#make sure the environment is clean
rm(list=ls())
args <- commandArgs(trailingOnly = TRUE)
num1<- args[1]
num2<- args[2]
library(MASS)
library(parallel)
library(signatureSearch)
library(tidyverse)
library(HDF5Array)
library(Summari... |
aad98997e7319dfeae738ddc744781c8f7d81e62783d20451ba69981e85c888f | R | 2,800 | 76 | #SR_TAU_CELL
.libPaths("/data/user/jfisher7/.conda/envs/SR_TAU_CELL/lib/R/library")
#make sure the environment is clean
rm(list=ls())
args <- commandArgs(trailingOnly = TRUE)
num1<- args[1]
num2<- args[2]
library(MASS)
library(parallel)
library(signatureSearch)
library(tidyverse)
library(HDF5Array)
library(Summari... |
50ecfc6f7030b1c75aeff7432028c12eed412e80b32a17c40fc54c9d3a17b1ec | R | 2,801 | 96 | # Canonical Correlation Analysis
#
# Script for canonical correlation analysis to investigate the relationship
# between behavioral changes and neurobiological variables
#
# Author: Rafael Naime Ruggiero 2022
library(lme4)
library(CCA) #facilitates canonical correlation analysis
library(CCP) #facilitates che... |
e130a2e321d67d14ae4226f58943321522f47141dddc5e38ce12d787adfe331d | R | 2,801 | 104 | urlfile="https://raw.githubusercontent.com/NathanSkene/ALS_Human_EWCE/master/Data/Raw/GSE67196_Petrucelli2015_ALS_genes.rawcount_EXP.csv"
ALS_patients<-read_csv(url(urlfile))
write.csv(ALS_patients, "ALS_patients-NatNeuro.csv")
ALS_patients = read.csv("ALS_patients-NatNeuro.csv", header=T)
colnames(ALS_patients)[1]<-"... |
09d7177bb8d82d26677a10f11991bafb16568c4f5deff62698a0057ac7839ea3 | R | 2,802 | 95 | importfiles <- function(name) {
namesoffiles <- list.files()
importedlist <- lapply(namesoffiles, read.csv)
unlisted <- unlist(importedlist)
numberofsamples <- as.numeric(length(namesoffiles))
numberofrows <- numberofsamples * 2
numberofbarcodes <- as.numeric(length(unlisted) / numberofsamples / 2)
dim(unlisted) <- ... |
3a3e18f6cb95358455ecc8d22a092dd26ef4f18b3687e0b93f5af4f3448c4451 | R | 2,802 | 76 | #SR_TAU_CELL
.libPaths("/data/user/jfisher7/.conda/envs/SR_TAU_CELL/lib/R/library")
#make sure the environment is clean
rm(list=ls())
args <- commandArgs(trailingOnly = TRUE)
num1<- args[1]
num2<- args[2]
library(MASS)
library(parallel)
library(signatureSearch)
library(tidyverse)
library(HDF5Array)
library(Summari... |
06cd0e0fda176141941e2132101199419b139dc0b76a3028a382715abeb06a61 | R | 2,803 | 78 |
#SR_TAU_CELL
.libPaths("/data/user/jfisher7/.conda/envs/SR_TAU_CELL/lib/R/library")
#make sure the environment is clean
rm(list=ls())
args <- commandArgs(trailingOnly = TRUE)
num1<- args[1]
num2<- args[2]
library(MASS)
library(parallel)
library(signatureSearch)
library(tidyverse)
library(HDF5Array)
library(Summar... |
68a3944b87820e14567eda0f0127c0e3265a3ebccd956f2927945888092cfeb6 | R | 2,828 | 52 | ################################################################################
# goHierarchCluster.R 1.0.0 #
# Authors: Valentin Sabatet (Institut Curie) #
# Contact: myproms@curie.fr ... |
80dde3124ffdd4b1c766977a2e05929cfddab0c2d344a647b9fbe6a7a6ea1059 | R | 2,862 | 86 |
# Plot the left EC six fold group comparison
library(ggplot2)
library(lme4)
library(gridExtra)
library(ggprism)
library(ggpubr)
library(coin)
library(car)
# Load the dataframe
# SC -----
SC_LeftECSubj<-read.csv('/Volumes/ROBBOT/Projects/GridMagination/Tables/02_QuadratureFilter/SC_LeftEC_Subj.csv')
SC_LeftECSu... |
e8e53f7984f156ad941925e9800c082b90f70adb5841d27d6b04025b1290b890 | R | 2,879 | 86 | ### DATA
# iPS = induced pluripotent stem cells (day 0)
# HE = hepatic endoderm (day 8)
# LB = liver bud = hepatic endoderm + supportive mesenchymal and endothelial cells
# MH = mature hepatocyte-like (day 21)
# DE = definitive endoderm (day 6)
# IH = immature hepatoblast-like (day 14)
# EC = endothelial cells
# HUVEC ... |
a764a952ea8dc90328786a39ed5e64f0cf399b09c2a5107bdfbdbccdf1688de2 | R | 2,881 | 87 | library("stars")
library("ggplot2")
library("cowplot")
library("ggnewscale")
dem = read_stars("data/variables/01_ELEVATION.tif", proxy = TRUE)
dem = st_downsample(dem, 15) # downsample to 480 m
dem[[1]] = as.integer(dem[[1]])
names(dem) = "dem"
tmp = tempfile(fileext = ".sdat")
system(
paste(
"saga_cmd ta_light... |
e3824d7eeb132569d3ab37c4e858031523bafcd080dfe0730dac1fb8132773e1 | R | 2,886 | 34 | ---
title: "About"
output:
workflowr::wflow_html:
toc: false
editor_options:
chunk_output_type: console
---
# Transient galanin expression shapes peripheral‑to‑central connectivity in the somatosensory thalamus during whisker development
This repository contains code and output of the analysis of single-nucle... |
9902a335123505001784cf15bdb3f87c79ddff9f530b623cfdb01cfd25870ca5 | R | 2,900 | 84 | # this is a toy example to illustrate how to use the BAUM package
# data is available under the same folder
# metabolites network, features, and potential matchings are a part of the
# covid-19 metabolomics data
# for details, please look at our paper:
# Guoxuan Ma, Jian Kang and Tianwei Yu. (2023+). Bayesian Function... |
118b2199d4c8db5829323f5cda1178435cb9c16dac9461db002fa596162fae9d | R | 2,905 | 80 | if (!requireNamespace("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("celda")
library(celda)
library(SingleCellExperiment)
setwd("/data/mayerlab/cmayer/meis2_paper/results")
P7GE_sub <- readRDS("/datastore_share/Users/mayho/meis2_paper/seurat_objects/P7GE_sub.rds")
DimPl... |
3d0ab61b58682aeb7ae25a18ab2936774685568907379431408ac3b71400d4f0 | R | 2,905 | 77 | source("/cbica/projects/network_replication/manuscript/code/scripts/main_analyses/computeConnectivityMetrics.R")
##################
# Set Variables
##################
args <- commandArgs(trailingOnly = TRUE)
dataset = args[1]
##################
# Set Directories
##################
config_data <- fromJSON(fil... |
141fde2f914032243bc7859134a307b11c04e256f246c631f2a7ad679e8bf80f | R | 2,929 | 110 |
#' @importFrom utils packageVersion
#' @noRd
.onAttach <- function(libname, pkgname) {
if (!grepl(x = R.Version()$arch, pattern = "64")) {
warning("The StellarPath package requires running R on a 64-bit systems
as it is dependent on torch from ptyhon; and you're
system is not 64-bit.")
... |
652514a04873cc59445986f361a50312c1ef2ea53d8d2903c7b4898bc6d05b06 | R | 2,929 | 102 | importfiles <- function(outputfilename) {
namesoffiles <- list.files()
namesoffiles <- namesoffiles[grep("mapping", namesoffiles)]
importedlist <- lapply(namesoffiles, read.csv)
unlisted <- unlist(importedlist)
numberofsamples <- as.numeric(length(namesoffiles))
numberofrows <- numberofsamples * 2
numberofbarcodes <... |
362cb5e091265f9649484bd0447fcfd6abe96310bc3c270dcef1ec344daf75d5 | R | 2,931 | 72 |
# Check whether the activity in IPC durign Clock Navigation Experiment correlates with Path Integration Performance
library('ggplot2')
library("ggpubr")
library(ppcor)
library(rstatix)
library(ggprism)
#SC -----
# Load the Df
SC_PI_IPC<-read.csv('/Volumes/ROBBOT/Projects/GridMagination/Tables/03_Correlations/SC_PICo... |
00abfaa843d795d24982fa9ad507b5394f1745c264f6d88e7204aafc0c8f0085 | R | 2,934 | 89 | ##DE Analysis
expr.c<-read.csv("~/Desktop/Rhythmicity Code and Output/Example_data.csv", row.names = 1)
clin_ctrl<-read.csv('~/Desktop/Rhythmicity Code and Output/Example_clinical.csv', row.names = 1)
n<-nrow(expr.c)
Symbols<-row.names(expr.c)
TOD<-clin_ctrl$TOD
night.index.neg<-which(TOD < 0)
night.index.pos<-which(... |
75eb4fae24601c99315830dd1707e31af611b8201f73d6b740bd303434a91309 | R | 2,942 | 106 | ####install libraries####
# {
# install.packages("nlme")
# install.packages("data.table")
# install.packages("emmeans")
# install.packages("lmerTest")
# install.packages("lattice")
# install.packages("plyr")
# install.packages("ggplot2")
# install.packages("Renext")
# install.packages("ggsn")
# ins... |
4937e8986df8b5bf2d516fcc2231de9e4b4b05b5cf81cd39d63949a50034d231 | R | 2,945 | 79 | library(pso)
library(dplyr)
library(ggplot2)
devtools::load_all('~/Workspace/breaktools/')
load("~/Workspace/Everything/DSB_Paper/data/data_matices.rda")
load("~/Workspace/Everything/DSB_Paper/data/training_parameters.rda")
mtx_source <- do.call(cbind, data_matrix)
mtx_target <- do.call(cbind, repliseq1_data_matrix)
... |
f4f657cb08d25cc51c67ebfb38497f8ec537b1fbc49625e9cd2e05a3f3fa8853 | R | 2,945 | 53 | ### DATA
Nestorowa = read.table("coordinates_gene_counts_flow_cytometry.txt", header=T)
Anno = read.table("all_cell_types.txt", header=T)
Anno <- Anno[order(rownames(Anno)),]
rownames(Anno) <- gsub("-",".",rownames(Anno))
### ANNOTATIONS
expr_mat = t(Nestorowa[,28:length(Nestorowa[1,])])
#rownames(expr_mat) <- ensg2sy... |
ccf37694d5ca3ca819d0a38a93551e83e5bd4db447d5e25631796d21576bdc76 | R | 2,962 | 97 | #########################################################################################
# R script to calculate and plot a 2-dimensional t-SNE (Barnes-Hut-SNE) projection map
# from a 13-dimensional mass cytometry data set ("Marrow1" from Amir et al. 2013, see
# Figure 1b), using Rtsne package
#
# References:
#
# Am... |
9fc60c1bb1fb21465a904670ed0e0ddd66cf046e0afc0ad24d856bb40ba9a7fa | R | 2,993 | 100 | test_that("consensusGRanges() works with minimum.width defined", {
# Testing by an artifical GRanges.
# NOTE - if the artifical GRanges described here changes,
# We need to save the `.rds` file again!
gr <- makeGRanges(
tibble::tribble(
~start, ~end, ~seqnames,
1, 1... |
d9fd47fe4741442b0481df224fa88546991e7c0841b132e4b16f73104e4a3ff6 | R | 3,014 | 72 | # Set directory to source file loaction
setwd(paste0(figureDirectory, "Figure6-candHeatmap" ) )
library(pheatmap)
library(stringr)
library(grid)
library(RColorBrewer)
load("geneCandidates.RData")
candGenes_biolFunction_summary <- read.csv("candGenes_biolFunction_summary_validation.csv")
rownames(candGenes_biolFu... |
ca3acba21997ee89aed3e6b4eb5711ba2e2f045da7d0616ec0b7d5f4ba2dffec | R | 3,016 | 81 | rm(list = ls())
options(stringsAsFactors = F)
library(tidyverse)
library(AnnotationForge)
library(stringr)
librarywd="build"
setwd(librarywd)
egg <- rio::import('out.emapper.annotations.tsv',fill=TRUE)
names(egg)[1]<-"query_name"
egg[egg==""] <- NA
egg[egg=="-"] <- NA
# colnames(egg)
gene_info <- egg %>%
dplyr::s... |
a43e9345874d7c8d5c908c38e11a95b56d81843835a43490581401843e163a11 | R | 3,018 | 80 | source("/cbica/projects/network_replication/manuscript/code/scripts/main_analyses/computeConnectivityMetrics.R")
##################
# Set Variables
##################
args <- commandArgs(trailingOnly = TRUE)
dataset = args[1]
##################
# Set Directories
##################
config_data <- fromJSON(fil... |
3a45ddd5dd276bbb83801b99aa99f1147a9a1203665e2e936beeab7ec7447182 | R | 3,019 | 90 | bestModelSelection <- function(aGeneIndex,expressionMatrix,covariables3,Diagnosis,UniqueID,VariableListOne,VariableListTwo){
library(lme4)
curGeneExpression = as.numeric(expressionMatrix[aGeneIndex,])
## mixed model with adjusting for covariates
basicFormula = update(curGeneExpression ~ Diagnosis+(1|Uniqu... |
8711a6c1878ab15f40bec7f77b075b1de18f4f23c9bb78a4dba8a22e20233c3f | R | 3,020 | 80 | source("/cbica/projects/network_replication/manuscript/code/scripts/main_analyses/computeConnectivityMetrics.R")
##################
# Set Variables
##################
args <- commandArgs(trailingOnly = TRUE)
dataset = args[1]
##################
# Set Directories
##################
config_data <- fromJSON(fil... |
5eac208de7c2bc056ace34b568344b9368631c87f97253e99f6f8008ba3ef0f7 | R | 3,027 | 70 | # required external packages for SIMLR
library("Matrix")
library("parallel")
# load the igraph package to compute the NMI
library("igraph")
# load the palettes for the plots
library(grDevices)
# load the SIMLR R package
source("./R/SIMLR.R")
source("./R/compute.multiple.kernel.R")
source("./R/network.diffusion.R")
s... |
28a225787c0935a7d1a2603c0bcc306513bad96e1d56bb4a6c56f70455e80f85 | R | 3,028 | 82 | source("/cbica/projects/network_replication/manuscript/code/scripts/main_analyses/computeConnectivityMetrics.R")
##################
# Set Variables
##################
args <- commandArgs(trailingOnly = TRUE)
dataset = args[1]
##################
# Set Directories
##################
config_data <- fromJSON(file... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.