sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
6007ecf74deb1f8ea4dc8343e099956230ece48055028fbe5fea6b1453006d58 | R | 4,383 | 97 | #' @title
#' CBDA Top-Ranked selection function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function has all the features of the *Consolidation()* function but allows to choose a
#' different **top** value (i.e., different from the one specified in the main *CBDA()* function
#' @param top_new ... |
89881d44f9d1675645aabf7f7c5678c735e183721533334e8fc7896f3ada96f8 | R | 4,400 | 92 | #' Main workhorse of dcca_factor
#'
#' Given the two matrices (given by \code{svd_1} and \code{svd_2}) and the
#' CCA solution in \code{cca_res}, compute the common scores.
#' This calls the functions
#' \code{.common_decomposition} and \code{.compute_distinct_score}.
#'
#' @param averaging_mat sparse matrix
#' @para... |
83da6ef8898024ac6555d773ba1586452e3516fb6d0a9e6cdfe689f554aab607 | R | 4,418 | 101 | # Data preprocessing Part 2 (generative values)
library(tidyverse)
# Load data
load("processed_data/allValues/allValuesAuditory_before_DPP_pt2_generative.Rda")
load("processed_data/allValues/allValuesVestibular_before_DPP_pt2_generative.Rda")
load("processed_data/allValues/allValuesVisual_before_DPP_pt2_generative.Rd... |
9d472acde5e6ac0744934b8f428939d357b7276e617b2c242cf2489da0485430 | R | 4,421 | 105 | context("Test .tiltedCCA_common_score")
## .tiltedCCA_common_score is correct
test_that("(Basic) .tiltedCCA_common_score works", {
# load("tests/assets/test_data1.RData")
load("../assets/test_data1.RData")
mat_1 <- test_data$mat_1
mat_2 <- test_data$mat_2
svd_1 <- test_data$svd_1
svd_2 <- test_data$svd_2
... |
11a7ad38769cf6086cd34021698b773327f4631682678704aabb3434b684c7f9 | R | 4,451 | 132 | ---
title: "add_motifs_run_chromvar"
author: "Nate Mullin"
date: "12/01/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(Signac)
library(Seurat)
library(patchwork)
library(tidyverse)
library(EnsDb.Hsapiens.v86)
library(Matrix)
library(SummarizedExperiment)
library(... |
bf874d1f80d0984365dce88f2073ec4f5dc1344f63bcc870b9fbd1a92e6b49de | R | 4,465 | 210 |
/home/toolkit/tools/R4.2.0/bin/R
setwd('/home/disk/database/data/ICS_scHIC/f04_multi')
library(Signac)
library(Seurat)
library(EnsDb.Mmusculus.v79)
#diyn.load('/usr/local/hdf5-1.8.17/lib/libhdf5_hl.so.10')
set.seed(1234)
rna <- Read10X("./data/rna/", gene.column = 1)
atac <- Read10X("./data/atac/", gene.column ... |
3cf2c6dfe79acee4fe02ba585601a3c3cf0dba18ceaaa50367de62bae75adf0d | R | 4,480 | 97 | #' @title
#' CBDA Top-Ranked selection function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function has all the features of the *Consolidation()* function but allows to choose a
#' different **top** value (i.e., different from the one specified in the main *CBDA()* function
#' @param t... |
9475894364b184dddb86ff466329d5bfeac06687300f7cf4b8cef34c21728f1e | R | 4,512 | 156 | ---
title: "dge_multiome"
author: "Nate Mullin"
date: "02/01/2023"
output: html_document
---
```{r}
knitr::opts_chunk$set(echo = TRUE)
library(Signac)
library(Seurat)
library(ggplot2)
library(patchwork)
library(tidyverse)
library(EnsDb.Hsapiens.v86)
library(Matrix)
library(SummarizedExperiment)
library(ggpubr)
#librar... |
0a85254da2ea1a813aafd21eee8e1d7612e8e21a7d217bb12b1887f8a8e52b78 | R | 4,553 | 100 | setwd(dirname(rstudioapi::getSourceEditorContext()$path))
library(ggplot2)
library(dplyr)
library(tidyr)
library(scales)
library(tibble)
library(ggpubr)
library(reshape)
library(taxonomizr)
library(data.table)
library(purrr)
library(forcats)
#fetch NCBI db
prepareDatabase('accessionTaxa.sql')
#load RefSeq data
table... |
d686a7163af6ec3272af20f04a13fcaa26826ed6df26a3ebba78c180519348e9 | R | 4,611 | 111 |
# Analysis of Microinjected dataset with RaceID
# For detailed explanation of each step,
#please refer to the RaceID Manual: https://cran.r-project.org/web/packages/RaceID/vignettes/RaceID.html
# load packages.
library(RaceID)
library(FateID)
require(tsne)
require(pheatmap)
require(MASS)
require(cluster... |
6c5342f481f8ff4d1d7022dedaefd0a9dd7810883e4231fafeefd2c985a3dba0 | R | 4,638 | 102 | library(Seurat)
library(ggplot2)
library(SeuratDisk)
library(plyr)
original_engineered_samples = c("Cre-P61-d112-Live", "Cre-P61-d50-Live", "Cre-P61-d50-Methanol",
"Cre-P63-d23-Live", "Flp-P61-d112-Live", "Flp-P61-d50-Live",
"Flp-P61-d50-Methanol", "Flp-P... |
a8cf659b529bef060a186c5f9038d69f6844f5cdc34030337d7629ee654049b1 | R | 4,647 | 90 | #Load Packages
library(Seurat)
library(dplyr)
library(Matrix)
library(ggplot2)
library(patchwork)
library(future)
library(DoubletFinder)
#Set it to 500GB
options(future.globals.maxSize = 500000 * 1024^2)
# Load Data
obj.data <- Read10X(data.dir = "filtered_feature_bc_matrix")
# Create Seurat object
obj <- ... |
2103859b8d43007a3559f463cb9c6df1f61030b61be88066d9d72b8a94d16a12 | R | 4,656 | 117 | # load environment
library(Seurat)
library(ggplot2)
library(ggpubr)
library(tidyverse)
library(plyr)
library(readxl)
source('../../functions/_global.R')
source('../../functions/_visualize.R')
# load data
fetal = readRDS('single_cell_projects/10X-Pak/reference/fetalBrain_Bhaduri_Nature/public_h5ad/Bhaduri_raw.rds')
fe... |
169ca4f619a141fcc7674566f7aeb796e064db6abfa8a855ea5881e004b2eaf1 | R | 4,707 | 135 | ---
title: "SiT - Supplementary Figure 01"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
7c883b82898f99229baabb169a7304b593e8b33b7e62961bc54ed11dc42ae401 | R | 4,721 | 136 | ### S.vickery October 2020 ##
### Age regression model for IXI human sample ###
library("readr")
library("plyr")
library("dplyr")
library("ggplot2")
library("stringr")
library("MatchIt")
library("optmatch")
library("rgenoud")
library("emmeans")
chimp.dat <- read.csv("Chimp_brain_meta.csv")
IXI.dat <- re... |
93263e8596b048d186ebf2781006940d0a9e2e62e87dece6d1fd92791e84c46f | R | 4,813 | 108 | library(data.table)
library(dplyr)
ptv_raw <- fread(paste0("/camhpc/dept/human_genetics/UKBB_exome_variant_450k/UKB_Freeze_456k_HC_LOF_canonical_bychr/chr2_lof_carriers_v3.txt")) %>% dplyr::filter(gene=="TTN")
ptv_raw <- ptv_raw[which(ptv_raw$alt_af<0.001),]
pheno <- fread("~/muscle_targets/regenie/UKBB_maxHGS_WBLM.p... |
622c12ea04b2af4680de9ab95d81694e176823bb85f8accce16dc054bc6d7854 | R | 4,820 | 117 | if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("clusterProfiler")
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("AnnotationDbi")
if (!requireNamespace("BiocManager", quietly = ... |
553d3cccd41db31ae49a01812196aa2153ba613de28b66fcb39a2da5b5e985a6 | R | 4,834 | 117 |
#' Friedman-Rafsky (FR) test
#'
#' FR test is a multivariate generalization of nonparametric two-sample test. This function is an implementation
#' with customized options, including a visualization of the minimum spanning tree (MST).
#'
#' @param samp1 Numeric matrix or data frame for Sample 1. Rows are multivariate ... |
3325d07efdc7ca4bd89a43e6380bdff8f6779cbb4fc58389d73be7035bf130a1 | R | 4,928 | 179 | # Observer Model (Transition Probabilities) - 2022
library(tidyverse)
# Add function to shift down vector n rows and replace with NA
shiftdown <- function(x, n){
c(rep(NA, n), head(x, -n))
}
# Main function
CountEventMarkov <- function(s, omega = Inf){
# Store input sequence in dataframe
df <- enframe(s, na... |
18755db3158ca9d08fce48fc9c706337c82a5e7cb367d64d258ceda2bbd2f386 | R | 4,951 | 99 | setwd(dirname(rstudioapi::getSourceEditorContext()$path))
library(ggplot2)
library(dplyr)
library(tidyr)
library(scales)
library(tibble)
library(ggpubr)
library(grid)
library(ggrepel)
table1 <- read.csv("heterozygosity_summary.txt", header=TRUE, sep="\t")
datalist = list()
for (i in 1:ncol(table1)) {
data<-data.f... |
3631c1f826d23f3825b2502d569830be9c6fa468047c1368f976feaa2e42a939 | R | 4,954 | 158 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
.getYesNo<-function(DType,CType){
DType=DType
CType=CType
i.yes=c()
j.yes=c()
i.no=c()
j.no=c()
... |
0f8fe56741a8086492d19af904e4d1772ed1ae13d4798f4a9e174bc436b6898c | R | 4,986 | 152 | ---
title: "SiT - Mouse Coronal Brain Section (CBS1) - 01.spatial"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landsc... |
1935253b2df88ed20f79bbb3567eae3c1d5f10ebe45b4d3474fc58a84cd38e06 | R | 5,002 | 153 | ---
title: "SiT - CBS1 deconvolution"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression isofor... |
07dac50b2df0f9b0f32ab96b1dafa1f27531bb23a10d96781a6e5c8384297dd4 | R | 5,014 | 87 | AAmpD = function(path_to_files,t_cluster, tumor_prefix, normal_prefix, normal_clusters,
mapp_file ,blacklist, genome_file ,bin_size,sd, cut_off) {
print(paste0("Processing cluster ", t_cluster))
mappability=read.table(mapp_file, stringsAsFactors = FALSE)
black_list=read.table(blacklist, stringsAsFactors = F... |
539d357b1246fe1b4e09481f27b409859c9729a3b1722d7444b9d7de475199db | R | 5,040 | 162 | Get_Number_of_Populations <- function(folder_name){
filename = list.files(path = folder_name,pattern="*_Data.dat");
print(filename)
full_filename <- paste(folder_name,"/",filename,sep="");
print(full_filename);
data <- read.table(full_filename, header = TRUE, sep = "\t")
data <- data[, colSums(is.na(dat... |
b8457c04e9f86db31e0d75d37a2ae767d058e8c3ea222a452fb72c4f45e82e4d | R | 5,093 | 119 | #' Perform CCA
#'
#' This function takes either two matrices or two SVDs. Both \code{input_1}
#' and \code{input_2} must be "of the same type." Calls the \code{.compute_cca_aggregate_matrix} function.
#'
#' @param input_1 first input
#' @param input_2 second input
#' @param dims_1 desired latent dimensions of data mat... |
a22ff35c66c6ce91b9e998003f0c654c84f36466db25d8d0e96a556caa696c1b | R | 5,098 | 170 | # /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(ggplot2)
library(patchwork)
set.seed(1234)
library(Signac)
library(GenomicRanges)
pbmc=readRDS('./scATAC_pbmc.rds')
TYPE=pbmc$type
#############################################################i
getCicero<-functio... |
db064598ad768116840b7d2a1850278b32ce371c8af61cdff230d3d9c68977b4 | R | 5,102 | 164 | Get_Number_of_Populations <- function(folder_name){
filename = list.files(path = folder_name,pattern="*_Data.dat");
print(filename)
full_filename <- paste(folder_name,"/",filename,sep="");
print(full_filename);
data <- read.table(full_filename, header = TRUE, sep = "\t")
data <- data[, colSums(is.na(dat... |
cf390dd3da32eaaf89cdd331fe25da26cae0eddb54039f5f31544c82d1ad0e56 | R | 5,149 | 118 | #' @title
#' Stopping Criteria function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function generates a stopping criteria for the *max_covs - min_covs* nested
#' predictive models generated in the previous step. It also populates the CBDA object.
#' @param label This is the label appended to ... |
38fc34b0022276235a447ae2ba570e8eb83513ca5278abe2358db901333bf569 | R | 5,165 | 120 | #This script is used to run FR-Match
#The goal is to test if the differentiated neurons at E18.5 match those at P5
#Ref: Zhang et al. 2022. Cell type matching in single-cell RNA-sequencing data using FR-Match. Scientific Reports
#Tutorials: https://jcventerinstitute.github.io/celligrate/FRmatch-vignette.html
#Ins... |
8f82f1b8f2234de2f9661124942462c9d77ba94b42cec4db1bdb5949c165ee2a | R | 5,171 | 112 | # RPCA (fast integration using reciprocal PCA)
library(Seurat)
library(ggplot2)
library(patchwork)
library(plyr)
library(SeuratWrappers)
library(monocle3)
options(future.globals.maxSize=(90*1024^3)) # set max memory size as 60G
# megre 10x outputs
organoid.list = list()
samples = list.files("../processed data/")
for ... |
19f5c20fa74d4c1d87ad9528568e9274d7890b39b488d3900573962bc3e37162 | R | 5,182 | 161 | ---
title: "SiT - Mouse Olfactory Bulb (MOB) - 01.spatial"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of g... |
83ce63641503d42b0dbf5f35dacdfb9222446ff79aee82ee671135cbf6a23dbd | R | 5,202 | 128 | context("Test SNN")
## .form_snn_mat is correct
test_that(".form_snn_mat works", {
load(paste0("../assets/test_data1.RData"))
svd_1 <- test_data$svd_1
dimred_1 <- .mult_mat_vec(svd_1$u, svd_1$d)
res <- .form_snn_mat(dimred_1,
num_neigh = 10,
bool_cosine = T,
... |
9bd392c28690cb74b858f06f3b4ea8bc009db04b58d1bb786ad2882b7c99a357 | R | 5,204 | 169 | #' cursub.closer
#' @export
#' @author Léo Botton-Divet
cursub.closer<-function(cur,req)
{
mat<-as.matrix(dist(cur))
DPO<-NULL
for (j in 1:nrow(cur)-1)
{
a<-mat[j+1,j]
DPO<-c(DPO,a)
}
DCO<-NULL
for (j in 1:length(DPO))
{
DCO[j]<-sum(DPO[1:j])
}
DN<-sum(DPO)/(req+1)
DCN<-DN*(1:req)
c... |
2fdfb6f94a8bb6241474f1e90c36e7fca928a36295a5e09247d73a2580fef9f4 | R | 5,225 | 114 | # Analysis steps of the PACNet web app, free of Shiny so they can be tested
# (tests/testthat) and reused by server.R.
PANEL_TISSUES <- c("heart", "hspc", "intestine_colon", "liver", "lung", "neuron", "skeletal_muscle")
OTHER_TISSUES <- c("esc", "endothelial_cell", "fibroblast", "kidney", "b_cell", "t_cell", "monocyte... |
43be9abe6f45b2aa27f7834ed1c3ecdfbba4d434cd41436dc650f34873b700df | R | 5,267 | 118 | #' @title
#' Stopping Criteria function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function generates a stopping criteria for the *max_covs - min_covs* nested
#' predictive models generated in the previous step. It also populates the CBDA object.
#' @param label This is the label appen... |
f525d00ce4b3a909726d71f4fbad898f061944ca7b903c189f37222493ab5dea | R | 5,269 | 120 |
#' "Barcode" plot
#'
#' This function plots expression pattern of a cluster by marker genes, a.k.a. barcode plot.
#'
#' @param sce.E1,sce.E2 Data objects of the \link[SingleCellExperiment]{SingleCellExperiment} data class. If only \code{sce.E1},
#' then the "barcode" is a self plot, i.e. both cluster (\code{cluster.na... |
5d12dfb0899972fa8fba31dc85544e806a5733a1da3d70c6d735cae051bbd810 | R | 5,306 | 155 | ---
title: "SiT - Mouse Coronal Brain Section (CBS2) - 01.spatial"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landsc... |
42af2e84d25958fbbb6e5a10743841354d83b7462d887dea168755db39054673 | R | 5,321 | 144 | library(logistf)
library(data.table)
library(dplyr)
irnt <- function(x, k=3/8, randomties=FALSE, seed=NULL){
if (randomties == FALSE){
set.seed(seed)
return(qnorm((rank(x,na.last="keep")-k)/(sum(!is.na(x))+1-(2*k))))
}else if (randomties == TRUE) {
# Modified from Pain et al. Eur J Hum Genet 2018
s... |
1d822b0e9bd481fbed31ab649e7add25bfec90722239257e9c9c8db94b25e49a | R | 5,355 | 154 |
library(data.table)
assemble_connections<-function (cicero_model_list, silent = FALSE){
types <- vapply(cicero_model_list, FUN = class, FUN.VALUE = "character")
char_hbn <- cicero_model_list[types == "character"]
gl_only <- cicero_model_list[types == "list"]
if (!silent) {
print(paste("Succes... |
19f31570b2b030b1a685bf9fc62c67d8aa903a5075f514f73f3068562ed56dc0 | R | 5,358 | 202 | suppressMessages(library(tidyverse))
suppressMessages(library(TmCalculator))
suppressMessages(library(optparse))
suppressMessages(library(fs))
#Parse arguments
option_list <- list(
make_option(c("-d", "--dataset"), type = "character", default = NULL, help="CSV file with dataset, efficiency, grna_energy, and grna_sca... |
b990369ceb60494a89925c12badbcae0de09876f223d3eabd258b8b1ca5b0fe7 | R | 5,418 | 143 | #' Build a PubMed search query programmatically
#'
#' It helps to build a valid PubMed search query using the Entrez query language,
#' combining multiple search terms with Boolean operators.
#'
#' @param terms is a character or character vector. Search terms to look for in title and abstract fields.
#' @param fields i... |
2550ebe1329c313d3d01d4eefb674c3d76f99175ef0b015aa77bdfae764479ac | R | 5,509 | 167 | ---
title: "SiT - Supplementary Figure 10"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
c6770a4e86cf036630034a247da2a98cb9c0e936df4762ecf197772dea049182 | R | 5,520 | 139 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
dataset_file = as.array(args[1])
workspace_directory=as.array(args[2])
label=as.array(args[3])
maxRowsValidationSet=as.numeric(args[4])
maxColumnsChunks=as.numeric(args[5])
jobID=as.numeric(args[6])
maxRowsChunks=as.numeric(args[7])
print(dataset_file)
prin... |
882c972922685e3df51a05eb5a3814062b33de6bafb79f90891d0f0def5ead6d | R | 5,641 | 229 | context("discovery_regression")
library(magrittr)
# Extract names of relevant NMR biomarkers
nmr_biomarkers <- dplyr::intersect(
colnames(df_demo_metabolic_data),
df_NG_biomarker_metadata$machine_readable_name
)
test_that(
"discovery_regression returns expected output for linear regression", {
# Select only... |
b537538492df07ac6f5cca868a0cad2e226a066bbbd549a2f3738523c74fb815 | R | 5,663 | 155 | #' Collect and process PubMed bibliographic data in one step
#'
#' A convenience wrapper that executes the full pubmedR workflow:
#' query building, record count check, metadata download, conversion to
#' data frame, and (optionally) citation enrichment via NCBI E-Link.
#'
#' @param query is a character. A PubMed searc... |
9b43ec1cf2beb373267e1fab1b13bd374c6e7313469fc45214fb4fe44af8e73e | R | 5,719 | 161 | ---
title: "heteroplasmy_plotting_06182021_nkm"
author: "Nate Mullin"
date: "11/04/2022"
output: html_document
---
```{r setup, include=FALSE}
library(Signac)
library(Seurat)
library(ggplot2)
library(patchwork)
library(tidyverse)
library(EnsDb.Hsapiens.v86)
library(Matrix)
library(SummarizedExperiment)
library(ggpubr)... |
06ccb186cd7b46af7b4f8c4732d61c223f86fe354a2cfb9c2b2e72b84c7e614c | R | 5,720 | 130 | plot_decomposition_2d <- function(vec1, vec2, common_vec,
gridsize = 100, col_levels = 21,
plot_bg = T,
renormalize = T,
xlim = range(c(0, 1.1*c(vec1, vec2, common_vec))),
... |
2a3aae9aa5614b078ae33b74a11b1de41d26464df9b4fb8b8e813605c5e17ce9 | R | 5,739 | 171 | ######################## Description and Setup #################################
# This script performs statistical analysis of neurite orientation and dispersion
# Load Libraries
library(sciplot)
library(rstatix)
library(ggthemes)
library(tidyverse)
# load preconfigured custom plot theme
source('code/analysis/paper... |
861638d30a7ecaae9522a1d7336dfbeddd7cca36c2fa28a8290be65abe9157ef | R | 5,755 | 120 | # Data preprocessing for quantifying orthogonal predicitons between
# response-based models and stimulus-based models
library(fs)
library(tidyverse)
### === SOURCE FUNCTION === ###
source("functions/CountRatio.R")
### === LOAD DATA === ###
file_list <- dir_ls("processed_data/LLhood_Easy_vs_Difficult/") |>
path_... |
81da0be6329891ab3d2502e9498c20c5e9dd87acb59a2d3d53bd689ccf47dcd6 | R | 5,830 | 193 | GetFiles <- function(test_no){
searchPattern = paste("Test",toString(test_no),"*",sep="");
all_files = list.files(pattern=glob2rx(searchPattern));
#filename = list.files(path = folder_name,pattern=glob2rx(searchPattern));
#full_filename <- paste(folder_name,"/",filename,sep="");
#df <- data.frame("data" = ful... |
c1b020770b1defd98e1ff6a7c3d795c176593e6df858853692000c4d6857be17 | R | 5,890 | 207 | context("Test variable selection")
## .generic_variable_selection is correct
test_that(".generic_variable_selection works", {
set.seed(10)
n <- 100; p <- 50
mat <- MASS::mvrnorm(n = n, mu = rep(0, p), Sigma = diag(p))
vec <- runif(p)
colnames(mat) <- paste0("g_", 1:p)
rownames(mat) <- paste0("c_", 1:n)
... |
a7a2b24335f9509abedbc8bab7c608f8f6ec1c3359b5f92dbe5c11ea75e22cad | R | 5,907 | 129 | #!/usr/bin/env Rscript
#############################################################
# crispro_stats.R
# Provides statistical analyses and visualization of CRISPR
# score and peptide annotations
#############################################################
library(ggplot2)
library(readr)
library(dplyr)
library... |
184b37c5bdb9c31ea98210275785520afbe14dcd4616c1f486c6e7ca1731543f | R | 5,929 | 134 | #' @title
#' Stopping Criteria function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function generates a stopping criteria for the *max_covs - min_covs* nested
#' predictive models generated in the previous step. It also populates the CBDA object.
#' @param label This is the label appended to ... |
fe08258600b227a49eac93d442bdb983d9d083afe7fe2934f561beb1e5f72937 | R | 5,936 | 102 | # Significance Tests for shifts in psychometric curves
library(tidyverse)
library(coin)
# Load the data
load("processed_data/Psychometric/auditoryPsychometricData.Rda")
# Reallocate df
dfa <- auditoryPsychometricData
aa_ba_fs <- wilcoxsign_test(dfa$auditory_shift_givenAA_fs ~ dfa$auditory_shift_givenBA_fs, di... |
014a0af0a38f23ba91a890769a00413085d750f2074cf459fe535c0bf82ae876 | R | 5,938 | 123 | server_fn <- local({
source(app_file("server.R"), local = TRUE)
server
})
upload <- function(f) list(name = basename(f), datapath = example_file(f), size = file.size(example_file(f)), type = "text/csv")
test_that("CellNet analysis runs end to end for a panel tissue", {
shiny::testServer(server_fn, {
session... |
3426fb3e3f1c7df283bc368c53256a60bd727c7513d5329090578393ac743e32 | R | 6,000 | 132 | library(crayon, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(backports, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(withr, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(rstudioapi, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAG... |
7655e6add9712f391f9b0e1f693888b9d7ac94ad2b8b85c3e13b98c2099a09fe | R | 6,026 | 122 | # PACNet web app UI. Packages and helpers are loaded in global.R.
page_head <- tags$head(
# Google Analytics
tags$script(async = NA, src = "https://www.googletagmanager.com/gtag/js?id=G-836SPJH7DH"),
tags$script(HTML(
"window.dataLayer = window.dataLayer || [];
function gtag(){dataLayer.push(arguments);... |
3669b348556f8ce060ea189aaa531a35e3f99ba1a6766ca82f641175b751cfb0 | R | 6,028 | 121 | #object_path: path to RData object
#clust_num_column: final_cluster_labels or equivalent (column of meta.data with cluster numbers)
#subset_logical: TRUE or FALSE (should a subset of the object be performed before dge?)
#subset_set_ident: "celltype" or equivalent (column name of meta.data for the subset). Unique values... |
c7a229165b8906ab0f195587ef03557b7ab3d04d90d5a91fca6d116a07ea1155 | R | 6,063 | 134 | #' @title
#' Stopping Criteria function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function generates a stopping criteria for the *max_covs - min_covs* nested
#' predictive models generated in the previous step. It also populates the CBDA object.
#' @param label This is the label appen... |
f3275988c0af70954df5dce45948a1702cff33cedd26136635046aeeac65f100 | R | 6,081 | 132 | # Author: Danyang Li
# Date: 21/05/08
# The brainspan data is downloaded from https://www.brainspan.org/static/download.html
#import significant genes
fh <- read.csv('FH_Global_Cells_Sig_Genes.csv', header=T)
pb <- read.csv('Pb_Global_Cells_Sig_Genes.csv', header=T)
colnames(fh)[2] <- 'ensembl_gene_id'
## brainspan ... |
ee7d05385e8fa7aac2b527568aeeeb788d2ff9907eb3b650de66a75a79fe8006 | R | 6,097 | 179 | ---
title: "Grabowski et al. - SBR2 - Bayou Setup - log Brain Size ~ log Body Size"
output: html_notebook
editor_options:
chunk_output_type: console
---
All Primates Analysis
```{r}
rm(list=ls())
require(bayou)
library(treeplyr)
library(dplyr)
library(plyr)
options(scipen=999)
#For execution on a local, multicore C... |
8051c51b6f891ee2afc6fd8243e54c8a5acd4b3ad63edf1fe7a390aeebe22f32 | R | 6,099 | 129 | ---
title: "CBDA-SL and Knockoff Filter Results on a AD HOC Binomial Dataset - 9000 jobs"
author: "Simeone Marino"
date: "December 24, 2016"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some useful information
This is a summary of a set of 30 experiments I ran on Cr... |
6ae2ba7465b2356be8f90d6bfa3ecb69ad4c86c33021346a7c81c63b6edd9106 | R | 6,100 | 117 | #' Compute how aligned the common component for a feature is with that feature in the original data modality
#'
#' The common matrix for modality \code{input_assay} is extracted, and then
#' compared against another matrix (depending on \code{bool_use_denoised} is set).
#' (For example, if \code{bool_use_denoised=TRUE}... |
fa908f8685aff6dfd6129d02db510a057d5aff4c9f5a459cb776a7e4151eb7df | R | 6,128 | 140 | context("Test common SNN")
## .compute_common_snn_hardclustering is correct
test_that(".compute_common_snn_hardclustering works", {
load(paste0("../assets/test_data3.RData"))
mat_1 <- test_data$mat_1; mat_2 <- test_data$mat_2
svd_1 <- test_data$svd_1; svd_2 <- test_data$svd_2
multiSVD_obj <- create_multiSVD(m... |
c45e83bc2fa5614cb25d2b1401e6d43dcecbc3ad8ca412b61ee07a904c939696 | R | 6,135 | 133 | # NOTE: THIS WAS EDITED FROM APV'S RIBO CCA SCRIPT TO CONVERT FROM RMD AND BE RUNABLE BY NKM ON ARGON.
# MODIFIED/TESTED ON 01NOV2022
library(crayon, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(backports, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(withr, lib.loc... |
f8322221ff3c50134ff651a2a3540d4f9cbdba7dd9b1f2ea5a9d3d14901d0c70 | R | 6,149 | 194 | ---
title: "SiT - MOB deconvolution"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression isoform... |
0bfaef37eeb7aa4e5303c69a58ed30d7cb04caa1632cc6bded2ef2b2c9514d06 | R | 6,219 | 130 | #' Compute the negative log-10 p-values across all cell types
#'
#' This function does the following: There are varible cell types
#' stored in \code{de_list$level_vec}. For each cell type, enumerate
#' the p-value (across different features, i.e., genes or proteins)
#' when comparing that cell type against all other... |
516d433b70fa181a3d629960a1c045c2a462ad0389d90ebcf53bf2c4ce5ac54f | R | 6,278 | 119 | ##' Import PTS files from Checkpoint
#'
#' @param ptslist a character vector of file names indicating the pts files to be imported
#' @param curveinfo the output of the function \code{create_curve_info}.
#' @param subsample a logical value specifying whether or not to subsample the semilandmark curves to equal lengths
... |
a0d8d48f1e26b392b2fe3579c3b176863a7ba9e48f42bc495fcac2af30eda659 | R | 6,299 | 128 | #' Information and Grouping for Nightingale's Blood Biomarkers
#'
#' A data frame with information on the Nightingale Health Ltd. NMR-quantified
#' blood biomarkers.
#'
#' @format A data frame (tibble) with 252 rows and 8 columns:
#' \describe{
#' \item{abbreviation}{Biomarker abbreviation, i.e. the one delivered wit... |
7d563e4278b57d878abf286a5a4aa24cfcc5b8dafcc5281790270eece60c2e65 | R | 6,353 | 166 | context("Test .compute_cca_aggregate_matrix")
## .compute_cca_aggregate_matrix is correct
test_that("(Basic) .compute_cca_aggregate_matrix works", {
set.seed(10)
n <- 20; p1 <- 50; p2 <- 40
mat_1 <- scale(MASS::mvrnorm(n = n, mu = rep(0,p1), Sigma = diag(p1)), center = T, scale = F)
mat_2 <- scale(MASS::mvrno... |
ba9f34d0497b44e39ae162d0478ccbb0c04a7362402761fc56a33bde91fa7939 | R | 6,371 | 206 | ---
title: "SiT - Supplementary Figure 05"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
8f7e02b572a704b3f7c444478b428298c6eeb8c3a4c7d0bd9e3a295b3e951961 | R | 6,411 | 203 |
# /home/toolkit/tools/R4.2.0/bin/R
library(Signac)
library(GenomicRanges)
library(Seurat)
#args <- commandArgs(TRUE)
#opath=args[1]
source('https://gitee.com/jumphone/BEER/raw/master/BEER.R')
COL_LIST=list(DType=c('scATAC'='indianred1','scHIC'='royalblue1'),
CType=c('Astro'='red','MG'='gold1','Neuron.... |
71d23a6f3d42397a551de219b4c914085014842b9ae9b842bcc0a64c6db9b02f | R | 6,462 | 155 |
#' Plotting function for FR-Match results
#'
#' This function takes in the \code{\link[FRmatch]{FRmatch}} output and generates plots for the matching results.
#'
#' @param rst.FRmatch The \code{\link[FRmatch]{FRmatch}} output.
#' @param type If \code{type="matches"} (default), it plots the one-way matches.
#' If \code... |
8e85b0afa4ff85620d4c9a37ce1475961ebb54f6b8bf94d088e6d647ff2f5258 | R | 6,505 | 337 | ---
title: "Figure 5"
output: html_notebook
---
```{r message=FALSE, warning=FALSE}
rm(list = ls())
library(circlize)
library(scales)
library(dplyr)
library("openxlsx")
```
# Read Data
```{r}
df.all <- readRDS("../Data/Physio_data.RDS")
demographics <- read.csv("../Data//Demographics.csv")... |
cd1e7eee3ab785bdde6414a0726349ef265a84f7738084c5bf69ab73a9f528b5 | R | 6,533 | 177 | # Plot Accuracy for all data sets
set.seed(81) # because of geom_jitter
library(tidyverse)
library(patchwork)
library(equatiomatic)
library(ggtext)
load("processed_data/allValues/allValuesAuditory.Rda")
load("processed_data/allValues/allValuesVestibular.Rda")
load("processed_data/allValues/allValuesVisual.Rda")
# ... |
d9dc889ede02eaf68546ea272ea6d95170ca3df03197de6cc659d8052bc021b5 | R | 6,543 | 177 | # Plot Accuracy for all data sets
set.seed(81) # because of geom_jitter
library(tidyverse)
library(patchwork)
library(equatiomatic)
library(ggtext)
load("processed_data/allValues/allValuesAuditory.Rda")
load("processed_data/allValues/allValuesVestibular.Rda")
load("processed_data/allValues/allValuesVisual.Rda")
# ... |
9b5e72c3d9e106b381b42df6e8ee5076dcc008f43fa4333d21e220be15632a09 | R | 6,598 | 164 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(label)
print(worksp... |
db84fbfd33707b0b969dabc5e60b3308292e41a40256714e2a4efcc0caadf599 | R | 6,658 | 154 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(label)
print(worksp... |
2c535156364b35268b242c7f7ab22e0c8cc30bda6e5b0b72eeecfc41bcd8fbfa | R | 6,732 | 161 | # Analysis of Electroporation datset using single cell gene regulatory network inference and clustering (SCENIC)
# Perform seurat analysis and then export the seurat objects for control and treated cells for this analysis
# Initial prepaprations
# All required objects and data files are provided in this repositor... |
b06134779d550e8c2f32b5d46e40604aefafeaff9401a960d05f6851b2e240e2 | R | 6,880 | 271 | ---
title: "Figure S6"
output: html_notebook
---
# Libraries
```{r message=FALSE, warning=FALSE, echo=FALSE,include=FALSE}
rm(list = ls())
library(lmerTest)
library(lme4)
library(emmeans)
library(dplyr)
library(ggpubr)
library(ggplot2)
library(rstatix)
library(effsize)
library(magrittr)
library(knit... |
10d8c9686e7d1e1655f01aa84b04c297d04ec375a4baeeae428fc65afebbc2df | R | 6,884 | 220 | ---
title: "SiT - Supplementary Figure 14"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
e284faeeade18f8234efb23ed9a5901f370458edef299ac6965e53fadc106abc | R | 6,928 | 208 | ---
title: "Introuduction of Github and Loni PipeLine"
output:
pdf_document: default
html_document: default
---
```{r,echo=F}
setwd("~/Desktop/fengyu/picture")
```
## Learn about Version Control with Git
Git is a free and open source distributed version control system designed to handle everything from small to... |
cd0139a746f26f663c9b8e5c268280b254c63ea809e455bd67650a5b7349fe94 | R | 6,964 | 233 | #' Construct cell-type subsamples
#'
#' Sample at most \code{min_subsample_cell} cells from each
#' unique level of \code{membership_vec}
#'
#' @param membership_vec factor vector
#' @param min_subsample_cell positive integer
#'
#' @return indices
#' @export
construct_celltype_subsample <- function(membership_vec, min... |
3230d4fd6a21c087c10b3e5e65ce247c2084ba55e0a87a1a9f934be2ce07db24 | R | 6,991 | 305 |
library(tidyverse)
library(foreign)
library(kableExtra)
library(pROC) ## ROC
library(caret) ## ROC
library(rpart)
library(rpart.plot)
library(gtsummary)
library("scales")
hex_codes1 <- hue_pal()(6)
# Data Preparation
ad.raw = read.csv("../data/ad_full_data.csv")%>%
mutate(PET = as.factor(PET),
... |
36e3a56eaf54a4acc0d8271b9acd77834f8f1ba65d832d1d766bf2ee9f27c657 | R | 6,996 | 311 | ---
title: "DEXSeq_AF22"
author: "Marika Oksanen"
date: "6/30/2021"
output: html_document
---
##DEXSeq
```{r}
library(DEXSeq)
library(biomaRt)
library(VennDiagram)
library(tidyverse)
```
## Preparations
Find the countfiles that are calculated using python scripts
```{r}
countFiles = list.files("/Users/k6-c02dm8d6ml... |
00af34b039a266733ce393225e46479284cb5a6f8a15081228f675b0ac0ec6df | R | 6,997 | 151 | #!/usr/bin/env Rscript
# Builds the slim reference data in data/ from the original PACNet web-app
# objects on S3, and records golden outputs of the ORIGINAL pipeline in
# tests/golden/ for the regression tests.
#
# Usage (from repo root):
# git show 748297c:webApp/pacnet_utils.R > /tmp/pacnet_utils_orig.R
# Rscrip... |
205fe3b8b65f19db4fa311d731250e57a52c63818e2e986af3a642dc0d5ea1af | R | 7,018 | 102 | library(data.table)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(forestplot)
pheno <- fread("/mnt/depts/dept04/compbio/human_genetics/users/yhuang5/muscle_targets/regenie/UKBB_maxHGS_WBLM.pheno")
cov <- fread("/mnt/depts/dept04/compbio/human_genetics/users/yhuang5/muscle_targets/regenie/UKB... |
a18a77733b53596031100c89bb85718cf1c165bac7b7efd2ffb115e8609ab850 | R | 7,078 | 222 | library(icd.data)
library(data.table)
get_icd10_chapter <- function(icd_code){
my_letters <- toupper(letters[1:26])
first_letter <- which(my_letters == substr(icd_code,1,1))
first_number <- as.integer(substr(icd_code,2,3))
if(first_letter == 21){
ch_name <- "ICD10: Codes for special purposes"
return(ch... |
13253e2d1791d446f5887910e3e6e5cf178351771a290c6dc29d56daa3294748 | R | 7,104 | 212 | library(rstatix)
library(car)
library(DescTools)
# for visualization
library(ggplot2)
library(ggpubr)
dat <- read.table('../Output/Classical_measures.txt', header = TRUE, sep = ",")
# total distance traveled
dat <- as.data.frame(dat[,c('day','group','mouse','tot_distance')])
dat$group <- as.factor(dat$group)
dat$day... |
dbccc40891c67cbc66f3a51468d16fbf0a8e448c818c338e390aed583a603d27 | R | 7,132 | 191 | # Data preprocesing for LLhood (without Stimulus-Only Model)
# IMPORTANT: It is absolutely necessary to start a new R Session before
# running this script!
# .rs.restartR()
# Purpose: For plotting!
library(fs)
library(tidyverse)
### === LOAD DATA === ###
file_list <- dir_ls("processed_data/LLhood_Easy_vs_Difficul... |
cc39b8da204e843a8f6ef647f54207e5e85533c4749b484aa5ef6df2cfc42c5c | R | 7,251 | 305 | ---
title: "Figure S12"
output: html_notebook
---
# Libraries
```{r message=FALSE, warning=FALSE, echo=FALSE,include=FALSE}
rm(list = ls())
library(dplyr)
library(ggpubr)
library(ggplot2)
library(rstatix)
library(magrittr)
library(knitr)
library(openxlsx)
library(tidyr)
```
# Read Data
```{r}
... |
4f73c4542d953b6b1cbcb27c7bd40feba2b86efe964d39521c14b3ab029d02ba | R | 7,285 | 136 | ---
title: "CBDA-SL and Knockoff Filter Results on a AD HOC Binomial Dataset - 9000 jobs"
author: "Simeone Marino"
date: "December 24, 2016"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some useful information
This is a summary of a set of 30 experiments I ran on Cr... |
cd0c91904571bef59026dd563cc661997c974a446a090b6c441618fc43032754 | R | 7,380 | 209 | ---
title: "SiT - Supplementary Figure 16"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression i... |
64298ff3240fea6ae55e98e3c5aae94b8ae2e4f9b9281fb62e8453a878baa004 | R | 7,496 | 217 | ---
title: "SiT - CBS2 deconvolution"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression isofor... |
204ce8361f1d287615c5fc705128d451e34ca87e44e3ec36cea58b07b97e1d59 | R | 7,519 | 86 | ---
title: Mulivariate analysis of neural development in GSE$^\dagger$ adolescents with
ANTs
author: "Brian B. Avants et al."
date: "March 16, 2015"
output: html_document
bibliography: ants.bib
---
# Specific Aims
The overall specific aims have not changed.
$\dagger$: Gestational substance exposure
# Studies and ... |
796fbd355c0f5c115f3bce68f266efdc03ca4bab4a01405bb7417ecdf963fff4 | R | 7,531 | 278 | context("Test utility")
## .mult_vec_mat is correct
test_that(".mult_vec_mat works", {
trials <- 100
n <- 10
bool_vec <- sapply(1:trials, function(x){
set.seed(x)
vec <- round(100*runif(n))
mat <- matrix(100*runif(n^2), n, n)
res1 <- .mult_vec_mat(vec, mat)
res2 <- diag(vec) %*% mat
... |
d7a132868963c924406be0094971005026e134c8bb61696f8a100cd5413e32ad | R | 7,588 | 151 |
#requires input files with clade associations, here from full_species_data, and output from BayesTraits analyses, here myrateslist.R, code for generating this from BayesTraits output included below
species_data<-read.csv("./Raw_Data/full_species_data.csv")
load(file="myrateslist.R")
tree_labels<-c("70_75","75_80","8... |
7748c92a72fb6c2984243a6336b0b4199708f2bfb35d08073d2bee8157d6ad0c | R | 7,617 | 116 | library(data.table)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(forestplot)
pheno <- fread("/mnt/depts/dept04/compbio/human_genetics/users/yhuang5/muscle_targets/regenie/UKBB_maxHGS_WBLM.pheno")
cov <- fread("/mnt/depts/dept04/compbio/human_genetics/users/yhuang5/muscle_targets/regenie/UKB... |
f9d997cb7662fffb00a52f3b0d6e29bf4097076d17e43e88d343ba8dff5be3b8 | R | 7,620 | 185 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(label)
print(worksp... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.