sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
c5bf36d371253e05b11f6e8afba150108345d4023cfa424deb36d32c694686b2 | R | 1,048 | 52 | #the shared script library, where all kinships are declared
source('./kinships.R')
#the common stats file with number of filtered SNPs for each dataset
#stats_infile = '~/Documents/deep_learning_for_breeding/datasets/filtering_stats.csv'
#actual elaboration config
config = NULL
#------- ADDITIVE
config = rbind(c... |
ef23bad0f3e6e7ddd0f8b6b33e8b19917c1cef8188946b948ca1153ddd4b8532 | R | 1,067 | 34 | library(LSD)
getvals <- function(dir, pattern, filter){
files = list.files(path=dir, pattern=paste0('^',pattern), full.names=T, recursive=T)
files = files[grep(filter, files)]
say(files)
table <- as.data.frame(do.call("rbind", lapply(files, FUN=function(file){
vals=readme(file,skip=95,header=T,gz... |
72fe9a2b4a4eda98526cb9723f8015d596dad58e907b018f31e9c00436b3f460 | R | 1,105 | 25 | test_that("heatmap raw data and rendering tolerate undefined clustering", {
grDevices::pdf(NULL); on.exit(grDevices::dev.off(), add = TRUE)
x <- .m61_fp3()
one <- x; one@result <- one@result[1, , drop = FALSE]
flat <- x; flat@result$Pvalue <- rep(0.02, 3)
cases <- list(list(one, one), list(x), list(flat, flat... |
20f70badd856dffb7147700185671afb274f745b8d8fded5a5d8abd672a9d7b0 | R | 1,111 | 34 | #' List Current Available Trained Keras Models
#'
#' @return A `tibble` containing summary models.
#' @export
#'
#' @examples
#' list_trained_models()
list_trained_models <- function() {
ext_path <- system.file("extdata", package = "sigminer.prediction")
if (!dir.exists(ext_path)) {
ext_path <- system.file("ins... |
7081e9e6e88a0f5d6e5e449ebc3c863c16fc8ee967011eb04fca112990b8d354 | R | 1,130 | 32 | # Contains <ggplot> themes for float_contrast == TRUE | FALSE
#
# Contains themes `float_contrast_theme` and `non_float_contrast_theme`.
# Theme for left-right graph
float_contrast_theme <- ggplot2::theme(
plot.margin = ggplot2::unit(c(0, 0, 0, 0), "pt"),
axis.line.x = ggplot2::element_blank(),
axis.title.x = gg... |
63b511f627c32c0025aa21557998e476821331e5534a4120c6859c385a06def2 | R | 1,148 | 31 | source(here("runMonteCarlo.R"))
for (section in unique(quant_cluster$Section_Type)){
quant_cluster %>% filter(Section_Type==section) -> dat
for (k in unique(quant_cluster$Cluster)){
concat_counts <- runMonteCarlo(dat,k,1000)
limits <- apply(concat_counts,1,apply_quantile)
actual_count <- rep(0... |
f7d7ba526679ba6e77c7a37944558bea26c6c54e5249334418a2d24823de9ae2 | R | 1,151 | 33 | #' triQP
#'
#'
#' @name triQP
#'
#' @param df dataset extracted from a QPath analysis and where annotation correspond to "Genotype Sex"
#' @description will create 2 columns containing respectively the genotype and the sex of the samples
#'
#' @examples
#'
#'
#' @export
triQP <- function(df)
{
df$genoty... |
57785f1299a4fd81f672f25e9bccbd3b1821ede70406cb04b5050383705a55aa | R | 1,156 | 30 |
########
options(future.globals.maxSize= 891289600000)
rm(list = ls())
if(T){
library(dplyr)
library(Seurat)
library(patchwork)
library(stringr)
library(ggplot2)
library(tidyverse)
} ##加载包
setwd("/public-supool/home/zhchen/XS/2_preproccess")
load("/public-supool/home/zhchen/Lab_share/public_source/cellcyc... |
37268ae91f050e11a0731d1d638c5d7b3c6ba781644e6df0df635e692c66d98d | R | 1,158 | 78 | # Load Packages
## Load Packages into environment
library(ggplot2)
library(tidyverse)
library(readxl)
library(openxlsx)
# library(xlsx)
library(plyr)
library(dplyr)
library(ggrepel)
library(grid)
library(gridExtra)
library(reshape2)
library(grid)
library(devtools)
library(RColorBrewer)
library(ggfortify)
library(veg... |
0e3e4749a3e77b926916146fffe24d3f3aa7856451ce47e4343a65e25a1e3c90 | R | 1,167 | 33 | # dat - dataset to subset from (e.g., pooled anterior sections)
# k - cluster number for which we are calculating enrichment
# n - number of simulations
# Subdivisions: list of strings of the names of subdivisions assessed for this current A-P axis position
runMonteCarlo <- function(dat,k,n){
dat %>% filter(Cluste... |
a4babbb335d69768e7c42fcbc875db2c898ba6dfab9549b24949a82f6f8ef96d | R | 1,203 | 44 | a=read.table('TAG.txt',sep='\t',header=T)
b=read.table('../../Tasic_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
c=read.table('../../Tasic_exp_sc_mat_cluster_merged.txt',sep='\t',header=T)
library('cidr')
library('NMI')
a[,2]=as.character(a[,2])
a[which(a[,2]=='Myelinating.Oligodendrocytes'),2]='Oligodendrocyt... |
71a9ad138ec1b4c85a495dc848dd93480c6ec81f83ba45afcc7cc47ed08a62b0 | R | 1,215 | 55 | ---
title: "Slide-seq v2 hippocampus"
author: "Will Townes"
output: html_document
---
```{r}
library(Seurat)
library(SeuratData) #remotes::install_github("satijalab/seurat-data")
library(SeuratDisk) #remotes::install_github("mojaveazure/seurat-disk")
library(scry) #bioconductor
fp<-file.path
pth<-"scrna/sshippo"
dpth... |
a00909863a16368a7acec98683a1d6441f96523d7e50fbef75d9db94d7594c42 | R | 1,219 | 52 | library(imputeR)
#half lives, simple seq features, & codon freqs
a=readme("seqFeatWithKmerFreqs.txt.gz",gzip=T)
a=a[,1:10]
dim(a)
say("RIP/m6aCLIP")
b=readme("allRIPCLIPdata.txt.gz",gzip=T)
a=merge(a, b, by=1, all.x=T)
dim(a)
say("ENCORI")
load("peakMatENCORI.RData")
colnames(b)[2:ncol(b)]=paste0("ENCORI.",colnames(... |
93cabd793ee0f2c998f383b1cd8dea9c99a2abef8840dce62af34738e700e959 | R | 1,232 | 45 | library('mclust')
library('NMI')
a=read.table('TAG.txt',header=T,sep='\t')
b=read.table('../../Zeisel_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
c=read.table('../../Zeisel_exp_sc_mat_cluster_merged.txt',sep='\t',header=T)
a[,2]=as.character(a[,2])
a[which(a[,2]=='Myelinating.Oligodendrocytes'),2]... |
ade55af2b4b6391b3d89a1d6ea92d031fc0032459ca9a1c9d7cc229015c45bc4 | R | 1,232 | 43 | library('mclust')
library('NMI')
a=read.table('Kendall.txt',header=T,sep='\t')
b=read.table('../../Zeisel_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
c=read.table('../../Zeisel_exp_sc_mat_cluster_merged.txt',sep='\t',header=T)
a[,2]=as.character(a[,2])
a[which(a[,2]=='Myelinating.Oligodendrocytes'),2]... |
c2014e049d6d3f62b55fb586db7d6b21fbba69747807c7c07dbd6b249f573a72 | R | 1,239 | 45 | library('mclust')
library('NMI')
a=read.table('TAG.txt',header=T,sep='\t')
b=read.table('../../Habib_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
c=read.table('../../Habib_exp_sc_mat_cluster_merged.txt',sep='\t',header=T)
a[,2]=as.character(a[,2])
a[which(a[,2]=='Myelinating.Oligodendrocytes'),2]='... |
75e698941d684f13a85615da489cb96d2b2e2ba6e6806d7a655607154c5ea0a3 | R | 1,240 | 44 | a=read.table('TAG.txt',sep='\t',header=T)
b=read.table('../../Hochgerner_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
c=read.table('../../Hochgerner_exp_sc_mat_cluster_merged.txt',sep='\t',header=T)
library('cidr')
library('NMI')
a[,2]=as.character(a[,2])
a[which(a[,2]=='Myelinating.Oligodendrocytes'),2]='Ol... |
635c27fcb5063ea6e3df6528c154e92f31b993d351fd4901e1f3b39d144fb8d9 | R | 1,248 | 45 | library('mclust')
library('NMI')
a=read.table('TAG.txt',header=T,sep='\t')
b=read.table('../../Hochgerner_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
c=read.table('../../Hochgerner_exp_sc_mat_cluster_merged.txt',sep='\t',header=T)
a[,2]=as.character(a[,2])
a[which(a[,2]=='Myelinating.Oligodendrocy... |
3f676845ac2c0d423204caf2f2d7b61dfd8783b805c415cf4449b1f97dbd101f | R | 1,251 | 45 | library('mclust')
library('NMI')
a=read.table('TAG.txt',header=T,sep='\t')
b=read.table('../../Tasic_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
c=read.table('../../Tasic_exp_sc_mat_cluster_merged.txt',sep='\t',header=T)
a[,2]=as.character(a[,2])
a[which(a[,2]=='Myelinating.Oligodendrocytes'),2]='... |
16beff905f77ea38812ef0cc9771f4c217521d94f137b47ee91366fb67026454 | R | 1,276 | 40 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017 Robert McCulloch and Rodney Sparapani
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
#... |
223222a36737abf6a90ef1532a98f6ba31f06f526ba49735af063c653eb5364e | R | 1,287 | 29 | ###
dataPath = "/Volumes/KausaliaHD/AllProjects/BRAIN DATA/MIBI/MIBIData Matlab/May_June2019/HiResScans_uci2717J/DeepCellDataAnalysis/"
outputFolder = paste0(dataPath,"/Pooled")
dir.create(outputFolder, showWarnings= F, recursive = T)
all_data_modMore <- read.csv("/Volumes/KausaliaHD/AllProjects/BRAIN DATA/MIBI/MIBIDa... |
c23f454978024e9c6016c65dd97626528405780af103850dda33c532293cc35c | R | 1,290 | 83 | ## Configure enviornment
#### Install CRAN Packages
install.packages(
c(
"ggplot2",
"tidyverse",
"readxl",
"plyr",
"dplyr",
"ggrepel",
"gridExtra",
"reshape2",
"devtools",
"RColorBrewer",
"ggfortify",
"vegan",
"MASS",
"compositions",
"zCompositions",
"g... |
655dc9958848a72b48bb83be4b3640073e6784085f8d9ed057af540bef431e12 | R | 1,292 | 35 | # Install kpcalg + its CRAN dependencies that are not already on conda.
# Run AFTER creating the conda env with environment.yml:
# conda env create -f environment.yml
# conda activate timeawarepc
# Rscript install_r_deps.R
#
# graph, RBGL, and pcalg are already installed by the conda env.
options(repos = c(CRAN ... |
d1794d4368b961db26c26f471e9788122698620bdeb667aedeab454a168b2113 | R | 1,297 | 44 | library(Seurat)
library(patchwork)
library(dplyr)
library(DoubletFinder)
rm(list = ls())
Tbx3_merge <- readRDS("/public-supool/home/zhchen/XS/3_cluster/Tbx3_merge_0911.rds")
if(T){
library(DoubletFinder)
sweep.res.list_kidney <- c()
sweep.stats_kidney <- c()
bcmvn_kidney <- c()
mpK <- c()
annotation... |
886825b65ac166de027b4d744546388fe87ab50b86318852223aaebcca13c68c | R | 1,315 | 45 | #' Normalise and scale counts matrix
#'
#' This function is called by the \code{preprocess} function and performs
#' log-normalisation and scaling of a counts matrix.
#'
#' @param x a matrix of counts
#' @param lib.size the library size
#' @param log logical, indicates whether the output should be on the log2 scale
... |
41e1a3047d5673aacfb1668f5aa8e9a76476bb430280e85f6c74f229b8ff9f3e | R | 1,322 | 36 | mrd <- function(input, scales=6, family='db2', output='waveletoutput', scale.of.interest = 3, energy.trace=0, coherency=0, orientation=0, max=0, min=0, maskoriginal=FALSE, out.SOI.only=FALSE) {
file <- as.character(input)[1]
family <- as.character(family)[1]
outputfile <- as.character(output)[1]
scales... |
43720368225789c96841a26df694a6eef67aea087bfcf51b9acdf236bc2766c5 | R | 1,323 | 25 | ##### Documentation for plots
library(ggplot2)
fn <- "/Users/dblyon/Downloads/Output/Deamidation.txt"
fn_plot <- gsub("txt$", "pdf", fn)
df <- read.csv(fn, sep="\t", header = TRUE)
df <- df[with(df, order(mean)), ]
df$sort_order <- seq(nrow(df))
df$Raw.File <- factor(df$Raw.File, levels=unique(df$Raw.File[df$sort_orde... |
f597f5bfefc7a41a7f223cbd5fa415523e422b3a63e7355342c811ebab95fe49 | R | 1,333 | 48 | #' Compute the cosine similarity
#'
#' @description Compute the cosine similarity between the columns of
#' a matrix or two matrices.
#'
#' @param x1 (matrix) A numeric matrix
#' @param x2 (matrix) A numeric matrix
#'
#' @return (matrix) A matrix containing the cosine similarity between
#' the columns of m1 (and m2)
... |
e6c77d40fbce9fbaddb78bfd9721d84f19155c2f8c073d16e6be45a5f4cab091 | R | 1,338 | 50 |
exp_data=read.table('exp_mat.txt',sep='\t',row.names=1,header=F)
meta_data=read.table('meta.txt',sep='\t',header=T)
library(pcaPP)
################################################################
Stage=meta_data[,9]
OUT=c()
i=1
while(i<=length(exp_data[,1])){
this_gene=rownames(exp_data)[i]
this_exp=t(exp_data[i,])
... |
aabd8ee348e18352a0d7160a3bcab62ebdd3642ebbb5339f845ef72094410836 | R | 1,353 | 41 | # Test cases
describe("Testing plot_delta function", {
test_that("Returns a list with correct components for valid inputs", {
# Test case: Valid inputs
np_dataset <- generate_non_proportional_dataset()
dabest_effectsize_obj <- dabestr::load(
data = np_dataset, x = Group, y = Measurement, idx = list(... |
dec71e208661c3ed1885dd664fff10b1c484a92d8cf92d0fa721bf719793af23 | R | 1,354 | 40 | batch_binding_annotation <- function() {
data.frame(
GeneID = LETTERS[1:6],
Annot = rep(c("T1", "T2", "T3"), each = 2),
Term = rep(c("Term one", "Term two", "Term three"), each = 2)
)
}
test_that("batchEnrich binds differing columns from real native ORA results", {
annot <- batch_binding_annotation()... |
548578fae4dc5fabd78c324f032ffd4741a70ed2b8216e65b4c189549264b687 | R | 1,383 | 49 | #' Generate example data
#'
#' @return a dataframe containing cell-level information for sample, group and
#' clusters
#'
#' @export
#'
#' @examples
#'
#' speckle_example_data()
#'
speckle_example_data <- function(){
# Make up some data with two groups, two biological replicates in each
# group and three cell t... |
c0579448410aca6c64f6acccee0b5dbf92891f7e535c938d0f43e2b72579600e | R | 1,390 | 31 | # Load data
source(here::here("code","01_load_data.R"))
# Create long dataset to plot
FBC_over_time_long <- PWAWM %>%
dplyr::select("patient", "FBC_acute_AF_L", "FBC_acute_AF_R", "FBC_acute_IFOF_L", "FBC_acute_IFOF_R",
"FBC_subacute_AF_L", "FBC_subacute_AF_R", "FBC_subacute_IFOF_L", "FBC_... |
d2ae9c7f1eda7384aa9d89ba6e80dbccd55702bfb0db7d6ab43ed53cf8746e10 | R | 1,400 | 43 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017-2018 Robert McCulloch and Rodney Sparapani
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License,... |
2f0fc96e91a2067a9b861869ad3a44bf5bfdad430a27e136f68ba433845649ed | R | 1,404 | 34 | # Test cases
describe("Testing print_each_comparism function", {
test_that("Prints each comparison correctly for valid inputs", {
# Test case: Valid inputs with list dabest_obj
dd_dataset <- generate_deltadelta_dataset()
dabest_obj <- dabestr::load(dd_dataset,
x = Genotype, y = Measurement,
de... |
67f8faacf0cf7068f85904da65e9724427648817adf26fc33d943bac32585c38 | R | 1,415 | 40 | test_that(context("Test GSEA"), {
library(liger)
data("org.Hs.GO2Symbol.list")
universe <- unique(unlist(org.Hs.GO2Symbol.list)) # get universe
gs <- org.Hs.GO2Symbol.list[[1]] # get a gene set
vals <- rnorm(length(universe), 0, 10) # simulate values
names(vals) <- universe
vals[gs] <- rnorm(length(g... |
b87d4efa1d65ff9e07f105dd912cfc13d54c545a68b58d0be87f6ad35391d301 | R | 1,423 | 30 | ## CANOES SPARK
cnv_espresso_transmission_analysis_file<- './cnv_info_w_img_tranmission_analysis_pilot_prediction.csv'
cnv_espresso_df = read.csv(cnv_espresso_transmission_analysis_file)
familyID_list <- unique(cnv_espresso_df$FamID)
print(paste("Number of family/trio:", length(familyID_list)))
transmission_rate_per_... |
460fd163f6db1790b687cb8378ec6519be3e1d7d44db9efc4405b2a42812e7fa | R | 1,428 | 45 | library(imputeR)
#half lives, simple seq features, & codon freqs
a=readme("seqFeatWithKmerFreqs.txt.gz", gzip=T)
a=a[,c(1,grep("Codon",colnames(a)),grep("3UTR",colnames(a)))]
dim(a)
b=readme("CWCS.txt.gz",gzip=T)
colnames(b)[2:ncol(b)]=paste0("MIR.",colnames(b)[2:ncol(b)])
b[2:ncol(b)] = -1*b[2:ncol(b)]
a=merge(a,b,b... |
0b80b6dd23f8e80a543991f12341a0761b972fdd160bf624346d577ffdb8a23d | R | 1,431 | 56 | ---
title: "Import alevin counts & generate Seurat object"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: true
toc: true
toc_depth: 3
fig_width: 16
html_document:
df_print: kable
theme: united
fig_height: 5
fig_width: 16
out_height: ... |
d4c51f4d5400905364a1422038add8356fcd82fd67c499b7354b6802149e0457 | R | 1,453 | 43 | #Packages
suppressPackageStartupMessages(library(tidyverse))
#Output directory
outdir='data/isocortex/'
#Create outdir if needed
if (!dir.exists(outdir)) {
dir.create(outdir, recursive = TRUE, showWarnings = FALSE)
}
#Input and output mouse files
infile <- 'data/MouseExpressionMatrix_voxel_coronal_maskcoronal_log2... |
91a523f70a2d0f6e1b9a25fa750d56b19c036c7d3b13a92748e66fe845668b66 | R | 1,456 | 47 | library('Seurat')
exp_mat=read.table('GSE92332_atlas_UMIcounts_reheader.txt',sep='\t',header=T,row.names=1)
pbmc <- CreateSeuratObject(raw.data = exp_mat, min.cells = 0, min.genes = 0, project = "10X_PBMC")
tag=cbind(as.character(pbmc@meta.data$orig.ident),as.character(pbmc@meta.data$orig.ident))
source('scRef.R')
NewR... |
2d7e4fb370deadeb80a80eeb0cb3380a061048b368e56a8b8f6dbfab187b2ad9 | R | 1,458 | 58 | ---
title: "Data Exploration with the UCSC Cell Browser"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: false
toc: false
html_document:
df_print: kable
theme: united
---
This vignette demonstrates how to run launch a UCSC Cell Browser instance popula... |
08e4494f3ad64bb683f686b02e6e0af6d86c10db8681bd698d130c6d38fa7194 | R | 1,463 | 32 | ###
### Find the unique sample types in the TCGA cancer types available in MEXPRESS.
### The idea is to assign a unique color to each sample type (for consistency
### accross figures), but because there are more than 12 possible sample types
### and I only have 12 unique distinguishable colors to assign, I want to find... |
3e676fe4c8a036396e2e91156e8ac4ab6a1f4a4aa3e073e2933f61f868273992 | R | 1,468 | 41 | setwd("C:\\Users\\zhenjiang fan\\Documents\\workspace\\Pythonworkspace\\Feature selection using deep learning\\data simulation\\RANK")
source('isee_all.R')
# load packages
library(MASS)
library(Matrix)
p = 40
n=2000
iterAll = 20;
dataDir = paste("/ihome/hpark/zhf16/causalDeepVASE/data/simulated_data/",... |
c0a09d67e72c6938e9a74593f6760b4d815f2f316e28b7a6158622f2de4e0446 | R | 1,474 | 53 | library(rhdf5)
library(ggplot2)
file = args[1]
getvals <- function(dir, pattern, filter){
files = list.files(path=dir, pattern=pattern, full.names=T, recursive=T)
files = files[grep(filter, files)]
table <- as.data.frame(do.call("rbind", lapply(files, FUN=function(file){
if (grepl("preds", file)) { vals = t(h... |
c02e625e3a99eda8c2ba79a00c798b807aecb7c513302c8ae95901a3f5dac661 | R | 1,518 | 47 |
library(Seurat)
exp_raw_data=read.table('MGH54_mat.txt',sep='\t',header=T,row.names=1)
pbmc <- CreateSeuratObject(raw.data = exp_raw_data, min.cells = 3, min.genes = 200, project = "10X_PBMC")
pbmc <- NormalizeData(object = pbmc, normalization.method = "LogNormalize",
scale.factor = 10000)
pbmc <- FindVariableG... |
69acf8a1e9d37f299910343250a271e5d420e3c0e5ebe8a9acbf36d8b1510d5a | R | 1,532 | 58 |
setwd("/home/yanglu/Downloads/RANK/")
source('/home/yanglu/Downloads/RANK/gknockoff_all.R')
source('/home/yanglu/Downloads/RANK/isee_all.R')
# load packages
require(graphics)
library(igraph)
library(MASS)
library(Matrix)
library(pracma)
regfactor = "log"
npermu = 5
sis.use = 0
bia.cor = 0
xURL = "... |
290663f6688a91dd0332d64ed4abbd024868b25afa78fe038998aaead24ef866 | R | 1,547 | 58 | library(data.table)
library(Biostrings)
library(reshape2)
kmerlen = 7
file1 = "all_HLs_human_featTable.txt.gz" #replace with "all_HLs_mouse_featTable.txt.gz" for mouse
inp.tbl <- fread(paste("zcat", file1),header=T,data.table=F,sep="\t")
rownames(inp.tbl) = inp.tbl[,1]
inp.tbl[,1] = NULL
#get codon frequencies
inp.... |
4c9331c757f0e8ccebd7512c5e2b461c1e03a2033e49c1b4559776d01f751d18 | R | 1,580 | 67 |
load('TSNE.RData')
library(Seurat)
.addclust <-function(TAG, TSNE_VEC, MINC=3, MAXC=5, random_seed=123456){
random_seed=random_seed
TSNE_VEC=TSNE_VEC
TAG=as.matrix(TAG)
TAG[,1]=as.character(TAG[,1])
TAG[,2]=as.character(TAG[,2])
library(factoextra)
set.seed(random_seed)
MAXC=MAXC... |
1fb75443e63a12cfb88a593e048ae56cdd1ef5f6d8f04f2f9dec9e21fb931e97 | R | 1,583 | 45 | #Natverse code to display and work with synapse and desmosomal connectors separately
#Gaspar Jekely, Sept 2021
library(natverse)
# catmaid connection
source("~/R/conn.R")
#read in dorsal MUSlong cells with connectors
MUSlong = nlapply(read.neurons.catmaid("^ventrolateral muscle$", pid=11,
... |
915a27ab1093b95e78debd922b07dd4f5e12be4774e70aa4e8d991faca17c020 | R | 1,607 | 59 | ####This script is intended to perform a PCA in order to reduce the dimensionality of cytokines
####in order to predict outcome of covid-19 patients
##data
cleveland <- read.csv("~/Documents/covid/COVID_AI/irish/covid_non_covid_all.csv")
cleveland <- na.omit(cleveland)
##pca
pca <- prcomp(cleveland[, 2:30], scale =... |
ad9248cfa4398d9738644f0035271e14deac874f88b99e4ea5969c70c7d16c5b | R | 1,615 | 52 | #Install R environment if using conda: #conda install -c r r
#Load the R files. Use "\\" on Windows and "/" on Linux
gknockoff_all_path = paste(normalizePath(dirname("gknockoff_all.R")),"/DL/knockoff/RANK/", "gknockoff_all.R", sep = "")
source(gknockoff_all_path)
#source('/ihome/hpark/zhf16/test/DeepPINK/knockoff/RANK/... |
022c9c0d23ddafc121705c119f477d58c2cf1cbeb0c11b423cf98d78dcbe77e0 | R | 1,616 | 62 | library(ggplot2)
library(tidyverse)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
figPath = "figures/"
############################################
# Fig 1B - Gene essentiality
############################################
# load data
# fns = c("data/geneEss_model1.txt",
# "data/geneEss_newal... |
2377f6494186586561548d24f649cb30d6b9be417405c7f0f1d8401e1255ce9c | R | 1,644 | 78 | a=read.table('Spearman.txt',sep='\t',header=T)
b=read.table('../../Zeisel_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
c=read.table('../../Zeisel_exp_sc_mat_cluster_merged.txt',sep='\t',header=T)
a[,2]=as.character(a[,2])
a[which(a[,2]=='Myelinating.Oligodendrocytes'),2]='Oligodendrocytes'
a[which(a[,2]=='Newly... |
68ac53d458ef47b8ccdca47d6832dba9c482b35f85a619f8c172ac784f175766 | R | 1,670 | 61 | # Functions that generate main_plots based on the main_plot_type of `create_rawplot_components`.
#
# Contains function `plot_slopegraph`.
# Function that plots slopegraph
# TODO add proper documentation
plot_slopegraph <- function(dabest_effectsize_obj, plot_kwargs) {
check_effectsize_object(dabest_effectsize_obj)... |
48eb659fe26fde65dd5e82a70dda7821c41df658c6f942a4a89bfac6b3eaceaf | R | 1,677 | 43 |
##############################Optimal Experimental Design######################### Amination
install.packages("AlgDesign")
library(AlgDesign)
cand <- gen.factorial(levels=c(3,22,3,4),
nVars= 4,
factors="all", varNames = c("aryl_halide",
... |
ad165a1c79c6f42f0ca2ff6f9609d675caac647cf8cd5acecf7cba41393ff573 | R | 1,681 | 64 | #' Comp2Moy
#'
#'
#' @name Comp2Moy
#'
#' @param Measure1 a measure from a dataset
#' @param Measure2 the same measure from a different dataset
#'
#' @description Compare 2 dataset using the right test according to normality and variances
#' @import rstatix
#' @import stats
#'
#' @examples
#'
#' Comp2Mo... |
e6ec4c417eccf61c99543b491d6388c2702922c48ecf7351c497e1a050f86b49 | R | 1,697 | 36 | ## data-raw/pathway.R -- rebuild data/pathway.rda (object `pathway`).
##
## NOT run as part of richR 0.2.0 M1: the bundled snapshot in data/ is left
## byte-identical. Running this script replaces data/pathway.rda with a fresh
## KEGG snapshot, so run it only when the annotation refresh is intended.
## The date the cur... |
e1dbe90ffae838b0004c85eed1b4f3d520bbd87b94f4d72d2a87f71a8fc63d0a | R | 1,704 | 88 | # @DESCRIPTION: filename
# Library -----------------------------------------------------------------
library(magrittr)
library(ggplot2)
library(patchwork)
library(rlang)
# Load data ---------------------------------------------------------------
a <- list.files("analysis/2021-11-05/raw", pattern = "All.xls", full... |
4af88110c386e0c792afc530b980b427b0a064038eb089624548071645f8f515 | R | 1,711 | 51 | # Plot Select Features of interest
source("src/load_packages.R")
source("src/load_phyloseq_obj.R")
source("src/Metadata_prep_funcs.R")
source("src/miscellaneous_funcs.R")
source("src/Community_Composition_Funcs.R")
#-----------------------------------------------------------------------------------------------------... |
2e5115544078f4f8319d5cf67b8bc4e610158efa89b7fe868cc3cf19c4b106aa | R | 1,722 | 54 | setwd("/public-supool/home/zhchen/XS/3_cluster")
options(future.globals.maxSize= 891289600000)
rm(list = ls())
if(T){
library(dplyr)
library(Seurat)
library(patchwork)
library(stringr)
library(ggplot2)
library(tidyverse)
} ##load package
Tbx3_merge <- readRDS("/public-supool/home/zhchen/XS/2_preproccess/T... |
2534b8d5ac3443f7a0430089d2fd3ae1e0739fde6396e1e2d36c400385dfb34a | R | 1,732 | 50 | library(devtools)
library(RColorBrewer)
library(plyr)
source_url("https://raw.githubusercontent.com/obigriffith/biostar-tutorials/master/Heatmaps/heatmap.3.R")
c = readme("all_HLs_mouse.txt.gz", gzip=T)
c$external_gene_name=NULL
code=as.factor(unlist(lapply(strsplit(colnames(c[,2:ncol(c)]),"\\_"),function(x) x[1])))... |
c9f0ec86df28659bcaede9ab4ecbf072d632d57d86fc2c6c3727284b90ca0ec1 | R | 1,756 | 59 | #http://dropviz.org/
anno=readRDS('annotation.BrainCellAtlas_Saunders_version_2018.04.01.RDS')
exp=readRDS('metacells.BrainCellAtlas_Saunders_version_2018.04.01.RDS')
COL=which(anno[,1]=='CB')
out=exp[,COL]
colnames(out)= anno[COL,5]
write.table(out, file='GRCm38.81.P60Cerebellum_ALT_mouse.txt', quote=F, sep='\t',ro... |
b436ae1a28b44fb777180a82700a9029199f555b6f0aa5a9044cf314ad8ba091 | R | 1,765 | 61 | rm(list = ls())
source("src/load_packages.R")
source("src/miscellaneous_funcs.R")
source("src/load_phyloseq_obj.R")
source("src/metadata_prep_funcs.R")
source("src/community_composition_funcs.R")
#----------------------------------------------
# Plot Seq Depth Distributions by group
#---------------------------------... |
21480f28e908784d013d6dedb52875d38a472530e125ba87e50c91d1147c848a | R | 1,785 | 63 | ---
title: "Whole-brain comparison of rodent and human brains using spatial transcriptomics"
subtitle: "Gene enrichment analysis"
author: "Antoine Beauchamp"
date: 'August 2nd, 2022'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
include = FALSE,
... |
3163504147a1fbd5064034b68854b85ebd31e3b816405ffa0fc0d16cbe35551d | R | 1,796 | 53 | #Plots the DSAVE score for all tissues
library(tidyverse)
library(DSAVE)
fig__dir = "D:/SingleCellModeling/MetAtlas/Figures/"
library(Matrix.utils) #for transpose of sparse matrix
dsBulk = DSAVE::bulkTotalVar1vs1[[4]] [[2]]
dsaveRes = readRDS("D:/SingleCellModeling/MetAtlas/DSAVEData.RDS")
tissClust3 = readRDS("D:/... |
cc8ffd025d1fcef785b322af25545bfb266ac869c2cb30b3bbb18b8eb46864fd | R | 1,796 | 50 | #' Estimate parameters of a Beta distribution from counts
#'
#' This function estimates the two parameters of the Beta distribution, alpha
#' and beta for each cell type. The input is a matrix of cell type counts,
#' where the rows are the cell types/clusters and the columns are the samples.
#'
#' This function is c... |
4ab1b2dc76fea5db6a38e1c5c66e01619ed79878a775e80500dfcce12175b388 | R | 1,800 | 46 | #Shelli Kesler 12/2/16
#Determines fit of degree distribution with power law
#Compares fit between two groups
#Assumes degree data are listed per subject per region and are in adjacent columns
#NOTE: Attach dataframe before running
#See https://cran.r-project.org/web/packages/poweRlaw/vignettes/b_powerlaw_examples.pdf... |
14224b453948831cfcfcf67b65793bf0600564639bcde37bac187cf2f04098c2 | R | 1,811 | 39 | # Load data
source(here::here("code","01_load_data.R"))
# Create long dataset to plot
datafr <- PWAWM %>%
dplyr::select("patient", "ScreeLing_1_fon", "ScreeLing_1_sem", "ScreeLing_2_fon", "ScreeLing_2_sem", "ScreeLing_3_fon", "ScreeLing_3_sem") %>%
pivot_longer(cols = !patient,
names_to... |
4a8a1e74b998bc73feef71a9b00de5638511b0c69a10ff2f2117ba739361e438 | R | 1,816 | 33 | # Regenerates plot_reference_m6base.rds (SPEC-RICHR-006 AC-009, REQ-006).
#
# The reference holds the plot data, layer data and titles of every plot of
# set P (richBar, richDot, richNetplot, richNetwork, richNetmap, richLollipop,
# richCircle, richNES, richScatter, richECDF, richTermSim, richGeneDot,
# richGeneHeat, r... |
baf31366ca0b6bbccdd2c6b30a772d9593e0544815478b27a86c580ac9afec40 | R | 1,817 | 54 | library(glmnet)
#mouse model tested w/ human data
load("../Robj/BC3MS_CV-Lasso.Robj")
#half lives, kmer kreqs, & codon freqs features
a=readme("seqFeatWithKmerFreqs.txt.gz",gzip=T)
a=a[,c(1:10,grep("Codon",colnames(a)),grep("3UTR",colnames(a)))]
#augment matrix w/ mouse miRs -- set to 0 since they don't exist in hum... |
5901290cd50e1c99b7ca1d6f5bd5ec7420c66d9138f98c990c6ac62088522a0a | R | 1,836 | 47 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'fdc' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(metr... |
6cddce1c12e77832816854837845a5b83c79ec6395f0f2de6898de5abbf11d72 | R | 1,841 | 48 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'fd' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(metri... |
bc56b15f720372f900e247bb28c15009c4cdc11cc0cde5ecd825e4fb8c3de8e1 | R | 1,855 | 48 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'fdc' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(metr... |
a9a3c5ba6578577f55129f71633afa0d3bfc9ee595752679980414686ca25947 | R | 1,861 | 48 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'fa_DKI' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(m... |
bda831afb986f4af06c3a054764fd037cd9f98b4396dd33aad20990b6bd94192 | R | 1,861 | 48 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'mk_DKI' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(m... |
e143647be2b2b7b709845ba7c947c5ec1664f56263bd089eff194d59f48e5aa8 | R | 1,861 | 48 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'md_DKI' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(m... |
7bbe111c67e9e100ecf1879d363450a2d6d2dc1bcb9039fa9ff39738a6eb2a86 | R | 1,862 | 48 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'log_fc' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(m... |
d5d16322101c3e5629c03cc0be9e00c28ecd60728c4276105a330292d6213027 | R | 1,863 | 48 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'kfa_DKI' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(... |
cf5c745ebd664c993f7c799743a046a9480c8fde861318f0ca6532e17865eacb | R | 1,865 | 49 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'OD_NODDI' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c... |
6053151ad2f3439698bff7f14f3270a7a82845fb2d334d309ad7662d8a56afe1 | R | 1,869 | 48 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'ICVF_NODDI' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric =... |
c3b48dcc88814afaa78ec52243736e367efebfd048f028fb93589434f2d75c72 | R | 1,896 | 49 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'fd' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(metri... |
8748e6b471ef9329dc0d1245131c98ab99e7a28a32b8d17fe340142f2f98692a | R | 1,910 | 49 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'log_fc' # metric to analyze (log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(metric)... |
c4fee9ca32b7faf532100c0c01cdf260631dd5cd9f2c7aad37152efca2eba4d2 | R | 1,926 | 74 |
---
title: "Fast Differential Expression with Presto"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: true
toc: true
toc_depth: 3
fig_width: 16
html_document:
df_print: kable
theme: united
fig_height: 5
fig_width: 16
out_height: 4
-... |
94fc6afbf1de6ecf9a481a06f491b0f4f3084149772ae006520a5e2a9bc8210f | R | 1,931 | 48 | # Load libraries
library(ModelArray)
library(testthat)
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
args = commandArgs(trailingOnly=TRUE)
fba_dir = args[1] # path to FBA derivatives directory
metric = 'fdc' # metric to analyze (e.g., log_fc, fd, or fdc)
# Define analysis and file paths
metric = c(metr... |
043884503c6d1d1a6348c32d45f89394cced39711eb304c420efb7848b9a7f0f | R | 1,935 | 59 | library(glmnet)
#mouse model tested w/ human data
load("mouse/Robj/BC3MSD_CV-Lasso.Robj")
#half lives, kmer kreqs, & codon freqs features
a=readme("seqFeatWithKmerFreqs.txt.gz",gzip=T)
a=a[,c(1:10,grep("Codon",colnames(a)),grep("3UTR",colnames(a)))]
a$Codon.TAA=0
a$Codon.TAG=0
#augment matrix w/ mouse miRs -- set to... |
a6d2296255d01db985ea57ded9874d777e80ccb4fcec9443d278928bd3e7135c | R | 1,935 | 61 | #Code to generate Video3 of the desmosomal connectome paper Jasek et al
#Gaspar Jekely
source("code/Packages_and_Connection.R")
#read the saved visNetwork file from supplements/
conn_graph.visn <- readRDS("source_data/Figure3_source_data2.rds")
# plot neurons by colours matching network pic -----------------------... |
15d06ae0e7309ff1cafb743d9897b41c560dce5d024bd4ce80a596be7bc5cb7d | R | 1,937 | 44 | #!/usr/bin/env Rscript
################################################
################################################
## REQUIREMENTS ##
################################################
################################################
## TRANSCRIPT ISOFORM DISCOVERY AND QUANTIFICATION... |
a15a4ae025c29ce617e67f654438282e14579dffeca5f6f5e0c9790ceb0dd277 | R | 1,949 | 63 | #' Prepare Training and Test Dataset
#'
#' @param data A `data.frame`.
#' @param col_to_vars A character vector specifying the predictive columns.
#' @param col_to_label A column indicating the labels/classes.
#' @param label_names Label/class names. The order is important. For example,
#' "a", "b", "c" will be transfo... |
fc6a394d9dc46a46c64ddf4949e98dfc1ffbd8fa3c79158fb339bdd744683adf | R | 1,953 | 58 |
### Read-in MaAsLin2 output
df.mas <-
read_tsv(
paste0(
"data/MaAsLin2_Analysis/Species_genotype_diet_maaslin2_output/all_results.tsv"),
col_names = T
)
df.mas.sig <- df.mas %>% filter(qval < 0.25)
df.plot.nonsig <- df.mas %>%
filter(qval > 0.25) %>%
pivot_wider(
!value,
values_fro... |
612c7dae27782ec720163fe4ad7f2d348fdb433c3a0ebe23d977908621a74b8c | R | 1,960 | 45 | # Create flowchart
flowchart <- grViz("digraph flowchart {
node [fontname = Calibri, shape = rectangle, fontsize = 16]
tab1 [label = '@@1']
tab2 [label = '@@2']
tab3 [label = '@@3']
tab4 [label = '@@4']
tab5 [label = '@@5']
tab6 [label = '@@6']
tab7 [label = '@@... |
3216bfe4d50aabde9b4fcf2f85e7b8f12c6e5194d8f853956f8e6296721f8b49 | R | 1,963 | 51 | # GSEA plot display labels with short = TRUE (SPEC-RICHR-003 REQ-011). Fork-only:
# relies on the GSEA result layout of SPEC-RICHR-002 (pathway = identifier,
# Term = label). Two pathways whose labels differ only in a whitespace run must
# stay two categories after shortening.
gsea_ws_result <- function() {
genes <-... |
75185e06939f582fcff40a366630e7973d18a660ace9bb46b63e6ef8b2fe4500 | R | 1,968 | 63 | # Contains functions responsible for separate plot functionalities via flow = FALSE.
#
# Contains `separate_idx`, `remove_last_ele_from_nested_list`, `create_xlabs_for_sankey` functions.
# Separate idx function
# TODO Add documentation with the roxygen format
separate_idx <- function(idx, paired) {
separated_idx <- ... |
a318427c09030b387062640d96e21d7ac9664a1dead70b059306f539e7829ac6 | R | 1,973 | 68 | #all kinship functions should follow the template:
#kinship_SOMETHING = function(SNP_matrix){...}
#where "SOMETHING" is the name of the algorithm or some distinctive string
#and SNP_matrix is SNP matrix, markers as rows, samples as columns, values in 0/1/2
#(or more) in case of polyploids
library("sommer")
library("ma... |
d029f6b649e984742e0a132ea135b7cf21c4baa565205a92cc1710a9b26cbfbe | R | 1,978 | 56 |
library(Seurat)
library(dplyr)
pbmc.data <- read.table('CASE.txt',sep='\t',check.name=F,row.names=1,header=T)
pbmc <- CreateSeuratObject(raw.data = pbmc.data, min.cells = 3, min.genes = 200, project = "Natalie")
#save(pbmc,file='CASE_raw.RObj')
mito.genes <- grep(pattern = "^mt-", x = rownames(x = pbmc@data), value ... |
31dadc70b1ea7ae1c06170bc55c24e501c9ddb17f7f6c1c3f816f5748857bddc | R | 1,982 | 74 | ---
title: "Zero-preserving imputation with ALRA"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: false
toc: false
html_document:
df_print: kable
theme: united
---
This vigettte demonstrates how to run ALRA on Seurat objects, which aims to recover mis... |
f21ee2cdd5db78f0c6f71cb8844687d1ab020c3c7d59a90f6741865de39248a4 | R | 1,992 | 72 | #! /usr/bin/Rscript
########## Hierarchical clustering ##########
#' Generate vectors containing needed variables
#'
#' norm_val: contains normalized values (column 9 or other depending on the normalization step)
#' geneID: contains gene names (unique occurences)
#' samples: contains sample names (unique occurences... |
b25a793d3717b20d2a9e4547f13a781bba5b9fd612fd1453f710eca680c40e4e | R | 1,996 | 69 | # alignReplicates() - model parsing ----------------------------------------------
test_that("alignReplicates() - model parsing", {
simDataWideFile <- system.file(
"extdata", "simDataWide.csv",
package = "blotIt"
)
input_data <- readWide(simDataWideFile, description = seq_len(3))
expe... |
94e0e2ccf69a83efed5c8399b2a616b0fdf297888aa45418660c9aa383539a0a | R | 2,011 | 66 | install_ez_packages <- function(answer = T) {
if (answer == T) {
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(pkgs = c("tidyverse", "R.matlab", "tidyr", "rlang", "flowCore", "ks", "flowVS", "flowViz", "RColorBrewer", "gtools", "gplots","ggpub... |
84c25e8cd218a7f2a663b6ad938fc8d1ac2e37a501bc92203f456946793ad169 | R | 2,023 | 69 | library(tidyverse)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
d = read_csv(paste0(rstudioapi::getActiveDocumentContext(), "/data/CCLE_expression_full.csv"))
dim(d)#1377 52055
#transpose
ds = as_tibble(cbind(gene = names(d)[-1], t(as.matrix(d[,-1]))))
colnames(ds)[-1] = d[[1]]
#make sure the columns... |
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