sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
0411b6775876a2ebf4e1878fa06c99ff0b7f621ff35e6e5ac7f99fcba0744974 | R | 1,934 | 73 |
#### Find spatial neighbors ####
## -----------------------------
setMethod("spatialDists", "ANY",
function(x, y, coord, r = 1, byrow = TRUE,
metric = "euclidean", p = 2, weights = NULL,
neighbors = findNeighbors(coord, r=r),
neighbors.weights = spatialWeights(coord, r=r),
verbose = getCardinalVerbose(), chu... |
2de6c43f88873d8ea659087ac71cfd59f47f385419ee95f36e3c3e1a290c6864 | R | 1,936 | 76 | #cd /mnt/scratch/quanjian/BS/OriginalRef/MethylationExt
#module purge
#module load GCC/11.2.0 OpenMPI/4.1.1 R/4.1.2
#R
library(methylKit)
library(stringr)
library(dplyr)
files <- list.files(pattern="_OriRef.deduplicated.sortbyName.CpG_report.txt.gz")
file.list <- list()
for(i in 1:47){
file.list[i] <... |
a437514390784b5fe0835588b2d7037effd772fc05fbaf4737a38e031119dc21 | R | 1,936 | 62 | ---
output: pdf_document
geometry: margin=0.5in
---
```{r, echo = F, warning = F, message = F}
library(dplyr)
library(cowplot)
library(ggplot2)
library(reshape2)
knitr::opts_chunk$set(echo = F, warning = F, message = F)
```
```{r}
plot.ci.coverage <- function(df, title='') {
df %>%
group_by(n) %>%
summarise... |
2eb12353a9a31c87a4ca847d8d35e6ac00589828e7023af4b74b827877d212a2 | R | 1,937 | 59 | library(dplyr)
library(logger)
library(purrr)
library(tidyr)
library(stringr)
## params to specify #####
#######
phenotypes = snakemake@params[['phenotypes']]
linear_model_res_path = snakemake@params[['input_res_path']]
phenotype_suffix = snakemake@params[['phenotype_suffix']]
out_dir = snakemake@params[['out_dir']]... |
3ca29bce17c890be15f6b0ea6f244469e33cc8b25970fe68c9b31ab4216f9708 | R | 1,938 | 67 | library(fmri.pipeline)
# note that HPC settings like nodes and batch_code are ignored for local scheduler
w <- R_batch_job$new(
batch_directory="~/batch_test",
job_name="step1",
n_nodes=1,
n_cpus=1,
cpu_time="10:00",
r_code=c(
"Sys.sleep(1)",
"print('hi')",
"x <- 2+2"
),
r_packages=c("lme4")... |
58a0827123097998ac292d166ef7282df83d3510c9ad4fc97773b0b033a43ecc | R | 1,941 | 38 | #!R
#!/usr/bin/env Rscript
# This script is used to modify the output scores metrics for common (freq > 50%) SVs.
# Sets scores to 0.51 (ie passing) for sites that passed at in at least one of SR_sum_log_pval, PE_log_pval, or PESR_log_pval
library("optparse")
option_list <- list(
make_option(c("-c","--cutoff... |
05bbd37321db8857afe70c7dad5042a80ab3216d8bdef57fd93045a3e68c0d28 | R | 1,946 | 69 | parseargs <- function(required=character(0), ..., silent=F, include.others=TRUE,
test=NULL) {
#' @title Function to parse arguments from command line
#' @keywords internal
opts <- list(...)
if(!interactive()) {
text <- commandArgs(trailingOnly = FALSE)
# str(text)
start <... |
d2fe49ed2618f13ff44c584312178dd6aaa2b045c14bdaa2234e42f2f73f1ba9 | R | 1,947 | 67 | library(ggplot2)
library(dplyr)
library(stringr)
stage <- c("40","70","115","168")
tissue <- c("Br", "Li", "Mu", "Pl")
##set color
color <- c("#8DD2C6", "#FFFFB3", "#BDB9DA", "#FA8071", "#80B0D2", "#FCB461", "#B3DE69", "#FBCDE4", "#D9D9D9", "#BB80BC", "#CCEBC5", "#FFEC6E")
###Br Li,Mu
for(i in 1:4){
fo... |
a142d59ba7a5396c2d9aa65d49cb10e7498f0169f3c2e5b9d3ef457a8f4bf270 | R | 1,949 | 66 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_firstorder_F3_independent"
ldsc.out <- "../2_ldsc/ldscout/MetS_noUKB_ldscout.RData"
sumstat.out <- "../7_sumstat/MetS_noUKB_sumstat.RData"
load(ldsc.out)
load(sumstat.out)
model <- "F1 =~ 1*BMI_GIANT18 + WC_Watanabe_UKB
F2 =~ 1*T2D_meta_FinR7_Mahajan22_M... |
a9be602d8711f9647968a6df6d912a933aa0717c174cfff83f31b905d2bcff79 | R | 1,949 | 74 | test_that("mixed_by does not mutate caller-owned data or external_df", {
data_df <- data.frame(
row_id = seq_len(8L),
id = rep(c(2L, 1L, 4L, 3L), each = 2L),
split = rep(c("b", "a"), 4L),
y = seq_len(8L)
)
external_dt <- data.table::data.table(
id = c(4L, 2L, 3L, 1L),
external_value = c("d... |
ec206fafd5f5e73c541c8e86bf50ceca9bf22b6d97c2a8c0b358ada217c61e30 | R | 1,950 | 73 | #' @title Spectral smoothing by splines
#'
#' @description
#' Spectral smoothing by splines.
#' @param spc `hyperSpec` object
#' @param newx wavelength axis to interpolate on
#' @param ... further parameters handed to [stats::smooth.spline()]
#' @return `hyperSpec` object containing smoothed spectra
#' @rdname spc-spl... |
1863d8744bf52125f968cd59240f71ae2acacfc5ac99cb67209685e00df9169b | R | 1,958 | 47 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
ID=args[1]
EXP=args[2]
print(ID)
.libPaths("~/miniconda3/envs/R/lib/R/library")
library(rtracklayer)
library(stringr)
remap<-read.table(paste0("/cluster/home/aanderson/allele_bias/Mapping/vg/",ID,"/trial/",EXP,"_remapped.txt"), sep="\t")
orig<-read.tab... |
9267fab6af61c3e96a76f7d24fcdd415c72a925de40705af1bf079a264a1a38a | R | 1,961 | 56 | # Compares parcel similarity between chimp and human OPNMF solutions #
# chimpanzee OPNMF solutions have been registered to human MNI space #
# using 03_Chimp_opnmf_def_batch.m SPM batch script #
# working project directory #
wd <- "~/projects/chimp_human_opnmf/"
setwd(wd)
# load in libraries needed for script #
# ne... |
0f5f09906552650ebb3047986d4fe74854d6b09c688a1bb1d274c1d3efb66f07 | R | 1,963 | 75 |
### -----------------------------------------------------------------------------
###
### .paste.row
###
###
#' @importFrom utils head tail
.paste.row <- function(x, label = "", name = "", ins = 0, i = NULL, val = FALSE,
...) {
.print.val <- function(x, range = TRUE, digits = getOption("digit... |
2efd4d222fe9cf4839709e53b3a579ae5106f75cc5e0c2914dd3e1942cce2e81 | R | 1,963 | 66 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_firstorder_F1_independent"
ldsc.out <- "../2_ldsc/ldscout/MetS_noUKB_ldscout.RData"
sumstat.out <- "../7_sumstat/MetS_noUKB_sumstat.RData"
load(ldsc.out)
load(sumstat.out)
model <- "F1 =~ 1*BMI_GIANT18 + WC_Watanabe_UKB
F2 =~ 1*T2D_meta_FinR7_Mahajan22_M... |
3ecf78f1dcf53c8d1ce66d32b874ef86df8e58487bc00382afd18b912ae4b1d6 | R | 1,973 | 53 | library(ggplot2)
library(ggseqlogo)
library(Biostrings)
# Neg_Xs
for (x in 2:100) {
for (target in c("up", "down")) {
if (target == "up") {
fasta_file <- paste("/ccb/cybertron/khchao/splam-analysis-results/train/results/Negs/Neg_", x, "/INPUTS/800bp/input_neg_", x, "_0.5_up.fa", sep = "")
} else {
... |
67846daec8e1fd04b32c01f477b44e5b4654c1a415639b15a3e06749cc668939 | R | 1,974 | 52 |
#' archstabms__plot_ddPCAvalidation_scatter
#'
#' Plot correlation with validation data.
#'
#' @param input_dt data.table with model free energy estimates (required)
#' @param ddPCA_inpath path to ddPCA free energies (required)
#' @param trait trait name to plot: folding or binding (required)
#' @param report_outpath ... |
48836069e5299d24a085fe807d0030aff64dc0b321c280a76a5b312dc23ff307 | R | 1,975 | 54 |
#' archstabms_fit_linear_model_datasets
#'
#' Fit linear models to multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param seq_position_offset list of sequence position offsets (required)
#' @param base_dir Base directory (required)
#' @param output_dir Output directory (re... |
12cc981493af5e73838f2e1dd6ec6beb1679dbf5c1e9c8a6fe705de03448b5b9 | R | 1,977 | 41 | library(data.table)
library(dplyr)
library(tidyr)
##generate .esd file for SMR tests
args <- commandArgs(T)
tis <- args[1]
n_chr <- args[2]
Phe1 <- args[3]
Phe2 <- args[4]
chr_file <- paste0("~/2SMR/", Phe1, "2", Phe2, "/", tis, "_", Phe1, "2", Phe2, "_pairs/", tis, ".", Phe1, "2", Phe2, "_chr", n_chr, ".txt")
if(Phe... |
16d38b444730654a35b56dcb4d44cb42cef4b888ef7e562b7a1c78c238e762f2 | R | 1,979 | 64 | ```{r init}
library(Matrix)
library(tidyverse)
library(biomaRt)
library(monocle3)
library(rgl)
library(tricycle)
library(SignacX)
library(princurve)
library(ggplot2)
library(Hmisc)
library(pheatmap)
library("ComplexHeatmap")
library(circlize)
library(dplyr)
library(ggrepel)
library(latex2exp)
```
```{r loadData}
cds<-... |
4227278a76ac875691fb8da5288309a9f9cdeeef84e0aed0af543bcb1ec99937 | R | 1,981 | 50 |
PeakCount_Analyser = setRefClass(
"PeakCount_Analyser",
contains = "Analyser",
methods = list(initialize = function()
{
callSuper(
description = "Counts Peaks within user defined CalculationWindow and returns
resulting paired or unpaired boxplot.
Grouping is done according to user-defined... |
7a52907fc406d255598b79c44158d6917532086894c6556bdc558a00e5f7901d | R | 1,987 | 67 | library(chromVAR)
library(SummarizedExperiment)
library(Matrix)
library(BSgenome.Hsapiens.UCSC.hg38)
library(motifmatchr)
library(chromVARmotifs)
# parse arguments
argReader = commandArgs(trailingOnly=TRUE)
base_dir <- argReader[1]
print(paste0("Loading files (peaks.bed, sampling_depth.txt, counts.txt, peak_names.t... |
acc28621a49c7966b44a7ea2889b987120118a40770037939a9fa0703fdcf05b | R | 1,989 | 41 | library(data.table)
library(dplyr)
library(tidyr)
##generate .esd file for SMR tests
args <- commandArgs(T)
tis <- args[1]
n_chr <- args[2]
Phe1 <- args[3]
Phe2 <- args[4]
chr_file <- paste0("~/2SMR/", Phe1, "2", Phe2, "/", tis, "_", Phe1, "2", Phe2, "_pairs/", tis, ".", Phe1, "2", Phe2, "_chr", n_chr, ".txt")
if(Phe... |
fd7ac3a1d25097e0b48ab0aebeae4c75f3ba0835563cfc9fc0d0ff06920dab78 | R | 1,994 | 43 | library(Seurat)
library(DAseq)
library(ggpubr)
library(SingleCellExperiment)
options(future.globals.maxSize = 4000 * 1024^20)
Sys.setenv('R_MAX_VSIZE'=2500000000000)
Oli<-readRDS("Oli.rds")
Idents(Oli) <- "updatedDX"
Oli_DEG.DX<-FindMarkers(Oli, slot = "data", assay = "RNA", test.use = "MAST", ident.1 = "AD", ident.... |
211835c5ab4226c7bca31b5df6bca391a6f63dedc25342f3138e39cee286b18b | R | 2,005 | 42 | test_that("cache_stan_model parameter works correctly", {
# Skip if cmdstanr is not available
skip_if_not(instantiate::stan_cmdstan_exists())
# Test that cache_stan_model parameter is available in all relevant functions
expect_true("cache_stan_model" %in% names(formals(sccomp_estimate)))
expect_true("cache... |
83d5a40a84f85c1171e7146f7f1950e1627cd4997febb73813acc01ff17500a1 | R | 2,015 | 76 | library(Signac)
library(Seurat)
setwd("~/multiome")
seuMulti <- readRDS('multiome-signac-noArchR.rds')
#### R2 atac
fragpath <- "~/nextseq/Multiome_CMN347SMN_e11-5_R2/outs/fragments.tsv.gz"
metadata <- read.csv(
file = "~/nextseq/Multiome_CMN347SMN_e11-5_R2/outs/singlecell.csv",
header = TRUE,
row.names = 1
) %... |
551f57738710d7fe0ba125584d3e74bcb037237faeacb89f440617dc3cb87865 | R | 2,016 | 52 | InputPath="F:\\RNA_20221101\\02_ModifiedGenome\\A_GeneExpressionMatrix\\"
OutputPath="F:\\RNA_20221101\\02_ModifiedGenome\\B_SampleEvaluation\\a_MappingBias\\Figure\\"
library(ggplot2)
library(ggrepel)
library(dplyr)
library(stringr)
library(ggsci)
RawReadCount <-read.table(paste(InputPath,"AutosomeGeneCount... |
b6b1f37da228f53efe5f9c2ddc1ca367e223383bed7e8364601a5703626a0719 | R | 2,024 | 74 | #!/usr/bin/R
# ----------------------------------------
# Plot neuron counts in entorhinal cortex:
# Updated: 12/06/2021
# ----------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){ source(paste0(sbindir, 'load_metadata.R')) }
source(paste0(sbindir, 'auxiliary... |
bf6324a5fb10726872015b85a79b7d25ad7af2f757fd55eca38457e1e2abcdf5 | R | 2,027 | 60 | #! /usr/bin/env Rscript
##meant to generate cutoffs for different copy states##
##first input is a Rd-test generated median_geno file from genotyping command on a set of know sites##
## second input is how many copy states to assess##
## third input is output filename for final matrix of cutoffs##
args <- commandArgs... |
a030231607bdb52dd7a099c10d713d14ffa49ced2952e305a0e01d321161dbf2 | R | 2,037 | 41 | library(Seurat)
library(SeuratObject)
# file <- "C:/Users/mauri/Desktop/scRNAseqR/results/Bas test/BL_C.qs"
# data <- qs::qread(file)
# FeaturePlot(data, features = markers)
# DotPlot(data, features = markers)
markers <- c("SLC17A7", "SNAP25", "SYT1", "SLC1A2", "SLC1A3")
file <- "C:/Users/mauri/Desktop/scRNAseqR/re... |
11ae0138fb4491a212495019374296c109202d0bbb0f34a5beeee4094071c295 | R | 2,040 | 49 | exDir = '/path/to/along-tract-stats/example'
subID = 'subject1'
trk_info = read.table(file.path(exDir, 'tract_info.txt'), header=T, sep='\t')
trk_avgs = read.table(file.path(exDir, 'trk_avgs.txt'), header=T, sep='\t')
library(ggplot2)
#############################################################################... |
d514d887ca2a4e2d68e320765e963c50ccfe1323ae87184dc59279da8ab70ac5 | R | 2,040 | 42 | ##------------------generated data ------------------
cd /f/Manuscript/ATAC/figure/POE05112023/ATAC_POE/method2/FilterDistal
ls *nodistal.csv|while read file
do
paste <(echo $file) <(cat $file|tr ',' '\t'|awk 'NR>1'|awk '$6<=0.05'|awk '$2>0'|wc -l) <(cat $file|tr ',' '\t'|awk 'NR>1'|awk '$6<=0.05'|awk '$2<0'|wc -l)... |
d51ac4ddd58663e0edf55e704001e3698d7747b8d9eb76cc171cee811a0e0574 | R | 2,046 | 71 | # Cluster and density plots over KMNN graphs
#
# input:
# data/06_DS1gdf_xy.txt: Layout computed using the ForceLayout2 algorithm in Gephi
#
# output:
# data/06_DS1_MKNN_partition.pdf
# data_figs/06_DS1_MKNN_cohort_*_.pdf
options(bitmapType = 'cairo')
# (0) Load data ----
data <- read.csv('data_aux/06_DS1gdf... |
13cdb307600b102b5d4b7c4cd548eccff593a96147b31335d7f2d5ca07e12258 | R | 2,048 | 51 | if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("VariantAnnotation")
library(VariantAnnotation)
# Function to read VCF and extract allele depth information along with chromosome and sample information
extract_allele_depth_chrom_and_samples <- function(vcf_path) {
... |
6d0e3f27c0a86723bb183ab5acd4b0376afdcd422b09191b08deda6a77231a93 | R | 2,058 | 83 | ---
title: "CS stats"
output: html_document
date: "Cazalé-Debat et al. 2023"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r include=FALSE}
rm(list=ls())
data<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/data/courtscape/sc... |
3508e7bea6e38e6b2549510d169dbd5b499a382fed4fd8d03a0032e243612bb2 | R | 2,060 | 66 | library(Seurat)
library(DAseq)
library(ggpubr)
options(future.globals.maxSize = 4000 * 1024^20)
Sys.setenv('R_MAX_VSIZE'=2500000000000)
cluster<-readRDS("combined.sct.dx.rds")
meta<-cluster@meta.data
meta.dx<-meta[,c(1,36)]
labels_res <- meta.dx[meta.dx$updatedDX == "AD", "orig.ident"]
labels_nores <- meta.dx[meta.dx... |
cbf1231303c8dc7c81a47620321caf1150c691d45e4d1b235dadd319fe5bbd83 | R | 2,060 | 65 | biomart <- read.table("./mart_export_human_mouse.txt", header=T)
biomart$link = paste0(biomart$Mouse_ID,"-",biomart$Human_ID)
pseudobulk_mouse_by_embryo <- function(obj,gene_id,liftover=T){
obj_mtx <- obj[['RNA']]@counts
if(liftover){
df_mouse_human = biomart[biomart[[gene_id]] %in% rownames(obj_mtx),]
... |
a750bea85a12dba9359aada2c819ecb89facb41b1070f2d159ecf910d7d0fecd | R | 2,063 | 91 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
name<-args[1]
dataColumn<-as.numeric(args[2])
skip<-as.numeric(args[3])
minY<-as.numeric(args[4])
maxY<-as.numeric(args[5])
fileArray=args[6:length(args)]
nFiles=length(fileArray)
cat("found ",nFiles," files.\n")
for (i in 1:nFiles) {
arg<-fileArray[i]
cat("\t"... |
9892f883d910623bfb01f185a0e12303846e614d2eeeb99cd1ebbdcb610e96ee | R | 2,064 | 72 | # Expensive pre-fitted sccomp objects shared by plot and association tests.
# Helpers run before any `test_*.R` file so objects exist regardless of
# alphabetical test order.
if (requireNamespace("instantiate", quietly = TRUE) && instantiate::stan_cmdstan_exists()) {
data("seurat_obj", package = "sccomp", envir = en... |
b51b7bbc314c35d6527ed96ffda568201f493d1a57fa66016d41be7c0205fbcc | R | 2,068 | 67 | require(GenomicSEM)
library(data.table)
NAME <- "MetS_firstorder_F2_independent"
ldsc.out <- "../2_ldsc/ldscout/MetS_noUKB_ldscout.RData"
sumstat.out <- "../7_sumstat/MetS_noUKB_sumstat.RData"
load(ldsc.out)
load(sumstat.out)
model <- "F1 =~ 1*BMI_GIANT18 + WC_Watanabe_UKB
F2 =~ 1*T2D_meta_FinR7_Mahajan22_M... |
00bf9c200a5c8ac4092cc8ea90a325d56818dddb5229e0bf0b71be9653386095 | R | 2,073 | 67 | #' @name DEPRECATED-orderwl
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Sorting the wavelengths of a `hyperSpec` object
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibilit... |
4c14b4b2a98beff12178560d3f390be0e349b9cf71fcd1f4bce3c025c21579bc | R | 2,074 | 92 |
# Get functions
library(monocle3)
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
# modStatus <- loadMonoclePackages()
# library(tidyr)
library(cicero)
library(ggplot2)
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-c", "--cds... |
8c2efea5ab8931ccc6d0f41596f39bebdd7d91b31e86e55fe30fc7d8c16a896a | R | 2,074 | 79 | library(data.table)
library(dplyr)
dir.result <- "./cml_ma_dp_result"
result.list <- list.files(dir.result, pattern = "*.rds")
### Result to dataframe
df.combined <- NULL
for (idx in 1:length(result.list)){
filename <- result.list[idx]
phecode <- strsplit(gsub(".rds", "", filename), "_")[[1]][5]
cML_dp_... |
f721d03d67f5426fddce04bb4b8be060d6a7474f78350b53f951cce48317feef | R | 2,075 | 92 |
# Get functions
library(monocle3)
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
# modStatus <- loadMonoclePackages()
# library(tidyr)
library(cicero)
library(ggplot2)
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-c", "--cds... |
5b71af301e0ad2a536b9be97e4e1287d3b8fbd9dd0d22dc173cee7654ef22b42 | R | 2,076 | 66 | require(plyr)
library(data.table)
library(tidyverse)
args = commandArgs(trailingOnly=TRUE)
input_gd <- args[1]
proband_gd <- args[2]
parents_gd <- args[3]
ped <- args[4]
chromosome <- args[5]
df_ori <- as.data.frame(read.table(input_gd, header = FALSE, sep="\t",stringsAsFactors=FALSE, quote=""))
colnames(df_ori) <- ... |
9b8d452fca15f4f1d59ff2bcb8eff3adee1dc84d910276513d039e2e08c1ed4f | R | 2,088 | 68 | ## usage: Rscript topGO.R [gene ids] [CC/BP/MF]
library("topGO")
# this is the file containing gene IDs
args = commandArgs(trailingOnly=TRUE)
ids <- read.table(args[1], header=F)
#ids <- read.table("pzq.genes", header=F)
myGenes <- as.character(ids$V1)
ndSize <- 5
goCat <- args[2] #'CC', 'BP' and 'MF'
nTerms <-50 # ... |
438fe67cc5897d6cfb5a946e4c104949010a273bc33e9c08fa1e54e5987ccf35 | R | 2,100 | 50 |
#' archstabms__predict_fitness
#'
#' Predict fitness from folding and binding energies for 3-state model.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param dataset_name dataset name (required)
#' @param folding_energy folding energies for 3-state model (required)
#' @param binding... |
d86240746607ecbd50e178c6a905c3c4785e78b73161d8a9d44aa457dd4a6346 | R | 2,106 | 57 | ## input two table, each is from one rep
##table1 and table2 are the filenames with data
organizeExp <- function(table1,table2,col1,gene1=NA,tx1=NA,align=c("gene","tx"),col2=col1,gene2=gene1,tx2=tx1){
align <- match.arg(align)
t1 <- read.delim(table1,header=T,stringsAsFactors=F)
t2 <- read.delim(tabl... |
9011eac106d4955fc0e744898268835797c5560b84ccabc6facb82a0da0f58a4 | R | 2,121 | 72 | #' @title Elastic net using summary statistics
#' @description Coordinate descent algorithm to solve:
#' 0.5 x'X'Xx - x'b + lambda1 ||x||_1 + 0.5 lambda2 ||x||_2^2
#' Function to get elastic net solutions given X, a reference panel, and
#' b, regression coefficients
#' @keywords internal
elnetR <- function(lambda1, la... |
7123c0404c419c9dad44d4f2221a93453f89d03f700f8ebd7a51ed31b7f8bbe3 | R | 2,122 | 70 | cohens_d <- function(x, y, DIM=1, SIGN=TRUE, na.rm=TRUE) {
#
# Function will compute cohen's d effect size.
# Generalized to work on either matrices, data frames, vectors
#
# INPUT
# x <- matrix or numeric vector
# y <- matrix or numeric vector
# DIM <- specify the dimension which samples are along
#
# Example usage
#
... |
00b27122ca9ee72dca5622a67c5c8da53d66bf41341b4cedc3b193f0d6415ff9 | R | 2,124 | 84 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
inputFile<-args[1]
signalColumn<-as.numeric(args[2])
nBreaks<-as.numeric(args[3])
skip<-as.numeric(args[4])
minX<-as.numeric(args[5])
maxX<-as.numeric(args[6])
if(is.na(skip)) {
skip<-0
}
inputFileName<-basename(inputFile)
myData<-read.table(input... |
959c6af00aead56e6e80a99dbdda1bc4e28046b3fd94235bf4e44172af2a0817 | R | 2,124 | 62 |
cat("Reading PRS...\n")
prs_file <- "../PRS-CS/PRS_MetS_noUKB.csv"
df1 <- read.csv(prs_file, sep=',', header=TRUE)
colnames(df1) <- c('FID', 'eid', 'PHENO', 'CNT', 'CNT2', 'PRS')
df1 <- df1[, c("eid", "PRS")]
df1$PRS <- scale(df1$PRS) # Normalize PRS
cat("Reading covariates...\n")
covariates_file <- "UKB_covariates.c... |
621bb8feede3ad08dff4e357ea5434ba49ecabb02ed58d35ffab06dd91e8644f | R | 2,126 | 68 | #' @name DEPRECATED-read.asc.Andor
#' @concept moved to hySpc.read.txt
#'
#' @title (DEPRECATED)
#' Import Raman spectra/maps from Andor cameras/solis ASCII files
#'
#' @description
#' These data input functions are **deprecated** and they will be removed in
#' the next release of \pkg{hyperspec} package.
#' Now... |
556e988119a4e5364354d75e7b4c4a21d2a314edf27f07ee32b47c6e3c525c35 | R | 2,128 | 58 |
library(Matrix)
motifMatFile = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2022_03_24_reprocessHeartAlone_nobackup/analyze_out/W145.heart.apex.s1/motif_matrices/W145.heart.apex.s1-peak_motif_matrix.mtx.gz"
motifMat = readMM(gzfile(motifMatFile))
matrixCol = read.table("/net/trapnell/vol1/home/r... |
6703f4faecf6bf6867086ee30ffdff6ecaaebd14c129692a72a6591799513ee3 | R | 2,128 | 59 | library(data.table)
library(dplyr)
library(TwoSampleMR)
dir_out_har <- "tsmr_harmonized_data/"
dir.create(dir_out_har, showWarnings = FALSE)
dir_out_tsmr_res <- "tsmr_ivw_result/"
dir.create(dir_out_tsmr_res, showWarnings = FALSE)
# Exposure data
df_exp <- fread("exposure_data.MetS_noUKB.txt",
sep=" ... |
4694517ae8c7b709bc3a687986001c35ab0e7de3c40c12231a3d1b484f66b63a | R | 2,130 | 90 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
name<-args[1]
dataColumn<-as.numeric(args[2])
skip<-as.numeric(args[3])
minY<-as.numeric(args[4])
maxY<-as.numeric(args[5])
fileArray=args[6:length(args)]
nFiles=length(fileArray)
cat("found ",nFiles," files.\n")
for (i in 1:nFiles) {
arg<-fileArray[i]
cat("\t"... |
9cfb7762cc594c76e4a5b3275b31462bef1d861a0ff395b2d65e8d319198b432 | R | 2,135 | 42 | test_that("correct calculation, unpaired", {
pdf(NULL) # to prevent generating an empty RPlots.pdf
yaml = get_testyaml_object("dir", analyser = "PeakCount", changes = c("DataSettings$CalculationWindow$Start = 3",
"DataSettings$CalculationWindow... |
9c37ca9caea4f88b3e602952ecaf8d9c0513278e3765c608e1ac782c41d74089 | R | 2,136 | 66 | #!/usr/bin/R
# ---------------------------------------------------------
# Run all of the modules resource creation steps, in order:
# Updated 11/29/2021
# ---------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
# Scripts prefix and location:
modbindir = paste0(sb... |
3a6ad4f5d20625fe8aca8ebfbf6e700b9b1cf892f4912136cbc50ae5a6aea394 | R | 2,143 | 61 |
cat("Reading PRS...\n")
prs_file <- "../../PRS-CS/firstorder/PRS_MetS_F3.csv"
df1 <- read.csv(prs_file, sep=',', header=TRUE)
colnames(df1) <- c('FID', 'eid', 'PHENO', 'CNT', 'CNT2', 'PRS')
df1 <- df1[, c("eid", "PRS")]
df1$PRS <- scale(df1$PRS) # Normalize PRS
cat("Reading covariates...\n")
covariates_file <- "../UK... |
97d5dc3e779b3e73242c68d5dee65ad7711eb666df40ec43f60f40512899a5c3 | R | 2,143 | 62 |
cat("Reading PRS...\n")
prs_file <- "../../PRS-CS/firstorder/PRS_MetS_F2.csv"
df1 <- read.csv(prs_file, sep=',', header=TRUE)
colnames(df1) <- c('FID', 'eid', 'PHENO', 'CNT', 'CNT2', 'PRS')
df1 <- df1[, c("eid", "PRS")]
df1$PRS <- scale(df1$PRS) # Normalize PRS
cat("Reading covariates...\n")
covariates_file <- "../UK... |
e83c200cbb822ca846197c1d33bf22aa02cf706cf85dde337072b6050b555e1e | R | 2,143 | 61 |
cat("Reading PRS...\n")
prs_file <- "../../PRS-CS/firstorder/PRS_MetS_F1.csv"
df1 <- read.csv(prs_file, sep=',', header=TRUE)
colnames(df1) <- c('FID', 'eid', 'PHENO', 'CNT', 'CNT2', 'PRS')
df1 <- df1[, c("eid", "PRS")]
df1$PRS <- scale(df1$PRS) # Normalize PRS
cat("Reading covariates...\n")
covariates_file <- "../UK... |
15b4ef5820598b4b689018294b8bbd71fe78ffdeaff87fba8abe7c14fe619cd0 | R | 2,148 | 62 | test_that("create_new_hi_model stops before contrasts for aliased higher-level terms", {
subject_data <- data.frame(
id = rep(sprintf("sub%d", 1:4), each = 3),
session = rep(1:3, times = 4),
session_label = rep(c("analgesic", "placebo", "opioid"), times = 4),
stringsAsFactors = FALSE
)
session_day... |
4b07b4b1f3438c73f15cfeac59ffef7d353cb00ef01feca5cbfe804bab170cad | R | 2,150 | 48 |
#' archstabms__predict_fitness_ddPCA
#'
#' Predict fitness from folding and binding energies for 3-state model.
#'
#' @param mochi_outpath path to MoCHI thermo model fit results (required)
#' @param folding_energy folding energies for 3-state model (required)
#' @param binding_energy binding energies for 3-state model... |
8c0706f9f696a07e56cb9466530274ab29e3d1f741ae093dd68a7debb4852ca2 | R | 2,151 | 61 | #!/usr/bin/env Rscript
# Helper script to write list of variant IDs, genotypes, and GQs per sample
###Set parameters
options(stringsAsFactors=F)
###Read arguments
args <- commandArgs(trailingOnly=T)
#Checks for appropriate number of positional arguments
if(length(args) != 3){
stop("Incorrect number of required pos... |
09c2fea02b59e44b8ad2374a98fa253ece218cde893d32929f8dcd6fae8a5989 | R | 2,163 | 50 | rm(list=ls())
data<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/data/courtscape/screen/text/soundxp_all.txt",
h=T,sep="\t",na.strings = "NA", stringsAsFactors=T)
attach(data)
library(beeswarm)
quartz()
line1 <- factor(line, levels = c("... |
c7a18ca6f16ef6e5c17260bce1a8598ba37bda5169e6909e4573aa8b2fba4891 | R | 2,166 | 71 | ---
title: "clean_data_for_classifier"
author: "Veronica Mandelli"
date: "10/11/2023"
output: html_document
---
# in this script I clean UCSD data before use the WEB APPLICATION to classify the subjects into theis subtypes
```{r}
work_dir = "..."
codepath = file.path(work_dir,"code")
data_long_path = file.path(work_d... |
84f932661f84df2f85dde504e9eeb032ded2a4f08f1ef9eb6fd708721c5dd60b | R | 2,169 | 82 |
# Function -------------------------------------------------------------------
.split <- function(x, f, drop = TRUE) {
validObject(x)
hyperlist <- split(seq(x, index = TRUE), f, drop)
for (i in seq_along(hyperlist)) {
hyperlist[[i]] <- x[hyperlist[[i]], ]
}
hyperlist
}
#' Split a `hyperSpec` object ... |
266661068bbc745a4fae7f0275ac595f40bf4949af0f26c0e126a626b73f0a5f | R | 2,171 | 101 | suppressPackageStartupMessages({
library(Seurat)
library(tidyverse)
})
mk_seurat <- function(dir_path) {
meta <- paste(dir_path, "meta.csv", sep = "/") |>
read.csv(
row.names = 1,
numerals = "no.loss"
) |>
mutate(
condition = ifelse(isKO == "True", "ncko", "nchet"),
within.bb... |
bccd30dd47557ff0904a21f2a384ebbbb24e4341e27abb20ee309ef39d6017e7 | R | 2,176 | 75 |
#' archstabms__relative_angle_from_PDB
#'
#' Get relative angle of c-beta to c-alpha vector w.r.t. center of mass to c-alpha vector from PDB file.
#'
#' @param input_file path to PDB file (required)
#' @param chain chain id (default:A)
#'
#' @return data.table with relative angles for all residues
#' @export
#' @impo... |
11385c65029221ed6e5733cce4671daa85f9464e44e02d22c70c8c31ad3c2b77 | R | 2,177 | 85 | ################################################
### Sanzhen Liu
### 1/27/2024
################################################
args <- commandArgs(trailingOnly=T)
miniscan_file <- args[1] # miniscan output
chrlen_file <- args[2] # chromosome lengths (2 columns)
outdir <- args[3] # output directory
prefix <- args[4]
if... |
66b9046405a0c09662d0aeb9d4054130deb798697acf74dded1b0a0925fd423c | R | 2,179 | 54 | # Define reusable variables
TR <- 2
n_vols <- 100
onsets <- seq(1, 80, by = 5)
# Unit tests for fmri.stimulus
# 1. Basic functionality - volume units
test_that("fmri.stimulus returns correct length for volume units", {
stim <- fmri.stimulus(n_vols = n_vols, onsets = onsets, durations = 1,
un... |
f648337afb79860e2e2e5d38d694420447dee8bdb104e1f1aed345e80df831ef | R | 2,185 | 67 | #!/usr/bin/env Rscript
print("Hello world")
loadMonoclePackages <- function(){
modStatus <- tryCatch({
suppressPackageStartupMessages({
# Adding this 5-14-21
# .libPaths('2')
library(argparse)
library(Matrix)
# library(dplyr)
... |
480bdfafe1cda728bf6dc4d47047ad939a1b005d8a459802cd72a2f3bb84daba | R | 2,189 | 72 | # load the reshape2 package
library(reshape2)
library(dplyr)
library(lme4)
library(lmerTest)
#metadata
setwd("D:/PFC_427_WS/Vanshika")
meta=read.csv("dataset_652_basic_04-23-2020.csv")
meta=meta[,c("projid","age_death","pmi","msex","gpath")]
setwd("D:/home_wkstn/F/Brain_region_project/LIANA/Analysis_aggreg... |
7661cbb098b41a08ba48689cf4c1d74a4bb8208fbea26aa4bd447d27a4961f76 | R | 2,190 | 105 | #Third script to launch
load(file = "sctransv2_pd_annotated_new.Rdata")
library(Seurat)
library(dplyr)
require('openxlsx')
library(ggplot2)
# Differential analysis - all cell-types combined
sctransv2 <- PrepSCTFindMarkers(object = sctransv2)
Idents(sctransv2) <- "cond"
table(Idents(sctransv2))
#DimPlot(sctransv2,... |
42bf69551344e12dc6667270271e7c3bfad8cc2bec2efcf1846b996e3dda5dfe | R | 2,191 | 68 | #!/usr/bin/env Rscript
# Talkowski SV pipeline downstream analysis helper script
# Merge AF tables across batches
###Set global parameters
options(stringsAsFactors=F,scipen=1000)
svtypes <- c("DEL","DUP","INS","INV","CPX","BND")
allpops <- c("AFR","ASN","EUR","HSP")
###################
###HELPER FUNCTIONS
#######... |
589acbb587bb7ec7177e9e79334ef5fd273f00a3b302a9828e4f1e93ae0cdea2 | R | 2,199 | 79 |
#### Principal components analysis ####
## ------------------------------------
setMethod("PCA", "ANY",
function(x, ncomp = 3,
center = TRUE, scale = FALSE,
verbose = getCardinalVerbose(), chunkopts = list(),
BPPARAM = getCardinalBPPARAM(), ...)
{
ans <- prcomp_lanczos(x, k=max(ncomp),
center=center, scale... |
947609e6723e03cae20887629ee1043f382f5772381d89804006e55ea27f5aec | R | 2,201 | 66 | #!/usr/bin/R
# -------------------------------------------------------
# Plot UMAP resources for figure panels + other analysis:
# Updated: 12/02/2021
# -------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
... |
309ae2c410bf7d25274180b82ff789159f595d241deceac42ef2524aa8b13816 | R | 2,204 | 72 | library("tidyverse")
library("clusterProfiler")
library('data.table')
library("BiocParallel")
# prepare from GMT files download from gprofile
ReadGMT_GSEA <- function (gmt.file) {
pathwayLines <- strsplit(readLines(gmt.file), "\t")
pathways <- lapply(pathwayLines, tail, -2)
fetchName <- function(x){
unlist(... |
37889091264c8de6e22c818a7be88f972dd5f7338b9d137fd78dabd46c28f0c7 | R | 2,207 | 47 | options(bitmapType='cairo')
library(fBasics)
library(qvalue)
args <- commandArgs(TRUE)
inputFile<-args[1]
inputName<-basename(inputFile)
loessData <- read.table(inputFile,header=TRUE)
print('attemping to model null ZSCOREs')
print('Assuming all negative zScores are true NULLs')
quant <- quantile(loessD... |
4e19d790772fc312a4df0ecc9b293d741fc5aa42ad6ae7981e06461858f051ba | R | 2,209 | 47 | normalized_to_dna <- function(dir_filtered,
network_inference,
lp_set = c(1, 2),
dir_label = "New results") {
if(is.null(dir_label) | dir_label == "") {
stop("dir_label cannot be null nor an empty string.")
}
# Load... |
87677f5a8c453d13322d64c6865e255d65e0b8429a57d6d72ed7b1b7ac377048 | R | 2,217 | 62 | test_that("concat_l1_confounds unions spikes and pads zeros", {
skip_if_not_installed("data.table")
tmp_dir <- tempfile("confound_concat_")
dir.create(tmp_dir, recursive = TRUE)
sqlite_db <- file.path(tmp_dir, "test.sqlite")
gpa <- create_mock_gpa(n_subjects = 1, n_runs = 2, include_l1_models = FALSE, outpu... |
7016307959542bd9c2bee797d1ff31c546d2c8dfc4bee6d3f80eba65d54db872 | R | 2,220 | 71 | test_that("run_feat_sepjobs returns an empty character vector when nothing needs execution", {
gpa <- create_mock_gpa(include_l1_models = TRUE)
gpa$output_locations$sqlite_db <- NULL
gpa$parallel$fsl <- list(
l1_feat_time = "0:10:00",
l1_feat_memgb = 1,
l1_feat_cpus_per_job = 1L,
l1_feat_runs_per_... |
325e1884b0f754e7c5c0a5b4f87845ff1e7b3e79fc9320faf81d9516b598904d | R | 2,230 | 74 | #!/usr/bin/env R
#.libPaths("/cluster/home/bmoyers/R/x86_64-pc-linux-gnu-library/4.1")
library(GenomicRanges)
library(VGAM)
# read in all counts tables
files=as.list(readLines("/cluster/home/aanderson/allele_bias/Counts_WASP_noMAPQ0/path_counts.txt"))
n=sapply(strsplit(unlist(files),"[/]"),"[[",7)
n<-gsub(".txt",""... |
6d117a109ab582a2788104b1e454a669ece33ab67d4b03fe414042575b0a5635 | R | 2,232 | 57 | # Title: SV nested nearest neighbor analysis
#
# Description:
# This script performs a nested nearest neighbor analysis of synaptic vesicles.
#
# Author: Daniel Parthier
# Date: 19.04.2024
# Last modified: 19.04.2024
# Version: 1.0
# Load libraries ####
library(readxl)
library(data.table)
library(ggplot2)
library(ggbe... |
0897a878d8bcdf25f140de30e5b5f085f917d6f19c34663b9abd200e6dffe21b | R | 2,235 | 87 | ---
title: "SEM Guide for NosaAnalysis"
author: "Julia Thüringer"
date: '`r Sys.Date()`'
output:
html_document: default
pdf_document: null
vignette: >
%\VignetteIndexEntry{SEM Guide for NosaAnalysis}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
### Description
```{r description, eval=T... |
feab8f6ce0a4815cfa0156b7d1ac710d2b423188f2bd7c0c3a7789efa7611ec9 | R | 2,239 | 61 | # Title: Nested SV density analysis
#
# Description:
# This script performs a nested SV density analysis.
# It calculates the density of synaptic vesicles within each nested bouton.
#
# Author: Daniel Parthier / Felicitas Brüntgens
# Date: 03.04.2024
# Last modified: 09.04.2024
# Version: 1.0
# Load libraries ####
li... |
2b9bcf901d3cf6cff4896d83f74018aeb2817de194ae9510e68cddc3073c7d55 | R | 2,241 | 54 | #' @keywords internal
"_PACKAGE"
#' sccomp: Differential Composition and Variability Analysis for Single-Cell Data
#'
#' @description
#' The sccomp package provides comprehensive tools for differential composition
#' and variability analysis in single-cell RNA sequencing, CyTOF, and microbiome data.
#' It implements... |
300cbd263cffe23131262b60730f4030caecccf6ae9c42fe30592a703a60ebb2 | R | 2,241 | 75 | library(colorspace)
library(doParallel)
library(dplyr)
library(foreach)
library(magick)
library(readr)
library(stringr)
num_to_sample = as.numeric(commandArgs(trailingOnly=TRUE)[1])
out_dirpath = commandArgs(trailingOnly=TRUE)[2]
out_filepath = commandArgs(trailingOnly=TRUE)[3]
alreadysampled_filepath = commandArgs(tr... |
8097bdad9a94490d97be1026ce115dc3c2076d57a19462684f09137b2d567a29 | R | 2,246 | 48 | #' @title Obtain the SNP-wise standard deviations from the PLINK bfile
#' @details \code{keep}, \code{remove} could take one of three
#' formats: (1) A logical vector indicating which indivduals to keep/remove,
#' (2) A \code{data.frame} with two columns giving the FID and IID of the indivdiuals
#' to keep/remove (ma... |
dbeb31614e59d581c99c7d1d1f0576e1efdbbef3f05fa0aca3fc7586c72a9546 | R | 2,250 | 49 | ## ----------Script-----------------
##
## Purpose: Compare and characterise Iso-Seq vs ONT targeeted datasets
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
##
## ---------- Source function and config files -----------------
# source all general scripts related to long-read sequencing
LOGEN = "/gpfs/mrc0/proj... |
5ee290714fa8af5e30fd8c7bfba0e3a834208f1aed3757197ac14a66fc36364d | R | 2,251 | 69 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
inputFile<-args[2]
name_1<-args[3]
name_2<-args[4]
correlationMode<-args[5]
outlierFraction<-as.numeric(args[6])
ymin<-as.numeric(args[7])
ymax<-as.numeric(args[8])
xmin<-as.numeric(args[9])
xmax<-as.numeric(args[10])
if(is.na(outlierFra... |
10cc5cbccf0ae3f4da975bdb46927430b198c27849482d60347458956a9c558e | R | 2,255 | 74 | library(scrattch)
library(scrattch.io)
library(scrattch.hicat)
library(scrattch.vis)
search_file_for_functions <- function(file,
funs) {
lines <- readLines(file)
found <- logical(length = length(funs))
names(found) <- funs
for(i in 1:length(funs)) {
search_str <- ... |
27fd3e55edee8499cc502941ab7ce99844a32b79ccd8496070db80019d384e35 | R | 2,255 | 75 | #' Print method for sccomp objects
#'
#' @description
#' Print method for sccomp objects.
#'
#' The print method for sccomp objects provides a summary of the model specifications,
#' data dimensions, and convergence diagnostics.
#'
#' The output is formatted to be easy to read and understand.
#'
#' @importFrom magri... |
025223b7af876e32b1318a8c8050a9a106443c750181cbf2478b0266fd728612 | R | 2,256 | 83 |
#### Find spatial neighbors ####
## -----------------------------
setMethod("spatialWeights", "ANY",
function(x, coord = x, r = 1, byrow = TRUE,
neighbors = findNeighbors(coord, r=r),
weights = c("gaussian", "adaptive"),
sd = ((2 * r) + 1) / 4, matrix = FALSE,
verbose = getCardinalVerbose(), chunkopts = list... |
bc16759575bd8548479063b03282ee36062add67e2302f0bef22dd2d34ceead3 | R | 2,257 | 54 | log <- file(snakemake@log[[1]], open = "wt")
sink(log)
sink(log, type = "message")
library(ggplot2)
library(readr)
library(ek.plot) # Github koncina/ek.plot
suppressPackageStartupMessages(library(dplyr))
message(paste("Reading in a list of", length(snakemake@input), "tsv"))
# Read in the tsv after flattening the inp... |
0e1b46dcfab48b6fa593ca2f60c667da322af6e4e503847b7e46701c795ab049 | R | 2,258 | 57 | rm(list=ls())
options(stringsAsFactors = F)
library(dplyr)
library(ggalluvial)
read.table("GBM_TCGA/HT_HG-U133A",header=T,row.names=1,sep="\t") -> U133A_exp
U133A_exp <- U133A_exp[,-(grep("11$",colnames(U133A_exp)))]
library(ssgsea.GBM.classification)
source("~/ssgseaMOD.r")
Wang_score <- as.data.frame(mod.analyze2(U13... |
5904552f851a6cfcdcbd854039995a31cab8c6ad8f38229fb0ba995f369a57d0 | R | 2,260 | 54 | library(data.table)
library(spacexr)
library(Matrix)
library(dplyr)
library(tidyr)
library(tibble)
hippo_ref <- readRDS('../inst/extdata/reference_scrna/scRefSubsampled1000.rds')
celltypes_hippo <- hippo_ref@meta.data$liger_ident_coarse
levels(celltypes_hippo)[levels(celltypes_hippo)=='Denate'] <- 'Dentate'
hippo_cou... |
606ac6a1b6b30429e913e1ad3a0da8b0c0a47c95dbaff60f2b4653ff16cfa88a | R | 2,267 | 72 | # Simulate Deutan Image
suppressPackageStartupMessages(library(magick))
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(colorblindr))
convert_hex_vector_to_image <- function(hex, dimensions) {
x = col2rgb(hex)
r = matrix(as.raw(x[1,]), nrow=dimensions[2], ncol=dimension... |
4e2e5116a4a65e0ebd5ebeb16579edd23c9114c8e22abfe86b16dd279337e312 | R | 2,268 | 67 | ---
title: "Deduplicating large datasets"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{deduplicating-large-datasets}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
## **Setup**
First,... |
e15d77d34390b061bbfb5b583b41fdfa217e7962e5741401ce74a44e89769592 | R | 2,268 | 67 | ## ---------- Script -----------------
##
## Purpose: input variables for characterising QC of ONT targeted mouse transcriptome datasets
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
## ---------- Packages -----------------
suppressMessages(library("dplyr"))
## ---------- LOGEN modules -----------------
L... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.