sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
9591ebf925209814c87aafc66d85b7a829a3d4d8786d98fe49559b9eaa3b2384 | R | 881 | 21 |
library(pheatmap)
heatmapgenes<- read.csv(ordered_c,"upset_FCby1.5_ordered.csv")
heatmapgenes1 <- na.omit(heatmapgenes)
paletteLength <- 100
myColor <- colorRampPalette(c("blue", "white", "red"))(paletteLength)
myBreaks <- c(seq(-4, 0, length.out=ceiling(paletteLength/2) + 1),
seq(max1/paletteLength, ... |
7b52d77c6564ac36c12f98c34be3bba97cdf36cafef4f644c6b22aa3dae28fb8 | R | 894 | 36 | rm(list=ls())
alldata_BD<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/ethograms/lisa_quantification/original.spreadsheet/analysisframeV2/Ethogramm_bending_V2.txt",h=T,sep="\t",na.strings = "NA", stringsAsFactors=T)
# Create a new Quartz window
quartz()
... |
dc18c3efcb6357ee97e9eb4d2b0e854580c32318a1cde82835a563e8c759f417 | R | 894 | 37 | #!/usr/bin/env Rscript
# Talkowski SV pipeline downstream analysis helper script
# Make list of all nonredundant pairs of batches from an input list of batches
###Set global parameters
options(stringsAsFactors=F,scipen=1000)
###Read command-line arguments
args <- commandArgs(trailingOnly=T)
in.list <- as.characte... |
2124e19f69a6fca441b23dd4fa583a60adfb5eceacab5d2fb4cbce2705e1dc5c | R | 904 | 31 | library(data.table)
library(dplyr)
trait_list <- c("MetS", "TG", "HDL",
"FG",
"BMI",
"HTN",
"T2D", "WC",
"F1", "F2", "F3",
"MetS_lind", "MetS_walree")
for (trait in trait_list){
print(trait)
dir_cojo <- paste0(".... |
6d88335077f9520cda84a800648bef457d59204d9ad30b3bb0f552c65c9fe480 | R | 907 | 23 | #' @title Independent LASSO using summary statistics (a.k.a. soft-thresholding)
#'
#' @param coef vector of regression coefficients (\eqn{r})
#' @param lambda a vector of \eqn{\lambda}s
#'
#' @details A function to find the minimum of \eqn{\beta} in
#' \deqn{f(\beta)=\beta'\beta - 2\beta'r + 2\lambda||\beta||_1}
#... |
b7b3464332cb23a52afbf0d62f82e71687b501feac1b6a9adea093b0a03e51ab | R | 907 | 32 | # common functions for reproducible reporting
require("tidyverse")
require("readxl")
require("openxlsx") # for normal write xlsx format
# require("patchwork")
require("officer")
require("rvg")
require("boot")
require("boot.pval")
library("RColorBrewer")
my.cores <- min(10, parallel::detectCores() - 1) # always leave ... |
0e0f1c10216c3da455afbbb968c11ec31cf18b4e8496231b39df0fd5d9cfda92 | R | 916 | 24 | library(data.table)
## Specify PRS file ID
prs_file <- "PGS_file_name_here"
## Read files for specified PRS
get_files <- list.files(path="/path/to/pgs_chr", pattern=glob2rx(paste0("plink_prs_", prs_file, "_chr*.profile")))
read_files <- lapply(get_files, function(i) fread(paste0("/path/to/pgs_chr/", i)))
read_files_... |
6a3558c20ed0794107f370d1661b937d9d515b34551f8303b6fdab674a9dcc8e | R | 916 | 35 | #' @name DEPRECATED-markpeak
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Find an evenly spaced grid for x
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and ... |
2a6f71a0432c4bcc8f55f22c159dbf61a99445f9f3ba93f068fd6776d7d24206 | R | 924 | 36 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
inputFile<-args[2]
span<-as.numeric(args[3])
inputFileArray<-unlist(strsplit(inputFile, "\\/"))
inputFileName<-inputFileArray[length(inputFileArray)]
wd<-paste(dir,sep='')
setwd(wd)
tmpData<-read.table(inputFile,header=F,sep="\t")
tm... |
fbb2c12faaf2786cc61323acb0575ebc04951b7b6dac08fb0d089ac64696ae52 | R | 925 | 26 | library(data.table)
library(dplyr)
for(tis in c("Brain", "Lung", "Muscle", "Heart")){
for(dir in c("m6A2ha", "ha2m6A")){
Coloc_res <- data.frame()
for(i in 1:22){
Coloc_res0 <- fread(paste0("~/Coloc/", dir, "/", tis, "_", dir, "_coloc/chr", i, ".coloc.res"), sep = "\t", header = TRUE)
Coloc_res <- bind_rows... |
49dab01e7861556c5cd22b1f19f3684dcac9736e9ee59ab76c90259a6b8c7200 | R | 928 | 20 | setwd("/media/hansruedi/3b32f153-0651-4d56-b863-9b070ac7dd55/home/hansruedi/Allen_Institute_06142020")
data=read.csv("matrix.csv",header=TRUE,row.names=1)
rows=row.names(data)
columns=colnames(data)
metadata=read.csv("metadata.csv")
rownames(metadata)<-metadata$sample_name
library(Seurat)
counts=t(data)
pbmc <-... |
2a1be010740a891ff4d3e5b95cef67ba099d211628345159145f35ad4e18751c | R | 929 | 36 | #' @name DEPRECATED-plotc
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Plotting spectra
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and will be
#' removed ... |
56762c48a21aa5d45b22d53e86bb3a0383e380c2a9a99ddb986bc893f7338eb5 | R | 934 | 30 | test_that("setup_parallel_settings enforces memory floors for estimation jobs", {
gpa <- list(
parallel = list(
l1_setup_cores = 1L,
l1_setup_memgb = "8G",
fsl = list(
l1_feat_memgb = "8",
l2_feat_memgb = "12",
l3_feat_memgb = "4"
),
spm = list(
l1_spm... |
9e60ab8a61d6a1999a3773d1b5b2a83389514abba2b27a1f6000ecf9c4aa1ad8 | R | 934 | 27 | #######
# Purpose: Visualize fastANI core-genome comparison
# Usage: Rscript <this_script> <query sequence in fasta format> <subject sequence> <fastANI visualization output>
# Output: <fastANI visualization output filename>.pdf
# Uses genoPlotR package: http://genoplotr.r-forge.r-project.org
#Parse command line argum... |
02e104b6eac366b0b2c45f9096cc877fa27e95d9762d9149219c2b062ae4580f | R | 937 | 29 | ##POE gene enrichement analysis
dat <- read.csv("F:\\ATAC\\processFiles\\POE05112023\\MG_AllGenomePeakReadNumber.NoDistal.GroupByGene.csv", header = T, row.names = 1)
geneList <- rownames(dat)
POE <- read.csv("F:\\RNA_20221101\\02_ModifiedGenome\\C_POEgeneSelection\\result\\POE.list2.csv", header = T, row.names = ... |
5e46e69c93a0d3e592c0a227cefc36b98533fe2088f72ca06ef74a7affe0d98c | R | 938 | 32 | #' @name DEPRECATED-stacked.offsets
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Calculate y-axis offsets
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and w... |
9bbcf9afad0729ff158413ca0453a380a9de8729d416fed86bfa5fe6626f06c4 | R | 941 | 29 | #!/usr/bin/env Rscript
# Helper script to merge outputs from count_svtypes task
# in final_outlier_sample_filter.wdl
###Set master parameters & read arguments
options(stringsAsFactors=F,scipen=1000)
args <- commandArgs(trailingOnly=TRUE)
INFILE <- args[1]
OUTFILE <- args[2]
###Read input data & reformat
dat <- read... |
98ad2cfae932faf392b99216597af84c47d40d22d3c3bc128be4c5ec29fc9ab0 | R | 944 | 36 |
#' archstabms_fitness_plots_datasets
#'
#' Fitness plots for multiple datasets.
#'
#' @param dataset_names character vector of dataset names (required)
#' @param base_dir Base directory (required)
#' @param output_dir Output directory (required)
#' @param stagenum stage number (required)
#' @param colour_scheme colour... |
246ea7fa54117e27ff5b81191de1231b1d64e96bc8dc5b5bef30fc076d64cde6 | R | 945 | 30 | test_that("intercept-only L2/L3 models auto-add diagonal contrast", {
df <- data.frame(y = rnorm(6))
lmfit <- lm(y ~ 1, data = df)
mobj <- list(level = 2L, lmfit = lmfit)
class(mobj) <- c("list", "hi_model_spec")
res <- fmri.pipeline:::specify_contrasts(mobj, spec_list = NULL)
expect_true(isTRUE(res$contr... |
d1bf8febff07f775b227550e43bf32e9c56f653bfc946e6bbd1285df7f190150 | R | 948 | 27 | DESeq2 <- function(comparisons, comparisons_dir, conditions, conditions_dir, results_dir, padj_threshold) {
# Load Counts
countData <- read.csv(paste0(comparisons_dir, comparisons, ".DESeq2_comparisons.csv"), row.names = 1)
colnames(countData) <- substr(colnames(countData), 2, nchar(colnames(countData)))
... |
6bdcb9aac491168c8dc9bfa00bb9753dff031f20f6b8868130c6b899b73dd56a | R | 958 | 19 | # *******
# 0a-import.r
# Code for initial construction of the seurat object holding UMI
# Note: Expected within Data/secondary are individual secondary 10X outputs for each organoid scRNAseq sample.
library(tidyverse)
library(magrittr)
library(Seurat)
source("Analysis/util/import_10x.r")
meta <- readxl::read_xlsx('Dat... |
30cebce391feb7617d0b10077e046161e46fba9d3ef1d77d54c8df2fcd4108f5 | R | 965 | 36 | #' @name DEPRECATED-plotmap
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Plotting spectra
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and will be
#' remove... |
9e7ea3554ce2e3dead491be91e6d2e55d43edd749122433daec17bce42f8cc7e | R | 968 | 42 |
#' archstabms__temperature_factor_from_PDB
#'
#' Get residue-level data from temperature factor (B-factor) columm in PDB file.
#'
#' @param input_file path to PDB file (required)
#' @param chain chain id (default:A)
#'
#' @return data.table with residue-level temperature factor (B-factor) column values
#' @export
#' ... |
9f4219362878fba86a9722568591ec8245a0a0ebb54e9cc81d7ad3f793c6dfec | R | 971 | 31 | load_matrix <- function(path){
expr_matrix = read.table(path,sep=',',header=T,row.names=1)
expr_matrix = as.matrix(expr_matrix)
return(expr_matrix)
}
load_pathway <- function(path){
gSet=getGmt(path)
return(gSet)
}
pathway_scoring <- function(gSet, mat_gene){
cells_rankings <- AUCell_buildRank... |
c2c47cb56f5afaba94fb475da5bde04d699f180eee9d323982c7fd46951bfbab | R | 971 | 17 | library(spacexr)
library(spASE)
cere3 <- readRDS('results/rctd_cere_3.rds')
cere3@config$max_cores <- 8
cell_types <- c('Fibroblast', 'Granule', 'MLI2', 'Purkinje', 'Bergmann', 'Oligodendrocytes')
cere3_intercept <- run.CSIDE.intercept(cere3, cell_types = cell_types, cell_type_threshold = 50, spase=F, logs=T)
cere3_i... |
f02f5b7d56a5b7474e0b60234f43faa2ee591f26fbcf00ad8eaac9fb913263b3 | R | 973 | 32 | library(data.table)
library(dplyr)
covariate_file <- "./UKB_covariates.csv"
df_cov <- fread(covariate_file, sep="\t", data.table = F, nThread = 1) %>%
dplyr::select(f.eid, SEX, BIRTH_YEAR, PC1, PC2, PC3, PC4, PC5, PC6, PC7, PC8, PC9, PC10) %>%
rename(FID = f.eid) %>%
mutate(IID = FID) %>%
dplyr::sele... |
a5e93b793c1bc027377fcd50d979119d97b96984cbd247f96c3f73c913d2c152 | R | 976 | 36 | #' @name DEPRECATED-plotmat
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Plotting spectra
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and will be
#' remove... |
22dcfdd62642efc98831260a6e0d0f1f2b1116ca730146a8cb2c300fe4b5d5de | R | 978 | 22 |
NMD <- lapply(TargetGene,function(x) class.files$protein_filtered_final[class.files$protein_filtered_final$is_nmd == "TRUE" & class.files$protein_filtered_final$associated_gene == x,"corrected_acc"])
names(NMD) <- TargetGene
NMDp <- lapply(NMD, function(x) plot_expression_summed(x, AgeDiv = TRUE))
for(i in 1:lengt... |
bb9a6bafcc93ebded4ab3d346f68746018568caf42992c758f8098af7db65b23 | R | 978 | 16 | library(spacexr)
library(spASE)
hippo1 <- readRDS('results/rctd_hippo_1.rds')
hippo1@config$max_cores <- 8
cell_types <- c('Astrocyte', 'CA1', 'CA3', 'Dentate', 'Interneuron', 'Oligodendrocyte')
hippo1_intercept <- run.CSIDE.intercept(hippo1, cell_types = cell_types, cell_type_threshold = 50, spase=F, logs=T)
hippo1_i... |
c6e9b3d09b5c59475529e31048cbe515867ac3d258bcf4debe7a8e3446c2320a | R | 978 | 16 | library(spacexr)
library(spASE)
hippo2 <- readRDS('results/rctd_hippo_2.rds')
hippo2@config$max_cores <- 8
cell_types <- c('Astrocyte', 'CA1', 'CA3', 'Dentate', 'Interneuron', 'Oligodendrocyte')
hippo2_intercept <- run.CSIDE.intercept(hippo2, cell_types = cell_types, cell_type_threshold = 50, spase=F, logs=T)
hippo2_i... |
c8bf6e5d5aa7ac200bad591628740e0d1ee51af2c9faaec7dbc990ce4042981e | R | 979 | 22 | library(Seurat)
library(EmptyNN)
library(tensorflow)
tensorflow::install_tensorflow(python_version="4.4")
# data.data <- Seurat::Read10X(data.dir = file.path(samples_dir, sample_name, "filtered_feature_bc_matrix"), strip.suffix = TRUE)
# data <- Seurat::CreateSeuratObject(counts = data.data, project = sample_name, mi... |
88656982e3fffb8c49b37d8a7d9dcfe6679a8db0b681ef6fa2fb0964a35f2553 | R | 980 | 53 | #General
library(tidyverse)
library(reshape2)
library(matlib)
library(matrixStats)
library(forcats)
#DeepComBat
library(torch)
library(torchvision)
library(neuroCombat)
library(CovBat)
#ML
library(caret)
library(randomForest)
library(xgboost)
library(e1071)
library(MASS)
#Statistics
library(twosamples)
library(group... |
d22b527891a5a61f49af005e88b0fbd3a4415b347de54087462757f0f9d9dcda | R | 983 | 17 | library(spacexr)
library(spASE)
hippo3 <- readRDS('results/rctd_hippo_3.rds')
hippo3@config$max_cores <- 8
cell_types <- c('Astrocyte', 'CA1', 'CA3', 'Dentate', 'Interneuron', 'Oligodendrocyte')
hippo3_intercept <- run.CSIDE.intercept(hippo3, cell_types = cell_types, cell_type_threshold = 50, spase=F, logs=T)
hippo3_... |
9404b2730d47337d50ff1cf7ea6ac9cf0308b2c6fcb8bcc321aa77ea5649c70b | R | 996 | 33 | library(rhdf5)
library(qvalue)
library(dplyr)
observedFeatures <- 0
results <- NULL
snpAnnotation <- NULL
featureAnnotation <- NULL
tmp=h5dump(file = "perm_results_22.h5")
if(length(tmp)>0){
for (j in names(tmp)) tmp[[j]][["feature"]] <- j
observedFeatures = observedFeatures+length(tmp)
df <- bind_rows(tm... |
f1ee3e223ef76255a5ffeb3860eed5abfb72c0c0d4936d6e8b14aed83c1f1182 | R | 1,001 | 29 | # csvFancGetNumInputSyn.r
#
# Function that takes in CSV file of FANC IDs and returns the total number of
# synapses that neuron receives
# Writes output CSV file with FANC IDs and number of synapses (name is same
# with _synNum appended)
csvFancGetNumInputSyn <- function(csvFilePath) {
# read in CSV file
csvDa... |
47a254e835e80bcf13e5fa84d2fc0c317b58c68700f170eb747548f07cfd8a75 | R | 1,005 | 34 | library("ukbtools")
library(dplyr)
library(ggplot2)
library(arrow)
library(stringr)
library(tidyr)
library(reticulate)
phenotype_df = read_parquet('phenotypes.parquet') #as in https://github.com/PMBio/deeprvat/tree/main/example
all_samples = phenotype_df %>% pull('sample')
ukb_relatedness = read.csv('ukb_rel_a8135... |
314c9e19646e404e88871e480c1f3dc519593b161652433e30c0bd5ad763fa91 | R | 1,006 | 35 | plinkfileStem <- "test"
plinkfile <- paste0(plinkfileStem, ".bed")
n <- nrow.bfile(plinkfileStem)
p <- ncol.bfile(plinkfileStem)
beta <- rnorm(p)
Xb <- pgs(plinkfileStem, beta)
X <- genotypeMatrix(plinkfile, n, p,
integer(0), integer(0), integer(0), integer(0), 1)
Xb2 <- X %*% beta
stopifnot(all.eq... |
b772aff2cdc3e212bbcd0c4daccc16d1cdf41adb96e67846a3378c0970f1abbe | R | 1,006 | 34 | #' @title pgs for a list of bfiles
#'
#' @keywords internal
pgs.vec <- function(bfile, weights, extract=NULL, exclude=NULL,
trace=0, ...) {
l <- splitvec.from.bfile(bfile)
if(is.vector(weights)) weights <- matrix(weights, ncol=1)
stopifnot(nrow(weights) == length(l$split_p))
weights <... |
acba9afd771b8fc9ac02814b221380ae1a22d0626d28054a5fbbd89f91c80cc4 | R | 1,009 | 44 |
#' archstabms__secondary_structure_from_PDB
#'
#' Get secondary structure.
#'
#' @param input_file path to PDB file (required)
#' @param chain chain id (default:A)
#'
#' @return data.table with secondary structure
#' @export
#' @import data.table
archstabms__secondary_structure_from_PDB <- function(
input_file,
cha... |
69e9f3e5251c74ff7adf1700419cbf30ba41265ff398fb4abbfd1f80bee316ea | R | 1,010 | 39 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
dir<-args[1]
inputFile<-args[2]
inputFileName<-args[3]
setwd(dir)
data<-read.table(inputFile,header=F,sep="\t")
x<-data[,2]
# calcuate for full data range
x.min<-floor(min(x))
x.max<-ceiling(max(x))
x.bound<-max(abs(x.min),abs(x.max))
if(x.boun... |
9ba67f9b01075c2827048c6b326e748bb432763f2611dd0e9b8213e51b948f45 | R | 1,018 | 36 | #!/usr/bin/env Rscript
# Generates a UMAP plot based on a GRM
###Set master parameters & load libraries
options(stringsAsFactors=F,scipen=1000)
require(umap)
#Inputs
grm.input <- "~/scratch/grm.txt.gz"
anc.input <- "~/Downloads/gnomAD_v2_SV.sample_population_assignments.rough_initial_simplified.txt"
seed <- 1234567... |
c930a74af977c7e62bd4b41a8ca6deb13632ec3155b50121f48842d3edc71a2b | R | 1,018 | 36 | #' @name DEPRECATED-plotvoronoi
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Plotting spectra
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and will be
#' re... |
aaf91c76e412b27b188c74e50a2bb71bd8483f2c87b64b798c23bac3af38e4d6 | R | 1,019 | 18 | #!/usr/bin/env Rscript
requireNamespace('jsonlite')
requireNamespace('pkgload')
opts <- list(
Azimuth.app.reference = 'https://seurat.nygenome.org/references/pbmc',
welcomebox = "box(\n h3(\"Please upload a dataset to map to the Multimodal PBMC reference\"),\n \"Upload a counts matrix from an scRNA-seq dataset ... |
8241544f61f85c3a3a539dbb8fcdae5049ddd7120174fecbe02161720b644261 | R | 1,030 | 29 | mega <- readRDS("/proj/mnhallqlab/users/michael/fmri.pipeline/local/mega_brms_results.rds")
library(data.table)
get_df_from_mega <- function(mega, nm) {
res <- rbindlist(lapply(mega, function(el) {
df <- el[[nm]]
cbind(el$metadata, df)
}))
res[, Evid.Ratio := NULL]
res[, Post.Prob := NULL]
res[, Hy... |
213648632581fd04b030feee10ba931c3326fdf6fef0ab21cca9bcd1497d0ee7 | R | 1,043 | 35 | #' @name DEPRECATED-mark.dendrogram
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Identifying spectra and spectral data points
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compati... |
aadf1078699613a5cfb29043d662f8f8c7f3de20d7a4615fba72df3db4d41448 | R | 1,043 | 27 |
##Plot the ibs distance mtarix between RNA-seq sample according to variants calling results
setwd("C:\\Users\\qjp\\Desktop\\Manuscript\\RNA\\Ori\\data\\IBS_heatmap")
dat_now <- read.table("Autosome.allSample.ibs.matrix.txt", header = F)
meta <- read.table("meta_data3.txt", header =T, row.names =1)
rownames(... |
fd1cbc510a9ddde3ce8fea1a80b136932c4c106710c79b71689ed0c7d04bd6a3 | R | 1,057 | 27 | # first argument is the chunk of params to run (1-18)
library(spacexr)
library(spASE)
devtools::load_all()
args <- commandArgs(trailingOnly=T)
source('../R/spase_sim_utils.R')
myRCTD <- readRDS('results/results_celltype_cere_4_visium_df_5.rds')
params <- read.delim('simulations_cere4_params.tsv')
df <- 5
cell_types... |
e30ea03523f227a63e171d2e3cb954e5a09a347e801e6bf8c557f6ac8c5d72d5 | R | 1,058 | 34 | rm(list = ls())
library(e1071)
MD_SVM <- read.csv("CDK_MOR_sn_svm_H_selected for Prediction.csv")
MD_x <- data.matrix(MD_SVM[, 3:ncol(MD_SVM)])
MD_y <- MD_SVM$Y
svm_model <- svm(Y ~ ., data=MD_SVM,na.cation=na.omit)
summary(svm_model)
library(caret)
train_index <- createDataPartition(y=MD_SVM$Y, p=0.8, list=F... |
a2b91b1f94f6769497ca2492a0b6344a696a5605bbd85b62733e16b7214cb2bc | R | 1,059 | 31 | test_that("runElnet" , {
plinkfileStem <- "test"
plinkfile <- paste0(plinkfileStem, ".bed")
n <- nrow.bfile(plinkfileStem)
p <- ncol.bfile(plinkfileStem)
beta <- rnorm(p)
Xb <- pgs(plinkfileStem, beta)
X <- genotypeMatrix(plinkfile, n, p,
integer(0), integer(0), integer(0), integer(0), 1)
Xb2 <- X ... |
177d67cdba1cf6b0fb1e78b22404d16947fb6853066778fdd07490df9553a6ad | R | 1,062 | 22 |
#he global methylation level of each sample was calculated according to bismark methylation extractor module
library(ggplot2)
library(ggsci)
library(ggtext)
path <- "C:\\Users\\qjp\\Desktop\\Figures\\TissueCpGMethylationLevel\\"
dat <- read.table(paste0(path,"MethylationLevelOfTissues.txt"), header = T, row.... |
c4e0786ffe91f4b9d10ef657bff2239a21b705c02dc47f4afb201b5cf2a4878e | R | 1,063 | 30 | rm(list=ls())
data<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/revision_progression/binary_table_revision_progression_latethreat.txt",h=T,sep="\t",na.strings = "NA", stringsAsFactors=T)
attach(data)
library(dplyr)
# Calculate the percentage of individuals pe... |
cc2273ffc837873cf41d4d1cae6e2143656aa6fbbf1f682fc3c597ba9b37ac8e | R | 1,063 | 34 |
#' List installed *R hyperSpec* family packages
#'
#' Convenience function that lists all installed
#' [*R hyperSpec*](https://r-hyperspec.github.io/) family packages.
#' These include **hyperSpec** and all packages that share common
#' **`hySpc.`_something_** package name pattern.
#'
#' @return Character vector with ... |
c34c128718e4c4d5738ef53dac2e2a1604492625f9f86e2c8d409bb78715ef1d | R | 1,067 | 39 | #' @name DEPRECATED-plotspc
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Plotting spectra
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and will be
#' remove... |
014e81b0eb8578cb6e4dbdcbbb1b72bc9be06a267e759f877c4ffefb174548b0 | R | 1,074 | 28 | #!/usr/bin/env Rscript
# override for debugging purposes
quiet <- as.logical(Sys.getenv("QUIET", unset="TRUE"))
library(parallel)
Ncpus <- detectCores()
# treat warnings as errors, otherwise script can fail silently if a package fails to install
options(warn = 2)
# packages to be installed
to.be.installed <- commandA... |
b557b33e2ca8863d01b715b8d71642f0258541c004996b79b20978f4eefdc426 | R | 1,077 | 25 |
##sorting out the biomarker mess
upennbio <- read.csv("./UPENNBIOMK_MASTER_FINAL_21Nov2023.csv")
roche_elecsys <- subset(upennbio, RUNDATE>=as.Date("2016-11-17"))
#go through each RID and compute median of values at duplicate time points
roche_elecsys$ABETA42 <- as.double(gsub("<","", gsub(">","", roche_elecsys$ABE... |
c02492e3774a2c8aef373cb1279f71a18427e739631eb9041a3ae8ef1bc1b03e | R | 1,096 | 31 | library(data.table)
library(dplyr)
dir_har <- "tsmr_harmonized_data/"
dir_out <- "cml_ma_input/"
dir.create(dir_out, showWarnings = FALSE)
# Metadata
file_in_mr_result <- "tsmr_best_sig_result.csv"
df_mr_result <- fread(file_in_mr_result, sep=",", data.table = F, nThread = 10,
colClasses = c("cha... |
8a5c5ee333abfee7b99f459a14c9ca0c31030893e21a5eac8304d2f884b469cd | R | 1,098 | 25 | setwd("/media/hansruedi/3b32f153-0651-4d56-b863-9b070ac7dd55/home/hansruedi/Brain_region_final")
library(Seurat)
adata <- ReadH5AD(file = "Brain_region_raw_highly_variable2.h5ad")
pbmc <- CreateSeuratObject(counts = GetAssayData(object = adata, slot = 'counts'), project = "pbmc3k", min.cells = 0, min.features = 0)
... |
e3f715670f7a333f31211b17d5665648d74b51ce9e1a9e7f9451df4afba6e032 | R | 1,101 | 48 | ---
title: "Normalisation of Nurr1 dataset"
author: "Yuvarani Masarapu"
date: '2022-11-30'
output:
html_document:
self_contained: true
number_sections: true
highlight: tango
df_print: paged
code_folding: hide
toc: yes
toc_depth: 3
toc_float:
collapsed: false
smooth_scrol... |
25b9c6ab5df3583285f87bd008d9f35993d8af501aa50a942921507069790173 | R | 1,103 | 40 | #' @name DEPRECATED-pooled.cov
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Pooled covariance matrix
#'
#' @description
#' These \pkg{hyperSpec} functions are **deprecated** and not maintained any
#' more. You should not use these.
#' Currently they are present due to back-compatibility reasons and will b... |
0d3c4af5f26e64e9483a31925236e1a89c6243af94a3c26953f6b89d9cbd4a7c | R | 1,105 | 45 |
```{r}
require(arrow)
require(ggplot2)
require(dplyr)
```
```{r}
source("../figure2_association_testing_benchmark/rvat_figure_utils.R")
```
```{r}
scores = read_parquet("burdens_with_has_known.parquet")
scores
p = ggplot(
scores %>%
# filter(abs(ASXL1 - median(ASXL1)) > 0.0001) %>%
arrange(ASXL1) %>%
... |
c4006f6cc66d2702f287c77728153ce7e91b84413bbb7edb8c037710394f7a79 | R | 1,123 | 45 | test_that("truncate_runs writes truncated NIfTI via subset_nifti_volumes", {
skip_if_not_installed("RNifti")
dims <- c(2, 2, 2, 15)
img_data <- array(rnorm(prod(dims)), dim = dims)
img <- RNifti::asNifti(img_data)
infile <- tempfile(fileext = ".nii.gz")
RNifti::writeNifti(img, infile)
gpa <- list(
c... |
5f1a144c6884c4c89bb39b86fde0cbcc40542292bb05ff5b61de289956b5757b | R | 1,130 | 32 | #!/usr/bin/env Rscript
# Script to QC merged per sample bincov files
# Created by: Chelsea Lowther <clowther@mgh.harvard.edu>
# Inputs:
# 1) A single bincov file per sample that contains bins from the last 1 Mb per chromosome
# 2) A sample ID string
# Usage: bincov_QC.R BINCOV SAMPLE_ID OUTFILE
## Libraries
req... |
d0ddb4a4e507ce17155b17b4495bcd8caa6aaa29fa77c5a491cfb58ecd3fc481 | R | 1,153 | 29 | library(tidyverse)
library(SingleCellExperiment)
library(scater)
library(Seurat)
options(future.globals.maxSize = 4000 * 1024^20)
Sys.setenv('R_MAX_VSIZE'=2500000000000)
library(scran)
celltype<-readRDS("Oli.rds")
DefaultAssay(celltype)<-"RNA"
celltype.sce <- as.SingleCellExperiment(celltype)
clusters <- quickCluster... |
cca4aa3455e34d2b8bff38340a9bc5042723a7039e184832fcb4bacb10a15dc3 | R | 1,155 | 40 | #%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Generics
#%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' Run Azimuth annotation
#'
#' @param query Seurat object or following type of path:
#' \itemize{
#' \item A \code{.h5} matrix
#' \item A \code... |
e6f496301a14f0d05bc925c1a6baff673cbccff136ddbe9648cb862b645f3f8d | R | 1,158 | 48 | #'
#' Yaml object for specific parameters.
#'
#' If the object has the param, then return it.
#' If the param is unknown, create it with the given default value and return the default.
#'
#' @field yaml_obj A Yaml object
#'
#' @import yaml
#' @importFrom methods new
#'
#'
YamlClass = setRefClass(
"YamlClass",
fiel... |
f3e4f75795d1a72a7ca45cbc63d0088dac85542dd33c98fee334f6120246a893 | R | 1,158 | 30 | library(data.table)
library(dplyr)
##Integrate m6A-H3K27ac MR results for the four tissues
for(tis in c("Brain", "Lung", "Muscle", "Heart")){
for(dir in c("m6A2ha", "ha2m6A")){
MR_res <- data.frame()
for(i in 1:22){
MR_res0 <- fread(paste0("~/2SMR/", dir, "/", tis, "_", dir, "_mr/chr", i, ".mr.res"), sep = "\t... |
64d2e6f80b855e08d1036400cf0f68889fd77a0c313332bedef3dab1e03c21b2 | R | 1,159 | 34 | rm(list=ls())
data<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/revision_progression/binary_table_revision_progression_earlythreat.txt",h=T,sep="\t",na.strings = "NA", stringsAsFactors=T)
attach(data)
library(dplyr)
quartz(title="title",width=10, height=12)
... |
6a1e59657ad9f46823c23eb228a34e27f8937f374701453c2f6488bd6b1ba611 | R | 1,160 | 28 | ###PCA plot of WGS individual
setwd("C:\\Users\\qjp\\Desktop\\Manuscript\\WGS\\data")
##pca value by gcta
pca <- read.table("snp.gcta.eigenvec.txt", header = F, row.names=1)
#data inpute
library(ggplot2)
library(ggord)
pca <- pca[,-1]
colnames(pca) <- paste0("pca",1:10)
pca1_2 <- pca[,1:2]
design <- da... |
70407c17c6d1f85cbc744986c17c298b2ce5742225c58ad4988477ca5b699c16 | R | 1,162 | 35 | #!/usr/bin/env Rscript
# Helper script to return list of outlier samples from an svcounts.txt file
# Read command line arguments
options(stringsAsFactors=F)
args <- commandArgs(trailingOnly=TRUE)
if(length(args)!=3){
stop("Three positional arguments expected: svcounts.txt, N_IQR_cutoff, and OUTFILE")
}
countsfile <... |
77a7b69c0e7ca57c90f058598d4f98efad116cd68e9a499d821565bb38066d14 | R | 1,162 | 20 | library(spacexr)
library(spASE)
cere4 <- readRDS('results/rctd_cere_4_visium.rds')
cere4@config$max_cores <- 8
cell_types <- c('Fibroblast', 'Granule', 'MLI2','Oligodendrocytes')
cere4_intercept <- run.CSIDE.intercept(cere4, cell_types = cell_types,
cell_type_threshold = 2^7, s... |
0e32bd134d5f9ad443cea9ac27ee60bf5c228fc4910ff120d01b302576ecad64 | R | 1,165 | 42 | library('RColorBrewer')
library('MAST')
library('data.table')
library('Seurat')
library('biomaRt')
library('lme4')
setwd("./Projects/SNCSynuclein")
# load the seurat object
Seurat.SNC.final <- readRDS("Step5.FinalClustering/Seurat.SNC.final.rds")
# set the levels for factors:
Seurat.SNC.final$TYPE <- factor(Seurat.S... |
347d321d5c39b9d251ae627abaeb258f191be1743b6c25ca3c9484fceb38b617 | R | 1,165 | 27 |
#' archstabms__get_reference_id
#'
#' Get reference id with absolute amino acid residue position.
#'
#' @param input_dt data.table with id and mut_order columns (required)
#' @param position_offset residue position offset (default:0)
#'
#' @return Reference id with absolute amino acid residue position
#' @export
#' @i... |
7c310d2a9c03258ad1a6f9f86720815582d9a09622066f7845e58746934f4e84 | R | 1,168 | 48 |
#### Find spatial neighbors ####
## -----------------------------
setMethod("findNeighbors", "ANY",
function(x, r = 1, groups = NULL,
metric = "maximum", p = 2, matrix = FALSE, ...)
{
.findNeighbors(x, r=r, groups=groups,
metric=metric, p=p, matrix=matrix)
})
setMethod("findNeighbors", "SpectralImagingData",
... |
2b24b1d77b90b119c93b225879d3315ee1f27430248a1ab55ea5c770dd805b7c | R | 1,169 | 52 | ---
title: "Demo Vignette"
output: html_document
---
Example of running spASE on some simulated data.
```{r setup, include=FALSE}
library(spASE)
```
```{r null-genes}
# Null simulation parameters
ngenes <- 100
numi <- 5
p <- 0.5
phi <- 0.2
a <- p*(1-phi)/phi
b <- (1-phi)*(1-p)/phi
# Simulate raw ASE data in a squ... |
a5e06c191afc91163e1cac4035136061f1778fdbceb2471e61990cd4c242f489 | R | 1,169 | 42 | library(data.table)
library(dplyr)
### F1
trait <- "F1"
file_gwas <- "MetS_noUKB_F1_GWAS.csv"
df <- fread(file_gwas, sep=",", data.table=F, nThread = 20) %>%
dplyr::select(SNP, CHR, BP, A1, A2, MAF, est, Pval_Estimate) %>%
rename(BETA = est,
P = Pval_Estimate)
write.table(df,
file = p... |
3dfd9ea4c6194eb2e7ea36532041daf5c9acc7b49dccc0a7d09dfc6ca6932882 | R | 1,177 | 31 | #' @keywords internal
"_PACKAGE"
## usethis namespace: start
#' @importFrom Rcpp sourceCpp
#' @importFrom rlang .data
#' @useDynLib fmri.pipeline, .registration = TRUE
## usethis namespace: end
NULL
if (getRversion() >= "2.15.1") {
utils::globalVariables(c(
":=", ".", "..split_on", ".filename", ".I", ".SD",
... |
f815bfd2c3f2d8bb2e7e18b2e96a6c6a8f2c87e75284b2bffca0dadbbbd51964 | R | 1,187 | 55 | ---
title: "Trace Guide for NosaAnalysis"
author: "Julia Thüringer"
date: '`r Sys.Date()`'
output:
html_document: default
pdf_document: null
vignette: >
%\VignetteIndexEntry{Trace Guide for NosaAnalysis}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
### Description
```{r description, ev... |
f6dff01d26e9660add786b2eb9a56725187e9237c24422ffa4eeeff0deaa334d | R | 1,197 | 29 | # *********
# 0c-ambient.r
# Script for estimating ambient RNA contamination in the dataset with DecontX.
# Expected within the folder Data/secondary is are folders BU-SNCA-1, BU-SNCA-2, ..., BU-SNCA-24,
# within which we expect the folder structure of:
# outs/
# - filtered_feature_bc_matrix
# - raw_feature_bc_matr... |
2c8b453b500e4441bafabaa3dfed78ffd7809a5dc4867825933a1eb05c5f0e0f | R | 1,199 | 44 | ## 3D scatter
library(scatterplot3d)
pc <- read.table("C:\\Users\\qjp\\Desktop\\BS\\PCA\\data\\SamplesPCA_prcomp_x.txt", header = T, row.names = 1)
Tissue <- c()
tis <- substr(rownames(pc),7,7)
for(i in 1:nrow(pc)){
if(tis[i]=="B"){
Tissue[i] <- "Br"
}else if(tis[i]=="L"){
Tissue[i] <- "Li"
... |
e70f28808a11996183df7929c9795e27d34538b8407f81bcfda7558b56fca1ba | R | 1,201 | 36 | rm(list=ls())
library(conover.test)
library(multcomp)
library(beeswarm)
data<-read.delim("/Users/cazaledl-admin/Library/CloudStorage/OneDrive-UniversityofBirmingham/Post_doc/XP/data/motioncontrols/foldchange.txt",h=T,sep="\t",na.strings = "NA", stringsAsFactors=T)
attach(data)
boxplot(cbind(MALE_THC1_CHRIMSON... |
a60185d40abe308151bbe5a44549e37f17535cf50e9d1f4fa2114c245d7a7888 | R | 1,203 | 35 | # Tmisc()
# Tim.load(Rplink)
# setwd(attachroot("~/WORK/myRpackages/lassosum/tests/"))
# Tim.load(lassosum)
#
# library(data.table)
# bfile <- "../inst/data/testsample"
# n <- nrow.bfile(bfile)
# ss <- read.table2("../inst/data/summarystats.txt", header=T)
# cor <- p2cor(ss$P_val, n=60000)
# lp <- lassosum.pipeline(co... |
3f4802dca60d52298894893706c46071b3a905fc9158efaef7f0bfca0e3fd114 | R | 1,208 | 54 | test_that("jlmer validates call-site inputs before starting Julia", {
testthat::skip_if_not_installed("JuliaCall")
df <- data.frame(y = 1:4, x = 1:4, id = factor(c(1, 1, 2, 2)))
expect_error(
jlmer(~ x + (1 | id), df),
"two-sided"
)
expect_error(
jlmer(y ~ missing_x + (1 | id), df),
"Variab... |
6d6379f80a13f99a177d6258e11f2d6bf75954c38733e6708fdd934b780f6b30 | R | 1,209 | 40 | library("harmony")
library("Seurat")
library("tidyverse")
setwd("./Projects/SNCSynuclein")
# load the Seurat object
Seurat.SNC <- readRDS("Step2.QC/Seurat.SNC.QC.R1.rds")
# run harmony for data integration
options(repr.plot.height = 2.5, repr.plot.width = 6)
Seurat.SNC <- FindVariableFeatures(Seurat.SNC,
... |
a6d67bfa838458ed245e47acc3faa0d630ffb773eefdf680b2ab89d509552ab0 | R | 1,210 | 19 | library(colorspace)
library(dplyr)
library(grid)
library(httr)
library(magick)
library(spacesXYZ)
library(stringr)
# This is where the images downloaded from eLife are stored.
images_dirpath = "Images"
source("Functions.R")
create_simulated_image("/shared_dir/ImageSample1to5000/elife-00640-fig6-v1/original_downloade... |
4b7fcfd84aa17bdfaedbc1cffecf5cbb4b53c0037c087e1cd2be81f1bb476937 | R | 1,211 | 39 | library("harmony")
library("Seurat")
library("tidyverse")
setwd("./Projects/SNCSynuclein")
# load the Seurat object
Seurat.SNC <- readRDS("Step2.QC/Seurat.SNC.QC.R1.rds")
# run harmony for data integration
options(repr.plot.height = 2.5, repr.plot.width = 6)
Seurat.SNC <- FindVariableFeatures(Seurat.SNC,
... |
5d7055120110d45ac85b32704ca899d5cff4b5651ae99c41524cbb981112837d | R | 1,215 | 47 | # subset columns of a matrix
# Mainly use for when we optionally subset the columns.
# In this case, the option subsample should be set to a number
subsample <- function(x, subsample=FALSE) {
if (subsample) {
keep = sample(1:ncol(x), size=subsample)
if ('Seurat' %in% class(x)) {
x <- subset(x, cells=col... |
f9ce6cbe95f4be9db31da57a34fafe47af80aeb25860d0a6910c4e60a9f11b4c | R | 1,220 | 31 | ---
title: "Compute word embeddings for Neurosynth functional terms"
author: "Evan Collins"
output: html_document
date: '2024-02-06'
---
All code in this markdown document has written by Evan Collins. Any questions can be sent to evantomcollins@gmail.com.
```{r}
# Load packages
library(word2vec)
set.seed(123456789)
`... |
3a85a4e73091f03cbae27687c836a1ef794d0126d691cfdd2589ff73ff905484 | R | 1,221 | 45 | options(bitmapType='cairo')
args <- commandArgs(TRUE)
inputFile<-args[1]
inputFileName<-basename(inputFile)
myTable <- read.table(inputFile, header=TRUE, sep = "\t",row.names = 1,check.names=FALSE)
for(group in colnames(myTable)) {
tmpTable<-myTable[[group]]
if( (max(tmpTable)==0) && (m... |
356481297406de55f00acbfbf506601a6b2f4d42c1aab2ffec20d56335584647 | R | 1,229 | 44 | #' Barbiturates spectra from `.spc` example files
#'
#' A time series of mass spectra in a list of `hyperSpec` objects.
#'
#'
#' @name barbiturates
#' @docType data
#'
#' @format The data sets consists of a list of 5 hyperSpec objects with a single
#' spectrum each. They are the first five spectra of the BARBITUATES.... |
d98a055ac12f66408d15196614921c70e6d2ea4c66fa3cd3906f5a81a7408b83 | R | 1,238 | 35 | #!/usr/bin/env Rscript
# override for debugging purposes
quiet <- as.logical(Sys.getenv("QUIET", unset="TRUE"))
library(parallel)
Ncpus <- detectCores()
# treat warnings as errors, otherwise script can fail silently if a package fails to install
options(warn = 2)
# packages to be installed
to.be.installed <- commandA... |
e0d8d5a1d038588ed950a5742e3a596242038fae6320dc971c8cffff52b18dec | R | 1,247 | 28 | ## ---------- Script -----------------
##
## Purpose of script: Identify Transcript ID associated with Bin1 isoforms for creating a blast library
##
## ---------- Notes -----------------
## Prerequisite for downstream script (3b_bin1_blast_primer.sh)
## To generate Transcript ID for subsetting and to generate a fasta ... |
ed5c1f4e62c8c0bfb6d065bf4a896e23ebdbbf3a3496bc9b5534c4e4bd119318 | R | 1,248 | 18 |
### ------------- Table 1 -------------------
mTable1 <- Merged_gene_class_df[c("Total.Number.of.Transcripts","Total.Number.of.Known.Transcripts","Total.Number.of.Novel.Transcripts","Total.Number.of.Coding.Transcripts",
"Total.Number.of.Transcripts.with.canonical.splice.sites","Total.Number.of.T... |
0b69f411e785b7126cfc14b0035a8e0b451ee6fc69f45c4fbbf279e497aeed80 | R | 1,255 | 36 | #' @name hyperSpec-package
#' @rdname hyperSpec-package
#' @aliases hyperSpec-package
#'
#' @title Package "hyperSpec": interface for hyperspectral datasets
#' @description
#' This package gives an interface to handle hyperspectral data sets in R.
#' Hyperspectral data are spatially or time-resolved spectra, or spectra... |
a33ba16bedda469872ff862b9a7220e975017b7902dd88a030f06042e2545a04 | R | 1,255 | 48 |
#' archstabms__distance_matrix_from_PDB
#'
#' Residue distance matrix from PDB.
#'
#' @param input_file path to PDB file (required)
#' @param residue_subset subset of residues (default:all)
#' @param query_chain query chain (default:A)
#' @param metric distance metric (default:'scHA')
#'
#' @return data.table with min... |
f434b56efa6dc80d31fd53a6d59f11af8fcabb1a9fabe397ef5a447781a8752b | R | 1,264 | 31 | .onAttach <- function(libname, pkgname) {
unlockBinding(".options", asNamespace("hyperSpec"))
desc <- utils::packageDescription("hyperSpec")
first_url <- sub(",.*$", "", desc$URL) # To use the first URL only
packageStartupMessage(
"\n\n",
"Package ", desc$Package, " (version ", desc$Version, ")\n\n",
... |
77eca576121d646f758bf68b40f8e7a24f5d75516ab6d75d09fc1dc3fdc00974 | R | 1,265 | 43 | #' @describeIn DEPRECATED-read.ENVI
#' @include DEPRECATED-read.ENVI.R
#' @export
read.ENVI.HySpex <- function(file = stop("read.ENVI.HySpex: file name needed"),
headerfile = NULL, header = list(), keys.hdr2data = NULL, ...) {
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... |
8338fa7ba58a416174d0797b1a5efd0a349ee4796597c58cfecb5c7373237f45 | R | 1,278 | 32 | merge.lassosum <- function(...) {
#' @title Merge lassosum results
#' @description e.g. when calculated over different blocks/chromosomes
#' @method merge lassosum
#' @export
#'
ll <- list(...)
stopifnot(all(sapply(ll, "class") == "lassosum"))
stopifnot(all(sapply(ll, function(x) all(x$lambda == ll... |
5d8fb5fa327a4fd8de8478142584bae829485353644cf5bb8dbce1c420d19bf5 | R | 1,281 | 50 | require(testthat)
require(Cardinal)
context("spatialWeights + spatialDists")
test_that("spatialWeights", {
set.seed(1)
x <- matrix(runif(81 * 6), nrow=81, ncol=6)
co <- as.matrix(expand.grid(x=1:9, y=1:9))
nb <- findNeighbors(co, r=1)
wts <- spatialWeights(co, r=1, weights="gaussian")
sd <- 3 / 4
d1 <- matte... |
fcb146a683393d0e30c6d497846a7d3b9759147bee38d1964b36027a35e5cccf | R | 1,284 | 41 | # Take .tsv of TE calls and convert from tall to wide format for hail matrix table import
#de novos
setwd('/hail-TE')
TE <- read.csv('TE-denovos.csv', header=TRUE)
R <- colnames(TE)
TE$Chr <- str_remove(TE$Chr,'chr')
TE$locus <- paste(TE$Chr, TE$Breakpoint, sep=':')
library(stringr)
#TE$famID <- str_split(TE$Sample, '... |
2368c5e5defbf8e104ea5ff46bd8a8e4784e1f36c83b3cff2bea9f178f7645f5 | R | 1,285 | 47 | plinkfileStem <- "test"
plinkfile <- paste0(plinkfileStem, ".bed")
n <- nrow.bfile(plinkfileStem)
p <- ncol.bfile(plinkfileStem)
beta <- rnorm(p)
Xb <- pgs(plinkfileStem, beta)
X <- genotypeMatrix(plinkfile, n, p,
integer(0), integer(0), integer(0), integer(0), 1)
Xb2 <- X %*% beta
stopifnot(all.eq... |
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