sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
bda8d882792e615aa10ae2b7df88a591bbe9dc7bf47a3c06edcb9f0b33f04d83 | R | 11,902 | 331 | #!/usr/bin/R
# -------------------------------------------------------------------------
# Run MAST + wilcoxon (Nebula in other script) using the precomp. matrices:
# Updated: 10/31/23 to add interaction
# -------------------------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'mu... |
7ca1e24af72da235105716713c1535a070cf3f7b8f829c34de8ebfb2cd5f32c3 | R | 11,917 | 339 | make_fwe_execution_test_gpa <- function(root, create_smoothness = TRUE) {
l3_feat <- file.path(
root, "feat_l3", "L3m-l3_group", "L1m-facehouse",
"L2m-l2_session", "l2c-overall", "FEAT_l1c-EV_face.gfeat"
)
image_feat_dir <- file.path(l3_feat, "cope1.feat")
stats_dir <- file.path(image_feat_dir, "stats")... |
0c0b5baffab172cbfb291bfa1da3a3d16c26cc7e43b737af276bc35fbaa79e02 | R | 11,931 | 369 | #' @name DEPRECATED-read.jdx
#' @concept moved to hySpc.read.jdx
#'
#' @title (DEPRECATED)
#' JCAMP-DX import for Shimadzu library spectra
#'
#' @description
#' This function is **deprecated** and will be removed in the next release
#' of \pkg{hyperSpec} package.
#' Please use function `hySpc.read.jdx::read_jd... |
69507617dbbdeb08b5d61d0f1572ff6a4ca9fe0d6b53120b49939a72a86146d5 | R | 11,931 | 309 | #####对于没有删除样本的组
Input="F:\\RNA_20221101\\02_ModifiedGenome\\A_GeneExpressionMatrix\\"
library(dplyr)
library(edgeR)
library(ggplot2)
data_filter <- read.csv(paste0(Input,"AutosomeGeneCount_filtered2.csv"), header = T, row.names = 1)
###------------------------------------Function----------------------------... |
307ccdb08d5db47639c5b39d7efd1fa51960cf12a9f3d86976080e42c2d8a947 | R | 11,940 | 338 | ### --------------------------------------------------------------------------~
###
### .extract - internal function doing the work for extracting with [] and [[]]
###
### --------------------------------------------------------------------------~
# Function ------------------------------------------------------------... |
c162cbbc3e1fa95d19c434f88df0e3bd35052d614c4541196fedd12f6b7cfb1c | R | 11,941 | 228 |
#' ---
#' title: "Clustering Polypeptide motifs using the Hammock hidden Markov model peptide clustering"
#' author: "Tomas Bjorklund"
#' header-includes: \usepackage{longtable}
#' output:
#' pdf_document:
#' highlight: tango
#' geometry: margin=0.6in
#' fontsize: 9.5pt
#' ---
#' This script clusters Polypeptide... |
d2c3a5d3364a00d4d820d53e0954bd0e45893115fa4cbac5a3355fa663b15121 | R | 11,978 | 262 | find_genes <- function(myRCTD, cell_types, MEAN_VAL_THRESH=5, TOT_THRESH=100) {
mean_val <- myRCTD@de_results$gene_fits$mean_val # Cell type mean gene expression from CSIDE
mean_val <- mean_val[,cell_types]
# find genes with well-behaved estimates i.e. low variance
tot <- rowSums(myRCTD@spatialRNA@maternalCount... |
3ce13c923cfdc9af4c5810352b3868a2ec8faba21dfa5dd7526df3e5aee5b322 | R | 11,985 | 362 | # Derived from revtools code, copyright Martin J Westgate
# Adapted by Lukas Wallrich, still unter GPL-3 licence
add_green <- function(x) {
paste('<span style="color: green">', x, '</span>')
}
clean_text <- function(str) {
str %>%
# Remove editions like "2nd Ed."
gsub("(\\d+(?:st|nd|rd|th)\\s*Ed[\\. ])", ... |
8682f64db6e24a1c0007e2f990a60d33d62dc11f97536c1da5ddecc720aaf584 | R | 12,066 | 288 | #!/usr/bin/env Rscript
# Helper script to determine categories of SV that
# don't meet a minimum Mendelian violation rate (MVR)
###Set master parameters
options(stringsAsFactors=F,scipen=1000)
###################
###HELPER FUNCTIONS
###################
#Clean input data
cleanDat <- function(dat){
#Drop variant I... |
d10289f106e79a3a091bd13856c207d323eadf66b7d8a60070e3b95e3ba2f60d | R | 12,071 | 303 |
#' ---
#' title: "Library fragment alignment"
#' author: "Tomas Bjorklund"
#' output:
#' pdf_document:
#' highlight: tango
#' geometry: margin=0.7in
#' ---
#' This workflow identifies correct fragments from the Cre-recombined AAV plasmid library and aligns them to the CustumArray ordered nucleotide fragments usi... |
c60e0bd1ed8163bcd26d93b4f94e3abe0599f40aa6f0d6ca70120f743262d9ed | R | 12,075 | 347 |
basepath = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2022_04_03_redoATAC_proc_nobackup/"
dir.create(paste0(basepath, "archr/results/"))
out_dir = paste0(basepath, "archr/results/NB2_Peak_UMAP/")
dir.create(out_dir)
setwd(out_dir)
# DFR add
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjec... |
60e687b60a3e09a47304fb3b67ad198c534af5d5490ea75d4da146e9f246c4ec | R | 12,092 | 315 | ##POE gene identification
Input="F:\\RNA_20221101\\02_ModifiedGenome\\A_GeneExpressionMatrix\\"
library(dplyr)
library(edgeR)
library(ggplot2)
data_filter <- read.csv(paste0(Input,"AutosomeGeneCount_filtered2.csv"), header = T, row.names = 1)
###------------------------------------Function------------------... |
7da6b7a46c57ac72aa00e17485bc3c6cbecb056f038ad10bbe6a9d45579953fb | R | 12,123 | 372 | #' @name DEPRECATED-wlconv
#' @concept deprecated
#'
#' @title (DEPRECATED)
#' Convert different wavelength units
#'
#' @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 ... |
ecaac3303e0e931c03cf390b7a9867065c5a17aec63dc384b968be9dbd8cbb7f | R | 12,158 | 226 | #!/usr/bin/env Rscript
## ----------Script-----------------
##
## Author: Szi Kay Leung (S.K.Leung@exeter.ac.uk)
## simple analysis rewiew
## find reference genes
## correlation
##
## --------------------------------
library("dplyr")
library("stringr")
library("cowplot")
library("data.table")
library("ggplot2")... |
b908907ef996b982576549ac5fa0f11c101e34288a4303bffe47fbe0761dbdf7 | R | 12,209 | 408 | #' The theta method as in the forecast package
#' but with seasonality forced to FALSE
#' @param y The time-series
#' @param h The forecast horizon
#' @param level Confidence levels for prediction intervals
#' @param fan If TRUE, level is set to seq(51,99,by=3). This is suitable for fan plots.
#' @param x Deprecated. I... |
b7814f60f8928246d6033aebbf4a1b79f31053b6e0c770c42061102b156e209b | R | 12,235 | 360 | rm(list = ls())
#############################
## Load Relevant Libraries ##
#############################
require(ggplot2)
require(Hmisc)
require(MASS)
require(mgcv)
require(ppcor)
require(stringr)
require(visreg)
require(parallel)
require(multilevel)
require(stats)
require(Formula)
require(lavaan)
require(lme4)
requi... |
ceec94dc2c8c8219f3d34d16693e000b964a2316309954ab4268c7b9fc3b5c63 | R | 12,236 | 325 |
library(tidyr)
library(ggplot2)
library(glmnet)
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-p", "--pValFIMOcutoff"), type="numeric",
default=1e-6,
help="Max FIMO p value to retain match", metavar="character"),
make_option(c("-f", "--featureSelection"),... |
eecad417f984215ed25c9e12043dc50bb8c6debe431286ca9c29a35fe54a342a | R | 12,283 | 355 |
### match IXI sample to chimp under 41 sample ###
wd <- "~/projects/chimp_human_opnmf/"
setwd(wd)
library("readr")
library("ggplot2")
library("MASS")
library("dplyr")
library("fs")
library("stringr")
library("MatchIt")
library("optmatch")
library("rgenoud")
library("broom")
#library("emmeans") no package by this name... |
5f3b27b750a68af9dc3e7ed8a734dfaab607331a869e9b728724196c6327c168 | R | 12,297 | 252 | #!/usr/bin/env R
#Construct_fasta_variant_sequences.R
################################################################################
#This script is used to convert a phased VCF to a table that can more easily be
#parsed and used by a later script, Construct_fasta_variant_sequences.R
#
#Run under R 3.4.3, but can be... |
d5b72247f526127bd14f127754e2cb234ab1e7def90679c5476860a40986250a | R | 12,301 | 404 | ---
# For vignette ---------------------------------------------------------------
title: Unstable Laser Emission
subtitle: "Example Workflow for Time-Series Spectra Dataset `laser`"
description: "laser: Example workflow for time-series spectra dataset `laser`."
# Authors --------------------------------------... |
40129d037984c73fe96d13d6e6dd0883d58a27d042598fd4b90a2c98054d9fc3 | R | 12,320 | 334 | library(readr)
library(dplyr)
library(tidyr)
library(data.table)
library(lgr)
library(foreach)
library(doParallel)
library(emmeans)
library(glue)
library(oro.nifti)
library(RNifti)
library(ggplot2)
# library(fmri.pipeline)
# setwd()
# setwd("~/Data_Analysis/r_packages/fmri.pipeline/R")
#file.sources <- list.files("~/D... |
72de6563be414f5c50f0ce83932c5c730065c8f6ec08652236dc8010ea8ab0e7 | R | 12,328 | 304 | library(data.table)
library(dplyr)
library(foreach)
library(doParallel)
require(argparse)
#######################
### Notes
# GWAS 1 is the reference SNP
# Compute per chromosome
#######################
#######################
# Parser arguments
#######################
### Define parser arguments
parser <- argparse::... |
2649b7f473b0e1317f528050c29333b6fee7eba90a3de23666ea9c96ca2fdcb7 | R | 12,338 | 346 | library(tidyverse)
library(broom)
library(glmnet)
library(glmnetUtils)
library(cowplot)
###########################################################################
# Load and clean data ----------------------------------------------------
###########################################################################
da... |
9d4a35b9eaa98c45113601824681818721b23cb8b74e222e0d978060cadeab70 | R | 12,388 | 317 | # EYE SIZE AND VISUAL ACUITY, EXPERIMENT WITH MALES AND FEMALES, UP- AND DOWN-SELECTED FOR TELENCEPHALON SIZE,
# GENERATION 3, 5% DIFFERENCES IN TELENCEPHALON SIZE, 234 FISH
# OCTOBER - NOVEMBER 2020, DATA COLLECTED BY MADDI GARATE-OLAIZOLA
#----------------------------------------------------------------------------... |
1633a30c9ef06eedda957c58080a5d228b31bbfdc03374c68596bef39a281c06 | R | 12,393 | 399 | ---
title: "DeepRVAT - Seed Genes Downsampled figures"
author: "Eva Holtkamp"
output:
pdf_document: default
html_notebook: default
---
Load libraries
```{r}
library(ggplot2)
library(arrow)
library(dplyr)
library(stringr)
library(tidyr)
library(latex2exp)
library(RColorBrewer)
library(ggpubr)
```
```{r}
source(f... |
30ae776bb29d00706526d48d2a2c3e4a6efcf91014847754a96c431ca639a694 | R | 12,405 | 320 | library(SingleCellExperiment);library(tidyverse);library(reticulate);library(tidyverse)
library(ggplot2);library(scmap);library(fmsb);library(ggsci);library(scibet)
library(Seurat);library(M3Drop);library(ROCR);library(cluster);library(parallel);library(ROGUE)
expr <- readr::read_csv("/data1/pauling/04_SEmodel/07_NC_r... |
0bddfbe814408933e748b19834b760e00676d86c77bc5f8737427604f49c52e1 | R | 12,426 | 341 | ---
title: "RTqPCR Data Analysis"
author: "Jenn Kim"
date: "17/10/2023"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# MAIN FIGURES
### Load libraries
```{r}
library(tidyverse)
library(xlsx)
library(ComplexHeatmap)
library(dendsort)
library(cowplot)
library(ggplot2)
th... |
e340d7a96a70fe0d0052c077cfd35e3fee721b076cb8ae489cf01c1537ba6ff7 | R | 12,442 | 328 |
library(tidyr)
library(ggplot2)
library(glmnet)
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-p", "--pValFIMOcutoff"), type="numeric",
default=1e-6,
help="Max FIMO p value to retain match", metavar="character"),
make_option(c("-f", "--featureSelection"),... |
190db4ca6156ab32b7df3334c9b0585ff5e65b885617c98b256e957a740aefa6 | R | 12,451 | 409 | #' Create a `hyperSpec` Object
#'
#' To create a new `hyperSpec` object, you can use one of the following functions:
#' - [new()] (i.e., `new("hyperSpec", ...)`);
#' - `hyperSpec()`.
#'
#' @note
#'
#' A `hyperSpec` object is an S4 object, so its initialization is performed
#' by calling `new("hyperSpec", ...)`. The fun... |
714ed506e39795361d0360f70f10260aae5b3df2a1ac234ed77dbe413b4dda88 | R | 12,469 | 292 | ---
title: "R Notebook"
output: html_notebook
---
```{r}
library(RaceID) #RaceID3
library(tidyverse)
library(ggsci)
library(ROGUE)
library(Seurat)
```
#Load data
```{r}
cl.sce <- readr::read_rds("/data1/pauling/04_SEmodel/07_NC_revision/03.data/01.clustering.ari/01.10x.5cl/sce.rds.gz")
cda <- readr::read_csv("/dat... |
7963bb21a68f854753f854eb6f0192b63a2623295d6cd83eb8ca33f9cb4df212 | R | 12,495 | 266 |
.libPaths('1')
# Get functions
library(monocle3)
source("/net/trapnell/vol1/home/readdf/trapLabDir/sharedProjectCode/utility/singleCellUtilFuncs.R")
# modStatus <- loadMonoclePackages()
library(tidyr)
library(ggplot2)
# Note: Need bedtools loaded for this! bedtools/2.29.2
library(cicero)
# Add this to ... |
c986413f99816499cd0d03f0f0f2a83634e3b4c848ef9f3c278a26e8f34430ba | R | 12,503 | 372 | ################################################################################
#MULTIVARIATE ANALYSIS ON BEHAVIOURAL DATA OF YOUNG AND OLDER ADULTS
################################################################################
#LOAD DATA
setwd("/Users/au685269/Library/CloudStorage/OneDrive-AarhusUniversitet/Add... |
730442cec78fdb46df359ddc9add31e663275fb5724240d05a5d179e664726d2 | R | 12,506 | 392 |
#'
#' Tests whether user-defined input and output directories and their paths exist.
#' The output directory should not yet exist, but the input directory should.
#'
#' @param input_dir Path of an existing directory.
#' @param output_dir A path whose base name exists but the destination directory does not.
#'
#' @retu... |
0645b659e4ab04382757da9bdac25816b5f477ad7530cfb3aeaccfdc43466bd4 | R | 12,517 | 278 |
################################# PCA based clustering ##########################################################
# http://www.sthda.com/english/articles/31-principal-component-methods-in-r-practical-guide/117-hcpc-hierarchical-clustering-on-principal-components-essentials/
library(FactoMineR)
library(factoextra)
lib... |
8c446d757b614c6c365c8ef26c8c0bb76de6b56c3028d07d91fd0fae4da14c24 | R | 12,546 | 406 | test_that("extract_spm_l2_projection aligns runs and drops intercept/constant columns", {
gpa <- create_mock_gpa(n_subjects = 1, n_runs = 2, include_l1_models = TRUE, include_l2_models = FALSE)
gpa$glm_settings <- list(spm = list())
gpa$l2_models <- list(
models = list(
l2_main = list(
name = "... |
9fe29885bdf469f6c0d60796ed6457a2f734250ac4c504abacee6d3120ea61cc | R | 12,547 | 288 |
#' ---
#' title: "Analysis plots for AAV plasmid library and coverage"
#' author: "Tomas Bjorklund"
#' output:
#' pdf_document:
#' highlight: tango
#' geometry: margin=0.7in
#' fontsize: 10pt
#' ---
#' This script provides a number of measurements on the AAV plasmid library and the readouts in vitro & in vivo. ... |
0052a39a4b5c34f82d80a1f9eb20665faaaa6c29031d0f8cb77ab74bee2d0c93 | R | 12,579 | 263 | # function to compute lme and make spaghetti plot
spaghettiPlot2 <- function(data2use, x_var, y_var, subgrp_var, cov_vars=NULL, xLabel, yLabel,
modelType = "quadratic",
fname2save = NULL, plot_dots = TRUE, plot_lines = TRUE,
dot_alpha = 1/... |
73bf19072eea15047d00eaeecf18296eb3bfad9f95716a3173e492455d380e8c | R | 12,665 | 322 | #' Calculate Proportional Fold Change from sccomp Estimated Effects
#'
#' @description
#' This function calculates the proportional fold change between two conditions using the estimated effects from a sccomp model.
#' The fold changes are derived from the model's posterior predictions rather than raw counts, providin... |
06b38ff20d2e09354fb644ce214f641e20638cb3ba033107be4a945472e13b50 | R | 12,686 | 402 | #!/usr/bin/R
# -------------------------------------------------------
# Plots for the integrated neuronal trajectory from DEGs:
# Updated: 03/01/2022
# -------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
library(tidyr)
... |
cd7bfa4a099ca6e288577e0dd14e888b5e2bfdf2a31494ab5d47422ca2be0273 | R | 12,688 | 361 | #!/usr/bin/env Rscript
# This script generates SPM contrasts from an fmri.pipeline L1 model contrast matrix.
args <- commandArgs(trailingOnly = FALSE)
scriptpath <- dirname(sub("--file=", "", grep("--file=", args, fixed = TRUE, value = TRUE), fixed = TRUE))
argpos <- grep("--args", args, fixed = TRUE)
if (length(arg... |
4c5efdfdffc033040a0743999a296000849db65c9bc9764c12e78433493d982a | R | 12,730 | 364 | #!/usr/bin/R
# ----------------------------------
# Put together the full GWAS figure:
# Updated 02/21/2022
# ----------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
if (!exists("cellmeta")){
source(paste0(sbindir, 'load_metadata.R'))
}
source(paste0(sbindir, 'auxiliary_pseudobulk_lo... |
676fc8620e893a96f71d0b93ad4d01c67ed3a3b1817ff3941ed70840f7189eed | R | 12,732 | 359 | #' Output GLM configuration to a '.yaml' or 'json' file
#' @param gpa the `glm_pipeline_arguments` object with relevant fields already populated, including
#' `$l1_models`, `$l2_models`, and/or `$l3_models`.
#' @param file the filename for the exported configuration file
#' @return invisibly return the YAML/JSON synt... |
bc43efbbb17befb6ebf5c2252b1b7699622f67149ae494027608e04d4001f0e8 | R | 12,756 | 308 |
basepath = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2021_07_15_Greg_ATAC_Code_nobackup/"
dir.create(paste0(basepath, "archr/results/NB8/"))
out_dir = paste0(basepath, "archr/results/NB8/")
setwd(out_dir)
set.seed(7)
# load requirements
suppressPackageStartupMessages({
library(ArchR)
library(monoc... |
abe209b7c5968c2759c7076c334f45c4dd8cc47a51987bfdfb1f1a0fb0049141 | R | 12,759 | 245 | #' Run DEA on processed Seurat object
#'
#' Perform differential expression analysis on processed and annotated Seurat object.
#'
#' @param sample_name sample name, string
#' @param qs_file character string file path to .qs file of processed Seurat object
#' @param output_dir output directory for plots, string
#' @para... |
2f73e3a6e3a76e474727c01ab39b75e16f4b3dbac971f46aa0cdc3a115be4d82 | R | 12,783 | 206 |
#' archstabms__random_library_design
#'
#' Random library design.
#'
#' @param ddpca_outpath path to ddPCA data (required)
#' @param position_offset residue position offset (default:0)
#' @param fitness_abundance_file path to abundance fitness data (required)
#' @param fitness_binding_file path to binding fitness data... |
f17102a9085ef58a8cf47955ee66c5b69c1169cd00cef0a9e7590451244ffc7b | R | 12,805 | 305 | plot.genotypes.qtl <- function(msg_cross_object, scanone_object, phenotype, format, zoom, plot_title){
####plot.genotypes.qtl plots all the genotypes of individuals ordered according to the value of a phenotype and the QTL corresponding to that phenotype
####USAGE
####plot.genotypes.qtl(msg_cross_object, scanone_obje... |
3cc956db0fa200dd78464d79e248a568e14b4970c5f6e4bc66f9f91f843aeac8 | R | 12,822 | 339 | ---
title: "Beta binomial cell type abundance testing"
output: html_notebook
date: "9/8/2021"
author: "Lauren Saunders"
---
```{r setup, echo=FALSE, warning=FALSE, message=FALSE}
knitr::opts_chunk$set(warning = FALSE)
knitr::opts_knit$set(progress = TRUE, verbose = FALSE)
suppressPackageStartupMessages({
l... |
fb025ed3dba1da2e246454df5bcaf158ceab61d091b8526bdc9db432f1c002ba | R | 12,842 | 295 | #' Main function for detecting spatial allele-specific expression (ASE) patterns
#'
#' Fits a beta-binomial thin plate regression spline for each gene.
#'
#' @description This function fits a spatial beta-binomial model, estimates a
#' 2D smoothed probability function, and stores information for plotting results.
#' Us... |
ee03b5b6e9df9c7f7d9a2aca123dd9e4e9374422c6b71f9b7f4eeeb7b12ec16f | R | 12,896 | 225 | ---
title: "Azimuth annotation"
output:
html_document:
theme: united
df_print: kable
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
---
***
```{r setup, include=FALSE}
all_times <- list() # store the time for each chunk
knitr::knit_hooks$set(time_it = local({
now <- NULL
function(before, option... |
7fb6d64e4f29c3857ea5f700ddb4e8aca164895474e660bd602a65ea994df9b8 | R | 12,899 | 253 | ---
title: "Nurr1 project clustering"
author: "Yuvarani Masarapu"
date: "11/18/2022"
output:
html_document:
code_folding: hide
toc: TRUE
---
```{r}
library(SeuratObject)
library(Seurat)
library(harmony)
library(sctransform)
library(Matrix)
library(RColorBrewer)
library(ggplot2)
```
### Load filtered seurat... |
6aedbc32baaa2ab4546c96200d9df864914164cf472068bbdd14b5cc98c568f1 | R | 12,947 | 378 | #!/usr/bin/env Rscript
# Script to score dosage bias per sample
#Inputs:
# 1) a binCov matrix subsetted to the testing bins of interest
# 2) a list of bins with signed weights per bin
####################################
#####Set parameters & load libraries
####################################
options(scipen=1000,st... |
62fb99be7ac7a3c7d7219042eada8eae8bd96d49339f263a5cf7b3ec2d1b7bc4 | R | 12,984 | 358 |
# Trying:
# qlogin -l mfree=20G -q trapnell-login.q -pe serial 16 -l centos=7
# Trying this on 7-20-21. (Didn't work, going back to 20g)
# qlogin -l mfree=40G -q trapnell-login.q -pe serial 16 -l centos=7
basepath = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2021_07_15_Greg_ATAC_Code_nobackup/"... |
a7f5e9d323773d2074eafbfc4e69d934416cff66a08213152a19c1cb420ef157 | R | 12,985 | 293 | #bsub -q taki_normal -J "ml[1-4]" -M 40000 -o ~/Documents/nnbatch/run_ml.txt bash -c "Rscript /home/fengling/Documents/nnbatch/code/run_ML_battery.R"
source("~/Documents/nnbatch/code/load_packages.R")
setwd("~/Documents/nnbatch")
#ML
library(caret)
library(randomForest)
library(xgboost)
library(e1071)
library(MASS)
lib... |
d770d76491ff4fe8b86f6ae1e4170ceca0f66d14baf755ca9795843a02feaec5 | R | 12,986 | 453 | ---
title: "Bilateral rhizotomy. Analyse PAS UBI"
author: "Yaromir Kobikov <kobikov.yaromir@gmail.com>"
date: "10/27/2021"
output:
word_document: default
html_document:
toc: true
toc_float:
collapsed: false
editor_options:
chunk_output_type: inline
---
```{r setup, include=FALSE}
source("../../BayesianPValue... |
563b4682a9fae5e58badcc1350a9c79e41fcbe7fcb29b8ec391f53c0739ee09a | R | 13,089 | 365 | #!/usr/bin/R
# -----------------------------------------------------
# Starting from all DEG results,
# - Plot the lFC for diff. pathology against each other
# NOTE: mostly preliminary, not finished analysis
# Updated: 05/18/23
# -----------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ',... |
8cf9a70a1298962e0c3c682498f4236bad0a69bee98a4c0787bd37b036ab80df | R | 13,115 | 303 | ---
title: "prep_for_velo_sporos"
author: "T Attenborough"
date: "16/01/2023"
output: html_document
---
The other packages are on CRAN.
```{r, message=FALSE}
library(remotes)
library(Seurat)
library(zeallot) # For %<-% that unpacks lists in the Python manner
library(DropletUtils)
library(tidyverse)
library(GGally) # F... |
9486cea6880a9b71c6176202efc61556b1898a5344bc1caa4678095aeb48e716 | R | 13,116 | 303 | ---
title: "prep_for_velo_sporos"
author: "T Attenborough"
date: "16/01/2023"
output: html_document
---
The other packages are on CRAN.
```{r, message=FALSE}
library(remotes)
library(Seurat)
library(zeallot) # For %<-% that unpacks lists in the Python manner
library(DropletUtils)
library(tidyverse)
library(GGally) # F... |
ebcd454a3b414d8366ce56028765db5f28ca56c814e846628b5ca93879ff2f0b | R | 13,118 | 303 | ---
title: "prep_for_velo_sporos"
author: "T Attenborough"
date: "16/01/2023"
output: html_document
---
The other packages are on CRAN.
```{r, message=FALSE}
library(remotes)
library(Seurat)
library(zeallot) # For %<-% that unpacks lists in the Python manner
library(DropletUtils)
library(tidyverse)
library(GGally) # F... |
96b13f440ef6512e04914938167b70a31db8921082ea75dec13ff3c97a7ef1f0 | R | 13,124 | 314 |
library(tidyr)
library(ggplot2)
library(glmnet)
library(ROCR)
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-f", "--featureSelection"), type="character",
default="Binary_Combined_Motif_Counts", # "Binary_Combined_Motif_Counts", "Binary_PromOnly_Motif_Counts"... |
27e34e36d382ffa8c8718406112777f318694edb2036cb7cf15252d36fe36bc3 | R | 13,126 | 303 | ---
title: "prep_for_velo_sporos"
author: "T Attenborough"
date: "16/01/2023"
output: html_document
---
The other packages are on CRAN.
```{r, message=FALSE}
library(remotes)
library(Seurat)
library(zeallot) # For %<-% that unpacks lists in the Python manner
library(DropletUtils)
library(tidyverse)
library(GGally) # F... |
2e2f23bb81553ad11e55ba4803d6eabec883b60af1117c74c1468163e43bbca0 | R | 13,132 | 472 | globalVariables("cont")
globalVariables("contrast")
globalVariables("grupo")
globalVariables("type")
globalVariables("table")
globalVariables("ranking")
globalVariables("association")
# READING AND NORMALIZATION FUNCTIONS
obtaining_annotations <- function(){
annotation_packages <- grep(x = as.data.frame(installed.p... |
e09e377c9a37d2a503c9e9f361957e32ecf637e4f15397c5110039e7b0b59822 | R | 13,163 | 387 | #!/usr/bin/R
# -------------------------------------------------------
# Plots for the integrated neuronal trajectory from DEGs
# Focusing on DPT pseudotime
# Updated: 03/02/2022
# -------------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_me... |
075a84b8785d18f1a6841a783bc40c8ae6f8ffe6c68b57e84b1e38c61b949524 | R | 13,174 | 329 | #' Setup a third-level SPM model using lower-level con images
#'
#' @param l3_df a data.frame containing one row per L3 input map, typically one row per subject/session, with fields including
#' id, session, l1_model, l3_model, l1_cope_name, and con_file
#' @param gpa a gpa (glm_pipeline_arguments) object containing ... |
4453d2f024ed132f1351d3e2c22e88c411f9c2e43b08882c4182da6eb6391ef2 | R | 13,188 | 323 | #!/usr/bin/R
# -----------------------------------------------------------
# Use the pathology to run some tests on linear mixed models:
# Using Liang's Nebula package to run the fast NBLMM
# Basic overall + per-region interactions, for relevant subtypes
# Updated: 10/28/2020
#
# Run with filters for low expr. genes, ... |
0a2a806b2fab83e7bdf962db7188461c95ca250eb3d63f4e0e48afd8750db3c8 | R | 13,191 | 340 | #########################
########Licensing########
#########################
# Breathe Easy - an automated waveform analysis pipeline
# Copyright (C) 2022
# Savannah Lusk, Andersen Chang,
# Avery Twitchell-Heyne, Shaun Fattig,
# Christopher Scott Ward, Russell Ray.
#
# This program is free software: you can redis... |
f3be89466fe5caa5fa44c28a7f127665eae2525663f2a2e9c0920a893207b080 | R | 13,418 | 426 | ---
title: "dartseq replicates of HEK293-T"
output:
html_document:
self_contained: false
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
In this document we show the variant count per sample, and the variant relationship between dart-seq replicates of HEK293-T cell line.
#### YTH, V1
`... |
98082f03cae3549027c10eb35026f2fc808e09c67506ce295e584ae16024fc77 | R | 13,480 | 357 | #!/usr/bin/R
# --------------------------------------------------
# Starting from all DEG results,
# - Predict each region from others + from pathology
# Updated: 11/02/23
# --------------------------------------------------
library(cbrbase)
set_proj('DEVTRAJ', 'multiRegion')
source(paste0(sbindir, 'load_metadata.R'))
... |
fea2677b7b6be3ddd5fd07b62db8c79f0599def51a5e7a260a06fe5b8e481581 | R | 13,496 | 431 | require(testthat)
require(Cardinal)
context("stats")
test_that("PCA", {
set.seed(1, kind="L'Ecuyer-CMRG")
s <- simulateImage(preset=1, dim=c(10L, 10L))
pc <- PCA(s, ncomp=2)
pred <- predict(pc, newdata=s)
x <- t(spectra(s))
pc2 <- prcomp(x)
pred2 <- predict(pc2, newdata=x)
expect_equivalent(pc$sdev, pc2$s... |
d964502e2aa515dc716a6aaeaa57c4cf95e52e85c50c73e21805d623ff872b66 | R | 13,572 | 395 | # Greg Booth 2021
# run with qlogin session on multiple cores:
# qlogin -q trapnell-login.q -l mfree=25G -pe serial 4
# This script integrates sciATAC and sciRNA from HEART using ArchR
# The sciATAC data is already stored in an ArchR project
# The sciRNA data is pulled in as a cds object and converted to seurat f... |
fe1c7806936bf470861ea667083011ac7fcd3431987d4b489a873a2a4053a139 | R | 13,581 | 350 | library(Matrix)
library(irlba)
library(Seurat)
library(plyr)
library(dplyr)
library(stringr)
library(GenomicRanges)
options(stringsAsFactors = FALSE)
.get_aux_files = function(mtx_file) {
base_name = str_replace(mtx_file, '[.]gz$', '')
base_name = str_replace(base_name, '[.]mtx$', '')
features_file = paste0(b... |
61bace130843f3a16c795dc67bd3edad553d33cdde3d2b4b916dc61a3003d505 | R | 13,590 | 249 | ---
title: "DeepCombat"
output: html_document
---
# Preprocessing
```{r}
source("./code/load_packages.R")
raw <- as.matrix(read.csv("./data/raw.csv", header = F))
covariates <- read.csv("./data/covariates.csv", stringsAsFactors = T)
covariates$X.1 <- covariates$X.1 + 1
final_visits_covariates <- covariates %>% group_b... |
d29b4dbc870cb5c004a82a78778e80f3510e33bf38226745e4069ca82754a401 | R | 13,662 | 264 |
fake_coords <- function(counts) {
coords <- data.frame(as.matrix(Matrix(0,nrow=dim(counts)[2],ncol=2)))
colnames(coords) <- c('x','y')
rownames(coords) <- colnames(counts)
return(coords)
}
#' Creates a SpatialRNA object from a 10x Genomics Visium `outs` directory
#'
#' Given a SpatialRNA directory 10x Genomi... |
57bae4c1d9077dc1d6416ba09f9f33a98b90f3a32f4fb70b3cbc21bd4f8ca78a | R | 13,711 | 416 | # For the specified k, conduct PCA on the cognition and network reconfiguration
# variables separately to check the relationship between the first PC and PLSC
# dimension. Also plot first and second PC loadings to see which network reconfiguration
# variables are similar and examine variance explained.
# Output:
# ne... |
591385190d077cd079aeb056bd0c8378dc4dde4dbb0b953fabe19b89db990b38 | R | 13,731 | 276 | ```{r init}
library(Matrix)
library(monocle3)
library(tricycle)
library(dplyr)
library(RColorBrewer)
library(scales)
library(ggplot2)
```
```{r load data}
cds <- readRDS('E36_v2.rds')
fname <- 'Figure5'
S_matrix <- SingleCellExperiment::reducedDims(cds)[['UMAP']]
data_df <- data.frame(S_matrix)
colnames(data_df) <- ... |
1bb5fedba8adc86d9133c2d8b17b5676d1118b9369068b8448ca5131c45c73c9 | R | 13,777 | 498 |
#### XDataFrame ####
## -----------------
.valid_XDataFrame <- function(object)
{
errors <- NULL
nms <- names(object)
keys <- object@keys
if ( length(keys) > 0L && is.null(nms) )
errors <- c(errors , "'keys' must be a named list")
strings <- vapply(keys, is.character, logical(1L))
if ( !all(strings) )
error... |
b1b6476a414947fe813a54e4e336e2444a38b1a50b8256f57500ed933da7ff4a | R | 13,779 | 490 | test_that("generate_spm_mat handles concatenated ts_files and non-concat mismatch", {
skip_if_not_installed("RNifti")
tmp_dir <- tempfile("spm_mat_")
dir.create(tmp_dir, recursive = TRUE)
make_nifti <- function(path, nvol = 5L) {
dims <- c(2L, 2L, 2L, nvol)
img <- RNifti::asNifti(array(0, dim = dims))... |
54f8e7b01aeee33ec80a80cbf77c2f88c49710175ddd98d152bb3c4f3d89b414 | R | 13,792 | 419 | library(cicero)
library(monocle3)
library(ArchR)
library(batchelor)
## Clusters 2 and 11 (CN7 based clusters)
proj <- loadArchRProject('/ArchR/subprojects/clusters_shendure_drop_SMN_CN12')
proj <- proj[proj$Sample %in% c('cluster2', 'cluster11')]
## Call new peaks on just this cluster; iterative sub-clsering - re-clus... |
142edf8c7888697b5dba92b3d98ad68a731bb473363304c538d204ca326bb05d | R | 13,842 | 189 | ####################################################################################################
###
### File: 06_EEG_results_data.r
### Purpose: Loading the results data to obtain the statistics
### and get differences among the work.
### Authors: Gabriel Rodrigues Palma, Rafael Moral
### Date: 08/0... |
c6228d213aef2ee1ae87cd5a7f8911b6fde12a2a03156b0f7a0d6c7b341a0c2d | R | 13,874 | 342 | #https://bids-specification.readthedocs.io/en/stable/appendices/entities.html
#' Function for extracting fields from BIDS filenames (mostly generated correctly by ChatGPT)
#' @param filenames a character vector of BIDS filenames
#' @return a `data.frame` containing the fields extraced from `filenames`
#' @importFrom c... |
7a788d1135c92d499a8e297072b5217fdb7793647a17fb055eea6d63d0bec732 | R | 13,878 | 322 | # Hypothesis testing for smooth (spline) terms.
#
# `sccomp_test_smooth()` tests whether the mean composition differs between two
# positions (or two intervals) of a continuous covariate entered as a smooth
# `s()` term. It is a thin wrapper that:
# 1. predicts the *mean* linear predictor (`mu_unconstrained`, the log... |
c9d8b5835da62b369105b63029e35332f92dbf91c2cbb82e9056cc274b0e2780 | R | 13,940 | 479 |
#### Spectral pre-processing ####
## ------------------------------
setMethod("addProcessing", "SpectralImagingData",
function(object, FUN, label, metadata = list(),
verbose = getCardinalVerbose(), ...)
{
args <- list(...)
step <- ProcessingStep(FUN, ARGS=args)
step <- setNames(list(step), label)
metadata <- s... |
9a57c4b904796aa481a0d61ed04972219841bf07d9d0ac716e9c063803289bf8 | R | 13,965 | 360 |
# load requirements
suppressPackageStartupMessages({
# library(ArchR)
library(monocle3)
library(dplyr)
library(ggrastr)
# library(Seurat)
})
library(tidyr)
library(plyr)
library(ggplot2)
library(Matrix)
library("lme4")
assignDonorAndSite <- function(inputCDS){
updatedColdata = separate(data... |
2fb82dbb0cfef980a35bd4b9c7854bbae3564ba710cf046875a034f59e2f3216 | R | 13,975 | 416 | make_lookup_feat_outputs_gpa <- function(root, l1_feat = NULL, l2_feat = NULL, l3_feat = NULL) {
if (is.null(l1_feat)) {
l1_feat <- file.path(root, "feat_l1", "sub-01", "ses-1", "facehouse", "FEAT_LVL1_run1.feat")
}
if (is.null(l2_feat)) {
l2_feat <- file.path(
root, "feat_l2", "sub-01", "ses-1", "L... |
1b80b32b8a1c5c5aea1625c9dbb75edfe9f7608def4552a32bd74e9d34c29620 | R | 13,980 | 375 | ## Simulation --- AUC
#library
library(SingleCellExperiment);library(tidyverse);library(reticulate);library(tidyverse)
library(ggplot2);library(scmap);library(fmsb);library(ggsci);library(scibet);library(ggpubr)
library(Seurat);library(M3Drop);library(ROCR);library(cluster);library(parallel);library(ROGUE)
#Functions... |
3dc690ec6b234c1867e4681a995709510ea24f0c72cb0d2969ff2232fbe57290 | R | 13,984 | 189 | ####################################################################################################
###
### File: 06_EEG_results_data.r
### Purpose: Loading the results data to obtain the statistics
### and get differences among the work.
### Authors: Gabriel Rodrigues Palma, Rafael Moral
### Date: 08/0... |
96d98769e516367a85ded2d156515e14bfcb028ab38a8186a84444ccfda67550 | R | 14,027 | 220 | # STAT_MODEL_TYPE1.R
# This is the main code that runs the first type of model, namely to compare the
# cortical activity of the PD group to the HC group for the various gait events
# when no freezing occurred.
# INPUT:
# - data_nrlgait.csv (created by create_dataframe_nrlgait.m)
# - participants.xlsx
# OUTPUT:
# - d... |
58da83395846a21170d1351133a32d228ba5f280051edb92b5a46e8d3e7b3354 | R | 14,042 | 387 | ---
title: "04_longitudinal"
author: "Veronica Mandelli"
date: "2/14/2022"
output: html_document
---
This script run the longitudinal model (statistics + graphics) for VABS and MSEL for the 2 ASD subtypes identified wiht NDA database usign the reval algorithm
```{r}
# load libraries
library(grid)
library(lattice)
lib... |
4a91964825367187afa9cfa13cd310bb100dcd34a4df1f233d71f57167e685d7 | R | 14,050 | 390 | # Note: This only runs if you get the right resources upon qlogin/qsub. Run:
# qlogin -q trapnell-login.q -l mfree=4G -pe serial 16
# to start up
# Trying:
# qlogin -l mfree=20G -q trapnell-login.q -pe serial 16 -l centos=7
# Trying this on 7-20-21. (Didn't work, going back to 20g)
# qlogin -l mfree=40G -q trapnell-... |
829170c6425c7fcb0d46fe6b9c6a407607a010d82194b1eb8192a7260298c933 | R | 14,080 | 394 |
#The input of the function is a black and white skeletonized
#image (using Zhang Suen method of skeletonization).
horizontal_ridge_count<-function(pictures){
#Image is opened as a function "im_orig" and stored as raw data as "image_data".
graphics.off()
im_orig<-load.image(as.character(pictures))
i... |
b9b1e9fc20096bdb96c6868fd4e83eacc8fe6a8421f50a43b757c5308d4baac4 | R | 14,087 | 368 | #' Internal helper function to submit a query to the tracking SQLite database
#'
#' @param str Character string SQLite query
#' @param sqlite_db Path to SQLite database used for tracking
#' @param param List of parameters/arguments to be used in query
#'
#' @keywords internal
submit_tracking_query = function(str, sqlit... |
f7ee908b0fe5d8aee16284241f54a060ba59b1a461b6b896ac77447eadbeb954 | R | 14,095 | 299 | library(TwoSampleMR)
library(MRInstruments)
library(MRPRESSO)
require(argparse)
func_run_MR <- function(exposure_name, outcome_name,
harmonized_rdsfile,
savedir_individual){
dat0 <- readRDS(harmonized_rdsfile)
no_MR <- FALSE
if (is.null(dat0)){
result ... |
d7d3828c48f7deec2b1597bcf322b4bb3553a9f3cbe7feceae8842709bb56d4e | R | 14,152 | 337 | #!/usr/bin/env Rscript
# Helper script to create final minGQ tree from
# combined outputs of ROC analysis at individual
# filter conditions
###Set master parameters
options(stringsAsFactors=F,scipen=1000)
###################
###HELPER FUNCTIONS
###################
#Read and merge all input data
process.inputs <- f... |
860e081dcf333eda92d46cd80c0a8ecf2a52362310aaa9fa6a4d29e0eae5b988 | R | 14,208 | 351 |
# load requirements
suppressPackageStartupMessages({
# library(ArchR)
library(monocle3)
library(dplyr)
library(ggrastr)
# library(Seurat)
})
library(tidyr)
library(plyr)
library(ggplot2)
library(Matrix)
library("lme4")
assignDonorAndSite <- function(inputCDS){
updatedColdata = separate(data... |
4952b488dc75d9b9ff12aacdd87213196d78566cca968d813c8ffa297ac1b04a | R | 14,234 | 329 |
library(ggplot2)
library(dplyr)
library("optparse")
library(stringr)
library(ggrepel)
options(ggrepel.max.overlaps = Inf)
print("Libraries loaded, starting now")
# Get the passed parameters
option_list = list(
make_option(c("-m", "--modelNotes"), type="character",
# default="_fix_Anatomical_Si... |
a50743bf3b7d8eb3e704c12a478fcf0005bcbd44995b90e5f8c61bbbb6870486 | R | 14,282 | 359 |
getInputDF <- function(opt, specifyFeatures=FALSE, specifiedFeatures="NULL"){
# Get the path
dirName = paste0("../2022_04_07_redoATACtoExprData_nobackup/fileOutputs/",
"Gene_Prom_Plus_Distal_WithSequence_Sites_Max", opt$maxNdistalSites, "_Upstream", opt$promoterUpstream,
"_Downstr... |
e774befc37a2757511e476322324b2072c18ac64eb12c2fc3eaf72f4e4f0bf11 | R | 14,297 | 359 |
getInputDF <- function(opt, specifyFeatures=FALSE, specifiedFeatures="NULL"){
# Get the path
dirName = paste0("../2021_07_26_setup_ATAC_to_expr_data_nobackup/fileOutputs/",
"Gene_Prom_Plus_Distal_WithSequence_Sites_Max", opt$maxNdistalSites, "_Upstream", opt$promoterUpstream,
"_Do... |
3cceeba3cf41cbc2a80320701016d7b1a16afdd29f033962576d508ccdb0d79f | R | 14,308 | 355 | #' sccomp_boxplot
#'
#' @description
#' Creates a boxplot visualization of the model results from `sccomp`. This function plots the estimated cell proportions across samples, highlighting significant changes in cell composition according to a specified factor.
#'
#' @import ggplot2
#' @importFrom ggrepel geom_text_repe... |
8e66878190514f880cc60a322cda83c82cb1b1a74bcb4caa622c74cff728645b | R | 14,356 | 363 | rm(list = ls())
####################
## Load libraries ##
####################
require(stringr)
require(R.matlab)
require(pracma)
require(Formula)
####################################################################################
### This script will construct a subject sample for Elis's Nback & Rest Analyses ###
#... |
34375d6cf0f0da6b006b83a6453915f6406d14606351e6299e5b340ac0568c2a | R | 14,361 | 351 |
basepath = "/net/trapnell/vol1/home/readdf/trapLabDir/hubmap/results/2022_04_03_redoATAC_proc_nobackup/"
dir.create(paste0(basepath, "archr/results/finalPlots/"))
out_dir = paste0(basepath, "archr/results/finalPlots/")
setwd(out_dir)
set.seed(7)
# load requirements
suppressPackageStartupMessages({
# librar... |
995f251e35e9a3e2c412114846c50274f6c6b7d9aa27524ea1e346954c409ebc | R | 14,409 | 351 |
library(tidyr)
library(ggplot2)
library(glmnet)
library(ROCR)
library("optparse")
# Get the passed parameters
option_list = list(
make_option(c("-p", "--pValFIMOcutoff"), type="numeric",
default=1e-5,
help="Max FIMO p value to retain match", metavar="character"),
make_option(c("... |
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