sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
206a426713c3709ba631d4ad796070972cc6337a529630a48746d73a2ae39943 | R | 8,544 | 242 | #' Create a future assignment
#'
#' `x %<-% value` (also known as a "future assignment") and
#' `futureAssign("x", value)` create a [Future] that evaluates the expression
#' (`value`) and binds it to variable `x` (as a
#' \link[base:delayedAssign]{promise}). The expression is evaluated in parallel
#' in the background.... |
cd7740be99f25ab473ddd6b9169a7893c1ed742dde4738c77b712fde4d5591b6 | R | 8,570 | 237 | #' Create a future
#'
## Rescaled to 120x138: convert -geometry 28.7% future-logo.png logo.png
#' \if{html}{\figure{logo.png}{options: style='float: right;' alt='logo' width='120'}}
#' Creates a future that evaluates an \R expression or
#' a future that calls an \R function with a set of arguments.
#' How, when, and wh... |
f80b7e5eb46b05e11b224dd430a3fabfc589fd0b9d2460b9d05b6202a9c55844 | R | 8,602 | 212 | ##########################################################################################
## Plot cell type enrichment results generated by ASD_CellTypeEnrichmentAnalysis.py
##
## Author: Yu-Han Hsu
##########################################################################################
rm(list=ls())
library(ggplo... |
a02bda9b9a1a844a639df5565f4f5a6bc094495b81283f38ce803ef02cdda698 | R | 8,613 | 239 | #' Clean homopolymers indels
#'
#' Detect indels exist in homopolymers and change the indel to be the same as in the reference genome.
#'
#'
#'
#' @param seq_ann_tbl (Required). The haplotype sequence table includes sample_id, marker_id, sequence (haplotype sequence), count (haplotype read counts).
#' @param marker_inf... |
886ccab4be7a57f38f132a3e5f0b54c30dc6ef520fdd716070d7107f1ea7a4d4 | R | 8,678 | 219 | #
# helper.functions.R
#
# Copyright (c) 2013-2013 GBIC: Danny Arends, Harm-Jan Westra, Lude Franke and Ritsert C. Jansen
# last modified Jul, 2013
# first written Jun, 2013
#
read.harmjan.zscores <- function(){
if(file.exists("metaRes.Rdata")){
load("metaRes.Rdata")
}else{
metaRes <- read.csv("MetaAnalysi... |
437d1edec27bf266445ece55d22b285eb4645f190c0666780d8d7844b285299b | R | 8,751 | 231 | ##########################################################################################
## Compare and plot co-expression z-scores of bait-interactor pairs subsetted by
## different variables, using co-expression data derived from 4 transcriptomic datasets:
## Stickels2021, Maynard2021, Velmeshev2019, BrainSpan
##
#... |
c565857bf961dd1f98573e3d3110fa9a4243eb48dc40ba884cd95deee915679d | R | 8,754 | 257 | ```{r}
library(ggplot2)
library(gridExtra)
library(readxl)
library(pheatmap)
library(RColorBrewer)
library(gridExtra)
source("../downstreamer_functions.r")
# Read reference datasets
ensembl <- read.table("~/Documents/data/reference/ensembl/ensembl_gene_position_b37_biotype.txt",sep="\t", header=T, row.names = 1, stri... |
3d908d068c1255b16f9a7f7165945893d90263f6ac624b815b3cc6ed414db4d8 | R | 8,764 | 186 | .LOG <- function(..., file, print = TRUE) {
msg <- paste0(..., "\n")
if (print) cat(msg)
cat(msg, file = file, append = TRUE)
}
.get_renamed_colnames <- function(hold_names, userprovided, checkforsingle=c(), filename, N_provided, log.file,
warnz=FALSE, warn_for_missing=c(), stop... |
1f36354ba44f70eaafbbb1c34f8ffc85283163ce4992cc666de5bba679f14e6d | R | 8,771 | 234 | #' Registry of Active Futures
#'
#' @param where (character string) The name of the registry, which is
#' decided by the [FutureBackend].
#'
#' @param action (character string)
#' If `"add"`, add `future` to registry.
#' If `"remove"`, remove `future` to registry.
#' If `"contains"`, checks whether `future` is in re... |
65b7e77c6bd879d371653d36def52af3052a8755b858449ee682799343d8fd41 | R | 8,777 | 226 | #!/usr/bin/env Rscript
library(data.table)
library(simGWAS)
library(mvtnorm)
library(corpcor)
library(GenomicRanges)
# Function definitions
#---------------------------------------------------------------
# Function to read haplotype block
gethap <- function(i) {
y=fread(ldd$comm[i], skip="#CHROM")
ha <- simGWAS::... |
cc3524079e6300be89f8de6b67af32809c610515ba6e24dd64f39094a3c3d62d | R | 8,782 | 301 |
getCorrelationCN <- function(CNVref, CNVcomp){
#TODO - add the standard output dir as var for the function
df_1 <- as.data.frame(approx(CNVcomp$Pos,CNVcomp$Mean, seq(min(CNVref$Pos,CNVcomp$Pos),max(CNVref$Pos,CNVcomp$Pos), by = 1), ties = "ordered"))
df_2 <- as.data.frame(approx(CNVref$Pos,CNVref$Mean, seq(m... |
203ea25d20524149505bbb7d92fd314047e43f3f9319b9eed9d189ce308b2554 | R | 8,856 | 259 | ##########################################################################################
## Generate volcano and scatter plots for ASD IP-MS datasets
##
## Author: Yu-Han Hsu
##########################################################################################
rm(list=ls())
library(readxl)
library(ggplot2)
lib... |
e08a2826fe532ff1e6e129f2cf30b1ace7cba2ef2ea1b04d4c33ae82d3db9d49 | R | 8,895 | 170 | setwd("C:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(readr)
#saveRDS(hpoMatrix, "../GeneNetwork/Data31995Genes05-12-2017/PCA_01_02_2018/PathwayMatrix/ALL_SOURCES_ALL_FREQUENCIES_phenotype_to_genes.txt_matrix.rds")
hpoMatrix <- readRDS("../GeneNetwork/Data31995Genes05-12-2017/PCA_01_02_2018/PathwayMatrix/ALL... |
2f12df5d1fde2841ed31eb6fd57efbb6e472d2512c8ff5bff48b5a17e93b3889 | R | 8,946 | 246 | ## --------------------------------------------------------------------------------------------------------------------------------------------------------------------
source("0_SharedCode.R")
stopifnot(SharedCode_version == c(11,0))
## ----------------------------------------------------------------------------... |
f8ba0bf5cd19955cfeb25e13d8daef1bafc9cba96ecf3c997ee10b309741d906 | R | 8,977 | 287 | # Tests for visualization functions
# library(SingleR); library(testthat); source("setup.R"); source("test-score-dist.R")
# pred1 = no pruned.labels
pred1 <- SingleR(test=test, ref=training, labels=training$label, genes="de")
# pred2 = 16 pruned labels
pred2 <- pred1
pred2$pruned.labels[seq(5,80,5)]<-NA
############... |
20bc247be60da83e2fdc9a26e38804f7e0b218936f25cc51f8acb106e1ec1349 | R | 8,984 | 226 | #######################################################
### Deep Brain Stimulation: When to go directional.
### Figures
### 2022 Katrin Petermann
#######################################################
## grey, green, blue, yellow, pink
cols <- c("#677078", "#009870", "#6ca5da", "#f8cf3b", "#ea8698")
################... |
e860de0a72bba31f991e23b46f254158171d653315130078421ba637500e4b8a | R | 9,020 | 220 | # Usage: Rscript code/ses_simulation/run_simulation.R "iter" "level_2_theta" "0/1"
iter <- as.integer(commandArgs(trailingOnly = TRUE)[[1]])
level_2_theta <- as.numeric(commandArgs(trailingOnly = TRUE)[[2]])
only_opt <- as.integer(commandArgs(trailingOnly = TRUE)[[3]])
library(tidyverse)
library(galamm)
library(lme4)
... |
88f4a2b85dc616a0783e6f72f46c852c84d0f927e5090dea5db12b78418747e6 | R | 9,021 | 231 | # Usage: Rscript code/ses_simulation/run_simulation.R "iter"
iter <- as.integer(commandArgs(trailingOnly = TRUE)[[1]])
library(tidyverse)
library(memoise)
library(gamm4)
library(galamm)
sim_params <- readRDS("data/ses_model/sim_params.rds")
outfile <- file.path(
"results/ses_model/simulation/model_fits", paste0("si... |
657e0d3c4c055c2d37bfc5c2954f22cbcfa3be4b828f324cf5771a7dc3c1da30 | R | 9,034 | 223 | #######################################################
### Deep Brain Stimulation: When to go directional.
### Data Cleaning
### 2022 Katrin Petermann
#######################################################
cleandata <- function(dd){
# cleaning up the original input file
# subsetting the data to raw
dd <- su... |
3b31a94bad55f7bfe38927904df996048013efaaa66890c4be937d7e7053dcae | R | 9,065 | 124 | ---
title: "Introduction"
output: rmarkdown::html_vignette
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Introduction}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
... |
074c4dc95d30cee6d9035548d0460aebce006810c5c68e51f16f0e055faafd2e | R | 9,066 | 235 | myplotEigengeneNetworks = function(
multiME, setLabels,
letterSubPlots = FALSE, Letters = NULL,
excludeGrey = TRUE, greyLabel = "grey",
plotDendrograms = TRUE,
plotHeatmaps = TRUE,
setMargins = TRUE,
marDendro = NULL, marHeatmap = NULL,
colorLabels = TRUE, signed = TRUE,
heatmapColors = NULL,
pl... |
dd11ea16a850ccd05d63c81744411b2296a80e8bcb6887df4c1ef58908373bdc | R | 9,078 | 204 | #' geneci
#'
#' Function to calculate confidence intervals for dN/dS values per gene under the dNdScv model using profile likelihood. To generate a valid dndsout input object for this function, use outmats=T when running dndscv.
#'
#' @author Inigo Martincorena (Wellcome Sanger Institute)
#' @details Martincorena I, et... |
f4982c3aa502a51fa965d770493d9b9faedb3374d431be0e4dc123426fe12e8f | R | 9,097 | 236 | #!/usr/bin/env Rscript
#need pandas pip install in the envrionment if you don't have it
#usage ./metacell.R [outputfolder] [data.name] [path to count data]
#example ./metacell.R /home3/junhacha/HumanNetv3/Benchmark/MetaCell/Bcell/ BC_metacell_Bcell /home3/junhacha/HumanNetv3/Benchmark/BC_countdata_f_B_cell.tsv
#Junha C... |
b56e6e24b7a5a2f63f563c7cac173d0bb52133aaf9d81843eaee8be5ebb354f0 | R | 9,165 | 327 | ---
title: "AD Genetics Descriptives"
output: html_document
date: "`r format(Sys.time(), '%d %B, %Y')`"
author:
- "Shea Andrews"
params:
rmd: "report.Rmd"
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(tidyr)
library(purrr... |
776f1af0383839a1bffd2521f474a39646f4c07b1d0b0abf13ae6cefd7893b2a | R | 9,190 | 302 |
getInitParameters <- function(segmVect,meanVect,sdVect, truncBoundRight,truncBoundLeft){
truncBoundLeftVect<-c(meanVect[2]*3,meanVect[2]-(3*sdVect[2]),-truncBoundLeft,truncBoundRight,meanVect[4]+(3*sdVect[4]))
truncBoundRightVect<-c(meanVect[2]-(3*sdVect[2]),-truncBoundLeft,truncBoundRight,meanVect[4]+(3*sdVect... |
afc4c798893bcfbe9445beee9159b3838de220a44d86c65a38c9ad5bb3d91bc8 | R | 9,208 | 189 | #' Classify cells with SingleR
#'
#' Assign labels to each cell in a test dataset, using a pre-trained classifier combined with an iterative fine-tuning approach.
#'
#' @param test A numeric matrix of single-cell expression values where rows are genes and columns are cells.
#' Each row should be named with the gene n... |
470afe1d953a3754654c8c6f0f67fb03f2cfff61334c77817a9ceaa06ea4f8f5 | R | 9,246 | 174 | library("optparse")
library(ggplot2)
library(ggpubr)
library(gridExtra)
library(matrixStats)
library(plyr)
library(data.table)
# Get command line arguments
option_list = list(
make_option(c("-c", "--cisQTLs"), type="character",
help="eQTLProbesFDR0.05-ProbeLevel.txt.gz of cis-eQTLs", metavar="character... |
f9239aaf3efa1952554e8fee28dc54c3a287bc658955bdd8f22b12f99954d3b4 | R | 9,276 | 322 | #=================================#
# 0.Metabolic correlates paper: #
# relevant analyses (WGCNA/ORA) #
#=================================#
## Set up ----
filepath <- ""
setwd(filepath)
### Load libraries ----
library(dplyr)
library(openxlsx)
library(tidyr)
library(WGCNA)
### Load data ----
# Cognition & KNN... |
441833653d4294f0e6e360bbf4263666fc7de38c3dc0c6f227798d6b5d419d93 | R | 9,327 | 270 | #!/usr/bin/env Rscript
#this code takes an expression data as an input, runs bigSCale correlation calculaion, and produce a unsorted PCC graph
#rds file must be a seurat file
#./Rscript.R [-i FULL path to exprs UMI data] [-s sort] [-o 20K_monocytes]
#./Rscript.R --help for detail
#RunbigSCale -> coverage filter -> cut ... |
690b1a88e562387bb1f92bee876aa683c6c3955143a8ca885e81719a8caa20a1 | R | 9,339 | 178 | #' Calculate starting points to be used in the likelihood function optimisation
#'
#' @param input.df The resulting data frame from merge_sumstats(), where the effect size, SE, RSID and other columns are present, in addition to columns representing LD scores, weights and local LD structure
#' @param trait.names Vector ... |
1a7ae5cd9d7a9304fa3ffeff8421055af85ba3f8112838c7db144624b9bd2f46 | R | 9,345 | 181 | library(ggplot2)
library(tidyverse)
library(ggpmisc)
# library(ggrepel)
# library(broom)
source("utils_Florian.R")
# dir.create(paste(root_dir, "output/Fig10a_10c-PE_analysis/", sep=""))
dir.create(paste(root_dir, "Ascending_neuron_screen_analysis_pipeline/output/Fig10a_10c-PE_analysis/plots/", sep=""))
possible_crf... |
0787987497bb00a911402ee197ea07943e28b7dbd656e64ef483f38fba7ef145 | R | 9,349 | 295 | #' Pruning function
#'
#' @param pkn Prior Knowledge Network (read into df)
#' @param bool_data (list of) booleanized data
#' @param rule "MR" for Majority Rule or "ID" for Inhibition Dominant
#' @param functional_only TRUE/FALSE return only the functional edges (from expressed nodes)
#' @param nsol number of expected ... |
74407d126b53dda1d7071d02b33ba44e9edf53c225e7c1c4fd29c954de9736e6 | R | 9,395 | 313 | ---
title: "Categorization of HCPEP COND based on VAR dynamic metrics - upsampling"
author:
- name: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
output: html_... |
9a38184302feb19567f535e5b49df7ee64d7e771ed9687edd978687bd26e49db | R | 9,426 | 229 | #remoter::server(verbose = T, port = 55557, password = "laberkak", sync = T)
remoter::client("localhost", port = 55557, password = "laberkak")
setwd("/groups/umcg-wijmenga/tmp04/projects/depict2/")
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
source(paste0("C:\\Users\\patri\\Documents\\GitHub\\systemsgenetics\\... |
b134f9716cd939f8695b2d16e50c59f40e5a6b9d07cdd9d92cfb47854f9c53ae | R | 9,427 | 313 | ---
title: "Categorization of HCPEP COND based on VAR dynamic metrics"
author:
- name: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
output: html_document
--... |
30143c372356ba7d9d33374e3f455533763f4695dfbe4c9efc32eccdc4ef2213 | R | 9,445 | 293 | ---
title: "Non-parametric anova testing dFC Metrics -HCPEP CON PAP NAP - RAW"
author: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
output: html_document
---
`... |
12330ed19331021af21ee6c6f8daba3f19e48e9912c8aabca780d01066ddd4d1 | R | 9,447 | 393 | ---
title: Effects of spatial separation on vowel discrimination in noise, and it's dependence
on Auditory Cortex
output:
pdf_document: default
html_notebook: default
---
Version History:
* Created: 2021-09-09 Stephen Town
* Ported to notebook: 2022-05-19
```{r}
rm(list = ls())
suppressPackageStartupMessages... |
c3ac0c760dddce4bbd8b7586a9711cf5ca3273fc2c1c8eedb4788977b9dd96d0 | R | 9,456 | 211 | #' Plot a heatmap of the markers for a label
#'
#' Create a heatmap of the log-normalized expression for the most interesting markers of a particular label.
#'
#' @param results A \link[S4Vectors]{DataFrame} containing the output from \code{\link{SingleR}} or \code{\link{classifySingleR}} on a single reference.
#' @par... |
7ca2a225675cb7d8a2801def1fb4836043a3e3c9a74d45df522c8a5501fb9d0b | R | 9,473 | 257 | #' Analysis script to evaluate the state distribution of a four-node gene circuit
#' @param rSet sRACIPE object. RACIPE simulation data.
#' @param numModels Numeric. Number of models to be simulated. Default: 10000
#' @return List. plot objects.
#' @importFrom webshot webshot
#' @import sRACIPE
#' @import SummarizedExp... |
59f96584f2658d5ac1ba723d6a7f956d807250d4ca087a10b9c7bb31266d6d4d | R | 9,481 | 241 | #====================#
# 7.PGS Analyses #
#====================#
## Set up ----
filepath <- ""
setwd(filepath)
### Load libraries ----
library(dplyr)
library(magrittr)
library(purrr)
library(tidyr)
### Load data ----
# Metabolite & outcome imputed data
dat <- readRDS("dat/ins_met_nodementia.Rdata")
# Metabolite... |
ff8efed522bd9a367b2f3d1cb80040ed42b0d6160c505537c5c690c9b2fba221 | R | 9,491 | 276 | ---
title: "Interactions Between Latent and Observed Covariates"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Interactions Between Latent and Observed Covariates}
%\VignetteEngine{knitr::rmarkdown}
... |
5b804ef107b018e57ae1a2cf2128f8e89f52754977d2d3ce6481a455e859184f | R | 9,497 | 275 | #' Computes a list of archetype-specific interactomes
#'
#' @param ace ACTIONet output object
#' @param G Baseline network as an adjacency matrix or igraph object.
#' If it is NULL, PCNet is loaded as the baseline from SCINET package.
#' @param min.edge.weight Used in post-processing to remove insignificant edges
#' @p... |
67c25ec385a1a4f59f7ad0abc7bc3fc63aafb7697d68c8e7fa4ddd026ae9b249 | R | 9,520 | 288 | #' Compartment score matrix plot
#'
#' Display a Hi-C matrix with compartment scores annotated in the margins.
#'
#' @inheritParams insulation_matrixplot
#' @param CS_discovery A \code{CS_discovery} object, as created by the
#' \code{\link[GENOVA]{compartment_score}} function. When \code{NULL}, the
#' compartment... |
c95bc62142ba85b8cf491ee5976961acee7e0ba07dc0d11e1e34dd86aba8da5e | R | 9,525 | 240 | rm(list = ls())
print("REPLACEME")
Sys.time()
load("/work/lulab/Ben/BenStuff/research/projects/oldhome/scripts/networks/rset/rsetlists/output/rsetlistREPLACEME")
library(parallel)
library(sRACIPE)
library(combinat)
detectCores()
ks.test.self.imp <- function(a,b){
x <- a
y <- b
n.x <- length(x)
n.y <- length(y)
... |
7cca0f1aca0f7cde08cc0c071168ec0ade132a565448f3dfbb4919c3366015da | R | 9,536 | 313 | ---
title: "Categorization of HCPEP COND based on META dynamic metrics"
author:
- name: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
output: html_document
-... |
c8beb56d80dfccf5d1c121e5fce6e9abbb46ac03c686fe35ca90b745258a9f84 | R | 9,536 | 212 |
library(data.table)
library(ggplot2)
library("dplyr")
library(stringr)
library("optparse")
library(RColorBrewer)
library(tidyr)
library("readxl")
library(tidyverse)
# Get command line arguments
option_list = list(
make_option(c("-i", "--input"), type="character", default=getwd(),
help="path to inpu... |
3548b32b1c115868b9a66d562eb417164a9b8507bc727eb98861ea548ba30385 | R | 9,538 | 241 | ##########################################################################################
## Compare SCN2A, SYANK3, SYNGAP1 interactors found in induced neurons (iN) vs brain IPs
##
## Author: Yu-Han Hsu
##########################################################################################
rm(list=ls())
library(... |
84347cd507f2228a0b2abcdf9813f7bcdc76475a1589e4a7fdbb898e2100e6be | R | 9,542 | 317 | #' Add factor variables as data columns
#'
#' @param load_var The \code{load_var} argument provided to
#' \code{\link{galamm}}.
#' @param lambda The \code{lambda} argument provided to \code{\link{galamm}}.
#' @param factor The \code{factor} argument provided to \code{\link{galamm}}.
#' @param data The \code{data} arg... |
9ad282279ea8d7b2d23310a3330f0006d60be8efa134fa40a672b16e28210160 | R | 9,590 | 243 | #conda activate HNv3
library(ACTIONet)
library(SCINET)
library(igraph)
library(SingleCellExperiment)
library(Seurat)
#load data and convert to sce#############################################################################
setwd('/home3/junhacha/HumanNetv3/Velemeshev2019/condition_merged_celltype/')
counts <- Seurat... |
556b0f6454d7e914ff55cc85810d5c9fe6de274f2daf4317ebcedc39a19f1f4a | R | 9,607 | 220 | #######################################################
### Deep Brain Stimulation: When to go directional.
### Prediction Model
### 2022 Katrin Petermann
#######################################################
#######################################################
### Create new Dataframe with labels for prediction... |
6c009871056d7aabd41e0a822c7f92b04cae659d8c495e1c4bc1cba261d840a3 | R | 9,609 | 238 | #' Epilepsy Data
#'
#' Longitudinal epilepsy data from
#' \insertCite{leppikControlledStudyProgabide1987;textual}{galamm}. This
#' documentation is based on Chapter 11.3 of
#' \insertCite{skrondalGeneralizedLatentVariable2004;textual}{galamm}, where
#' the dataset is used.
#'
#' @srrstats {G5.1} Dataset used to test pa... |
5194b0ae2dcdbd5cd7019bef124b732313bce9452538367ab4c10bb6d9424621 | R | 9,637 | 224 | setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(readr)
hpoMatrix <- readRDS("../GeneNetwork/Data31995Genes05-12-2017/PCA_01_02_2018/PathwayMatrix/ALL_SOURCES_ALL_FREQUENCIES_phenotype_to_genes.txt_matrix.rds")
#hpoMatrix <- hpoMatrix[,hpoTerm, drop = F]
trait <- "height_2018_30124842_hg19"
table_tmp... |
89a0dbdbef7474f9f0ef2130028ef6aa003991fade42d141b0eca9437cb64e29 | R | 9,691 | 317 | ---
title: "Categorization of HCPEP COND based on VAR dynamic metrics - SVM"
author:
- name: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
output: html_documen... |
f4f4774fe10955bf0cba509bf7c7e04391d1b96c2ca13fdc4cfe3fc64c7521dd | R | 9,698 | 192 | ---
title: "Optimization"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Optimization}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
... |
414e9851e8a3b9e609f1069d2e4ee5b61589065d40a56f2dec45327550f93d32 | R | 9,699 | 317 | ---
title: "Categorization of HCPEP COND based on VAR dynamic metrics - Logistic regression"
author:
- name: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
outp... |
41dab23a9f10fb5b208a0149924382e2bb5369faa8094eb7ddee1cba50c2b660 | R | 9,711 | 291 | #' @tags value
#' @tags list listenv environment
#' @tags sequential multisession multicore
library(future)
library(listenv)
options(future.debug = FALSE)
## https://stat.ethz.ch/pipermail/r-devel/2025-March/083892.html
## Fixed in R-devel (>= 4.6.0) /HB 2025-03-23
attr_on_primitive_is_error <- local({
res <- NA
... |
3213570518a82aa33cf3364a036b4586d8bfa56e0a825ea6ce85ac1fbb9989b8 | R | 9,727 | 278 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("evaluating_coregulation")} else {setwd("../evaluating_coregulation")}
source("../downstreamer_functions.r")
source("data/source_olivier.r")
library(pROC)
library(matrixStats)
# Cached 2021-04-16
#datasets <- read.downstreamer.batch(main.downstr... |
2921499b4672f1f3cd967275cc50ae20f263579c9b20b78663447ad7372760b0 | R | 9,745 | 54 | # Discussion
**Word limit**: `r wordcountaddin::word_count("04-discussion.Rmd")` of 800.
Here, we show that deep learning architectures available in the PyTorch open source machine learning framework [@paszkePyTorchImperativeStyle2019] can classify radiographic views (anatomical location and projection) of horse limb... |
7e3ffb1be099dba7bb276fda53f790165b67baf6526ed0833b136fae7a610757 | R | 9,752 | 305 | library(data.table)
source('../config.r')
source('../utils.r')
source('../read_data.r')
extra_path <- file.path('inputs')
fname <-
'post_predictive_analysis_all_models_combined_imaging_non_imaging.csv'
DT_pred <- assemble_inputs(fname, path1 = extra_path)
#' Get point estimates + standard outputs for conditional... |
c7f5cd710c6350abaf0d5d16c2e51866205b7580ebccf62c7bf1b59cbcbe8467 | R | 9,766 | 247 | ---
title: "Generalized Linear Mixed Models with Factor Structures"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Generalized Linear Mixed Models with Factor Structures}
%\VignetteEngine{knitr::rmarkdo... |
b08785e8a0f6e5b5755400858d522a7502ab86fb16cec248ab889f97f1d4cb3a | R | 9,767 | 241 | ```{r}
if (basename(getwd()) == "downstreamer_main") {setwd("coregulation_calculations")} else {setwd("../coregulation_calculations")}
source("../downstreamer_functions.r")
library(GenomicRanges)
# Read Ensembl
ensembl.file <- "data/ensgR75_protein_coding.txt"
ensembl <- read.table(ensembl.file, sep="\t... |
42b814723fabb16a022e5ceceef2e51b8369ce034029946d91600143867ea870 | R | 9,785 | 211 | setwd("C:\\UMCG\\Genetica\\Projects\\Depict2Pgs")
library(readr)
hpoMatrix <- readRDS("../GeneNetwork/Data31995Genes05-12-2017/PCA_01_02_2018/PathwayMatrix/ALL_SOURCES_ALL_FREQUENCIES_phenotype_to_genes.txt_matrix.rds")
hpoTerm = "HP:0000002"#HP:0001511
hpoMatrix <- hpoMatrix[,hpoTerm, drop = F]
table_tmp <- rea... |
7bd6e2d550c0301f69223bc3c42d3ab634219aaaca5d699f87670f31fd8b953c | R | 9,801 | 124 | #' Title
#'
#' @param input.files - list of data frames, where each data frame contains the summary statistics of a trait to use
#' @param trait.names - vector containing the trait names in the order they're found in 'input files'
#' @param log.name - the name of the log.file connection to append log descriptions
#' @... |
763960f541046dcc174322bba5e319f68a546ba8cae39e9e0ef191a80b8a41d3 | R | 9,852 | 182 | ---
output:
github_document:
fig_width: 6
fig_height: 4
bibliography: inst/REFERENCES.bib
link-citations: yes
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
... |
879a2ac9e70be9bad01be897777e1a8fb9bd2cad242353c9d6a828b00bdc126f | R | 9,937 | 197 | #srun --cpus-per-task=1 --mem=50gb --nodes=1 --qos=priority --time=168:00:00 --pty bash -i
#remoter::server(verbose = T, port = 55001, password = "laberkak", sync = T)
#remoter::client("localhost", port = 55001, password = "laberkak")
library(uwot)
setwd("D:\\UMCG\\Genetica\\Projects\\Depict2Pgs\\Recount3\\")
setwd... |
4fe4a298da53a2adba5c46aa0e49444989c4a9d8356a3d520ebbc522c45b171f | R | 9,962 | 252 | #!/usr/bin/env Rscript
##
## Differential gene expression with DESeq2.
##
## usage: Rscript --vanilla dge-deseq2.R genome_build genes.gtf counts.txt groups.csv
##
# increase output width
options(width = 120)
# print warnings as they occur
options(warn = 1)
# java heap size
options(java.parameters = "-Xmx8G")
# get... |
3074314066056ad4d77109f19c270a0bf68291eedb9577e3f34972febd195a2f | R | 9,974 | 327 | ---
title: "Categorization of HCPEP COND based on VAR NM dynamic metrics"
author:
- name: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
output: html_document
... |
24399239b406baab84399e179a41c960855291d60b353d83da066a5e17d558aa | R | 10,004 | 209 | .commonfactorGWAS_main <- function(i, cores, n, S_LD, V_LD, I_LD, beta_SNP, SE_SNP, varSNP, varSNPSE2, GC, coords, k,
smooth_check, Model1, toler, estimation, order, utilfuncs=NULL, basemodel=NULL, returnlavmodel=FALSE) {
if (!is.null(utilfuncs)) {
for (j in names(utilfuncs)) {
... |
879702379cece88b1bb2316072e1e3eae18f0b6dcc7c70a9b5b7440d6c556286 | R | 10,057 | 281 | #====================#
# Figure 2 #
#====================#
## Set up ----
filepath <- ""
setwd(filepath)
### Load libraries ----
library(dplyr)
library(ggplot2)
library(grid)
library(gridExtra)
library(gtable)
library(ggtext)
library(magrittr)
library(openxlsx)
library(tidyr)
## Figure 2a (module heatmap) ... |
e583f85cf1927abcb73f318c51781745d475ad2ee24b46ae4c46f2ba3a316f97 | R | 10,080 | 225 | library(readxl)
library(tidyverse)
library(reshape2)
library(plyr)
library(DescTools)
library(abind)
library(xlsx)
library(ChIPpeakAnno)
library(org.Hs.eg.db)
setwd("/Users/philippehabets/Dropbox/Git/fMRI-transcriptomics-cortisol/data")
###define functions##############################################################... |
21e18f29e4f5bbeaf645150de1dd6410574e6a1429f02208cbb5d4c1a5d3e8a9 | R | 10,103 | 177 | source("utils_Florian.R")
# behaviours <- c("rest", "walking", "antennal_grooming", "eye_grooming", "foreleg_grooming", "hindleg_grooming", "abdominal_grooming", "PER_event", "pushing", "CO2")
behaviours <- c("rest", "forward_walking", "backward_walking", "antennal_grooming", "eye_grooming", "foreleg_grooming", "h... |
52112958bd9bed0c6392bdb28022affd604093f8bffa76d74a91db3fdd5bf830 | R | 10,121 | 222 | ### Functions to perform the permutation test to validate the identified modules
#' Shuffle protein labels
#'
#' @param FC Named vector with log2FC values. Names should be protein identifiers (e.g. UniProt IDs)
#' @param P Named vector with P-values. Names should be protein identifiers (e.g. UniProt IDs). Should... |
a77bc5d2473053c262eb079c0b4cc450ffa33ab30780c58900af6e3937f84b8b | R | 10,181 | 344 | ---
title: "Cross validaiton of categorization of COND based on COBRE in HACEP dynamic metrics"
author:
- name: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
o... |
5e9d72ed14382b329adf011e6a6889a586837aed7f19b757752ef3218b1f2715 | R | 10,206 | 266 | ##########################################################################################
## Enrichment analysis for IGF2BP1-3 targets
## (1) overlap enrichment between IGF2BP1-3 targets vs. Satterstrom ASD genes
## (2) plot ASD common variant enrichment of IGF2BP1-3 targets (from MAGMA)
## (3) overlap enrichment betw... |
1b028c763add84c868155eb1ffd4e4d02339bcabe907f7f34015117ba8600099 | R | 10,239 | 224 | ---
title: "Making Spearman correlation distributions"
output: html_notebook
---
I needed a way to quantify that the decreased correlation in expression between the combined pair bond and separation time points is "real." So I'm plotting a distribution of Spearman corrleation Rho and pvalues between the true combin... |
6cab23a31fb490ad9334c8e3bd461ff1f66cf4f46b6d31e7cb27beb1d41c9929 | R | 10,260 | 207 | library(gplots)
########### Identify the input arguments
args <- commandArgs(trailingOnly = TRUE)
jobID <- args[1]
stringency <- as.numeric(args[2])
fitMode <- args[3]
tempFolder <- paste("./tmp/",jobID,sep="")
outputFolder <- paste("./out/",jobID,sep="")
########### read the exon and intron centered normalized rea... |
608f134e17d9e2595cee58610e1a8effd89e8d0307511d9b0cd633d35aedf005 | R | 10,285 | 309 | # code for analyze LC4 neurons and downstream target neurons
# There are 3 .R files, run "LC4_startup.R" first.
# Then "LC4_proc.R" and "LC4_target_proc.R" can be run independently.
# To search for code for all plots, try "PLOT",
# for a particular plot, try eg. "Fig.3g" or "ED Fig.5b"
# library and cleanup ------... |
f6d1de06af2a93157cca5663d72ff46eef125a90692b86004362756e88f0766a | R | 10,287 | 251 | #' Plot score distributions
#'
#' Plot the distribution of assignment scores across all cells assigned to each reference label.
#'
#' @param results A \link[S4Vectors]{DataFrame} containing the output from \code{\link{SingleR}}, \code{\link{classifySingleR}}, or \code{\link{combineRecomputedResults}}.
#' @param show D... |
349a943fe4467277845fa45c7fd5b4a2be194af1638ff19f6835770f077acc69 | R | 10,314 | 262 | #' Optimize amplicon haplotype calling
#'
#' This function optimize amplicon haplotype calling and generate an amplicon haplotype sequence table.
#'
#' @param seq_ann_tbl (Required). The amplicon haplotype sequence table includes: sample_id, marker_id, sequence (haplotype sequence), count (read counts), status (haploty... |
51183b70c16a8fc3fc43b4d1dda7179a960e2c86fe31bfcba5f7dc3de71feebd | R | 10,317 | 302 | ---
title: "SPATA2_DMG1_use"
author: "junwei 2048"
date: "2022/3/30"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,warning = FALSE)
knitr::opts_knit$set(root.dir = "/mnt/raid61/Personal_data/songjunwei/Tumor_visium/12SPATA/")
library(SPATA2)
library(Seurat)
library(magrittr)
l... |
9aef057e4d2800aec7ead9d8e84c3da895e509e0bbfd17160baf4fdb3b71eb01 | R | 10,352 | 225 | # Title : create_seurat_object.R
# Objective :
# Created by: mvochteloo
# Created on: 22/10/2020
# https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html
# https://satijalab.org/seurat/v3.2/sctransform_vignette.html
# install.packages('Seurat')
library(Seurat)
library(Matrix)
library(ggplot2)
work_dir <- '/group... |
188352fa4b133a522b23b062ef30cf9d037c7b1fd0cd9951806fc7927b9ca4f1 | R | 10,400 | 364 | # Contacts class ----------------------------------------------------------
#' @export
#' @keywords internal
print.contacts <- function(x, ...) {
res <- attr(x, "res")
res <- if (res %% 1e6 == 0) {
paste0(res / 1e6, " Mb")
} else if (res %% 1e3 == 0) {
paste0(res / 1e3, " kb")
} else {
paste0(res, ... |
5fa56ff523a08523504e7626203c3fc2f3c1e8525957af10fc4be17763b2d152 | R | 10,406 | 226 | # Title : sn_rnaseq_analysis.R
# Objective :
# Created by: mvochteloo
# Created on: 22/10/2020
# https://satijalab.org/seurat/v3.0/pbmc3k_tutorial.html
# https://satijalab.org/seurat/v3.2/sctransform_vignette.html
# https://www.nature.com/articles/s41586-019-1195-2.pdf
# install.packages('Seurat')
library(Seurat)... |
c50c5660fa61d45995c49ceb8dc6a9d5ab59744d71b45b753e6c6dfd90144f3d | R | 10,437 | 340 | ---
title: "Categorization of HCPEP COND based on VAR dynamic metrics - Internal validation"
author:
- name: "Fran Hancock"
affiliation: Department of Neuroimaging, Institute of Psychiatry, Psychology and Neuroscience, King's College London, London, UK
date: "`r format(Sys.time(), '%B %d, %Y')`"
fontsize: 14pt
outp... |
03e5429aeadfd2d2e3b9f034db8d76c4337fa6fb7b77cbdbc47182c3eee71fdd | R | 10,466 | 217 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# scHumanNet
Construction of cell-type-specific functional gene net... |
88a033725e5dc977e8d5c2d942d8c623a6e8a34bc21e74c7bafa9bb8c3517d18 | R | 10,506 | 283 | #conda activate HNv3
library(ACTIONet)
library(SCINET)
library(igraph)
library(SingleCellExperiment)
library(Seurat)
#load data and convert to sce#############################################################################
setwd('/home3/junhacha/HumanNetv3/Velemeshev2019/merged_celltype/')
counts <- Seurat::Read10X(... |
917a2f8cdb6db4d7a1a57fdc4a47579785d157722ff1e14df81498a793b76477 | R | 10,512 | 447 | ---
title: "Computational Scaling"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Computational Scaling}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
``` r
library(galamm)
```
... |
3ddacadfe60dfc0bf1ffb3497f132563d33c73c4f8d3479051b6a381a5be9c53 | R | 10,599 | 301 | # Entorhinal Cortex: Final Script
# 2020-07-03
# Loading libraries
{
library(readxl)
library(tidyverse)
library(ggplot2)
library(cowplot)
library(scales)
}
# Writing folder directories (if not available)
{
dir.create("output")
dir.create("output/shapiro")
dir.create("output/baseline")
dir.create("output/primary")
dir... |
540741f6ca79aef20a87afa9517bc91935ae4e0881f5f80fc5211d3db697f069 | R | 10,612 | 385 | #' Set row-associated networks
#' @return List of adjacency matrices
#' @rdname rowNets
#' @export
setReplaceMethod("rowNets", "ACTIONetExperiment", function(object, value) {
value <- as(value, "SimpleList")
object@rowNets <- value
validObject(object)
object
})
#' Set column-associated networks
#' @r... |
9672a217d695c8a5db9b0ad4731e66b8e23c3f834b56dd18c23856a64b0a0e2a | R | 10,651 | 271 | #' Plot a heatmap or profile of insulation scores
#'
#' Takes pre-calculated insulation scores around a set of genomic locations and
#' plots these in a heatmap and/or profile.
#'
#' @inheritParams call_TAD_insulation
#' @param bed A \code{data.frame} with 3 columns in BED format, containing
#' genomic locations for ... |
1eae1decc4af208576f8f758c9ce5995964595ba7eb80afb57561d6118954b97 | R | 10,679 | 301 | library(tidyverse)
library(patchwork)
library(galamm)
library(mgcv)
options(bitmapType = "cairo")
theme_set(theme_bw())
theme_update(panel.grid = element_blank(),
strip.background = element_blank())
library(Matrix)
mod <- readRDS("results/cognition_model/cognition_model.rds")
dat <- readRDS("data/cognitio... |
010740fcb4025b61d2ea0e1b2c8c8225117baee3ee082b53fd4b4d0bd7c38b36 | R | 10,737 | 339 |
#' removeSyntheticBaseline Removes a synthetic baseline from a tumour pure matrix
#'
#' @param count_mtx count matrix
#' @param par_cores number of cores for parallel computing.
#'
#' @return relative matrix
#'
#' @examples
#'
removeSyntheticBaseline <- function(count_mtx, par_cores = 20){
d <- parallelDist::pa... |
2cb70113be21115c835512c0e5c1863ae3ba4c8d1d49b8a5179bc36f7673902c | R | 10,737 | 295 | library(galamm)
library(memoise)
library(tidyverse)
library(gamm4)
library(furrr)
dat <- readRDS("data/ses_model/dat.rds")
base_formula <- "value_z ~ 0 + itemedu_father + itemedu_mother + itemedu_self +
itemincome_father + itemincome_mother + itemincome_self +
itemgrouphippocampus:(siteousAvanto + siteousPrisma +... |
92ce79fe7b09bfcc9b9a63cb92570b4b969deb92cb0df5ddf2456bc9ff5ba0ff | R | 10,751 | 415 | ---
title: "Computational Scaling"
output:
rmarkdown::html_vignette:
fig_width: 6
fig_height: 4
bibliography: ../inst/REFERENCES.bib
link-citations: yes
vignette: >
%\VignetteIndexEntry{Computational Scaling}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr... |
2a96e00fd4d5c4eff69881a1466e998a5b1abc1218bfdc329899a64ffa447dd9 | R | 10,774 | 307 | rm(list = ls())
library(Biobase)
options(stringsAsFactors = FALSE);
library(WGCNA);
allowWGCNAThreads()
library(flashClust);
library(gplots)
library(RColorBrewer)
set.seed(12345)
setwd(dirname(rstudioapi::callFun("getActiveDocumentContext")$path))
# use data from prior step or start from load data below
eset<-readRD... |
ca3f29c9e17dcc8793e684a2c6ab6cee2370f4afe273b7040c5aa106d91270bd | R | 10,829 | 151 | # Materials and methods
**Word limit**: `r wordcountaddin::word_count("02-materials-and-methods.Rmd")` of 800.
## Study Design
The Hong Kong Jockey Club would retrospectively collect and provide pre-import radiographic sets to us.
We would select complete sets to use and no further selection criteria would be applie... |
651f272d169dad1c0104f2f8112cb735bc0220c9a42342639165037b0122a9e0 | R | 10,836 | 233 | ---
title: "00_Count.C4"
output: html_document
---
## Install packages #
```{r}
rm(list = ls())
options(stringsAsFactors = FALSE)
library(tidyverse)
```
## Import C4 imputation results #
```{r}
c4.imputed <- read.delim("../data/C4.imputed.multethnic.transposed.txt", header = FALSE, na.strings = c("", "NA"))
c4.im... |
b043d46a15556853a2f9fa0b1cd603e764b48bf298f3cf050d1495679303c3d5 | R | 10,897 | 269 | options(stringsAsFactors=F)
library(tidyverse)
library(janitor)
library(openxlsx)
library(stringr)
library(survival)
setwd('~/d/sci/src/anle138b/')
#### RML STUDY
read.xlsx('received/promising/RMLinocGFAPlucDec2014.xlsx', sheet='Master') %>%
clean_names() %>%
filter(harvest_date=='age to endpoint') %>%
mutate(... |
fb77deaf9daeeaa5c8dfc13ac171e40f7cd2c6869d6b582723319d15bd047996 | R | 10,923 | 311 | #' Signals Captured Conditions
#'
#' Captured conditions that meet the `include` and `exclude`
#' requirements are signaled _in the order as they were captured_.
#'
#' @param future A resolved \link{Future}.
#'
#' @param include A character string of \link[base:conditions]{condition}
#' classes to signal.
#'
#' @param ... |
de78cc44a6b5a570ad98986c5f87ac978f8ad803a7889a7aa6c1cadf6bf2837d | R | 10,964 | 327 | rm(list = ls())
set.seed(12345)
library(Biobase)
options(stringsAsFactors = FALSE);
library(WGCNA);
allowWGCNAThreads()
library(flashClust);
library(gplots)
library(RColorBrewer)
setwd(dirname(rstudioapi::callFun("getActiveDocumentContext")$path))
# use data from prior step or start from load data below
eset<-readRD... |
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