sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
7e9e736b8cda75734e1eeb956a0a3e2513df7a244e1760f6652c985f82d6c23e | R | 6,119 | 207 | #' @importFrom R6 R6Class
#' @importFrom randomForestSRC rfsrc vimp get.mv.vimp
#' @include utils.R
#' @include model.R
#' @include model_helpers.R
RandomForestModel <- R6Class(
classname = "RandomForestModel",
inherit = Model,
public = list(
# Properties --------------------------------------------------
... |
188addfdbbc1f39437cd543c5fdf63a2156fcf639392ecd0262eef2440d9ba91 | R | 6,161 | 126 | library(dplyr)
library(purrr)
library(SummarizedExperiment); library(qs);library(ggplot2); library(gridExtra);library(pryr); library(tibble); library(furrr); library(tidyr); library(readr)
library(dplyr); library(data.table); library(grid); library(limma); library(pheatmap); library(RColorBrewer); library(broom); li... |
9458c817ede81ac61f7b9587a04942b6277ab60718de04f1747c28609de2798e | R | 6,219 | 168 | # Prepare covariates
#------------caQTL--------------#
options(stringAsFactors = F)
outdir = "/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/phenotype/caQTL/"
library(DESeq2);
library(limma);
library(statmod);
options(stringsAsFactors=FALSE);
##load vst count number
load("/proj/steinlab/projects/R00/ATACpreproce... |
4ca1f3e0fa501bd8ec4199fb34fbab0632f0a11d5667381a79eb63a6c41b96b7 | R | 6,227 | 269 | data(Iris)
test_that("Univariate numeric (no tuning)", {
model <- generalized_boosted_machine(x_num, y_num, seed = 1, verbose = FALSE)
expect_generalized_boosted_machine(
model = model,
x = x_num,
y = y_num,
hyperparams = list(
trees_number = 100,
max_depth = 1,
node_size = 10,
... |
22855f253b8d2095c544c63d9361645e16bb7d227fa3e16952b63fcbbb1400e0 | R | 6,248 | 137 | groupEBE <- function(data=NA,idCol="subject",RefCol="reference",deviceCol="device",
stage=0,stageLabel="wake",metricsType="avg",CI.type="classic",CI.level=.95,boot.type="basic",boot.R=10000,
advancedMetrics=FALSE,doROC=FALSE,digits=2){
# renaming variables
colnames(... |
d5016b9f3039f6fab283d7885a66eeaf35af9059410e39a80e711076e585d9ea | R | 6,255 | 131 |
#' get_celltype_mapping
#'
#' For each cell, returns an estimate of its cell type based on cell type labels of neighbors in Selection graph.
#' Requires at least 2 genes for the Selection graph. If cell type mapping for the Selection graph is not possible, returns NULL.
#'
#' @param sce SingleCellExperiment object co... |
18f7e288cae0fe115b0e315cffbcb456eab5066c8ba4cf02da60f950119768dc | R | 6,317 | 150 | options(stringsAsFactors = FALSE)
library(data.table)
##################################################################################
##################################################################################
###
### Condition mediators for each pair of listed SNP-gene candidates
###
### Author: Nil... |
c805171858967673f58a91aa4aceb66abcc9d747833b8d62594153e394978f72 | R | 6,346 | 162 | #' @include utils.R
#' @include validator.R
#' @include random_forest_model.R
#' @title Fit a Generalized Boosted Machine (GBM)
#'
#' @templateVar ClassName GeneralizedBoostedMachineModel
#' @templateVar XType matrix
#' @templateVar YType `vector`
#' @templateVar refFunction gbm::gbm.fit()
#'
#' @description
#' `gener... |
fc333249f3bc1f35a772142100047303ad380635023f0e02413b60a0396ba744 | R | 6,402 | 184 | #Statistical analysis (Relationsip between Volume & BodyMass as an example)
##"SurfaceArea", "Main_V", "Main_SA" can be inserted instead of "Volume".
##"SkullVolume" "HeadModel" can be insterted instaed of "BodyMass".
setwd("")
library(caper)
library(smatr)
library(maptools)
library(phytools)
library(geiger)
library... |
fbb68ae8e77a5970b7932f5676236bce351ab340caf37c6a393104460f7cb8d1 | R | 6,407 | 213 | library(reshape2)
library(ggplot2)
source('global.R')
Filter_gene <- function( order.gene.df, P.val.cutoff,
FC.val.cutoff, gender ) {
if(gender =='F'){
logFC <- log2(1/as.numeric(FC.val.cutoff))
gene.sig <- order.gene.df[ order.gene.df[["P.Value"]] <= P.val.cutoff
... |
759770243f14f19555eb0cec0e47600992ac5076e7a579f69a422caedaf570e9 | R | 6,493 | 139 | ####################################################################################################
# Script to erform WGCNA to obtain samples match HP-DG networks #
# "Cell-population enrichment" studies #
# ... |
5eac8f85ececc9f318615008e1ff3d2bfb6d3e16a7bb84a4c4fa5cb37a124a44 | R | 6,521 | 137 | ####################################################################################################
# Script to erform WGCNA to obtain samples match HP-DG networks #
# "Cell-population enrichment" studies #
# ... |
9c9b4b721fbb18e56f7c0ae6b13dfe1d1b5e64fb88236ef07ba7262946d9528f | R | 6,555 | 154 | ---
title: "Custom code for 'Lonely individuals process the world in idiosyncratic ways'"
author: "Elisa Baek"
date: "9/22/2022"
output: html_document
---
This is the custom code that was used for the main analyses for the manuscript "Lonely individuals process the world in idiosyncratic ways" under revision at <i>Psy... |
476277442b1508de5d7dd49678f2a6a0be1e1464c8243cb47405ed870ae40a99 | R | 6,585 | 144 | ###############################################################################################
# Script to Estimate beta for WGCNA via connectivity match #
# iPSC data[noKO] #
#############################... |
7d2b98ddcbc6f2791ea627f432161beb58dc644988dbd6bb684cc22a4c36a0f0 | R | 6,619 | 176 | # This script generates plots with the results of multivariate modeling
insert_head()
# data container -----
multi_plots <- list()
# globals: lasso coefficients, fits, stats ------
insert_msg('Globals setup')
## variable extraction regex
multi_plots$var_regex <- multi_cox$var... |
0b30b45b1984b36cdf3c0a0b57e922f27561dd689ca09f072b52b3ddc6e8d6a2 | R | 6,653 | 150 | ###############################################################################################
# Script to generate WGCNA networks and save output #
# "Regional-coexpression" and "Age-period" studies #
# ... |
61dddddc03c990802de4a0b963c67e4b3a94aead467a4920542ea1119f257088 | R | 6,661 | 148 | ##########################################################################################
## Significant interactors overlap enrichment Heatmap for each bait IPs
##
## Author: Joshua King To Ching
##########################################################################################
library(circlize)
library(Com... |
8fc096e8fd8a461a3dd7bc5c5170adef6c446684bf84ca41cbda1c4e5102ed98 | R | 6,670 | 163 |
#' plot_coexpression
#'
#' @param sce SingleCellExperiment object containing gene counts matrix (stored in 'logcounts' assay).
#' @param genes Character vector containing gene names to evaluate for co-expression.
#' @param title String to be passed as a title. Default title=NULL.
#' @param ... Additional arguments to... |
7fe83af37d4fa01912dcb7b3486fbca443103ff89502db879fb6b05f9f7f771d | R | 6,702 | 132 | library(dplyr)
library(purrr)
library(SummarizedExperiment); library(qs);library(ggplot2); library(gridExtra);library(pryr); library(tibble); library(furrr); library(tidyr); library(readr)
library(dplyr); library(data.table); library(grid); library(limma); library(pheatmap); library(RColorBrewer); library(broom); li... |
2fcd23ebc553c1174a50118e18c76587fae2e6f11a9e7b8ababef95c09dc4d1f | R | 6,724 | 180 | options(stringAsFactors = F)
setwd("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/trans-eQTL/scripts/model/")
files = list.files(path = "/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/trans-eQTL/scripts/model/progenitor",
pattern=glob2rx("*.hsq.RData"),
full.names = T)
pro... |
75ac9dade47bdb0a392938623b286f79a4405e60e14bb29a3d0f9f51cbe3fe21 | R | 6,755 | 188 | # This script contains functions to construct kNN_graphs (designated for the internal use)
#'
.get_mapping = function(sce , genes = rownames(sce), batch = NULL, n.neigh = 5, nPC = 50 , cosine = F){
if (is.null(batch)){
out = .get_mapping_single_batch(sce , genes = genes, n.neigh = n.neigh, nPC = nPC , cosine = c... |
1245513e22931e518cfb3812aff80874e0a40bf84bae594dfbbc840fe8f07c25 | R | 6,814 | 171 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Benckmark scSpace with other methods on 140 simulated datasets
```{r}
library(ggplot2)
library(ggthemes)
library(ggsignif)
library(mclust)
library(RColorBrewer)
```
### Load data
```{r}
# Ground Truth
sim_gt <- readRDS('../data/simulation/sim_grou... |
799f2409b339e24d5e0e963941dd93f5214fbb6a85ed226de4175e8777e40e02 | R | 6,824 | 152 | #!/home/ajl1213/anaconda2/bin/R
library(Seurat)
library(ggplot2)
library(RColorBrewer)
library(viridis)
seuset <- readRDS('SeuratObjects/PD.SN.snRNA.postAlign.rds')
table(seuset@meta.data$orig.ident)
table(seuset@meta.data$disease)
##================================================================================... |
b75ecd0296ee773b9c33ce41471a76bd9120da9a924458141e655ef4b49c65db | R | 6,830 | 157 | ###############################################################################################
# Script to Perform WGCNA to obtain iPSC network #
# iPSC data[noKO] #
#############################... |
913f18a6bbc77fcf8f2148c76a27707113e77921157f62bf898b960e43df7f2a | R | 6,869 | 201 | ---
title: "graphAnalyses_Hubs_Matlab_BCT"
author: "Sandra Martin"
date: "2/8/2022"
output:
md_document:
variant: markdown_github
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE, echo=FALSE}
rm(list= ls()) # clear all
knitr::opts_chunk$set()
options(scipen = 999)
```
# Load package... |
d7804129101437e6e1c785e00e3971d4abd904a60b5fad686e66f1d5674f71f2 | R | 6,876 | 199 | ##########################################################################################
## Compare and plot co-expression z-scores between bait-interactor pairs vs. other
## gene pairs, using co-expression data derived from 4 transcriptomic datasets:
## Stickels2021, Maynard2021, Velmeshev2019, BrainSpan
##
## Autho... |
d2a21245f6c87cceaec6674cd5939a694fba6b37834dc252ea647f330112c04c | R | 6,914 | 168 | ##########################################################################################
## ##
## Functions to combine pipeline runs for individual samples into something more useful ##
## ... |
898c2fe348fdc79c865a8647ecc02c413a09c96ab9e6fed5c83c8bd5acb6e17a | R | 6,986 | 156 | ###############################################################################################
# Script to Perform WGCNA to obtain iPSC networks #
# iPSC data[shuffle/KO] #
#############################... |
dce6706c635178d3e5403bfcc7ea46bc622af6432e0ce1c7be21feeb8309f995 | R | 7,016 | 157 | ###############################################################################################
# Script to estimate beta for WGCNA based on connecitivity match #
# "Regional-coexpression" and "Age-period" studies #
# ... |
4902630aab102d7b04c97668a1fccd183f2a17147198fc94c6e921a87482c524 | R | 7,079 | 168 | #' @include utils.R
#' @include validator.R
#' @include bayesian_model_model.R
#' @title Fit a Bayesian Generalized Linear Regression Model (BGLR)
#'
#' @templateVar ClassName BayesianModel
#' @templateVar XType list
#' @templateVar YType `vector` or `matrix`
#' @templateVar refFunction BGLR::BGLR()
#'
#' @description... |
08dd60eaac9f3a5184a9cd5bf56df15854db1e4d77974cc4d2b8fc7acd62ce57 | R | 7,090 | 147 |
#' raw_to_sce
#'
#' Essentially a parser: raw counts (or log-normalized counts) stored in a file (e.g. .txt) -> SingleCellExperiment object of the right format.
#' If raw counts are passed, log-normalization is performed (optional but recommended) and logcounts will be used downstream.
#' If no batch is supplied, si... |
a4bb94e2cb6c0f483a0fe44733c35322451949d72c81e2c7f1fbf3287ad63d3c | R | 7,118 | 153 | ###############################################################################################
# Script to generate WGCNA networks and save output #
# "Regional-coexpression" and "Age-period" studies #
# ... |
28e9a4b26b18789b83c0ce2c939c9bcfb2089fec94fcaed68e63c17a1eee94c4 | R | 7,138 | 184 | #' Sci Data Paper Figure 2
#' @import grid
#' @import gridExtra
#' @import cowplot
#' @import ggplot2
#' @import reshape2
#'
#' @return figure2.pdf
#'
#' @export
get_fig2=function() {
# warnings off
w <- getOption("warn")
options(warn=-1)
if( !exists("fig"))
dir.create("fig")
# load data into local env... |
a738a585f819cd094f3d6ee9fa5bef700b8bb573954d48b5d53d4c2b5b2fc27c | R | 7,195 | 177 |
options(stringAsFactors = F)
library(GenomicRanges)
library(data.table)
library(rtracklayer)
k = commandArgs(trailingOnly=TRUE)[1]
bim = fread(paste0("/work/users/n/i/nil1/replication_data_xQTL/mQTLchr",k,"_50Kb.csv"), data.table=F)
# liftover the data
path = "/proj/steinlab/projects/R00/eQTLanalysis/hg19ToHg38.o... |
afa278bb44a84c55d479454177176178bf31bf6f0e1b6c61c0400c7e1853adf4 | R | 7,202 | 131 | ###############################################################################################
# Script for additional sliding window plots #
# "Sliding window" study #
# ... |
80dc287a03d80c6708cc9e8f412fd8746250ccfaf947808aca7e2fdb4df1aead | R | 7,204 | 239 | #' @importFrom R6 R6Class
#' @importFrom nlme fixef
#' @include lme4GS.R
#' @include model.R
#' @include model_helpers.R
MixedModel <- R6Class(
classname = "MixedModel",
inherit = Model,
public = list(
# Constructor --------------------------------------------------
initialize = function(..., testing_i... |
ef273075e9055d3554c694a68510ddb0e3d1eaa3af629f0c32e93a100634992c | R | 7,269 | 199 | # Explorative data analysis and comparison of the study features
# between the cohorts
# tools -------
library(plyr)
library(tidyverse)
library(trafo)
library(soucer)
library(exda)
library(survival)
library(survminer)
# container list ----
data_ex <- list()
# globals -----
... |
acf3cb1374e3f5785b5a76171d319ae4d44b985bf890d930edef6393eaa40886 | R | 7,318 | 173 |
#------------------------------------------------------------------------------------
# PACKAGES #
packages = c("tidyr", "plyr", "dplyr", "reshape2", "seqinr", "ggplot2", "coefplot",
"forcats", "tibble", "stringr", "purrr", "gridExtra", "pracma", "hablar")
invisible(lapply(packages, library, characte... |
4cb2d56b191dbd8295659586fd5e32ca4c66a770ec926a8e1e66a1eaa9ddd2ec | R | 7,381 | 178 | #GNT SVM 3 Group Classifier build process:
library(minfi)
library(ConsensusClusterPlus)
library(Rtsne)
library(ggplot2)
library(caret)
library(doParallel)
library(MLmetrics)
#Read in raw training (450k) and test (EPIC) data and combine into one array
baseDir <- getwd()
targets <- read.metharray.sheet(baseDir, pattern ... |
3b9b1d3ce3aaa6866b7913c0c4fe923fd56cbc7f88bbc0a448b7cb9fa46c45d8 | R | 7,416 | 157 | ###############################################################################################
# Script to estimate beta for WGCNA based on connecitivity match #
# "Regional-coexpression" and "Age-period" studies #
# ... |
fd2d89c455d30678e3eb4175bcf15088a498445c2506f12995d547ff813dc490 | R | 7,425 | 191 | #' @include utils.R
#' @include validator.R
#' @include random_forest_model.R
#' @title Fit a Random Forest Model
#'
#' @templateVar ClassName RandomForestModel
#' @templateVar XType `matrix`
#' @templateVar YType `vector` or `data.frame`
#' @templateVar refFunction randomForestSRC::rfsrc()
#'
#' @description
#' `rand... |
0b1c5a6bd6eb76b1bfa5c76a7da2821317dad9b36a48f48c75738504deeede61 | R | 7,449 | 176 | #!/home/ajl1213/anaconda2/bin/R
library(Seurat)
library(Rmagic)
library(ggplot2)
library(RColorBrewer)
library(viridis)
seuset <- readRDS('SeuratObjects/PD.SN.snRNA.postAlign.rds')
##=======================================================================================================
## theme:
my_theme <- theme(... |
749a7bf13c97bcf6a55f6757b511847637670784c7da54b6857081ac397ffac3 | R | 7,474 | 132 |
#' gene_search
#'
#' Main function of the package - returns optimal library of the selected size.
#'
#' @param sce SingleCellExperiment object containing gene counts matrix (stored in 'logcounts' assay).
#' @param genes_base Character vector specifying base genes to construct first Selection graph. Default=NULL in ca... |
56667d6d9e4c7ce67ddc5c11c3a8c284fc4b15a0e60360c6a993dce50dfec50e | R | 7,499 | 229 | # Clustering of the participants with the optimal clustering algorithm
# chosen by cross-validation and variance fraction comparison
insert_head()
# container list -----
clust <- list()
# parallel backend ------
insert_msg('Parallel backend')
plan('multisession')
# globals ---... |
ce6c6afecde04a6596a5ef8de2b19580809ca6f97e1d06e20ccd7bcbf2938e1a | R | 7,587 | 244 | # Random Forest ensemble of the established risk assessment tools
# and of the newly developed Elastic Net score
insert_head()
# container ------
rf_tools <- list()
# parallel backend -------
insert_msg('Parallel backend')
plan('multisession')
# analysis globals ------
i... |
2fec5195baec65c256f45b8b8470c7827f28a9709240833fc8058c60318078c8 | R | 7,588 | 150 | #################################################################
# Function for MAGMA enrichment preprocessing #
# Annotation and Gene Analysis steps #
#################################################################
#### MAGMA enrich preprocess ####
MAGMA_enr... |
9afedf74d0479c7d1ee3cdc46fc81d838a9fa0e682263491de14f4798b04abdb | R | 7,607 | 215 | #' @include utils.R
#' @include validator.R
#' @include support_vector_machine_model.R
#' @title Fit a Support Vector Machine (SVM)
#'
#' @templateVar ClassName SupportVectorMachineModel
#' @templateVar XType matrix
#' @templateVar YType `vector`
#' @templateVar refFunction e1071::svm()
#'
#' @description
#' `support_... |
76d35d539962f19f4f51b177ebaf8b8cd3d908c7693596f4632e2d94b8fb69c3 | R | 7,627 | 159 | #################################################################
# Function for MAGMA enrichment processing #
# Gene Level Analysis step #
#################################################################
#### MAGMA enrich ####
MAGMA_enrich = functio... |
df63afd96baf8b9682719cf00a879e0c34c3088f9f837af84cf0a17f23942e8c | R | 7,690 | 212 | ##########################################################################################
## Generate volcano and scatter plots for SCZ IP-MS datasets
##
## Author: Yu-Han Hsu
##########################################################################################
rm(list=ls())
library(readxl)
library(ggplot2)
lib... |
95688571e820f8888333631032a57cc13252dfc5aae02ecd6647bf37aa36e454 | R | 7,757 | 220 | ##########################################################################################
## Generate SCZ social Manhattan plot using PGC3 GWAS data
##
## Author: Yu-Han Hsu
##########################################################################################
rm(list=ls())
library(ggplot2)
library(dplyr)
librar... |
075ccd464db27ca2bac2067e4eb804b0325c6fe021f13b8c6e16886a33fb2ef4 | R | 7,868 | 229 | # Ridge ensemble of the established risk assessment tools
insert_head()
# container ------
lasso_tools <- list()
# parallel backend -------
insert_msg('Parallel backend')
plan('multisession')
# analysis globals ------
insert_msg('Analysis globals')
## analysis variables
l... |
31f481d8d4513996c443e502d83f37492a393bafdfd73d4a374c5d344f39440a | R | 8,032 | 204 |
############################################################################
######## Figure 2A
###########################################################################
library(ggplot2)
link = ""
raw = read.table(paste0("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/genotype/caQTL/",type,"/chr",chr,"/chr3_7452... |
f2d8f2583551c59d20d7a08e245925ccde5a6e0009cfe8c9a24e3615e553d8ce | R | 8,088 | 189 | ###############################################################################################
# Script to create shoffle/KO networks #
# Script to Estimate beta for WGCNA via connectivity match #
# ... |
d09d4143f6ba283c60f72ef2cf7c2047532abe55c44cbb1686299addbd970879 | R | 8,102 | 145 | errorMatrix <- function(data=NA,idCol="subject",RefCol="reference",deviceCol="device",
staging=TRUE,stages=c(wake=0,light=1,deep=2,REM=3),matrixType="prop",
CI.type="classic",CI.level=.95,boot.type="basic",boot.R=10000,digits=2){
# renaming variables
colnames(... |
69d084fab5ca27d548b39d50a7688cbfbc24be663a64839e562cca5ba40a56fc | R | 8,115 | 125 | setwd("C:/GitHub/twin-data-models")
rm(list = ls())
# Load libraries
library(tidyverse)
#### Setup participants datafile ####
qtab.participants <- read_delim("non_imaging_phenotypes/participants_restricted.tsv", delim = "\t", na = "n/a")
# Recode sex from M/F to sex_female 0/1
qtab.participants <- qtab.participants ... |
3eb3c02fd9b8f7ae14724031a9f88d1bc3a7d3cde1b9afbac78d5a0d3cb827bd | R | 8,139 | 279 | # custom functions for downstream analysis
run_RCTD <- function(sc_meta,
sc_data,
st_meta,
st_data,
celltype_index){
### Create the Reference object
cell_types <- as.factor(sc_meta[[celltype_index]]); names(cell_types) <- colnames... |
ca2005ac5ea966d16e4f8227572c900a057675648453b5c42908524e61720f89 | R | 8,173 | 212 | # DESCRIPTION:
# The degree to which edges are more dense within communites and more
# sparse between communities, which quantifies the segregation of a
# weighted network (Chan et al. 2014).
#
# Inputs: M, Correlation matrix
# Ci, Community affiliation vector (e.g., system labels)
# Optiona... |
6ab63eff9c2f538c2f2db851202b3e5679b08f405da71711823d66eefd534c6e | R | 8,227 | 224 | #
# ggbiplot.r
#
# Copyright 2011 Vincent Q. Vu.
#
# This program is free software; you can redistribute it and/or
# modify it under the terms of the GNU General Public License
# as published by the Free Software Foundation; either version 2
# of the License, or (at your option) any later version.
#
# This... |
084d6b61ea662c083b14a1a925c9e7ec49b0f5f0f17581582cf46b8fb47c1b1d | R | 8,235 | 155 |
#' evaluate_library
#'
#' For the selected library, returns estimates of the library quality (at cell, gene and/or celltype levels) as a function of number of genes.
#' Grid of number of genes is specified with 'library.size_type' and 'n_genes.step' arguments. For each type of stat (cell, gene and/or celltype) return... |
306f0e45724dfa2f876ccb612a5362eece0308a499b6dc721588b5ef9e535780 | R | 8,484 | 285 | #' @importFrom R6 R6Class
#' @importFrom pls plsr selectNcomp MSEP RMSEP mvrValstats
#' @include utils.R
#' @include globals.R
#' @include model.R
#' @include model_helpers.R
PartialLeastSquaresModel <- R6Class(
classname = "PartialLeastSquaresModel",
inherit = Model,
public = list(
# Properties -----------... |
9bc0b536388e65c4f6b4187da9150261c184a43bee698ab005f6667defc84bcd | R | 8,650 | 321 | #' @import dplyr
#' @include utils.R
#' @include metrics.R
#' @include validator.R
lm_intercept <- function(x, y) {
if (all(is.na(x)) || all(is.na(y))) {
return(NaN)
}
Data <- data.frame(x = x, y = y)
return(lm(y ~ x, data = Data)$coefficients[1])
}
lm_slope <- function(x, y) {
if (all(is.na(x)) || al... |
5d50c627df31dd8127e918176e6eabcab79c3b3d30e1d36e3f708bca3ffb9548 | R | 8,671 | 266 | options(stringAsFactors = F)
library(bmediatR)
library(data.table)
type = "progenitor"
setwd("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/bmediate/scripts/")
##################################################################################
temp.link = "/pine/scr/n/i/nil1/tmp/"
pheno.link = "/proj/steinlab/pro... |
417caf0ef3c0a91b16cb8087e7673a853b0511b5fb2584db49af1f8f515f9d09 | R | 8,693 | 266 | options(stringAsFactors = F)
library(bmediatR)
library(data.table)
type = "progenitor"
setwd("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/bmediate/scripts/")
##################################################################################
temp.link = "/pine/scr/n/i/nil1/tmp/"
pheno.link = "/proj/steinlab/pro... |
c27d2f1da7855b53d2a4d8ff0039bfab885091880c6273e2a6cdd03a947af522 | R | 8,700 | 221 | ###############################################################################################
# Script to Perform WGCNA to obtain sliding window networks #
# "Sliding window" study #
# ... |
e3a795463dd2ea80bd9de0df342b271dc2bfabfdb89a2b979919b9fded04051f | R | 8,704 | 273 | #' @import dplyr
#' @importFrom R6 R6Class
#' @include utils.R
#' @include bayesian_model.R
#' @include gs_cross_evaluator.R
#' @include gs_bayesian_predictor_preparator.R
GSFastBayesianCrossEvaluator <- R6Class(
classname = "GSFastBayesianCrossEvaluator",
inherit = GSCrossEvaluator,
public = list(
# Prope... |
61ede35cbee70189c7f3ac16d86652315847ca1bbbc5458d3945d0814537e0fc | R | 8,710 | 241 | #####################################################
#####################################################
## Eliminate FP cis-eQTLs
##
## Author Nil Aygun
#####################################################
#####################################################
setwd("/proj/steinlab/projects/R00/eQTLanalysis/path_Q... |
4b109667a5e830894c45c5229d35bc22e1c09f213f8a6bf3c480e1b40cf13c4b | R | 8,847 | 202 | ####################################################################################################
# Script to Preprocess samples match rse data for HP-DG #
# "Cell-population enrichment" studies #
# ... |
a4c5879561fda991f5f498c6d8793af514156c938912a7b5c255611d5272c1af | R | 8,858 | 219 | ###############################################################################################
# Script to Perform WGCNA to obtain sliding window networks #
# "Sliding window" study #
# ... |
d7176cf8b9af3ae77decac9addff29ff23c3c56dbd32936d5a36505c96736c52 | R | 8,912 | 253 | # Characteristic of the participant clusters. The features are compared with tow-tailed T test
insert_head()
# container list ----
cl_chara <- list()
# analysis tables ------
insert_msg('Analysis tables')
## analysis tables
cl_chara$analysis_tbl <- clust$clust_obj %>%
ma... |
53d36b71de27ab1c6e4f99780801f5cb56f6c559d3d873fa9b39580d1ec5d745 | R | 9,031 | 244 | # ------------------------------------------------------------------------------
# Program: miFunctions.R
# Author: Hermine Maes
# Date: 02 21 2018
#
# Set of my options & functions used in basic twin methodology scripts
# Email: hmaes@vcu.edu
# -------|---------|---------|---------|---------|---------|-------... |
3a338cc5ba719b114363aedbc7c8bda329f538ed3c67aa01c7b2d499b678cedc | R | 9,053 | 264 | # Searches of the optimal clustering procedure in the exploration IBK cohort
# The cslustering features identified by elastic net regression
insert_head()
# container list ----
clust_dev <- list()
# parallel backend ------
insert_msg('Parallel backend')
plan('multisession')
# g... |
4f6a43931f911e80ae643f79bbb72cf05dd472aaa65b2abd644e156526c632c8 | R | 9,086 | 221 | ##########################################################################################
## Generate plots to show common variant enrichment results of SCZ PPI networks
## (1) MAGMA results
## (2) GRS results
##
## Author: Yu-Han Hsu
####################################################################################... |
cda4da11c0106b24848c8bf843c61180b583d9cf674a145cdc170dbdab72fe5a | R | 9,195 | 203 | library(data.table)
library(DESeq2)
library(readxl)
library(stringr)
samples_DRG_paired <- paste0('SRR853396', c(0:5))
samples_DRG_single <- paste0('SRR85339', c(66:86))
samples_DRG_paired
samples_DRG_single
# trimming - single-----
dir <- '/data/NCATS_ifx/data/dbGaP/DRGs/SRA/FASTQs'
samples <- unique(samples_DRG_si... |
b83de0ffaf2607b48b334f890d18edcd9d8c86a0dbf2cde7421529783cefd00a | R | 9,216 | 254 | # context("Testing calc_Minkowski_distances")
library(geneBasisR)
### set up inputs
## libs
library(SingleCellExperiment)
set.seed(32)
## toy data
counts = matrix(c(rep(0,10),1,1,1,0,0, 1,1,1,0,0, 1,1, 0,0,0,1,0,0,0,0,0,1,1,0,0), nrow = 5, ncol = 7)
sce_correct = SingleCellExperiment(list(logcounts = counts))
colnam... |
9e7de018c208069a2d5939c29dc0d04d7c407a55e4bceb8d51fb626b90ef1c66 | R | 9,247 | 210 | ###############################################################################################
# Script to Preprocess tissue rse data "Sliding window" study #
# (SCZ samples only) #
################################... |
6ffb3a2019953114c866ee5c48120fda5d4a563c08cd79ca247e12369971472d | R | 9,271 | 285 | # Motif serach for figure 5, SLC26A7 gene locus
setwd("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/")
options(stringsAsFactors = F)
library(data.table)
library(Biostrings)
library(BSgenome.Hsapiens.UCSC.hg38)
library(TFBSTools)
library(JASPAR2020)
# chr8:91180477:GT:G
start = 91180477 - 50
end = 91180477 + 5... |
0e6f0cfd44a5a6738689c24bf8e6609ede85f1bab503bf56ede53750918e9ce4 | R | 9,275 | 281 | ---
title: "OMST"
author: "Sandra Martin"
date: "2/2/2022"
output:
md_document:
variant: markdown_github
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE, echo=FALSE}
rm(list= ls()) # clear all
knitr::opts_chunk$set()
options(scipen = 999)
```
# Load packages
```{r packages, message... |
29b3703b217c7a5c508a1e75633db2bc41bd5d2e2ceaf3f9629dba7b3e889512 | R | 9,278 | 183 |
#' get_gene_prediction_scores
#'
#' Calculates gene prediction scores by comparing True and Selection k-NN graphs.
#'
#' @param sce SingleCellExperiment object containing gene counts matrix (stored in 'logcounts' assay).
#' @param genes.selection Character vector containing names of selected genes.
#' @param genes.al... |
d7551f92e40ef86a7f896f7857afb2131a981188e8cee6e79b01bfe662f9f812 | R | 9,298 | 168 | # Lachlan T Strike
# Based on scripts provided by Hermine Maes (https://hermine-maes.squarespace.com)
# Direct estimates of variance components
#### Load libraries ####
library(OpenMx)
library(dplyr)
source("miFunctions.R")
#mxOption(NULL, "Default optimizer", "CSOLNP") # NPSOL SLSQP CSOLNP
Univariate_ACE <- function... |
dfa57a14a74835d852cbaf37c44352d92327c4a5329cb1382c26fe96b7ad981f | R | 9,337 | 411 | data(Iris)
test_that("Univariate numeric (no tuning)", {
model <- random_forest(x_num, y_num, seed = 1, verbose = FALSE)
expect_random_forest(
model = model,
x = x_num,
y = y_num,
hyperparams = list(trees_number = 500, node_size = 5),
responses = list(
y = list(type = RESPONSE_TYPES$CONT... |
4782f8ab289cbcc02ef2d3c658c59e6d1983dd620115a2320d97cd6b3c2b71e5 | R | 9,380 | 428 | data(IrisMatrix)
test_that("Univariate numeric (no tuning)", {
model <- generalized_linear_model(x_num, y_num, seed = 1, verbose = FALSE)
expect_generalized_linear_model(
model = model,
x = to_matrix(x_num),
y = y_num,
hyperparams = list(alpha = 1),
responses = list(
y = list(type = RESP... |
47b39e00ee19a1203fda933cf27f75ea9ea6d128b307a0c1e7cb78fd08eb1ad5 | R | 9,555 | 160 | ---
title: "The Combination of Supervised and Unsupervised Learning based Risk Stratification and Phenotyping in Pulmonary Arterial Hypertension - a Long-term Retrospective Multicenter Trial"
subtitle: "Figures and Tables"
author: "Innsbruck PAH registry"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output:
bo... |
7c5ba4991c4eeed3713039f33847a04344edbd1318bd9cc41792714f9931b3f0 | R | 9,614 | 246 | library(shiny)
library(shinydashboard)
library(ggplot2)
library(reshape2)
library(DT)
source("global.R")
source("Fun_Rank_disease.R")
source("enrichr.R")
HealthyAllUI <- function(id,label="RankAll"){
fluidPage(
### First Row ###
fluidRow(
h3("Rank aggregation of Sex-biased genes of all brai... |
3221a335bb5edefb24dba94298451b28d06b7830863727a5484af9f0d460fdaa | R | 9,728 | 196 | # Lachlan T Strike
# Based on scripts provided by Hermine Maes (https://hermine-maes.squarespace.com)
# Univariate model specifying path coefficients and covariates
#### Load libraries ####
library(OpenMx)
library(dplyr)
source("miFunctions.R")
mxOption(NULL, "Default optimizer", "CSOLNP") # NPSOL SLSQP CSOLNP
#### M... |
c13d93daff2038d7f2fa14cfdeee375c50c74102b4edc67ab84c02fce7c6faec | R | 9,780 | 222 | ###############################################################################################
# Script to Preprocess tissue rse data "Regional-coexpression" and "Age-period" studies #
# (NC samples only) #
################################... |
37f433cbe8f6214728e459ca1dcd810b5162b7f73148be45d98d97b8834b7544 | R | 9,837 | 290 | ##########################################################################################
## pLI score enrichment analysis for various SCZ gene sets using KS test
##
## Author: Yu-Han Hsu
##########################################################################################
rm(list=ls())
library(genoppi) # for p... |
a434d5ecde0a70bac8e6c9b5f683ea83358b765bc9fc5d0eb9719424d908649b | R | 9,969 | 360 | #' @include utils.R
#' @include globals.R
#' @include validator.R
# Helpers --------------------------------------------------
l2norm <- function(x) sqrt(sum(x^2))
diagAK_f <- function(dAK1) {
AKAK <- dAK1^2
costheta <- dAK1 * AKAK^(-1 / 2)
costheta[which(costheta > 1, arr.ind = TRUE)] <- 1
theta <- acos(cos... |
42dce16157e3a1ad3ea74e7e82ee919c3fad8de74fe7811478333634dd0e7521 | R | 10,016 | 257 | ---
title: "cPPI Results - Plots"
author: "Sandra Martin"
date: "10/27/2021"
output:
md_document:
variant: markdown_github
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE, echo=FALSE}
rm(list= ls()) # clear all
knitr::opts_chunk$set()
options(scipen = 999)
```
# Load packages
```{r... |
9a8ad7728a912c15f6c215b3d4e3a623f18491da451e9da7c9790c1ce66cf4ce | R | 10,034 | 341 | #' @importFrom R6 R6Class
#' @importFrom BGLR BGLR Multitrait
#' @include utils.R
#' @include model.R
#' @include model_helpers.R
#' @include validator.R
BayesianModel <- R6Class(
classname = "BayesianModel",
inherit = Model,
public = list(
# Constructor --------------------------------------------------
... |
c57b3b35eb2d56406d26940333f4e2589e5751c8e846e32dc3578a120bce6ae2 | R | 10,092 | 356 | ####################################################################################################
# Script to test module preservation using WGCNA #
# Preserving DLPFC Perinatal modules in DLPFC Prenatal networks #
# ... |
57a2ffb7add3bda61d7cb09705ee233a2b1f3d6557d677a50dd98b8052b29d3a | R | 10,386 | 270 | ---
title: "NBS results from Matlab - Plots"
author: "Sandra Martin"
date: "11/10/2021"
output:
md_document:
variant: markdown_github
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE, echo=FALSE}
rm(list= ls()) # clear all
knitr::opts_chunk$set()
options(scipen = 999)
```
# Load pa... |
c321d77cc5c0140011f6b82a755df2eaf3c2559dbb00e25f00f121d954932ccc | R | 10,388 | 283 | options(stringAsFactors = F)
library(bmediatR)
library(data.table)
options(stringAsFactors = F)
files = list.files(path = "/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/trans-eQTL/",
pattern = glob2rx("FDR25_MAF025_trans.progenitor.big.bmediate.chr*_updated_w_complete.csv"),
... |
695224f10b5c3c85a6bfe5f1d24af98ee072181f47a468df814239a8cb925680 | R | 10,508 | 285 |
## PURPOSE: generate unique motifs from miRBase data
## INPUT: miRBase data miRNA.xls
## OUTPUT: fasta file motif_list_species_mirbase_22.fa
## table containing motifs species-miRBase22.tsv
require(gdata)
require(plyr)
require(data.table)
base_path<-'C:/Users/Susanna/Documents/NCI/miRBase/'
setwd(base_pat... |
0425f24410e2228d4f4b5033bfab385f08853cee61a2fbb1adf5f1c47b231719 | R | 10,637 | 216 | .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)) {
... |
09dbdab250240121d36a4cd37bc0dde331b706c8b3812325a1bad1e26f189cca | R | 10,663 | 330 |
# Figure 3A-E, evaluate the features that can be predictive to assume causality
options(stringAsFactors = F)
# Data input
pro = read.csv("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/bmediate/data/progenitor_bmediatR_results_final.csv")
neuro = read.csv("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/bmedi... |
0be7c02ac88ade233ff2d3b6e5a728138d4742c90913019a66ef37c6514ba03c | R | 10,672 | 288 | ---
title: "geneBasis, mouseEmbryo."
author:
- Alsu Missarova
output:
BiocStyle::html_document:
toc_float: true
code_folding: hide
number_sections: yes
toc: yes
BiocStyle::pdf_document: default
package: geneBasisR
vignette: >
%\VignetteIndexEntry{geneBasis_mouseEmbryo}
%\VignetteEngine{knitr... |
c9e2e2dbe3f90013d4e3cea54d98dd33f25067efe1c1626ada21cf1246b0e18c | R | 10,995 | 239 | # Taisz, Galili et al., (2023)
# Figure S1F, H, I
# find all downstream cell types of DA1 (Or67d) ORNs that are not ALLNs in the hemibrain dataset
# ORN cell type and hemisphere annotation from Schlegel, Bates et al., (2021)
# see https://github.com/natverse/hemibrainr on how to access hemibrain data
# ----------------... |
14fd78b4a5037b22e54d4d3b842f83812bf94bb576df92c171708883acb3512e | R | 11,098 | 221 | #' @importFrom GPfit predict.GP
#' @title Bayesian Optimization
#'
#' @description
#' Bayesian Optimization of Hyperparameters.
#'
#' @param FUN The function to be maximized. This Function should return a named list with 2 components.
#' The first component "Score" should be the metrics to be maximized, and the seco... |
64ffd5dba74f9a02dc4f36f3869ddc2b24b7dcd7c3a212d1749b05e2fde18675 | R | 11,099 | 224 | #Written by Cameron Wehrfritz
#and Natan Basisty, PhD
#Schilling Lab, Buck Institute for Research on Aging
#Novato, California, USA
#March, 2020
#updated: February 09, 2021
# PROTEIN TURNOVER ANALYSIS
# STEP 4:
# Linear model of log(Percent.Newly.Synthesized) by timepoints and cohorts and their interaction
#
# OUTPUT:... |
2f5f0d971e2d2debc78a9e4f417c282df5c561af16743e8abe7559214c7915c0 | R | 11,259 | 256 | # This script reads and clears data from the PAH Innsbruck study
# libraries -----
library(plyr)
library(tidyverse)
library(readxl)
library(stringi)
library(survival)
library(soucer)
library(figur)
library(trafo)
source_all('./tools/project_tools.R',
message = TRUE,
... |
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