sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d67b587259f45218eb139c7a4c7ab6e29261e583903fe609d94d3dce9bd9fea9 | R | 11,260 | 292 | require(dplyr)
require(optparse)
require(MSstats)
booleanOrString <- function(argument) {
if (argument == "FALSE") {
return(FALSE)
}
if (argument == "TRUE") {
return(TRUE)
}
return(argument)
}
booleanOrNumber <- function(argument) {
if (argument == "FALSE") {
return(FALSE)
}
if (argument ==... |
7d43b0bcd37c58346745e53b851bb613e4bbf2974cdc07dfb8f5015311ce390a | R | 11,343 | 242 | ###############################################################################################
# Script to Arrange samples in sliding age windows and estimate beta for WGCNA #
# via connectivity match #
# ... |
6c0d2084c2ec034caad88eb344f1b947c1d3f6d330055798d4cdb36bccd5a1e8 | R | 11,387 | 242 | ###############################################################################################
# Script to Arrange samples in sliding age windows and estimate beta for WGCNA #
# via connectivity match #
# ... |
fd8c350ce760783c0f4ca02c42ecfed36c77b32874a4bda50e97bc993dc950a3 | R | 11,444 | 252 | ###############################################################################################
# Script to preapre and preprocess iPSC data #
###############################################################################################
options(java.parameters = "-Xm... |
cf84512c747b80c82827f9600a26459f8c1b44ee10ae99261ecf01ca3800b845 | R | 11,558 | 290 |
library(Seurat)
library(Signac)
library(ggplot2)
library(RColorBrewer)
library(viridis)
dir.create('Plots')
##===================================================================================================================================================
## theme:
my_theme <- theme(
axis.line=element_blank(),
... |
aae30c4d4ed2b35b5e04412687efb66a741d971b2185e5db3dcd396ff17f0cbf | R | 11,564 | 322 | #' Calculate relative motion of two bodies
#'
#'This function, similar to oRel in Maya, calculates the relative morion of
#'either 'mobile' 3D point/s or rigid body relative to 'proximal'/reference
#'rigid body. The function is written to handle multiple frames/timepoints of
#'data (see arguments for format), and will ... |
bdcd03e13811125d749e329e2129d242fbe0a82d354380eee29110cc653d4ca3 | R | 11,794 | 233 | ###############################################################################################
# Script to Preprocess tissue rse data "Regional-coexpression" and "Age-period" studies #
# (NC samples only) #
# ... |
4ea2c055103b9128cf2e9df488c37c74ff355ff1155ffd8a43a9b8ab42e562e3 | R | 11,859 | 427 | # Produces Shewhart Control Charts and Pareto Analysis for Evaulating Proteomic Experiments
# for Rv2.13 or later must be installed along with the qcc library from R and Skyline
# (https://skyline.gs.washington.edu/labkey/project/home/software/Skyline/begin.view)
options (echo = FALSE)
debug <- FALSE
# Retention Tim... |
fd4d7e00dbdab98578aedfd02fdedd934cca5ae7992160cdf5493c4142079ca5 | R | 11,955 | 316 | # Comparison of performance of risk stratification tools at predicting
# overall survival
insert_head()
# container ------
surv_tools <- list()
# analysis globals -------
insert_msg('Analysis globals')
## analysis variables
surv_tools$var_lexicon <-
rbind(tibble(variabl... |
36b1fb1f2f376026cee1ef6c85c5d376e12a6182768f8a66d269ffdbab5ece7f | R | 11,978 | 276 | commonfactorGWAS <-function(covstruc=NULL,SNPs=NULL,estimation="DWLS",cores=NULL,toler=FALSE,SNPSE=FALSE,parallel=TRUE,
GC="standard",MPI=FALSE,TWAS=FALSE,smooth_check=FALSE){
# Set toler to machine precision to enable passing this to solve() directly
if (!toler) toler <- .Machine$double... |
8af60c2c7b5765ddacf67591a84626733878f397aa66b037568a1e1ab59e06f4 | R | 12,003 | 340 |
#######################################################################
####### Create Figure 4 to evaluate trans-eQTL
#######################################################################
options(stringAsFactors = F)
library(bmediatR)
library(data.table)
link = "/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/t... |
beff2d84f205d4c9a9f7bbb14761c7b4a93d7b3bcab25ed26d761d331ac2cdc3 | R | 12,008 | 244 | #' get_neighborhood_preservation_scores
#'
#' Calculates cell neighborhood preservation scores by comparing distances to the neighbors from True and Selection k-NN graphs.
#'
#' @param sce SingleCellExperiment object containing gene counts matrix (stored in 'logcounts' assay).
#' @param neighs.all_stat If not NULL, sho... |
a365ba02c31953b306b5d6e12429109c843ff59e9a9d5c2d2f08ce21d292256c | R | 12,143 | 261 | # This script generates paper tables
insert_head()
# data container ----
paper_tables <- list()
suppl_tables <- list()
# Table 1: cohort characteristic ------
insert_msg('Table 1: cohort characteristic')
paper_tables$cohort_features <- data_ex$result_table %>%
filter(variabl... |
0fc9911adf80ac4eb0ed238f143fcbde85e983d567dc4f74f1b3d2847015d256 | R | 12,236 | 257 | library(ggplot2)
library(ggpubr)
library(gridExtra)
setwd("/Volumes/SizhuFiles/TMS+EEGstudy/Main/visualization/data")
## Oscillation: hc vs. patients ##
rm(list=ls(all=TRUE))
data <- read.csv("HCvsPats.csv",header = T)
Group <- c(rep(c(rep("HC",64),rep("MDD",53)),5))
Freq <- c(rep("Delta",117),rep("Theta",117),rep("... |
cbc5b832f15b1237ce344baa2e1f061414620c71ac9c6cdf603440ba1c4b693f | R | 12,414 | 366 |
#################################################################################
# FigureS3 A
#################################################################################
options(stringAsFactors = F)
link = "/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/conditional/"
cell = "progenitor"
L = "P"
setwd("/pro... |
0003931deee1e7542f8b04741abb791c2cb337caecdb77c57673d9cbd0c5d756 | R | 12,502 | 273 | # $Id: QuaSAR-GP.R 107 2012-06-25 18:37:49Z rahmad $
print ("$Id: QuaSAR-GP.R 107 2012-06-25 18:37:49Z rahmad $", quote=FALSE)
# The Broad Institute of MIT and Harvard
# SOFTWARE COPYRIGHT NOTICE AGREEMENT
# This software and its documentation are copyright (2009) by the
# Broad Institute. All rights are reserved.
# ... |
4b41311ea22d7558b5c2e9098a903e89f2c8b304180bd56851da3fa2a4a82631 | R | 12,651 | 398 | #' @importFrom R6 R6Class
#' @include utils.R
#' @include tuner.R
#' @include model_helpers.R
#' @include validator.R
Model <- R6Class(
classname = "Model",
public = list(
# Properties --------------------------------------------------
name = NULL,
is_multivariate = NULL,
allow_coefficients = NUL... |
fb2d4ae3e21ad4c5405b171cded9003d01cc1bf1a28be0fdb0a0633971ca0d9d | R | 12,866 | 353 | # Contains various check functions to examine whether variables are of the right format
#' @importFrom SingleCellExperiment logcounts counts
#' @importFrom SummarizedExperiment assays
.prepare_sce_counts = function(sce){
if (!is(sce , "SingleCellExperiment")){
stop("sce should be a SingleCellExperiment object."... |
7d9abb2376625a5d80ca38d2c0f874a4924fcc04fe3c80cb33012394074f1133 | R | 12,872 | 357 | ---
title: "Graph Analyses- Segregation"
author: "Sandra Martin"
date: "11/17/2021"
output:
md_document:
variant: markdown_github
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE, echo=FALSE}
rm(list= ls()) # clear all
knitr::opts_chunk$set()
options(scipen = 999)
```
# Load package... |
942de855f5e48119b69b1bcea025542e05c8232b993a30ef2fdee29637715fcf | R | 12,959 | 361 |
library(Seurat)
library(Signac)
library(ggplot2)
library(RColorBrewer)
library(viridis)
seuset.atac <- readRDS('SeuratObjects/PD.SN.snATAC.postAlign.geneExp.label.anno.rds')
##===========================================================================================================================================... |
40d1eb2e1622038173a5afdd1e51c3db9a8871eeda644976609e07eef885c4f7 | R | 13,266 | 479 | ################################################################################
# This file has been taken from the lm4GS github package:
# https://raw.githubusercontent.com/perpdgo/lme4GS/master/pkg_src/lme4GS/R/lmerUvcov.R
################################################################################
#' @import l... |
b89a02f449b72b124baf73da65439a13790de69f0c321d68c1c1a2131d63d0b0 | R | 13,284 | 241 | ####################################################################################################
# Script to collate and combine network data from other published networks #
# "Regional-coexpression" study #
# ... |
fb6fe78372b798b7b83547e9186961a6a176154d48b2a56b0dd2a30295d26116 | R | 13,421 | 428 | # A medley of functional project tools
require(plyr)
require(tidyverse)
require(rlang)
require(cowplot)
require(survival)
require(stringi)
require(caret)
# import helpers ----
det_mortality <- function(inp_tbl, update = T) {
# identifies if the patient survived in the study... |
a7fe71ac5ac4c360d803bfbcdfe6cc6e64d74d406973ee8622c82df6ba24ffdb | R | 13,453 | 369 | options(stringAsFactors = F)
library(bmediatR)
library(data.table)
type = "progenitor"
setwd("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/bmediate/scripts/")
gtf = fread("/proj/steinlab/projects/R00/eQTLanalysis/phenofiles/gene_transcript_v92.csv",data.table = F)
gtf = gtf[which(gtf$type == "gene"),]
idx = whic... |
cc58f124f64ed67316fe59536f7e65963b0433a4aece1b275f44db7d0c0c72d2 | R | 13,732 | 354 | ##################################################################################################
# Figure 5A
##################################################################################################
chr = 8
trait = "G-temporal-middle_cortical_area"
gwas_dir = "/proj/steinlab/projects/R00/eQTLanalysis/Disease... |
e400d76d80b06b546af4bfc1353069cb17e6a0b6da818e6d08836c1b1285d911 | R | 13,746 | 283 | ###############################################################################################
# Script to calculate and plot SCZ enrichment measures for sliding window networks #
# "Sliding window" study #
# ... |
4566382317aec93af16b258045d2472bb210555cc689338519e3502177c4c894 | R | 14,111 | 318 | # context("Testing gene_prediction_scores")
library(geneBasisR)
### set up inputs
## libs
library(SingleCellExperiment)
set.seed(32)
## toy data
counts = matrix(c(rep(0,10),1,1,1,0,0, 1,1,1,0,0, 1,1, 0,0,0,1,0,0,0,0,0,1,1,0,0), nrow = 5, ncol = 7)
sce_correct = SingleCellExperiment(list(logcounts = counts))
colnames... |
d95638dbba189fccaf8015a934055e203adbce1045ff63c58b23fc3e6fb36f18 | R | 14,166 | 345 | ---
title: "Behavioral relevance - cPPI results"
author: "Sandra Martin"
date: "11/14/2021"
output:
md_document:
variant: markdown_github
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE, echo=FALSE}
rm(list= ls()) # clear all
knitr::opts_chunk$set()
options(scipen = 999)
```
# Load... |
3fa0008ba281520a73839274c383c8eebc5582a34ab8c03dd9f2514032bbdf95 | R | 14,276 | 393 | ---
title: "DEMO : Spinal Cord Axis Projection Example"
output:
tufte::tufte_html: default
author: Alma Andersson
date: 2021-10-01
---
## Prep
This notebook illustrates how the distribution of certain features of interest
(e.g., genes or cell types) along a specified axis can be analyzed. To simplify
things, I've ... |
bb14bfe785ca8056fdbd004fcd7d0a67c114728ff42488ab34d255fde6250f3c | R | 14,536 | 301 | ---
title: "xrommRtools Vignette"
output: rmarkdown::html_vignette
description: Use this if this is your first time using xrommRtools. It will teach you the basic ideas and workflow.
vignette: >
%\VignetteIndexEntry{xrommRtools Vignette}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, in... |
ccfe14047be8f6565cb6f51395c5dd7a339901a41e4981963ea0939970040786 | R | 14,551 | 408 |
library(Seurat)
library(Rmagic)
library(ggplot2)
library(RColorBrewer)
library(viridis)
seuset <- readRDS('SeuratObjects/PD.SN.snRNA.postAlign.Anno.rds')
sampleList <- c(
'X5628NOSN',
'X4689NOSN',
'X4870NOSN',
'X4996NOSN',
'X5006NOSN',
'X5130NOSN',
'Public1NOSN',
'Public2_1NOSN',
'Public2_2NOSN',
'Public3_1NOSN',
... |
d782e78e30bef7e94f6f17a13d3b2aa09e2f5c20d49d79c36677780628f017d1 | R | 14,623 | 526 |
#----a
options(stringAsFactors = F)
library(data.table)
link = "/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/bmediate/icc/"
icc_gene_p = data.frame()
for (i in 1:22) {
dt = read.table(paste0(link,"progenitor_chr",i,"_icc_gene.txt"),header = T)
icc_gene_p = rbind(icc_gene_p,dt)
}
icc_gene_n = data.frame()
... |
a4f2fc660f795b67f94f17d0882e9a05b162c5170ea393e703128db880685ab4 | R | 14,747 | 346 | #' @importFrom tensorflow set_random_seed
#' @include utils.R
#' @include validator.R
#' @include deep_learning_model.R
#' @title Fit a Deep Learning Model
#'
#' @templateVar ClassName DeepLearningModel
#' @templateVar XType `matrix`
#' @templateVar YType `vector` or `matrix`
#' @templateVar refFunction keras::keras_... |
a9292ca258687e44e6d2297763bdd952c81a05b0f145032d329b2cbf98d9d87f | R | 15,224 | 370 | library(Seurat)
library(SingleR)
library(DoubletFinder)
library(tidyverse)
library(patchwork)
library(dplyr)
library(GSVA)
library(infercnv)
library(AnnoProbe)
library(ComplexHeatmap)
library(limma)
library(clusterProfiler)
library(msigdbr)
library(org.Hs.eg.db)
library(monocle3)
library(SCENIC)
## ... |
a15242c36edf81b5140580043d01bcaa7ca52ac06f7e73a6da8630754a44f2fb | R | 15,288 | 341 | # context("Testing evaluate_library")
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))
colnames(sce_c... |
7e53f21578448b2ea6d671398102af83cf639897b9bfae7d213499b9b1f2573a | R | 15,403 | 357 | library(shiny)
library(shinydashboard)
library(ggplot2)
library(reshape2)
source("Fun_Rank_disease.R")
source("global.R")
source("enrichr.R")
Healthy_UI <- function(id,label="Rank"){
fluidPage(
## First Row: Filtering sex-biased genes from differential expression topTable.
fluidRow(
h3("Step1... |
b5e425213337d76b3085a83c83f7de27a16618bfd09f32518b86f95c1826a66c | R | 15,413 | 627 | suppressMessages(library(e1071))
data(Iris)
expect_tuning <- function(tuner,
loss_function,
cv_class,
folds_number,
testing_proportion,
hyperparams,
grid_proporti... |
f61b3529180fd1112ee352d306be51ed8b039a8d7fba82614a2bd79fd82dada4 | R | 15,551 | 516 | 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... |
ababb1fa060299996bd78b9c548f858e1e0f76d026538f202a067cfa9fc4385c | R | 15,679 | 369 | # context("Testing get_DE_genes")
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))
colnames(sce_corre... |
8bc80585843b0064258f4343f6c3976d9f37c081fb8e22752badd6dbb40dad2a | R | 15,952 | 275 | groupDiscr <- function(data=NA,measures=c("TST_device","TST_ref"),size="reference",logTransf=FALSE,
CI.type="classic",CI.level=.95,boot.type="basic",boot.R=10000,digits=2,warnings=TRUE){
require(BlandAltmanLeh)
# setting measure name and unit of measurement
measure <- gsub("_ref... |
c7387e8c50024ec4c88e9cfb80f330bdd66d45fb50e2977db6a44b7561932c6e | R | 16,148 | 429 | ####################################################################################
# Table S1
####################################################################################
dat = read.csv("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/bmediate/data/progenitor_bmediatR_results_causal_final.csv")
dat = dat[,... |
84d0ca833bd07c68b6334a9ae3cf95e21331ee9d50c394d78bd022d66accc2a0 | R | 16,190 | 483 |
##################################################################################################
# Figure 2A, prepare a Manhattan plot to view causal forward and reactive models (Model 1A and B)
# Manhattan plot for caQTL->eQTL mediation
options(stringAsFactors = F)
library(data.table)
gtf = fread("/proj/steinlab/... |
c9a389333306863343445a3383c969b55b6690ea86ddd3736ca415886565d493 | R | 16,267 | 377 | 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... |
2f4b8634d2dd4d4e99e1607c9c693e67e9b6a4907c18e9ec24558c6ceb1422cb | R | 16,360 | 296 | ###############################################################################################
# Script to plot enrichment in PGC lists vs enrichment in consensus list in stemcell networks #
# iPSC data[noKO+shuffle/KO] #
################################... |
8aba93d62a425a709fd5a87049d97064307c9529e5415d5dfc3cd5074ded3b52 | R | 16,416 | 348 | #Written by Cameron Wehrfritz
#and Natan Basisty, PhD
#Schilling Lab, Buck Institute for Research on Aging
#Novato, California, USA
#March, 2020
#updated: February 08, 2021
# PROTEIN TURNOVER ANALYSIS
# STEP 3:
# NON LINEAR FIT OF TURNOVER, THROUGH THE ORIGIN with single parameter: rate of change
#
# OUTPUT:
# i. PDF... |
5b30f4cca4154f5bd85820b52e7e611d46fcef4cf3296c49e12f1c660d9f5a44 | R | 16,424 | 412 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Sptial analysis of T cell subpopulations in melanoma
```{r}
library(Seurat)
library(ggplot2)
library(ggthemes)
library(ggsignif)
library(cowplot)
library(dplyr)
library(ggpubr)
library(viridis)
library(cgdsr)
library(survival)
library(survminer)
lib... |
d7a893b9e7dfd17793f5d2d9013144fa0eb615b4ff8b6afcb914fde4366b3930 | R | 16,471 | 305 | con_ensembl = c(
"ENSG00000101204"
,"ENSG00000119522"
,"ENSG00000162104"
,"ENSG00000173175"
,"ENSG00000196588"
,"ENSG00000198010"
,"ENSG00000215012"
,"ENSG00000002746"
,"ENSG00000105649"
,"ENSG00000174437"
,"ENSG00000175931"
,"ENSG00000184156"
,"ENSG00000198929"
,"ENSG000000847... |
9967e7baada5f6bef7029be8563a1c4894f542391a4a1a7eb973a079dd9540dc | R | 16,531 | 446 | # This script generates main and supplementary figures -----
insert_head()
# data containers ----
paper_figures <- list()
suppl_figures <- list()
rev_figures <- list()
# Figure 1: STROBE ----
insert_msg('Figure 1: STROBE')
paper_figures$strobe <- plot_grid(ggdraw() +
... |
bbd378cd5150fe008cc7a14c66808577478a732d880645d3a2687fe672d68b1c | R | 16,709 | 359 | #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 2:
# NON LINEAR FIT OF TURNOVER
# CALCULATE X-INTERCEPTS
#
# OUTPUT:
# i. PDF of non-linear regression plots:... |
aa6a861f444f967a8585c14820a8d9f9dccafd659111b35dcf11f3179f9737fe | R | 16,987 | 405 | .userGWAS_main <- function(i, cores, k, n, I_LD, V_LD, S_LD, std.lv, varSNPSE2, order, SNPs, beta_SNP, SE_SNP,
varSNP, GC, coords, smooth_check, TWAS, printwarn, toler, estimation, sub, Model1,
df, npar, utilfuncs=NULL, basemodel=NULL, returnlavmodel=FALSE,Q_SNP,mod... |
cfa86b7532debf257f58896e8151f1622449ec76928b0262d68491684e741134 | R | 17,958 | 654 |
local({
# the requested version of renv
version <- "0.13.2"
# the project directory
project <- getwd()
# avoid recursion
if (!is.na(Sys.getenv("RENV_R_INITIALIZING", unset = NA)))
return(invisible(TRUE))
# signal that we're loading renv during R startup
Sys.setenv("RENV_R_INITIALIZING" = "true"... |
0519f121363a69dd39684b632c0b2de10288707ee1b04d0eef910364d4dcf434 | R | 18,428 | 433 | # context("Testing gene_prediction_scores")
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))
colnames... |
6cc3c92731b93bb41ae6f182b250a892decc7163121d8bc5eb83b719b187d411 | R | 18,592 | 601 | #' @importFrom R6 R6Class
#' @import keras
#' @include utils.R
#' @include model.R
#' @include globals.R
#' @include deep_learning_grid_tuner.R
#' @include deep_learning_bayesian_tuner.R
#' @include model_helpers.R
DeepLearningModel <- R6Class(
classname = "DeepLearningModel",
inherit = Model,
public = list(
... |
45c2c6ee4a6db4eaf839d3845d9536241baef5100c443568eee798c2aef31fc7 | R | 19,315 | 730 | set.seed(1)
test_that("to_matrix", {
categories <- factor(sample(c("A", "B", "C"), 10, replace = TRUE))
expect_matrix(to_matrix(1), nrows = 1, ncols = 1, any.missing = FALSE)
expect_matrix(to_matrix(1, TRUE), nrows = 1, ncols = 2, any.missing = FALSE)
expect_matrix(to_matrix(1:10), nrows = 10, ncols = 1)
e... |
559401c1697f0d12c41a450e9388a46a6f02ca7bd4124aad4d5ca2d4aa8c2601 | R | 19,503 | 339 | # Lachlan T Strike
# Based on scripts provided by Hermine Maes (https://hermine-maes.squarespace.com)
# Saturated model used to check assumptions of twin data. Is setup for QTAB data (i.e. 5 zyg groups, with opposite sex twin pairs coded females as 01 and males as 02).
# Take rMZ/DZ & covariate estimates from model wit... |
d13e2e85c736b9b20f05fdf38eebac072366db01650804024b590498d31363f0 | R | 19,650 | 496 | ---
title: "Graph Analyses with Subnetworks"
author: "Sandra Martin"
date: "12/1/2021"
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 pack... |
3960915d6bc0d9a480c915c901317232fcd2ab57c776770a704f50dca3469044 | R | 19,908 | 507 | ##########################################################################################
## BrainSpan expression profiles for various SCZ gene sets
##
## Author: Kalliopi Tsafou, Yu-Han Hsu
##########################################################################################
rm(list=ls())
library(dplyr)
librar... |
e7536aa48771a847b14479a444e2e37c378040b4696b3728a171e92c77f83d5d | R | 19,916 | 505 | library(tidyverse)
library(ggtern)
library(hrbrthemes)
library(ggtext)
library(ggplot2)
library(ggpubr)
library(patchwork)
library(scales)
library(tidyr)
library(matrixStats)
library(ggrepel)
require(pals)
library(rdist)
library(rpart)
library(ggthemes)
library(ComplexHeatmap)
library(ggdendro)
###1.dr... |
5bfb9d2133b1caf5e5e5a0ec8c786c6784ba0b7b73eb2c3eed19d5b7bb3d289b | R | 19,923 | 347 | BAplot <- function(data=NA,measures=c("TST_device","TST_ref"),logTransf=FALSE,
xaxis="reference",CI.type="classic",CI.level=.95,boot.type="basic",boot.R=10000,
xlim=NA,ylim=NA,warnings=TRUE){
require(BlandAltmanLeh); require(ggplot2); require(ggExtra)
# setting labe... |
9735a0f9dec9faf0bc5d6a5bfe8428ab39df127311da0e7a7fb10359de8b5279 | R | 19,930 | 505 | library(tidyverse)
library(ggtern)
library(hrbrthemes)
library(ggtext)
library(ggplot2)
library(ggpubr)
library(patchwork)
library(scales)
library(tidyr)
library(matrixStats)
library(ggrepel)
require(pals)
library(rdist)
library(rpart)
library(ggthemes)
library(ComplexHeatmap)
library(ggdendro)
###1.dr... |
50db4e261b0b9c0abe216dda720a39bf980042589e644e9d9ae681374c4a016f | R | 19,944 | 719 | # The Broad Institute
# SOFTWARE COPYRIGHT NOTICE AGREEMENT
# This software and its documentation are copyright (2003-2006) by the
# Broad Institute/Massachusetts Institute of Technology. All rights are
# reserved.
# This software is supplied without any warranty or guaranteed support
# whatsoever. Neither the Broad I... |
9466f837f8006be498a1dc269aec355b0ee8529950138b7cb4e0946e6b0fdc86 | R | 20,057 | 496 |
########################################################
# Figure S5C
########################################################
type = "progenitor"
setwd("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/bmediate/scripts/")
##################################################################################
temp.lin... |
56ddff9b153f4469affcf5c471a577c88583fd899af93ee50b117c261d10241e | R | 20,146 | 492 | join <- function(...) {
paste(..., sep = "")
}
#
# Global values for sharing scales between sites (comment out for site-specific scales)
#
acceptQCutoff = 0.01
acceptCvCutoff = 0.2
acceptRunsPercentile = 0.5
#
# Reading data
#
command_args <- commandArgs(trailingOnly = FALSE)
script_args <- commandArgs(trailingOn... |
ca29fc26166ec38d354a89048c6b672be69e68ddd42e64e30dc4b5da7dd4d88a | R | 20,147 | 693 | expect_difftime <- function(x) {
expect_class(x, "difftime")
expect_number(as.numeric(x), lower = 0, finite = TRUE)
}
expect_model <- function(model,
x,
y,
hyperparams,
responses,
tune_grid_... |
caed44e03a075b7bc3c524fbf0ff5c53d2248eeda2e44fe3147e343a924d7cce | R | 20,379 | 840 | data(IrisMatrix)
test_that("Univariate numeric (no tuning)", {
model <- deep_learning(
x_num,
y_num,
epochs_number = 5,
verbose = FALSE
)
expect_deep_learning(
model = model,
x = to_matrix(x_num),
y = y_num,
hyperparams = list(
learning_rate = 0.001,
epochs_number = 5... |
a8c8a4e349d289acb0298ead6d8f6747fe0f04ed66af76eb0bd2b8421c30edf8 | R | 20,661 | 265 | ---
title: "The Combination of Supervised and Unsupervised Learning based Risk Stratification and Phenotyping in Pulmonary Arterial Hypertension - a Long-term Retrospective Multicenter Trial"
subtitle: "PULM-D-22-00488, point-to-point reply"
author: "Innsbruck PAH registry"
date: "`r format(Sys.time(), '%Y-%m-%d')`... |
5bfd0e52cda060a740df536ce138e449f0e345739a582ae2e59834436bec98ca | R | 21,020 | 649 | library(ggmsa)
library(Biostrings)
library(msa)
library(tidyverse)
library(ggpubr)
library(rstatix)
library(emmeans)
library(ape)
library(ggtree)
library(ips)
library(gt)
library(webshot2)
#--Multiple Sequence Alignments (MSA)---(figure 1)---##########
oxtr_seq <- readDNAStringSet(filepath = "rodent_oxtr_mRNA_seq1.fa... |
b0c6690d873c950e85c6bd91865bcbd4a5bd9dfa71dbe11c967161789bfe87fc | R | 21,466 | 490 | userGWAS <- function(covstruc=NULL, SNPs=NULL, estimation="DWLS", model="", printwarn=TRUE,
sub=FALSE,cores=NULL, toler=FALSE, SNPSE=FALSE, parallel=TRUE, GC="standard", MPI=FALSE,
smooth_check=FALSE, TWAS=FALSE, std.lv=FALSE,fix_measurement=TRUE,Q_SNP=FALSE,
... |
8d59938bf175ff40dbf20a460641c0811d52d8c62542863529ef704ca8ed454f | R | 21,531 | 861 | #' @import keras
#' @import dplyr
#' @importFrom glmnet cv.glmnet
#' @include utils.R
#' @include validator.R
#' @include kernels.R
#' @include bayesian_model.R
prepare_univariate_y <- function() {
if (is.data.frame(self$y)) {
self$y <- unlist(self$y, use.names = FALSE)
} else if (is.matrix(self$y)) {
se... |
7cd9f3717b57b3165ec3a0d9ff9943b5009bd0e538617e431150054b280e95a4 | R | 21,918 | 403 | ####################################################################################################
# Script to plot main enrichment plots for SCZ risk modules for all networks #
# Script to plot GO, TF and Pathology enrichments for SCZ risk modules for all networks #
# "Regi... |
54e4c0d2e5502933c0861012b28db34157d336c49655b22d88f23219e2136d7b | R | 22,592 | 477 | library(DESeq2)
library(data.table)
library(RUVSeq) # http://bioconductor.org/packages/release/bioc/vignettes/RUVSeq/inst/doc/RUVSeq.pdf
library(EDASeq)
library(readxl)
#setwd("/data/NCATS_ifx/data/dbGaP/DRGs/SRA/DESeq2")
setwd("~/Downloads/DRGs/")
sampleTable <- as.data.table(readxl::read_xlsx('DRGs_merged_sampletab... |
ae05eb24f6b4ac4b22a11112e04d69515577fc4d18b6fa07d9cba7d1575fb09a | R | 23,000 | 509 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Sptial analysis of the invasion of myeloid subpopulations in COVID-19
```{r}
library(GSVA)
library(Seurat)
library(ggplot2)
library(ggthemes)
library(ggsignif)
library(ggforce)
library(dplyr)
library(reshape2)
library(ggbeeswarm)
library(cowplot)
li... |
633de6673d5f355fff95649c476fd578f792eaddcbeaecdd43c98d9516915713 | R | 23,838 | 497 | # Project: Run economic evaluation and sensitivity analyses
# Created by: James Hedley
# Date created: 8th November 2021
# Last updated: 22nd June 2022
# Load libraries ----
# Set directories ----
user <- Sys.getenv("USERNAME")
if (user == 'jhed6231') {user <- paste0(user, '.MCS')}
paper3loc <- paste0('... |
3cbb480f84a9b6d7662a76991e6f95d3db80209485df8f8a53b55ce4635c7e46 | R | 25,640 | 873 | set.seed(1)
numeric_vars <- list(
observed = round(rnorm(5, 10, 1), 2)
)
numeric_vars$predicted <- round(numeric_vars$observed + rnorm(5), 2)
categorical_vars <- list(
observed = factor(c("A", "B", "C", "A", "B", "A", "C")),
predicted = factor(c("A", "C", "C", "B", "B", "A", "C"))
)
test_that("confusion_matrix... |
a79c134995458d42bfa56c98f3f559ef3bda70c2e5ab80d12dd7584e1ba20b21 | R | 27,259 | 611 | enrich <-function(s_covstruc, model = "",params,fix= "regressions",std.lv=FALSE,rm_flank=TRUE,tau=FALSE,base=TRUE,toler=NULL,fixparam=NULL){
time<-proc.time()
##determine if the model is likely being listed in quotes and print warning if so
test<-c(str_detect(model, "~"),str_detect(model, "="),str_detect(model, ... |
e4ca0e4f355e9fc67f6e6c184d8a4825b1a552e0bcfaaecbd6a6aa62d3db245a | R | 27,413 | 648 | ####################################################################################################
# Script to run various enrichments on gene_module_list: #
# SCZ, DEGs, DMGs, TWAS, GO, MAGMA, Cell Specificity etc. #
# ... |
e40fbad06ce4452a830c58a6c210f5c3f21442885880056989f6f280c19ca8c4 | R | 28,285 | 705 | library(RUVSeq) # http://bioconductor.org/packages/release/bioc/vignettes/RUVSeq/inst/doc/RUVSeq.pdf
library(EDASeq)
library(RColorBrewer)
library(DESeq2)
library(data.table)
library(ComplexHeatmap)
library("scales")
setwd('/Volumes/ncatssctl/NGS_related/BulkRNA/ISB012/Tao/')
sampletable <- as.data.table(readxl::read_... |
1a084765caead260ca957c34db86aed2c47b17b4a7f6f8c5a9ca7247285fa277 | R | 30,623 | 700 | # DEPENDENCIES
# nnls, Hmisc, mgcv, plyr
Test <- function(){
# read in the data
gc <- read.table("gc.txt")$V2
canoes.reads <- read.table("canoes.reads.txt")
# rename the columns of canoes.reads
sample.names <- paste("S", seq(1:26), sep="")
names(canoes.reads) <- c("chromosome", "start", "end", sample.na... |
7820cbdc6827bff81692bf10924718234e91a52d67d9fed45f016dab9fea31b7 | R | 33,339 | 685 | usermodel <- function (covstruc, estimation = "DWLS", model = "", CFIcalc=TRUE,
std.lv = FALSE, imp_cov = FALSE, fix_resid = TRUE, toler = NULL,Q_Factor=FALSE)
{
time <- proc.time()
test <- c(str_detect(model, "~"), str_detect(model, "="),
str_detect(model, "\\+"))
if (any(tes... |
0c4df31231bcb4bb333e3285e2a153938e5111488ee330df6c1fb6a7978e2d65 | R | 33,864 | 816 | #Prepare genome feature file
library(GenomicFeatures)
library(BSgenomewheatpart)
spompe <- makeTxDbFromGFF('/public/workspace/xuyongyue/ZLH/scATAC-seq/161010_Chinese_Spring_v1.0_pseudomolecules_parts.filter.gtf')
#saveDb(spompe, file="/public/workspace/xuyongyue/ZLH/scATAC-seq/wheat_txdb.sqlite")
wheat_txdb <- loadDb("... |
e8099a6d9a866e0ffeef471ac43cd93a723a601214b2729661054a28613fa976 | R | 34,068 | 390 | ---
title: "The Combination of Supervised and Unsupervised Learning based Risk Stratification and Phenotyping in Pulmonary Arterial Hypertension - a Long-term Retrospective Multicenter Trial"
subtitle: "Supplementary Material"
author: "Innsbruck PAH registry"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
output:
... |
8021f43fd25601f0796289b33f783839a011ab2835425d5cb392d998d4189a82 | R | 34,408 | 818 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Validation of scSpace on human middle temporal gyrus (MTG) scRNA-seq data
```{r}
library(Seurat)
library(ggplot2)
library(ggsignif)
library(ggforce)
library(mclust)
library(dplyr)
library(cowplot)
library(reshape2)
```
### Load data
```{r}
# Ground... |
a63d247ab0d92791b8aaf4d1836454956df1bc99ef7ee7b2c0144e1781144349 | R | 35,420 | 914 | #install.packages("devtools")
library(devtools)
library(ggplot2)
library(reshape2)
library(gridExtra)
library(ggpubr)
setwd("/Volumes/SizhuFiles/TMS+EEGstudy/Main/visualization/data")
#### plot LMFA-AUC/TEPs: bar plot ####
rm(list=ls(all=TRUE))
color1<-c("#222222","#E42600") # black=hc, red=patients
data <- read.csv... |
f3bcac2f02414f47f9cf5c5a3bcb3438f279e127178e803b31fd0182201832ce | R | 35,455 | 754 | ####################################################################################################
# Script to run GO aqnd SCZ enrichments on consensus gene environment: #
####################################################################################################
##Function... |
580c4560031d914c3415e4e80296691a752af58968571636f479d59fed83b32b | R | 36,280 | 795 | ######
# This script builds multitissue gene-gene coexpression network byusing GTEx data.
# The
options(warn=-1)
#install.packages("R.utils")
#BiocManager::install("EnsDb.Hsapiens.v86")#BiocManager::install("EnsDb.Hsapiens.v79")
library(R.utils)
library(EnsDb.Hsapiens.v86)
setwd("/data/users/cs15d017/backup_from_h... |
9e6dd9bcc0925555b8be98834d6b883cc22407024b8bf0d2ae4345a0b975ef4c | R | 37,319 | 1,406 | #' @importFrom reticulate py_suppress_warnings py_capture_output
#' @importFrom data.table fwrite fread
#' @importFrom dplyr as_tibble
#' @include globals.R
#' @include validator.R
# Computations --------------------------------------------------
lm_intercept <- function(x, y) {
if (all(is.na(x)) || all(is.na(y)))... |
39c6383f4f25faa9ddfdf9c214df4b5df6534017688bf1ea5bf88997bab30ef0 | R | 38,600 | 821 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Validation of scSpace on human embryonic heart scRNA-seq data
```{r}
library(Seurat)
library(ggplot2)
library(ggsignif)
library(ggforce)
library(mclust)
library(dplyr)
library(cowplot)
library(reshape2)
library(ggExtra)
library(NMI)
```
### Load da... |
36a9137d9744b2c7d13be874879b2c7c2caea1b67e8d71c87659ee2e2b04cf33 | R | 43,595 | 1,523 | #' @import checkmate
#' @include globals.R
#' @include utils.R
#' @include kernels.R
# Checkmate extension --------------------------------------------------
checkSubsetString <- function(x,
choices,
empty.ok,
unique,
... |
b555a7ced15586d5c21435789a5d58314c8fdac7f4bc022613db56df780f2ced | R | 44,687 | 1,106 |
#---------S4A
#-----caQTLs
cpro = fread("/proj/steinlab/projects/R00/eQTLanalysis/path_QTL/EMMAX/progenitor_caQTL_inpeak_snps.txt", data.table=F)
cpro$fdr = p.adjust(cpro$V3, method = "fdr",n=nrow(cpro))
cpro = cpro[which(cpro$fdr < 0.05),]
cpro$peak = sapply(cpro$V4, function(x) unlist(strsplit(x,"/",fixed="TRUE... |
93774b00efd13b028587eeb2c6570f438e3d2a956e31679ac131ff25d764d98c | R | 45,665 | 1,152 | ---
title: "Differential expression and exon usage analyses"
author: "MW"
editor_options:
chunk_output_type: console
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
bookdown::html_document2:
code_folding: show
toc: yes
toc_float:
collapsed: no
params:
dataset: 1
---
```{r setup, include=FAL... |
f8cf2a75ef7144fece022464ea0e5b1a3c489dd12fde91624dd8feb0b30e3d64 | R | 45,672 | 1,152 | ---
title: "Differential expression and exon usage analyses"
author: "MW"
editor_options:
chunk_output_type: console
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
bookdown::html_document2:
code_folding: show
toc: yes
toc_float:
collapsed: no
params:
dataset: 1
---
```{r setup, include=FAL... |
b0b6cb453518b93008fa88a1d574cd6e8bb01b69d24a6ac47d44c2b8aee5e045 | R | 46,014 | 1,699 | #' @import checkmate
#' @importFrom MLmetrics PRAUC
#' @include validator.R
# For categorical data ---------------------------------------------------------
#' @title Mathematical Mode
#'
#' @description
#' Obtain the most frecuent values (mode) with the frequency from a vector. By
#' default all values with the gre... |
21eb3694dd29e82de0dcd3d2415989523f1aa9157d3e34c9173bfe371ee3c1d1 | R | 49,935 | 912 | ####################################################################################################
# Script to run various enrichments on gene_module_list: #
# SCZ, DEGs, DMGs, TWAS, GO, MAGMA, Cell Specificity etc. #
# ... |
917a69ce5f63b8a3d71c481374f1bd683757ff7339ec86bdf48d0430086e2d04 | R | 50,107 | 1,508 | #!/bin/env Rscript
#### License Notice ####
##
# Copyright (c) 2023 Cedars-Sinai Medical Center
# 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 3 of the License, or
# (at your o... |
88f42d3508e80d8bbb957cd2edff29455f8b558a31436d707294444123173484 | R | 52,421 | 1,285 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Validation of scSpace on DLPFC and STARmap spatial transcriptomics data
```{r}
library(Seurat)
library(ggplot2)
library(ggsignif)
library(ggforce)
library(mclust)
library(dplyr)
library(cowplot)
library(reshape2)
library(ggExtra)
library(NMI)
```
... |
8fd5e405d2358af30bec276ea0656ea60eb40d91791b68f0498d74d3e4d311f6 | R | 55,943 | 1,074 | ---
title: "Variant_Analysis"
author: "MW"
editor_options:
chunk_output_type: console
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
bookdown::html_document2:
code_folding: show
toc: yes
toc_float:
collapsed: no
params:
dataset: 1
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo ... |
ba3d08ccbc244a0779ae7f2e64fce30f65621608c0eea0741d21643e0ac98e85 | R | 57,370 | 945 | ####################################################################################################
# Script to run various enrichments on gene_module_list: #
# SCZ, DEGs, DMGs, TWAS, GO, MAGMA, Cell Specificity etc. #
# ... |
f3e88df0ffd9c1767f893951182b7f7cb348f50b32272eafe7cab61524b731ca | R | 65,018 | 759 | ####################################################################################################
# Script to plot main enrichment plots for SCZ risk modules for all networks #
# Script to plot GO, TF and Pathology enrichments for SCZ risk modules for all networks #
# "R... |
40f1c272df45014557ea8d74f60daf9ec1fbcf945c9cd3abcdba60c958726ac5 | R | 67,683 | 1,218 | ##########################################################################################
## ##
## Functions to combine pipeline runs for individual samples into something more useful ##
## ... |
c68dfdc73cd1475bb5763278e9932f2f6725d518e038807beae8a90587f56f1f | R | 78,150 | 1,203 | ---
title: "Flanking Intron Analysis"
author: "MW"
editor_options:
chunk_output_type: console
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
bookdown::html_document2:
code_folding: show
toc: yes
toc_float:
collapsed: no
params:
dataset: 1
---
```{r setup, include=FALSE}
knitr::opts_chunk$s... |
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