sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
52d96502b4f53024464d5d0f3909f961577525347bc293e8d07739f451e4c37b | R | 10,802 | 255 | library(here)
library(ggplot2)
library(rlang)
library(rstatix)
library(nlme)
library(cowplot)
library(ggbeeswarm)
library(ggpubr)
library(ggsignif)
library(knitr)
library(dplyr)
data <- readr::read_csv(here("Analysis_Files", "ASO","ASO Rotarod - Rotarod.csv"))
data$SLC_Genotype <- factor(data$SLC_Genotype, levels=c... |
98d729d3d3fd2f1cf8da6821bb540a3ee460edebb7f80808ecf1f5e0f9b3922d | R | 10,885 | 195 |
#' Create the Quality Control report
#'
#' Normally used as part of the analysis_quickstart() function, refer to its implementation for example code.
#'
#' @param dataset your dataset
#' @param output_dir output directory where all output files are stored, must be an existing directory
#' @param norm_algorithm normali... |
cef723e3544f2cbd5144d4658edf45dbb05d96666e2f7ef6a855d0ea318166fa | R | 10,892 | 367 | library(here)
library(tidyverse)
library(cowplot)
library(ggplot2)
### Establish location ---
here::i_am("src/PFF/PFF_Correlate_DAT_with_Puncta.R")
### Read in input files ---
PFF_lum_col_counts <- read.delim(here("data/PFF/PFF_Microbiome/differential_taxa/collapsed_ASV_tables/PFF_L6_Subset_Luminal_Colon.tsv")) %>%... |
bd8442544d6c0db2fc68ceef5f5c8f49f12a4ac3d378aef26c5fd120d85bc19c | R | 10,931 | 325 | ---
title: "Fig 3 Rising Falling Plots"
output: html_document
date: "2024-11-20"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figures
... |
b48f05f34ce434195069840c6de69607d6e7f2e85338bb69c2a705fe0d1f215f | R | 10,940 | 309 | # Topic_distribution.R
rm(list=ls())
library(topicmodels)
library(MetaTopics)
library(ggplot2)
load('./Gibbs_model.RData')
map=read.csv('./01_taxa/feat_genus.csv',header=T)
#1.topic-genus probability----
P1=plot_beta(Gibbs_model,prob=0.01)
#pie chart: Topic_8----
my_beta <- Gibbs_model@beta
colnames(my_beta) <- Gi... |
9a934ad7214ca8197989c8bf70ac18bf80abeeef434d7513bb268d29b19aae7c | R | 10,941 | 398 | ---
title: "Chromatin modifier DRIAD run plots"
author: "Clemens Hug"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(here)
library(tidyverse)
library(data.table)
library(synExtra)
library(qs)
library(powerjoin)
library(plotly)
library(ggrepel)
synapser::synLogin()
s... |
5b133cfa43a51da7bf0d7eb72bcbdd94fc8adababe43bee06c99a68cd69ee0a9 | R | 10,990 | 348 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figures
fig.height = 6, # set default height of figures
fig.align = "center", # always a... |
c1f372f4253147ef5086ed1dd74f076016e55110f27c451d6d037c124cd60f9b | R | 10,990 | 348 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figures
fig.height = 6, # set default height of figures
fig.align = "center", # always a... |
995f0a0775c6d77da8c273e03ab914238d44224f7a94c88317e95d3d1dbd8582 | R | 11,003 | 347 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figures
fig.height = 6, # set default height of figures
fig.align = "center", # always a... |
74d612c4271b6defabfc92e9320ab9353cac6787b0c5722cb156877a5074783d | R | 11,027 | 348 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figures
fig.height = 6, # set default height of figures
fig.align = "center", # always a... |
ce449309cd689790ce3f918161d68a4f1e4eaaee0ce5d1a999fba46878f5d653 | R | 11,049 | 175 | # UI-elements for Explore tab
tabPanel(title = "Explore", icon = icon("magnifying-glass-chart"), id = "explore",
# link to the style.css file.
tags$link(rel = 'stylesheet', type = 'text/css', href = 'styles.css'),
sidebarLayout(fluid = T,
sidebarPane... |
e6e05817741a9f2ed6dbe232c44880d60cd4e91fb54f388868f239522b1b90a0 | R | 11,059 | 338 | ---
title: "Attack Chase Death"
output: html_document
date: "2024-10-29"
---
```{r setup, include=FALSE}
## libraries ##
library(tidyverse)
library(ggplot2)
library(lmerTest)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(lmtest)
library(blme)
library(scales)
library(ggthemr)
... |
23338f7d83131ed1b8d44f7f12ebfbabdfb56681eeb1e5e9206b06f2d447aeb9 | R | 11,100 | 159 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
#' GLMM parameter estimation using pseudo-likelihood with a custom covariance matrix
#'
#' Iteratively estimate GLMM fixed and random effect parameters, and variance
#' component parameters usi... |
7e87182f1fceab2f0081a23fac7f626b9273079818c1e853799ce990552b8126 | R | 11,110 | 265 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(multcomp)
source("src/scales.R")
res <- read_csv("results/metrics_all_env.csv", show_col_types = FALSE)
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y <- scale(ref$GrainYield[ref$Set == "Train"])
yc <- attr(y... |
16fa5ab1c6a8291b2e824c52d2ee3835608e7da7bb96be11f0d43b7753ce034f | R | 11,132 | 359 | ---
title: "Cleaning LL19"
output: html_document
date: "2023-12-06"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figures
fig.height =... |
15acd33757adc44b300997a7509c97c7cacc42a51bfe066f0731e70f1a148833 | R | 11,155 | 358 | ---
title: "Cleaning BJH041"
output: html_document
date: "2023-12-06"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 5, # set default width of figures
fig.height... |
c8db2c92072ebf1448558e500d96f7ec7aec02c705ce0646aeeb3d73e954327c | R | 11,176 | 350 |
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figures
fig.height = 6, # set default height of figures
fig.align = "center", # always a... |
841cb1e0c11789ca0cf1624371c2178300c2216b7db47635fb229af2130cf722 | R | 11,211 | 366 | ---
title: "Create Supp Turn Time Tables"
output: html_document
date: "2024-12-12"
---
```{r setup, include=FALSE}
## libraries ##
library(tidyverse)
library(ggplot2)
library(lmerTest)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(lmtest)
library(scales)
library(ggthemr)
libra... |
7f2dba87743dc230bda4c3b2a8be762e7c38cfdc9d683a2b8d289b09de275633 | R | 11,215 | 245 | col_subtype = c('ZFTA-RELA' = "#B44672",'ZFTA-Cluster 1' = "#B47846", 'ZFTA-Cluster 2' = "#46B478",
'ZFTA-Cluster 3' = "#46B4AF", 'ZFTA-Cluster 4' = "#4682B4",'ST-YAP1' = "#B4AF46")
colors_metaprograms_Xenium <- c("gray50","#F99E93FF","#9E5E9BFF","#74ADD1FF",'#0F4F8B', "#ACD39EFF","#96410EFF", "#96410E... |
bec143eb169d793465a7f3dd9814f143cbdc21e0cd7e1ca7bc621160ea7700c0 | R | 11,216 | 366 | ---
title: "Cleaning LL17"
output: html_document
date: "2023-12-06"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figures
fig.height ... |
8bee2ae1bd3bc51b499eab0c80a52e41e9c7daacbf6e7d8165c58a608b0f7eb1 | R | 11,235 | 299 | library(here)
library(dplyr)
library(funrar)
library(ggplot2)
library(cowplot)
library(paletteer)
library(rlang)
library(wakefield)
library(vegan)
library(here)
library(tidyverse)
### Declare path to current script
here::i_am("src/ASO/ASO_Beta_Diversity.R")
### Load metadata and count table
metadata <- read.csv... |
fcaf04bd9da6831df9d161a8ebd507ef786b1da0934972abc377f391301f8b0f | R | 11,265 | 361 | ---
title: "Cleaning BJH026"
output: html_document
date: "2023-12-05"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 5, # set default width of figures
fig.height ... |
86a35e333d14e87f4913ce9ff90061ffc545765abc4bc54e9b4d59668757ffb7 | R | 11,335 | 177 | #' Import a label-free proteomics dataset from EncyclopeDIA
#'
#' This function imports peptide intensities, retention times and peptide-to-protein mappings from a 'Quant Report' .elib file generated by EncyclopeDIA.
#'
#' Because this 'Quant Report' does not contain confidence scores per peptide per individual sample,... |
c775e67bb578c94854160a66fd0fba8b70a47789dc61d1a404687460b24d426f | R | 11,344 | 209 | ---
output:
rmarkdown::github_document:
html_preview: true
toc: true
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
fig.path = "images/custnorm-",
comment = "#>"
)
```
```{r setup, include = FALSE}
devtools::load_all()
# ! change to some temporary working directory on your compu... |
ec609a179c9d61dd7011612d9d7e0b52e9496fae9ddb14c8130d1b028392ba56 | R | 11,361 | 421 | ```{r}
source(here::here("src/init.R"))
```
<!----------------------------------------------------------------------------->
<!----------------------------------------------------------------------------->
# I. Data
```{r}
local({
tmp <- tempfile(fileext = ".R")
knitr::purl("analysis/clearing.Rmd", output = t... |
5a90fccc95d0a05b033ea4af16b73b9e9b8397bdd7901f378fb5a1949c8f49d9 | R | 11,435 | 460 | library(tidyverse)
library(synExtra)
library(data.table)
library(powerjoin)
library(here)
library(qs)
library(ComplexHeatmap)
library(ggbeeswarm)
library(broom)
library(ggpubr)
synapser::synLogin()
syn <- synDownloader("~/data", .cache = TRUE)
rosmap_tdp43_classification <- syn("syn44277761") %>%
read_csv()
rosmap... |
075d8b7e29d5a4170dea725ba0b8fef09cf9cfb7b044c570c2c9d2ab9ab1a328 | R | 11,439 | 267 | #' Estimate cellular composition and associated error
#'
#' This is an adaptation of estimateCellCounts() from the minfi R package to
#' include the calculation of CETYGO alongside cellular deconvolution.
#' The arguments are as described in the minfi package. The estimation
#' of cellular composition is an implementai... |
0fac490067b1958c42cfca187986e14da7ef176357b0345fa96d5e0adb0f96ba | R | 11,457 | 326 | ---
title: "Plots of ST-EPN programs - neuroepithelial vs embryonic"
author: "Sara Danielli"
output:
html_document:
toc: yes
df_print: paged
---
```{r, setup, include = FALSE}
library(knitr)
opts_chunk$set(
echo = TRUE, cache = TRUE, warning = FALSE, comment = FALSE)
```
```{r preparation environment, mes... |
c391262c15264b4825e7ee50d9d28a029aed1da3fe9d6172fa945b6ca7a97325 | R | 11,524 | 400 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(robustlmm)
library(glmmTMB)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\i... |
009edfeb8d263bab6a08ca1478e4367faec251a47a5be4256d550449272ce81c | R | 11,549 | 289 | ## Auxiliary function to extract genotypes (if geno = TRUE) or phenotypes (if geno = FALSE) stored within inDir
extractData = function(inDir, drugList = NULL, geno = FALSE) {
## Record the starting directory
startDir = getwd()
## Change into the input directory
setwd(inDir)
## Identify sub-directories
useDi... |
8deb72ea5f687c7537690371ff78ebff337986f17363c1485af64d8ca046d6b9 | R | 11,583 | 493 | ---
title: "DRIAD Tau signature"
author: "Clemens Hug"
output: html_document
---
```{r setup}
library(tidyverse)
library(synExtra)
library(data.table)
library(powerjoin)
library(here)
library(qs)
library(batchtools)
synapser::synLogin()
syn <- synDownloader("~/data", .cache = TRUE)
data_dir <- file.path("driad", "... |
218f1d787f64267149391cafbdf83f5ce045d8797b88779134a646fd722b3e5b | R | 11,595 | 254 | context("Testing plotting functions")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
library(miloR)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen values for the... |
d02e8ea8601231fdd84e5682ffad2f21d418a0e8c052f3ea884c21a2973fd8dc | R | 11,610 | 555 |
# ---- libs ----
library("arrow") # reading in parquet data
library("dplyr") # piping and manipulation of data
library("tibble") # prettier data.frames
library("ggplot2") # plotting
library("ggthemes") # plotting
library("knitr") # to print kable(.) tables
library("tidyr") # pivot_wider() function
library("readr") # ... |
437e1b822d8406ecda66efdcca9bfeb7f51dbea231f5b62d63d0890b38e6d20f | R | 11,664 | 253 | library(here)
library(ggplot2)
library(rlang)
library(rstatix)
library(nlme)
library(cowplot)
library(ggbeeswarm)
library(ggpubr)
library(tidyr)
library(dplyr)
library(knitr)
setwd("../pdbehavior/")
data<-read.csv("ASO Pole Test - All_Cohorts_Assign_Maximum_Time.csv", header=TRUE)
data <- data %>%
rowwise() %>%
... |
0257e31aabb8c227d4ec5cbed9128b032d0f971175711116f4c13c914573c9f3 | R | 11,681 | 262 | library(Maaslin2)
library(funrar)
library(dplyr)
library(ggplot2)
library(cowplot)
library(plyr)
setwd("C:/Users/Jacobs Laboratory/Documents/JCYang/SLC_GitHub/slcproject/PFF_Microbiome/")
## PFF Jejunum ---
input_data <- read.delim("starting_files/picrust2_output_min10000_no_tax_PFF_ASV_table.qza/export_ec_metagenom... |
93d5a7581ec4693c2ef2dd3d8dc3c4b4c9431cb2d0e9a47f1b9338de794ab640 | R | 11,686 | 360 | ---
title: "Cleaning BJH039"
output: html_document
date: "2023-12-05"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 5, # set default width of figures
fig.height... |
f055f934ed9bec27f44fe5150177a368f965d3c8ae899d2f90d93c190770d3b5 | R | 11,729 | 307 | # =============================================================
# ASSOCIATIONS OF ALL 21 AGEING BIOMARKERS WITH ALL-CAUSE MORTALITY
# =============================================================
# COHORT:
# Lothian Birth Cohort 1936
# COX MODEL:
# Surv(time_to_death, dead) ~ scale(biomarker1) + ... + scale(biom... |
893f6fe82fa7e51b57fefcb8cd19fb6ea1889b2c119d6a2535d48a4dd53bde80 | R | 11,737 | 246 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# scWGCNA V 1.0.0
<!-- badges: start -->
<!-- badges: end -->
scW... |
a832595311acccac45e3b5fdfc775888cdfdba1fb09dc143ada5d7c565d45cbd | R | 11,742 | 378 | ---
title: "LL13 Cleaning"
output: html_document
date: '2022-10-31'
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo <- FALSE, # don't print the code chunk
warning <- FALSE, # don't print warnings
message <- FALSE, # don't print messages
fig.width <- 5, # set default width of figures
fig.heigh... |
12bec5da7492020e645bb3df17972ad0081092986dd227473c98a0430ba904d5 | R | 11,758 | 238 | library(tidyr)
#' This function plots values from column 1 of a dataframe against the
#' corresponding values in column 2. The values can be numeric or character.
#'
#' @param df dataframe with two columns
#' @param logscale set to character string log10 or log2 to scale y-axis
#' @param axlabels vector of axis labe... |
606be519d8013c8e3263c803cfbb66efd300886252331ad35a4e0a6f39dcfa90 | R | 11,758 | 267 | ## This script performs developmental projections of tumor cells onto developmental reference (Fig. 1f and E2b)
# Load packages -----------------------------------
rm(list = ls())
library(data.table)
library(tidyverse)
library(crayon)
library(ape)
library(readxl)
library(qs)
library(cowplot)
library(Seurat)
# Organi... |
bc3c749c683b66963c083329d96a87d650b21a5f9e953c7d76165b69e18d9d29 | R | 11,843 | 404 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(robustlmm)
library(glmmTMB)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\i... |
10d42685e85bb2c6c8d0be966f81939fcec5f300404337f4e2ef1fc1f15da754 | R | 11,946 | 368 | ---
title: "BJH016 Cleaning"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo <- FALSE, # don't print the code chunk
warning <- FALSE, # don't print warnings
message <- FALSE, # don't print messages
fig.width <- 5, # set default width of figures
fig.height <- 8, # set ... |
c69f5c483bec4db6d429cd8533aa072f0c945d1712bb49354a84a7d49873f2f7 | R | 11,958 | 263 | ---
title: "Using NHANES to Train and Project into HRS"
author: "Dayoon Kwon"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Using NHANES to Train and Project into HRS}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_c... |
52396ca55edbbb9b04518449867de1cec0ad3c0004ab61d81ed799b2d5b69112 | R | 11,969 | 275 | library(Maaslin2)
library(funrar)
library(dplyr)
library(ggplot2)
library(cowplot)
library(here)
library(glue)
library(tidyverse)
here::i_am("src/ASO/ASO_L2_L6_Maaslin2.R")
### Note: First remove "#Constructed from biom file row"
### Fraction ASV table into respective subsets ---
# Load metadata once
metadata <- rea... |
1acc1f48c923c0e51992e83d2161ae6f3bac6474fd8dbb912a9a289ec157e306 | R | 11,986 | 296 | # ==== TODO
# * Make sure BLUP/BSLMM weights are being scaled properly based on MAF
suppressMessages(library("optparse"))
suppressMessages(library('plink2R'))
suppressMessages(library('glmnet'))
suppressMessages(library('methods'))
suppressMessages(library('Seurat'))
suppressMessages(library('caret'))
option_list = l... |
12ff3041a9882c3df46d7ea153d8e174f80ebfd2f4f924f7718bdf26a617bedc | R | 12,017 | 377 | #02_ES-Ana_associated_with_AD.R
rm(list=ls())
library(topicmodels)
library(MetaTopics)
library(dplyr)
load('./Gibbs_model.RData')
#1.ES-Ana probability: logistic----
map=read.csv('./03_table/00_meta_all.csv',header=T)
map$age=map$Age
load('./Gibbs_model.RData')
n_topic=15
LDA_20_1_df <- as.data.frame(Gibbs_model@... |
cec7304c6b96ea651d629a529e14603fe369a79bb7550bcc7f36960b92f19d2a | R | 12,034 | 446 | ---
title: "Bayesian Assessment of HFA Correlations And Time to Turnaround"
output: html_document
date: "2024-10-14"
---
```{r setup, include=FALSE}
## libraries ##
library(tidyverse)
library(ggplot2)
library(lmerTest)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(lmtest)
lib... |
1b22925c1d1463eda497dcebb887fb590762e6f5eaf8b919951949a3e371546b | R | 12,051 | 370 | ---
title: "Bayes Factors for Rising/Falling Coherence"
output: html_document
date: "2025-08-14"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default wi... |
5217f88d0b7fa55f70b1b9414eac03ae041aa5b568fcb82d2f0c68ea210a5249 | R | 12,115 | 367 | ---
title: "Calculate Correlations for Turn Time Analysis"
output: html_document
date: "2024-04-29"
---
```{r setup, include=FALSE}
## libraries ##
library(tidyverse)
library(ggplot2)
library(lmerTest)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(lmtest)
library(scales)
lib... |
e600aeee841b10d44cdace5ab064906ce9114cd7d30dc815de85020e047002fc | R | 12,166 | 418 | # =============================================================
# GENE SET ENRICHMENT ANALYSIS OF MORTALITY-ASSOCIATED SOMASCAN 11K PLASMA PROTEINS
# =============================================================
# COHORT:
# Lothian Birth Cohort 1936 (LBC1936)
# COX PROPORTIONAL HAZARDS MODELS:
# Protein-level ass... |
ce284403318d472047c8aa7ca0354d49a3092aedafad13fdf6cab628220c0cc6 | R | 12,193 | 291 | ##' Internal cross-validation can be used for estimating the level of
##' structure in a data set and to optimise the choice of number of
##' principal components.
##'
##' This method calculates \eqn{Q^2} for a PCA model. This is the
##' cross-validated version of \eqn{R^2} and can be interpreted as the
##' ratio of va... |
1f4153787d06732dc18611595b34b3d35014d6f9032204c3286431831f3da09d | R | 12,196 | 327 | # ------------- Figure S5 --------------
#----Figure S5A----
module2 <- readRDS("./yang/wang/figure5/module2.rds")
module5 <- readRDS("./yang/wang/figure5/module5.rds")
M2.epi <- subset(module2,CELLTYPE %in% c("Epithelial"))
M5.epi <- subset(module5,CELLTYPE %in% c("Epithelial"))
M2.5.epi <- merge(M2.epi,M5.epi)
M2.5.e... |
bd92c8fc7148ec9aad9ff280dde63b48861377a6f6c6317397b4538ea4570e0a | R | 12,221 | 445 | library(tidyverse)
library(synExtra)
library(data.table)
library(powerjoin)
library(here)
library(qs)
synapser::synLogin()
syn <- synDownloader("~/data", .cache = TRUE)
rosmap_res <- syn("syn45053249") %>%
read_csv()
msbb_res <- syn("syn50934769") %>%
read_csv()
combined_res <- list(
rosmap = rosmap_res,
ms... |
9ec74b8f51c8ef8da350404a1c687e78e161e2bec1f43baa38d8d442d1eeef86 | R | 12,227 | 164 | #
#
# ### test: import various "datasets" generated from the same raw data, by processing them with various FragPipe versions and settings in the workflow tab ("experimental designs")
#
# devtools::load_all()
#
# dataset = import_dataset_fragpipe_ionquant(path = "C:/VU/projects/Remco - Shisa6 IP WT-KO (2015)/fragpipe/v... |
55f3bef9dac191dade1a1e2977b1a4770b1ea051efd1cafff3fad425a8dc327d | R | 12,281 | 356 | # Get all the sample directories
sample_dirs <- list.dirs(path = "Box-Box/Harini_RNASeq_NGN2-iN/files_code/STAR", recursive = FALSE)
# Find ReadsPerGene files in each directory
count_files <- file.path(sample_dirs, paste0(basename(sample_dirs), "_ReadsPerGene.out.tab"))
count_files <- file.path(sample_dirs, paste0(bas... |
35dfbbc7a3cd290fe833e16fe2952f830e8e664e24831afc4cd704b16e7a8fd3 | R | 12,288 | 243 | library(Seurat)
library(tidyverse)
library(harmony)
#loading inHouse data
counts <- Read10X_h5("CS12_1_matrix.h5")
CS12_1 <- CreateSeuratObject(counts$`Gene Expression`,assay = "RNA",project = "CS12_1")
counts <- Read10X_h5("CS12_2_matrix.h5")
CS12_2 <- CreateSeuratObject(counts$`Gene Expression`,assay = "RNA",project ... |
fa2b3e4aeb38f9ca1ecda358a6f393ff3e0d66dea56a4d8c2d18a39277149162 | R | 12,354 | 385 | ---
title: "Subplots for Supplemental Figure 5"
output: html_document
date: "2024-11-19"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of fi... |
9df47838cdf0d102a93bc2a94a3c6138d819f08ebf952bff70aad630f95248df | R | 12,387 | 306 | ### Imputation
# # # # Imputational relations were chosen based on the correlationmatrix and additionally
# # # # logical relations were taken into account
library(jsonlite)
pfad <- "C:/Users/Masterthesis_Mayla/Step4_specific_match all_ and_some_ROI/Result/"
paths <- list.files(path = pfad, pattern = "*.txt", full.... |
1e305c5fdb8fd89b3168ecf0061bd47468e79613fae85ea798d88dd85a22ab22 | R | 12,413 | 227 |
#' wrapper function for peptide-to-protein rollup
#'
#' converts a table with peptide-level abundance values to protein-level data by combining data from respective peptides per protein (per sample).
#'
#' @param tib peptide tibble in long-format. missing values should be set NA, not zero. required columns: peptide_id... |
71eab91c455fb1bc8eb10006e290bc5673f1ae277bf4718be94ffe45bd028d89 | R | 12,422 | 294 |
#' Guess experiment data from a set of filenames
#'
#' main function, remaining functions are internal / utility
#'
#' Assuming input files are somewhat well formatted,
#' we here try to detect sample groups and other metadata to populate metadata table for user's convenience
#'
#'
#' example; wt1, wt2, ko1, ko2, ...
... |
0bb5f15eb32159c28e101c1a3540256d84c6b0748e20c31f653dce6c2126a786 | R | 12,431 | 255 | library(qs)
library(Seurat)
library(tidyverse)
library(stringr)
library(patchwork)
library(glue)
base_dir <- "/n/scratch/users/c/cao385/Ependymoma/dj83"
script_dir <- "helper_scripts"
source(file.path(base_dir,script_dir, "single_cell_preprocessing_helper_functions.R"))
source(file.path(base_dir, script_dir, "NMF_help... |
547d3367757712960fde49e2995a550a21947e6505a3569ef1f5ee4e8d61d8eb | R | 12,463 | 160 |
#' write statistical results to file
#'
#' combines both the DEA and "differential testing" results into output file differential_abundance_analysis.xlsx
#'
#' @param dataset your dataset
#' @param output_dir path to existing directory
#'
#' @importFrom openxlsx createWorkbook addWorksheet createStyle writeData saveWo... |
ca29d7539d8d1516a921f0c5d007d3c04cb4c2bdae3043aef4f4b1c955564881 | R | 12,469 | 518 |
# ---- libs ----
suppressPackageStartupMessages({
library("readr")
library("dplyr")
library("tidyr")
library("lubridate") # way to handle dates better than default R way
library("ggplot2")
library("ggthemes")
library("purrr") # map(), map2() functions etc
library("stringr")
library("knitr")
li... |
276d8fe3cafd81681b87bd08802248f54bafdc70cd6129ac200ce3f0e8fb46f0 | R | 12,489 | 413 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(robustlmm)
library(glmmTMB)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\i... |
4021e7e4882189b1d26935e85287dc7c7bb2d43404deef5ca9a306122c0895cb | R | 12,507 | 389 | ---
title: "Cleaning BJH025"
output: html_document
date: "2023-02-09"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo <- FALSE, # don't print the code chunk
warning <- FALSE, # don't print warnings
message <- FALSE, # don't print messages
fig.width <- 5, # set default width of figures
fig.he... |
1a0822a720c23d411e5aaaa46ad37a855e354628700b36e2dd0ba87d46fb322f | R | 12,628 | 454 | ---
title: "Bayesian Assessment of Theta Correlations And Time to Turnaround"
output: html_document
date: "2024-09-23"
---
```{r setup, include=FALSE}
## libraries ##
library(tidyverse)
library(ggplot2)
library(lmerTest)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(lmtest)
l... |
eaa1ad708977431621976245e454ff6ade01ccd6d852d695c9a9cfec2f4f2d87 | R | 12,634 | 553 | ---
title: "DRIAD additional JAKs"
author: "Clemens Hug"
output: html_document
---
```{r setup}
library(tidyverse)
library(synExtra)
library(data.table)
library(powerjoin)
library(here)
library(qs)
synapser::synLogin()
syn <- synDownloader("~/data", .cache = TRUE)
data_dir <- file.path("driad", "additional_jaks")
d... |
63061771e9cf2e0e5005fb917c9d075e6222dffd1527125e218efbe7b628542e | R | 12,662 | 417 | library(tidyverse)
library(synExtra)
library(data.table)
library(powerjoin)
synapser::synLogin()
syn <- synDownloader("~/data", .cache = TRUE)
rosmap_quant <- syn("syn43841162") %>%
fread()
rosmap_key_mapping <- syn("syn3382527") %>%
read_csv()
rosmap_clinical <- syn("syn3191087") %>%
read_csv()
specimen_met... |
509ccc20a585151ac2205abcc22503c070b662ca6ccbb58addd12ba35c3d5a06 | R | 12,696 | 234 | testthat::context("assert pipeline output equal to example from MSqRobSum R package")
msdap::enable_log(FALSE)
cl <<- msdap::initialize_multiprocessing()
######################################################## generate results ########################################################
library(limma)
library(MSnbase)
... |
837f2c7bcb1cb7a9c44889da74c165d48a5e1c0b966410abb45f9072669820a3 | R | 12,752 | 287 | ##' Perform cross validation to estimate the optimal number of
##' components for missing value estimation. Cross validation is
##' done for the complete subset of a variable.
##'
##' The assumption hereby is that variables that are highly correlated
##' in a distinct region (here the non-missing observations) are also... |
c9faaa66ca945326ac8f2981a2046878cbea0d27cc77f4de18392b144239ee37 | R | 12,772 | 307 | ##' @include errorHierarchic.R
##' @include derrorHierarchic.R
NULL
##' This is a class representation of a non-linear PCA neural
##' network. The \code{nlpcaNet} class is not meant for user-level
##' usage.
##'
##' Creating Objects
##'
##' \code{new("nlpcaNet", net=[the network structure],
##' hierarchic=[hierarchic... |
f77e116c87866825f56b5df7e21338e98058916eb6eb4e17617db7eafe25b93a | R | 12,775 | 390 | ---
title: "Fig 3 Roi Rising/Falling Plots"
output: html_document
date: "2024-11-21"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figure... |
6ca5ee135f0fa45e07dbb0359c5236b798e1c53713ed879f63d005576ca47b8e | R | 12,918 | 258 | # Here are the functions which deal with doing something with the actual spatial coordinates of the file (like subsetting according
# to spatial coordinates etc.). These were originally written for the G34RV project, but are useful for other projects too.
# function to return the coordinates of the centroid of a cell... |
da42919dc705480ea2986076de74fc5017788f30d08a2b09f48adb4dc19e6ee4 | R | 12,954 | 462 | #!/usr/bin/Rscript
#' ----Goodness of Fit comparison across distributions----
#' Code to generate the results presented in Supplementary Figure 21 - 37.
#' Provide (either via the CLI or with hardcoded paths) the count data and genes
#' that are of interest to assess. The code will then :
#' 1. Normalize and Cluster t... |
4fd85d3a257285a8ca7517718f29e640bb99a8b83c0aa454b028e125f2e37110 | R | 12,981 | 421 | library(dplyr)
library(ggplot2)
df<- read.table("taxon_ec_sel.tsv", header = TRUE, sep = "\t" )
new_df <- subset(df, select = c('X1812935', 'X2052056', 'X2714945', 'X33887', 'X37919','X1.13.11.37'))
predictors <- c('X1812935', 'X2052056', 'X2714945', 'X33887', 'X37919')
df_clean <- new_df[complete.cases(df$X... |
af1a88d31b87a8057390f1f8e11cf0ed8067a626ea7b6a25956eebfacf231e19 | R | 13,016 | 295 | #' Perform post-hoc differential gene expression analysis
#'
#' This function will perform differential gene expression analysis within
#' differentially abundant neighbourhoods, by first aggregating adjacent and
#' concordantly DA neighbourhoods, then comparing cells \emph{within} these
#' aggregated groups for differ... |
b841adb7749d518d12f6cefcfa73197495d0a734c8c94af383e2b899c53487d0 | R | 13,025 | 358 | # ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
# This script is to fit deep learning models using H2O for the manuscript in PLOS ONE:
# "Growth rates on coral reefs peaked at 25 °C through the Holocene"
# by Tonya Macedo and Robert van Woesik
# ~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~... |
50829b334185b72cc6333a0c913503ea2d35a9dc1197bd4a560294e2679aa833 | R | 13,131 | 331 | create_last_away_events <- function(df, fname) {
last_away_df <- df %>%
filter(Trial != "ITI") %>%
filter(trial_numeric != 0) %>%
filter(died == 0) %>%
mutate(neural_trial_numeric = trial_numeric - 1) %>%
group_by(neural_trial_numeric) %>%
mutate(towards_ghost = if_else(Direction == "Left" ... |
3b6ebf80b4fcacdf485232c647688a44479f48f6ab6ff49f79a1c9aed3e3d818 | R | 13,163 | 228 |
#' Merge technical replicates prior to downstream analysis
#'
#' @description
#' Replicate measurements of the same "biological sample ID" are merged by this function, updating the peptide and
#' sample tables; foreach set of rawfiles (sample_id in the sample metadata table) from the same biological sample,
#' all but... |
36c1f1d86389e45cfb8ea9553330b2112371fc3d789935347dfd9527a71501f2 | R | 13,174 | 443 | ---
title: "Compile iEEG Onset ITI LOGGED Baseline"
output: html_document
date: "2023-09-12"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo <- FALSE, # don't print the code chunk
warning <- FALSE, # don't print warnings
message <- FALSE, # don't print messages
fig.width <- 5, # set default wid... |
47dbb34423eb4f128043c0567879fcce2cd52200255aafd8dd12a4788b7403e9 | R | 13,176 | 340 | ## DoAll
# INACT_TIME: -1: pre only 0: peri only 1: post only 2: pre+post
# LESION vs CONTROL, ZEN:
# 11685 11670 11696 1703 1706 1712 1718 1722 (1726) vs 1700 1709 (1714)
# Often exclude 1726
# LESION vs CONTROL, TYR:
# 1964 1986 1992 1995 1998 2002 2005 vs 1968 1971 1973 1978
# 4,-5 6,4 6,8... |
02be2e2f67876a47d83947cee67cec937acedb3251cbee570a00c50fceba42fc | R | 13,230 | 348 | ---
title: "Average anterior-posterior difference plots"
output: html_notebook
---
Version 1.0, July 2025, SA
This script takes matrices from ClustRobust.m, which are adjacency matrix that show where clustering is consistent across people. It reads this information in and selectively averages together the anterior-po... |
c263e7fe96f4ae4132257bfd1a2c9d4e09416f8a86cc1b151986d4427adb8f8e | R | 13,285 | 410 | ### Bayesian Helpers
pull_bayesian_model_summaries <- function(model, coef_name){
post <- as_draws_df(model)[[coef_name]]
# Replace mean=0, sd=2 with your declared prior for this coefficient
bf <- bayesfactor_parameters(
posterior = post,
prior = distribution_normal(n = 50000, mean = 0, sd = 2... |
b593b9199e777af0f144e4b6b8d3f5353ce6ebeb87d75b1cfcf3b97a16c624ff | R | 13,314 | 280 | # Load packages -----------------------------------
rm(list = ls())
library(data.table)
library(tidyverse)
library(crayon)
library(ape)
library(readxl)
library(qs)
library(cowplot)
library(Seurat)
library(ggpubr)
library(future)
library(SingleCellExperiment)
library(SingleR)
library(celldex)
# Organize environment an... |
7de7af8ae3e49aad05d122474cb74b33b0cb6359ba69f1464757e696b4ca52bf | R | 13,374 | 658 | ---
title: "Cleaning BJH027"
output: html_document
date: "2023-09-01"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo <- FALSE, # don't print the code chunk
warning <- FALSE, # don't print warnings
message <- FALSE, # don't print messages
fig.width <- 5, # set default width of figures
fig.hei... |
2c9d3eb25aed262084e86347254a28499d94ef5755d2d38e57bbd9b2edffccae | R | 13,453 | 411 | ---
title: "hNPC_scRNAseq_analysis"
author: "SabaShahin"
date: "06/06/2023"
output: html_document
editor_options:
chunk_output_type: console
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
load required libraries
```{r}
suppressWarnings(library(dplyr))
suppressWarnings(library(Seurat))... |
0f440966518395f3770a4f017e2c8fc4afb6aefcf7df4c1a3f6c5924bdf138bc | R | 13,612 | 418 | # helpers.R
# ------------------------------------------------------------------------------
# Helper utilities for protocol-style examples
#
# These functions support streaming FASTQ parsing, pattern alignment/extraction,
# Phred-quality based filtering, and conversion to a flat table.
#
# Dependencies: ShortRead, Bio... |
81a62d5e99600a94ae6c1f384abcd39a655cd22ab21962d09b92b01e0d2b5952 | R | 13,698 | 407 | ---
title: "Rising Connectivity - Imaginary Coherence"
output: html_document
date: "2024-06-02"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default wid... |
a12c05ad15a2b995d1ced26de781f45d4c8057d804da3bad09a1dfa1fe371c47 | R | 13,735 | 520 | ---
title: "AD vs healthy GSEA"
author: "Clemens Hug"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(here)
library(tidyverse)
library(data.table)
library(synExtra)
library(qs)
library(powerjoin)
library(ggrepel)
synapser::synLogin()
syn <- synDownloader(normalizePat... |
dd651de072b2729b8141b5f08bbeae9798512a92454863d78f2f2152cc37fcd9 | R | 13,742 | 237 |
#' placeholder title
#' for reference, standard pca (does not cope with missing values); summary(stats::prcomp(t(matrix_sample_intensities), center = T, scale. = F))
#' @param matrix_sample_intensities peptide or protein abundance matrix
#' @param samples sample metadata table
#' @param samples_colors sample colors in... |
419655f4d16cf00d11aea57e298a6bb08ffc9fdc654c250fea218f555b1e4059 | R | 13,834 | 250 |
#' Import a label-free DDA proteomics dataset from OpenMS, experimental feature !
#'
#' This parser is based on output we generated with OpenMS 2.5 using input data and parameters/instructions from: https://abibuilder.cs.uni-tuebingen.de/archive/openms/Tutorials/Data/iPRG2015_Hannes/
#'
#' Check the file "run_lfq.sh"... |
6222b4d3cf5bd12d3188671ca175641c03c1a9109aa679e0e73b5ee3bb7b73c2 | R | 13,966 | 770 | ---
title: "Cleaning BJH029"
output: html_document
date: "2023-09-01"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo <- FALSE, # don't print the code chunk
warning <- FALSE, # don't print warnings
message <- FALSE, # don't print messages
fig.width <- 5, # set default width of figures
fig.he... |
bdcf17bf32f4752e9223315428ac5afefc0cc9c24f5de83fd00b5995cb435225 | R | 13,975 | 393 | ### Approach Theta Coherence ~ Time ###
## libraries ##
library(tidyverse)
library(ggplot2)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(scales)
library(brms)
## load data ##
conn_df <- read_csv(path(here(), "munge", "combined_ghost_connectivity_newsubs.csv"))
## split the... |
e540f1cde6e002051f8039642f8942018aed607a8acf491829bccb93cd4d8dec | R | 14,018 | 424 | ---
title: "Subplots for Supplemental Figure 6"
output: html_document
date: "2024-11-19"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of fi... |
f6ad41c0ea4edc4f95cec2ca40ed3a1b10e78de4abbce953b0ae5df3c1f09010 | R | 14,024 | 388 | # doPower.r
###########################################################################
# Set basic parameters #
########################
AREA = "LIP"
MONK = "tyr" # zen, tyr, both (don't use both: R calls will merge monks)
MEMORY = F # T or F - can only be run with Zen, LIP (have not tested...)
ALIGN = "" # "" (targ... |
e0f47a9a0037f6df390cee76b60be568e0ab603dceae5ee8438d44b640bbc1cd | R | 14,074 | 284 | library(Seurat)
library(tidyverse)
library(harmony)
library(patchwork)
#loading inHouse data
counts <- Read10X_h5("/Volumes/Extreme SSD/Mouse_cellranger/E9_1_matrix.h5")
E9_1 <- CreateSeuratObject(counts$`Gene Expression`,assay = "RNA",project = "E9_1")
counts <- Read10X_h5("/Volumes/Extreme SSD/Mouse_cellranger/E9_2_m... |
cafd749cbd74d9bb7efdac5c7a9c0fefd6b07cc451d20472d9a18720264d1165 | R | 14,118 | 399 | ### Avoid Theta Coherence ~ Time ###
## libraries ##
library(tidyverse)
library(ggplot2)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(scales)
library(brms)
## load data ##
conn_df <- read_csv(path(here(), "munge", "combined_ghost_connectivity_newsubs.csv"))
## split the dl... |
881e2e4d828a5bf2efbaae762f45000b14166a30878c57f4fc96e93f6d3f8743 | R | 14,190 | 275 | rm(list = ls())
library(data.table)
library(tidyverse)
library(readxl)
library(crayon)
base_dir <- "/Users/sdaniell/Dropbox (Partners HealthCare)/Sara Danielli/Project/Ependymoma/6 - scRNAseq models"
resources_dir <- file.path(base_dir, 'scripts/resources')
source(file.path(resources_dir, "single_cell_preprocessing... |
5624cc1984ee35860274c6c218d418a8b61193ad1bb43a6bfdac962ab95f8356 | R | 14,195 | 434 | # Taken from https://github.com/Vivianstats/scImpute
# find_hv_genes
#' @importFrom stats quantile
.find_hv_genes <- function(count, I, J){
count_nzero = lapply(1:I, function(i) setdiff(count[i, ], log10(1.01)))
mu = sapply(count_nzero, mean)
mu[is.na(mu)] = 0
sd = sapply(count_nzero, sd)
sd[is.na(sd)] = 0
... |
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