sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
8b294ed048e3ffbce314ddd37c6763fab3f1cb5e6e0d56649358571e196a31fe | R | 7,491 | 175 | #install.packages("colorspace")
#install.packages("stringi")
#install.packages("ggplot2")
#install.packages("circlize")
#install.packages("RColorBrewer")
#if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("org.Hs.eg.db")
#BiocManager::install("DOS... |
ca77ed7f674ab057f6c4862bbcabf9b2d84923229e26a04f1e6b1545c67a200a | R | 7,507 | 210 |
#' Select probes for use in cellular deconvolution
#'
#' Using a DNA methylation dataset containing profiles for a panel of
#' reference cell types this function selects the sites for cellular
#' deconvolution and estimates the nessecary coefficients
#' This function is adapted from minfi pickCompProbes()
#' to take a... |
7605180bad39eea5a96fbbe267d0003cb4e58ffc04af62bf213509dbfdcc0e77 | R | 7,523 | 188 | library(Seurat)
library(tidyverse)
library(cluster)
#library(factoextra)
library(dendextend)
library(weights)
library(ggpubr)
library(matrixStats)
library(readxl)
library(glue)
setwd("/n/scratch/users/s/sad167/EPN")
base_dir <- "/n/scratch/users/s/sad167/EPN/scRNAseq"
resources_dir <- file.path(base_dir, 'scripts/res... |
9d3bf564cc6d83d0fa9d1288da108471f714a956508610b44da75a8cf283b1e6 | R | 7,523 | 284 |
# ---- funcs ----
# The below function is thanks to Curtis:
# https://github.com/curtis-murray/MedicalDevicesNLP
#' Function to get data for disproportionality analysis
#'
#' @param group_1 any vector combination of "pelvic_mesh", "hernia_mesh",
#' "other_mesh", "other_device".
#' @param group_2 any vector combinat... |
c086c8efeadc5f4aa68bdf93c24325fe823e5d14710e6e476c1f83d016143282 | R | 7,558 | 169 | context("Testing nhood marker gene function")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen values for the covari... |
e92585ef15c051932eae872b06bfb56e177e6ccc4424ca55d1de27229ee69b01 | R | 7,564 | 250 | detrend_signal <- function(signal) {
time <- 1:length(signal) # Create a time index
lm_fit <- lm(signal ~ time) # Fit a linear model
detrended_signal <- signal - predict(lm_fit) # Subtract the trend
return(detrended_signal)
}
calculate_overall_ccf <- function(sig_pairs, theta_df){
results_df <- tibble("s... |
06ffb7a8d080e5569c9034450278462b54b210c0a32f5844dfaeee2ba34f814a | R | 7,588 | 191 | ##' Neural network based non-linear PCA
##'
##' Artificial Neural Network (MLP) for performing non-linear
##' PCA. Non-linear PCA is conceptually similar to classical PCA but
##' theoretically quite different. Instead of simply decomposing our
##' matrix (X) to scores (T) loadings (P) and an error (E) we train a
##' ne... |
2af97529f9f84023367a6999d36b5e04e8df5144df6ef0fd26807dce35c53007 | R | 7,607 | 202 | rm(list = ls())
# Load packages -----------------------------------
library(dplyr)
library(Seurat)
library(ggplot2)
library(SingleCellExperiment)
library(tidyverse)
library(ggpubr)
library(readxl)
library(qs)
library(SeuratWrappers)
library(circlize)
library(ComplexHeatmap)
library(RColorBrewer)
library(dendextend)
li... |
8313126f27e6989e325b85bf023ac5d520595c0b08889aed80e1a518d8f6cb94 | R | 7,739 | 203 | # Load packages -----------------------------------
rm(list = ls())
library(data.table)
library(tidyverse)
library(R.utils)
library(ggpubr)
library(dplyr)
library(Seurat)
library(ggplot2)
library(writexl)
library(tidyverse)
#library(paletteer)
library(readxl)
library(cowplot)
#library(scCustomize)
#library(ComplexHeat... |
266266886b68efa3a12ab915e2e7de57c33f9ca0af0b66fcc222b2d3328c8576 | R | 7,750 | 228 | library(here)
library(tidyverse)
library(cowplot)
library(ggplot2)
### Establish location ---
here::i_am("src/ASO/ASO_Correlate_DAT_with_Rotarod.R")
### Read in input files ---
ASO_rotarod <- read.csv(here("data/ASO/ASO Rotarod - Rotarod.csv"))
ASO_rotarod <- ASO_rotarod %>% filter(Day=="One") %>%
filter(ASO_Tg==... |
ca99bd98a20e8cd3497ad9202f4425e6a904c9f5ba86ca873eaa8de53d689927 | R | 7,766 | 231 | ---
title: "Correlation_Analysis"
output: html_document
date: "2025-06-09"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Configuration
## Load library
```{r}
library(data.table)
library(dplyr)
library(pheatmap)
library(ggplot2)
library(ggpubr)
library(cowplot)
int_dir = "analysis/adata_o... |
db417371968fff7d4d47bc106bf71e942e12557f1c1b604eb5cc78ab7752f834 | R | 7,824 | 239 | library(NMF)
## preprocess cm for nmf analysis
## @param cm log transformed and centered cm
nmf_df_preprocessing <- function(cm){
## convert negative values to zero
cm = ifelse(cm < 0, 0, cm)
## remove genes with zeros in all cells
cm = cm[Matrix::rowSums(cm) != 0,]
}
## Find genes with high NMF score
## @par... |
a215da92eaf53ad58151a4da97eef39888b7fb735eb1bb0e1ab6d80b6809ebdd | R | 7,847 | 271 | ---
title: "P3 amplitude"
output: html_document
---
```{r include = FALSE}
# clear old outputs
if(dir.exists(file.path("../output/p3_amp"))) {
unlink("../output/p3_amp", recursive = TRUE)
}
output_dir <- file.path("../output/p3_amp")
dir.create(output_dir)
nice_tables_file <- paste0(output_dir, "/nice_tables.m... |
d3b711f87f77529b7abc993f3989ce29237406692ec831568926bf2ec2dbc8bb | R | 7,875 | 225 |
library(clusterProfiler)
library(org.Hs.eg.db)
library(AnnotationDbi)
library(rrvgo)
library(purrr)
library(stringr)
library(ggplot2)
library(patchwork)
library(dplyr)
name <- 'exneu'
path_rs <- sprintf('~/../jinandmaya/perturbseq/results/DE/')
method <- 'ruv'
df_deseq <- read.csv(sprintf('%sres.%s.%s.csv', path_... |
586a70738bcdd3ade8b3c7f6c806a9c45b3e90a2fba25cb93879ca4e79684eb7 | R | 7,905 | 248 | ---
title: "right_left_mfg"
output: html_document
date: "2024-10-30"
---
```{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)
library(RColorBrew... |
3c03e6f29e2a0cebce3132c1508e6213ebdf651b5519957dccb0945e781b0b96 | R | 7,919 | 199 | # Load packages -----------------------------------
rm(list = ls())
library(data.table)
library(tidyverse)
library(crayon)
library(ape)
library(readxl)
library(ComplexHeatmap)
library(qs)
library(cowplot)
library(SingleR)
# Organize environment -----------------------------------
base_dir <- "/Users/sdaniell/Dropbox... |
1d53a74b131500bbec8af4e248189944da2ad59f069255651ee2bb92f9bbbda4 | R | 8,002 | 245 | # Ensure ggplot2 package is installed
if (!requireNamespace("ggplot2", quietly = TRUE)) {
install.packages("ggplot2")
}
library(ggplot2)
# ================================ Data Preparation (English) ================================
# Your provided silhouette scores (corresponding to K=2 to K=10)
silhouet... |
17550640ed4b8d1de51c0850a45736c6183ffb48bfd5b9eafbd45af095a27367 | R | 8,003 | 175 | # On-Demand Seizures Facilitate Rapid Screening of Therapeutics for Epilepsy
# Authors: Yuzhang Chen, Brian Litt, Flavia Vitale, Hajime Takano
# DOI: https://doi.org/10.7554/eLife.101859
# Step 0: Epileptic (1) or Naive (0)
ep_or_nv = 1;
# Step 1: Import Libraries and Master Spreadsheet
library(readxl)
library(ggpl... |
84a0601a81839e23bde5bacb7e1a1beaf899d84d4ed0dd31cfb9128cbc021414 | R | 8,020 | 220 | library(ggplot2)
library(dplyr)
library(cowplot)
library(nlme)
data <- read.csv("Analysis_Files/ASO/ASO GI Motility - ASO_FP_Output.csv",header=TRUE)
data_long <- pivot_longer(data,
cols = starts_with("X"),
names_to = "timepoint",
values... |
a3c6fe1e51070e5e3f76ac76db1e544d12465cc8ad05a632396c1310cdbc6bb5 | R | 8,026 | 217 | ---
title: "Hemisphere x Long-Axis x Age Bin Interactions"
output: html_notebook
---
Version 1.0, July 2025, SA
We ran a linear mixed model predicting hippocampal-voxelwise whole brain connectivity as a function of hemisphere, long-axis, and age bin. We identified neocortical clusters where there was a significant in... |
5aa114c8b53b33643fe5dd926d8df1f242017b53847816e96ee6b3ea3ef72cf6 | R | 8,036 | 196 | ## This script imports the two references preprocessed in the script before and combines them into a single dataset
## It also calculates pseudobulked cm, HVGs and DEGs for projection in the next script
# Load packages -----------------------------------
rm(list = ls())
library(data.table)
library(tidyverse)
library(... |
d55f36a4d333a0d6f71ef1b71e671a811779d004fceba176cc977dc2c7333ced | R | 8,094 | 260 | # CpG Feature Selection via Age–Correlation Stratified by Sex
#
# - Builds a common CpG set across multiple cohorts
# - Computes Spearman correlations with age in 4 age strata,
# separately for females and males
# - For each stratum, keeps CpGs with the top 1% |rho|
# (sex with higher |rho| wins for each CpG... |
a04538518ea5579260666fe621b0b0eae59deeb45a624bee3f64921c64f581ff | R | 8,098 | 96 | # Step 2: Nuclei segmentation of individual capture areas images
The functions [VNS](https://github.com/LieberInstitute/VistoSeg/blob/main/code/VNS.m) (Visium Nuclei Segmentation) and [refineVNS](https://github.com/LieberInstitute/VistoSeg/blob/main/code/refineVNS.m) from the pipeline are used to perform nuclei segmen... |
1c567ad5c98b7565a8e805216c17a2bbe2ddf25ff50702597a8e4028f976bff6 | R | 8,139 | 205 | library(Rtsne)
library(here)
library(dplyr)
library(ggplot2)
library(cowplot)
### Compiling ASO data into one single sheet
## Rotarod --
data <- readr::read_csv(here("data", "ASO","ASO Rotarod - Rotarod.csv"))
subset <- data %>% select(c("MouseID","ASO_Tg"))
summary_rotarod <- data %>%
group_by(MouseID) %>%
#sum... |
0086f5f96c565d8054636483007f281275e5496c8fa6d310aac74635a6729442 | R | 8,148 | 228 | library(ggplot2)
library(dplyr)
library(cowplot)
library(nlme)
library(here)
library(tidyverse)
here::here()
data <- readr::read_csv(here("Analysis_Files", "MPTP","MPTP_FP_Output.csv"))
data_long <- pivot_longer(data,
cols = c("5","10","15","30","45","60"),
names... |
7ae97578adb699f155b13850b7bff9fd5124ddbc6faf207f69eecb70aa1518fe | R | 8,218 | 318 | #####################################
# Estimate correlation coefficient between metacognitive effiency
# estimate between two, three, or four domains.
#
# Adaptation in R of matlab function 'fit_meta_d_mcmc_groupCorr.m'
# by Steve Fleming
# for more details see Fleming (2017). HMeta-d: hierarchical Bayesian
# estim... |
fc3ffbe6ac6a68fe6e55ff663641c9efa8e8fba8197865ab0a9291ac37b69f29 | R | 8,238 | 143 | ## This function executes the original SOLO algorithm, following Timothy Walker's implementation (PMID 26116186).
## The inputTab has columns sample_id, mutation, phenotype and [SOnly or het]; maxIter is a positive integer or Inf.
## If removeSOnly is FALSE, the first stage of classifying and removing the variants occu... |
9b1c631f20e8f8ce1e66b6a57dc4ff51d2009f9a132694a051438a111278c61f | R | 8,252 | 207 | library(ggplot2)
library(dplyr)
library(patchwork)
library(cowplot)
#for plot
latent_data_X <-as.data.frame(cbind(design,spls.final.4comp.multilevel.canonical$variates$X))
colnames(latent_data_X) <- c("sample", "X1", "X2", "X3", "X4")
latent_data_Y <-as.data.frame(spls.final.4comp.multilevel.canonical$variates$Y)
lat... |
d65cabb19db6cde7d4a956b30ef31366deb9c3150831399e48f16828418243a8 | R | 8,266 | 207 | ################################################################################
# Visualizes change in environmental variables for Figures S1 and S5
# Visualizes the diversity of Durusdinium and Symbiodinium ITS2 profiles
################################################################################
###############... |
1e1bdc7653c865c9067a899a8d350c19d948ac88b5808a323ac96796fdb023ae | R | 8,274 | 207 | library(here)
library(ggplot2)
library(rlang)
library(rstatix)
library(nlme)
library(cowplot)
library(ggbeeswarm)
library(ggpubr)
library(ggsignif)
#ROTAROD
here()
rotarod_data<-readr::read_csv(here("Analysis_Files", "Spontaneous", "Data_Rotarod_Analysis.csv"))
generate_boxplots <- function(input_data, X, Y, min,ma... |
d86790b86b5c66d17805f4c9803d828c3433bd4cfd5f3655490dfec33952a541 | R | 8,281 | 211 | ---
title: "Comparing Electrode Groups on Turnaround Effect"
output: html_document
date: "2025-07-03"
---
```{r setup, include=FALSE}
knitr::opts_chunk$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 defaul... |
5c128304cf3104bf034165bdeea66112c892ca044d82ca2cf8a2f8c5faf4103f | R | 8,286 | 272 |
top_markers <- function(markers, ntop=10) {
c_names <- levels(markers$cluster)
top <- lapply(c_names, function(x) markers$gene[markers$cluster == x][1:ntop])
top <- lapply(top, function(x) x[!is.na(x)])
names(top) <- c_names
return(top)
}
find_common_variable_genes <- functio... |
77fe126bf976c7be52e07cc8d2757f49f7751f755f114d236e0e2abb08ec5d4d | R | 8,390 | 288 | ---
title: "Heatmap of spatial composition of each section"
output: html_document
date: "2025-06-09"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Configuration
## Load library
```{r}
library(data.table)
library(dplyr)
library(pheatmap)
library(ggplot2)
library(ggpubr)
library(cowplot)
li... |
f3355991f4e24a1ed8bf793573a409e2bc9f97f4794b598b69f39bddf3e732cb | R | 8,440 | 202 | library(ggplot2)
library(here)
library(ggplot2)
library(rlang)
library(rstatix)
library(nlme)
library(cowplot)
library(ggbeeswarm)
library(ggpubr)
library(ggsignif)
data_box_plot<- read.csv("Data_OLM_Analysis.csv", header=TRUE)
mytheme <- theme(panel.grid.minor=element_blank(), #gets rid of grey and lines in the midd... |
be9d598a5a2d214f4e84c8a89466f787ae1813bcef4e4f6535b9aa39ce63fc8b | R | 8,604 | 174 | context("Testing makeNhoods function")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen values for the covariance ma... |
9d62d15fde84f1d382d5293874e387641d2d0a5b68fd28a823f94ac41154bab1 | R | 8,657 | 213 | ---
title: "Making comparisons for differential abundance using contrasts"
author: "Mike Morgan"
date: "27/01/2022"
output:
BiocStyle::html_document:
toc_float: true
BiocStyle::pdf_document: default
package: miloR
vignette: |
%\VignetteIndexEntry{Using contrasts for differential abundance testing}
%\Vignett... |
c197771da6e781461071d4b9f6c04972466e7ce33c337ef2f718cbb0f77b9c0c | R | 8,701 | 312 |
##############################
## Utils
##############################
load_single_file <- function(file_path) {
tryCatch(
{
args <- readRDS(file_path)
do.call(Epoch, args)
},
error = function(e) {
file.remove(file_path)
stop(paste("Error loa... |
56e5298da234d99445f064ae79a86cafe9981c309d2175e4b6ad839dc0f64a96 | R | 8,702 | 282 | make_plot = function(axis_type, comp_df, dat, label,
scatter_cols, cor_zlim, white_borders = FALSE) {
num_vars = length(levels(comp_df$x_var))
ind_mat = matrix(
seq(1, (1 + num_vars)^2),
nrow = 1 + num_vars,
ncol = 1 + num_vars
)
index_df = get_indexes(comp_df, ind_mat, num_v... |
f54966ff777697636053c59b630c8633324bdd980a503bd78660bee69bc6d992 | R | 8,775 | 184 | #' ### foldchange and standard error in each contrast, one plot for each protein
#' # ggplot is too slow for thousands of plots
#' # instead, use a vanilla R implementation (downside: more hardcoding and fiddling, upside: few seconds to plot 2000+ proteins)
#' #' @param dataset todo
#' #' @param pdf_file_path todo
#' #... |
5bb20d996c8020e8195923f36e43e95a84743432bde7c3125e13932fe71a4300 | R | 8,866 | 177 | #' Control the spatial FDR
#'
#' Borrowing heavily from \code{cydar} which corrects for multiple-testing
#' using a weighting scheme based on the volumetric overlap over hyperspheres.
#' In the instance of graph neighbourhoods this weighting scheme can use graph
#' connectivity or incorpate different within-neighbourho... |
08f4938f8c94f95666ba196ad844d06e15b90cbb775b83d0be079b0fe5fb2204 | R | 8,906 | 253 | ############
# LIBRARIES #
############
library(readxl)
library(dplyr)
library(edgeR)
library(ggplot2)
library(ggrepel)
library(paletteer)
library(biomaRt)
library(xlsx)
library(clusterProfiler)
library(org.Hs.eg.db)
library(ComplexHeatmap)
library(RColorBrewer)
library(enrichplot)
library(pathview)
... |
3e05c015b9dfb2017c60caec0acb02964315337c13efb500a61e130ba872f1ac | R | 8,912 | 226 | cli::cli_h2("┗ [Vasc-AoP] Loading data ingestion functions")
#-------------------------#
####🔺Helper functions ####
#-------------------------#
## Loading the data dictionary
load_data_dict <- function(path = configs$data$data_dict) {
purrr::map(readxl::excel_sheets(path) |> purrr::set_names(), \(sheet) ... |
29d61bbc3cb814c723ea091a9e10516a0892a438d0a43c1cd90a4ee3f3315680 | R | 8,914 | 277 | ######## getter/setter Methods ########
#' @title Get and set methods for Milo objects
#'
#' @description
#' Get and set methods for Milo object slots. Generally speaking these methods
#' are used internally, but they allow the user to assign their own externally computed
#' values - should be used \emph{with caution}.... |
e82fbd1f0169edf6af517000cb1390296172d03622f12e3aca09288facfd4c38 | R | 8,941 | 252 |
# Simulation Parameters ---------------------------------------------------
#' @importFrom MASS rlm
#' @importFrom stats residuals na.exclude
.lfc.evaluate <- function(truth, estimated) {
# input
SE <- ((truth - estimated)^2)
AE <- abs(truth - estimated)
RMSE <- sqrt(mean(SE, na.rm = T))
MAE <- mean(AE, na... |
5e06de962f36825eec5deb59d719c82388d0e29bce7b8b348dfae597a123d848 | R | 8,961 | 224 | library(EWCE)
library(HPOExplorer)
library(MSTExplorer)
library(ggplot2)
library(readr)
library(data.table)
library(dplyr)
library(tidyr)
library(scales)
# for figure 7B
hpo <- HPOExplorer::get_hpo()
ymat <- HPOExplorer::hpo_to_matrix()
##unique genes
length(unique(ymat@Dimnames[[1]]))
# [1] 5180
##unique HPO phen... |
e18d77fddc208f53a18e1afc91b8a6da023aeba48b2e77fff5a784cbe482f9e6 | R | 8,966 | 171 |
#' placeholder title
#' @param peptides todo
#' @param samples todo
#'
#' @importFrom viridis scale_fill_viridis
#' @importFrom ggpubr theme_pubr
ggplot_peptide_detect_frequency = function(peptides, samples) {
## peptide detect counts, mapped to samples
tib = peptides %>% filter(detect) %>% select(peptide_id, samp... |
561837658d370c40e150689d03dcbac3b1b7f195f46f43cc51ae192246611f12 | R | 9,024 | 201 | # doPowerVsDistance.r (Seul Ah Kim)
#
rm(list = ls())
AssumeLIP_PD = F
rundoPower = F
# Looping through monkey, NEAR, and Hemisphere
for (monk in c('tyr','zen')) {
for (NEAR in c(0)) {
for (Hemisphere in c('R','L')) {
## Section 1 - Nearest to sort all sites based on distance in monk and hemi
##-----------... |
bf212abc5cbff1e4108294c6b42f3a9161e2e785d7f15e62ae1725517b34acf9 | R | 9,032 | 217 | ### Grouping neighbourhoods ###
#' Group neighbourhoods
#'
#' This function groups overlapping and concordantly DA neighbourhoods, using the louvain
#' community detection algorithm.
#'
#' @param x A \code{\linkS4class{Milo}} object containing single-cell gene expression
#' and neighbourhoods.
#' @param da.res A \code... |
075ef6981de3a08785deab6cd7f4415a7ef4ae1f2ec34eb8474aa2c126ab0216 | R | 9,145 | 259 | # 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... |
96fc9e4c8d4f4320d5c2911aa8133d32185dec0ab1e761245743d151fb3d71ff | R | 9,216 | 175 | ses_res = function (dat, agevar, exposure, covar, label) {
covars = paste(covar, collapse = "+")
res = dat %>%
select(all_of(agevar), all_of(exposure), all_of(covar)) %>%
tidyr::pivot_longer(all_of(agevar), names_to = "y", values_to = "yvalue") %>%
mutate(y = factor(y, levels = agevar, labels = label)... |
95324ce8248d99fcf031991b973fca96058d3fc9f130910b588f4266ee5eb52f | R | 9,232 | 218 | #' Calculate within neighbourhood distances
#'
#' This function will calculate Euclidean distances between single-cells in a
#' neighbourhood using the same dimensionality as was used to construct the graph.
#' This step follows the \code{makeNhoods} call to limit the number of distance
#' calculations required.
#'
#' ... |
2447765c2e55e30330df57a09932904d8bde81c5b98bd2c392ec14991644e950 | R | 9,249 | 226 | library(here)
library(tidyverse)
library(ggvenn)
library(ggplot2)
library(cowplot)
### Read in ASO results ---
ASO_lc_dat <-read.table(here("results/ASO/differential_taxa/L6_Luminal_Colon_Maaslin2_Sex_Site_Genotype/all_results.tsv"), header=TRUE)
ASO_lc_dat_het <- ASO_lc_dat %>% filter(value=="HET") %>% filter(qval<0.... |
7c7ac3ff96dcb6367b4057224afea6a590bdca1e68e74759bff4f5f1f2c3f736 | R | 9,249 | 217 | ##' Missing value estimation using local least squares (LLS). First,
##' k variables (for Microarrya data usually the genes) are selected
##' by pearson, spearman or kendall correlation coefficients. Then
##' missing values are imputed by a linear combination of the k
##' selected variables. The optimal combination ... |
b4d32776b12ac98842537cd2acae46ec5b1e3e503209d69d24a5e0656866e547 | R | 9,292 | 153 |
#' Import a label-free proteomics dataset from MetaMorpheus
#'
#' @param path the directory that contains the search results (eg; present files are AllProteinGroups.tsv, AllQuantifiedPeaks.tsv, etc.)
#' @param protein_qval_threshold qvalue threshold for accepting target proteins
#' @param collapse_peptide_by if multip... |
b6cf3b354ce406d28c3660090158827ea731d210af3688e6c4dd85fa78fd8af6 | R | 9,312 | 183 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
<img src="images/logo_UCR.png" width="300"/>
<img src="images/logo_with_text-01.png" width="300"/>
<img src="images/lo... |
8c350d169283223ca74c5522fbc5f3164b0f3cf9fc3a6ee1c354050fbd540282 | R | 9,332 | 248 | library(ggplot2)
library(vegan)
library(dplyr)
library(rlang)
library(cowplot)
library(viridis)
library(here)
here::i_am("src/PFF/PFF_RSJensen_Beta_Diversity.R")
fp <- "data/PFF/PFF_Microbiome/"
metadata <- read.table(here(paste0(fp,"/starting_files/PFF_Mapping.tsv")),header=TRUE)
counts <- read.table(here(paste0(fp,"... |
654202ca3d7d5ee79c749f8a43589427be0ff090ff50721e06b22fd5a6774352 | R | 9,420 | 245 | ---
title: "Differential abundance testing with Milo"
author:
- Emma Dann
- Mike Morgan
output:
BiocStyle::html_document:
toc_float: true
BiocStyle::pdf_document: default
package: miloR
vignette: |
%\VignetteIndexEntry{Differential abundance testing with Milo}
%\VignetteEngine{knitr::rmarkdown}
%\Vign... |
fcc0cff708bc8cb9fb7df872e0e94c8d455e6ca64d24d0e4273b44967d413cf2 | R | 9,464 | 222 | ##' This is a PCA implementation robust to outliers in a data set. It
##' can also handle missing values, it is however NOT intended to be
##' used for missing value estimation. As it is based on robustSVD we
##' will get an accurate estimation for the loadings also for
##' incomplete data or for data with outliers. ... |
d67670219e328c01b43debda434614c4c7acbb779278284690abcf06e94277c5 | R | 9,465 | 260 | library(ggplot2)
library(dplyr)
library(cowplot)
library(here)
library(tidyr)
library(ggbeeswarm)
## Environment --
here::i_am("Rscripts/Figure_S4_ASO_PFF_Behaviors.R")
## Functions --
generate_boxplots <- function(input_data, X, Y, min,max){
data<-as.data.frame(input_data)
#Ensure correct ordering of levels
#... |
9d19ce2372f9f468af193bab82055de33b28ec23ce81fabfbfe9c3d45d6d2b47 | R | 9,489 | 245 | #' server.R
# Copyright (C) Carlos Biagi Jr
#
# This is a 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 option) any later
# version.
#
# This software is distribute... |
bef460772258b83986219a9ecce91393c82d38104cb1b30589b84af33a125f11 | R | 9,489 | 312 | ---
title: "Behavioral Models"
output: html_document
date: "2024-10-03"
---
```{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.h... |
32244e9c6220b8b31981975036713367d76036163475d0257baa10c5550fa92b | R | 9,511 | 288 | ########## R scripts used to produce plots for
########## Lewin, Liao and Luo 2024
########## Brachiopod genome and the evolution of BMP signalling
#### Initially created: 02/06/2023 (Thomas D. Lewin)
#### Last edited: 23/05/2024 (Thomas D. Lewin)
############################# Load general packages #################... |
cab0c19ef5f687743716ced4ffa03685247bdce1fd789ab3ece9a12489cd4997 | R | 9,534 | 270 | library(ggplot2)
library(dplyr)
library(cowplot)
library(nlme)
library(tidyr)
setwd("C:/Users/Jacobs Laboratory/Documents/JCYang/pdbehavior/")
data <- read.csv("Analysis_Files/SMT/Fecal Pellet Output (SMT) - Total_FP_output.csv",header=TRUE)
data_long <- pivot_longer(data,
cols = starts_wit... |
c184ba7c8ccc766b51becad150373cd73ffc64133228648e9db2887a19487200 | R | 9,536 | 222 |
#' placeholder title
#' prior to this; species %in% c("YEAST", "HUMAN")
#' add extra column predictor; true for spike-in, false for rest
#' @param tib todo
#' @param mtitle todo
#' @param universe todo
#' @param plot_coords todo
#'
#' @importFrom pROC plot.roc coords
#' @importFrom gtools mixedsort
#' @importFrom colo... |
d8a7a98af6ce3af9c488d523e47f1c94f0873acaeb48091f76e476f90a9ee773 | R | 9,572 | 324 | ---
title: "MMN amplitude"
output: html_document
---
```{r echo = FALSE}
# create dir to save output
ifelse(!dir.exists(file.path("output/mmn_amp")),
{
dir.create(file.path("output/mmn_amp"))
print("results will be saved in output/mmn_amp")
},
"Dir exists, if you continue, i... |
0b3e8102e467b7fdb396bc1e787f64583ca3943db2eb1e2af17d3c80a915fdd8 | R | 9,590 | 180 | health_res = function (dat, agevar, outcome, covar, label) {
covars = paste(covar, collapse = "+")
res = dat %>%
select(all_of(agevar), all_of(outcome), all_of(covar))%>%
tidyr::pivot_longer(all_of(outcome), names_to = "y", values_to = "yvalue") %>%
tidyr::pivot_longer(all_of(agevar), names_to = "x", ... |
9c2d0cf2c91508047f65f0baffd636000bcb61142c2babfe2f5d083b5c29722f | R | 9,693 | 170 | ---
title: "Sulcal Phenotype Network Analysis"
author: "Will Snyder"
output:
pdf_document: default
html_document: default
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,message = FALSE, warning = FALSE)
#install and load required packages
if(!require("corrplot")) install.packages("corrplot"); l... |
59bf40716a58d0755e14385ed23fe80427dc3b62a2e28228e36d52b08fa33d63 | R | 9,705 | 310 |
---
title: "iEEg Cleaning"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$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 d... |
28da8de970d2fcf8b5f7edebc2a35ec8ec25bae7e63029dbcd3b0f2d5cdd67cc | R | 9,715 | 163 |
#' Plot the variance explained of sample metadata properties in the protein-intensity matrix
#'
#' precondition: the input dataset$peptides tibble must have a "intensity_all_group" column,
#' e.g. obtained by first calling the filter_dataset() function with parameter all_group=TRUE
#' (when using this function to anal... |
35f1a6f8886b5e6cc6fb04cb691954b566fadfdb3c3bb35184de6fb61fb0ab3a | R | 9,727 | 234 | library(here)
library(ggplot2)
library(rlang)
library(rstatix)
library(nlme)
library(cowplot)
library(ggbeeswarm)
library(ggpubr)
library(ggsignif)
library(knitr)
library(dplyr)
data <- readr::read_csv(here("Analysis_Files/PFF/PFF Rotarod - PFF_Rotarod_Analysis.csv"))
data$SLC_Genotype <- factor(data$SLC_Genotype, ... |
41fc32ebf81a7e1d31e64b253199dddf1cee305060e070dd7e64f916395f1d49 | R | 9,781 | 215 | # 01_statistic_of_gut_microbiome.R
rm(list=ls())
library(dplyr)
library(ggplot2)
group_col=c("#982b2b","#db6968","#EDB3B3","#D0E5D0","#459943")
names(group_col)=c('AD','MCI','SCD','SCS','NC')
#1.meta:group-age-sex----
map=read.csv('./03_table/00_meta_all.csv',header=T)
df_count_sex <- map %>%
group_by(Age, gender... |
b8ada12c6ea40c6271440ef24d0824c283a2f74478a79a413e66a6d70f78fe36 | R | 9,790 | 175 | ---
output:
rmarkdown::github_document:
html_preview: true
toc: false
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
fig.path = "images/dd-",
comment = "#>"
)
```
```{r setup, include = FALSE}
devtools::load_all()
```
This vignette demonstrates a basic metric for "differential... |
ff8e1472b4f40e5395a6c902e2f617e72f6c08f74396aab70cf9dc1cec218122 | R | 9,791 | 224 | ---
title: "Comparing Electrode Groups on Turnaround Effect"
output: html_document
date: "2025-07-03"
---
```{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 defa... |
2c1c15da8076dcbc25dd28b347ff0c21e7f3c1792c44f378e4cbac25057d4478 | R | 9,806 | 114 | ---
title: "VistoSeg: Visium Histology Image Segmentation and Processing Pipeline"
author:
- name: Madhavi Tippani
affiliation:
- &libd Lieber Institute for Brain Development, Johns Hopkins Medical Campus
email: madhavi.tippani@libd.org
site: bookdown::bookdown_site
apple-touch-sin: "icon_192.png"
apple-t... |
7f89f00d6b7076f2dcfb051a472dfe61a71d2b63bb8d7289fe32b9c0f103e321 | R | 9,857 | 198 | #Loading required libraries####
library(ranger)
library(data.table)
library(ggplot2)
#set.seed(42) #Uncomment to reproduce the published results
#Parse args####
args = commandArgs(trailingOnly = TRUE)
if(length(args) < 2){
print("Usage: Rscript GenomicPrediction_with_IncrementalFeatureSelection plinkBinaryPrefix th... |
fbb2027314618e5cdf587b59f013ce88089a6d3030ff8ba93b0e2c541329b1f4 | R | 9,950 | 240 |
#' Identify post-hoc neighbourhood marker genes
#'
#' This function will perform differential gene expression analysis on
#' groups of neighbourhoods. Adjacent and concordantly DA neighbourhoods can be defined using
#' \code{groupNhoods} or by the user. Cells \emph{between} these
#' aggregated groups are compared. For... |
f9407c82e7192cd0bc95168fb0061a6707f2992594b0782386076fc6e70eae75 | R | 9,974 | 248 | if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
setTimeLimit(100000); setSessionTimeLimit(10000)
library(ggplot2)
library("cowplot")
library("gridExtra")
library(ggpubr)
library(plyr)
# load data
data <- read.csv(file="//lexport/iss01.charpier/analyses/stephen.whitma... |
87d0e360fa10e8920cbf622c4c3a1f28e8adad1d45a3f027523d1216afc6d923 | R | 9,991 | 248 | if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
setTimeLimit(100000); setSessionTimeLimit(10000)
library(ggplot2)
library("cowplot")
library("gridExtra")
library(ggpubr)
library(plyr)
# load data
data <- read.csv(file="//lexport/iss01.charpier/analyses/stephen.whitma... |
f1c8f715a6a9d7ffdf8c53442f8fc7716591c13e041b40ee75345039ce7f35f3 | R | 10,008 | 295 |
library(VennDiagram)
library(dplyr)
library(VennDiagram)
library(readr)
library(data.table)
ados <- fread("ADOS_mri_cor_anova_outlier_removed2.tsv",
sep = "auto",
encoding = "UTF-8") %>%
.[, .(feature, feature_pvalue)]
cars <- fread("CARS_mri_cor_anova_outlier_rem... |
ebf386f6f93b482a1195b0afc95d31f343513b0344d52cff9d2f540a950c850e | R | 10,033 | 323 | ---
title: "LL10 Cleaning"
output: html_document
date: '2022-08-02'
---
```{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 <- 5, # set default width of figures
fig.heig... |
76b123eeb81d8b9a07e44fe85b426495621256682fa3691cbd5f57e1b5988a97 | R | 10,045 | 283 | library(Maaslin2)
library(funrar)
library(dplyr)
library(ggplot2)
library(cowplot)
library(here)
library(glue)
library(tidyverse)
library(circlize)
here::i_am("Rscripts/ASO/ASO_L2_L6_Maaslin2.R")
### Note: First remove "#Constructed from biom file row"
### Fraction ASV table into respective subsets ---
# Load metada... |
802764717ddf6aabcc016a9ca4c7f1d314e32224b875e74fbc410addb1374abf | R | 10,068 | 323 |
```{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... |
0d994f4bb3ebbd595a280122d0a2a25286faaaa850cfdf515005b9c28542ebd9 | R | 10,096 | 328 | # This test will try to download actual data, ensure network connectivity
# and that the downloader is configured correctly.
# Initialize the downloader
dl <- EpochDownloader()
test_that("EpochDownloader initialization and listing", {
expect_s4_class(dl, "EpochDownloader")
# Check if names(dl) returns a character... |
4dbe4b459c91cae2b0a9bbd4ade0451185cd765de4110ee61ce183af4a3a6c89 | R | 10,144 | 217 | #install.packages(c("seqinr", "plyr", "openxlsx", "randomForestSRC", "glmnet", "RColorBrewer"))
#install.packages(c("ade4", "plsRcox", "superpc", "gbm", "plsRglm", "BART", "snowfall"))
#install.packages(c("caret", "mboost", "e1071", "BART", "MASS", "pROC", "xgboost"))
#if (!require("BiocManager", quietly = TRUE))
... |
ab896507036269443bde0125eb8ec7924240d3fba24883aac44befa0ab3c04a0 | R | 10,145 | 348 | #' @include zzz.R
#'
NULL
#' Get list of available datasets
#'
#' @return A dataframe with available Seurat datasets. Rownames of the dataframe are the actual package names
#' \describe{
#' \item{Dataset}{Name of dataset, usable for other functions in SeuratData (eg. \code{\link{InstallData}})}
#' \item{Version}{V... |
2f2464728cd49430f5becdf149619fedb00fda49f02beb4287514a4c852b4dd4 | R | 10,239 | 248 | if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
setTimeLimit(100000); setSessionTimeLimit(10000)
library(ggplot2)
library("cowplot")
library("gridExtra")
library(ggpubr)
library(plyr)
# load data
data <- read.csv(file="//lexport/iss01.charpier/analyses/st... |
0d93dfdbe2a9e696c6e76b7d2f9d64d5c936cfa7a097548fe2484e974ffa4d85 | R | 10,297 | 248 | # Load packages -----------------------------------
library(tidyverse)
library(ggpubr)
library(dplyr)
library(Seurat)
library(ggplot2)
library(writexl)
library(paletteer)
library(readxl)
library(writexl)
library(qs)
library(ggrastr)
library(cowplot)
library(openxlsx)
library(SingleR)
# Organize environment ----------... |
7e919acd2b580126e376adab7d10a1ea95de43b714020ff99f796156844ea846 | R | 10,308 | 252 | 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")
### PCs vs. SNPs ---------------
pcs_snps <- res %>%
filter(Data %in% c("G", "A"))
res_pcs_snps <- pcs_snps |>
mutate(Genetics = relevel(factor(Genet... |
a6e36f06f86aa7ec5d97ed5a3364db9c0e4587c2ad46f1406d694567669a18f2 | R | 10,412 | 179 | name="MergeGLU.V20241022"
outdir = "/cluster/share/atac_group/PublishedData/HumanMouseMacaque/GLUintegrated"
setwd(outdir)
homoGeneDir = "/cluster/share/atac_group/PublishedData"
oneToOneOrthGeneTb = readr::read_tsv(paste0(homoGeneDir, "/mart_export.humanMacaqeMouse.oneToOneOrth.ensembl91.20220428.txt"))
head(oneT... |
7c48055ebd0f2bab0e6ed1741793c05a4ec62364a0201ce9016b79f9f5f5ba87 | R | 10,459 | 246 | library(Maaslin2)
library(funrar)
library(dplyr)
library(ggplot2)
library(cowplot)
library(here)
library(glue)
library(tidyverse)
here::i_am("src/PFF/PFF_L2_L6_Maaslin2.R")
### Note: First remove "#Constructed from biom file row"
### Fraction ASV table into respective subsets ---
# Load metadata once
metadata <- rea... |
98ebff14fafa9390bcb466610192b11ee1e1eb005bd3dfc654d35b941b3eaeca | R | 10,460 | 283 |
## libraries ##
library(tidyverse)
library(ggplot2)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(scales)
library(brms)
## hand written functions ##
source(path(here(), "R", 'mutate_cond.R'))
source(path(here(), "R", "bayesian_helpers.R"))
### Load and Prep DF ###
# load c... |
b8c466f7ffaad7640e4d836402eafd2eac3c19b5afbd2eada0fce2a63ad25d95 | R | 10,460 | 225 | context("Testing nhood marker gene function")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen values for the covari... |
164fdfea49e0a1eae7bfc7dbcb473bc0bee76934db9c9372b847c204d92876bc | R | 10,469 | 282 |
## libraries ##
library(tidyverse)
library(ggplot2)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(scales)
library(brms)
## hand written functions ##
source(path(here(), "R", 'mutate_cond.R'))
source(path(here(), "R", "bayesian_helpers.R"))
### Load and Prep DF ###
# load c... |
cd6eca5dfa5aa0f8a85e994eb0b9faac04133ea4d887daf52f96a1e4be6b1f89 | R | 10,494 | 344 | ---
title: "LL12 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... |
583e41c67ea23307cda5b679293849529bbc1f22311836665c305290aa5d4681 | R | 10,566 | 287 | ---
title: "Rising Falling Supplementary Tables"
output: html_document
date: "2024-12-09"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of f... |
a4c66a06979ea2d5329aaaea31d6f4e1f96001a69b8690ae8c9dbdd3a632264b | R | 10,638 | 245 | #' Define neighbourhoods on a graph (fast)
#'
#' This function randomly samples vertices on a graph to define neighbourhoods.
#' These are then refined by either computing the median profile for the neighbourhood
#' in reduced dimensional space and selecting the nearest vertex to this
#' position (refinement_scheme = "... |
2cde294ea74db95753ab5f58ba071138a2ea42e07399660140195757362576e6 | R | 10,700 | 303 | # ==== TODO
# * Make sure BLUP/BSLMM weights are being scaled properly based on MAF
suppressMessages(library("optparse"))
suppressMessages(library('plink2R'))
suppressMessages(library('glmnet'))
suppressMessages(library('methods'))
suppressMessages(library('Seurat'))
suppressMessages(library('caret'))
option_list = li... |
ccb23d889ecd0e1faca721c92000028d0ac7d686edfdf644f9086a9e3b61c5c8 | R | 10,715 | 339 | ---
title: "Cleaning BJH021"
output: html_document
date: '2022-11-01'
---
```{r setup, include=FALSE}
knitr::opts_chunk$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... |
d9fa553b0c0648bbeaeee29e8e121a619b90f7e58a9196637c6f9387886ac778 | R | 10,775 | 510 |
# ---- 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") # ... |
cab9b97ab43d5f53e059cc46954be54a15fd3f5072a4af35153db2fc41357bc8 | R | 10,793 | 245 |
#install.packages("ggplot2")
library("ggplot2")
library("grid")
library("magrittr")
library("viridis")
#install.packages("raster")
library("raster")
library("readxl")
library(ggplot2)
library(viridisLite)
library(grid)
library("cowplot")
library("cowplot")
#install.packages("scales")
library("scales")
if (!requireNa... |
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