sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
2f876153e8b85c60250596620760f2c638984dab29f98052ac51e208e41ca45e | R | 14,198 | 273 | ---
output:
rmarkdown::github_document:
html_preview: true
toc: true
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
fig.path = "images/customlimma-",
comment = "#>"
)
```
```{r setup, include = FALSE}
devtools::load_all()
# ! change to some temporary working directory on your co... |
6e3c7bb2b0341688599ebb9ce7ff4bfde6fe91ee6b6965a49ec8d07296632269 | R | 14,223 | 320 | library(Seurat)
library(SeuratDisk) # new, for h5Seurat objects
library(dplyr)
library(data.table)
library(Matrix)
require(SeqArray)
source('../../R/scTWAS_IRLS.R')
gene_info <- fread('../Onek1k/1k1k_gene_GRCh37.txt')
suppressMessages(library("optparse"))
option_list = list(
make_option("--run_subtype", action="sto... |
c1156f8fb9a1a3121580c7285528be53411449699b6e0b849a80bcd96ab3fa17 | R | 14,235 | 285 | context("Testing testNhoods function")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
library(BiocParallel)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen values... |
5ed970c5e4a84892e8dfe0a33f6309307dfb12a9687e11682c8353e3325f01a4 | R | 14,279 | 431 | # ====================================================
# CALCULATING PLASMA PROTEOMIC ORGAN AGE GAPS
# ====================================================
# MODEL DEVELOPMENT (by Oh et al. 2023; https://doi.org/10.1038/s41586-023-06802-1):
# - Bootstrapped LASSO regression models trained in an independent cohort (K... |
07c496635732a953df86304712aa2bcdbd291f32bfbe40f2ad9ed267a6451207 | R | 14,325 | 300 | # 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... |
6214b844a449499724bcdecaeb19be3124956c4d4dd0be9b9a6cb05dcd02d3df | R | 14,478 | 353 | #' Runs a semi-automatic, iterative scWGCNA analysis
#'
#' This function runs our semi-automatic single-cell WGCNA analysis. It runs in an iterative way. Based on single-cell or pseudocell data.
#' @param p.cells Seurat object. The expression data used to run the co-expression analysis. Can be pseudocell or single-cel... |
c27d1350f5439ff7590d427a113c5c9dc318674a983838bf2c95940d5d5a8c14 | R | 14,521 | 423 | # ReadAll.r Plot power spectra for classes.
# In correlation, 'flip' irrelevant to LIP-LIP and PRR-PRR, but matters in
# a straightforward way for cross.
# Crossx2 is trickier; have to do it in Select
CRITERION_Z = 0 # [0] Remove if greater than (3?)
SCALE = F # [T] Everyone gets equal weight
REMOVE_POWER_OUTLIE... |
12576010af4a50340f4827f4f74cc93cb40e70a69955ac05de31eb53257003b1 | R | 14,537 | 453 | ---
title: "Calculate Theta Correlations"
output: html_document
date: "2025-01-13"
---
```{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)
libr... |
297561cd66e4464a1353bf31065cba6db362288d4dd4165652489dfc55055209 | R | 14,592 | 255 | ### If running the sensitivity and specificity analysis separately, uncomment the lines below after changing to the right directory
# source("Driver.R")
# source("ComputeStatsNew.R")
# source("Constants.R")
# source("Utilities.R")
computeSensSpec = function(version = 2, relaxed = FALSE, safe = TRUE, skipEpistasis = TR... |
4b19156db9894298b255620dc5efd9f4ce75e49fd9e10187f329dd57f2e0d6b4 | R | 14,627 | 538 | ---
title: "Coherence ~ Region Models (IMCOH)"
output: html_document
date: "2024-10-23"
---
```{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 fig... |
a42f1a50c6f2a8f4edcc0b3589037a6a0e5d8a0a8ae480ccb210023ec53254db | R | 14,641 | 302 | # ------------- Figure S4 --------------
#----Figure S4A----
groupSize <- as.numeric(table(cellchat@idents))
par(mfrow = c(1,2), xpd=TRUE)
#CM2
celltype_col <- c('#f1b38a', '#f0db69', '#a2c246', '#f5cee0', '#b6d2b7','#cbd2e5','#ad98c3','#be95db','#53A85F','#E5D2DD', '#F1BB72', '#F3B1A0',"#d6d5b7",'#D6E7A3',"#0073C... |
4e2ac4c2771c7ff824efc651fd0f1d439a6cd819504753c35af6c28121d68453 | R | 14,699 | 336 | library(Maaslin2)
library(funrar)
library(dplyr)
library(ggplot2)
library(cowplot)
library(plyr)
library(circlize)
library(here)
library(stringr)
here::i_am("src/PFF/PFF_PWY_Maaslin2.R")
## PFF Jejunum ---
input_data <- read.delim("data/PFF/PFF_Microbiome/starting_files/picrust2_output_min10000_no_tax_PFF_ASV_table.q... |
266363e21c59c2d1bd36c002f1459ff623773afde46740f6b9256d5799c50d14 | R | 14,700 | 450 | # Create a dummy Epoch object for testing
set.seed(1)
row_num <- 10
col_num <- 100
dummy_data <- matrix(rnorm(row_num*col_num), nrow = row_num, dimnames = list(paste0("Elec", seq_len(row_num)), NULL))
expected_times <- seq(0, by = 0.1, length.out = col_num)
dummy_epoch <- Epoch(dummy_data, time = expected_times)
test_... |
72270c9c323ea587e6be385876a191e58ff1007128742cd3b134fce78f02ce3d | R | 14,784 | 331 | library(ggplot2)
library(vegan)
library(dplyr)
library(rlang)
library(cowplot)
library(viridis)
library(Microbiome.Biogeography)
metadata <- read.table("../starting_files/PFF_Mapping.tsv",header=TRUE)
counts <- read.table("../starting_files/PFF_ASV_table_Silva_v138_1.tsv", header = TRUE, row.names=1)
## Store taxonom... |
c932e6d02634aa160f7411dccae796b77651fad1fa41744a78592d05b31b1c3e | R | 14,822 | 299 | context("Testing post-hoc DGE 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 ... |
438e631056f74d45c07fe099988d2c481a4d0af7bfe90e8d9e0e199e9ab4fe8e | R | 14,840 | 409 | ##' Vector with current valid PCA methods
##' @title List PCA methods
##' @param which the type of methods to get. E.g. only get the PCA
##' methods based on the classical model where the fitted data is a
##' direct multiplication of scores and loadings.
##' @return A character vector with the current methods for doi... |
86106f6d0272e98a1dd62079857a3e685e445572d8fba9156d76512987619645 | R | 14,979 | 441 | ---
title: "Hemisphere x Long-Axis Interaction"
output: html_notebook
---
Version 1.0, July 2025, SA
This script plots hippocampal connectivity with neocortical clusters identified by a significant hemisphere x long-axis interaction.
Input: Hippo_AxisxHem_F_betas.txt
Output: Plots in Fig S3C
# Packages and functi... |
7956118e9e9cf33e51d9c0df1587e92e5483c8689d967b8b3f9a62b94d8ccfe4 | R | 14,982 | 504 | #' Install and manage Seurat datasets
#'
#' @section Package options:
#'
#' SeuratData uses the following options to control behaviour, users can configure
#' these with \code{\link[base]{options}}:
#'
#' \itemize{
#' \item `SeuratData.repo.use`: Set the location where the SeuratData datasets
#' are stored. Users g... |
0c5718fb3b32aa14aa29ece98648c9c40b6677df97ee883edd1276ccf8622d37 | R | 15,003 | 490 | ---
title: "All ROI HFA Ghost Attack"
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(ggt... |
104acd51e43ccf5db771eefbf3cbb7db3a0fe591b5b9df9f52f86920995a4240 | R | 15,029 | 516 | ---
title: "MMN amplitude"
output: html_document
---
```{r echo = FALSE}
# clear old outputs
if(dir.exists(file.path("../output/mmn_amp"))) {
unlink("../output/mmn_amp", recursive = TRUE)
}
output_dir <- file.path("../output/mmn_amp")
dir.create(output_dir)
nice_tables_file <- paste0(output_dir, "/nice_tables.... |
cfdf8e2e3737a1c6788d39d8a7f9f3628bd46df8e9e731ebe18cef7de8d032a9 | R | 15,041 | 356 | context("Test spatialFDR 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 matri... |
3a5565a47b85ad63327d7e15b7cb7aa319729c8c2aead2225524f9d7cf71ec7b | R | 15,104 | 325 | #' Identify post-hoc neighbourhood marker genes
#'
#' This function will perform differential gene expression analysis on
#' differentially abundant neighbourhoods, by first aggregating adjacent and
#' concordantly DA neighbourhoods, then comparing cells \emph{between} these
#' aggregated groups. For differential gene ... |
edf85764f4240dbcb949388684798aa53ae4944fed8463c45aaacc43b235095d | R | 15,156 | 249 | # NicheAnalysis function takes in the location of the annotated seurat object, the minimum and maximum number of niches
# and builds niche assay for all the values of niche numbers between min and max, and the given number of neighbors for
# the same, and saves the seurat object appropriately.
NicheAnalysis <- functi... |
4ce2139356d8130f0d2ad98256f7661187d8f00b0bccb6e340cd352f6b89c1f8 | R | 15,210 | 419 | #Deconvolution Functions
#install these packages if necessary...
if (!require("quadprog")) {
install.packages("quadprog", dependencies = TRUE, repos="http://cran.r-project.org")
}
if (!require("reshape")) {
install.packages("reshape", dependencies = TRUE, repos="http://cran.r-project.org")
}
if (!require("e1071"))... |
b5d154f1e95706d8232f3cdd9ca5691268c118403a5b39282ac90aab28e7e094 | R | 15,294 | 456 | # power2.r Plot power spectra for 5 stacks, > 1 class
# Overlay two classes
# For grant
# Split: 3 & 5: '1' is purple (2 & 4 are orange)
# 12: '8' is purple (4 is orange)
LINES = T # Solid line to show mean
RIBBONS = T # Ribbon of +/- 1 SEM (can have both)
SECOND_CLASS_RIBBONS = (CLASS.OVERRIDE==... |
521e3aec280f60f769efc250c77009218593f7c474927cc729c2c5c2858c5c71 | R | 15,387 | 409 | ---
title: "Quantifying the error associated with estimating cellular composition
from DNA methylation profiles"
author:
name: Eilis Hannon
affiliation: University of Exeter
email: E.J.Hannon@exeter.ac.uk
package: CETYGO
bibliography: cetygo.bib
abstract: >
A tutorial on calculating the CETYGO scor... |
df537b030b3639c27fd71e58d78c9febea9aea3f178c47f4490d200043302ba7 | R | 15,433 | 231 | ---
output:
github_document:
html_preview: true
toc: false
---
<!-- README.md is generated from README.Rmd using devtools::build_readme() -->
<!--  -->
<img align="left" alt="MS-DAP logo" hspace="20" vspace="10" src="doc/logo/msdap_logo_small.png">
Th... |
b6d5d773f555cdb663c266ef4e09e2723ef41eaa3febd81204d6510bdcb9f77e | R | 15,449 | 320 | # Setup -----------------------------------------------------------------
#' @name Setup
#' @aliases Setup
#' @title Setup options for RNA-seq count simulations
#' @description This function generates the settings needed for \code{\link{simulateDE}}.
#' Firstly a set of differential expressed gene IDs with
#' associat... |
34704f556325811813c3063b5928ca0904675312e719a45dd35a76dab2157e68 | R | 15,591 | 353 | suppressMessages(library('plink2R'))
suppressMessages(library("optparse"))
option_list = list(
make_option("--sumstats", action="store", default=NA, type='character',
help="Path to summary statistics (must have SNP and Z column headers) [required]"),
make_option("--out", action="store", default=NA, t... |
c213579b7e373372279be2ab42c8c9a1241ca43681b405c96f6cf5a77df5ef15 | R | 15,610 | 311 | ### original code from this commit: https://github.com/statOmics/MSqRobSum/commit/2875dcde1f89578685ed0a3316d6f66fa510b732
### here adapted to get optimal multiprocessing for vastly reduced computation times & compatibility with latest dplyr
### FRANK: removed all documentation/examples/"additional functions we don'... |
338972726b77f6ba81805800a1a5f97021c09d50346cd7cdc4a878445962530d | R | 15,660 | 230 | ---
output:
github_document:
html_preview: true
toc: false
---
# About Docker
The MS-DAP R package depends on many other tools / code libraries, so in order to work with MS-DAP one needs to install all these dependencies as well as the R programming language and RStudio (ref; MS-DAP R package install guide)... |
9c3805b73deb682bdf4cbe674b65356c439f6a8f0d5ec7939b7d56f59d905d3d | R | 15,816 | 307 | ## This function executes the neutral algorithm, which identifies variants that should be considered as neutral
## If safe = FALSE, existing conversion files for mapping version 1 to version 2 are used directly (no checks!)
## The last option is only used for naming the (intermediate) output files, and should not norma... |
c032f2c76d3ba37466cb9c5c3ef9eb93b1ab98b69edbf5828a7875e34e5df0e0 | R | 15,870 | 337 | library(Seurat)
library(tidyverse)
library(glue)
library(SeuratWrappers)
library(ggpubr)
library(SingleCellExperiment)
library(scDblFinder)
library(SoupX)
library(qs)
source('~/Projects/General-Codes/Resources/Plotting_helper_functions.R')
source('/home/cao385/Projects/General-Codes/Resources/single_cell_preprocessing... |
14009efed5590b523179e12687a930254314fbb76594aafba0468aa40ac09d7d | R | 15,982 | 422 | ---
title: "Example Regional Differences based on Coherence ~ Region Analyses"
output: html_document
date: "2024-11-19"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width ... |
ae507c152daebfeb6c9700aba53011c35c017666022e6760b144945f26966264 | R | 16,284 | 567 | ---
title: "Coherence ~ Region Models (PPC & PLV)"
output: html_document
date: "2024-10-23"
---
```{r setup, include=FALSE}
knitr::opts_chunk$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... |
38aa900e3d89635b15b281515acd70dcc81b6fbee6855b511952e84b1070dbfc | R | 16,382 | 438 | # doPower.r
# Cannot run simultaneous analyses if SPLIT_SACCADE !
###########################################################################
# Set basic parameters (override if DoAll) #
############################################
AREA = "PRR"
MONK = "tyr" # zen, tyr, both (don't use both: R calls will merge monks... |
71883d58c37b27eecacdd375c6f4dd2828e5b5685f4bf29755400ebd03cf3432 | R | 16,415 | 350 | library(biomaRt)
library(gprofiler2) # g:Convert for robust, cross-namespace symbol -> Ensembl ID
library(dplyr)
library(readr)
library(stringr)
library(purrr)
# biomaRt pulls in AnnotationDbi, whose select()/etc. mask dplyr verbs.
# Force dplyr versions so the pipeline doesn't break mid-run.
select <- dplyr::sele... |
b933b1c3c9c272f5e33b160da37eb3398bb82d7b6df4f862ac761a01a5d94c83 | R | 16,472 | 344 | library(Seurat)
library(tidyverse)
library(harmony)
library(SeuratWrappers)
library(patchwork)
#load full processed human data
cds2<- readRDS("data/cds2.rds")
#create an ensembl gene annotation table linking mouse and human genes
require(biomaRt)
human <- useMart("ensembl", dataset = "hsapiens_gene_ensembl", host = "ht... |
d07b24147dedefc153512c0dd9e9122101b81e777556f2385bf48b7cb4e23258 | R | 16,526 | 311 | ---
title: "Mixed effect models for Milo DA testing"
author: "Mike Morgan"
date: "14/03/2023"
output:
BiocStyle::html_document:
toc_float: true
BiocStyle::pdf_document: default
package: miloR
vignette: |
%\VignetteIndexEntry{Mixed effect models for Milo DA testing}
%\VignetteEngine{knitr::rmarkdown}
%\Vig... |
fc15f7dad2e563212311a4be20e07ef575cf3b378b32d0ca01a50f2ffa5615c5 | R | 16,594 | 377 |
#' Parse HGNC gene identifier lookup table that was downloaded from genenames.org
#'
#' download link: https://www.genenames.org/download/statistics-and-files/
#' table: "Complete dataset download links" -->> "Complete HGNC approved dataset text json" -->> download the "TXT" table
#' filename is typically something li... |
3d00c89560ead02daf11ae598ae24cbf7467dcdefc373009428fad33d5ee65bc | R | 16,653 | 336 |
#' Normalize a numerical matrix by the Variation Within, Mode Between (VWMB) algorithm
#'
#' @description
#' The normalization algorithm consists of two consecutive steps:
#' 1) samples are scaled within each group to minimize the overall `metric_within` among replicates
#' 2) summarize all samples per group by respec... |
f354226731485ff7292fa2a7b27a78d8a3f0e2b2fe7ace10de2adc2d1177d4dd | R | 16,862 | 479 | ---
title: "Rat cocultures "
output: html_document
author: "Daeun Jeong"
date: "2025-08-07"
---
```{r preparation environment, message=FALSE, warning=FALSE}
# Load packages -----------------------------------
library(dplyr)
library(qs)
library(Seurat)
library(ggplot2)
library(patchwork)
library(SeuratObject)
library... |
d9b9eb2fd444fff7aa1876a0f06ee195f1b6ac4e30566a6ed1d418538943d74c | R | 16,958 | 296 | ## ----knitr_init, echo=FALSE, results="asis", cache=FALSE----------------------
library(knitr)
library(rmdformats)
## Global options
options(max.print = "75")
opts_chunk$set(echo = FALSE,
cache = FALSE,
prompt = FALSE,
tidy = FALSE,
comment = NA,
... |
9da1e8122b399959af9424e95c066264e144ba204a4daf05ab1e93ba24dbbe86 | R | 16,991 | 319 | #' global.R
# Copyright (C) Carlos Biagi Jr
#
# Tis 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 distributed... |
f428eb0598d1320a5450b92e60b8e8a720da5799423869f523b0e6e3cdbad22f | R | 17,133 | 334 | library(Seurat)
library(tidyverse)
library(harmony)
library(patchwork)
#subset E11 matching TW E11 data
cds <- readRDS("data/cds.rds")
Idents(cds) <- "stage"
E11 <- subset(cds, idents = "E11")
Idents(E11) <- "cell_type"
#subset E11 mesenchyme and transfer subtype annotation from TW
mes <- subset(E11, idents = "mesenchy... |
6fd2f04a7f79e655d2f5f49085e0eb3c359b3230ed49a7d20a5c2cbe8c0a57a2 | R | 17,135 | 297 |
#' Summarize DEA and/or differential detection results in a dataset into a table with a single statistic per gene
#'
#' @description
#'
#' In most cases, you probably want to use the `export_stats_genesummary()` function instead.
#' That is a wrapper function that uses this function but also adds additional functional... |
31d34749be3ad2b7b3aa735777861058da3928924d8bade508cfa4dbe5bfc3a6 | R | 17,162 | 399 | suppressMessages(library('plink2R'))
suppressMessages(library("optparse"))
option_list = list(
make_option("--sumstats", action="store", default=NA, type='character',
help="Path to summary statistics (must have SNP and Z column headers) [required]"),
make_option("--out", action="store", default=NA, t... |
a2e6ab68d110dd3f3fbeb1873a193b6c775c06b5d54d27a652c6a8075bff9ddf | R | 17,175 | 466 | ## Make a taxa summary plot --
generate_L6_taxa_plots <- function(path_to_csv, titlestring,greppattern, fillvector){
#L2_lum<-readRDS("Long_Term/taxa_barplots/LuminalColon_level-6.RDS")
#taxa <- gsub(".*g__","",taxa)
#cols<-assign_cols
titlestring<-c(titlestring)
L2_lum<-read.csv(path_to_csv,header=TRUE,row.n... |
8aca77e649ce89922e47237749c72a5eeb31443173a6751938013a86b9b094e7 | R | 17,230 | 505 | ---
title: "Identification of transcriptional programs differentially regulated between canonical ZFTA-Cluster and ZFTA-Cluster 3 tumors"
author: "Sara Danielli"
output:
html_document:
toc: yes
df_print: paged
---
```{r, setup, include = FALSE}
library(knitr)
opts_chunk$set(
echo = TRUE, cache = TRUE, warn... |
43ced6e64fd93202ab184a44fb4afa0a7c9c89801f6e3f8a445360237ad2d96a | R | 17,301 | 243 | gradeMutations = function(LoF = TRUE, NON_DATABASE_DIRECTORY = NULL) {
## Prepare the master variant table, compute genes and mutations, and conduct a basic consistency check
Tab0 = read_csv(paste0("Stats_WHO" , ifelse(LoF, "_withLoFs", ""), ".csv"), guess_max = Inf, show_col_types = FALSE)
Tab1 = read_csv(paste... |
cee23e4678c0a3fd5270445e76a9e9028276c45faaa14262fc3be51fc63e308e | R | 17,484 | 340 | rm(list = ls())
library(DESeq2)
library(pheatmap)
library(RColorBrewer)
library(org.Mm.eg.db)
library(ggplot2)
library(dplyr)
library(tidyr)
library(UpSetR)
library(tibble)
library(gridExtra)
library(poolr)
library(reshape2)
library(WGCNA)
library(PMCMRplus)
library(readr)
library("tidyr")
library(ggplot2)
library(tidy... |
52d3fab49208df2c247e7a074969eecf5378a3c82096c2cd93bc17f67f55d38e | R | 17,495 | 516 | # power.r Plot power spectra for 5 stacks, ONE class (Larry)
# And scatter plot
LINES = T # Solid line to show mean
RIBBONS = T # Ribbon of +/- 1 SEM (can have both)
PRINT.MEANS = F # Won't work for other alignments!
PRINT.DIVERGE = T
PRINT_DATA_FOR_FIGURE = F
StimAt = c(495, 520) # Contaminated range
... |
eb0a09802bd24fda0113e99ed66d951bee470ea4d49733670160a2866e591240 | R | 17,580 | 393 | ---
title: "Perturb-seq analysis (R)"
output:
md_document:
variant: markdown_github+tex_math_dollars
date: "`r format(Sys.time(), '%d %B, %Y')`"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Perturb-seq Tutorial (R)
This tutorial uses an excitatory-neuron subset from [Jin et al. (202... |
36b928ca47f69adcf10295a957a260224b950771a77eda18c8cbdf72e531e1d9 | R | 17,646 | 270 |
#' Import a label-free DDA proteomics dataset from a ProteomeDiscoverer PSM result file
#'
#' @description
#' ProteomeDiscoverer workflow must include Percolator so MS-DAP can parse peptide confidence scores.
#'
#' Example ProteomeDiscoverer workflow:
#' - Processing Step: PWF_QE_Precursor_Quan_and_LFQ_SequestHT_Perco... |
fab9f921ce9fafff7b5627e1fdefae3360fb6cfc3b3d7381ca2be0e74a96b504 | R | 17,705 | 477 | # PowerVsFreq.r (Seul Ah)
# ----------------------------------------------------
# Plot LFPeffect as a function of frequency bands
# Power vs. 1 variable (freq)
rm(list=ls())
NORMALIZE.OVERRIDE = F # [T] usually, but [F] for raw power (1/f) plots
SCALE.OVERRIDE = F # [T] Everyone gets equal weight (F for raw pow... |
db28b555e4a4eb97aaa0f2c3b6096921a858e95a06e225bd0605915bcebb3d27 | R | 17,707 | 290 |
#' THIS FUNCTION SHOULD ONLY BE USED FOR LEGACY FRAGPIPE DATASETS THAT PRODUCED 'mbr_ion.tsv' OUTPUT FILES (e.g. FragPipe v15)
#'
#' To generate output files that we here require in MS-DAP, configure FragPipe as follows:
#' - assign Experiment IDs in the workflow tab (you may simply set these all to 1)
#' - enable Ion... |
6002189aa4e51426a0c41573ae16d57412f0319ef627cffc35f897fd49b4ab53 | R | 17,735 | 447 | Sys.setenv('R_MAX_VSIZE'=64000000000)
library(tidyverse)
library(cowplot)
library(Matrix.utils)
library(edgeR)
library(Matrix)
library(reshape2)
library(S4Vectors)
library(SingleCellExperiment)
library(pheatmap)
library(apeglm)
library(png)
library(DESeq2)
library(RColorBrewer)
library(data.table)
library(scater)
libra... |
e29981dcbc87c716b31e087ade397b1129fd900dad876dd0c7a3b667343c860f | R | 17,748 | 414 |
#' Plots the gene / module WGCNA tree, or trees
#'
#' This function will help us plot the gene / module WGCNA tree.
#' @param scWGCNA.data scWGCNA.data. An scWGCNA.data object, as calculated by run.scWGCNA().
#' @param tree numeric. Which of all the trees in the iterations should be plotted?
#' @return Plots the las... |
a41614a1c0038f165c1209fb3ae4ca0638b24cc0d5b2f8cfcc91df737c160169 | R | 17,807 | 502 | ---
title: "Average Theta & HFA Profiles"
output: html_document
date: "2024-05-20"
---
```{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 = 5, # set default width of figures
... |
c0130cfb16064872c4213c3878f43de3f14de4283ce2f14e76c682bc169e5eea | R | 17,874 | 359 | rm(list = ls())
library(data.table)
library(tidyverse)
library(readxl)
library(crayon)
base_dir <- "/n/scratch/users/s/sad167/EPN/scRNAseq"
resources_dir <- file.path(base_dir, 'scripts/resources')
source(file.path(resources_dir, "single_cell_preprocessing_helper_functions_CBJr.R"))
analysis_dir <- file.path(base_... |
f385f5e6ee2059a8854e727562c5a0002c5099f0535a2bb7b1d22b22d6f7b02f | R | 17,932 | 349 | library(dplyr)
library(ncdf4)
library(ggplot2)
library(dichromat)
library(scales)
library(RColorBrewer)
library(maps)
library(patchwork)
library(raster)
library(sf)
library(readr)
library(terra)
library(tidyverse)
##########################
#Loading environmental data
###########################
sites<-read.csv("Data... |
a41f6a199bfc6aac4fa91a64f5f668abbb14d724be32351bf79d5c9576d3eb32 | R | 17,965 | 549 | ---
title: "Differential Connectivity (Coherence ~ Region) Tables"
output: html_document
date: "2024-12-04"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set d... |
cfa894996dfb6b9ea65c141445f50e16d2a6f3d7033d4545b83b48e5ba5eb320 | R | 18,084 | 498 | # VsFreq.r
# If 'SUBTRACT.BASE' is true, then save the base (target align) and
# subtract it from cue align.
LO_FREQ = 18 # [18, 16, 12, 8] (short intvl: 18?)
HI_FREQ = 100 # [100,100,50] (movement period:120)
LO_NOTCH = 57 # [56-7] if SUBTRACT.BASE: 56 if 200 ms: 46
HI_NOTCH = 62 # [64-2] if ... |
e7f9012e2b50a7b6ecc7ee3b9325d1facaa2d5a9d89ce3be77586fa31bb2b38c | R | 19,114 | 323 |
#' Import a label-free proteomics dataset from MaxQuant
#'
#' @param path the directory that contains the search results (typically the 'txt' filter that contains files 'proteinGroups.txt' and 'evidence.txt')
#' @param collapse_peptide_by if multiple data points are available for a peptide in a sample, at what level s... |
a625b3440dae3b91d1e83ff06c979766256dc5594b825ec6c1154284dd1b6848 | R | 19,417 | 462 |
#' Test if the dataset contains DIA data, simply testing if the acquisition_mode equals "dia" (case insensitive)
#'
#' @param dataset dataset to test
#' @export
is_dia_dataset = function(dataset) {
if(length(dataset$acquisition_mode) != 1) {
append_log("'acquisition_mode' attribute missing from dataset", type = ... |
2b4338d6b728676cc2e243fbfe2bb2fa57ffc5a1c0b02b3877a3a508079806ee | R | 19,431 | 533 | ---
title: "Create Sig Theta Coherence CSV"
output: html_document
date: "2024-09-20"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 8, # set default width of figure... |
d03b09dfa10f7ecc83797a8c660ff013f01e6b49487aeca30f2f4ae6ce861151 | R | 19,546 | 537 |
# IMPUTATION WRAPPER ------------------------------------------------------
.impute.calc <- function(Imputation,
countData,
spikeData,
batchData,
clustNumber,
Lengths,
... |
122e7906ed53bf2327b16fa1cd1ad983d0a85bf9beab5de480c9b4d41b1f3ec0 | R | 19,565 | 576 | ########################################################################
#
# Load the Python package
#
########################################################################
# require(reticulate)
# # create a new environment
# virtualenv_create("py")
# virtualenv_install("py", c("numba", "pandas", "numpy", "scipy",... |
54d3a74e6871303b9162a6a5cd429fbffbb166ea9ee4b2507f7d04bb4a2fc677 | R | 19,565 | 497 | ---
title: "Figure 1 Behavioral Data"
output: html_document
date: "2024-10-14"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo <- FALSE, # don't print the code chunk
warning <- FALSE, # don't print warnings
message <- FALSE, # don't print messages
fig.width <- 5, # set default width of figures
... |
a073b52fca35b57e2cab48806af97c965d8484b8adb7fec007f6243fb3f8bbb2 | R | 19,590 | 320 |
#' returns all normalization algorithms integrated with MS-DAP
#'
#' @description
#'
#' median: scale each sample such that median abundance values are the same for all samples in the dataset.
#'
#' loess: Loess normalization as implemented in the limma R package (PMID:25605792) <https://bioconductor.org/packages/rele... |
07f37ebf87fe9197c72e7ec879f8dbfe5ea0be788d80bcd855817ddffcecefca | R | 19,653 | 506 | ## Make a taxa summary plot --
generate_L6_taxa_plots <- function(path_to_csv, titlestring,greppattern, fillvector){
#L2_lum<-readRDS("Long_Term/taxa_barplots/LuminalColon_level-6.RDS")
#taxa <- gsub(".*g__","",taxa)
#cols<-assign_cols
titlestring<-c(titlestring)
L2_lum<-read.csv(here(path_to_csv),header=TRUE... |
bb354b0252630cdea35f2ab90c2c6c6206ae4bcb05116588e32fb73afa8ae6cb | R | 19,765 | 956 |
# ---- libs ----
suppressPackageStartupMessages({
library("readr")
library("dplyr")
library("tidyr")
library("forcats")
library("purrr")
library("furrr")
library("lubridate") # way to handle dates better than default R way
library("tictoc") # measure time elapsed in calcs
library("ggplot2")
l... |
014927f723df2e0b5fca50c6cffa23789b5b2c05830ef784bacd8160c73ded19 | R | 20,037 | 759 | library(tidyverse)
library(synExtra)
library(data.table)
library(powerjoin)
library(here)
library(qs)
synapser::synLogin()
syn <- synDownloader("~/data", .cache = TRUE)
rosmap_quant_clinical <- syn("syn44335073") %>%
read_csv()
# Use all ROSMAP patient data from PCC region
rosmap_quant_clinical_filtered <- rosmap_... |
0f5cc6ddbbd384ff17e8385261454b498534dea865b8900ad3a1b53c0612db27 | R | 20,058 | 356 |
#' volcano plot for DEA results, split into 4 panels; with/without labels and with/without thresholding foldchange-outliers
#'
#' If there are multiple contrasts in your DEA results you should either use the 'wrapper function' plot_volcano_allcontrast(), OR, first subset the DEA result table for 1 contrast before call... |
3f2de775ef21c2a43bf403d51e7ae9c155f018cbac308bf4e54932199fd7c84a | R | 20,170 | 539 |
---
title: "Coculture - rat cell types"
output: html_document
author: "Daeun Jeong"
date: "2025-05-01"
---
```{r, setup, include = FALSE}
library(knitr)
opts_chunk$set(
echo = TRUE, cache = TRUE, warning = FALSE, comment = FALSE)
```
```{r preparation environment, message=FALSE, warning=FALSE}
# Load packages ---... |
b38ee92a52c72dfe7c2dd41796e2df17ffe6a0c1804ec6ceea6cae7ad6c42d2a | R | 20,307 | 351 | library(Seurat)
library(ShinyCell2)
library(stringr)
library(grid)
library(scales)
library(scCustomize)
library(SeuratDisk)
# Code to generate Shiny App
setwd("/scr1/users/manchela/Data")
seu <- readRDS("human_face_no-neuro_clustering_cellrangerARC-raw_emptyDrops_singlets_finalannot_27Mar.rds")
seu$subtype_reduced[w... |
797ce6f67ea20cc77330a455691bc9fca1014591e15f4490758cea9d17f29c1f | R | 20,375 | 750 |
# ---- libs ----
suppressPackageStartupMessages({
library("readr")
library("dplyr")
library("tidyr")
library("forcats")
library("lubridate") # way to handle dates better than default R way
library("stringr")
library("ggplot2")
library("ggthemes")
library("ggrepel")
library("knitr")
library(... |
b45b5cb018474ea9026d09ed16ae6572feb8537281b1cbef6c8150eb3b4dae8f | R | 20,587 | 754 | library(tidyverse)
library(synExtra)
library(data.table)
library(powerjoin)
library(here)
library(qs)
synapser::synLogin()
syn <- synDownloader("~/data", .cache = TRUE)
# fnROSMAP <- DRIAD::wrangleROSMAP("~/data")
rosmap_tdp43_classification <- syn("syn44277761") %>%
read_csv()
# > rosmap_tdp43_classification %>%... |
2d12fdbf453bfc941f0bf543d716e40c1f8c513807bab4aaea75a8fcdb22f428 | R | 20,855 | 719 | ---
title: "Theta Ghost Attack"
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(blme)
library(scales)
library(ggthemr)
... |
473f80ec51e553641b76280731a80a6e1a1087251aae900800a126091a83c6cd | R | 21,274 | 384 | library(ggplot2)
library(tibble)
library(tidyr)
library(tidyverse)
library(plyr)
library(dplyr)
library(vegan)
library(glmmTMB)
library(arm)
library(nnet)
library(gamlss)
######################################################
#Summarising assemblages to symbiont genera, Figure S4
#####################################... |
94ad15bfe961f1477fc83cdff41f5e30753fadafa9a21a9a7079bd0a465b3f56 | R | 21,307 | 556 | # TODO
# change behaviour of minus mean values ?
# Simulate DE between 2 groups (DE of mean) -------------------------------
#' @importFrom stats model.matrix rbinom
.simRNAseq.2grp <- function(simOptions, n1, n2, verbose) {
set.seed(simOptions$DESetup$sim.seed)
if(is.null(simOptions$DESetup$bLFC)) {
... |
37a45c43ed2b3c4d6d758f6ce60142fed6408c9be3c0be3c16b12e24bfe0de03 | R | 21,317 | 455 | library(Maaslin2)
library(funrar)
library(dplyr)
library(ggplot2)
library(cowplot)
library(plyr)
setwd("C:/Users/Jacobs Laboratory/Documents/JCYang/SLC_GitHub/slcproject/PFF_Microbiome/differential_taxa/")
### Note: First remove "#Constructed from biom file row"
### Run Maaslin2 and get table of relative abundances
ru... |
adb496174fac416de7af3d1219747379de9ba4cb6235ce853124e40ab474b507 | R | 21,332 | 834 | # =============================================================
# ASSOCIATIONS OF INDIVIDUAL SOMASCAN 11K PLASMA PROTEINS WITH ALL-CAUSE MORTALITY
# =============================================================
# COHORT:
# Lothian Birth Cohort 1936
# COX MODELS:
# Models are fitted separately for each protein.
#... |
1697ec473c2b61588b8cf73043e967fd8acc39180ad7628c403d4b4361b56257 | R | 21,404 | 624 | ---
title: "EP1NS_hiNs"
output: html_document
author: "Daeun Jeong"
date: "2025-7-15"
---
```{r preparation environment, message=FALSE, warning=FALSE}
# Load packages -----------------------------------
library(dplyr)
library(Seurat)
library(ggplot2)
library(patchwork)
library(SeuratObject)
library(data.table)
librar... |
f8d7abd503cfef43be2a90851ef71754181054ee513fbeeb7a9a86e3e403a20f | R | 21,437 | 481 | #######################################################################################################################################################################
#######################################################################################################################################################... |
b89ed71b861869c97e847ffa2864b716a61b234ca2cec131dde1832ce37a6893 | R | 21,614 | 458 | ---
title: "Differential abundance testing with Milo - Mouse gastrulation example"
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 - Mouse gastrulat... |
805c8916fc3c4a1bdfdbd38858949d4d3d291a97e5f69fc2cb398661e2c92dda | R | 21,644 | 335 | library(conflicted)
library(haven)
library(igraph)
library(magrittr)
conflicts_prefer(magrittr::extract)
conflicts_prefer(magrittr::set_names)
library(tidyverse)
conflicts_prefer(dplyr::filter)
## This function executes the complete analysis of the dataset required to create version 2 of the WHO catalogue.
## If fast ... |
c41aa58e3a6e45189dcfd40fbea384621c413d72cdf55de49b76ff2ccc3c6127 | R | 21,914 | 419 | #spls analysis
# Bioconductor install
if (!requireNamespace("BiocManager", quietly = TRUE)){
install.packages("BiocManager")
}
# Install BiocParallel
BiocManager::install("BiocParallel")
BiocManager::install(update = TRUE)
# over GitHub
install.packages("devtools") # restart no
devtools::install_github("mixOmics... |
dfdc6d300a837f33352850f619d60d8dfe778e27d6fd2db3737f57f6467402af | R | 22,114 | 503 | ---
title: "Normative Gameplay on the Pacman Task from Clinical/Nonclincal Set"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo <- FALSE, # don't print the code chunk
warning <- FALSE, # don't print warnings
message <- FALSE, # don't print messages
fig.width <- 5, # set d... |
b3aa3cbe1591f0e62943c4a1abb224efd9c96f5e5de3cb541f2cea3cfe1a39c6 | R | 22,230 | 293 |
#' Quickstart for analyses in this pipeline
#'
#' all-in-one function that covers the vast majority of use-cases of analyzing a dataset imported into MS-DAP.
#' (assuming you already loaded peptide data, sample metadata and fasta files using MS-DAP import functions).
#'
#' @section Filtering:
#'
#' Peptide filter crit... |
cffde0016aa6b0bef51e37843eda4e9cf576d0e32449e365e8786a3a932dd8ff | R | 22,518 | 450 | ################### ################### ################### ################### ################### ###################
#Run this script for sensitivity analyses to reproduce Section S3.2 (Sex-Stratified Analyses)
################### ################### ################### ################### ################### ######... |
16c21b579b1a1b69e48fb655f0807f050e80176dd41a36bce10d1bd51e1b5035 | R | 22,603 | 695 | # power8.r Plot power spectra for 5 stacks, 8 class
# Should have scale turned OFF for these!
ALIGN_ON_STACK = 0 # 0 or stack number [-1]: align each stack
MIMIC.ALIGN_ON_STACK = 0 # [>0]: rm class 1+5 in this stk [-1]: all stks
# Values of -1 only work for FREQ_PLOT
ALIGN_ON_UNIT = 0 # 0:ignore 1:align... |
4a0695392b6bbe480e2b72f178f3aaf6bbd7436712d547fd0e22612f7bb13fac | R | 22,618 | 491 | ---
title: "DEGs of scRNAseq results of mono- vs. co-culture"
author: "Sara Danielli"
output:
html_document:
toc: yes
df_print: paged
html_notebook:
toc: yes
toc_float: yes
---
```{r, setup, include = FALSE}
library(knitr)
opts_chunk$set(
echo = TRUE, cache = TRUE, warning = FALSE, comment = FALS... |
bd814616a49228d3544184df40a7cbbd0c4aa1282ed8d0ad1298f7a6fb2cf5e4 | R | 22,712 | 478 |
#' append file to path, then call file_check() for validation of the file. Does not validate illegal characters in the path
#'
#' returns a (potentially cleaned) file path
#'
#' @param path a directory on this computer. eg; "C:/temp" on windows or "/home/user1" on unix
#' @param file filename that should be appended t... |
bde86f753225f4b41ba8dc3720a3574c6d80922eed7d9c103044d5fd8c41916f | R | 22,892 | 520 | library(ggplot2)
## Make a bar graph to summarize proportion of clusters/programs/etc in each sample
## @para: x, x-axis data
## @para: y, y-axis data
## @para: x_order: order of x-axis variables
## @para: y_order: order of y-axis variables
## @para: col_names: column names for df for plotting
## @para: x_var: name of... |
53d8aa9ac52489b11d15f92f18bda5c920f7a4702c37abac76bde29ee057d37f | R | 22,926 | 515 | ################### ################### ################### ###################
#Visualization
################### ################### ################### ###################
bluegreen <- c("#261861", "#016DAB", "#03ABB1", "#6FCFB6")
###################
#Main Paper Plots
###################
#Figure 1: Standardized ... |
4dfa0304928f88a7efb7215e7674c74565efcc08218d439d2c1024682138cc66 | R | 23,179 | 523 | rm(list = ls())
library(DESeq2)
library(pheatmap)
library(RColorBrewer)
library(org.Mm.eg.db)
library(ggplot2)
library(dplyr)
library(tidyr)
library(UpSetR)
library(tibble)
library(gridExtra)
library(poolr)
library(reshape2)
library(WGCNA)
library(PMCMRplus)
library(readr)
library("tidyr")
library(ggplot2)
library(tidy... |
7f85a034927707a8b248a440b8fa8e421e3b3abe5854bec7a27ee5789547eef2 | R | 23,314 | 666 | ## PD library in flies paper
################################
################################
# This file contains the functions needed to extract the sleep features used in Kaempf et al 2026
# The functions are organized in sections, which start with the header ## --
library(data.table)
library(behavr)
library(scopr... |
afbfdebf52e54cf3c042ccfdda29bd591f751d99cf3591c15ced5420748cdb69 | R | 23,476 | 846 |
# ---- libs ----
library("arrow") # parquet files
library("binom") # wilson confidence intervals for binomial counts
library("foreach") # flexible looping and return amalgamation
library("episensr") # misclassification error contingency table adjustments
# library("simdata") # NORTA method to get correlat... |
f78193058aaed61e93863a9a08e437b6f2d8162463f2cce0a199605a7acaac96 | R | 23,593 | 964 | ---
title: "Analyze Theta CCF"
output: html_document
date: "2025-02-04"
---
```{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(RColorB... |
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