sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
2c633fe6a99889bc3844360cfc6c9fa30206ee6289624701371d2637bbe6cdb6 | R | 29,081 | 1,415 | ---
title: "SEFL10K_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::opts_chunk$set... |
b01ed8084e4bedc33dc9bd58b13927a78b07c48d7a5f9ba942df939946214645 | R | 29,174 | 716 | ---
title: "TEBC faecal samples: shotgun taxonomic data statistical analysis"
author:
- "Kadi Vaher"
- "PERMANOVA and Maaslin analyses"
date: '`r Sys.Date()`'
output: html_document
---
```{r setup, include=F, message=F}
library(tidyverse)
library(here)
library(phyloseq)
#library(decontam)
library(RColorBrewer)... |
7558e4215527127100e924002badbe8fd4e578727174c229e52e0e41bdbd6cad | R | 29,260 | 859 | ---
title: "ONS76-2D_Seurat_EXPLORE1_50K"
author: "JJ"
date: "18/01/2023"
output: html_document
---
```{r graphical_output}
options(bitmapType='cairo-png') # this line of code needed BEFORE knitr code in chunk below
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(dev = "png",
dpi = 300,
... |
c0292bdbc6d9842cd09b24347f68359396d43fe95a4604e6245d42085b9e917b | R | 29,295 | 596 | ### script pre-process H1 Teratoma Time Series snRNAseq data
### doublet identification method: DoubletFinder
### Annotation method: scanvi, manual
### Integration method: log normalized seurat integration
### following Blue's advice from presentation 20230118, just transfer all labels using SCANVI
library(Seurat)
li... |
5808ee37c991cebe32ddb948e076f2e48bcc2e10af8c53aa1ef9a75a911fb2df | R | 29,581 | 790 | #### Notes ####
#### Libraries ####
library(ggplot2)
library(ggpubr)
library(cowplot)
#### Sourced functions ####
#### Functions ####
plot_sd_vs_mean <- function(summary.stats, metadata, log2=FALSE) {
Tissue <- as.factor(metadata$Tissue)
if ("Sex" %in% colnames(metadata)) {
Sex <- as.factor(metadata$Sex)
... |
f4f919d8e79e3d705b847d00bcb4c846705c5911ba46e1c085f42036c037276c | R | 29,833 | 810 | ######################################################################################
# CLUSTER HEATMAPS
#' Heatmap plots of group summary statistics. THIS FUNCTION IS NOW IN library(scrattch.vis)
#'
#' @param data A data frame containing gene expression values. The first column should be sample_name
#' @param anno... |
8eed8bd47539e4b6e4cfcb2e4529e6e089a599a121f7f02d6c5b2decee77f93d | R | 29,898 | 729 | ---
title: "Estimating onsets using cluster statistics: simulation using EEG-like noise"
author: "Guillaume A. Rousselet"
date: "`r Sys.Date()`"
output:
# pdf_document:
# fig_caption: no
# number_sections: no
# toc: yes
# toc_depth: 2
github_document:
html_preview: yes
toc: yes
toc_dep... |
899017b29dee1afdc7be4e8e3d715b433c64618cf8a9b4e7b537683b9de271ab | R | 29,967 | 748 | ---
title: "TEBC pre-discharge faecal samples: microbiota associations with brain imaging markers: global approach"
author:
- "Kadi Vaher"
- "This script is for the statistical analyses linking gut microbiota from 16S-based sequencing and brain MRI features"
date: '`r Sys.Date()`'
output: html_document
---
```{r ... |
a61fa72b03529f11522d82e94918af76ff5851832c61d7a8ed6a6986193bbcb9 | R | 30,016 | 573 | ### data from 4 H1 teratomas, generated by Daniella & processed by Yan before
### reprocessing using GRCh38_and_mm10 reference
### to save time, only processing the cells that are annotated as singlet by Yan's processing
# ----------------------------------------------------------------------
### preprocessing
## loa... |
3447bae73baf2f9563476efbd6cc8f4520105e403e5605881b2c4511b9545cd9 | R | 30,050 | 705 | #!/usr/bin/env Rscript
# ==============================================================================
# == Main CNV analysis script
# ==============================================================================
# ------------------------------------------------------------------------------
# -- Variables
# -----... |
a588abd9d539321a17c92a7fc2aa25ec4a6e8693cf24605e316f8a58eb2a14e3 | R | 30,086 | 942 | #' Funnel plot
#'
#' @description
#' Draw a funnel plot which can be used to assess small study effects
#' in meta-analysis. A contour-enhanced funnel plot can also be
#' produced to assess causes of funnel plot asymmetry.
#'
#' @aliases funnel funnel.meta
#'
#' @param x An object of class \code{meta}.
#' @param typ... |
da12cd31415ff28bcb1570d1efeaa592f226a7a456d584c6234571627e9e897c | R | 30,472 | 520 | .libPaths(c("/sc/arion/projects/roussp01a/jaro/programs/R_libs_4_0_3/", .libPaths(),''))
cellType= commandArgs(T)[1]
ANALYSIS_LEVEL=commandArgs(T)[2]
sampleFilteringFunction = function(x){x=x[x$cell_subtype==cellType,]}
samplesToExclude=vector()
mySdCutOffForCorrelationOutliers=Inf #"Inf" means don't remove anyt... |
8f1a2cbf4dc6d9d6332e75b3a5bad076238becef0b74f36fc81021b92c8c9ef2 | R | 30,672 | 1,207 | # Auxiliary functions
#
# Package: meta
# Author: Guido Schwarzer <guido.schwarzer@uniklinik-freiburg.de>
# License: GPL (>= 2)
#
allNA <- function(x)
all(is.na(x))
bylevs <- function(x) {
if (is.factor(x))
res <- levels(factor(x))
else
res <- unique(x)
res
}
byvarname <- function(argname, matchcall)... |
5529b10a6155d819228ceaef27315b8062bc7b844805f04f43b3f8f0dcddf96e | R | 30,849 | 928 | #' Meta-analysis of correlations
#'
#' @description
#' Calculation of common effect and random effects estimates for
#' meta-analyses with correlations; inverse variance weighting is used
#' for pooling.
#'
#' @param cor Correlations.
#' @param n Number of observations.
#' @param studlab An optional vector with study l... |
3654a9ed9dbfd0159c009acacd2d3f25aec2b19fea71e7c921a3d1de63241d5a | R | 30,879 | 654 | ---
title: "20250105_invivo_plots"
output: html_document
date: "2025-01-05"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load libraries}
library(tidyverse)
library(ggplot2)
library(agricolae)
library(ggsignif)
library(multcomp)
library(nlme)
```
```{r load data}
#manual_synbot_data ... |
301f077b1b2cbdd17172331db55ddf95ddbcd6addeb683f9fb883a43143b282b | R | 31,062 | 856 | ---
title: "Lipids"
output: html_document
---
```{r}
library(tidyverse)
library(ggsignif)
library(ComplexHeatmap)
library(ggpubr)
library(RColorBrewer)
library(rstatix)
```
#Load data
```{r}
data <- read_csv("//atlas/LCSB_Cellular_Biology/16-Our_Papers/In Preparation/PD_Diabetes_Alise/Figures/Figure... |
a2fec4ae9ab743c12e08336666595291cd873ed1b42e423841d5d41dda7fbd5c | R | 31,089 | 1,086 | ---
title: "Previous_studies"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Custom functions
```{r}
#' calculate total number of cells
#'
#' @param celltypes vector; cell type labels for each cell
#' @param dataset string; name of dataset
#' @param region string; name ... |
6dda365502c2d539cfa014850d560540a4fd7d1b967118a353b9fc578ce2d9cd | R | 31,109 | 786 | #========================================================================================#
# Written by James M Roe, Ph.D.
# Contributors: Øystein Sørensen
# Center for Lifespan Changes in Brain and Cognition, Department of Psychology
# University of Oslo, Norway
#=======================================================... |
4a16dc8be6a94374c06cd2d9e9973dd4b165c090608f017555c30086b7937b90 | R | 31,682 | 860 | library(Cardinal)
# library(rayshader)
library(dplyr)
library(tidyverse)
library(ggplot2)
library(M3C)
library(spatstat.geom)
library(RColorBrewer)
library(keras)
library(reshape2)
res_ppm <- 20
mass_range = c(50, 650)
setCardinalBPPARAM(BPPARAM=SerialParam())
normal_peak_2 <- 157.0771
normal_peak <- 208.1144
#### d... |
d286804cdf2662079b0084adb2c3375be68b98b6eb4e53aae89fadc626ae683f | R | 31,919 | 1,054 | catmeth <- function(x,
common, random, prediction, overall, overall.hetstat,
prediction.subgroup = FALSE,
#
func.transf, backtransf, func.backtransf,
#
big.mark, digits, digits.tau, text.tau, text.tau... |
c2143da96f1b7818b77cfe161525088a4042717f74c8046e7affcaf49a44402e | R | 32,150 | 580 | setwd("/home/wangmg/Documents/deconvolution/Data_and_code_for_reproduction")
source("Code/utils/utils.R")
source("Code/utils/plot.R")
library(ggplot2)
library(ggpubr)
library(ggprism)
library(ggplotify)
#################### Preprocess the original scRNA-seq data
### The original scRNA-seq data can be downloaded fro... |
16394fb573c6c1430175d8b719c99c723602848ad894a311ea5b3d7b98b96873 | R | 32,172 | 705 | # edit to your filepath containing csv-files from "refine_FI_analyses.Rmd"
wd <- ""
setwd(wd)
#Edit to name of csv-files (with .csv as suffix)
ADNI1_file = ""
ADNI2_and_ADNIGO_file = ""
library("gmodels")
library("car")
library("DescTools")
library("ggplot2")
library("qqplotr")
library("dplyr")
library(moments)
lib... |
f4c0ea1c576cee4cacd53d25caf4c33b4fb89699439020965c42c86ebbf9aa51 | R | 32,539 | 706 | ######################################################################
# Program to produce the regression models for the ADNI-NACC dataset
######################################################################
setwd("/Users/elinorvelasquez/Desktop/adni_nacc/")
library("tidyverse")
library("dplyr")
library("readxl")
l... |
b9e35bb90127a4ed3f174fd22c06274cf30be4d0b12e39a6fdf4c66dddf2df94 | R | 32,852 | 1,120 | .onAttach <- function(libname, pkgname) {
msg <-
paste0("Loading 'meta' package (version ",
utils::packageDescription("meta")$Version,
").",
"\nType 'help(meta)' for a brief overview.")
packageStartupMessage(msg)
}
updateversion <- function(x) {
#
# Update older meta object... |
8a7a4d6f35120e3512e64a641ca33ed78a42fe5abdd8e778ccc32cb688c26b55 | R | 32,948 | 1,515 | ---
title: "SEFL1c1d_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::opts_chunk$se... |
6ac31a23538092319f15496e9631df04510570656cc80a4426a89dfe4638f4c4 | R | 32,998 | 856 |
Testing the adult brain coexpression (MTG human data; https://www.biorxiv.org/content/10.1101/2022.09.19.508480v1.full.pdf)
Get adult metamarkers and an adult coexpression network
Test the conserved coexpression of the fetal and organoid neurons and non-neurons
Compare against the fetal results
Expecting perfor... |
8f7964e1c6cab9631df4365c615e71b94d471d035e2f27ffbc297a126ee13c0f | R | 33,031 | 638 | #----Figure_07_E_S16------------------------------------------------------------
#-------------------------------------------------------------------------------
# This is the code for Figure 07 Panel E and Figure S16 for the connectivity
# analysis for Reinhard and Fukuda et al. 2024
# swc_L2 files have a resolut... |
0b3e6a24be4b1a63ff59d990ebb79b8923e16207c41d7e8e2ac0a50711e2da11 | R | 33,154 | 759 | ## FUNCTIONS FOR VERTEX-WISE TFCE ANALYSIS
## FOR USE IN THE COGNITIVE AND BRAIN HEALTH LABORATORY
############################################################################################################################
###############################################################################################... |
6f9343d335e5298d38cd2f8fb5066d95aea71a441d3ab65341146dbd257036f9 | R | 33,203 | 776 | #' meta: Brief overview of methods and general hints
#'
#' @description
#' R package \bold{meta} is a user-friendly general package providing
#' standard methods for meta-analysis and supporting Schwarzer et
#' al. (2015),
#' \url{https://link.springer.com/book/10.1007/978-3-319-21416-0}:
#'
#' @details
#' R package ... |
a8211fbaf64b04c5e788c5d825bb905e3b7a71b9f379a789f936a047042e0487 | R | 33,275 | 1,511 | ---
title: "SEFL1c1d_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::opts_chunk$se... |
05b462b525de9ee9524213ddfb25563f8d19592581d1885ed46b3347d52596e2 | R | 33,506 | 1,011 | #' Test for funnel plot asymmetry
#'
#' @description
#' Test for funnel plot asymmetry, based on rank correlation or linear
#' regression method.
#'
#' @aliases metabias metabias.meta metabias.default
#'
#' @param x An object of class \code{meta} or estimated treatment
#' effect in individual studies.
#' @param se... |
c251c0f5b2e0b16ae897099d494e53c6fb96a0ddbfbc4d2c8f8aeba389d5d378 | R | 33,700 | 992 | #!/usr/bin/env Rscript
# dupHMM.R
# TODO:
###############################################################################
library(expm, quietly=TRUE, warn.conflicts=FALSE)
library(truncnorm, quietly=TRUE)
library(docopt, quietly=TRUE)
###### functions ######
initializePi <- function() {
# initialize initial distr... |
8c756803c0314d6f757721e3e534ee2e7cf002810185c50c80bb4be289c9e04f | R | 34,024 | 582 | #' defineUnifiedFocalLoss
#'
#' Define a loss for semantic segmentation using a modified unified focal loss framework as a subclass of torch::nn_module()
#'
#' Implementation of modified version of the unified focal loss after:
#'
#' Yeung, M., Sala, E., Schönlieb, C.B. and Rundo, L., 2022. Unified focal loss:
#' Gener... |
11665633729af95f16df42c69fc0befcbe21c6df40f8ddb523b274f2d0cb101c | R | 34,100 | 1,622 | ---
title: "SEFL10K6_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::opts_chunk$se... |
b5c9a146ef15f2dfbb3d17581d2e518d5b318a0b08f1a340b8e153fd4b08af9c | R | 34,169 | 916 | ---
title: "WGCNA (*`r params$species.name`*)"
author: "Bucao, Christabel Floi"
output: html_document
date: "`r Sys.Date()`"
params:
input.samples.rds.path: "../../results/01_Filter_Samples"
input.variability.rds.path: "../../results/03_Variability_Plots"
input.bias.rds.path: "../../results/12_Organ_Bias"
outpu... |
208b82363ebcc96c13b92e65207a79782543ccc659446e89f00bce5202d45de7 | R | 34,321 | 897 | ---
title: "CBO_Seurat_EXPLORE1_50K"
author: "JJ"
date: "07/08/2022"
output: html_document
---
```{r graphical_output}
options(bitmapType='cairo-png') # this line of code needed BEFORE knitr code in chunk below
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(dev = "png",
dpi = 300,
... |
8742df38df14a27393a428cca86723e7b0b9989c436db9c10b14db78b5c9d87b | R | 34,328 | 920 | ---
title: "Code for plotting Figure 1"
author: "Amy Tresenrider"
date: "2024-03-18"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
suppressPackageStartupMessages({
library(tidyverse)
library(ggplot2)
library(tidyr)
library(viridis)
library(ggridges)
library(pur... |
864f1cf6897928f8eab76238d2ce6138e150d1bc4d12b43abc85997cdfed606f | R | 34,390 | 1,408 | ---
title: "Full TCGA dataframe with missing values"
output: html_notebook
---
## Purpose of the script
Data collection of the proteomic expression data and other features that
may be of interest of 28 cancer types.
**Input data sets:** Follow the [[Downloading the data from the GDC
website]] instructions for the fo... |
08adc0964d13b12f418c630a057885036d7fbd34ccc3d4f3bc5828ac0019cda0 | R | 34,524 | 778 | ---
title: "Relative abundance: plots and stats"
author:
- "Kadi Vaher"
date: '`r Sys.Date()`'
output: html_document
---
```{r setup, include=F, message=F}
library(tidyverse)
library(here)
library(phyloseq)
# library(decontam)
library(RColorBrewer)
library(knitr)
library(ggpubr)
library(vegan)
library(ggrepel)
l... |
d1f2e0f7be9aeada162fa143ae1bf759860dd47cb5046ead261d06cb964c178b | R | 34,553 | 956 | ---
title: "Variability of organ-biased genes (*`r params$species.name`*)"
author: "Bucao, Christabel Floi"
output: html_document
date: "`r Sys.Date()`"
params:
input.variability.rds.path: "../../results/03_Variability_Plots"
input.tau.rds.path: "../../results/10_Expression_Specificity"
output.rds.path: "../../re... |
16e73322f5078b4572d4cf05b0b131355f5aad0122c9e38a7ae45195f2b70616 | R | 34,865 | 834 | ---
title: "panel_plasticity_500ms_train"
author: "Franz Xaver Mittermaier"
date: "2023-08-28"
output: html_document
---
I performed paired patch-clamp recordings of synaptically connected pyramidal neurons. I then used 500-ms suprathreshold current injections to induce associated pre- and postsynaptic trains o... |
c4cec21ddd8175efbc553b560698b08ea7cae263f34cb39ce916f3f1db83b52f | R | 35,075 | 600 | ### script pre-process H1 Teratoma Time Series snRNAseq data
library(Seurat)
library(ggplot2)
library(cowplot)
library(DoubletFinder)
library(SeuratDisk)
library(cellMapper)
library(swne)
library(RColorBrewer)
library(stringr)
# read Scanvi output
TS_allRNA_scanvi.meta <- read.table('anndata_anno_20230121.csv', sep =... |
7079820dc0c06add02c55745a7dad304747f67261fe0f30da4e39fce0d995f4c | R | 35,099 | 650 | # METHYLATION DELTAS BARPLOTS ########################
#####################################################
# Obtain the bedgraph files from the ATAC-seq bam files to do map the ATAC coverage to the genomic regions.
system ("
cd ~/p/openchr/noack
cd cluster_bams_imputation
module load SAMtools
# Merge proj... |
28c25a955868fa51535addb9da4f705a855f9910c513637172223dc18251924a | R | 35,641 | 817 | ---
title: "SupplFig_OtherTests"
author: "Fabienne Roessler"
date: '2024-03-17'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer.R")
source("BFA_BFF.R")
library(sp) #tested with v1.3-2
library... |
bccff6d1299fed407f803bc6a6c9d657519916f1bc3b3b8397aae4a04027659d | R | 35,675 | 705 | if (!require(pacman)) {install.packages("pacman")}
pacman::p_load('this.path', 'glue', 'tidyverse')
# set path to current file
setwd(dirname(this.path()))
# source main results data
source('all_results.R')
# initialize figure list
figure_list <- list()
# overwrite saved figures?
overwrite_saved <- TRUE
# NOTE: Whe... |
b5f4f161e40bd9653694ee61df88b6efa80ffce67d032da137f06aabb5d205f5 | R | 35,691 | 873 | #========================================================================================#
# Written by James M Roe, Ph.D.
# Center for Lifespan Changes in Brain and Cognition, Department of Psychology
# University of Oslo, Norway
#========================================================================================... |
d31992505034f7c645e08e600d13ed0388e46749d85fc88900057c0e56c702e4 | R | 35,706 | 1,180 | # load libraries
library(psych)
library(tidyverse)
library(dplyr)
library(reshape2)
library(viridis)
library(hrbrthemes)
library(ggpmisc)
library(cowplot)
library(ggpubr)
library(lavaan)
library(lcsm)
library(OpenMx)
library(rrr)
library(doBy)
library(Hmisc)
library(corrplot)
library(mice)
packages <- c("corrplot", "fo... |
783b7ca14b0502f53a197f34c12337f66f7e6f0d46c73c4dcf6daad578b0b647 | R | 35,745 | 987 | ---
title: "DAOY-SPHER_Seurat_EXPLORE1_50K"
author: "JJ"
date: "2023-01-26"
output: html_document
---
```{r graphical_output}
options(bitmapType='cairo-png') # this line of code needed BEFORE knitr code in chunk below
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(dev = "png",
dpi = 300,
... |
eeb6da2a7dccc8162e843867017d205b27e0c0e6725733082d1c447c5365bb73 | R | 35,916 | 620 | # List of Color Palettes and the order in which they are printed
#' Complete list of palettes.
#'
#' Use names(MetPalettes) to return all possible palette names. Current choices are:
#' \code{Archambault}, \code{Austria}, \code{Benedictus}, \code{Cassatt1}, \code{Cassatt2}, \code{Cross}, \code{Degas},
#' \code{Demuth... |
7cbaa76fb171dc74d8649b5f77509836668c3cfa165cde2a675c172b0c5cafc0 | R | 35,990 | 748 | ---
title: "Pupilla::Advanced_statistical_analyses"
author: "Elvio Blini"
date: "February 2024"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Pupilla::Advanced_statistical_analyses}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
resource_files:
- images/mathot_plot_data-1.png... |
e66c131e51d4706ff7db7dc0ab3ffe0813dc1d8d7f068b4d4372b1bffd3975a6 | R | 36,565 | 829 | ---
title: "SupplFigure_VAME_AllData"
author: "Fabienne Roessler"
date: '2024-03-05'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r, message=FALSE, warning = FALSE}
source("DLCAnalyzer.R")
source("BFA_BFF.R")
library(sp) #tested with v1.3-2
li... |
e8ef0d09bd5f8f43bd86862a35d5ba5627434360ae60e6f264cebbdf31aea87d | R | 36,626 | 1,033 | ---
title: "04_AMT_Outliers_Diversity_Groups_Genes"
date: "`r Sys.Date()`"
output:
prettydoc::html_pretty:
theme: cayman
highlight: github
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
# AIM
- Examine nature of selection on AMT outliers based on Tajima's D
# M... |
927232c03671e896d5bc09da98e37518c2eeb1e3aa5dd7c537775135625df6a2 | R | 36,947 | 1,057 | ---
title: "Descriptive analysis"
author: "Giulio Centorame"
date: "`r Sys.Date()`"
format: html
editor: visual
output:
rmdformats::downcute:
toc: 6
number_sections: yes
---
This notebook contains the descriptive analysis for exposures and outcomes of interests.
The results of these analyses are available i... |
1861a2e0fe002ed5ba3720ba273078b84cc2fc31fd36e8388a265f984a18463f | R | 37,006 | 1,149 | ### read.spc - Import Thermo Galactic's .spc file format into an hyperSpec Object
###
### C. Beleites 2009/11/29
###
#####################################################################################################
## Define constants --------------------------------------------------------------------------------... |
cd04ed6bc4e3f440c010056a20a5df94b66289dbb8eec5f9fb43cefd1c8b96b0 | R | 37,178 | 945 | ---
title: "Reliability_SimSummary"
author: "<h3>by Michael Demidenko</h3>"
date: "`r format(Sys.time(), '%B %Y')`"
output:
html_document:
theme: united
highlight: tango
toc: yes
number_sections: yes
toc_depth: 2
toc_float:
collapsed: yes
smooth_scroll: yes
code_folding: hide
... |
4fe29625712e89d2ee1ef5ba53722fd3773bc5522c90a079d994321959ce4220 | R | 37,210 | 517 | # script for running cohens'd analysis on AEI and adar exp for organ development + visulization
aei_table_df <- read.table('/media/Scratch_SSD_Voyager/sammi/RNA_editing/AEI_OrganDev_v2/output_index_20230607/OrganDev_v1_outputindex/EditingIndex.csv', header = T, sep =',')
adar_tpm <- read.table('/media/Scratch_SSD_Voya... |
9be82b6e1eea5bbc0b3c5bb3b07f19da315406530a2211ace30b14f216fcd22c | R | 37,982 | 797 | #######################################################
### Deep Brain Stimulation: STN vs GPi.
### Data Preparation
### 2022 Katrin Petermann
#######################################################
## patients 23 and 78 have two postop assessments
dd_raw$redcap_repeat_instance <- as.numeric(dd_raw$redcap_repeat_insta... |
c0e58b8408a7077162405826a79ebef2476e690037b3c017d626a95f2edfba2b | R | 38,170 | 714 | ---
title: "Area Postrema Cell Types"
author: "Judith Kaye"
date: "January 5, 2021"
output:
html_document:
code_folding: hide
number_sections: false
toc: true
toc_float:
collapsed: true
smooth_scroll: false
number_sections: false
---
```{r, include = FALSE}
knitr::opts_chunk$set(war... |
67cef01e92839867a5686b5bfcda3d5606e43cf0c6c1d5f1f1226761a2cf0489 | R | 38,285 | 681 |
############################################################################################################################
############################################################################################################################
#' @title VertexWiseR system requirements installation
#'
#' @descri... |
780f22f8cbe4a861bd6d93e004a1c6e781c70678b9afce1d7909f0c27798806f | R | 38,443 | 783 | # ---------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
# ------------------------------------------------------------------------------------------------------------------... |
efcdf82ce11f2594b320417dd9c5b10a3f896a509c8a553d53253f0dec5141ad | R | 38,475 | 603 | Sys.which("make")
#Step 1 Load All packages first
rm(list=ls())
## Loading library
library("readxl")
library(metafor)
library (meta)
#NDDx Overall###########################################
## Loading raw data to merge NDDx vs. Control groups
NDDx_Control_Rawdata <- read_excel ("DataExtraction_NDDx_Controls.xlsx")
#... |
c8771e2cb3744b1b531214e4e80c6a0584cbe0545117bbb55cdcfccce7bc05c7 | R | 38,554 | 1,104 | #' Cumulative meta-analysis
#'
#' @description
#' Performs a cumulative meta-analysis.
#'
#' @param x An object of class \code{meta}.
#' @param pooled A character string indicating whether a common effect
#' or random effects model is used for pooling. Either missing (see
#' Details), \code{"common"}, or \code{"r... |
ee47271663716f02d1ac5b6f502b30a023e561095f4b678ea6dbbc139dca3eff | R | 38,899 | 1,112 | #' Influence analysis in meta-analysis using leave-one-out method
#'
#' @description
#' Performs an influence analysis. Pooled estimates are calculated
#' omitting one study at a time.
#'
#' @param x An object of class \code{meta}.
#' @param pooled A character string indicating whether a common effect
#' or random ... |
81263d8fcc3937f1e21e1638a0ea57248bc189191914017c759e8f60fb045543 | R | 39,454 | 795 |
Using the primary tissue metamarkers to generate broad cell-type annotations for the organoid datasets
Quantify the compositional variation across the protocols
Check out the arlotta datasets first since they have annotations, see how well the MetaMarker based annotations line up with the author provided annotations.... |
061a55216655e8905f84af2056f57b71eddaf7a6f0275fb782ab80c7a7eb67b9 | R | 39,550 | 1,059 |
Processing the massive fetal arealization dataset from the kreigstein lab
each timepoint and area is saved in a different directory
```{r}
library(Seurat)
library(dplyr)
library(MetaMarkers)
library(data.table)
library(ggplot2)
```
Read in the metadata
cells are labeled by their age_region_barcode
This is metadat... |
8f00b7b39298c45af0503a95f40ab69fa02662fc2a228b52cdc87409719e5bc5 | R | 39,596 | 836 | # FUNCTIONS #################
###############################################
## FUNCION PARA SACARTE LOS MOTIVOS EN LOS PEAKS
find_motifs<-function(peaks, peaks_file) {
require(dplyr)
require(bedr)
require(pracma)
options(scipen = 999)
write.table(peaks,peaks_file,sep="\t",quote=F, col.names=F, row.names = ... |
eb69707489421cfe437e892da33637579b72ed655b5275acdc29816bb6490d7e | R | 39,606 | 739 |
Misc. plots and things for the organoid project
```{r}
library(ComplexHeatmap)
library(ggplot2)
library(circlize)
library(viridis)
library(Rlab)
```
making visual heatmaps for metamarkers
one plot with noist differential expression signal
one plot with clean differential expression signal
Showing DE cleans up... |
e4d4a0561a6fb19525149bd426ac36cb97a72ce355988ad1dde594b3f79988c7 | R | 39,718 | 666 | #Cluster nuclei on the minimal marker genes
#Import the integrated seurat object (after SCT but prior to UMAP)
#Run UMAP but change the features to the minimal markers and to a random set of variable features
library(SingleCellExperiment)
library(Seurat)
library(tidyverse)
library(Matrix)
library(scales)
librar... |
da01be8bc2353be679a1c6b797a8865c95d15cdc036f442969ddf133c349004d | R | 40,326 | 1,155 | compute_aspect <- function(dx, dy) {
# Case 1: When dx is non-zero
aspect_rad_nonzero <- torch::torch_atan2(dy, -dx)
aspect_rad_nonzero <- torch::torch_where(aspect_rad_nonzero < 0,
aspect_rad_nonzero + (2 * pi),
aspect_rad_nonzero)
# Case... |
6af4a199e5221998e715ec13e95bf8a0775010956b08b6ec5a8461249badda80 | R | 41,529 | 1,458 | ---
# For vignette ---------------------------------------------------------------
title: Plotting Functions in "hyperSpec"
sutitle: "Manual of hyperSpec's Plotting Functions"
description: "plotting: Manual of hyperSpec's plotting functions"
# Authors ----------------------------------------------------------... |
8fe9ab2ae8732ebf494acaa1556b87787d04927806d495a2c955ac29be3d8610 | R | 41,550 | 1,886 | ---
title: "SEFL10j10e3_Combined_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::o... |
01c3a8d5b29c532b4d43d86ea7872465d5127bec6f60a8dbf6cab9cd81bb6676 | R | 41,699 | 1,086 | #' Print detailed meta-analysis results
#'
#' @description
#' Print method for objects of class \code{summary.meta}.
#'
#' @aliases print.summary.meta
#'
#' @param x An object of class \code{summary.meta}
#' @param sortvar An optional vector used to sort the individual
#' studies (must be of same length as \code{x... |
6deb8ce623f0fe5b1ff9dc81a0af49185a327ac23efa3c60cefc4f35292a07a6 | R | 41,892 | 890 | plot_simulation <- function(counts, simulations, counts_labels = NULL, simulations_labels = NULL, fname = "simulation", save_dir = ".", width = 7, height = 7){
library(ggplot2)
if (!dir.exists(save_dir)){
dir.create(save_dir, recursive = TRUE)
}
if (is.null(counts_labels)){
counts_lab... |
cc686523579cf388c8e4a58e545346c9bd636af855eee23139c0b6f32d98a06b | R | 41,985 | 1,070 | ---
title: "Alpha diversity for TEBC faecal microbiome data"
author:
- "Kadi Vaher"
- "Took examples for Wouters scripts for the niches paper"
- "added 22/03: saving the sample type alpha diversity plots as gg-object to make a panelled figure"
- "added 30/03: degrees of freedom for t-tests"
- "note that this... |
bf729892888419c80b11575e05069951dfdeca150b43bd74122d18cbbbaf29c1 | R | 42,096 | 1,112 | #' Merge results of two meta-analyses on the same data set
#'
#' @description
#' This function can be used to merge results of two meta-analyses
#' into a single meta-analysis object if they are based on the same
#' data set. This is, for example, useful to produce a forest plot of
#' a random-effects meta-analysis wi... |
45b64ac6ffdbbcc99b499a2be499f9a43dea1e685449e979617f9660a0b7d2f9 | R | 42,189 | 1,189 | ---
title: "ONS76-SPHER_Seurat_EXPLORE1_50K"
author: "JJ"
date: "19/01/2023"
output: html_document
---
```{r graphical_output}
options(bitmapType='cairo-png') # this line of code needed BEFORE knitr code in chunk below
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(dev = "png",
dpi = 300,... |
8d146fe77471c7f3797769b1e8dce1362cd7950ada5553ed003df80efc42ac69 | R | 42,587 | 805 | rm(list = ls())
library(openxlsx)
library(ggplot2)
library(pheatmap)
library(CRISPRcleanR)
library(MAGeCKFlute)
library(mixOmics)
setwd('directory')
r1 = read.delim('scores-FP_GPP655_Merk_CP0043_20190409_sgRNA.txt')
r2a = read.delim('scores-FP_GPP843_Merk_20190906_CP0043_Plate1.txt')
r2b = read.delim('score... |
25533c6aa21882632922949b3b9aac91ee3875c5ccbfb89b32eebe220d1448bb | R | 42,692 | 1,057 | #=======================================================================================================#
#
# Author: James M Roe, PhD.
# Center for Lifespan Changes in Brain and Cognition, University of Oslo
# Purpose: estimate CVLT slopes
# define quadrant risk groups
# test whether high PRS-AD hig... |
15b4cfc886579ddb96c3222443840c0aa84f6cba9869df22d6934e38606e9b6a | R | 42,773 | 1,174 | subgroup <- function(x, tau.preset = NULL, subgroup.rma,
seed = NULL, ...) {
subgroup <- x$subgroup
method.predict <-
replaceVal(x$method.predict, "", gs("method.predict"))
#
levs <- bylevs(subgroup)
n.levs <- length(levs)
#
methci <- paste(x$method.random.ci,
... |
c83f4ec7a5fc9b81d2bc496f8ef856fbc674b1db259b8bfe94886c2b3bcbeff2 | R | 42,857 | 778 |
Getting time series analysis using the annotated arlotta organoid dataset
Celltype prediction AUCs using individual fetal dataset markers, see if there is a trend across time points. Show the AUC using the full list of MetaMarkers
Conservation of Coexpression AUC using the individual fetal datasets as references, s... |
600663be1313b08b89900211f4a85bb9217fdf02fea9dd2311b3f889523a1296 | R | 43,461 | 712 | setwd("/home/wangmg/Documents/deconvolution/Data_and_code_for_reproduction")
source("Code/utils/utils.R")
source("Code/utils/plot.R")
library(ggplot2)
library(ggpubr)
library(ggprism)
library(scatterpie)
#################### Preprocess the original scRNA-seq data
### The original scRNA-seq data can be downloaded fro... |
0bea56bfdfd3db9e12c7c18eae1ab0f31daffd728ea52041265a49ec29c8faa0 | R | 43,790 | 1,116 | #############################################################################
# Copyright (c) 2009 Marie Laure Delignette-Muller, Christophe Dutang
#
# This program is free software; you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software ... |
00458c7daf86b8bf2fc71f0c7442645ab3359a35a29726b5eaa2abced0d989be | R | 43,893 | 2,067 | ---
title: "SEFL10j10e3_Combined_Analysis"
author: "Zach"
output:
html_document:
fig_height: 4.5
fig_width: 4.5
highlight: kate
number_sections: yes
theme: journal
toc: yes
toc_depth: 6
editor_options:
chunk_output_type: console
---
# Set Global Options
```{r global_options}
knitr::o... |
f50456861e96b1fe66db96e272da337eb66531c42f6d7f4ddd1196de2875263a | R | 44,955 | 1,158 | #' Meta-analysis of outcome data from Cochrane review
#'
#' @description
#' Wrapper function to perform meta-analysis for a single outcome of a
#' Cochrane Intervention review.
#'
#' @param x An object of class \code{rm5} or \code{cdir} created by R
#' function \code{read.rm5} or \code{read.cdir} .
#' @param comp.n... |
f3d2153124028030299218aa7f9dbd85abff7339fb7d149a18418d2a80f5050b | R | 45,020 | 1,369 | #' Meta-analysis of single incidence rates
#'
#' @description
#' Calculation of an overall incidence rate from studies reporting a
#' single incidence rate. Inverse variance method and generalised
#' linear mixed model (GLMM) are available for pooling. For GLMMs, the
#' \code{\link[metafor]{rma.glmm}} function from R p... |
3fe656c1710f84a35d0f12604592163dd7fee8ead60ac780d7949a588ff488cd | R | 45,513 | 1,188 | #
#
# Definition of auxiliary functions to import RevMan 5 analysis datasets
#
#
extract_outcomes <- function(txt, outcome.type, res,
comp.no, complab,
debug = 0) {
#
pattern1 <- paste0("<", outcome.type, "_OUTCOME")
pattern2 <- paste0("</", outcome.type... |
5e34b86585ece4517f7431d9614fda20743cd93f0c94c21e6efc0d32bf91a0d4 | R | 45,666 | 927 | #' @title Surface overlay plotter
#'
#' @description Plots surface data in a grid with one row in a .png file as in plot_surf(), with parameters to overlay two maps on top of eachother.
#' @param model_output A list object outputted by RFT_vertex_analysis() or TFCE_threshold(). The 'tstat_map' will automatically be tr... |
4c4aabf7cf3d3524add1bc3f3d5a00fa9d0908dd4a50310dbb91329c0a2f42c7 | R | 45,989 | 1,061 | ---
title: "Extended Data Figure 1, Extended Data Figure 3"
author:
- name: "Emir Turkes and Stephanie Fowler"
date: '`r strftime(Sys.time(), format = "%B %d, %Y")`'
link-citations: true
output:
html_document:
code_folding: show
number_sections: true
theme: lumen
highlight: haddock
toc: true
... |
45884c37c3b7ccda3dd8134b300dfdec9cd0b80b39a40692546314067b225d7a | R | 46,142 | 1,308 |
reportExists = function(experiment,tissue,module){
if(exists("coexp.report.go.all"))
return(!is.null(coexp.report.go.all[[experiment]][[tissue]][[module]]))
return(FALSE)
}
reportGet = function(experiment,tissue,module){
if(reportExists(experiment,tissue,module))
return(coexp.report.go.all[[experiment]... |
7622349ca7c8372574ed7b684c15e94dd8aa222b7e9709997d71b254bcbb4da0 | R | 46,486 | 1,236 | ---
title: "Filter Samples (*`r params$species.name`*)"
author: "Bucao, Christabel Floi"
output: html_document
date: "`r Sys.Date()`"
params:
counts.path: "../../data/counts"
sample.metadata.path: "../../data/sample_metadata"
gene.metadata.path: "../../data/gene_metadata"
output.rds.path: "../../results/01_Filt... |
d7680cfb8079313f21be784e887828758f829c6c93d31e5d57463f09f743b48b | R | 46,727 | 1,298 | ---
title: "ONS76-CBO_Seurat_EXPLORE1_50K"
author: "JJ"
date: "11/08/2022"
output: html_document
---
```{r graphical_output}
options(bitmapType='cairo-png') # this line of code needed BEFORE knitr code in chunk below
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(dev = "png",
dpi = 300,
... |
021368731c10b992b269116f405046e790e6a815378ba57166f8a11ffe949da3 | R | 46,771 | 1,056 | #Function Bank and Variable Definitions for FNIH Project 2
#Crossectional Part of Study
#### Coded: Benjamin Saef
#### Date as of commenting and documentation - 05/28/2024
library(tidyr)
library(data.table)
library(RVAideMemoire)
library(pROC)
library(RColorBrewer)
library(MuMIn)
####### Variable creation - centralize... |
d51c032a55bc0031de65fc8c7053e7e972b8ffaf2cc44c7f5aac7f1f7fd97547 | R | 46,986 | 968 | #### Notes on revisions ####
#### Set species ####
species.name <- c("Lepisosteus oculatus","Esox lucius","Danio rerio")
names(species.name) <- c("LOC","ELU","DRE")
#### Step 1: (Skipped) Filter samples ####
#### Step 2: Estimate gene expression variability - With jackknife ####
##### Set input and parameters ###... |
4e7ffd562d09d7c1368a9d158ee96ab7aa1577666987060714a84749bc8dd125 | R | 48,125 | 1,093 | library(Seurat)
library(dplyr)
library(data.table)
library(ggplot2)
library(ggpubr)
library(ggisoband)
library(pheatmap)
library(RColorBrewer)
library(stringr)
library(ggnewscale)
library(WGCNA)
library(future)
##########################################################################################
###... |
3b0ee71b6937689f8cf58f73a68698db0284eec7acf3ee8b98f410e9116f4181 | R | 48,202 | 990 | ---
title: "Github Figure 3 AB, Github Figure S4 ABC"
author:
- name: "Emir Turkes and Stephanie Fowler"
date: '`r strftime(Sys.time(), format = "%B %d, %Y")`'
link-citations: true
output:
html_document:
code_folding: show
number_sections: true
theme: lumen
highlight: haddock
toc: true
toc_d... |
8b745b8328c9eb4eef99a4149b1a1d6e78c6a837093a0c231fd1dbe96eb5b046 | R | 48,301 | 949 | ---
title: "scRNA experiment report- Experiment for Aysegul"
output:
html_notebook:
code_folding: hide
toc: true
toc_float: true
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Experiment
This experiment was designed as a first stage project to evaluate Parse snRNA preps withi... |
7bc69fd3b5af82b5c35cd05cc82c1da04677a127f502e201757d8f58cd94e598 | R | 48,757 | 1,508 | #' Meta-analysis of single means
#'
#' @description
#' Calculation of an overall mean from studies reporting a single mean
#' using the inverse variance method for pooling; inverse variance
#' weighting is used for pooling.
#'
#' @param n Number of observations.
#' @param mean Estimated mean.
#' @param sd Standard devi... |
1927be1fd2352c4d4eb8bbd1e7abeb5f7774014883e148ee86482c240fb98509 | R | 49,220 | 1,207 | #' countParams
#'
#' Count the number of trainable parameters in a model.
#'
#' Count the number of trainable parameters in an instantiated model subclassed from nn_module().
#'
#' @param model instantiated model object as nn_module subclass as opposed to luz fitted object.
#' @param test_data Example tensor of the cor... |
dbf5ab2cb95cb76b9ab5b8ac805e6ec873d3e39613592ef50e15dc22e26ee83d | R | 50,764 | 1,245 |
Going through each organoid dataset individually for processing
Load up the downloaded data and split datasets accordingly. Cell lines, age, batch, which ever batch information is provided
Get the QC metrics and normalize each dataset
Save a final processed seurat object for each dataset
```{r}
library(Seurat)
libra... |
37e9a745f849663095612a95b6f8d041031c44d40fd4544eb52cd9e63718ae9d | R | 51,099 | 1,365 | ---
title: "INTEGRATE_DAOY-CBO_DAOY-SPHER_DAOY-2D_50K.Rmd"
author: "JJ"
date: "04/11/2022"
output: html_document
---
```{r graphical_output}
options(bitmapType='cairo-png') # this line of code needed BEFORE knitr code in chunk below
```
```{r setup, include=FALSE}
knitr::opts_chunk$set(dev = "png",
... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.