sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
eac5cba3ecd99ec60c7ec54316d9d34bc773a7a5eb1a7da56b8dbf41f3159744 | R | 5,126 | 130 | ---
title: "Visualize spatial importance"
author: "Will Townes"
output: html_document
---
```{r}
library(tidyverse)
theme_set(theme_bw())
fp<-file.path
pth<-"scrna"
plt_pth<-fp(pth,"results/plots")
if(!dir.exists(plt_pth)){
dir.create(plt_pth,recursive=TRUE)
}
#dd<-c("sshippo","visium_brain_sagittal","visium_scra... |
eba0de16c0768eb9105509729801463ac19e18c96210aba154c5310eaac678ad | R | 5,126 | 149 | #' Validate the input parameters for the load function
#'
#' TODO Add description of parameters
#' @param data A tidy dataframe.
#' @param name_x x Column in `data`
#' @param name_y y Column in `data`
#' @param id_col_params id Column in `data`
#' @param colour_params Column in `data` that determines colour grouping
#'... |
f0f75620b36f9025b2695b0e1f382c48f1517638a6a080e6e4e4271c8596eb20 | R | 5,137 | 176 |
library(Seurat)
source('scRef.R')
exp_raw_data=read.table('GSE72056_melanoma_single_cell_revised_v2.txt.pure',header=T,row.names=1)
tag1=read.table('GSE72056_cnv.txt',header=T,row.names=1)
tag2=read.table('GSE72056_tag.txt',header=T,row.names=1)
tag1=t(tag1)
tag2=t(tag2)
tag1[which(tag1!='malignant')]=tag2[which(ta... |
5d8dcddeb7295f74a15bc37d41af1a6589da7aaf8fe63e4e5fd825b887b85664 | R | 5,142 | 147 | # Characterization baseline (REQ-004): the S4 accessor, detail(), rbind() and
# coercion surface of richResult, GSEAResult and Annot as produced by richR
# 0.1.5. These are exactly the registrations that R/misc.R and R/accessors.R
# define twice; the tests exercise whichever definition is live, so that
# removing a dup... |
065f54ae5911d7301894ee973b2ce101494c1caf6daa98d7d2bbe9e6663ee76b | R | 5,144 | 143 | # 07/23/21: Ran through with no errors.
# INPUT:
# Under PARAMETERS heading:
# 1. Choose whether or not you want to save plots out (save variable)
# 2. Choose whether you want to plot density maps and individual cells
# for clusters with previously-studied markers (Figure 4 currently) or with novel markers (Figure 5... |
588e87c6b7d82174e3aada67542943ab8b3b60f1175939558dca99ddd3a5356b | R | 5,153 | 132 | #' Central species lookup table used by all species-mapping functions.
#' Each row: common name, Bioconductor OrgDb, KEGG 3-letter code,
#' msigdbr scientific name, Reactome scientific name.
#' @return data.frame with columns: species, dbname, kegg, msigdb, reactome
.species_table <- function() {
data.frame... |
336a2e736b2db212a3d45c0201d6825a1916e132ff2c28f24983a6c4fec4a4ce | R | 5,180 | 116 | # setup -------------------------------------------------------------------
library(tidyverse)
library(rstatix)
library(corrplot)
library(formattable)
library(webshot)
library(htmltools)
library(ggstatsplot)
library(webr)
setwd("/home/eli/Desktop/Neurobiologia/projects/ongoing/verbal.irony/facilitators/experiment.3/")
... |
73a0b3f3f58748eabafb460695eae25b3e99a72abd636cd6b1c81487debdcfec | R | 5,264 | 135 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2020 Robert McCulloch and Rodney Sparapani
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of the License, or
#... |
94a12a15e1bd4900a7a997a63d4fd12e3ea70fbcf1ff1c760af0198a0dfaf8c2 | R | 5,270 | 153 | # This is the R script for batch query
# Designed for command line use, given the required packages are installed
#
# Example usage:
# Rscript Batch_query_db.R ./GeoDB ./example/input.csv ./example/output.csv
#
# ./GeoDB the folder containing the query database generated in previous step
# see: Gen_data_proc.R... |
91317b391f5fc291ce5a4f2140d7ad977b232a8e8cb4e37d4bbfaa7db81c35c1 | R | 5,283 | 132 | # Description: script to run coloc analyses using PsychENCODE
#---Import data and libraries-----------------------------------------
library(BSgenome)
library(coloc)
library(colochelpR)
library(data.table)
library(GenomicRanges)
library(MafDb.1Kgenomes.phase3.hs37d5)
library(tidyverse)
RBD_tidy_w_varbeta <-
fread... |
5fa47590ae8f22d36a9bab77b7ea9be4d9a4980f8ae1fa9297ab3481b547ebf1 | R | 5,284 | 142 | # Shelli Kesler 7/19/19
# Connectome-based predictive modeling with LOOCV
# Adapted from Matlab code Copyright 2015 Xilin Shen and Emily Finn
# Reference: Finn ES, et al. Nature Neuroscience 18, 1664-1671.
# Uses edge sums for each of 8 networks (Shen, X, et al. 2013. NeuroImage, 82, pp. 403-15.)
# Conducts simple lin... |
d84ebfe8da0353114dd872edef3a02fb0af602d394e21415a341f6dddd7c3a75 | R | 5,308 | 91 | library(tidyverse)
library(lme4)
source("scripts/R/ReadDataMTurk.R")
# STATISTICS score evaluation
all_score = read.table("results/csv/score-evaluation.csv", header=T, sep=",")
l=6
all_score_layer = all_score %>% filter(layer==l)# %>% filter(aq != -1)# %>% filter(aq<=17)
dist.interact = lm(scoreByMean ~ aq * age, data... |
883a43cb1017915a9ce402cfe682d70b21bc4843be4ad4fa56c64dea01184c6f | R | 5,313 | 125 | # setup -------------------------------------------------------------------
library(tidyverse)
library(rstatix)
library(corrplot)
library(formattable)
library(webshot)
library(htmltools)
library(ggstatsplot)
library(webr)
setwd("/home/eli/Desktop/Neurobiologia/projects/ongoing/verbal.irony/facilitators/experiment.2/")
... |
c2c0866facf56e7b8fd2a844c63a2af70da28817c7f591fbb6e34c0e3e3f2ec8 | R | 5,322 | 122 |
source("code/Packages_and_Connection.R")
#read the saved visNetwork file from source_data/ ------------------------------
conn_grouped_graph.visn <- readRDS("source_data/Figure3_figure_supplement1_source_data1.rds")
# plot grouped graph ----------------------------------
#overwrite group value (partition) with s... |
05752c64d4b23195d1f48fe8aa5a42c10b7ddfea9d33f9cebe728228265493a9 | R | 5,336 | 165 | # ----------------------------------------------------------------------------
# build_human_tree.R
# Author: Antoine Beauchamp
# Created: March 9th, 2021
#
# Build the gene expression tree for the Allen Human Brain Atlas
# Packages -------------------------------------------------------------------
suppressPacka... |
ffeb4215595e93ae53660eafb87fee46464eea692c338b7d556108ec5fdd7c23 | R | 5,352 | 205 | ---
title: "richR: Functional Enrichment Analysis and Visualization"
author: "Kai Guo"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{richR: Functional Enrichment Analysis and Visualization}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, in... |
a1446850584fecf1cddc16935a59de020488938db7d1f9852c0fc89004b11146 | R | 5,367 | 149 | pdf('COM_HEAT.pdf',width=12,height=5)
##################################
RESULT=c()
setwd('F:/SCREF_COM')
source('evaluate.R')
result <- evaluate('./OUT/scRef_True_Labels.csv', './OUT/scRef_Pred_Labels.csv')
RESULT=cbind(RESULT,result$F1)
result <- evaluate('./OUT/scPred_True_Labels.csv', './OUT/scPred_Pred_Labels.cs... |
a031e9dcb78d1361865f2878fcba734016209e89a38a281bc947169cf245b64d | R | 5,466 | 136 | ---
title: "Splice-site level analysis: E-MTAB-599"
---
```{r}
#Load in required library
library(data.table)
```
```{r}
Data_compiler <- function (filename, annotation, extended){
print("Reading in files")
dt <- fread(filename) #RNA-seq raw count table (STAR output .jcount file)
isoforms <- fread(a... |
fb2d624461ede23ad0598591319855791ab531100168542a121f5f7d04430928 | R | 5,467 | 155 | #' Plot colocalisations
#'
#' @param coloc_hits Character. Vector of coloc hits as ensembl ids.
#' @param eQTL_GWAS_to_plot Dataframe. Dataframe of p-values for SNPs
#' overlapping between GWAS and eQTL. Columns to include: chr, chromosome;
#' pos, position; gene, ensembl id; hgnc_symbol, gene symbol; Dataset, whet... |
a98e3283992e37a0a28d32ac838774d4cc74a56a2eda3339e55f9a4f59a55793 | R | 5,472 | 194 | describe("Testing add_swarm_bars_to_raw_plot function", {
# Test Case 1: Custom Bar Color Provided
test_that("Adds swarm bars with custom color when bars_color is specified", {
# Generate dataset
dataset <- generate_non_proportional_dataset()
# Create dabest_effectsize_obj
dabest_effectsize_obj <- ... |
17669500d480257d9793c7cb705ef9a0fdc53372bb653d28eafcc39ab763023c | R | 5,489 | 128 | ---
title: "Comparison of `liger` with `fgsea`"
author: "Jean Fan"
date: "June 26, 2018"
output: rmarkdown::html_document
vignette: >
%\VignetteIndexEntry{Comparison of liger with fgsea}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
`liger` is just one of many methods for gene set enrichment a... |
f00681e5e0ce6f24ec51f87e433fce6dcfee989b2f9e3ef0b4cae26faa46740a | R | 5,493 | 108 | #' GO term to offspring mapping
#'
#' A named list that maps each Gene Ontology term to itself and all of its
#' offspring terms, covering the biological process, molecular function and
#' cellular component ontologies.
#'
#' @format A named list of 42,984 character vectors. Each name is a GO
#' identifier (for examp... |
4381666380442afeddb0caf6ea757bc31b651048d8b960ea32ff111a335208d8 | R | 5,551 | 180 | setwd("~/Dropbox (ASU)/Placenta/BATCH2_PLACENTA_DECIDUA_ANALYSIS/HISAT_FeatureCounts/batch1_and_batch2/")
library(VennDiagram)
library(RColorBrewer)
PisarskaALL <- read.table("DEGs/pisarska/DEGs_placentas_genes_fdr1_lfc0_NOFPKMFILTER.txt", header = TRUE)
WilsonALL <- read.table("DEGs/placentas_batch_1and2/batch_BirthW... |
6f85925ff23d3a331e34696ca4ed3a92acf958b3cd3242580c2f0cb6507b1e13 | R | 5,576 | 127 | #R code to generate Video7 (related to Figure8-figure-supplement1) of the Jasek et al. Platynereis desmosomal connectome paper
#Uses Natverse and accesses the data on catmaid
#Gaspar Jekely
source("code/Packages_and_Connection.R")
# Motoneuron plotting -------------------------------------------------------------
M... |
b4e45ca8cd6fff0404f1feeeba951122f4cb219259820ecb2dc3f07ad21a26d7 | R | 5,579 | 234 | #code for Video4 of the Jasek et al 2021 desmosomal connectome paper
#Gaspar Jekely
source("code/Packages_and_Connection.R")
#read the tibble with desmosome ids, xyz coordinates and partners 1 and 2
desmo_with_partners <- read_csv("data/desmo_with_partners.csv")
celltype_names <- read.csv("data/non_neuronal_celltype... |
b0a7fba578634ff731cc41a52612407094a8dc03653831f39864bcc0c0c25008 | R | 5,668 | 108 | ##' generate network based on Enrichment results
##' @rdname richNetmap
##' @param richRes list of enrichment object
##' @param gene vector contains gene names or dataframe with DEGs information
##' @param top number of terms to display
##' @param top.display top number to display
##' @param pvalue raw p-value cutoff: ... |
a4198796da7b467f2eb463d58896ec190cf8d0ed4e5b6a17187f12eb3b1e4f38 | R | 5,685 | 130 | # SPEC-RICHR-010: separator metacharacters are literal gene delimiters.
.review010_annot <- function() {
data.frame(GeneID = paste0("G", 1:60),
Term = rep(paste0("T", 1:6), each = 10))
}
.review010_enrich <- function(x, sep = ",", annot = .review010_annot(), ...) {
suppressMessages(enrich(x, annot, pva... |
474ec2eed740397044e2223a1c98d55c50bc098188bd0ed93ca90ed3d4b33594 | R | 5,687 | 141 | library(catmaid)
library(tidyverse)
source("~/R/conn.R")
################################################################################
# Calculate percentages of desmosomes which are:
# - hemidesmosomes
# - muscle to muscle
# - muscle to muscle, same muscle type
# - muscle to muscle, different musc... |
1e147e0bdfff64478f239ef4b5a5b193b4cb06fc584b9e1fc46222cbcc745f4b | R | 5,693 | 160 | ---
title: "Using schex with Seurat"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: true
toc: false
html_document:
df_print: kable
theme: united
---
This vigettte demonstrates how to run schex on Seurat objects,
which aims to provide better plots. If ... |
755b91aeed41fa3cd5cdfe5a3e4035ceb99911f7e1df51f2d72cfb651aa2bd01 | R | 5,757 | 144 | ---
title: "Tutorial: Mini-Meta Delta"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Tutorial: Mini-Meta Delta}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, warning = FALSE, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
When scie... |
8733c07680004ffb7049b60cea5ce4194292d79a06575dcd09c0617e50ce94b7 | R | 5,767 | 125 | # Bundled KEGG snapshots (SPEC-RICHR-003 REQ-019, REQ-020): the shipped
# kegg.db, module and path objects say when they were retrieved and from which
# KEGG version, agree with what their documentation states, and are internally
# consistent, so that richLevel, richKEGG and richGSEA attach one hierarchy row
# to each ... |
843c5af72aff50a1ab918675504c4c69ce3cfa926fdaad26128dc57563493e81 | R | 5,783 | 146 | # INPUT:
# Under PARAMETERS heading:
# 1. Choose whether or not you want to save plots out (save variable)
# 2. Choose whether you want to plot density maps and individual cells
# for clusters with previously-studied markers or with novel markers
# using only_novel and only_old parameters
# OUTPUTS:
# If save set to... |
a40a5d8c2a3c88a41e9ff7639b0720ac25e93eb4a5a22f156d605cee244c0375 | R | 5,796 | 154 | # Shelli Kesler 6/5/19
# Connectome-based predictive modeling with split-sample CV
# Adapted from Matlab code Copyright 2015 Xilin Shen and Emily Finn
# Reference: Finn ES, et al. Nature Neuroscience 18, 1664-1671.
# Uses edge sums for each of 8 networks (Shen, X, et al. 2013. NeuroImage, 82, pp. 403-15.)
# Conducts s... |
f10595f4d84dd281358fcab69e6ff115a68c6815b3ba2c6b2186aa331dc07e9c | R | 5,797 | 166 | # Create a toy dataset to demonstrate usage of the Magellan package.
setwd("~/git/Magellan")
options(stringsAsFactors = FALSE)
library(tidyverse)
library(magrittr)
library(Seurat)
library(Matrix)
library(RANN)
library(splatterBatch) ## https://github.com/jordansquair/splatter_batch
library(scater)
library(Magellan)
# ... |
e191285576b02f9dbc27d2936c7dde63a686313febf8988ea73f9aca0945be7d | R | 5,880 | 238 | # Differentially Abundant Features (DAFs)
# Load data & functions ----
rm(list = ls())
source("src/load_packages.R")
source("src/miscellaneous_funcs.R")
source("src/load_phyloseq_obj.R")
source("src/metadata_prep_funcs.R")
source("src/community_composition_funcs.R")
source("src/daf_functions.R")
datfiles <- list.files... |
b6b992e30f6ee9ded11e9b4af7ba1eac787a0ab76f0ea6999cbaf11d64ff18d2 | R | 5,885 | 152 | #' Build a MINC array from structure-wise data
#'
#' @description
#' Map a vector of structure-wise values to voxels in an image.
#'
#' @details
#' This function matches the values defined for every region of
#' interest (ROI) in an atlas to the voxels that lie within those
#' ROIs in an image. This is done eithe... |
bf32f7b2e35911c22719d4646b142d585e788ea6255475d850f11a8621f7a8d7 | R | 5,887 | 173 | # Gut-Brain Module (GBM) and Gut-Metabolic Module (GMM) Analysis
rm(list = ls())
source("src/load_packages.R")
source("src/load_phyloseq_obj.R")
source("src/miscellaneous_funcs.R")
source("src/metadata_prep_funcs.R")
source("src/community_composition_funcs.R")
source("src/daf_functions.R")
wkd <- getwd()
#-----------... |
52a0281ee04ea0546eecb8a150fb68049c922be049f19a24278a9fe13c35eea2 | R | 5,922 | 128 | ##' @importFrom ggplot2 ggplot
##' @importFrom ggplot2 aes
##' @importFrom ggplot2 geom_bar
##' @importFrom ggplot2 element_text
##' @importFrom ggplot2 geom_text
##' @importFrom ggplot2 theme
##' @importFrom ggplot2 scale_fill_gradient
##' @importFrom ggplot2 xlab
##' @importFrom ggplot2 ylab
##' @importFrom ggplot2 y... |
bf108e38a59a74044e8c710e3237c10b443697eeee3393ce438ad526ea813095 | R | 5,925 | 132 | test_that("ORA uses the supplied effective universe for its hypergeometric test", {
annot <- data.frame(
GeneID = c("A", "B", "B", "C", "D", "E", "F"),
Term = c("T1", "T1", "T2", "T2", "T3", "T3", "T3")
)
query <- c("A", "B")
expected_p <- c(T1 = stats::phyper(1, 2, 2, 2, lower.tail = FALSE),
... |
32a48d0da9b8651f93998ec361c96924b14ebe19e1e72e9ff01df4901eb5dc24 | R | 5,932 | 197 | #R code to generate the Sankey graph in Figure 8 panel B of the 2021 Jasek et al Desmosomal connectome paper
source("code/Packages_and_Connection.R")
MN_names <- c(
"MNacicX", "MNchae", "vMN1-2",
"MNbow", "MNwave", "MNbiramous",
"MNhose", "MNcrab", "MNladder",
"MNspider type1", "MNspider type2", "MNring"
)
... |
9f4659edf9bf12696fef66d446486a648e6a52d578b07336fd96367aa3600f33 | R | 5,943 | 148 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2020 Robert McCulloch and Rodney Sparapani
## read.trees.R
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of t... |
cab3a3f505b87aadc53ca94a6e487ebaebad0650c9f7a35f20ffc4dfa29d4ae3 | R | 5,954 | 129 | #R code to generate Figure8-figure-supplement1 of the Jasek et al. Platynereis desmosomal connectome paper
#Uses Natverse and accesses the data on catmaid
#Gaspar Jekely
source("code/Packages_and_Connection.R")
# Motoneuron plotting -------------------------------------------------------------
MN_names <- c("MNacic... |
692512961fc5071ad91233b755a7e750dd6c378fdf039885abd4caee086bfdcf | R | 6,002 | 185 | ---
title: "Whole-brain comparison of rodent and human brains using spatial transcriptomics"
subtitle: "Allen Mouse Brain Atlas legend slice"
author: "Antoine Beauchamp"
date: 'March 14th, 2022'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE,
include = FAL... |
b3d613b0b3dabeaad54462f545386ff514aa143e2942f56ba30252b1c4f46a98 | R | 6,010 | 122 |
library(ggplot2)
library(hrbrthemes)
library(gridExtra)
experimentsFolder <- "/Volumes/Bathyscaphe/Mask RCNN for Kelp Detection/Experiments/" # "/Volumes/Jellyfish/Dropbox/Manuscripts/Convolutional Neural Networks for kelp canopy identification/Results/Experiments/"
resultsFolder <- "/Volumes/Jellyfish/Dropbox/Manusc... |
cdb706c5a02d6d9e1d8fadc38e5a0c12e860a31173e8954bb6f8dc7dbf44e434 | R | 6,011 | 256 | ## ----------------------------------------------------------------
## Re-exported dplyr / magrittr functions
## ----------------------------------------------------------------
#' @importFrom magrittr %>%
#' @export
magrittr::`%>%`
#' @importFrom magrittr %<>%
#' @export
magrittr::`%<>%`
#' @importFrom dplyr filte... |
7d7a70c5e08fe7c30b803cd666469955faff3eac43d23859b6d3d0abbd9d6d61 | R | 6,013 | 118 |
require(mgcv)
require(itsadug)
require(brms)
options(show.signif.stars=FALSE)
load('rt_data.rda')
############
### GAMM ###
############
summary(gam.model <- gam(RT.log ~
(Type + Llama_F + SRT) * Condition +
TrialOrder.z +
s(Item, bs='re') +
s(TrialOrder.z, Participant, bs='fs', m=1),
data = rt... |
dc5d00b195ceb7acf980f123ada5c21a5449ad87a239f5de3feb27fd2b6c48c3 | R | 6,017 | 141 | #!/usr/bin/env Rscript
################################################
################################################
## REQUIREMENTS ##
################################################
################################################
## DIFFERENTIAL TRANSCRIPT EXPRESSION ANALYSIS
... |
234fd79c3461ba3f7c1d008b5ea6f9da5361ae111d2666cb60d256554c306eff | R | 6,029 | 236 | # @DESCRIPTION: filename
# Library -----------------------------------------------------------------
library(magrittr)
library(ggplot2)
library(patchwork)
library(rlang)
# src ---------------------------------------------------------------------
source(file = "analysis/2022-07-20/script/utils.R")
# Load data ----... |
8b5ad2cba9833f342c61e8ca9f16933b90c0223f0924d5db51073bc1feca4553 | R | 6,053 | 198 | ###############################################################
# Define the tests
###############################################################
testgrouplinear = function(y, X, ALTERNATIVE = 'greater')
{
var.names = c("INTERCEPT", "TIV", "GROUP", "TRAINING", "GROUP_x_TRAINING")
contrast.names = c("GROUP_x_TRAI... |
43bcba4ff884163721204f268dd1af9ec5d56a25ed0652a9ad06d0894b4c2213 | R | 6,059 | 149 | #packages to source and details of CATMAID connection
rm(list = ls(all.names = TRUE)) #will clear all objects includes hidden objects.
gc() #free up memory and report the memory usage.
Sys.setenv('R_MAX_VSIZE'=8000000000)
# load nat and all associated packages, incl catmaid
library(nat)
library(catmaid)
options(nat.... |
5cb1c991146cab04f63646de91d547f99ace233775db08cf5f780d825b70255e | R | 6,070 | 156 | #' Read gene sets from a GMT file
#'
#' Reads a Gene Matrix Transposed (GMT) file and returns an Annot object
#' for use with \code{enrich()} or \code{richGSEA()}.
#'
#' @param file path to a GMT file
#' @param species species name (default: "custom")
#' @param keytype gene ID type in the file (default: "SYMBOL")
#' @r... |
13fe4d6d0f590603431fabd343cd328162fb19193923d09176399525e74e77fe | R | 6,156 | 177 | library(ggplot2)
library(tidyverse)
figPath = "Z:/projects/Single-cell modeling/figures/"
setwd("C:/Work/MatlabCode/projects/SingleCellModeling/SingleCellModeling")
############################################
# Fig 1B - Gene essentiality
############################################
# load data
fns = c("d... |
b1dcb4a007fc2ec7ca5a1cfb1aeaa9833209b2368ac3489cb32d36a4f06f72f9 | R | 6,170 | 150 | library(ggplot2)
library(glmnet)
library(LSD)
a=read.csv("experimentResults.csv")
round(a,3)
results = data.frame(tests = c("B vs B5","B vs BO","B vs B3","B vs BC","BC vs BCO","BC vs B5C",
"B3 vs B3M","BC vs BC3M","BC3M vs BC3MS","BC3M vs BC3MD","BC3MS vs BC3MSD"), pvals =
c(t.test(a$B, a$B5, paired=T,alternative='le... |
9c7d2ae26e70da5d15316c551acee167b81e0cbe1f88580079201ff7716af21f | R | 6,179 | 197 | # Shared helpers for the characterization baseline (test-characterization-*.R).
#
# The characterization tests pin the 0.1.5 behaviour of richR on small
# committed fixtures (tests/testthat/fixtures/, regenerated by
# fixtures/make-fixtures.R). Every pinned value is recorded with
# expect_snapshot_value() / expect_snap... |
dd1a8269601e5deee8eb404148dedc02cbf962b926979c0320441963f0137606 | R | 6,215 | 153 | describe("Testing forest_plot function", {
skip_on_cran() # skips all tests below if env var NOT_CRAN=false
skip_on_ci() # skips all tests below if env var CI=true
np_dataset_1 <- generate_non_proportional_dataset(seed = 1)
np_dataset_2 <- generate_non_proportional_dataset(seed = 2)
np_dataset_3 <- generate_... |
ba2a338a9f390b8bb90619647bcb33c3a183faf8fae26d3cf31c5d6e4b29838c | R | 6,248 | 153 | # Characterization baseline (REQ-004): richLevel(), compareResult(),
# compareGSEA() (padj = NULL, and non-NULL padj after the SPEC-RICHR-002 B1 fix)
# and richCluster(), as produced by richR 0.1.5. Known defects are pinned as
# they stand (REQ-005): a later fix must show up as a snapshot change.
compare_query_a <- fu... |
7808d0b67e9819e6a81cf8dd60155d8fe6ea6d62a6dfc8670a2a598a0f252eb0 | R | 6,259 | 158 | #' Create 5-layer Keras Model and Fitting Datasets
#'
#' @inheritParams keras::compile.keras.engine.training.Model
#' @inheritParams keras::fit.keras.engine.training.Model
#' @param data_list A `list` containing predictor and label matrix of training data and test data.
#' Please use [prepare_data] to generate this.
#'... |
3c88f45bd33ae19d59157c7209565511e82ae6399f966cf0818015f53141984b | R | 6,288 | 166 | # This script contains helper functions and a data loading flow
# Will load summarized data from Stage I integration, together with user input to plot.csv, samples.csv, and plot.yaml
# Note - dots is a summarized data.frame for count plot only;
# dots.full is non-summarized data.frame with all the columns; howev... |
951662d9d512f859fa9d5116785e40dd979ea2939da3526edd489e4b5a848001 | R | 6,291 | 222 |
source('evaluate.R')
DataPath='Zeisel_exp_sc_mat.txt.csv'
LabelsPath='./Zeisel_exp_sc_mat_cluster_original.txt.csv'
CV_RDataPath='./OUT/CV_folds.RData'
OutputDir='./OUT_REV_NOI/'
GeneOrderPath = NULL
NumGenes = NULL
#scPred Seurat 2
###################################
source('./scRNAseq_Benchmark-master/Scripts/rev... |
c2ea968d870b6193ce4a6149b5b7b39f2fd26f06ca0c04b95b17928b57ea9ef1 | R | 6,307 | 162 | library(ggplot2)
library(glmnet)
library(LSD)
a=read.csv("experimentResults.csv")
round(a,3)
results = data.frame(tests = c("B vs B5","B vs BO","B vs B3","B vs BC","BC vs BCO",
"B3 vs B3M","BC vs BC3M","BC3M vs BC3MS","BC3M vs BC3MD","BC3MS vs BC3MSD"), pvals =
c(t.test(a$B, a$B5, paired=T,alternative='less')$p.value... |
7673813d36adba56f9013b15f94e9197c78ea9c42817d46b71cb077443e2e4f4 | R | 6,332 | 91 | # Load data
source(here::here("code","01_load_data.R"))
# Set theme
gtsummary::theme_gtsummary_compact()
# Create table
patientchars <- PWAWM %>%
dplyr::select(age, sex, handedness, education,
stroke_type, stroke_laterality, stroke_history, stroke_area,
stroke_size,... |
e5907fb4317d9516eaf36d7bd3873a479f26198cfba66d23dce47efcf5e7b58c | R | 6,400 | 174 | ## ----------------------------------------------------------------
## Kappa-related helper functions
## ----------------------------------------------------------------
#' Compute kappa statistic between two gene sets
#' @param x comma-separated gene string
#' @param y comma-separated gene string
#' @param geneall v... |
b9fa50bea056df71044a7376e1693e689304a0523d78729f4060938d2c3c34ed | R | 6,432 | 111 | # ----------------------------------------------------------------------------
# create_neuro_pairs.R
# Author: Antoine Beauchamp
# Created: August 4th, 2021
#
# Description
# -----------
# This script defines a set of canonical neuroanatomical pairs between the
# mouse and human brain atlases
# Packages -------------... |
34613fa776280a73ffb7dee5f0371deb496bc489257284f95829a9745367d4a1 | R | 6,497 | 262 |
library('Seurat')
library(dplyr)
load('this.RData')
ls()
exp_ref_mat=a
#########PCA########
pca=prcomp(exp_ref_mat,scale=T,center=T)
PC1=pca$rotation[,1]
PC2=pca$rotation[,2]
xmin=min(PC1)-0.1
xmax=max(PC1)+0.1
ymin=min(PC2)-0.1
ymax=max(PC2)+0.1
LABEL=colnames(exp_ref_mat)
plot(PC1,PC2,xlim=c(xmin,xmax),ylim=c(ym... |
ac488d9c81a7ec1821a981aba2876f07dbeb1bdbe912a3049c053b0a35f268c7 | R | 6,601 | 144 | # setup -------------------------------------------------------------------
library(tidyverse)
library(rstatix)
library(webr)
library(ggstatsplot)
library(ggcorrplot)
library(formattable)
library(webshot)
library(htmltools)
setwd("~/Desktop/Neurobiologia/projects/ongoing/verbal.irony/discrepancy/experiment.1/")
# read... |
96cbd664ad6a5d03b8449cc8507d119ce0a024a6f9cec69df76bfbe47fb7684e | R | 6,606 | 231 | #a=read.table('Zeisel_exp_sc_mat.txt',header=T,row.names=1)
#write.table(t(a),'Zeisel_exp_sc_mat.txt.csv',quote=F,sep=',',row.names=T,col.names=T)
#a=read.table('Zeisel_exp_sc_mat_cluster_original.txt',header=T,row.names=1,sep='\t')
#write.table(a,'Zeisel_exp_sc_mat_cluster_original.txt.csv',quote=F,sep=',',row.names=T... |
4200ca2e0a0be17731ef4d5597207e4ce7abfa240562604ff8684ab8b9bfa1de | R | 6,642 | 134 | #' Plot compare heatmap of Enrichment result among DEG groups
#' @importFrom dplyr full_join
#' @importFrom dplyr arrange
#' @importFrom ggplot2 ggplot
#' @importFrom ggplot2 geom_tile
#' @importFrom ggplot2 scale_fill_gradient2
#' @importFrom ggplot2 theme
#' @importFrom ggplot2 theme_minimal
#' @importFrom ggplot2 co... |
283a05d8ed565af94223b43d77d9e2be6a5fbea06bcf0cc65668c86bd4279533 | R | 6,688 | 147 | # 07/23/21: Ran through with no errors.
# Only parameter to edit- "save" if you want to save your plots or not. Right now have set to FALSE to prevent overwriting
library(tidyverse)
library(ggplot2)
library(here)
###################
### PARAMETERS ####
###################
# TO EDIT: Would you like to save these plot... |
d869c63d291e955edf1034551b8419d599ffd8a9540f1521c4d1f09fc3a356b1 | R | 6,812 | 186 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017-2020 Robert McCulloch and Rodney Sparapani
## ip.gbart
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of ... |
558483d3dabce1e42814c4f691766cbb12cfec7745772b1b227b71355d0f4d5c | R | 6,834 | 147 | setwd(
"~/Dropbox (ASU)/Placenta/BATCH2_PLACENTA_DECIDUA_ANALYSIS/HISAT_FeatureCounts/batch1_and_batch2/"
)
library(reshape)
library(ggpubr)
library(ggpubr)
library(dplyr)
library(lattice)
library(car)
library(splitstackshape)
library(ggrepel)
# read in data
# df_pvals_decidua_ImmuneDBgenes.txt
# df_pvals_term_Immun... |
5ab51ecca164dc2c0e99f368079687d44a06dbde9aa43b8223df5add9cef04fa | R | 6,857 | 157 | #' Segment features
#'
#' This function performs image segmentation.
#' Returns a list object.
#' @param input file path to a valid monochrome 16-bit TIFF.
#' @numthresh unsigned integer. Number of threshold that should be used. Default is 8. Increase to pick up more fine grained intensity differences if cellbodies exi... |
31cf52b082baced491256c4e0a70e4f5383690ba38e08a5030ffca212cbd8151 | R | 6,860 | 219 | library(biomaRt)
library(tidyr)
library(ggplot2)
ensembl = useMart(host='uswest.ensembl.org', biomart='ENSEMBL_MART_ENSEMBL', dataset='hsapiens_gene_ensembl') #ensembl75 feb2014.archive.
annotations = getBM(attributes=c('ensembl_gene_id', 'external_gene_name','refseq_mrna', "chromosome_name"), mart = ensembl)
head(ann... |
900372ee29f8464a5c49bce893ec0bf329bcae6803b3d6dfdb45e40a7b5f36a0 | R | 6,928 | 165 | #! /usr/bin/Rscript
### Data normalization (Livak's method) ###
######################################## Reference ########################################
# "Analysis of relative gene expression data using real-time PCR and the 2^-ââCt method"
# Kenneth J. Livak and Thomas D. Schmittgen
# Methods, 25, 402-4... |
59b6a5eb18535e0ed4ef0b8fd80cbd5b8a80a37bef8fed21c517e4677f36994e | R | 6,934 | 170 | # Annotation identity (SPEC-RICHR-003 REQ-001 .. REQ-005).
#
# One identifier rule for every builder, ORA and GSEA: the gene is column 1;
# the identifier is the first of GOALL, PATH, Module, Term present (else column
# 2); the label is the column named Annot. ORA results report the identifier in
# `Annot` and the labe... |
83de2b33dc77af6f1caf6ac8f25f32d1ecc560968d89ddd6e65df4252c6c7cc6 | R | 6,934 | 196 | library(ggplot2)
library(scales) # for muted function
library(tidyverse)
figPath = "Z:/projects/Single-cell modeling/figures/"
setwd("C:/Work/MatlabCode/projects/SingleCellModeling/SingleCellModeling")
library(R.matlab)
##################################
# Structural comparison
##################################
S_... |
1b5c833438b0157f3a0898ed1f5b1c05c3be94981c670cde06a822e61976472d | R | 6,942 | 207 | #' @include internal.R
#'
NULL
#' scVI Integration
#' @param object A \code{StdAssay} or \code{STDAssay} instance containing
#' merged data
#' @param features Features to integrate
#' @param layers Layers to integrate
#' @param conda_env conda environment to run scVI
#' @param new.reduction Name under which to store r... |
c13f0afe4f469e21dc5eb2f8dad27ff7a7c435aaf43450c81dd239f58247747b | R | 6,980 | 166 | #' Pre-processing function for sex classification
#'
#' The purpose of this function is to process a single cell counts matrix into
#' the appropriate format for the \code{classifySex} function.
#'
#' This function will filter out cells that are unable to be classified due to
#' zero counts on *XIST/Xist* and all ... |
e4be2558eb3c10d58ce2792b14d5bd7c37201ab8e0f2feacd67852756b6d08d5 | R | 7,002 | 236 | # This only include the knockoff filter to computer FDR and Power
# This master R file includes all the R functions for implementing the knockoff filter
# in high-dimensional linear regression with precision matrix of covariates estimated
# by the innovated scalable efficient estimation (ISEE) procedure introduced in... |
56684a56475bca895b04563b4144415fd21cc8a629b446b1ee7a052a1b04ba1a | R | 7,012 | 226 | #' @include internal.R
#'
NULL
# Runs Wilcoxon Rank Sum using the Presto package
#
# @param data.use Data matrix to test
# @param cells.1 Group 1 cells
# @param cells.2 Group 2 cells
# @param verbose Print a progress bar
# @param ... Extra parameters passed to wilcox.test
#
# @return Returns a p-value ranked matrix of... |
6fdc05901fe1aab744c86ab5afaa041c953dcb7fe38afcd34e7894e2616c1ce8 | R | 7,026 | 155 | #!/usr/bin/env Rscript
################################################
################################################
## LOAD LIBRARIES ##
################################################
################################################
library(optparse)
library(ggplot2)
library(reshape... |
81774ca4a7600c0bec20aec6ae785f41ec2586b8fb90985b01a17c1d80e989f1 | R | 7,064 | 164 | batch_test_annotation <- function() {
data.frame(
GeneID = LETTERS[1:6],
Term = rep(c("T1", "T2", "T3"), each = 2),
Annot = rep(c("Term one", "Term two", "Term three"), each = 2)
)
}
batch_test_result <- function(genes, cutoff = 0.1) {
suppressMessages(richR::enrich(
genes, batch_test_annotation(... |
1e7953da3bc2958b60e1a90b45b7fdc559b04eb3d6af67ba00ab3e3056a3986a | R | 7,086 | 161 | library(ggplot2)
library(scales) # for muted function
library(tidyverse)
figPath = "Z:/projects/Single-cell modeling/figures/"
setwd("C:/Work/MatlabCode/projects/SingleCellModeling/SingleCellModeling")
library(R.matlab)
##################################
# Structural comparison
##################################
m... |
a755e5dc94fa603e7fc8a72caceda97d6594833a29071a9f781bf305af95d25e | R | 7,151 | 169 | #Gating & Clustering
# START OF GATING
# all gated data set together
master_data_all$cluster_id = 0
gateNames = c("Ungated","oligodendrocytes","astrocytes","microglia","endothelial","Dopaminergic-TH","Inhibitory-VGAT")
## To Gate Population. ## if not running FlowSOM
gatedIndexes = c()
## visualization
master_data_... |
c3b1f1d3f534562d1b081f7d1962a44b75d99af4e646a4bff2f1b85be536dca2 | R | 7,158 | 184 | # TODO add proper documentation
print_greeting_header <- function() {
dabest_ver <- utils::packageVersion("dabestr")
line1 <- paste("DABESTR v", dabest_ver, sep = "")
now <- Sys.time()
now_hour <- as.integer(format(now, "%H"))
if (now_hour < 12) {
greeting <- "Good morning!"
} else if (now_hour >= 12... |
93dc6433089d4e3da5fbdabfd1e81c407c58ba75be9acc8c89eae637f1617d3d | R | 7,241 | 170 | # Shared fixtures of the theme tests (SPEC-RICHR-007), built from base R,
# testthat and the public API, with no network.
#
# F-TH two hand-built ORA groups (no tested table): g1 reports A..F, g2
# reports D (another Term) and G (Term NA). Kappa A-B = A-C = D-E =
# 0.8224, B-C = 0.6447, every other pair... |
1496e11b300bfc47901134c5c563693ec27afc2ee749287ffd0dd9e92d6693cb | R | 7,247 | 174 | wd <- getwd()
library(Seurat)
library(dplyr)
library(ggplot2)
RG_4884_03 <- Read10X('Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_CellRangerFiles/Day_0/4884-RG-3/filtered_feature_bc_matrix')
RG_4884_04 <- Read10X('Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_CellRa... |
c24578433153c1a3913da159dfeac6cd582191e3adb32ebe18c4a89a3b837f84 | R | 7,254 | 187 | describe("Testing effect_size functions", {
# pre-loading all dataset types
np_dataset <- generate_non_proportional_dataset()
p_dataset <- generate_proportional_dataset()
describe("Given valid params", {
test_that("it should create dabest_effsize_obj with no issue", {
dabest_obj <- dabestr::load(data... |
220a8ab5099d18f2dafc490f46adc3adec26a8b685fec061d57e690acee63457 | R | 7,332 | 149 | ### LIBRARIES ###
libs <- c('ggplot2','RColorBrewer','reshape2','devtools',
'pheatmap','flowCore','plyr','scales','coin','grid',
'gridExtra','tidyr','randomcoloR','viridis','tidyverse','ggpubr')
for (L in libs){
if(!require(L, character.only = T)) {
install.packages(L, dep = T, quiet = T)
... |
b5c3e5971315d376a8abf4b447f0e6a864017136e92c578c03a1ce1ac83ab04e | R | 7,399 | 241 | library(tidyverse)
setwd("C:/Work/MatlabCode/projects/SingleCellModeling/SingleCellModeling")
metaDat = read_tsv("F:/GTEx/GTEx_Analysis_v8_Annotations_SampleAttributesDS.txt")
tissues = unique(metaDat$SMTSD)#55 types
length(metaDat$SMTSD)#22951
#grab 5 of each
RNASeq = read_tsv("F:/GTEx/GTEx_Analysis_2017-06-05_v... |
c42f0466ac5e4faed0c30a86f891b6ac3c34857757e001c1b03ddbca58ebbc98 | R | 7,419 | 121 | ##### object_image_writer ##### - Repeat for Hi, Med, and Ctrl Conditions
# Working with post-ez_segmenter data ann deepcell (MATLAB GUI generated) for single object image creation
# Author: Bryan Cannon 2020 (multiple code snippets taken or built from DM, FH, EFM, DT)
# install and / or load ez_pkgs if you have... |
6913ad62f3c21c0afaad51d4dce75f724f2e8751110fe95a3aadc6ad9ac950ce | R | 7,443 | 156 | ---
title: "Batch-corrected imputation using magicBatch"
author: "Kevin Brulois"
output: html_document
date: "2024-05-29"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Introduction
Marcov Affinity-based Graph Imputation of Cells (MAGIC) is an algorithm described by [David Van Dijk, et al]... |
825b331446cc72410c089a1f73603cf8185ff29b8585131c08534b5eb8833ad7 | R | 7,443 | 267 | #a=read.table('Zeisel_exp_sc_mat.txt',header=T,row.names=1)
#write.table(t(a),'Zeisel_exp_sc_mat.txt.csv',quote=F,sep=',',row.names=T,col.names=T)
#a=read.table('Zeisel_exp_sc_mat_cluster_original.txt',header=T,row.names=1,sep='\t')
#write.table(a,'Zeisel_exp_sc_mat_cluster_original.txt.csv',quote=F,sep=',',row.names=T... |
82778cdfab87b120feaf4c078e6406dbc9b5d86ba8e94c0afb20193ef10e3a92 | R | 7,452 | 156 | # AC-001 .. AC-004 (SPEC-RICHR-006 REQ-001, IC-1): a table plot draws exactly
# the rows its selection keeps and, when that drops rows, signals exactly one
# `richR_plot_selection` message per call naming the counts and the rule.
# The richR_plot_selection conditions signalled while evaluating `expr`, and
# the value;... |
c05b0abac261dee39f7b561b09f5e39aaca1d5e450c6b1fa61118d8706e6c341 | R | 7,468 | 179 | # setup -------------------------------------------------------------------
library(tidyverse)
library(rstatix)
library(webr)
library(ggstatsplot)
setwd("~/Desktop/Neurobiologia/projects/ongoing/verbal.irony/discrepancy/experiment.1/")
# reading data ------------------------------------------------------------
experim... |
9d76a0b19e6ebd8730094117b21828cf65e3642b7ee1264cef56c21a6543a594 | R | 7,476 | 250 | ---
title: "Visualization of gene expression with Nebulosa"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: true
toc: true
toc_depth: 3
html_document:
df_print: kable
theme: united
---
```{r r setup, include=FALSE}
knitr::opts_chunk$set(
tidy = TRU... |
f3605965dbe0b8e399a7cd15c10334958ca521078c23851682ff4220f52cf188 | R | 7,638 | 156 | if (F){
"
This script implements all QC plots except the batch faceted count plot.
It requires Stage I integration outputs dots.csv, samples.csv.
The following plots will be generated:
1. per sample basis, intensity of the recognized & overlapped FISH dots
2. per sample basis, residual of the ... |
5e7ae88befe6f69d3008d80cc728ae82b6651aa5f55315294b98af7e92b24d19 | R | 7,651 | 185 | # Aitchisons Distance Analysis
####### Load PhyloSeq objects #######
source("src/load_phyloseq_obj.R")
source("src/miscellaneous_funcs.R")
beta_diversity_summary <- function(x, z, dist = "Aitchisons", tag){
# # TROUBLE
# dist = "Aitchisons"
# load("files/Phyloseq_objects_Woltka/Species_PhyloseqObj.RData")
... |
84ac3492b057bf40e75e85c6bfccea2a78ecbf0b7c126ca62a7ead1264d71dd6 | R | 7,673 | 185 | wd <- getwd()
library(Seurat)
library(dplyr)
library(ggplot2)
RG_3088_01 <- Read10X("Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_CellRangerFiles/Day_1/3088-RG-1/filtered_feature_bc_matrix")
RG_3088_02 <- Read10X("Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_CellRa... |
238eaeb10f253be5347e15adb3af7033b85b85a95c0f9c9c1b60fabc6dc2b891 | R | 7,686 | 179 |
randomized.svd <- function(A,K, q, method = 'rsvd', mkl.seed = -1) {
out <- setNames(vector("list", 3), c("u", "d", "v"))
if (method == 'rsvd') {
out <- rsvd::rsvd(A,K,q=q)
}else if (method == 'rsvd-mkl') {
requireNamespace("fastRPCA")
fastPCAOut <- fastRPCA::fastPCA(A, k=K, its=q, ... |
238130b9e7f30112c88afc428af1f0fb6ac57c6e1e9af6951f7371fcf0b09d76 | R | 7,693 | 203 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017-2020 Robert McCulloch and Rodney Sparapani
## ss.gbart
## This program is free software; you can redistribute it and/or modify
## it under the terms of the GNU General Public License as published by
## the Free Software Foundation; either version 2 of ... |
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