sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
d7cf403b2ed0187b279f1d273e1788bcc3f3e91b4c1acde47b06b59be8f8faec | R | 2,029 | 43 | test_that("KEGG falls back to term IDs for missing or blank labels", {
annotation <- data.frame(
GeneID = c("A", "B", "C", "D"),
PATH = c("99999", "99999", "10000", "10000")
)
object <- new("Annot", species = "custom", anntype = "KEGG",
keytype = "SYMBOL", annot = annotation)
lookup_with... |
c7df14afd0e51a5e43081cf2db7482d66a2f6c4031e1150e7d7c91cbd02eb88d | R | 2,037 | 65 | install_ez_packages <- function(answer = T) {
if (answer == T) {
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install(pkgs = c("tidyverse", "R.matlab", "tidyr", "rlang", "flowCore", "ks", "flowVS", "flowViz", "RColorBrewer", "gtools", "gplots",
... |
4c2193414006a8d35f39522261f6744d46e58eee8ab5f4e1d5f90bf84a922382 | R | 2,063 | 72 | # Formulas used
#
# Contains functions `sigmoid`, `flipped_sig`, `calculate_group_variance`, `calculate_weighted_delta`, `confinterval` and `var_w_df`.
# TODO Add documentation
sigmoid <- function(x_from, factor, y_from, y_to, smooth = 5.5, n = 300) {
x <- seq(-smooth, smooth, length = n)
y <- exp(x) / (exp(x) + 1)... |
3ab1a0d85e0ec64e4a1b70ef5660f1acd15fd0d17ca87457fad05dc8382f2eba | R | 2,075 | 48 |
library('Seurat')
exp_sc_mat=read.table('./Benchmark/Zeisel_exp_sc_mat.txt',header=T,row.names=1,sep='\t')
exp_ref_mat=read.table('Reference_expression.txt',header=T,row.names=1,sep='\t')
sc=CreateSeuratObject(raw.data = exp_sc_mat, min.cells = 0, min.genes = 0)
ref=CreateSeuratObject(raw.data = exp_ref_mat, min.c... |
5a6389192250670feea2c68087b9cd924e8a62c7367c20f5436e2975677d4bdc | R | 2,079 | 68 | # Libraries
library(ggraph)
library(igraph)
library(tidyverse)
library(RColorBrewer)
##data
data <- read.csv("./mental_8_GatLogit_normLong_R1_norm.csv")
dis_id <- data.frame(ids = unique(c(as.character(data$dis1), as.character(data$dis2))))
edges <- data.frame(from = "origin", to = dis_id)
vertices ... |
71f3fed649f70ceb7951edba0b6a81ef6aa3d8af184c8a449f27f5986c76413b | R | 2,080 | 81 | # Helper to create a test richResult
make_test_result <- function() {
new("richResult",
result = data.frame(
Annot = c("GO:0001", "GO:0002"),
Term = c("apoptosis", "cell cycle"),
Annotated = c(100, 200),
Significant = c(10, 20),
RichFactor = c(0.1, 0.1),
FoldEnrichment = c(2.0,... |
7990c8147fc433fb1d076894c21f1c7c7fad8a22ee657971f2f417ce00f693d8 | R | 2,099 | 67 | #source("http://bioconductor.org/biocLite.R")
#biocLite()
#install.packages("devtools")
#devtools::install_github("cole-trapnell-lab/monocle-release@develop")
#biocLite(c("DDRTree", "pheatmap"))
library(monocle)
load('OligTumor.RData')
COL=c()
i=1
while(i <=length(pbmc@ident)){
this_col=which(colnames(pbmc@raw.da... |
fa7f098a0216232989a35e69a05bb78b3b1dc6856581f8ff43fee90c7ff3f0f4 | R | 2,110 | 46 | ## data-raw/module.R -- rebuild data/module.rda (object `module`).
##
## Last run for richR 0.2.0 (SPEC-RICHR-003 M3.6). Running this script replaces
## data/module.rda with a fresh KEGG snapshot, so run it only when an
## annotation refresh is intended. The result depends on live KEGG on the day
## it runs and is not ... |
b3899eef37370ccd35c82684c76e2bc6c452d85b93a60e59ab2e2467ccfcaf24 | R | 2,117 | 40 | setwd("/public-supool/home/zhchen/XS/merge")
rm(list = ls())
library(Seurat)
library(patchwork)
library(dplyr)
##### load data############
path_way <- c("/public-supool/home/zhchen/XS/data/Mouse_Hyp_male_Ctrl_0520_12_20-1",
"/public-supool/home/zhchen/XS/data/Mouse_Hyp_male_Ctrl_0520_12_20-2",
... |
e689a180669618cab29563a9ab0b8be785292772d5d64d5bf20e3f8bcdf4f4a0 | R | 2,118 | 57 | skids_from_connector <- function(connector_id, pid) {
cat_path = paste("", pid, "connectors", connector_id, sep="/")
connectors_response = catmaid_fetch(cat_path)
skid1 = connectors_response$partners[[1]]$skeleton_id
connector1_type = connectors_response$partners[[1]]$relation_name
if (length(connectors_respo... |
c67b8e8d46010a657f83a95799bba6b6017f1ee3d59928559f303669aa51a8a1 | R | 2,119 | 66 | library(Seurat)
load('pbmc.RData')
source('scRef.R')
########
current.cluster.ids <- c(0, 1, 2, 3, 4, 5, 6, 7)
new.cluster.ids <- c("CD4 T cells", "CD14+ Monocytes", "B cells", "CD8 T cells",
"FCGR3A+ Monocytes", "NK cells", "Dendritic cells", "Megakaryocytes")
pbmc@ident <- plyr::mapvalues(x = pbmc@ident, from =... |
e1b528ba4e4d67ab9875623c0ec9c6bd983f3ccbbaded70ec925ce75fc84309e | R | 2,137 | 63 | #' Construct A Batch of Keras Models
#'
#' @inheritParams modeling_and_fitting
#' @param param_combination A parameter `matrix`/`data.frame` with each row representing the parameters
#' for run Keras model once. Column names should indicate parameter names and should be same as in modeling function.
#' [base::expand.gr... |
3d891d6f29ef6d4bf1dc6505e8753402a7bbc2bd44295d26a15a305fa1c3bd96 | R | 2,144 | 57 | library(devtools)
library(RColorBrewer)
library(plyr)
source_url("https://raw.githubusercontent.com/obigriffith/biostar-tutorials/master/Heatmaps/heatmap.3.R")
c = readme("all_HLs_human.txt.gz", gzip=T)
c$external_gene_name=NULL
code=as.factor(unlist(lapply(strsplit(colnames(c[,2:ncol(c)]),"\\_"),function(x) x[1])))... |
97aa22482f5381115bcaa08f69e307ddd5542b31e81c4fb0c78d80044519822f | R | 2,191 | 77 | #' Non-proportional data for Estimation plots.
#'
#' Contains 3 Control Samples and 6 Test Samples.
#'
#' @keywords internal
#' @format A data frame with 180 rows and 4 variables:
#' \describe{
#' \item{Gender}{Gender of each observation}
#' \item{ID}{Identity of each observation}
#' \item{Group}{Whi... |
6ca8ad34a1391a6afe2e918f2ca50925d0e85fd90ada130621f49e6554328461 | R | 2,199 | 52 | # Function to generate a p-value for the spatial correlation between two parcellated cortical surface maps,
# using a set of spherical permutations of regions of interest (which can be generated using the function "rotate_parcellation").
# The function performs the permutation in both directions; i.e.: by permute bo... |
dcd00aaff33866e3f89f2ab91aeccdb4213eb826a68aab93b057bcc847fc111f | R | 2,209 | 57 | #! /usr/bin/Rscript
library(conflicted)
### Opening file ###
#' Make sure you are in the correct working directory (use getwd() and setwd() functions)
#'
#' enter your csv file name (e. g. "file.csv")
#' @param skip = 11 : allows to skip the first eleven rows of the csv file (contain infos about your Biomark run)
#'... |
47fc70ab93230baa1dfb7561b320c01c6f4f9faf208da8cec47d244178274bc5 | R | 2,225 | 81 | #This code extracts the data from a huge tsv file and saves it as a sparse matrix
library(tidyverse)
memory.limit(size = 100000)
fig__dir = "D:/SingleCellModeling/MetAtlas/Figures/"
#use the data.table package, it is better for reading large datasets
#install.packages("data.table")
library(data.table)
Sys.setenv("VRO... |
372611001b20c41de56e41d72ca715858e3d8d216e22ea4ea1f97678f12b885c | R | 2,231 | 48 | ## data-raw/kegg.R -- rebuild data/kegg.rdata (object `kegg.db`).
##
## Last run for richR 0.2.0 (SPEC-RICHR-003 M3.6). Running this script replaces
## data/kegg.rdata with a fresh KEGG snapshot, so run it only when an
## annotation refresh is intended. The result depends on live KEGG on the day
## it runs and is not b... |
daf5fd3b2a19a95e81de18993deabbf4760b6d77f771a93ca48794a598158c11 | R | 2,237 | 67 | # Load libraries --------------------------------------------------------------
# Assuming these packages are already locally installed
library(here)
library(dplyr)
library(stringr)
library(tidyr)
library(labelled)
library(VIM)
library(mice)
library(ggstatsplot)
library(lavaan)
library(multicon)
library... |
49e66a6e58e1c62c8b79ad785bd2c179188ff33eab33e8f917dadb3d0d71de2b | R | 2,238 | 52 | ###
### Process the annotation data of the Infinium probes.
### All data was downloaded from:
### Wanding Zhou, Peter W Laird & Hui Shen
### Comprehensive characterization, annotation and innovative use of Infinium DNA
### methylation BeadChip probes
### Nucleic Acids Research 45, e22 (2016)
### https://academic.oup.co... |
39b66e593fc9a40ae31b716ab6075ebf1f79bd30844f5e7f2623cefd0e2564bb | R | 2,244 | 56 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2020 Robert McCulloch and Rodney Sparapani
## write.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 ... |
4eb1cb99e34f1107440dbc09c48f642ae5138f0a31e8274d5d63e6e7920310b7 | R | 2,261 | 76 | library(rhdf5)
library(ggplot2)
library(LSD)
file = args[1]
getvals <- function(dir, pattern, filter){
files = list.files(path=dir, pattern=pattern, full.names=T, recursive=T)
files = files[grep(filter, files)]
table <- as.data.frame(do.call("rbind", lapply(files, FUN=function(file){
if (grepl("preds", file))... |
357e704576aebfaf0e9f877b11a490259656e94ec76b8f914daa3f628b2ae35e | R | 2,267 | 78 | # Feature Correlation Analysis
source("src/load_packages.R")
source("src/miscellaneous_funcs.R")
source("src/metadata_prep_funcs.R")
base::load("files/Phyloseq_objects_Woltka.RData")
#_______________________________________________________________________________
##### Correlation Analysis ... |
69779d4978760ea30b417b0e33482a34f996baacb409a7dfdc65d5570b7b34d5 | R | 2,276 | 47 | # Live providers (SPEC-RICHR-003 REQ-022): the default suite mocks KEGG REST and
# msigdbr; these tests run the real services, so a change in what they return
# (and so a drift of the mocks) is caught. They reach the network and run only
# with RICHR_LIVE_TESTS=true.
skip_unless_live <- function() {
skip_if_not(iden... |
2fb49d478c105d38205b0f5b54b028ae92520b349645fee29bc4f32bf1779c52 | R | 2,286 | 73 | test_that("empty gene input raises error", {
expect_error(
richR:::.validateGeneInput(character(0), func_name = "test"),
"input gene list is empty"
)
expect_error(
richR:::.validateGeneInput(c(NA, NA), func_name = "test"),
"input gene list is empty"
)
})
test_that("NAs and duplicates are remove... |
980a95dcc8bfd25fd9cd991c54ecf6561bc7b6a472c5e89bbec4b6b19c96a0a9 | R | 2,292 | 56 | ###
### Process the genomic annotation data. These include:
### - CpG islands (source: UCSC genome browser)
### - non-coding RNA (source: UCSC genome browser)
### - gene, transcript and exon (source: Ensembl)
### All genomic features have been mapped to the human genome version
### GRCh38/hg38.
###
### The following as... |
ede1191868f4e9bce471f5e19da1edccf83266489ea061087dfb1b193241f0c3 | R | 2,297 | 124 | #' sumo_palette
#'
#'
#' @name sumo_palette
#'
#' @param x number of colour to display, by default 6
#' @param diverging if TRUE, creates a purple to yellow diverging palette
#' @param reverse to change direction
#' #'
#' @description create a palette of colours based on Hokusai wave
#'
#'
#' @examples
#'
... |
f5f8cddbb1ed867ed1fb269504317f79d45842971afe905e632d0a5a6d9d5d4e | R | 2,305 | 63 | library(here)
library(tidyverse)
library(scales)
genes <- c("Nr2f2","Isl1","Prkcd") # c("Sst","Prkcd","Nr2f2","Isl1") # Genes for connectivity
vis_colors <- c("#FF0000","#00FF00","#0000FF") #c("#223f99","#8bc53f","#ec1c24","#ECEA13")
quant_cluster <- read.csv(here("Hiplex_QuantificationTable_with_clusters.csv"))
animal... |
518eb1d7d3333baea31a748cef88a02520e2e28073c4b1ff9c4c3c522527ff66 | R | 2,314 | 59 | ## Suppress R CMD check NOTEs for non-standard evaluation variables
## used in dplyr pipelines and ggplot2 aes() calls
utils::globalVariables(c(
# ggplot2 aes variables
"x", "y", "xend", "yend", "label", "Group", "NES",
"x_start", "y_start", "x_end", "y_end", "x_group", "y_level2",
"neg_log10_Padj", "annotateTe... |
98a7b2b21093eb526664ff22fde2cc188bb0e442a8529506f9d09d4d4078e690 | R | 2,319 | 71 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017-2020 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,... |
a92e96a176915a499f8de712ce4dcb54730393f8aa65453470e7e6e74a409fc3 | R | 2,354 | 55 | #### create data.frame containing neighborhood data from csv files - for each run ####
cytomap_files_path <- "/Volumes/BryJC_Stanford/paper1_analysis/Fig6/single_object_pipeline/cytomap_files/Analysis_FSOM3_v3"
setwd(cytomap_files_path)
conditions <- c('Ctrl', 'MedAD', 'HiAD')
master_neighborhood <- data.frame()
for ... |
d3fd9cd46df7d44c4932c169b4412a32469a8658770c4bfd73a0d9e1bf888aab | R | 2,354 | 63 | library(ggplot2)
library(tidyr)
getvals <- function(dir, pattern, filter){
files = list.files(path=dir, pattern=paste0('^',pattern), full.names=T, recursive=T)
files = files[grep(filter, files)]
say(files)
table <- as.data.frame(do.call("rbind", lapply(files, FUN=function(file){
vals=readme(file,... |
46481710219e1b858b47af3b4166338d534d93d95558a3d2a3709933889e292c | R | 2,370 | 67 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2020 Robert McCulloch and Rodney Sparapani
## read.forest.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 ... |
f0086b22e24c8d5e90521d859a35b2d5383cf12a731b98a3a2b9403e6dd9f48d | R | 2,375 | 62 | # Characterization baseline (REQ-001, REQ-002): the full ORA result surface of
# enrich(), richGO() and richKEGG() on small committed fixtures, as produced by
# richR 0.1.5. Every test asserts a non-empty result before snapshotting so an
# empty sweep can never pass as a pin.
ora_query <- function() .char_read("ora_qu... |
759a6369c800774233be8992525e7be1160c85544a1325c2300f8025a5a827b0 | R | 2,386 | 83 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017-2020 Rodney Sparapani
## stratrs
## 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
## (at... |
8271f0e6e36b9f26f70c107ab9404fad6cdf9bbc5d5a64d285b28b4240300ff0 | R | 2,436 | 67 | # Shared fixtures of the tested-table tests (SPEC-RICHR-004), built with the
# public API and no network.
#
# F-ORA: a 20-gene universe and five gene sets in four roles under the
# reference call (.fora_reference(): pvalue = 0.05, padj = NULL,
# minSize = 2, minGSSize = 3, keepRich = FALSE):
# T1 reported (overlap 5 ... |
6d393c5ea6c89f24b2c964d36c4f9dac7ed06c4a4f323874bf69932942cacfe7 | R | 2,482 | 66 | test_that("ORA treats repeated vector genes as one input gene", {
annot <- data.frame(
GeneID = c("A", "B", "C", "D", "E", "F"),
Term = c("T1", "T1", "T2", "T2", "T3", "T3")
)
unique_result <- suppressMessages(richR::enrich(
c("A", "B"), annot, pvalue = 1, minSize = 1, minGSSize = 1,
keepRich = F... |
78b33a064b59e7a62ec9d6a50882725cea32572aff4cd48b58962621cdfc0b09 | R | 2,483 | 64 | ###
### Process the TCGA variant data.
###
### The sample names need to be normalised:
### The DNA methylation data uses this sample name format:
### TCGA-D1-A0ZV-01, while all other data types use TCGA-2E-A9G8-01A,
### indicating that sample repeats have already been aggregated for
### the DNA methylation data... |
d56eeafc13653db3f463f3e5f2205e36e760387bfac186c42817ea350dba6b69 | R | 2,492 | 76 | #' @include internal.R
#'
NULL
#' Run CoGAPs on a Seurat object
#'
#' @param object Seurat object
#' @param assay Assay to pull data from
#' @param slot Slot to pull data from.
#' @param params \code{\link[CoGAPS]{CogapsParams}} object for specifying parameter settings
#' @param temp.file Name of temporary data matrix... |
f968e8ba442206dfd8c35d54925147489907f2a7a77a6b44e266d6b5a12cf53c | R | 2,508 | 67 | library(topGO)
loadings2go<-function(W, gene_2_GO, numtopgenes=100, min_genescore=1.0,
rowmean_divide=TRUE){
#This blog was helpful: https://datacatz.wordpress.com/2018/01/19/gene-set-enrichment-analysis-with-topgo-part-1/
#divide each gene by its rowmean to adjust for constant high expressio... |
32707c4279ccb3f19b14840a71137dd9fc21ebbf9223cb458581a0a42fcbb687 | R | 2,513 | 95 | # library
library(igraph)
data <- read.csv("./cal_glu_pathway300_unique.csv")
class_go <- read.csv("./scz_logits_300_annot_bp_class.csv")
colorr <- c()
# create data:
links <- data.frame(
source=data$node1,
target=data$node2,
#importance=log(data$importance,10)
importance=data$importance,10
#i... |
7a5d3a136f6de9796d9eb5ad422224f0226f0272ef5e40a83758b3f047c48b0f | R | 2,515 | 95 | # library
library(igraph)
data <- read.csv("./scz_300_unique_pair.csv")
class_go <- read.csv("../fig4-e/scz_logits_300_annot_bp_class.csv")
colorr <- c()
# create data:
links <- data.frame(
source=data$node1,
target=data$node2,
#importance=log(data$importance,10)
importance=data$importance,10
... |
51c8af45d3fcc1f82681e2c1b2d9b91d28b0d3a985bfa13beb86cae26da3170a | R | 2,521 | 65 | #' @include internal.R
#'
NULL
#' Load alevin quantification data
#'
#' A wrapper around tximport to create a \code{SeuratObject}
#' from alevin quantification data.
#'
#' @param file path to \code{quants_mat.gz} file within
#' alevin directory
#' @param getMeta logical, option to use \code{tximeta} to
#' programmatic... |
21ebac9668f496ef91c4efe486db298005f5909bd6e382ae8e6e9014e291ea4a | R | 2,527 | 54 | # Load data
source(here::here("code","01_load_data.R"))
# Check assumptions
shapiro.test(PWAWM$ScreeLing_1_tot)
shapiro.test(PWAWM$ScreeLing_1_fon)
shapiro.test(PWAWM$ScreeLing_1_sem)
shapiro.test(PWAWM$ScreeLing_2_tot)
shapiro.test(PWAWM$ScreeLing_2_fon)
shapiro.test(PWAWM$ScreeLing_2_sem)
shapiro... |
8ab313d9fcc455392fca571fcf9f1766dfccd26741915aee8ee1b1df7bc17a3d | R | 2,566 | 65 | ---
title: "Benchmarking Visualization"
author: "Will Townes"
output: html_document
---
```{r}
library(tidyverse)
theme_set(theme_bw())
fp<-file.path
pth<-"simulations/bm_mixed"
plt_pth<-fp(pth,"results/plots")
if(!dir.exists(plt_pth)){
dir.create(plt_pth,recursive=TRUE)
}
```
```{r}
d<-read.csv(fp(pth,"results/b... |
9bf7284888e464462ff67debb1ed9ffde61e92b6ec830b20830dcf2c83744b75 | R | 2,569 | 92 | #' graphsumo
#'
#'
#' @name graphsumo
#'
#' @param dataset dataset organised as col = c(condition, value) and 1 row = 1 sample
#' @param abs parameter to be plotted in x ex : genotype
#' @param ord parameter to be plotted in y ex : value
#' @param MFV can be vir for viridis scale, male for b&w or female for red... |
b5c824a2a083f9cf7802c11d025c0aa9dc181dc81aef36505225fff4de2e6be3 | R | 2,577 | 91 | ---
title: "Running GLM-PCA on a Seurat Object"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: false
toc: false
html_document:
df_print: kable
theme: united
---
This vignette demonstrates how to run GLM-PCA, which implements a generalized version of ... |
32e3a26068effde5dfc285d50c65f310f1a8f6abde22b3ddf337520575cf2650 | R | 2,586 | 69 | ---
title: "Exploring Permutation P-values"
author: "Jean Fan"
date: "7/15/2020"
output: rmarkdown::html_document
vignette: >
%\VignetteIndexEntry{Exploring Permutation P-values}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
I... |
fd86c90d5a60a22b8bb7d04368b9a8b8f7b73635856b34dba2f82576ee4a2917 | R | 2,593 | 77 | #' Plot cell type proportions versus variances
#'
#' This function returns a plot of the log10(proportion) versus log10(variance)
#' given a matrix of cell type counts. The rows are the clusters/cell types and
#' the columns are the samples.
#'
#' The expected variance under a binomial distribution is shown in the so... |
48ca205edc0f568ed7d8c5328817a58ebca97d6d973690a0995f556169973ac9 | R | 2,608 | 53 | #' @include internal.R
#'
NULL
#' Run estimate_cycle_position on a Seurat object
#'
#' This function run estimate_cycle_position function on Seurat object. It uses
#' the tricycle internal reference projection matrix.
#'
#' @param object Seurat object
#' @param assay Assay to use, defaults to the default assay
#' @par... |
dbf7927e20cb3af7262b17f4cded24e37aa6b119fbf779610ccaf0b42930c165 | R | 2,619 | 77 | #' Normalise a counts matrix to the median library size
#'
#' This function takes a \code{DGEList} object or matrix of counts and
#' normalises the counts to the median library size. This puts the normalised
#' counts on a similar scale to the original counts.
#'
#' If the input is a DGEList object, the normalisation f... |
1f44c3868e70f92cf75be7be7aec1821ef137907c651c39bb1a5762a3375a6c5 | R | 2,648 | 64 | # Helper functions that deal with assignment of colour palettes for the overall plots
#
# Contains function `apply_palette`.
# Applies palettes to <ggplot> objects
# TODO add proper documentation.
apply_palette <- function(ggplot_object, palette_name) {
ggplot_object <- switch(palette_name,
"npg" =
ggplot_... |
7704b18898af11ac94077235f3c26a4b2a20d6e63b02f2a1ad4c6a36490ada7c | R | 2,660 | 90 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2018 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
#... |
ab8c4deff6cd89ca60c907b537cb852fa7977110ee78e4ee6063828441ff9390 | R | 2,664 | 76 | #' @include internal.R
#'
NULL
#' Run GLMPCA
#'
#' @param object A Seurat object
#' @param L The number of dimensions to return (defaults to 5)
#' @param assay Assay to use, defaults to the default assay
#' @param features A list of features to use when performing GLM-PCA. If null, defaults to variable features.
#' @p... |
a5b4d7906f3e58806ec5f7a68a19959bae6d03614d5a9adac61eccabd9e2b1c0 | R | 2,708 | 47 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# Background
This repository contains scripts used to perform a col... |
0fc2e816c30d19ebb1ed9757e59f145003544f7bcdf0fec5c8b7e0d0fed520a1 | R | 2,719 | 81 | ---
title: "Estimating RNA Velocity using Seurat"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: true
toc: false
html_document:
df_print: kable
theme: united
---
This vignette demonstrates analysing RNA Velocity quantifications stored in a Seurat objec... |
ddb549dda07e87515641efb833e062b57563ea70b73063b29d3018d95aab9100 | R | 2,719 | 90 | ---
title: "DLB.EPIC.neuronalPropotionEstimation.Rmd"
author: "Xiaojian Shao"
date: "02/10/2020"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r load packages}
# load packages required for analysis
library(knitr)
library(limma)
library(minfi)
library(IlluminaHumanMet... |
ac40cdad91c9b3c33672618c8e0e774f96caa8e8f156e7813113bdec798405dc | R | 2,730 | 83 | test_that("richResult can be constructed and displayed", {
res <- new("richResult",
result = data.frame(
Annot = "GO:0001", Term = "test term", Annotated = 100,
Significant = 10, RichFactor = 0.1, FoldEnrichment = 2.0,
zscore = 3.0, Pvalue = 0.001, Padj = 0.01,
GeneID = "A,B,C", stringsAsF... |
b614aa0368724713cb4f5e86fca221bf72d652e0320e6d45a9df2b5139f886b9 | R | 2,735 | 87 | #' Build a similarity matrix
#'
#' @description
#' `buildSimilarityMatrix` computes the similarity between inputs x1 and x2
#'
#' @details
#'
#'
#' @param x1 A matrix or data tree containing values to use to calculate similarity.
#' @param x2 A matrix or data tree containing values to use to calculate similarity.
#' @... |
0a733468e96b446e0fec37330daf63e155e5ae28716c23c43af3941aee57985a | R | 2,761 | 54 | describe("Testing add_scaling_component_to_delta_plot function", {
test_that("Returns a list with correct components for valid inputs", {
# Test case: Valid inputs with float_contrast = TRUE
delta_plot <- ggplot2::ggplot() # Dummy plot object
float_contrast <- FALSE
boot_result <- list(difference = 0.... |
ddd69b8de26f1240df5091fdfdbb5125caa3d8e6bb8992d97cdd9ea91ac82fb7 | R | 2,811 | 68 | ---
title: "Benchmarking Visualization"
author: "Will Townes"
output: html_document
---
```{r}
library(tidyverse)
theme_set(theme_bw())
fp<-file.path
pth<-"simulations/bm_sp"
plt_pth<-fp(pth,"results/plots")
if(!dir.exists(plt_pth)){
dir.create(plt_pth,recursive=TRUE)
}
```
```{r}
d<-read.csv(fp(pth,"results/benc... |
35c969820c0f05420114bdec64d16819107cb4596bdcbed0625412f2c0bb4989 | R | 2,854 | 89 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017-2018 Robert McCulloch and Rodney Sparapani
## mc.pwbart
## 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... |
db625e3c40d843a8bb973d9b08a8417c76c93c91b6b69abe69e6965cd464d6a5 | R | 2,854 | 89 | library(missMDA)
library(RColorBrewer)
library(plyr)
library(preprocessCore)
library(ggplot2)
c=readme("all_HLs_mouse.txt.gz",gzip=T)
numassays = apply(c[,3:ncol(c)],1,function(x) length(x)-sum(is.na(x)) )
nrow(c)
c=c[numassays >= 5,]
"Genes with >=5 measurements:"
nrow(c)
b=c[,1:2]
c=c[,3:ncol(c)]
c = scale(c)
ncom... |
f293c6445d828cafb126b6dfb842eb5dbb4fe4483aa8f82c1b81df2385b54219 | R | 2,863 | 135 | # @DESCRIPTION: all data
# Library -----------------------------------------------------------------
library(magrittr)
library(ggplot2)
library(patchwork)
library(rlang)
# src ---------------------------------------------------------------------
source(file = "analysis/2022-07-20/script/utils.R")
# Load data ----... |
13110570ee4cebbb30c0d7f12a17b189e37d9e16b991e34349b40f2c55616f4f | R | 2,882 | 74 | # REQUIREMENTS:
# Setwd() to specific animal and section.
# Sub_folder: Name of folder nested within your animal/section folder where you ROI coordinates are (e.g., subdivision_analysis)
# Within sub_folder, use the following naming convention: "{SUBDIVISION}_XY_coords.csv" (e.g., {SUBDIVISION} could be medial in the c... |
3b8e455eb03ca88d0eaaf0804a4dc272e6219a5009d5abbe59a8519d86acafd2 | R | 2,923 | 82 | setwd(
"~/Dropbox (ASU)/Placenta/BATCH2_PLACENTA_DECIDUA_ANALYSIS/HISAT_FeatureCounts/batch1_and_batch2/"
)
library(reshape)
library(ggpubr)
library("gridExtra")
df_merged_pisarska <-
read.delim ("genelists/pisarska/vfit_placenta_df_genes.txt")
df_merged_pisarska$group <- "late first trimester"
df_merged_placenta ... |
c7f2140a31c10efa695f6fef5cbca4268ce88312b3f68f56261b072927920923 | R | 2,935 | 48 | # Load Phyloseq Objects
load_all_cohorts <- function(){
#### Taxa
load("files/Phyloseq_objects/Species_PhyloseqObj.RData")
cat("Loading: Species \n"); assign("dat.species", dat.species, envir = .GlobalEnv)
load("files/Phyloseq_objects/Genus_PhyloseqObj.RData")
cat("Loading: Genera \n"); assign("dat.genus", d... |
04923836b919c10b3501b7ad50511cb97a69ee660ad3648f6b259ede934bc8aa | R | 2,969 | 74 | # PERMANOVA PLOTS
source("src/load_packages.R")
source("src/miscellaneous_funcs.R")
source("src/load_phyloseq_obj.R")
source("src/metadata_prep_funcs.R")
permdf <-
read.csv(file = "files/permanova_analysis_2021-04-12.csv", header= TRUE)
#---------------------------------------------------------------------------... |
8f181940e380b0d0084ab00143860747927c639ac403bcc958ffb99510589f74 | R | 2,979 | 82 | #!/home/zhangfeng/bin/bin/Rscript
#######################################################################################
# Description: Run pagoda2 for single cell expression data
# Date: 2018-10-24 22:20
# douyaguang
# package required:
# pagoda2: basic pagoda2 algorithm package
# NMI: calc... |
7fff798e21bc1d0f76997363ed465d4ffad1cbe5fa898a01d086178481364abb | R | 2,988 | 94 | # Metadata_Prep_Functions
#----------------------------------------------------------------------------
##-------------- Metadata lists --------------
#----------------------------------------------------------------------------
motortest_vars <- c(
"pole_test_s",
"sticker_test_s",
"x_of_... |
fc6fe3d149d041319a3bd771cba2221c150ee9d63d7dac55ffdb33deea98c552 | R | 2,996 | 73 | library(Seurat)
library(patchwork)
library(dplyr)
options(future.globals.maxSize= 891289600000)
rm(list = ls())
load("/public-supool/home/zhchen/Lab_share/public_source/cellcycle_gene/mouse_cell_circle_gene.Rdata")
setwd("/public-supool/home/zhchen/XS/5_subset_neuron")
Tbx3_neuron <- readRDS("/public-supool/home/zhche... |
d889ef4d3f7d5a68218c6d394d6ec3a5c00a0bdf2bf5b2e6ca5e5db79933dba2 | R | 3,031 | 137 | #devtools::install_github(repo = "mojaveazure/loomR", ref = "develop")
#library(loomR)
#BiocManager::install("rhdf5")
#lfile <- connect(filename = "D:/SingleCellModeling/L4/tissue-stability-human-lung-10XV2.loom", mode = "r+")
library(rhdf5)
library(tidyverse)
setwd("C:/Work/MatlabCode/projects/SingleCellModeling/Si... |
caaa044362237d7a6357dd43949d8cdf2f8900b21c7a1932f954dd592911678b | R | 3,064 | 91 |
# INPUT CONFIGURATION MANAGEMENT ------------------------------------------
args = commandArgs(trailingOnly=TRUE)
############################################################################
#USAGE: Rscript createKinship.R do_kinship.conf.R FOLDER SNP_FILE.csv HEADER[True|False]
#Rscript createKinship.R do_kinship.c... |
94c2b1a8784dec5fc0ecbb5f4e29273704756250c8a5f03d17abd2bf24b8f392 | R | 3,076 | 85 | library(ggplot2)
getvals <- function(dir, pattern, filter){
files = list.files(path=dir, pattern=paste0('^',pattern), full.names=T, recursive=T)
files = files[grep(filter, files)]
say(files)
table <- as.data.frame(do.call("rbind", lapply(files, FUN=function(file){
vals=readme(file,skip=95,header=... |
b8a93b4256cf04df51be4be52e1927e42e35683c1bc1fdf0e5febeed2177083b | R | 3,095 | 88 | #' Calculates and transforms cell type proportions
#'
#' Calculates cell types proportions based on clusters/cell types and sample
#' information and performs a variance stabilising transformation on the
#' proportions.
#'
#' This function is called by the \code{propeller} function and calculates cell
#' type proportio... |
a04bb323d2944107a019fa02cc2a6fd4fc3ee57968eeed9dd3699758aba0d805 | R | 3,097 | 85 | setwd(
"~/Dropbox (ASU)/Placenta/BATCH2_PLACENTA_DECIDUA_ANALYSIS/HISAT_FeatureCounts/batch1_and_batch2/"
)
library(reshape)
library(ggpubr)
library("gridExtra")
df_merged_pisarska <-
read.delim ("genelists/pisarska/cpm_placenta_df_genes.txt")
df_merged_pisarska$group <- "late first trimester"
df_merged_placenta <... |
b536327aee3ae381164953ea80119c6c7f0f1f0b616207fc457faacab7172d51 | R | 3,150 | 84 |
require(qgam)
require(itsadug)
require(brms)
options(show.signif.stars=FALSE)
load('sd_data.rda')
#############
### QGAMM ###
#############
qgam.model.simple <- mqgam(rollSD ~
Type + Condition +
TrialOrder.z +
s(Item, bs='re') +
s(TrialOrder.z, Participant, bs='fs', m=1),
data=sd_data, qu=0.5, ... |
05f433a0ecfaaf0e7b1dc944eb99496b5423bfc3f1bbdf7a6b565424d18bbd7e | R | 3,168 | 79 | #' make annotation database using bioAnno results
#' @importFrom AnnotationDbi keys
#' @importFrom dplyr distinct
#' @param dbname database name from bioAnno
#' @param anntype GO or KEGG
#' @param OP BP,CC,MF default use all
#' @param species species name
#' @param keytype gene ID type (e.g. "ENTREZID", "SYMBOL")
#' @e... |
870e3160ae91d2fd21807c58635c2469c542cfbb53fbd7b48be3c298c94d4cef | R | 3,173 | 95 | ---
title: "Integration of datasets using Harmony"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: true
toc: true
toc_depth: 3
fig_width: 16
html_document:
df_print: kable
theme: united
fig_height: 5
fig_width: 16
out_height: 4
---
Th... |
1536877eee4738bde25d0b00aae75c5f22b8b00608d6ff892168e7a847fd8318 | R | 3,174 | 110 | ---
title: "Running fastMNN on Seurat Objects"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: true
toc: true
toc_depth: 3
fig_width: 16
html_document:
df_print: kable
theme: united
fig_height: 5
fig_width: 16
out_height: 4
---
This ... |
3caae2cf993905d34c1eb55f267aaf656f07c4c2ac1510b3ce5f17ef93fcec5a | R | 3,185 | 90 | # Load necessary libraries
library(rrBLUP) # for GBLUP analysis
library(data.table) # for data manipulation
library(BGLR)
############################################################################
#USAGE: Rscript RunGBLUPDaniele.R ../data/kinshipMatrices/kinship_additive_minMAF0.01_maxMAF0.5.csv ../data/simulated_... |
e8b8d198ab7817970409c38a31fef9e905334fac72c8ea1ff9040306f7800362 | R | 3,202 | 75 | library(DoubletFinder)
library(Seurat)
library(patchwork)
library(dplyr)
rm(list = ls())
load("/public-supool/home/zhchen/Lab_share/public_source/cellcycle_gene/mouse_cell_circle_gene.Rdata")
text <- readRDS("/public-supool/home/zhchen/XS/4_rm_doublet/seurat_filterDouble_0911.rds")
doublet_cell_id <- colnames(text)... |
d007bf827e1e88595550df62c4d584058b54646c417d4d8c0768480b46ce61a2 | R | 3,221 | 109 |
#### correlation
wt_10x <- as.data.frame(tbx3_10x_WT_ave$RNA)
colnames(wt_10x) <- c("x3","x12","x13","x14","x16","x18")
wt_10x$gene <- rownames(wt_10x)
head(wt_10x)
wt_BGI <- as.data.frame(tbx3_BGI_WT_ave$RNA)
colnames(wt_BGI) <- rev(c("d9","d14","d17","d41","d42","d46","d53","d58","d60","d65","d66","d67"))... |
c2cda08e0a6bf42d0f2a9d5c5cc98a2c8af007bc7f3f7e1d7e0e8727009fc506 | R | 3,225 | 92 | #' Plot cell type counts means versus variances
#'
#' This function returns a plot of the log10(mean) versus log10(variance) of
#' the cell type counts. The function takes a matrix of cell type counts as
#' input. The rows are the clusters/cell types and the columns are the samples.
#'
#' The expected variance unde... |
ca2f17a38e63a74aa83ac1ad6d76a698000ed4a9e9f41d8d3165108d4807c813 | R | 3,227 | 101 | library(rhdf5)
library(ggplot2)
library(abind)
files = args
getdata = function(track){
do.call(abind, lapply(files, function(x){
h5read(x,track)
}))
}
values = getdata("ism") #(1, 4, 12288, nseqs)
coding = getdata("coding") #(12288, nseqs)
seqs = getdata("seqs") #(12288, nseqs)
preds = getdata("ref") #(12... |
c0630735c24380f637de9f76f507c32947c7484b8161d02b45046560038431b3 | R | 3,252 | 50 | #### Neighborhood Analysis ####
#Heatmaps
# prepare quantile normalized neighborhood data (note: neighborhood counts are summed from each object within each neighborhood)
master_neighborhood_c <- master_neighborhood
nu_panel <- c(panel,"Area","Circularity","Eccentricity","MajorAxisLength","MinorAxisLength... |
da7ae6cb7d1186904e468fd1738b140c282d0bc17af4e6108c0b90bcf5967525 | R | 3,265 | 105 | library(natverse)
source("~/R/conn.R")
# get info about all desmo connectors in the Naomi project
all_desmo_connectors <- catmaid_fetch(path = "11/connectors/", body = list(relation_type="desmosome_with", with_partners="true"))
# extract ids of desmo connectors
all_desmo_ids <- sapply(all_desmo_connectors$connector... |
32dadb9d331df79faf352f6267063c8f710168ff50243eeda470f27bce1ff957 | R | 3,279 | 75 | #' Tested terms of an enrichment result
#'
#' Returns the tested table of an enrichment result: every term the analysis
#' tested, not only the terms it reported in \code{result(x)}.
#'
#' For an ORA result (\code{richResult}) the table holds one row for every
#' term of the tested family whose overlap with the query i... |
5af0e353342002cdecff7f5337dc57125e2164f7cf7967f69e5ab64656a8c600 | R | 3,300 | 77 | # Regenerates the legacy fixtures used by test-tested-legacy.R and
# test-rich-io.R: result objects serialized by a richR build that predates the
# `tested` slot (richR 0.2.0 before M4, commit fca5276), plus the outputs that
# build computed from them. No network.
#
# Run from tests/testthat/fixtures/ against an instal... |
be107201e5d34600afa7d5fbd1998f907f8260b7913b97a57cec8f00485faf3c | R | 3,304 | 121 |
library(Seurat)
source('scRef.R')
load('CASE.RObj')
TSNE_VEC=pbmc@dr$tsne@cell.embeddings
IDENT=as.character(pbmc@ident)
COL=c()
i=1
while(i <=length(pbmc@ident)){
this_col=which(colnames(pbmc@raw.data)==names(pbmc@ident)[i])
COL=c(COL,this_col)
i=i+1
}
tag=cbind(names(pbmc@ident),as.character(p... |
0a2e04d1715d0bb9f97db209bd493f5d2c477e54bb78580b48f42f9531748246 | R | 3,324 | 109 | rm(list = ls())
# Define working directory and source ImageLMMR.R script
setwd("/path/to/base/folder")
source('/path/to/base/folder/ImageLMMR.R')
# Define tests to perform
source('/path/to/base/folder/mytests.R')
NCLUSTERS = 12
###############################################################
# Prepare the data prev... |
ac2e1b9c3dfed6160c3f12f35366c5aad993cf5298aa488ef86b2815f64cf5a8 | R | 3,340 | 83 | ---
title: "06_interpretation_genes"
author: "Will Townes"
output: html_document
---
```{r}
library(biomaRt)
source("./scrna/utils/interpret_genes.R")
fp<-file.path
pth<-"scrna/sshippo"
```
loadings from NSFH
```{r}
W<-read.csv(fp(pth,"results/sshippo_nsfh20_spde_loadings.csv"),header=TRUE,row.names=1)
rownames(W)<-t... |
955846fa4c6a8cc6e7498799b244a88181cbfeefb545eb95c04c63870d82bb34 | R | 3,355 | 81 | #!/home/zhangfeng/bin/bin/Rscript
#######################################################################################
# Description: Run SAFE and all the methods compared in SAFE for single cell data
# Date: 2018-10-22 14:40
# douyaguang
# package required:
# SAFEclustering: for SAFEclustering algorithm
# N... |
f2a8e0dbcd149ef352d96782543f73e107e584f70eaa363ca25ed6caacbaef64 | R | 3,355 | 89 | # compareGSEA() per-group value tables.
#
# compareGSEA() must key its NES / adjusted-p / raw-p rows by group and pathway
# identifier, so groups whose retained pathway sets or orders differ still get
# each value from the matching (group, pathway) row of their own result.
#
# Fixtures are built inline; expectations ar... |
057a4e1423686c96c001a249f5c72f5342e472d4bf9b17e40724939d3d6c5865 | R | 3,371 | 77 | ---
title: "Interpreting `liger` results"
author: "Jean Fan"
date: "8/23/2018"
output: rmarkdown::html_document
vignette: >
%\VignetteIndexEntry{Interpreting liger results}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
We will use a simulation to help us interpret the results of `liger`.
```{... |
a1239f11174a69a3b0b2c8600b0b0f85e04a0259bc2a40d340c6541dabceecb1 | R | 3,378 | 86 | ---
title: "05_interpretation_genes"
author: "Will Townes"
output: html_document
---
```{r}
# library(tidyverse)
library(biomaRt)
# library(rPanglaoDB)
source("./scrna/utils/interpret_genes.R")
fp<-file.path
pth<-"scrna/xyzeq_liver"
```
loadings from NSFH
```{r}
W<-read.csv(fp(pth,"results/xyz_liv_nsfh6_spde_loadings... |
7e3bbaee01db57f21f6ce9cb394244593f40ffa8c4ef6ddf10b140ab4c4206de | R | 3,382 | 94 | library(Seurat)
load('TSNE.RData')
TAG=read.table('Zeisel_semi.txt',header=T,sep='\t')
TAG[,2]=as.character(TAG[,2])
TAG[which(TAG[,2]=='Myelinating.Oligodendrocytes'),2]='Oligodendrocytes'
TAG[which(TAG[,2]=='Newly.Formed.Oligodendrocyte'),2]='Oligodendrocytes'
pbmc@meta.data$zhang2014 = TAG[,2]#as.character(pbmc@ide... |
5d6e68c29d6a052aa9db00b78525bfc3a0291d05992334ddbd94880df4f8a2a9 | R | 3,385 | 118 | get_gender_column <- function(N) {
return(c(rep("Male", N / 2), rep("Female", N / 2)))
}
get_genotype_column <- function(N) {
return(c(rep("M", N / 2), rep("W", N / 2)))
}
# TODO Add documentation
generate_non_proportional_dataset <- function(N = 40, seed = 12345) {
set.seed(seed) # Fix the seed so the results ... |
a7ba4d60834a13b397b2fd99fc2a40abffe65010465989e4281c4c0ee429dd28 | R | 3,403 | 91 | #' Plot cell type proportions for each sample
#'
#' This is a plotting function that shows the cell type composition for each
#' sample as a stacked barplot. The \code{plotCellTypeProps} returns a
#' \code{ggplot2} object enabling the user to make style changes as required.
#'
#' @param x object of class \code{SingleCe... |
4088e330498696b8a402c81ffb80c5b89e7d98b565fccba46db4d8acdbed45d5 | R | 3,404 | 87 | ---
title: "Gene Set Enrichment Analysis with LIGER"
author: "Jean Fan"
date: "September 27, 2015"
output: rmarkdown::html_document
vignette: >
%\VignetteIndexEntry{Gene Set Enrichment Analysis with LIGER}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
Gene Set Enrichment Analysis (GSEA) is a c... |
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