sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
060085bbf41b2423450317903f0baa0962e10c67d128d3d27a0fff8ad90692c0 | R | 3,417 | 79 | # Result objects without a recorded tested table (SPEC-RICHR-004 REQ-005,
# AC-004): objects serialized by richR before the tested slot existed
# (fixtures/legacy_*_fca5276.rds, made by fixtures/make-legacy-fixtures.R with
# the fca5276 build), objects built by as(), and objects whose slot and
# metadata are gone. Catc... |
3c199e782e4191353ca7c322483b1a74dbe7754d2526cd2d9e3b669532294b47 | R | 3,435 | 82 | #!/usr/bin/env Rscript
################################################
################################################
## REQUIREMENTS ##
################################################
################################################
## DIFFERENTIAL GENE EXPRESSION ANALYSIS
## - ... |
115348eb9119b7e37fec585ed86173ae5a97cce81c3c9909302cec95955a2639 | R | 3,442 | 89 | # This script was written by Dr. Michael Hallquist and is copied from https://github.com/UNCDEPENdLab/fmri_processing_scripts/blob/master/fsl_regfilt.R
#!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
if (length(args) < 3 || length(args) > 5) {
stop("Expects at least three arguments: input_file, melodic... |
6dba1527005a4def2bf6c26f933d37295e604dc32d51e734cf86108679f270ad | R | 3,443 | 89 | ################################################################################
# DNA methylation analysis with RnBeads
# For project Stewart_DLB
# ------------------------------------------------------------------------------
# Vanilla analysis of the Stewart DLBvsHealth EPIC 850K dataset
# -------------------------... |
d8b00b66bdcca1d25d659af81316d2bc86af1a9e5ed7d412f43c57342cff183e | R | 3,448 | 84 | ---
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/visium_brain_sagittal"
```
loadings from NSFH
```{r}
W<-read.csv(fp(pth,"results/vz_brn_nsfh20_spd... |
e92078200203d90a17887539f5ec8052bce1e2e1fa7e13e85bc364e868e1c028 | R | 3,450 | 140 | #' Comp3Moy
#'
#'
#' @name Comp3Moy
#'
#' @param Condition the different conditions named in the same column
#'library
#' @param Measure the values measured for every sample
#' @param n is the number or replicates per condition
#'
#'
#' @description Compare 2 or more datasets using the right test according t... |
ed7be381e7d0bfc0f44bc9af158fef85151931503bc18c5ef11ce1b1299826b3 | R | 3,467 | 99 |
library('Seurat')
source('scRef.R')
exp_raw_data= read.table('MGH54_mat.txt',sep='\t',header=T,row.names=1)
#exp_raw_data= read.table('GSE70630_OG_processed_data_v2_MGH53.txt',sep='\t',header=T,row.names=1)
#exp_raw_data= read.table('GSE70630_OG_processed_data_v2_MGH60.txt',sep='\t',header=T,row.names=1)
#exp_raw_dat... |
7518b79982af094ee34f944b74c40e736964350dce9cc0764a43c22561a96c7f | R | 3,470 | 72 | # SPEC-RICHR-012: exact leading-edge identifiers and comparison group identity.
review_identity_groups <- function() {
fc <- stats::setNames(seq(2.3, -2.3, length.out = 24), sprintf("g%02d", 1:24))
result <- data.frame(pathway = c("P1", "P2"), pval = c(.01, .03),
padj = c(.02, .09), NES = c(1... |
2fce4e5cfeb013fda65ca6dd703c3ef541e0f6aadb30a703b86a0914f69ca207 | R | 3,489 | 99 | #' make_project
#'
#'
#' @name make_project
#'
#' @param path path where to create project
#' @param IHC true if it's an IHC and I want tiff, czi and QP folders
#' @param path_to_QuPath the path where the QuPath (console).exe is located, should look like this on windows "C:/Users/user_name/AppData/Local/QuPath-0... |
c3cdf0c2666077013e5f95de3e3b80f5fedf691c9d96422cc7786c118e729984 | R | 3,505 | 122 |
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
BiocManager::install("TSCAN", version = "3.8")
library(TSCAN)
load('OligTumor.RData')
library('Seurat')
source('scRef.R')
library(TSCAN)
library('Seurat')
#exp_raw_data=read.table('MGH36_mat.txt',header=T,row.names=1)
#exp_... |
f067d8e4e16bb1389a77d438641ba913fb412addad65bab34f42a8bbb765c63a | R | 3,531 | 99 | # group_by() on richResult, GSEAResult and Annot objects.
#
# The methods passed `add = add` to dplyr::group_by(), an argument dplyr
# (>= 1.0.0) made defunct, so every call failed with "The `add` argument of
# `group_by()` was deprecated in dplyr 1.0.0 and is now defunct." These tests
# catch that failure and an `add ... |
8a290428f525f2ff955fd6602210d7f95fe280dafeb959d1d641467c12ea387a | R | 3,532 | 87 | # Live KEGG pathway labels (builtin = FALSE) must survive an unreachable or
# incomplete KEGG listing: fall back to the bundled labels and say so.
# Catches: a crash when KEGG is unreachable, a silent fallback, and a spurious
# warning on a healthy listing. keggList is mocked; nothing reaches the network.
collect_warn... |
a9fb7e2f2b826588dde51bc634736399c66c8c90cb15022eae23b7c419be5bd8 | R | 3,559 | 88 | # Regenerates the small deterministic fixtures used by the characterization
# tests (test-characterization-*.R). No random numbers, no network.
#
# Run from tests/testthat/fixtures/: Rscript make-fixtures.R
# The committed CSV files are the source of truth for the tests; this script
# documents how they were derived s... |
bfa9ccdc86891af07466b8c041d6a2bf94fc214dc130505f2466167c04b54673 | R | 3,561 | 111 | ###
### Explore the different GDC & TCGA data types available through the
### Xena public data hubs.
###
### This script assumes that the working directory is the main MEXPRESS
### folder.
###
library(data.table)
cancerType = 'acc'
plotCol = '#ffa96a'
# Gene expression RNA-seq data
x = fread(paste0('data/', cancerTy... |
5877eee4cf7eb0cf929051f2b4050e2406f01671bb09ce9bf5a7779bcd31afb7 | R | 3,577 | 91 | # compareGSEA() significance cutoffs.
#
# compareGSEA() must keep a group's pathways only when the caller's cutoffs
# hold: with a non-NULL `padj` both the raw and the adjusted cutoff apply; with
# `padj = NULL` only the raw cutoff applies. Values equal to a cutoff are kept.
# The cutoffs must never be resolved to the ... |
ea9c1c009a87f63d34b84789fee88d6cad7eb91b1a97ffa1a409e76f1aae4019 | R | 3,625 | 94 | # load libraries ----
library(readxl)
library(ggplot2)
# clear workspace ----
rm(list = ls())
source('/Users/vkumar/Documents/smith10/2D_plots/geom_flat_violin.R')
source('/Users/vkumar/Documents/smith10/2D_plots/summarySE.R')
# Set path ----
setwd("/Users/vkumar/Documents/smith10/2D_plots")
setwd(dirname(rstudioa... |
b2f3824e3745c5cf82858f3a0fa1b37f5d427062bdb752e3896268cfc4837cee | R | 3,630 | 135 |
exp_data=read.table('GSE104276_all_pfc_2394_UMI_count_NOERCC.xls',header=T,row.names=1,sep='\t')
TYPE=read.table('NPCneuglia.txt',header=T,sep='\t')
exp_raw_data=c()
COLNAME=colnames(exp_data)
i=1
for(cell in TYPE[,1]){
exp_raw_data=cbind(exp_raw_data,exp_data[,which(COLNAME==as.character(cell))])
#print(cell)
pri... |
eb4ccc943ee76bc1a568853854dd01870390c2bb4a5fc8c0973db75071e721f9 | R | 3,681 | 86 | # Assigns cells that are not assigned automatically by assignSubdivisions
manualAssignments<-function(animal,section,dat){
if (animal=="640696" & section=="Intermediate RH"){
indices <- c(5,150,245,270,308,319,414,594,792)
if (any(dat[indices,"Subdivision_Assignment"]!="Unknown")){
print("Warning: One ... |
9d3fab19fccd4874f42291a41b77851046600d7fabef242e9766a6db58a68ff9 | R | 3,687 | 107 | #! /usr/bin/Rscript
#' Required packages
library(reshape2)
library(ggplot2)
### Add efficiency values to data set ###
#' Create matrix to be melted
#'
#' value: vector containing Ct values
#' geneID: vector containing gene names
#' conc: vector containing log(concentration) values
#' if there is a NA value in this v... |
efc474ad93f00dbf44e9505dd038eed0264a8313b2eabaf4e39354de3d3dc893 | R | 3,698 | 93 | #' Validate gene input for enrichment analysis
#'
#' Checks that gene input is non-empty, removes NAs and duplicates,
#' and reports mapping statistics.
#'
#' @param x gene vector or data.frame with gene rownames
#' @param annotation annotation data.frame (two-column: GeneID, Term)
#' @param func_name name of the calli... |
ea5073bf56666d2a959300035268386a3166669309036f37f66db8335ce4be0f | R | 3,708 | 93 | ###
### Process the epigenomics TCGA data. These include:
### - RNA-seq gene expression
### - DNA methylation
### - copy number
### - miRNA expression
###
### The sample names used in the different data sets need to be normalised:
### The DNA methylation data uses this sample name format:
### TCGA-D1-A0ZV-01, while... |
106754a0aac87b76130e26970d4e9eec105cfe3c6bb64f48c5c5f3df8cd4bb9d | R | 3,721 | 105 | # This is the R script for generating sampling density map
# Designed for command line use, given the required packages are installed
#
# Example usage:
# Rscript Get_density_map.R ./GeoDB ./example/input.csv ./example/output.csv titlename
#
# ./GeoDB the folder containing the query database generated in previous step... |
5170976e90b97966044c9241dcc58ebe8a15cc2144dac596fc4939f4b3b8b1a2 | R | 3,735 | 91 | #! /usr/bin/Rscript
### Data normalization (Pfaffl's method) ###
######################################## Reference ########################################
# "A new mathematical model for relative quantification in real-time RT_PCR"
# Michael W. Pfaffl
# Nucleic Acid Research, 2001, Vol 29, N°9 00
# h... |
54afa5a8c476331e10887c9cce3435f94700b47b20444439fd87002bfa21775f | R | 3,746 | 108 | # SPEC-RICHR-015: reject lossy integer metadata through the public folder API.
.mi_folder <- function(x = .rp_fq()$q1) {
p <- tempfile("manifest-integers-")
writeRich(x, p)
p
}
.mi_edit <- function(p, edit) {
f <- file.path(p, "manifest.json")
m <- jsonlite::fromJSON(f, simplifyVector = FALSE)
writeLines(js... |
a8a160fcb58119640fd37a5ac6580b15f416ff73bccd65f726611b221171a545 | R | 3,747 | 84 | # AC-009 (SPEC-RICHR-006 REQ-006, REQ-008): every plot of set P (each method),
# richClusterDot and richUpset draw on the current build exactly what the M6.1
# base (fca5276) drew, on inputs whose rows all pass the default selection.
# The reference fixtures/plot_reference_m6base.rds was recorded by
# fixtures/make-plo... |
1949956195f64d77001aeac291f3758565a68aee6b69b685ead3084d1238c548 | R | 3,757 | 70 | mirror <- function(DF, CHR, START, END, SENTINEL = "sentinel",
GENE_START = NA, GENE_END = NA,
T1THRESH1 = NA, T1THRESH2 = NA, T2THRESH1 = NA, T2THRESH2 = NA,
TITLE = NA,
COLOURS = c("blue", "red3", "orange", "yellow", "lemonchiffon2", "gray60"... |
e804c74944382ca17d2e06ab9c942c705a0cbda342d4c1db50908cc89205a740 | R | 3,771 | 93 | wd <- getwd()
library(Seurat)
library(dplyr)
library(ggplot2)
library(sessioninfo)
session_info(to_file = "session.log")
s.1.obj <- Read10X("Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_CellRangerFiles/Blood/5619-RG-14/filtered_feature_bc_matrix") %>% CreateSeuratObject(project="PBMC_Naive_F... |
2da9a15fdf86468417793f00a34d7e7695c530c0fe86e12282f2589d2296c3eb | R | 3,775 | 96 | library(LSD)
getvals <- function(dir, pattern, filter, refValt=F){
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,... |
227f8c45b5e7e7f068c304b0a078e4d81ae4f9bc42ba3884acfdf7b27fdaa1bb | R | 3,780 | 119 | ---
title: "Estimating RNA Velocity using Seurat and scVelo"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
html_document:
df_print: kable
theme: united
github_document:
html_preview: true
toc: false
---
This vignette demonstrates analysing RNA Velocity quantifications stored in a S... |
69f6494d8fc48ac4993753941e277613d6c7626b131181c7191c04fafdc5a328 | R | 3,783 | 125 |
## version using an array object
## these are wasteful since there are many zeros
## surprisingly, testing shows this is more efficient than sparse arrays
EXPVALUE = function(trees,
x.test,
S,
call=FALSE)## default to R vs. C++ code
{
if(call) {
#... |
cf14b64ff6953d5734ac44642f53f6ecdfa1e98605582b8735620ba6e2394133 | R | 3,796 | 98 | #code for Figure6 figure supplement5 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")
#read skids of connectome neurons ... |
434344debf351be5183ad5db6262f8df813df1bf4d7905157fd0514877623a10 | R | 3,804 | 113 | ---
title: "Running PaCMAP on a Seurat Object"
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
output:
github_document:
html_preview: false
toc: false
html_document:
toc: false
df_print: paged
---
This vignette demonstrates how to run PaCMAP, a dimensionality reduction method that can
be us... |
bb569a38b0886cc6c2633ef6acd38ed6e1592e33b123159a091c24242a85f063 | R | 3,828 | 85 | ## data-raw/kegg-refresh-helpers.R -- shared steps of data-raw/kegg.R,
## data-raw/module.R and data-raw/path.R. Sourced by those scripts; not run on
## its own.
##
## The KEGG version is derived by the installed richR's own .kegg_release()
## (the rule richR records for live KEGG labels: the release KEGG states,
## ve... |
9ef2e9464bb943d18961a58483a83ec9e0cfb744baf60d886ea0e9995101a5d8 | R | 3,830 | 105 |
load('loglin_data.rda')
##################
### Log-Linear ###
##################
freqdat = data.frame(tabdat <- xtabs(~
Match +
Type +
Condition,
data=loglin_data))
ftable(tabdat)
# Condition ST CT
# Match Type
# 0 subordination 6 6
#... |
fbd59a152e59d6667a84ffea4c4fc0629a74ca663cf762052a8714ea5b4289a9 | R | 3,836 | 109 |
#load packages
{
library(igraph)
library(visNetwork)
library(ggplot2)
library(ggraph)
library(rgexf)
library(RColorBrewer)
}
#read adjacency matrix
desmo_conn <- read.csv("data/adjacency_matrix_desmosomal_connectome_CATMAID.csv", sep=",")
desmo_conn <- as.matrix(desmo_conn)
#with the make_graph function of igraph ... |
1d4b56484c075403ccce0f252297ba5660daf9e5a12e7ff6b500a677093aeeeb | R | 3,851 | 114 | knitr::opts_chunk$set(echo = TRUE)
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(rstatix)
library(ggpubr)
library(officer)
#library(ggprism)
#load fpr data for cross-sectional data
vol_cx <- read.csv("Data/fp_vol.csv")
vol_cx$Metric <- "Volume"
corthick_cx <- read.csv("Data/fp_... |
f5feb2f183faf831c66c0a1bf0ddbad954e40da2616a59d7cbfe544bd4fddc41 | R | 3,860 | 106 | #' Predict sex of cells in scRNA-seq data
#'
#' This function will predict the sex for each cell in scRNA-seq data. The
#' classifier is based on logistic regression models that have been trained
#' on mouse and human single cell RNA-seq data.
#'
#' For bulk RNA-seq, checking the sex of the samples for mouse and huma... |
2bd91e13e80835e9b75caf3e0b1cf9f5726485a2e6f6227e626190cbd14e7962 | R | 3,869 | 103 | #' @include internal.R
#'
NULL
#' Run fastMNN
#'
#' @param object.list A list of Seurat objects
#' @param assay Assay to use, defaults to the default assay of the first object
#' @param features Either a list of features to use when calculating batch
#' correction, or a number (2000 by default) of variable features to... |
b566796cebd07fd16dfaff73c55eebd499e89ef488bea0be9879e7a410755363 | R | 3,888 | 90 | # Reactome annotation identity (REQ-006 .. REQ-008), offline: the reactome.db
# maps are replaced by small in-memory maps through the .reactome_map accessor,
# so these tests need neither reactome.db nor the network. Upstream-portable:
# they assert only the annotation frame and ORA gene-set keys, never result
# labels... |
d1fe9d5b2d2144d8d8693e830dbc754f56a29580ba80c7311d7168756bcc0768 | R | 3,897 | 141 | ---
title: "Integration of datasets using Conos"
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
---
Thi... |
476411a4378ffda9400eeede3f9ce77f10c7c1af87f9418a9e56e9b4bd95f11f | R | 3,939 | 96 |
#' Get probe paths
#'
#' This function uses a dataset data frame and returns probe paths.
#' Returns a probe.paths class.
#' @param dataset a data frame generated by getcell.ids() or inspect.registration().
#' @id vector indicating segmented points which belongs to single probe path.
#' @surface.point real-valued, sta... |
52e7aabf330102298404d96b09e5537f966dd17353e616583925d7546cdc79b7 | R | 3,944 | 106 | ##' Class "richResult"
##' This class represents the result of enrichment analysis.
##'
##'
##' @name richResult-class
##' @aliases richResult-class plot,richResult-method
##'
##' @docType class
##' @slot result enrichment analysis results
##' @slot detail genes included in significant terms and original information
##... |
3dc0838102b71f91efacbe504c9630a32807435675dc2d289ed444066867ce23 | R | 3,946 | 140 | ### QC_Seq_Depth 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/alpha_diversity.R")
source("src/beta_diversity.R")
base::load("files/Phyloseq_objects_Woltka.RData")
base::load("files/Phyloseq_o... |
85c8502c459dc7de83b07707cd8dcf48755fd46b430bb429feb23a552bbf24c5 | R | 3,953 | 131 | #' Producing an estimation plot
#'
#' @name dabest_plot
#'
#' @param dabest_effectsize_obj A dabest_effectsize_obj created by loading in a
#' dabest_obj along with other specified parameters with the [effect_size()] function.
#' @param float_contrast Default TRUE. If TRUE, a Gardner-Altman plot will be produced.
#' If ... |
520ab0d008eef7c8298feaf321eb0af9b34cc307b2c8e510f298b8a6fca2645b | R | 3,960 | 68 | # sample (sample_n) for later use by object type
n_sub_fraction <- 0.7
#tau_datsets
train_mglia_T <- sample_frac(filter(disease_cell_overlap, FlowSOM_ids == 'microglia' & tau_obj_POS == 0), n_sub_fraction)
train_mglia_T <- rbind(train_mglia_T, sample_frac(filter(disease_cell_overlap, FlowSOM_ids == 'microglia' & tau_o... |
a6ec7f7495664bd21bf496b1c6f7b05b642fc036a9fc24fe96aa648c269e475f | R | 3,981 | 77 | #' Flat-field correction
#'
#' An image sensor of a camera is composed of a two dimensional array of light sensitive detectors or pixels. The sesnor array is #'mechanically quite stable with the pixels retaining a rigidly fixed geometric relationship. Each pixel within the array, however, #'has its own unique light sen... |
06be24e79ba07a617058993a1b27732ad32a76193232913b508ec4f4245e4f92 | R | 4,019 | 67 | # setup -------------------------------------------------------------------
rm (list = ls())
library(data.table)
library(tidyverse)
setwd("~/Desktop/Neurobiologia/projects/ongoing/verbal.irony/discrepancy/experiment.1/task/data/")
#setwd("discrepancy/experiment.1/task/data/")
# general --------------------------------... |
e8f10a2a9737826c0a5ecf22596f9fa2d0f5a3b9027ffba068be25ebfaf176cb | R | 4,046 | 174 | #####################################
# Simulated benchmark
load('TSNE.RData')
ori_label=read.table('Zeisel_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
pbmc@meta.data$ori=ori_label[,2]
library(Seurat)
library(SingleCellExperiment)
library(scmap)
head(ann)
tmp=as.matrix(as.factor(pbmc@meta.data$ori))
#tmp[,1]... |
ae27d1cc6538a0477cc60c20d52de7caacdd7a375fc86dccd3e11634d79e4c0e | R | 4,056 | 120 | load('pbmc.RData')
source('scRef.R')
library(Seurat)
######human pbmc####
COL=c()
i=1
while(i <=length(pbmc@ident)){
this_col=which(colnames(pbmc@raw.data)==names(pbmc@ident)[i])
COL=c(COL,this_col)
i=i+1
}
sc_tag=cbind(names(pbmc@ident),as.character(pbmc@ident))
exp_raw_mat=as.matrix(pbmc@raw.d... |
bc2f9f4888ff54324b5951227e859127960052ba4b781218a938a9c2ef2d8a34 | R | 4,079 | 146 | ---
title: "Make_combat_figures"
author: "Sophie"
date: "07/01/2022"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(dplyr)
library(tidyr)
library(stringr)
library(ggplot2)
library(rstatix)
library(ggpubr)
library(officer)
#library(ggprism... |
f24041b24155be63691f8da0aa19b46bee01e3081d5ac177ec7dbd2f00587d35 | R | 4,085 | 98 | if (F){
"
This script implements count ~ group plotting integration.
It requires Stage I integration outputs dots.csv, samples.csv.
Two possible output files: merged.pdf and batch.qc.pdf
"
}
suppressPackageStartupMessages(library(tidyverse))
suppressPackageStartupMessages(library(ggrepel))
source("w... |
e9287e7d5875b7dedb4bba868567efa544901825602dedcb138b5348c8162e3e | R | 4,120 | 149 | ---
title: "Calculating Trajectories with Monocle 3 and Seurat"
output:
html_document:
df_print: kable
theme: united
github_document:
html_preview: no
toc: no
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
---
This vigettte demonstrates how to run trajectory inference and pseudotime calcul... |
584cee00e3385a06e3b37ade9d99bce84934f67d5f8efe0edd8533a580c9ee52 | R | 4,137 | 87 | # Regression controls for SPEC-RICHR-013; no shared fixture state.
.review_access_result <- function() {
methods::new("richResult",
result = data.frame(
Annot = c("A", "B", "C", "D"),
Term = c("B", "unique label", "shared label", "shared label"),
Pvalue = c(0.001, 0.02, 0.03, 0.04),
Padj =... |
d5b7e52f69e8b4aeda21af46ff33ef019e461146a31653aeaa9c250c466674a6 | R | 4,145 | 101 | library(ggplot2)
library(glmnet)
library(LSD)
a=read.csv("experimentResults.csv")
round(a,3)
results = data.frame(tests = c("B vs BG","B vs Bb","Bb vs BbG"), pvals =
c(t.test(a$B, a$BG, paired=T,alternative='less')$p.value,
t.test(a$B, a$Bb, paired=T,alternative='less')$p.value,
t.test(a$Bb, a$BbG, paired=T,alternati... |
0b25c0403a0ab468ca21566f1db6f0b62e1b10d4b24bf9500d9cbcc7908ad595 | R | 4,193 | 99 | #! usr/bin/Rscript
#' Required package
library(reshape2)
library(data.table)
### Apply statistical test to the qPCR data set ###
#'
#' The data that has been used so far is long-format data:
#' all genes are stored in one column and their corresponding values in another column.
#' To perform a statiscal test across t... |
06beaa418b482b111e682fa6b638673abcb730d5b7711262b33e13a2bc28acd4 | R | 4,194 | 101 | library(glmnet)
database = args[1] #name of database {choose a code below}
load("alldataGeneticBiochemical.RData")
if(database == "B") a=a[,1:10]
if(database == "Bb") a=a[,c(1:10,grep("^BIOCHEMICAL",colnames(a)))]
if(database == "BG") a=a[,c(1:10,grep("^GENETIC",colnames(a)))]
if(database == "BGb") a=a[,c(1:10,grep(... |
c8cba68641d438a4b3b34aa1fbb8a2a887042bedaaa8c402f281c65f4cc315da | R | 4,202 | 81 | # SPEC-RICHR-014: all fixture rows have overlapping genes except the explicit control.
.review014_inputs <- function() {
a <- .m61_fp3()
b <- a; b@result$Pvalue <- c(0.02, 0.03, 0.04)
b@result$Padj <- c(0.025, 0.035, 0.045)
b@result$Significant <- c(12, 13, 14)
# Repeated identifiers within a source must also... |
5bfdae04103b10f77d458530c4edf50dd7e88df580a8bf46d8629759f951f65e | R | 4,291 | 106 | setwd("~/Desktop/INTACT6/bivalency/")
library(ggplot2)
library(reshape)
#######
### riboTag D1 gencode vM23 cuffdiff
D1_bivalency <- read.table("D1_RPKM_bivalency_gencode_vM23.txt", header = TRUE)
D1_bivalency$bivalency_status <- factor(D1_bivalency$bivalency_status, levels = c("none", "H3K4me3", "H3K27me3", "Bival... |
d9241cf94a834e4b1c880bce1bde3863829c1923dcba45338b22394210d978af | R | 4,397 | 92 | microglia_umap_data <- filter(disease_cell_overlap, FlowSOM_ids == "microglia")
microglia_umap_data <- left_join(microglia_umap_data, master_gated_anat2[,c("spatial_id","anat_id")])
microglia_umap_data <- filter(microglia_umap_data, anat_id %in% c("DG","CA4","CA3","CA2","CA1"))
n_sub_fraction <- 554 #total Ctrl microg... |
8d1074146945076fad10afcf44bc8311a86505c8064dd8a892e07481c61727c9 | R | 4,414 | 121 | #' Extra conversions to Seurat objects
#'
#' @inheritParams Seurat::as.Seurat
#'
#' @rdname as.Seurat.extras
#' @name as.Seurat
#'
#' @seealso \code{\link[Seurat]{as.Seurat}}
#'
#' @aliases as.Seurat
#'
NULL
#' @param method Name of matching method graph was built using
#' @param reduction Name of graph embedding, if ... |
9ddbc0e537942fd7024fd7badc4dafab340fffdd5027285bea6aff3e31549d4f | R | 4,417 | 115 | #Generates DSAVE data for the clusters, decides which are large enough, and
#saves the DSAVE data, and generates bootstrap data for the selected clusters
library(tidyverse)
memory.limit(size = 100000)
fig__dir = "D:/SingleCellModeling/MetAtlas/Figures/"
library(Matrix.utils) #for transpose of sparse matrix
sparseMa... |
4d3af2c3070d53a49aae84a7918028e9d4df626bbea4a89db949dc6fe6c39853 | R | 4,495 | 96 | wd <- getwd()
library(Seurat)
library(dplyr)
library(ggplot2)
samples <- paste0("4361-RG-",1:4)
s.1.obj <- Read10X("Y:/CDBLabs-Bioinformatics/Some_Place_Where_Big_Files_Can_Sit/Giger_2022_CellRangerFiles/Distal_Injury/4361-RG-1/filtered_feature_bc_matrix") %>% CreateSeuratObject(project=samples[1])
s.2.obj <- Read10X(... |
82c7367aa41991dc79fd4207b9bbee6dc6bcb3e16933a04b37a82af78369493d | R | 4,502 | 108 | #' Batch enrichment analysis across multiple gene lists
#'
#' Runs enrichment analysis on a named list of gene vectors and returns
#' combined results ready for \code{richCompareDot()} visualization.
#'
#' @param gene_lists Named list of character vectors (gene IDs per group)
#' @param annot Annot object or annotation ... |
ebcc9c55203d90fbf29a76a4c8fd7671ce59d0cdb3e909f9c67023798b5196c4 | R | 4,505 | 161 | ---
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%"
)
```
# sigminer.prediction
<!-- badges: start -->
[![Lifecycle: experim... |
45e3b7a3b4b2f4e4bd1932e5b2abb35a6e4497365c44bf1eb4782b9979e2817b | R | 4,508 | 93 | ---
title: "Highlighting the Power of Gene Set Enrichment Analysis using Simulation"
author: "Jean Fan"
date: "December 23, 2015"
output: rmarkdown::html_document
vignette: >
%\VignetteIndexEntry{Highlighting the Power of Gene Set Enrichment Analysis using Simulation}
%\VignetteEngine{knitr::rmarkdown}
%\Vignette... |
555ffaabd5f6ff5ccf5c3ca220bcc147d3bda63024bcb42ae78910e0ac9d5fc5 | R | 4,512 | 127 | library(missMDA)
library(RColorBrewer)
library(plyr)
library(preprocessCore)
library(ggplot2)
c=readme("all_HLs_human.txt.gz",gzip=T)
c$Bazzini_ActD_HeLa_1 = -1*c$Bazzini_ActD_HeLa_1
c$Bazzini_ActD_RPE_1 = -1*c$Bazzini_ActD_RPE_1
c$Akimitsu2_BrU4sU_HeLa_1 = -1*c$Akimitsu2_BrU4sU_HeLa_1
c$Marks_ActD_HeLa_1 = -1*c$Mark... |
a2358c37f669e72a6a269036efc7607f4506d1cff2716288a50bed325cb48486 | R | 4,513 | 106 |
## 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
#... |
d2939542c9aca45976c04c718ca2e3f073e78a8818195ce32d646f54b55c2b4e | R | 4,515 | 139 | # ----------------------------------------------------------------------------
# build_mouse_tree.R
# Author: Antoine Beauchamp
# Created: January 31st, 2022
#
# Build a gene expression tree
#
# Description
# -----------
# This script maps the values from a voxel-wise expression matrix to the
# Allen Mouse Brain Atlas ... |
27a585c036e638ddd0e70dae5221b9a501ad8c2454628e79bb35f103e6b6bb20 | R | 4,541 | 102 | encoding_identity_pair <- function(text = "té") {
utf8 <- enc2utf8(text)
latin1 <- iconv(utf8, from = "UTF-8", to = "latin1")
Encoding(latin1) <- "latin1"
c(utf8, latin1)
}
test_that("native groups and counts use text identity across encodings", {
pair <- encoding_identity_pair()
expect_identical(length(un... |
6e7c0a5f5990b14f3c8cedac20b1a5efef4387b75ff5e2ea58464614f7a2ddef | R | 4,557 | 92 | ---
title: "Benchmarking Visualization"
author: "Will Townes"
output: html_document
---
```{r}
library(tidyverse)
theme_set(theme_bw())
fp<-file.path
pth<-"scrna/xyzeq_liver"
plt_pth<-fp(pth,"results/plots")
if(!dir.exists(plt_pth)){
dir.create(plt_pth,recursive=TRUE)
}
```
```{r}
d0<-read.csv(fp(pth,"results/ben... |
9091d8fcebd58b6728732d759fdbc76575d80994ceadc7ba715d02a6d7be7bac | R | 4,570 | 124 | library(glmnet)
database = args[1] #name of database & subset of features to evaluate
#half lives, kmer freqs, & codon freqs features
a=readme("seqFeatWithKmerFreqs.txt.gz",gzip=T)
if(database == "B") a=a[,1:10]
if(database == "BC") a=a[,c(1:10,grep("Codon",colnames(a)))]
if(database == "B5") a=a[,c(1:10,grep("5UTR"... |
eb478363b12bf565039e883ef3af39b64c9e0689eb6e4376533521b806e39b62 | R | 4,581 | 107 | library(glmnet)
database = args[1] #name of database {choose a code below}
load("alldata.RData")
if(database == "B") a=a[,1:10]
if(database == "BE") a=a[,c(1:10,grep("^ENCORI",colnames(a)))]
if(database == "BP") a=a[,c(1:10,grep("^PARCLIP",colnames(a)))]
if(database == "BR") a=a[,c(1:10,grep("^RIP|^TE",colnames(a)))... |
2c9e87bdd4b196f6136efacf85a4ed5a3b93116cb41c246107844b5f510bee87 | R | 4,669 | 125 | library(glmnet)
database = args[1] #name of database & subset of features to evaluate
#half lives, kmer freqs, & codon freqs features
a=readme("seqFeatWithKmerFreqs.txt.gz",gzip=T)
if(database == "B") a=a[,1:10]
if(database == "BC") a=a[,c(1:10,grep("Codon",colnames(a)))]
if(database == "B5") a=a[,c(1:10,grep("5UTR"... |
14da1e2ad56b6de5411e6db8c41ea3f521ee40bbb3844c5f0e54d83f5110bbb9 | R | 4,683 | 97 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-"
)
```
# dabestr <img src="man/figures/logo.png" align="right" height="139" /></a>
<!-- badges: st... |
f949fd7ceeb4ead729a99f2cd3562396f34738f0bde128b28903d739cae6a494 | R | 4,687 | 131 | #' @include internal.R
#'
NULL
#' Run fastMNN in Seurat 5
#'
#' @param object A merged seurat object
#' @param groups A one-column data frame with grouping information
#' @param layers Layers to use
#' @param assay Assay to use, defaults to the default assay of the first object
#' @param features Either a list of feat... |
4a79961530e958b1db5bfb88f7bbde92fdd043a7dc04987636d148cf5200cd44 | R | 4,697 | 155 | ---
title: "Homologous gene enrichment"
author: "Antoine Beauchamp"
date: '2022-07-25'
output:
html_document:
theme: paper
highlight: pygments
toc: true
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# Packages
```{r}
suppressPackageStartupMessages(library(t... |
6023aa36c2688623b0889435f7a3c665311c9cf9adc32acaaf092c3b9e67c79e | R | 4,712 | 133 | #' Performs F-tests for transformed cell type proportions
#'
#' This function is called by \code{propeller} and performs F-tests between
#' multiple experimental groups or conditions (> 2) on transformed cell type
#' proportions.
#'
#' In order to run this function, the user needs to run the
#' \code{getTransformedPro... |
5ece19954c8cf4caafafda8e0389b74806700d282da356e6bd516adb08a23a3e | R | 4,727 | 118 | libs <- c('ggplot2','RColorBrewer','reshape2','devtools','readr',
'pheatmap','flowCore','plyr','scales','coin','grid',
'gridExtra','tidyr','randomcoloR','viridis','tidyverse')
for (L in libs){
if(!require(L, character.only = T)) {
install.packages(L, dep = T, quiet = T)
if (!require(... |
da9fba596642e301ed9a87f857f6d4050c5c11998956731227fec6c262b1292b | R | 4,755 | 178 |
a=read.table('Kendall.txt',header=T,sep='\t')
a[,2]=as.character(a[,2])
a[which(a[,2]=='Oligodendrocyte.Precursor.Cell'),2]='Oligodendrocyte'
a[which(a[,2]=='Myelinating.Oligodendrocytes'),2]='Oligodendrocyte'
a[which(a[,2]=='Newly.Formed.Oligodendrocyte'),2]='Oligodendrocyte'
K=a
a=read.table('Multinomial.txt',head... |
01ba19c0cb45b3a45b84330a67973260480604cc26b4f8f015e323021353e02a | R | 4,774 | 120 | package_data_reference <- function(name) {
reference <- new.env(parent = emptyenv())
utils::data(list = name, package = "richR", envir = reference)
reference
}
with_global_data_state <- function(values = list(), absent = character(), code) {
names_to_restore <- unique(c(names(values), absent))
previous <- la... |
7ee7d38bb5a2aa2d1e9dcf4dc82e38ccea7f5462cf89511e9bd421f4a1ed53d6 | R | 4,788 | 112 | library(ggplot2)
library(glmnet)
library(LSD)
a=read.csv("experimentResults.csv")
round(a,3)
results = data.frame(tests = c("B vs BP","B vs BM","B vs BR","B vs Be","B vs BE",
"BE vs BER","BE vs BEM","BE vs BEe","BEe vs BEeM"), pvals =
c(t.test(a$B, a$BP, paired=T,alternative='less')$p.value,
t.test(a$B, a$BM, paired=... |
03c47373ebb105e6d6b6bf43efbfe7a2d45aeb2181be4f2cbdffd5edfdca4cf0 | R | 4,800 | 101 | # Reactome identity against the real reactome.db provider (Suggests; local, no
# network). Guards the in-memory maps of test-reactome-identity.R against
# drifting from the shape of the real maps. reactome.db is loaded, never attached.
# Fork-only: also holds the result-label checks (ORA Term, GSEA Term) that the
# ups... |
4d136acde1cfa39da942926c080002a250c416c04d24e9c6c3462d9a757e3f0c | R | 4,804 | 160 | # ----------------------------------------------------------------------------
# process_labelled_matrix.R
# Antoine Beauchamp
# Created: August 25th, 2021
#
# Process a labelled gene expression matrix
#
# Description
# -----------
# This script implements processing for a labelled gene expression matrix
# by scaling t... |
4bd8f4afdf0601810eca48ac28f8654d14dfb0800474808fbc66ac2840a5331b | R | 4,838 | 136 | load('TSNE.RData')
library('Seurat')
source('scRef.R')
ori_label=read.table('Zeisel_exp_sc_mat_cluster_original.txt',header=T,sep='\t')
pbmc@meta.data$ori=ori_label[,2]
#TSNEPlot(pbmc, group.by="ori")
table(pbmc@meta.data$ori)
####### Data preparation ##############
ref_vec=pbmc@dr$tsne@cell.embeddings
USE=which(pbm... |
525c245612229acab71a30d5b7cae43f3cd0d8b699928e11f10a74567bc9eb1c | R | 4,838 | 119 | # Characterization baseline (REQ-003): GSEA NES / ES / pval / padj / leadingEdge
# for a fixture ranked list under a fixed seed, as produced by richR 0.1.5.
# The fgsea version is part of every exact-numeric snapshot (key
# `fgsea_version`) so a diff caused by an fgsea upgrade can be told apart from
# one caused by ric... |
410b006b4a97940a3b49e84912227a3c1458004245ef15cf998af1487f24272a | R | 4,846 | 146 |
## BART: Bayesian Additive Regression Trees
## Copyright (C) 2017-2021 Robert McCulloch and Rodney Sparapani
## bartModelMatrix.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 vers... |
a145ebed02002580be9dc787115b2a73b2843010345e58250b77f0e1635138a7 | R | 4,848 | 164 | library(biomaRt)
library(tidyr)
library(ggplot2)
ensembl = useMart(host='ensembl.org', biomart='ENSEMBL_MART_ENSEMBL', dataset='mmusculus_gene_ensembl')
annotations = getBM(attributes=c('ensembl_gene_id', 'external_gene_name','refseq_mrna', "chromosome_name"), mart = ensembl)
head(annotations,10)
validchr = c(1:100,'X... |
0a792d2a08a6ce9a898db8a400f2be1305c8886de7dc754eb0cf29d60cec4343 | R | 4,859 | 121 |
## 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,... |
25ee5e29ba14a2604d4755bbfb4fe0b97fe1f81b21e5fc63eae4b7a16a29e99f | R | 4,866 | 177 | #' @importFrom BiocManager install
#' @importFrom remotes install_github
#' @importFrom Seurat IsGlobal Reductions
#'
NULL
#' @docType package
#' @name SeuratWrappers-package
#' @rdname SeuratWrappers-package
#'
"_PACKAGE"
# Set a default value if an object is null
#
# @param lhs An object to set if it's null
# @para... |
8d7e434ea47fb930a7c560b2b73e450012cc3b513a1f9dbd6fa6a51a615bde0b | R | 4,872 | 130 | # Description: script to run coloc analyses using eQTLGen
#---Import data and libraries-----------------------------------------
library(BSgenome)
library(coloc)
library(colochelpR)
library(data.table)
library(here)
library(MafDb.1Kgenomes.phase3.hs37d5)
library(tidyverse)
RBD_tidy_w_varbeta <-
fread("/home/rrey... |
dc21d92cd3f66c4100c95929e3cbfdd83c2375a0ac724653dc4588af2993b07e | R | 4,876 | 169 | ## PERMANOVA script
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")
load("files/Phyloseq_objects_Woltka/Species_PhyloseqObj.RData")
#-------------------------------------------------------------------------------
... |
f97cd396445f87363cca0f6db3ca04c5a771b20dbdff7fe6f8d4a9baf38d3444 | R | 4,891 | 105 | batch_test_annotation <- function() {
data.frame(
GeneID = LETTERS[1:6],
Annot = rep(c("T1", "T2", "T3"), each = 2),
Term = rep(c("Term one", "Term two", "Term three"), each = 2)
)
}
test_that("batchEnrich combines exact returned rows and reports every group outcome", {
annot <- batch_test_annotation... |
1ba058b4879ba16ee7b43b1ea289c956c6cbefe558e6eca42ddaeab40793cea9 | R | 4,932 | 193 | #' Contains custom <ggproto> geom_objects for plotting.
#'
#' List of geom_*:
#' - `geom_halfviolin`
#' - `geom_bootci`,
#' - `geom_proportionbar`
#' - `geom_sankeyflow`.
#'
#' @importFrom ggplot2 .pt
#' @noRd
# Halfviolin Geom
draw_group_halfviolin <- function(data, panel_scales, coord) {
coords <- coord$transform(d... |
ea4b32e7779a5bc11d1c921a4bbaed1b19c94938afd9a9667613379cd6fc3813 | R | 4,941 | 110 | ## data-raw/path.R -- rebuild data/path.rda (object `path`).
##
## Last run for richR 0.2.0 (SPEC-RICHR-003 M3.6). Running this script replaces
## data/path.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 byte-rep... |
7d8e7d66e6fe870312bfb158b9daaeb547ac8394e0266205b8d24def04fa2d85 | R | 5,006 | 199 | #' Intersect gene homologs
#'
#' @param data
#' @param homologs
#'
#' @return
intersectGeneHomologs <- function(data, homologs){
require(data.tree)
require(assertive)
#Check that data is a named list
assert_is_list(data)
assert_has_names(data)
#Check that names are Mouse and Human
if(!(any(c("M... |
486f7f59239913c27c4f7c8da24706d827580b5392af9838b4ba64569ff4936c | R | 5,016 | 143 | #' Performs t-tests of transformed cell type proportions
#'
#' This function is called by \code{propeller} and performs t-tests between two
#' experimental groups or conditions on the transformed cell type proportions.
#'
#' In order to run this function, the user needs to run the
#' \code{getTransformedProps} function... |
9eb0b0e74b9e2773cf1b4a3791deefdfb2ce44d8657ec859ad1954bb8e2526c9 | R | 5,016 | 133 | describe("Testing create_df_for_tufte function", {
describe("Given effsize_type is median_diff", {
test_that("it should return calculations for tufte lines based on median", {
np_dataset <- generate_non_proportional_dataset()
result <- create_df_for_tufte(
raw_data = np_dataset,
enquo_... |
7d039ac0d455943a3e372d2b1a9bf6b2a717e97f4845880893dc3366a775f9d9 | R | 5,047 | 175 | describe("Testing validate_load_params function", {
# pre-loading all dataset types
np_dataset <- generate_non_proportional_dataset()
p_dataset <- generate_proportional_dataset()
# for paired data test
np_dataset_trunc <- np_dataset[-2, ]
test_that("it should detect invalid x value", {
expect_error(... |
02849c5588df6685478dc5e370c599105e63afcda75128a156b7b430e8e7ee55 | R | 5,104 | 135 | library(R.matlab)
library(tidyverse)
library(ggplot2)
library(ggpubr)
fig____path = "Z:/projects/Single-cell modeling/figures/"
setwd("C:/Work/MatlabCode/projects/SingleCellModeling/SingleCellModeling")
###################################################
#New tINIT
#################################################... |
88af45fadb64469c7c24737d31c4ea10c49ea5bf98f98e99fa9144bfd5102756 | R | 5,110 | 131 | ---
title: "Integrating Seurat objects using LIGER"
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
---
... |
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