sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
f7661898e1b2e7b64bbd79da5f5041d60329a5c15e0b5c3183f0e91fbcdfe9d5 | R | 5,085 | 114 | ---
title: "230623_depmap_drug_target"
author: "Jennifer Fisher"
date: "06/23/2023"
output:
html_document: default
---
Goal/Purpose of operations:
The Archilles’s project investigated essential genes in cancer cell lines via CRISPR-Cas9 lost of function screen. We download the “CRISPR (DepMap Public 23Q... |
526e0f1b602cfe2a970f929752a0558e11d2f5d4584b2b2c10d291c9f08fe8cc | R | 5,108 | 127 | library(dplyr)
library(gratia)
library(janitor)
library(magrittr)
library(mgcv)
library(purrr)
library(rjson)
library(tidyr)
library(tidyverse)
source("/cbica/projects/network_replication/manuscript/code/scripts/main_analyses/GAM_functions.R")
##################
# Set Variables
##################
args <- commandArg... |
af6c1487c8a04afb339c006f31d1fb87c40d7d9727097b1af047f8bd1563d9a4 | R | 5,112 | 154 | library("stars")
library("xgboost")
set.seed(1)
## MODEL TRAINING --------------------------------------------------------------
## train model on whole dataset
df = readRDS("data/dataset.rds")
df$class = as.numeric(df$class) - 1
num_class = length(unique(df$class))
xgb_train = xgb.DMatrix(as.matrix(df[, -1]), label =... |
360b839543a94e22084327707c4d66ac4f909de29b67496fc2791a1dbe7aa7a0 | R | 5,143 | 162 | #!/usr/bin/env Rscript
#' COVID-19 Multi-omics Immune Response Analysis Pipeline
#' Dataset: Stephenson et al., 2021 (PMID: 33879890)
#' Description: Multi-omics single-cell longitudinal analysis of COVID-19 patients
#' Data: COVID-19 Portal (E-MTAB-10026)
# 1. Setup and Configuration -------------------------------... |
6bc8b03cc5085347233b9926449a12a24f5bda4384846a075529f5f467b45f90 | R | 5,177 | 136 | ---
title: "Bap1 Neuron Differential Gene Expression"
author: "Kat Beigel (beigelk@chop.edu)"
date: "`r Sys.Date()`"
output: html_document
---
# Neuron KO. v. WT by cluster, using Seurat v3.1.2
```{bash, include=FALSE}
wget https://github.com/satijalab/seurat/archive/refs/tags/v3.1.2.tar.gz
```
```{r setup, includ... |
661877ddb2d562239801cc99fc5cf6efba5ab12f0cf5449299b170ddff34ebac | R | 5,190 | 130 | source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/community-based_nodal_metrics.R');
source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/multi_scale_comm.R');
cbm_permutation2 <- function(df, memb, to_sample = c('signal', 'group') , seed=1987, resample=10000,
m... |
8247bf01276fd7e0c36c8e568d6ed65097814eb3d764de788fa0b19fa3647cc1 | R | 5,191 | 125 | ########################################@
## Assess conservation of modules ----
rm(list=ls())
gc()
library(CoReMo)
library(randomcoloR)
library(TBTools)
library(RTBKM)
library(here)
library(parallel)
library(tidyverse)
library(DT)
#*******************************************************************************@
## ... |
66bde1a61ac92abc9ab975393b08f128e4eddcdd419a057263c56560490bdf77 | R | 5,198 | 171 | # Parallel computing supporting functions
# Uses global options, refer to `edgeCounterOptions`
#' Set parallel computing options of edgeCounter
#'
#' @description `ecParallel()` sets global parallel computing options.
#'
#' @param PSOCK.names `name` of [parallel::makePSOCKcluster].
#' If `NULL` is provided, paralle... |
dfe78f325aff2bab1723a1651ae605ba7a102b17465adbe90833905912be3b4f | R | 5,212 | 122 | ########################################@
## Assess conservation of modules ----
rm(list=ls())
gc()
library(CoReMo)
library(randomcoloR)
library(TBTools)
library(RTBKM)
library(here)
library(parallel)
library(tidyverse)
library(DT)
#*******************************************************************************@
## ... |
665dbcd52b40ad49be8df326aeb1403a27a40647556c6892d6d4d3b6c9cdc144 | R | 5,217 | 116 | library(Seurat)
library(ggplot2)
library(dplyr)
ferret_seurat_sub <- readRDS("/data/mayerlab/neuhaus/ferret_proj/final_results/01/ferret_seurat_clean_wDF_sub.rds")
ferret_ref <- readRDS(file = "/datastore_share/Users/neuhaus/ferret_proj/data/Mustelaputorius_integrated.rds")
ferret_ref <- FindVariableFeatures(ferret_r... |
995959b43a710372fd04584c8e7c51c71617c007c92b91bda1a352c7207d3812 | R | 5,217 | 113 | # File for cleaning and integration of d9 objects
library(Seurat)
library(dplyr)
library(ggplot2)
library(plotly)
reticulate::py_run_string("import sys")
# This has been replaced by the creation of sub objects in separate analysis
# # Load in each object
# D9RA <- Read10X("D9RA")
# D9BMP <- Read10X("D9BMP"... |
6be430525dd46df29df0228fab0d054d177b3b165eb6ab2ca1162d9a0054ae81 | R | 5,224 | 137 | ############### SYNOPSIS ###################
# Create gene coordinate file compatible with CELLECT-LDSC and CELLECT-MAGMA
### DESCRIPTION
# ...
### OUTPUT:
# ....
### REMARKS:
# ....
### REFERENCE:
# GRCh37 assembly in Ensembl: https://www.ensembl.org/info/website/tutorials/grch37.html
# https://grch37.ensembl.org... |
5634ebf49f56821dc64bc0c2f3239dfa99687926054b2a7be9b068830c785bdb | R | 5,231 | 165 | library(beeswarm)
library(BiocParallel)
library(DT)
library(edgeR)
library(ggplot2)
library(here)
library(limma)
library(MASS)
library(openxlsx)
library(plotly)
library(RColorBrewer)
library(readxl)
library(scales)
library(tibble)
library(tximport)
library(reshape2)
library(WGCNA)
library(CoReMo)
library(lme4)
library(... |
339591a5a84ded26644e644f4c63bca867934b70577e7deaf518b9f18f1fba48 | R | 5,236 | 122 | ---
title: "Using UCell with SingleCellExperiment"
author:
- name: Massimo Andreatta
affiliation: Department of Pathology and Immunology, Faculty of Medicine, University of Geneva, 1206, Geneva, Switzerland; Swiss Institute of Bioinformatics, 1015, Lausanne, Switzerland
- name: Santiago J. Carmona
affiliation: Depa... |
75b1398b16b539d7b2875198c51439cae140d1cd04be6174d8d8884c4d96a534 | R | 5,250 | 157 | # This script adjusts an ART error profile to increase the cumulative error
# rate to a given value.
## Optimization of multiplier of counts vector to generate
## a distribution with (approximately) a given error rate
q2p <- function(q) 10^(-q/10)
cum_err <- function(p, counts) {
s <- sum(counts)
sum(p * count... |
665ec515ea89a11dd6125d45c5a4dc5cc02b18ec0e2bbd0f9a84da45c8193100 | R | 5,258 | 139 |
# Plot the Accuracy and RTS of the Nav-Math Experiment
library(ggplot2)
library(ggforce)
library(ggdist)
library(gghalves)
library(ggprism)
library(dplyr)
library(rstatix)
library (lsr)
library(lme4)
library(effectsize)
library(ez)
library(lsmeans)
library(lmerTest)
## Nav-Math Experiment
# Accuracies -----
# Loa... |
c62c9221a164ed23b57c57b0475aef032ed92c587aa2fd9b0c32b6b52cd5f798 | R | 5,285 | 120 | ---
title: "Salicylate Statistics"
author: "Aaron Wong"
date: "15 September, 2022"
output:
html_document: default
pdf_document: default
---
```{r setup, include=FALSE}
library(lme4)
library(nlme)
library(ggplot2)
if(!require(here)){
install.packages("here")
}
library(plyr)
if(!require(multcomp)){
install.packa... |
63b7e068cb8a5aeb392cd18a8bef42fc1f157f2b532a6544fd6b8fc042b30407 | R | 5,346 | 167 | library(beeswarm)
library(BiocParallel)
library(DT)
library(edgeR)
library(ggplot2)
library(here)
library(limma)
library(MASS)
library(openxlsx)
library(plotly)
library(RColorBrewer)
library(readxl)
library(scales)
library(tibble)
library(tximport)
library(reshape2)
library(WGCNA)
library(CoReMo)
library(lme4)
library(... |
987afb7108091bdfb7d7b2e78b71b0623f9548ab0870590154c13bb1adc83e69 | R | 5,355 | 118 | library(openxlsx)
library(ggplot2)
#Figure 3A
## MS4A1 2,5-DMP
File = "17 ms4a screening/20211203_Ms4a2,6D.xlsx"
raw1 = read.xlsx(File, sheet = "20211203_well6_cd20")
raw1 = raw1[,grep("cell",colnames(raw1)), drop = F]
raw1 = raw1[-(1:329),]
F0=apply(raw1[1:30,],2,mean) # generate F0
delF=sweep(raw1,2,F0,"-") ... |
9c0f347a18d220226c9854ca546b12db9935d0d428739e5d84ad4ec59c0f4225 | R | 5,381 | 176 | ## ---Libraries ------
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
library(patchwork)
library(rtracklayer)
library(cowplot)
library(BSgenome.Hsapiens.UCSC.hg38)
library(JASPAR2020)
library(TFBSTools)
library(patchwork)
##--- Functions --------
howMa... |
82785d7f3ac1131c17815e3c21593fbc693fb0a9d3976187927265972b4b8a4b | R | 5,388 | 169 | # perform the SIMLR clustering algorithm for large scale datasets
"SIMLR_Large_Scale" <- function( X, c, k = 10, kk = 100, if.impute = FALSE, normalize = FALSE ) {
# check the if.impute parameter
if(if.impute == TRUE) {
X = t(X)
X_zeros = which(X==0,arr.ind=TRUE)
if(length(X_zeros)>... |
6bec987d3645f53e1f1f3fb514f56a4b1c3269568e5b81b714c5e48352e2dcd1 | R | 5,396 | 117 | ## Survival for gene
library(plyr)
library(dplyr)
library(survival)
library(surviplot)
library(ggplot2)
library(ggsignif)
setwd("/home/bnovotny/proteomics/proteomics_analysis/CSF")
## Read in protein data
features <- readRDS("data/00-csf_feature_data.rds")
# Read in protein expression data and reconfigure
log_readin... |
9b5edace2c9ef68d79ac2afea8151e303945c641f3d4238c84b0b86cdde12014 | R | 5,399 | 147 | # Initialise
pacman::p_load('R.matlab','tidyverse','magrittr','emmeans','patchwork','multcomp','bbplot')
set.seed(17)
# Load and prepare the data ----
m <- readMat(here::here('data','PD_model_pars.mat'))
m = m$cellV
m = rbind(m[,,1],m[,,2])
m <- m[complete.cases(m),]#remove NaNs
df_lba_par = as.data.frame(m)
df_lb... |
8b5f61aac878ead9dfd4f90704e98ea9e0d5dcd493a12206cfe267b2a8919572 | R | 5,402 | 132 | # Load necessary libraries
library(dplyr)
library(Seurat)
library(EnhancedVolcano)
library(clusterProfiler)
library(org.Hs.eg.db)
library(enrichplot)
# Set global options and paths
dpi <- 300
# Function to perform differential expression analysis and generate plots
analyze_clusters <- function(dat0, cluster1, cluster... |
7de115ba60a1cea4e7f52eff01a79277dd523bcad0c357aeee0b84c3ae9aad73 | R | 5,417 | 157 | #### code for Figure 6
library(Seurat)
library(ggplot2)
library(sctransform)
library(dplyr)
library(CellChat)
library(patchwork)
library(ComplexHeatmap)
#### load the objects ####
setwd("~/Downloads/Yi Huang et al objects/")
fHFDcci <- readRDS(file = "fHFDcci.rds")
fLFDcci <- readRDS(file = "fLFDcci.rds")
mHFDcci <... |
c87db05413ee344e6e726273c405c1ee8f53016d22bad9bec883b2ff3058b8db | R | 5,424 | 180 | ---
title: "figure 2"
author: "Pavitra Roychoudhury"
date: "2023-07-16"
output: html_document
---
Panels for Figure 2
Import matrices
```{r}
rm(list=ls())
library(Seurat)
library(tidyverse)
options(dplyr.width=Inf)
library(cowplot); options(scipen = 999)
all3<-readRDS('./seurat_analysis_all3.rds')
```
TSNE plot t... |
18200a16725abd71178185a8c17ca4e8617ed52469fc186db67be3c26fb668ef | R | 5,450 | 137 | source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/community-based_nodal_metrics.R');
source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/multi_scale_comm.R');
cbm_permutation <- function(df, memb, to_sample = c('signal', 'group') , seed=1987, resample=10000,
me... |
1eec026421883c62ebe1c2904b28db41787013f8b2fff54454b90135870005d4 | R | 5,451 | 87 | ### Hong X. et al 2023
### produce figure 3
### hongxiaoning@outlook.com
library("ggplot2")
library("ggpubr")
library("ggsci")
#Figure3 A&b
for (group in c("Young", "Aged")) {
data<-read.table(paste0(group, "_Genomic_coverage.txt"), sep = "\t", h = T, check.names = F)
data$Group <-factor(data$Group, levels =rev(... |
31950215e0d3643f74c24ff2c77ca54efb0f3ddd7c584125b313d19b2168bd94 | R | 5,512 | 163 |
############################################################################################################
########## R script for:
##########
########## Statistical indices of masculinity-femininity: A theoretical and practical framework.
##########
########## Marco Del Giudice
##########
########## Script for Fi... |
cf7aa7703a265e18365a0c418c69ed780ff17f02ed84767e40b96ac68bbe3977 | R | 5,512 | 145 | ---
title: "Intro to the qqman package"
author: "Stephen D. Turner"
date: '`r Sys.Date()`'
output:
html_document:
toc: yes
---
<!--
%\VignetteEngine{knitr::knitr}
%\VignetteIndexEntry{Intro to the qqman package}
-->
```{r, include=FALSE}
library(qqman)
library(knitr)
opts_chunk$set(comment=NA, fig.width=12, fig... |
a434863bcbeea3bb4581bfa337287cbcc64e61b9f4a309813f311c01c12bd51f | R | 5,514 | 127 | ## Packages required
if (!('fda' %in% installed.packages()[,'Package'])){install.packages("fda")}; require(fda);
###########################################################################################################
modularity_maximization <- function(g, gamma = 1, weighted = T, class = TRUE){
#
# This functio... |
e4312bffa0e4eb2dd084f97a6584e84ceabb813d91b6a890b88057ad5612ed8d | R | 5,524 | 81 | # Figure 1
rm(list = ls())
require("writexl")
library("dplyr",quietly=T)
library("ggplot2",quietly = T)
library("ggtree",quietly = T)
library("ggnewscale",quietly = T)
library("ggtreeExtra",quietly = T)
setwd("Source_Data/Figure_1/Figure_1A/")
data.df<-read_excel("Figure_1A.xlsx")
All.Sp = read.tree("Figure_1A.all.s... |
4f9983896851b701f531db5988512b219818fcdc77e98c61ccf761ba12c52a8f | R | 5,534 | 120 | #########
## motif analysis MEIS/DLX
library(plyranges)
library(tidyverse)
library(data.table)
library("BSgenome.Mmusculus.UCSC.mm10")
library(seqinr)
library(ChIPpeakAnno)
genome.mm10 <- getBSgenome("BSgenome.Mmusculus.UCSC.mm10")
merged_peaks.df5 <- read_rds("./Table_S5_ChIP-seq.RDS")
meis2_peaks.df <- merged_pea... |
6ea65ab10212b53045797a9514e7b48b187d5e53bc0aafb07a51e34d4d1cb810 | R | 5,541 | 78 | # Set directory to source file loaction
setwd(paste0(figureDirectory, "Figure5-GOenrichment" ) )
load("resultGO.RData")
load("numCandGens.RData")
resultPlus <- resultGO[resultGO$Expression.direction == "Increased",]
resultPlus <- resultPlus[1:20,]
resultPlus$Description[resultPlus$Description == "calcium-dependen... |
c5da1d7cb07e0cff1ca99fee5ba2aa5d602459af9e2dc1d330386ff566100258 | R | 5,544 | 118 | data <- Read10X("/data1/zj_wt/outs/filtered_feature_bc_matrix/")
wt <- CreateSeuratObject(counts = data, project = "wt", min.cells = 3, min.features = 200)
wt[["percent.mt"]] <- PercentageFeatureSet(object = wt, pattern = "^mt-")
VlnPlot(wt, features = c("nFeature_RNA", "nCount_RNA", "percent.mt"), ncol = 3)
plot1 <- ... |
3af9f714a74a43f6714129004f63f75a5b4d693f3db0a25c47b2b245bfe471de | R | 5,569 | 170 | #!/usr/bin/env Rscript
#' COVID-19 Single-cell Atlas Processing Pipeline
#' Dataset: Ren et al., 2021 (PMID: 33657410)
#' Description: Large-scale single-cell atlas of COVID-19 immune features
#' GEO: GSE158055
# 1. Setup and Configuration -------------------------------------------
suppressPackageMessages({
librar... |
974739dc4e5cb880990724b0fcd6b7a97cef7de3a37fccade34d227a9cfb7acf | R | 5,596 | 163 | get_clusters_mat_kmeans <- function(path,
nbins=40,
min_frames=500,
max_nc=20,
nreps=20,
override=F,
clu... |
b174c72c71f4e7f5ab26ee786ddc3dbc6d20bea4ec879360e6fc52f55126c84d | R | 5,602 | 178 |
suppressPackageStartupMessages({
library(Seurat)
library(stringr)
library(sctransform)
library(future)
require(scales)
library(RColorBrewer)
library("readxl")
library(dplyr)
library(dendextend)
})
# After alignment with cellranger, (see cellrangercount.sh)
# the "filtered_feature_bc_matrix" per sam... |
3fd9220b2c0d3081a4e4e70781139f20618bb99cb9beca4519a191880f8e2607 | R | 5,616 | 118 | #check here for example on how to plot average across landscape:
#https://gadm.org/download_country.html
rm(list=ls())
#load required packages
library(ggplot2)
library(rgdal)
library(tmap)
library(dplyr)
library(sf)
library(raster)
library(rnaturalearth)
############### Fig. 1A - P. muralis distribut... |
69f1813109796567a65d2c26c15bdd6dbf019a76ba65bbef15c00213a9b791f3 | R | 5,618 | 132 | ## Prepare temporal variance and network connectivity for ABCD
## Network connectivity between 12 networks:
# auditory (AUD)
# cingulo-opercular (CON)
# cingulo-parietal (CP) aka ParietoOccip?
# default mode (DMN)
# dorsal attention (DAN)
# frontoparietal (FPC)
# retrosplenial (RSP) aka MedialParietal
# sensorimotor-ha... |
1d2f3f1a1685b51c8d6d00575f6e89200bac606b35d562ad4cfe2262cb2c9202 | R | 5,636 | 179 | source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/nested_list_functions.R')
source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/consensus_community.R')
source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/signed_louvain.R')
source('/home/cesar/Dropbox/R/Network sufficiency/Modular_c... |
fbd2bf3ddf7957a5f18d5a570e7e652b588a4b9e0dba5d1d34f52ecb6d877c92 | R | 5,640 | 193 | #!/usr/bin/env Rscript
#' Description: Process and using Seurat v4 to label cell types
#'
#' Datasets:
#' - PMID: 32514174 (Wilk et al., 2020)
#' - PMID: 32651212 (Lee et al., 2020)
#' - PMID: 32810438 (Schulte-Schrepping et al., 2020)
#' - PMID: 32788292 (Arunachalam et al., 2020)
#' - PMID: 33657410 (Ren et al., 20... |
87cf48281e34a01c626aa1b3c1f064c7e0a9c6c6cb2b6d9e392f582bad4ac7ff | R | 5,662 | 117 | ################################################################################
# AffectParametersSwath.R 2.0.2 #
# Authors: Alexandre Sta (Institut Curie) #
# Contact: alexandre.sta@curie.fr ... |
b7852d6c8965a9123d6c97487b582fb06ae8487ccdce9ad11720a4fd4517fff8 | R | 5,675 | 190 | if (!('aricode' %in% installed.packages()[,'Package'])){install.packages("aricode")}; require(aricode);
source('/home/cesar/Dropbox/R/Network sufficiency/Modular_codes/consensus_community.R');
multi_scale_comm <- function(g, ALG = signed_louvain, gamma = seq(-1, 2, 0.05), R = 1, seed = 87, param_name = 'gamma', sec_pa... |
8c7122496f856833c9812b8571a9ebb1ef258436bbfcdb5abbe2155b66dc0269 | R | 5,679 | 138 | ########################################@
## Assess conservation of modules ----
rm(list=ls())
gc()
library(CoReMo)
library(randomcoloR)
library(here)
library(parallel)
library(tidyverse)
library(DT)
#*******************************************************************************@
modAname <- "Correlated genes in H... |
4553350692a9f976936065c5c6c1a175546070fba6c0557e03573eaafe3a5f73 | R | 5,683 | 124 | #' Calculate module enrichment scores from single-cell data
#'
#' Given a gene vs. cell matrix, calculates module/signature enrichment scores
#' on single-cell level using Mann-Whitney U statistic.
#' UCell scores are normalized U statistics (between 0 and 1), and they are
#' mathematically related to the Area under th... |
ee4b51da68c480f7e860fe8c5286ae9a24280a1f2b9c732097e88c36d57a92f4 | R | 5,707 | 123 |
main = function()
{
extention = 1e2
# file = "~/Workspace/Datasets/ORM/allfiber_All.HeLa.async.gtf"
# file = "~/Workspace/Datasets/ORM/allfiber_C.0.gtf"
for(file in paste0("~/Workspace/Datasets/ORM/", c("D.20.gtf", "D.30.gtf", "D.45.gtf", "D.60.gtf", "D.90.gtf"))) {
data_df = readr::read_tsv(file, col_na... |
10d148e764d1aad70a943465a39491eca42f7a0c4399df505d29c652248ea968 | R | 5,719 | 217 | ---
title: "220427_transfer_learning_gene_label_testing"
author: "Jennifer Fisher"
date: "4/27/2022"
output:
html_document:
toc: true
theme: united
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
# START
Goal/Purpose of operations:
For this analysis, GTEx Brain Frontal Cort... |
6617b9f9afab9919bd027dff5cf0db33227568c2526e9a6ea9b07831cf4dac61 | R | 5,733 | 191 | ---
title: "top_markers_3perc_sporos"
author: "T Attenborough"
date: "05/04/2023"
output: html_document
---
Load packages
```{r}
library(Seurat) #Seurat v4.0.2
library(dplyr)
library(patchwork)
library(here)
library(ggplot2)
library(clustree)
library(ggrepel)
library(tidyverse)
#sessionInfo()
```
This saves today's... |
fcf7d3f2dcd6aaa89a998bd0170536725257725c57204dba90d3ee78f9c0a35e | R | 5,772 | 140 | #!/usr/bin/env Rscript
# plot-qq.R <stats TSV> <destination image file> [<comma-separated "aligner" names to include> [title]]
filename <- commandArgs(TRUE)[2]
# Install required packages
list.of.packages <- c("tidyverse")
if (endsWith(tolower(filename), ".svg")) {
list.of.packages <- append(list.of.packages, "s... |
430d0d8f6ca65e6ec2ab572d92bacc16fdf74700bc3f77da458bc1b7a9880f22 | R | 5,795 | 137 | #' Plot a Patient Similarity Network
#'
#' Plot a Patient Similarity Network
#'
#' @param info Dataframe, two character columns, first has sample's IDs (have to match with column names of the count matrices),
#' second has sample's groups (only two as a pairwise DE analysis, for example c(AD,AD,AD,HEALTHY,HEALTHY,HEALT... |
8364479291dfc19f256f87d495e8afd7ea2ed05d567cecf257d9cdcaebce4a18 | R | 5,808 | 142 | buildReference_on_UMAP <- function(exp_ref, metadata_ref, vars = NULL, sc2d, K = 100, verbose = FALSE,
do_umap = TRUE, do_normalize = TRUE, vargenes_method = "vst",
vargenes_groups = NULL, topn = 2000, tau = 0, theta = 2,
... |
1ced4add8d795cb38a6cc8176ab61efb5abb7691ae55ed14baf0706f4627105e | R | 5,809 | 203 | # compute and returns the multiple kernel
"multiple.kernel" = function( x, cores.ratio = 1 ) {
# set the parameters
kernel.type = list()
kernel.type[1] = list("poly")
kernel.params = list()
kernel.params[1] = list(0)
# compute some parameters from the kernels
N = dim(x)[1]
KK =... |
d13bf4150fbf7938337986eee3e6c2e80abc961894f3a693a98e7a228c2520ec | R | 5,809 | 152 | Dependency file generated on Sat Mar 2 15:13:30 2024
####################################################################################################
gruffi.optional.R
####################################################################################################
$`c("package:Seurat", "package:SeuratObjec... |
760e707e71f82ca17492279c551145f60a33de14188781ebb5dfacf5a9d8b932 | R | 5,819 | 205 | ---
title: "Compare eisaR spliced & ensembl transcriptomes"
output: html_notebook
---
## Setup
### Load Libraries for import
```{r setup}
library(Biostrings)
library(BSgenome)
library(eisaR)
library(GenomicFeatures)
library(tidyverse)
library(rtracklayer)
```
### File and Directory Setup
```{r}
## get file directo... |
fc77d45f2ea1ef629ed80d4f51b02c8b1d9aac5e61438eec506c3f508d8835ea | R | 5,851 | 141 | setwd('/work/AnneSophiaHartebrodt#6698/ms-project/')
require(rhdf5filters)
require(rhdf5)
require(Rhdf5lib)
renv::activate('/work/AnneSophiaHartebrodt#6698/ms-project/r-single-cell')
renv::repair()
set.seed(11)
require(Seurat)
library(tidyverse)
require(data.table)
require(ggrepel)
require(colorspace)
library(circlize)... |
3eeea07450e43f893ababcd887299213c78c64fd0a41e7febc58b240fa32642b | R | 5,893 | 154 | #' Load a VCF file
#'
#' This function loads a VCF file as a matrix.
#'
#' @param VCF_DATA loaded VCF file (read_vcf() output)
#' @param SEQINFO the object with chromosome characteristics: load_chr()
#' @param SAMPLE (optional) samples to plot (default is all samples in VCF file)
#' @param VAR_FLAG (optional) the VCF v... |
bc9c2ad952dd7e5f6e466cc4f195fd02ed2a4e0d4f2692f33421fc3c33f3964c | R | 5,893 | 169 | ---
title: "Integration of WT and KO data with Glia and Neurons for RNA velocity of all cells"
subtitle: "Following Seurat 4 Integration Methods (https://satijalab.org/seurat/articles/integration_introduction.html)"
author: "Kat Beigel (beigelk@chop.edu)"
date: "`r Sys.Date()`"
output: html_document
---
```{r setup, i... |
cd1b0137d34cbd7228dee1d507f43a31f845e49500aab3f8e8c233096978c220 | R | 5,930 | 109 | ################################################################################
# PCA.R 1.1.1 #
# Authors: Patrick Poullet, Stephane Liva, Guillaume Arras (Institut Curie) #
# Contact: myproms@curie.fr ... |
03e5a54d1204332d6df9c0409b2589e679140219f25270991c56df1cd9a6413d | R | 5,931 | 246 | ---
title: "QC"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(Seurat)
library(cowplot)
library(SCFunctionsV3)
library(dplyr)
library(tidyr)
library(stringr)
library(tidyverse)
li... |
40da8edd8cfb46740af18fe2d82627cc22514d8864f71276f289d0e0e66d2da4 | R | 5,948 | 162 | # Load packages
renv::restore()
library(lme4)
library(lmerTest)
options(scipen=10000)
# Load data
dat <- read.csv('../data/aggregated/concat/fpt-del.csv', header=TRUE)
# Scale
dat$length <- scale(dat$length)
dat$prev_length <- scale(dat$prev_length)
dat$count_ave_kika <- scale(dat$count_ave_kika)
dat$prev_cont_ave_k... |
d6086315839aaabf85343202f4e09e4ebb2e5dda98f0892877353426829b2358 | R | 5,955 | 234 | ---
title: "`r thetitle`"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r}
library(Spaniel)
library(scran)
library(scater)
library(batchelor)
library(dplyr)
library(Seurat)
library(SCFunctionsV3)
library(cowplot)
l... |
fa5674c179a343e4ab2c90cbb7c42651742b9b620a93a4ba2ed34a011fe89dee | R | 5,972 | 132 | #! /usr/bin/env Rscript
rm(list=ls())
library(GenomicRanges)
library(data.table)
library(eulerr)
options(bitmapType="cairo")
Path <- "/path/"
setwd(Path)
###########################################
# point towards loci txt
###########################################
t1 <- fread(paste0(Path, "results/T1/T1_mostest... |
89f5f7dfe9400b6e118069c580a773773a73e01066d0decaece08ccd4a3599f2 | R | 5,991 | 145 | #!/usr/bin/env Rscript
#
# plot-log-roc.R <stats TSV> <destination image file> [<comma-separated "aligner" names to include> [title]]
#
# plots a pseudo-ROC that allows the comparison of different alignment methods and their mapping quality calculations
# the format is clarified in the map-sim script, and should be a t... |
faa999dd8cd3b15c27627034e0de55110ddf9d5a91ec976c9bf3b22886379955 | R | 6,005 | 179 | ## infer clone IDs from sparse matrix ##
library(readr)
#library(philentropy)
#library(dendextend)
library(data.table)
library(dplyr)
library(tibble)
library(igraph)
library(ggplot2)
library(ggrepel)
## function to create network from sparse matrix ##
build_graph_from_sparse_mtx <- function(mtx) {
## init isolated ... |
3990e0f1d21f8215c8fe64f7c019f02c257304b4632e22666d32b7c460942c5a | R | 6,008 | 196 | #!/usr/bin/env Rscript
################################################
################################################
## Fucntions ##
################################################
################################################
# Helper function for NULL condition
string_to_nul... |
3db18736fcac6d753960a00149cfda69a2b7d14a70270a07081c2f697d7df7a0 | R | 6,011 | 132 | library(base64enc)
library(cifti)
library(dplyr)
library(gifti)
library(magrittr)
library(rjson)
library(stringr)
print("This script 1) creates a list of ciftis to extract timeseries data from, and 2) concatenates timeseries and makes connectivity matrices ")
##################
# Set Directories
################## ... |
b14bb7ab2ca0bf7a182381c27e16bec2887ccc18b2d532d68dd749463170215d | R | 6,011 | 124 | #' Calculate module enrichment scores from single-cell data (Seurat interface)
#'
#' Given a Seurat object, calculates module/signature enrichment scores at
#' single-cell level using the Mann-Whitney U statistic.
#' UCell scores are normalized U statistics (between 0 and 1), and they are
#' mathematically related to t... |
8848591151da77e51d5083f9e15708b9a49fb7e12e5b1681f9cca7033d051d0a | R | 6,016 | 121 | ####################prep###############
rm(list = ls())
options(stringsAsFactors = F)
library(ggplot2)
library(ggrepel)
library(dplyr)
candidate_genes = c('AKT1','APOE',"BDNF","CHRNA7","COMT","DAO","DAOA","DISC1","DRD2","DRD3",
"DRD4","DTNBP1","GRM3","HTR2A","KCNN3","MTHFR","NOTCH4","NRG1","PPP3CC,... |
f231a542721c16f735537cd4502e13330e23b9203dad71e1e7febfd42094a4f9 | R | 6,043 | 131 | library(base64enc)
library(cifti)
library(dplyr)
library(gifti)
library(magrittr)
library(rjson)
library(stringr)
print("This script 1) creates a list of ciftis to extract timeseries data from, and 2) concatenates timeseries and makes connectivity matrices ")
##################
# Set Directories
################## ... |
207040a64ada6e3f2cdb0196faa13eebfa890d1ad044be02b43c89c2fcc08ed7 | R | 6,044 | 211 |
suppressPackageStartupMessages({
library(Seurat)
library(stringr)
library(sctransform)
library(future)
require(scales)
library(RColorBrewer)
library("readxl")
library(dplyr)
library(dendextend)
})
# After alignment with cellranger, (see cellrangercount.sh)
# the "filtered_feature_bc_matrix" per sam... |
8ca31dfee0436c6862531da24d3b0547e24897eccd80c62c9b46e34950651291 | R | 6,062 | 104 | ## Fig 3a
library(tidyverse) #v1.3.1
library(plyranges) #v1.18.0
# import peak table
merged_peaks_meis2.df <- read.table("./Table_S5_ChIP-seq.txt",header= T, sep="\t") %>%
filter(meis2_peak == "y") %>%
dplyr::select(12:17) %>%
mutate(strand = "+") %>%
dplyr::rename("seqnames" = "chr_meis2", start ... |
d10369ed9ce64429e0930b2c936ad2af37973b2e8692e8b6c70d838d087567e3 | R | 6,067 | 150 | ## logistic regression: interneuron vs PN ##
library(Seurat)
library(dplyr)
library(glmnet)
library(ggrepel)
library(MASS)
library(bmrm)
library(ggsignif)
set.seed(4)
## helper functions ##
train_model <- function(expr_mtx, target_vec, regr_model = "binomial", type.multinomial = "no") {
## account for balanced des... |
fe56adae26e5014879a34b1f43a24424b925f11722e70d58fda398b35eebe83c | R | 6,072 | 194 |
significant_comps <- function(metrics_list = NULL, comp_list, alpha = 0.05, trim = FALSE,
coi = NULL){
# metrics_list List of network metrics
# comp_list output of the cbn_permutation() or nodal_permutation_test() functions.
# this output has to be from t... |
1dad0e195bf5f78c49a889c9fcd649424908dad162a388c80923b235bee81a8f | R | 6,096 | 133 | # Plot summary of burst features
#
# Vinding, M. C., Eriksson, A., Low, C. M. T., Waldthaler, J., Ferreira, D., Ingvar, M., Svenningsson, P., & Lundqvist, D. (2021). Different features of the cortical sensorimotor rhythms are uniquely linked to the severity of specific symptoms in Parkinson's disease [Preprint]. medRx... |
ddfc30ca73f8b76cc83751a720366f5dbab4b33ed7630c4139bdd3b4a8b4d8be | R | 6,101 | 201 | ResiliencyGeneticDistance <- function(ReadsTableName, expname, ComparisonMetaDataName, limit) {
ReadsTable <- read.csv(ReadsTableName, row.names = 1)
MetaData <- read.csv(ComparisonMetaDataName)
if( length(which(MetaData$Sample == "")) != 0) {print("STOP!!!")}
SampleNames <- MetaData$Sample[order(MetaData$G... |
1cc865e00373c6a3adda4ee398773ecbffadb7ab0856c912be65adfe89d4c2c1 | R | 6,132 | 97 | # Gene ontology of peaks that happen between 0-5, 5-10, 10-15, 15-20
library(gprofiler2)
library(data.table)
# Starting by just doing dI1
# THIS IS A LITTLE TRICKY AS THE MISSING DATA AT LOW PSEUDOTIME VALUES REALLY SKEWS THE RESULTS
# IT PRACTICALLY STARTS AT 5
# Get marker genes from all dI1 clusters
dI1... |
f4d3a22019b8f36469f699be92e6f6db81b9df7b800fe9d6d68fa361a993f478 | R | 6,132 | 215 | ---
title: "MeniscusTrackingRefinement"
author: "Tomas Pierce"
date: "2022-10-23"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
Load libraries
```{r lib load, include=FALSE}
require(ggplot2)
require(dplyr)
require(psych)
require(sys)
require(purrr)
require(gridExtra)
```... |
e0d9a749b42fe1c8f26e1f1e0c8bb1271f2f9098678a7325cf84427bf42556ad | R | 6,147 | 173 | #!/usr/bin/env Rscript
#' COVID-19 Temporal Immune Response Analysis Pipeline
#' Dataset: Liu et al., 2021 (PMID: 33713619)
#' Description: Time-resolved systems immunology of COVID-19 progression and recovery
#' Data: COVID-19 Cell Atlas (https://www.covid19cellatlas.org/)
# 1. Setup and Configuration --------------... |
468960653846e9a928130575dca5327a0e4c709a516593292bd7daa9fb1887c1 | R | 6,157 | 140 | rm(list=ls())
library(openxlsx)
library(tidyverse)
library(Hmisc)
library(corrplot)
library(pcaMethods)
library(dplyr)
options(scipen=999)
#Import the current new data set - this includes data up till 2018
data <- read.csv("Phenotype_data_final_June2023.csv")
#Import lineage assignments per population... |
1a18ddd8a87edb9f65143107a29a88eef0d62b2091e7914e1eea622685b255b7 | R | 6,162 | 247 | ---
title: "QC"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(Seurat)
library(cowplot)
library(SCFunctionsV3)
library(dplyr)
library(tidyr)
library(stringr)
library(tidyverse)
li... |
746314cabf398a50cac82c0b270ad7daa71543c06c125dc2fc1d9c4b2c7ce697 | R | 6,167 | 138 | library(base64enc)
library(cifti)
library(dplyr)
library(gifti)
library(magrittr)
library(rjson)
library(stringr)
library(tidyr)
print("This script 1) creates a list of ciftis to extract timeseries data from, and 2) concatenates timeseries and makes connectivity matrices ")
##################
# Set Directories
##... |
5c0c536f5cf4776f0eca0deb47e4cc568a0ba3732a526a885ddb7d64bf684f0a | R | 6,183 | 130 | ## import package
.libPaths("~/R/x86_64-pc-linux-gnu-library/4.2")
library(Seurat)
library(ggplot2)
library(ggpubr)
library(sctransform)
library(harmony)
library(dplyr)
library(plyr)
## load all the objects
Hypo_integrated <- readRDS("~/Yi_Huang/objects/Hypo_integrated.rds")
AgRP_subclusters <- readRDS("~/Yi_Huang/obj... |
ee21cc551f970a3f66bf313507fe8fc3725553a97047893efe487b50c3552d58 | R | 6,204 | 150 | #
# This is a Shiny web application. You can run the application by clicking
# the 'Run App' button above.
#
# Find out more about building applications with Shiny here:
#
# http://shiny.rstudio.com/
#
# Initial is copy and paste from other application. will adapt
# PACKAGE LOADING ----
library(shiny)
... |
d9be89040ed4b72748d219ddb54eb3f62fe17a9997540fde391df0a6e2a5b4f2 | R | 6,218 | 170 | #!/usr/bin/env Rscript
################################################
################################################
## Functions ##
################################################
################################################
#' Check for Non-Empty, Non-Whitespace String
#'
#... |
ca2cf6a1af66f0e69b75afecdfc8c752e7e1376282047e1a879554f4d956bfd8 | R | 6,226 | 141 | library(base64enc)
library(cifti)
library(dplyr)
library(gifti)
library(magrittr)
library(rjson)
library(stringr)
library(tidyr)
print("This script 1) creates a list of ciftis to extract timeseries data from, and 2) concatenates timeseries and makes connectivity matrices ")
##################
# Set Directories
##... |
7914e079e19e56df4f8f1165111f7b0801fa3cd73eca10b62cc30f0795112a91 | R | 6,240 | 111 | tlx_read_paper_samples = function(annotation_path, data_path)
{
tlx_read_samples(annotation_path, data_path) %>%
dplyr::filter(
organism=="mouse" & celltype=="NPC" & sample!="VI035" & (
experiment=="APH concentration" |
grepl("Nrxn1|Ctnna2 promoter/enhancer", experiment) & grepl(".*\\(... |
4c2096481e8d959c08b48b50bb7c606e24d2a7bc3e581f00f2480d65bc0b46a6 | R | 6,259 | 138 | #!/usr/bin/env Rscript
########################################################
#
# Reusable evaluation containing typical columns
# used in experiments using reading latency as a
# dependent variable with memory and syntactic
# predictors.
#
########################################################
#################... |
fbb35bfe0c56e574b154ba330692fb1b6db25b7c2f2cf10f783f4c92cbf06138 | R | 6,283 | 136 | library(base64enc)
library(cifti)
library(dplyr)
library(gifti)
library(magrittr)
library(rjson)
library(stringr)
print("This script 1) creates a list of ciftis to extract timeseries data from, and 2) concatenates timeseries and makes connectivity matrices ")
##################
# Set Directories
##################... |
ec0329629c42e3f3e2f182d52bb059d2f1ca6656a010064da53daab3bad2f19d | R | 6,296 | 146 |
mosaick_ll <- function(varn=NULL, i, out.tif, in.path="./tt", out.path="/data/LandGIS/predicted250m", ot="Int16", dstnodata=-32768, dominant=FALSE, prj=NULL, resample="near", metadata=NULL, aggregate=FALSE, te, tr, only.metadata=TRUE, pattern=NULL){
if(missing(out.tif)){
out.tif <- paste0(out.path, "/", varn, "_... |
1b3125eb1c7b7fea7f5ad10f211544907c9138d9f1f8babafc74afbbd767bf32 | R | 6,300 | 286 | ---
title: "Monocle RPCs T1 RGCs"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(tidyverse)
library(Signac)
library(Seurat)
library(JASPAR2020)
library(GenomeInfoDb)
library(EnsDb.Hs... |
5496e81dddd897e17fe1ee09c01759c280021dfc05a93b21aaa7077c588b640f | R | 6,302 | 114 | source('code/misc/miscfxns.R')
lm.mask.ant.ret <- function(y,Xt.retro,Xt.antero){
# fit linear model: y = b0 + b.r*Xt.retro + b.a*Xt.antero + error
# given Xt.antero and Xt.retro for a given set of time constants
# return pearson r between y and y-hat (predicted vs actual path)
#
# INPUTS:
# y: observed l... |
d6658ac07d1ba3441fb9a167c359324f83872671ecac739843346ac254a31921 | R | 6,302 | 201 | ResiliencyGeneticDistance <- function(ReadsTableName, expname, ComparisonMetaDataName, limit) {
ReadsTable <- read.csv(ReadsTableName, row.names = 1)
MetaData <- read.csv(ComparisonMetaDataName)
if( length(which(MetaData$Sample == "")) != 0) {print("STOP!!!")}
SampleNames <- MetaData$Sample[order(Meta... |
1489cb1ad4879fe0f5f7b7095a471a31e435baa5298d1f38181a4437fdb3a61d | R | 6,310 | 286 | ---
title: "Monocle RPCs T1 T3 - organoids"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(tidyverse)
library(Signac)
library(Seurat)
library(JASPAR2020)
library(GenomeInfoDb)
librar... |
b79a1b4cd50417b7271573ec7e2e9b27d8f1caeb1e337e014efbbf1815c24740 | R | 6,318 | 231 | #!/usr/bin/env Rscript
#' Platelet Classification Pipeline Using XGBoost
#' Description: Predict cell outcomes using gene expression data
# 1. Setup and Configuration -------------------------------------------
suppressPackageMessages({
library(xgboost)
library(Seurat)
library(caret)
library(tidyverse)
lib... |
dbc01bf99333c03cbfc7030f6ef7bb2ed3e0f89aa4c0d894703601803d26ccc3 | R | 6,319 | 135 | library(base64enc)
library(cifti)
library(dplyr)
library(gifti)
library(magrittr)
library(rjson)
library(stringr)
library(tidyr)
print("This script 1) creates a list of ciftis to extract timeseries data from, and 2) concatenates timeseries and makes connectivity matrices ")
##################
# Set Directories
###... |
5deae0158b0a4be8acff06a9966816d9090eb930f6abfc348d6441ad9d74ad05 | R | 6,327 | 243 | #!/usr/bin/env Rscript
#' Platelet Classification Pipeline Using Deep Learning
#' Description: Classify cell outcomes based on gene expression
# 1. Setup and Configuration -------------------------------------------
suppressPackageMessages({
library(reticulate)
library(Seurat)
library(keras)
library(tensorfl... |
89afc584a68500d3ae2eea4713a8b2618afe94289b133f6943b3385f3b6fcc3b | R | 6,333 | 172 | #!/usr/bin/env Rscript
################################################
################################################
## Functions ##
################################################
################################################
#' Check for Non-Empty, Non-Whitespace String
#'
#... |
9a8b5439ebfaa7fc505419e74c882419e9121d3fbbceb23f4c45564360432952 | R | 6,338 | 273 | ---
title: "QC - ATACseq"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(Signac)
library(Seurat)
library(GenomeInfoDb)
library(EnsDb.Hsapiens.v86)
library(ggplot2)
library(dplyr)
li... |
d48cf34e7dcc02a51b5aa65cd850bd19fc3aadf2491324bf5e6caab5b4ea604a | R | 6,344 | 308 | ---
title: "Monocle ATAC all"
output:
html_document:
df_print: paged
---
```{r global_options, include=FALSE}
knitr::opts_chunk$set(echo=FALSE, warning=FALSE, message=FALSE)
```
```{r, libraries}
library(tidyverse)
library(Signac)
library(Seurat)
library(JASPAR2020)
library(GenomeInfoDb)
library(EnsDb.Hsapie... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.