sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
307c56397b09e4e4d3f38f22f976535d55e172cbeba972bcb7ab1bc9af45ed1e | R | 2,124 | 60 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
library(stringr)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_me... |
7c46610ed0ca76dab92bdd19c1de489a2aeb4480901fe09394eefd470c05eb86 | R | 2,131 | 84 | # load the counts of each sample
DMSO<-read.table(file = "DMSO_gene_count.tsv",header = TRUE,row.names = 1)
Riluzole<-read.table(file = "Riluzole_gene_count.tsv",header = TRUE,row.names = 1)
DMSO_Riluzole<-cbind(DMSO,Riluzole)
DMSO_Riluzole<-DMSO_Riluzole[-(60672:60682),]
# rename the colname to the group name
grou... |
f4802dce73ada94f268850bc4dcded6be609362f2601552095221d8cbb69b72f | R | 2,131 | 84 | # load the counts of each sample
DMSO<-read.table(file = "DMSO_gene_count.tsv",header = TRUE,row.names = 1)
imatinib<-read.table(file = "imatinib_gene_count.tsv",header = TRUE,row.names = 1)
DMSO_imatinib<-cbind(DMSO,imatinib)
DMSO_imatinib<-DMSO_imatinib[-(60672:60682),]
# rename the colname to the group name
grou... |
201460386e59e5a82bdf1ebd111231f1d66bb226a262a1918ef2fc75408e3a15 | R | 2,132 | 67 | library(monocle3)
library(tricycle)
library(SingleCellExperiment)
library(scater)
library(cowplot)
library(ggplot2)
library(here)
tabula_muris <- readRDS(here("tabula_muris/data/10x_tabula_muris_annotated.rds"))
tabula_muris_sub <- tabula_muris[!is.na(fData(tabula_muris)$ensembl_id_trimmed),]
rownames(tabula_muris_s... |
ffa50df462a5aacbf462cc679f6e1e443c9cb6e4be1b94ff94f200b2e136e7da | R | 2,133 | 87 | # 05/23/2023 Fengyao Yan
# Apply NNLS deconvolution on patient 2224 data.
library(tidyverse)
library(ggplot2)
library(nnls)
library(glmnet)
library(Metrics)
library(data.table)
data_folder <- '../../data/2224'
data_raw <- '../../data/raw'
dat<-fread(paste(data_raw, '/selected_gtex_top_1_NT_1_fc_3_data.csv', sep=''),... |
e6d1ff20e42141e7602111a0fc2a2b2c6476a802e30165c6d7abc14153304847 | R | 2,141 | 60 | ## The following script creates the ../inst/extdata/tcga_cohort.txt.gz dataset
## Requires TCGAmutations R package (https://github.com/PoisonAlien/TCGAmutations) and Agilent sureselect target_regions BED file (https://kb.10xgenomics.com/hc/en-us/articles/115004150923-Where-can-I-find-the-Agilent-Target-BED-files-)
#Ge... |
161058f62bea9e2df3b6e8e09ffa3ee3ff3e883059c877870ded4266f4c0df6e | R | 2,145 | 84 | # load the counts of each sample
DMSO<-read.table(file = "DMSO_gene_count.tsv",header = TRUE,row.names = 1)
rapamycin<-read.table(file = "rapamycin_gene_count.tsv",header = TRUE,row.names = 1)
DMSO_rapamycin<-cbind(DMSO,rapamycin)
DMSO_rapamycin<-DMSO_rapamycin[-(60672:60682),]
# rename the colname to the group nam... |
6abb8d6dfcee34a2d4eee2fd998ff38bbcb0a1d53fa1b2310575d1bc67b35911 | R | 2,145 | 74 | library(BPCells)
library(Seurat)
options(Seurat.object.assay.version = "v5")
library(stringr)
library(purrr)
Sys.setenv("_R_USE_PIPEBIND_" = TRUE)
projdir <- here::here()
rdir <- file.path(projdir, "package/R")
import::from(.from = "utils.R", .directory = rdir,
setupLogging, closeLogging)
## library(future)
## pla... |
c820aebf8ad31ec5bb04c46ff6a91bae0ac4de4cf2e52f22a703024f86ac87f9 | R | 2,145 | 84 | # load the counts of each sample
DMSO<-read.table(file = "DMSO_gene_count.tsv",header = TRUE,row.names = 1)
Metformin<-read.table(file = "Metformin_gene_count.tsv",header = TRUE,row.names = 1)
DMSO_Metformin<-cbind(DMSO,Metformin)
DMSO_Metformin<-DMSO_Metformin[-(60672:60682),]
# rename the colname to the group nam... |
86a88ca2e94a86eb2f036ab2b43c3b97b515966b91c8c5c4a7d5e1d9923d551e | R | 2,151 | 84 | ##################################################################
# LC snRNA-seq analyses: build SCE object from Cell Ranger outputs
# Lukas Weber, Oct 2022
# using code by Matthew N Tran and Leonardo Collado-Torres
##################################################################
# run in interactive session on JHP... |
c732350bc6840c2f2def2fc20d07050d9b36dadd649c1df0c1db30180fc5290a | R | 2,152 | 59 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
cc71a1ce68d196cc7d2db4f86fafed5a84e3952bab70581c5ae26ae6a14d8ef4 | R | 2,154 | 57 | # Load library
library(tidyverse)
library(rpart)
library(rpart.plot)
# Load data
df<-read.csv(file.choose(), header=TRUE)
# csv file is 5 columns with headers: sex; drug; genotype; region; L_effect.
# each row is experimental replicate.
#L_effect is [DA] in isradipine normalised to control
# tidy data ... |
49a60b2765dfde4822c7a9ef9f9060fe885f623b83b15a3ceb6578019bb727c9 | R | 2,155 | 66 | # This script is prepared by Sai.
library(Matrix)
library(monocle3)
# should be cicero for monocle3
# devtools::install_github(
# "cole-trapnell-lab/cicero-release", ref = "monocle3")
library(cicero)
projdir <- here::here()
rscdir <- file.path(projdir, "src/main/resource")
args <- commandArgs(trailingOnly = TRUE)
Ce... |
893b0f5000c19de2de7916ea05588ed024dc572f4f0e112991f5408379d04b30 | R | 2,158 | 59 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
316ed720d9163c1e15e857adb783c76d04d0ec177ad3620db79a2170e1d4b768 | R | 2,159 | 22 | #' 178 Signatures for Normal Brain Cell Types
#'
#' Signatures were derived from 7 publications (below). Clusters were taken from each publication, and differentially expressed genes were computed for each cluster using scalop::dea. If the publication had subclusters, then scalop::dea was computed for that subcluster a... |
01fe21b1e91af1b69837eccc44d04c6d043f04782c32793ea8468726c4658e54 | R | 2,163 | 63 | library(ggplot2)
library(tidyverse)
mse_dots <- data.frame(matrix(ncol=4, nrow=0))
colnames(mse_dots) <- c('tissue', 'mse', 'n_gene', 'model')
mse_nnls <- read.csv('../../data/cmp/mse_all_cmp_nnls.csv', row.names = 1)
mse_deep <- read.csv('../../data/cmp/cmp_res/mse_all_cmp_deep.csv', row.names=1)
mse_nnls <- mse_nn... |
ffbc75c9b1055f90ef2e461422f25a64bd965aac714d43649751bf6adce7beef | R | 2,163 | 59 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
f0461d659d645f521e8cb71a555dc353900bc60f8874c429dbb44756d1b2be32 | R | 2,167 | 94 | #################################################
# LC Visium analyses: normalization and logcounts
# Lukas Weber, Oct 2022
#################################################
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcolladotor/pilotLC_LIBD001/locus-c/
library(here)
library(Spa... |
f1275d7ed0bc8e1ab3911057e545df32b150bb41dea63cb0d0d8ef4cf2916038 | R | 2,167 | 60 | # this script will compare the cnv burden between celltype annotations in the aggregated atac object
library(data.table)
library(dplyr)
library(tidyr)
library(here)
library(stringr)
library(tidyr)
# get library_ids from aggregation file
aggcsv <- read.csv(here("ckd","cellranger_atac_aggr","outs","aggregation_csv.csv"... |
254a95ff3c5d4ace04b95e7cffe1e9f0ccd8fb63127391a63ce689c62b0216d6 | R | 2,170 | 62 | # Install required packages if not already installed
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!requireNamespace("IRanges", quietly = TRUE))
BiocManager::install("IRanges")
# Load required packages
library(dplyr)
library(stringr)
library(fuzzyjoin)
librar... |
06abd4c1c25af84df8f04b4b9e77dedfb7cbd561c3a0eff92c78591cf509180f | R | 2,173 | 61 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
c02a4c1abbfa60d955b8568e013d886b9f98f4aac737107d601950df218e26ac | R | 2,179 | 82 | gierer.f <- function(rgc.dat, tec.dat,
nterm = 16, #number of terminals of each RGC.
epsilon = 0.005, # rate of growth of r
kappa = 0.00000, # rate of decay of r
p0.fn=2 #which energy term to use, p0db
... |
0edc23aa70046b884f360ab770fa15f6dd79117f75f2117a097fe018365e67fe | R | 2,193 | 66 | library(monocle3)
library(tricycle)
library(SingleCellExperiment)
library(scater)
library(cowplot)
library(ggplot2)
library(here)
tabula_muris <- readRDS(here("tabula_muris/data/smartseq2_tabula_muris_annotated.rds"))
pData(tabula_muris)$tissue <- pData(tabula_muris)$sample
tabula_muris_sub <- tabula_muris[!is.na(f... |
54cf57d1e134ddadf62f0d31ad0bec04db986aa50f2e340251914a40e865fad2 | R | 2,195 | 60 |
library(STRINGdb)
string_db <- STRINGdb$new( version="10", species=10090,score_threshold=0, input_directory="" )
data(diff_exp_example1)
example1_mapped <- string_db$map( diff_exp_example1, "gene", removeUnmappedRows = FALSE)
hits <- example1_mapped$STRING_id[1:80]
enrichmentGO <- string_db$get_enrichment( hits, ... |
82edd1b39ca8b51e8ed64cce82f048bae7cb95010f68ca0ff9f3573b40fcb860 | R | 2,197 | 50 | #' Function to get consensus sequences from BAM alignments
#'
#' @param ref path to reference genome
#' @param bam path to BAM alignment file
#' @param call whether to call variants
#' @param index whether to index the VCF variant file output
#' @param cons whether to generate consensus sequence
#' @param suffix suffi... |
145cbedaa12d6ccb5061989aa44b58f4df0fa8de5f4995a9ab157728ea5e5235 | R | 2,204 | 60 | library("methylKit")
file.list = list("input_test1.myCpG.txt", "input_test2.myCpG.txt", "input_control1.myCpG.txt", "input_control2.myCpG.txt")
myobj=methRead( file.list,
sample.id=list("test 1","test 2","control 1","control 2"),assembly="hg18",pipeline="amp",treatment=c(1,1,0,0))
pdf('output_statist... |
86f9aead951a9cfe941ede0b60f9ecc8d3f8285a87185ebde05e130c4cff5564 | R | 2,210 | 63 | # roc curves for HLA-I,
# DePTH trained on Emerson 2017 and testted on Emerson 2017
# random forest trained on Emerson 2017 and tested on Emerson 2017
library(ggplot2)
library(pROC)
# load scores from DePTH
pos_depth_file = "HLA_I_all_match_test_pos_20.csv"
neg_depth_file = "HLA_I_all_match_test_neg_20.csv"
df_po... |
b484efb627ce209361c072066657d87cd8c13c20a76e341b45c696b245ac8fca | R | 2,222 | 34 |
#' @title Fraction of a Cell's Expression Level Coming From Mitochondrial Genes
#' @description Often used as a QC parameter for cells in scRNA-seq data. The higher the fraction of mitochondrial expression (i.e. of expression coming from mitochondrial genes), the higher the implied level of stress a cell is in and by... |
5541376d80674aa63da57a2c65bfb06b28678088d0d11771eb9c9e9022b464f1 | R | 2,223 | 62 |
################################################################################
# Glm analysis for percentage
# Pauline Spéder
# 06-09-2023
################################################################################
library(readxl)
library(tidyverse)
library(ggbeeswarm)
library(emmeans)
# Import data --------... |
3f624df60ac114e6162a137a1084dcf023942b421c714a98902e77b91e95c584 | R | 2,227 | 84 | # this file is the second part for getting p-value cutoff for FDR 0.05
args = commandArgs(trailingOnly = TRUE)
eval(parse(text=args[[1]]))
library(parallel)
library(doParallel)
library(doRNG)
library(foreach)
nCore = 4
#nCore = Sys.getenv("SLURM_CPUS_ON_NODE")
nCore
registerDoParallel(cores=nCore)
options(mc.core... |
78d7af0abd873be935a5ad0b5c43b467c3de7230f41a68f0701952aa5c3a7776 | R | 2,229 | 42 |
#' @title Grouped Reorder and Ungroup
#' @description Keep desired groups together but reorder members within each group. E.g. If cells from multiple samples are being reordered, samples can be kept together and cells reordered within. Option to also reorder the order of groups or samples, such that groups with simil... |
38275a0dc2e3576f6fa59f179c89310c429d119999cccb66f3e218e715116ce2 | R | 2,237 | 62 | #05/23/2023 Fengyao Yan
#Plot the scatter plots for predicted tissue fractions for Gtex semi-silico data.
library(ggplot2)
library(tidyverse)
library(data.table)
data_t <- fread('../../data/gtex/true_gtex_nnls.csv', data.table=FALSE)
data_p <- fread('../../data/gtex/pred_gtex_nnls.csv', data.table=FALSE)
data_t <- d... |
98dbf326c0b33e8650407a99da62bd1c5b426f9b416cafe203bbaeddd43bac04 | R | 2,240 | 58 | ####╔═════ ═════╗####
####💠Stats helpers💠####
####╚═════ ═════╝####
cli_h2("┗ [SCRIPTS] Loading stats functions")
#------------------------------------#
####🔺Summarizing data or a model ####
#------------------------------------#
distribution_summary <- function(data, dvs, between = "Condition") {
data ... |
ec21ea7ea5ca878e58201acc67335ae4df90448628691efe5ed1fbe32f98eb6c | R | 2,259 | 94 | SyntheticNet<-function(filename){
library(mclust)
library(igraph)
print(filename)
A=read.csv(filename, header = TRUE, sep = ",", quote = "")
A=as.matrix(A)
A=A[,c(2:dim(A)[2])]
B=matrix(0,dim(A)[1],dim(A)[2])
B[which(A=="True")]=1
colnames(B)=c(1:dim(B)[2])
rownames(B)=colnames(B)
#This is now a 01 n... |
563b3176979a41b40953d8360b5e24ad39b6cdc37b32e9a2e334f459f6635469 | R | 2,261 | 50 | #' Write an output Slurm submit script
#'
#' Generate an Slurm submit script for the given workflow
#' @param prefix Prefix for output, to be prepended to outputs
#' @param resultnames List of names for the expected outputs
#' @param modelfile Name of the model file that contains the processVoxel command
#' @param desi... |
e317a142e73b164563436ef464108e460304563992fa8673b9e4084c4f003aa2 | R | 2,262 | 29 | ########################################################
## Define CIFTI template made in Connectome Workbench ##
########################################################
glasser_template.path <- "/ncf/hcp/data/analyses/myelin/parcellations/Q1-Q6_RelatedValidation210.CorticalAreas_dil_Final_Final_Areas_Group_Colors.32k... |
9e300eefa764d68b9f29e116c43bc81945c30a33d7539b9e6545ec0661af0806 | R | 2,265 | 75 | #!/usr/bin/env Rscript
# to run locally:
# SCRATCH1=/mnt/g/scratch
# docker run -it \
# --workdir $HOME \
# -v /mnt/g/ckd:$HOME/ckd \
# -v $HOME:$HOME \
# -v $SCRATCH1:$SCRATCH1 \
# -e SCRATCH1="/mnt/g/scratch" \
# p4rkerw/sctools:R4.1.0
#
# to run interactively on the RIS compute1 cluster:
# export LSF_DOCKER_VOLUMES=... |
c8d48be750a9ed48eba45c076c9100b2883e6469a5d1db038052d0e74db6b0af | R | 2,266 | 61 | library(AUCell)
library(readxl)
library(tidyverse)
library(here)
## main Seurat package snRNA-seq pacakges
library(Seurat)
library(SeuratDisk)
library(future)
library(BiocParallel)
library(doParallel)
DATADIR = 'data/tidy_data/AUCell_gene_set_activities'
here(DATADIR,c( 'loom', 'regulon')) %>% sapply(dir.create)
###... |
ff37e1e87d3439c834c00e064c743aca9a105b293b377c5081c5c90e8f9b2099 | R | 2,270 | 71 | #' Transform the 'TreeSummarizedExperiment' object to 'microtable' object of 'microeco' package.
#'
#' @param tse a TreeSummarizedExperiment object <doi:10.12688/f1000research.26669.2>.
#' @param ... parameter passed to microtable$new function of microeco package, such as auto_tidy parameter.
#' @return microtable obje... |
9ce451732bfece2e3e7ae7307a0ff336763d40092c1f8e61299da453e7aeb4ed | R | 2,273 | 48 | m0 = readr::read_csv('FACS/Brain_Non-Myeloid-counts.csv')
annotation0 = readr::read_csv('annotations_facs.csv')
# set gene rownames
# note re the ~2k gene ~names suffixed with 'Rik':
# https://bioinformatics.stackexchange.com/questions/3205/what-are-riken-genes
# they are new genes without proper gene names yet
m = as... |
73d74f5b644e2c4761518756c43c593ca822b50af8d9e97b1c38ba5418123195 | R | 2,282 | 44 |
#' @title Filter Out Lowly-Correlated Genes from Signature(s)
#' @description This function removes signature genes whose expression level is poorly correlated to the signature score. This is relevant if the signatures themselves were defined from a different dataset, as might be the case in applying single-cell si... |
94ff6ec64931e6988e79c6c0d48fbc2fcf38bd523fb9f204aae54e133e0bd90d | R | 2,283 | 47 |
library(gifti)
library(R.matlab)
library(RNifti)
library(ciftiTools)
ciftiTools.setOption('wb_path', '/Users/askeller/Documents/workbench/')
library(ggplot2)
PFNs_hardparcel<-read_cifti('/Users/askeller/Documents/Kellernet_PrelimAnalysis/hardparcel_group.dscalar.nii')
map_predacc_PC1_lh <- PFNs_hardparcel$data$corte... |
8e05cb0c06e07781cdea736f7b7df264f64e24f41baedd6f10de4b79e54c3ec0 | R | 2,289 | 62 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
library(stringr)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_me... |
c9ae04090ef4bda6852020de5646ca84c43a5503b4e252b392c5cd4247970d70 | R | 2,294 | 98 | ---
title: "Clusters"
author: "Sergey Naumenko"
date: "`r Sys.Date()`"
output:
html_document:
code_folding: hide
df_print: paged
highlight: tango
theme: default
number_sections: true
toc: true
toc_float:
collapsed: true
smooth_scroll: f... |
a4152b6346d51d2bc7950e71bcbeec3b22db6a4e731aaf83e743efeefedc4a62 | R | 2,296 | 74 | # Author: Luisa Cutillo
community.significance.test <- function(graph, Nodeids, ...) {
if (is.directed(graph)) stop("The graph is not undirected")
subgraph <- induced.subgraph(graph, Nodeids)
in.degrees <- degree(subgraph)
out.degrees <- degree(graph, Nodeids) - in.degrees
res=wilcox.test(in.degrees, out.degr... |
0c31686e06298ddc26bb097241f07af0b6ebd17c57e46b8d49c2bdcdac572a8b | R | 2,298 | 63 | # this code gets the side-by-side violin plots based on the scores on
# test positive pairs, test negative pairs, Szeto 2021 54 pairs
library(ggplot2)
library(ggpubr)
library(pROC)
theme_set(theme_classic())
results_dir = "../results/ensemble_scores/"
res_emerson_szeto_folder = "HLA_I_all_match_szeto/"
res_emerson... |
1daae36d2f86ca8eeedbff0f26f184f8a70b39b1ad6682895f6dc5686e0583b8 | R | 2,298 | 55 | # %% Plot results nice(r)
library(R.matlab)
library(tidyverse)
library(ggpubr)
library(ggsignif)
source("C:\\Users\\user\\Desktop\\brain_stuff\\ggplot_theme_Publication-master\\ggplot_theme_Publication-2.R")
setwd("C:\\Users\\user\\Desktop\\brain_stuff\\philipp\\sleeppaper\\data")
restacws <- readMat("restacws.mat")$a... |
1fab8b5413b9c122727408e96790eacb59a883ba3b369d725d98a3f906549f99 | R | 2,299 | 55 | #' @title Extract substrings between delimiters by position
#' @description substri() splits each element in 'x' into substrings and returns the desired substring(s). 'sep' defines the delimiters separating the strings into substrings, such that the input data between the matches become the fields themselves. 'pos' de... |
d03d2b054a89b6f3767a8c9e080f1900ff4487ee8fd635b7eb82eb0b5929e1bc | R | 2,300 | 78 | #####################################################################
# LC analyses: spot-level deconvolution using cell2location: plotting
# Lukas Weber, Apr 2022
#####################################################################
library(SpatialExperiment)
library(here)
library(ggplot2)
library(viridis)
# direc... |
ef12b4e46e4866d9e6074a019e26e9cabeaf993b1d089699f3cb49409bb93644 | R | 2,300 | 63 | # roc curve for DePTH trained on mcpas and tested on mcpas
# in comparison with CLAIRE on mcpas
library(ggplot2)
library(pROC)
# load scores from DePTH trained on McPAS training data and prediction on McPAS testing data
# (only with HLA-I alleles that we have pseudo sequence information for)
DePTH_neg_score_file = ... |
babdc0d520e42bb04476d78379a0974d242607a135091749a5ebf474ce0f824c | R | 2,301 | 55 | library(susieR)
set.seed(1)
args = commandArgs(trailingOnly = TRUE)
# [1] is summary statistics,[2] is LD matrix, [3] is the true causal file, [4] is number of causal snps,
# [5] is output subsets file name, [6] is output set file name, [7] is accuracy (sensitivity) file name,
# [8] is set size file name, [9] is the n... |
9d17266c9819b063f175994a2f87d7280d6102e9e8cd3e600789cb3bf5fb4e35 | R | 2,305 | 58 | #############################################################
# Generate Figure 2A
#############################################################
library(ggplot2)
library(scales)
library(ggpubr)
setwd("D:\\PhD\\Lab\\Project_3_SVG\\Figures\\")
Input_data <- read.csv(".\\Data\\Manuscript_2D_Simulation_Pattern... |
82862cd2484ed38eb49463bfb7356e23d4e1250f4651c27ac6fc7e6ea7134cd0 | R | 2,313 | 61 | #Sparklines expression of cdh3, clic3, ntf3, snap25 in mens/nens/glia
library(SingleCellExperiment)
library(monocle3)
library(scrattch.vis)
library(scuttle)
library(here)
library(dplyr)
source(here("scripts/accessory_functions/prep_sparklines_data.R"))
cds <- readRDS(here("6month_LMMP.rds"))
cell_types_to_keep <-... |
43d8b61a96491f7a83e2cd89218e816b592878fe9344eb3fc8a87165e6ce22ea | R | 2,314 | 61 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
7ee5fd6a57943e6a36509732f79da17c4d23776f38437d9171afc15f17c1d5fe | R | 2,322 | 60 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
1b91d01e228901e77f096d29b07b1b72bbec8a86fc601be8092903f2893b3e71 | R | 2,323 | 83 | require("categoryCompare")
require("Homo.sapiens")
require("KEGG.db")
do.GOEA = function(cls,univ) {
univ = subset(select(Homo.sapiens, keys=univ, columns=c("ENTREZID"), keytype="SYMBOL"),!is.na(ENTREZID))
geneLists = vector(mode = "list",length = length(cls))
names(geneLists) = names(cls)
for (i in 1:length(g... |
892cd213f0f85cb3b7be36d98ff3cafd5bbb497456a4be1133488776793de8c4 | R | 2,340 | 82 | # this script will analyze degs for pathway enrichment
library(clusterProfiler)
library(enrichplot)
library(ggplot2)
library(org.Hs.eg.db)
library(openxlsx)
library(RColorBrewer)
library(DOSE)
library(here)
library(dplyr)
library(gridExtra)
# create a plot dir
plots <- here("ckd","figures")
dir.create(here(plots))
s... |
0339747fc4313a59e4b9f03e93fbb3f4c2ceb9b2a675563f3915bd4f983d6a54 | R | 2,357 | 58 | # Gregory Way 2017
# PanCancer Classifier
# scripts/gestalt_pathway_analysis.R
#
# Performs Overrepresentation Pathway Analyses (ORA)
#
# Usage: Run in command line
#
# Rscript --vanilla scripts/gestalt_pathway_analysis.R
#
# Output:
# ORA results for Ras Classifier Genes (all genes and positive/negative genes)
li... |
1546f00910522c4451cf9a9cdd1c650e784dd849abb7a68726090ebbeee62cd5 | R | 2,357 | 91 | #!/usr/bin/env Rscript
# this script will preprocess aggregated snATACseq data
# counted and aggregated by cellranger-atac v2.1 without library normalization
#
# to run locally
# counts=/mnt/g/cellranger_atac_counts
# project=/mnt/g/ckd
# SCRATCH1=/mnt/g/scratch
# docker run -it \
# --workdir $HOME \
# -v $project:$HOM... |
8f7fb84277143a7434cc5264789047191c6844b176d768efe4a9d6d610a4a467 | R | 2,357 | 60 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
0653cbadce875130b15d6558153924432179f6f378e43036e36dd5cf9b5af238 | R | 2,360 | 68 | #-----------------------------------------------
# LICENSE
#-----------------------------------------------
# Copyright 2019 Novartis Institutes for BioMedical Research Inc.
# Licensed under the GNU General Public License, Version 3 (the "License");
# you may not use this file except in compliance with ... |
1e41591d10ce09859360b8445a845c4c243b6f6c9be1692fda5bfc6e6e1c05d2 | R | 2,360 | 80 |
# var2char <- function(v1) {
# deparse(substitute(v1))
# }
# retrieve vector of clust ids (with cell names -- column names in x)
# with option to return as a dataframe of two columns: cell and clust
# x can be expression matrix of genes by cells
# common to use reduced dims instead of genes
# eg. (50) PCs by cells... |
b1b31a1eb240fe03d2d2a5c5b7c5f717c87bb1e9ff82a33fa49a8e42affe5a16 | R | 2,366 | 64 | ## Author: Youtao Lu <luyoutao@sas.upenn.edu>
## Copyright (c) 2017-2023, Youtao Lu and Junhyong Kim, Department of Biology, University of Pennsylvania
## Copyright (c) 2017-2023, James Eberwine, Perelman School of Medicine, University of Pennsylvania
## All Rights Reserved.
## This Source Code Form is subject to th... |
1f0e1568c2138b3d1cd25680bcbe4985fe48f95cb366c279a86628cb622d1636 | R | 2,369 | 61 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
8f9c2fc547b60d9f8a3f01986d39cea3e0a0d2da18192a28db38490d76445d98 | R | 2,373 | 46 | library(misha)
library(ggplot2)
library(cowplot)
library(ggrastr)
theme_set(theme_cowplot())
gsetroot('/home/hpc/bonev/trackdb/mm10/')
options(gmultitasking = F)
options(gmax.data.size = 5e7)
ins_tracks <- c("hic.ES.ins_250","hic.NPC.ins_250")
for (track in c(ins_tracks)){ ### Create vi... |
24f7d23515b16beeb2f9bd02efab369a88cf97aa74227c0d59f9acb57e51662d | R | 2,375 | 64 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
library(stringr)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_me... |
93e52a392644df3fe0f0d6fc4657dba5f54a1a4d303d0013f0f5db731d419c4c | R | 2,380 | 80 | # this file writes out the associated (TCR, HLA) pairs based on p values
# from v allele level TCR into a csv file of
# the associated pairs and corresponding p values
data_dir = "../../data/intermediate_data/"
pvalues_big_list = readRDS(paste0(data_dir, "pvalues_big_list.rds"))
pvalues = unlist(pvalues_big_list)
... |
4906e8c230b3a221e34ae40de9e37465895107c14d04052aa9db484888a28d04 | R | 2,386 | 43 | #' Extract nucleotide counts for targeted variants from BAM files
#' @description bamreadcounts queries a BAM file at specific genomic positions and returns nucleotide counts (A, T, G, C) as well as insertion and deletion counts at each position. Function name is an homage to https://github.com/genome/bam-readcount
#' ... |
9a2befa653edd2fe114aa779c5e147035573415bc4f331f1dbc452cfeea728ef | R | 2,398 | 61 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
a322ff2f48bd5f82a693a17897dea9549bb2b286988989a22415cd4f2b9035f8 | R | 2,402 | 80 | #!/usr/bin/env Rscript
# This script expects one filename to be passed, the parameter file.
# This parameter file (which is just a R script) must stores the values of
# 3 variables:
#
# rgc.file
# sc.file
# op.file
#
# Other parameters (e.g. kappa, epsilon) can be set.
#
## The name of the paramfile is used as the p... |
b9fc93513f7e1c5e0856859b7ef26c484af81510e33332ff39c0d12f4e13c3bd | R | 2,406 | 88 | library(Seurat)
library(dplyr)
library(patchwork)
library(rhdf5)
library(hdf5r)
library(Matrix)
library(igraph)
library(SeuratObject)
library(RcppCNPy)
library(reticulate)
np <- import("numpy")
library(utils)
dir<-paste0('../datasets/time/')
t<-c('2000','4000','8000','16000','32000','64000')
for ( i in 1:length(t)) {... |
c2a0c567f2a1a28a7826fe0f0632dad3b3f5b40b371e4c978150909d4250e94c | R | 2,410 | 62 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
64eb7ec45acbabf6dcc5acb8cd8077357e5ced32a72bc8f2ee377083c69323e3 | R | 2,411 | 62 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
30e283268810868dbcc7afb5b957abbcf3bfce31777ea6f5b3a5c6081be37724 | R | 2,412 | 53 | library(tidyverse)
library(ggplot2)
library(ggpubr)
library(patchwork)
### Load performance results------------
landmarks <- data.table::fread('../results/LatentDimAnalysis/validation_results.csv')
colnames(landmarks)[1] <- 'fold'
landmarks <- landmarks %>% mutate(feature_space = 'landmarks')
allgenes <- data.table::... |
723d226557dfa40cacd17232171ceeb6b23f81fa735acf57e23ce7a607655cd0 | R | 2,423 | 63 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
26624fbbb89f0331be6635ce13804a61e0ddebb565aa8fddc2aaa11fda2243df | R | 2,424 | 117 | ---
title: "Averge of predicted scores from 20 models"
author: "Si Liu"
date: "`r Sys.Date()`"
output:
html_document:
theme: journal
highlight: tango
toc: true
toc_depth: 3
toc_float:
collapsed: true
smooth_scroll: false
number_sections: false
code_folding: hide
df_print: ... |
dd2675a0e3a596411be19e75255436dc0b572afd7a6ba9dc71fbec0770a91c9c | R | 2,426 | 109 | ###############################################
# LC Visium analyses: identify SVGs using nnSVG
# Lukas Weber, Oct 2022
###############################################
# qrsh -pe local 10 -l mem_free=5G,h_vmem=6G,h_fsize=200G -now n
# module load conda_R/4.2
# Rscript filename.R
# file location:
# /dcs04/lieber/lcoll... |
f087a49c4b7281724ebf203294749935a7caf3aaa7e632c527f36a56ed30294f | R | 2,426 | 63 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
library(stringr)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classifi... |
84b7796a9fd078adc363f5d20a40a6dcae1ff607e5cd36bccbc24e1b427d74f0 | R | 2,432 | 58 | #' From a file containing a table with gene and disease information generates a \code{molecularComorbidity}
#' object for comorbidity analysis
#'
#' Given a file with gene and diseases, the \code{dataframe2molecularcomorbidity}
#' generates a \code{molecularComorbidity} object.
#'
#' @param filePth The file name and wi... |
001bf3f9f9183773ff8272c27ffe93aaf932ea124116757e8254a779f6b7d524 | R | 2,434 | 59 |
# roc curve for DePTH trained on Emerson 2017 and testted on Emerson 2017
library(ggplot2)
library(pROC)
# load scores from DePTH trained on McPAS training data and prediction on McPAS testing data
# (only with HLA-I alleles that we have pseudo sequence information for)
DePTH_neg_score_file = "HLA_I_full_mcpas_neg_... |
3013e69b8ee32276627c209658d9a2947704483d7ef2d5314d9b97a7b370e92a | R | 2,445 | 62 | "pwr.norm.test" <-
function (d = NULL, n = NULL, sig.level = 0.05, power = NULL,
alternative = c("two.sided","less","greater"))
{
if (sum(sapply(list(d, n, power, sig.level), is.null)) !=
1)
stop("exactly one of d, n, power, and sig.level must be NULL")
if (!is.null(d) && is.character(d))
... |
211310c315665eee40484d002bd2ab132e1c1070e90ae9898e5b89721a929d04 | R | 2,448 | 102 | #####################################################
# LC snRNA-seq analyses: SCE object for ExperimentHub
# Lukas Weber, Oct 2022
#####################################################
library(here)
library(SingleCellExperiment)
# ----------------
# Load SCE objects
# ----------------
# load SCE objects from prev... |
0f1b51e3492a5d278ff6a3c0282757fabf520a992baa0af77a3347d969279e62 | R | 2,455 | 64 | #' Reorder an x or y axis within facets
#'
#' Reorder a column before plotting with faceting, such that the values are ordered
#' within each facet. This requires two functions: \code{reorder_within} applied to
#' the column, then either \code{scale_x_reordered} or \code{scale_y_reordered} added
#' to the plot.
#' This... |
866f5c715debaeb7e38311fb181443185a2ce15687b464ea6889e547bd49af80 | R | 2,457 | 74 | suppressPackageStartupMessages({
library(caret)
library(doParallel)
library(e1071)
library(scran)
library(bluster)
library(scater)
})
# First method, KNN classifier
doKKnn_simple_union <- function(train.sce, train.cellTypes, test.sce, test.cellTypes, anchor = NULL){
cat("do KkNN union classification \... |
cbded0e565ad8496bf3223ac8110ca0ff73cf388fb577ed5ac985217957c84b8 | R | 2,457 | 60 | "pwr.p.test" <-
function (h = NULL, n = NULL, sig.level = 0.05, power = NULL,
alternative = c("two.sided","less","greater"))
{
if (sum(sapply(list(h, n, power, sig.level), is.null)) !=
1)
stop("exactly one of h, n, power, and sig.level must be NULL")
if (!is.null(h) && is.character(h))
... |
ab165ec00477d60e0c2fcff42d45c97b10cc603cdead416ded46ec566ff208f2 | R | 2,467 | 66 | #############################################################
# Generate Supplementary Figure 4D
# Plot 3D plots of Discrete 3D patterns
#############################################################
library(plotly)
library(scales)
library(plot3D)
InputData <- read.csv("..\\Data\\discrete\\Discrete_900by10_widt... |
b07df44fe56401412c99ac20138ed97c31db49db00620b9035799419efd211bc | R | 2,469 | 84 | #TimesVector
#
#Created by Inuk Jung on 2016-08-3.
#Copyright (c) 2016 Inuk Jung. All rights reserved.
#
#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 3 of the License, or
#(at you... |
747c18a7719bf696c2b6a8ddd193156ecc230b4c840d7b7de97447d87e7b826e | R | 2,474 | 65 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
7d9d518329545edd13490b791c8f21aa57b53dfeaf320c7f4fb7029a8ee0f4b5 | R | 2,478 | 85 | #####################################################################
# LC analyses: spot-level deconvolution using cell2location: plotting
# Lukas Weber, Apr 2022
#####################################################################
library(SpatialExperiment)
library(here)
library(ggplot2)
library(viridis)
# direc... |
d9a0c1af0ec4d6d12048a18099e34e860462133b0cec32e21788da74f9c0d3f9 | R | 2,479 | 50 | DESeq_wrap = function(data = data.phenotype.clean, genes = data.gene.count.clean, effects = NULL, formula = NULL, alpha = 0.05, estimate_disp = TRUE, detail = FALSE, lfc = 1){
if(is.null(effects) & is.null(formula)){stop("Must specify either a list of additive effects or a character string specifying a formula")}
... |
9fbfc3961e76c3d74e8d879bfccec9183ee5c89fd6a681f974f4d05a998d77f6 | R | 2,481 | 85 | #####################################################################
# LC analyses: spot-level deconvolution using cell2location: plotting
# Lukas Weber, Apr 2022
#####################################################################
library(SpatialExperiment)
library(here)
library(ggplot2)
library(viridis)
# direc... |
35e48ebe32bc3132a601a708886901f5424d840977653d38e323316cf98d5946 | R | 2,484 | 65 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
ecd4bfc263507c8986be4f368e2505844381985564a1658964057c3f725c0cdb | R | 2,484 | 80 | #TimesVector
#
#Created by Inuk Jung on 2016-08-3.
#Copyright (c) 2016 Inuk Jung. All rights reserved.
#
#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 3 of the License, or
#(at you... |
4fe48268a647a71de63843f516c53eeba1cb63587b49b560e273bc68d217f8e8 | R | 2,485 | 65 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
8039694f0d24455c20736ca00af15410a24aaa03ef7fbd26f1ab66b751680660 | R | 2,495 | 65 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classification_methods/doEva... |
561f3478ae69690626e4aee2f0d7494a2f6d3831ffab8da4fafc66ea910e5a33 | R | 2,497 | 66 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
library(stringr)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classifi... |
80f7e24409e3548923b723e52ec5a3e53fa23e32524ee1d86a15991e9a35f68b | R | 2,505 | 57 | # Seurat label transfer wrapper script.
# Following from here: https://satijalab.org/seurat/articles/spatial_vignette.html
# See the section 'Integration with single cell data'
library(Seurat)
library(dplyr)
label_transfer <- function(st_expr_df, sc_expr_df, sc_labels) {
###### Performs Seurat label transfer from t... |
a955c1d45a3425b421046a399bdbe71cdfda03a5e1652dee8252d6f7160267e2 | R | 2,511 | 66 | #setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
suppressPackageStartupMessages({
library(SingleCellExperiment)
library(Seurat)
library(glue)
library(stringr)
source("../../Utils/utils.R")
source("../../Utils/Feature_selection_methods/Traditional/doDE.R")
source("../../Utils/Classifi... |
98a75124db6c8b4afac120846fe4d1dd1b555437371165becd07c24b9c23a522 | R | 2,514 | 67 | #=========================================================================
#
# Copyright NumFOCUS
#
# Licensed under the Apache License, Version 2.0 (the "License");
# you may not use this file except in compliance with the License.
# You may obtain a copy of the License at
#
# http://www.apache.org/license... |
a7ecd601cf6c36cdb831ddf5f494111432afd718b4e5de03d711d51f43b3bfdd | R | 2,522 | 62 | ---
title: "QC of Library Cloning"
author: "Stephen P Plassmeyer"
date: "4/14/2021"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
source('../../../code/mpra_tools/mpra_tools.r')
```
# Load and Format Data
```{r}
compiled_counts <- fread('../../../data/compiled_counts/asd3u... |
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