sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
e618487acdecc0805eab110cd5f1beec3e663214303b0c43df822d72b357ab5e | R | 7,510 | 175 | library(tidyverse)
#' Factory function to process a chunked 'coverage ratio' BED-like file produced by pipeline (to be used with readr::read_tsv_chunked())
process_chunk <- function(le_id_filter = NULL) {
# https://stackoverflow.com/questions/49238163/how-to-pass-arguments-to-a-callback-function-for-readrread-... |
fb684a8ed88f27dd138f5c2e8613b092904e84f364030d036058f17db95b7124 | R | 7,515 | 132 | ###------------- Code for formally testing pleiotropic association of two phenotypes with SNPs using GWAS summary statistics-----------------
#
message("====================================================================")
message(" PLACO v0.2.0 is loaded")
message("===================================... |
7cfb0f13258cf2fc50f9f1fcf22395594890d2e7603b6b3d3a7d2d792c8c0edd | R | 7,528 | 209 | # Tidy sample info table
library(tidyverse)
library(readxl)
library(janitor)
theme_set(theme_minimal() + theme(text = element_text(size = 16)))
# Read sample info --------------------------------------------------------
# master table with information about each sample
sample_info <- read_excel("data/raw/RNA seq - ... |
58b4938c8be2e1b8e06bfb5481104d453e64c47c98236aff7877092a4535be71 | R | 7,537 | 160 | ```{r}
# gene panel includes cell type and neuromodulator genes on sagittal slices
# whole-brain BAR-seq data registered to the Allen Common Coordinate Framework version 3 (CCFv3)
# data is quality controlled by keeping cells with genes/cell >= 5 and reads/cell >= 20
# load libraries
suppressPackageStartupMessages(li... |
a535d372e63a351c4c0b1b49c91bfc57d222bfe5debce7e7c1ec1ae7d1cb6350 | R | 7,541 | 162 | ---
title: "Convert tree shrew orthologous genes to mouse genes"
author: "Yuanming Liu"
date: "2024/4/14"
output: Orthl_document
---
# R version 4.3.1 (2023-06-16 ucrt)
# Platform: x86_64-w64-mingw32/x64 (64-bit)
# Running under: Windows 11 x64 (build 22631)
# Matrix products: default
# locale:... |
3fd04227357261e17b57a76108f8557386f220cd906dc26ed22d29e34c5e6f1f | R | 7,542 | 218 | library(tidyverse)
library(scrattch.hicat)
library(scattermore)
library(here)
#load data and calculate cl means/medians
orthologous_genes <- readRDS(here("data", "orthologous_genes.RDS"))
#human
human_mat <- readRDS(here("data", "human_mat.RDS"))
human_meta <- readRDS(here("data", "human_meta.RDS"))
human_meta <- hu... |
9d232f785407f47b2d8feb3f081ab2d7d7547cb3893eed8f4be5bbdf8b7dfb60 | R | 7,542 | 178 | library(tidyverse)
library(here)
#read in species subclass marker gene lists
human <- readRDS(here("DEG_lists", "human_subclass_wilcox_markers.RDS"))
chimp <- readRDS(here("DEG_lists", "chimp_subclass_wilcox_markers.RDS"))
gorilla <- readRDS(here("DEG_lists", "gorilla_subclass_wilcox_markers.RDS"))
rhesus <- readRDS(... |
3184df193b4b19ef4a048a620f121448a27d22859560fe29e0f7504e34df4e54 | R | 7,579 | 285 | ---
title: "CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project - Common variants: cell-type enrichment"
author: "Isabel Castanho"
date: "`r Sys.Date()`"
output:
html_document:
toc: true
toc_float:
collapsed: false
toc_depth: 4
code_folding: hide
---
---
... |
e0e2c438dc08c8b1fe08da352e3abe7240fc300be52026068ebdc5cf26e4c2ee | R | 7,586 | 177 | #!/usr/bin/env Rscript
# FFERREIRA 03/24/2024
# Nina Project - Neanderthal
## Runs differential gene expression (DEG) analysis
### PS: it should have more than 1 sample by group for comparison (at least 2x2), otherwise it throws an ERROR
#### NICE TUTORIALS / EXPLANATIONS
# https://hbctraining.github.io/DGE_workshop/... |
46add992f53d5b4fd506ce18d65590bfaeba4384ad2a4fd2b00546ec635257d6 | R | 7,608 | 192 |
#' Plot pain score distributions
#'
#' This function plots the distribution of pain scores, at either pre-peak or post-peak.
#'
#' @param csv_path The path to the aggregated CSV exported from the `paws_analysis` function
#' @param peak Whether to plot pre-peak (`pre`) or post-peak (`post`) pain scores.
#' @return A d... |
45ac207e782d2b357965b9dd387bb2cd416f85e5e10ea7b02e1cff376752a2f8 | R | 7,610 | 194 | #!/usr/bin/env Rscript
# FFERREIRA 06/11/2024
# BEPE Collabs - Nina Neanderthals
## Makes PCA plots from COUNTS data generated in 'st1_TXimport.R'
### Apply filters: (i) protein-coding only; (ii) |L2FC| > 1 | 2
################
# 0. SETS UP ENV
################
# Loads LIBs
library(ggplot2)
library(DESeq2)
# Sets ... |
f09e82c2d1c84e125d275db1756e2f52b903f4286498de01f49d3b6677e88e83 | R | 7,617 | 151 | suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-c", "--ciri.ref"), type='character',
help="Reference database (ciri2)"),
make_option(c("-g", "--gtf"), type='character',
help="genome annotation (gtf)"),
make_option(c("-i", "--introns"), type='ch... |
e980f35f4874f67671a4c276c3be416cfd5ff9c6a1b4953727daa8c0c932a6a6 | R | 7,621 | 183 | suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-b", "--iclip_bedgraph"), type='character',
help="iCLIP bedgraph (iCount)"),
make_option('--intron_split', type = 'logical', action = 'store_true', default = FALSE,
help = "split introns into 5\' and 3... |
f0fd59d89ddda9216ca628d896f8560df50384871fb8865814e16d27a9956ac3 | R | 7,638 | 195 | #---------- Load Required Libraries ----------
library(TwoSampleMR) # For MR analysis
library(dplyr) # For data manipulation
library(pbapply) # For progress bars in loops
library(data.table) # For efficient file handling
#---------- Custom Function for Path Concatenation ----------
'%+%' <- f... |
22dafd21ffc0d79276ee91270df86dbe536a971361c403d88e2d6e6c7639efb4 | R | 7,647 | 227 | # Siwei 18 Feb 2024
# make a GRangesObject of TSS -2/+1 kb
# Need to test which genes to include (if only protein_coding, 19 k)
# init ####
{
library(Seurat)
library(Signac)
library(EnsDb.Hsapiens.v86)
library(GenomicFeatures)
library(BSgenome.Hsapiens.UCSC.hg38)
library(GenomicRanges)
library(org.Hs.eg.... |
2907abe3a7da792fe772a8297299738cf5ca71904bfc7303dba14c511906f91e | R | 7,652 | 124 | #library(magrittr)
library(tidyr)
library(data.table)
library(biomaRt)
library(org.Hs.eg.db)
source_folder <- "/servers/iss-corescratch/am3019/250220_SP_alex_bulk/X204SC25013974-Z01-F001"
expr_folder <- file.path(source_folder, "07.Expr_matrix")
bulk_folder <- file.path(source_folder, "08.Bulkanalyser")
output_folder ... |
b4e995a584f2321bdee0dc89bce8172470101e658e8ebd9617b7b50e8e4236ce | R | 7,656 | 138 | library(Seurat)
TPM = readRDS("embryo.14082cells.TPM.new.rds")
metadata = read.table("embryo_14082cells.metadata.upload.txt",row.names=F,header=T,stringsAsFactors=F,sep="\t")
rownames(metadata) = metadata$Cell.Id
metadata = metadata[colnames(TPM),]
embryo = CreateSeuratObject(counts = TPM,meta.data = metadata,min.cell... |
f2b0e56e2972fd62c5a8d13b75f8fb7e5c0e5523392992ad368d4ea7fbd1b09e | R | 7,658 | 166 | #Adaptation to ccn_hmClass to prevent re-ordering the groups by name
acn_queryClassHm <- function (classMat, grps = NULL, isBig = FALSE, cRow = FALSE,
cCol = FALSE, fontsize_row = 4, fontsize_col = 4, main = NA,
scale = FALSE, customAnnoColor = NULL, ...)
{
cools ... |
d9da9a2f46f3f39ea3bb5706b9f4b01cc293956d669f13f8e96cd05cdf06bbcd | R | 7,660 | 185 | #!/usr/bin/env Rscript
#### Title: Integration of T cells using STACAS and LISI score
#### Authors: Jana Biermann, PhD; Massimo Andreatta
library(Seurat)
library(dplyr)
library(ggplot2)
library(ggrastr)
library(gplots)
library(lisi)
library(tidyr)
library(magrittr)
library(viridis)
library(scales)
library(STACAS)
lib... |
ac4e2a783b5301f9c3f85821b13831a216a5b1ecdac1de5c410ff7c8d7ffba0d | R | 7,661 | 151 |
## Perform logistic regression to test enrichment of cell-type peaks in DMPs, whilst controlling for contributions from other cell-types ##
library(dplyr)
cells <- readRDS(paste0(refPath,"tissueNames_peakEnrichment.rds"))
#1. Load EWAS results annotated to cell-type-specific peaks ===============================... |
917c9a933ceead3343a29e76518655bb0159eae280bc727f372bdc5eeac0a7c6 | R | 7,673 | 172 | # FFERREIRA 05/26/2024
# Sanford Consortium - UCSD
# DEG results from bulk "full" normal brain organoids - Volcano Plot
################
# 0. SETS UP ENV
################
# Loads LIBs
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(tidyverse)
# Figure CONFIG
dpi <- 1000
#formats <- c("jpeg","pdf","png","sv... |
b487eb778a79dd47eb61b765de60ea64401290e7572081f37b9a62e0394ee5fd | R | 7,679 | 216 | ---
title: 'A simple introduction to seismicGWAS'
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{A simple introduction to seismic}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
html_notebook:
fig_height: 5
fig_width: 5
---
This vignette introduces the `seismicGWAS` packa... |
1b8b78c23422fa443f6f71bc7ece93234b939926907fe7fa921302f5db2c2602 | R | 7,683 | 187 | #function to calculate mean: (truncated from calc_specificity)
calc_ct_mean <- function(sce, assay_name = "logcounts",
ct_label_col = "idents",
min_ct_size = 20) {
ct <- N <- nz.count <- ave_exp_ct <- NULL # due to non-standard evaluation notes in R CMD check
... |
d38b00bacfd63b39de95bb2a172fc266afb5ee389ffc0785155b0e990b3c5cb2 | R | 7,687 | 251 | library("SummarizedExperiment")
library("tidyverse")
library("sessioninfo")
library("here")
library("jaffelab")
library("recount")
library("viridis")
library("ggrepel")
library("GGally")
## prep dirs ##
plot_dir <- here("plots", "02_quality_control", "03_qc_pca")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursi... |
e3d26646b511db18981636c0c4916fc8083226c96d01d3fb6fdb326fd0d0212a | R | 7,698 | 151 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
library(readr)
library(plotrix)
library(GenomicRanges)
library(scales)
### Test functional elements for all
exp = gtex.str
dt.out <- data.frame()
for(typeseq in unique(as.character(exp$typeseq_priority))){
variable.count <- sum(exp$typeseq_priority == type... |
4a7e7c9e9abf4d5435c33eb4682c3ec8ae82c62c3336e99971a8eaa3a216e191 | R | 7,702 | 184 | #!/usr/bin/env Rscript
### title: Overlap of MBM enriched genes from cell line RNA-seq, ATAC-seq, TF and motif enrichment
### author: Jana Biermann, PhD
library(dplyr)
library(ggplot2)
library(gplots)
library(viridis)
library(scales)
library(ggrastr)
library(ggrepel)
library(ggpubr)
library(patchwork)
library(ggrastr... |
d8e73c4e26563603c748dbcf92b5547072e0187ac7f3069dca9c01aa337fc3ba | R | 7,710 | 235 | ####################### load all data and process #####################
suppressPackageStartupMessages({
library(tidyverse)
library(readxl)
library(ggplot2)
library(gridExtra)
library(Matrix)
library(matrixStats)
library(scrattch.hicat)
library(tibble)
library(patchwork)
library(dplyr)
... |
2f0ca0cf23c466d523c29d40d7ec9db68659ae3c7bfd9da1450aebda1ef33abf | R | 7,723 | 206 |
library("SummarizedExperiment")
library("tidyverse")
library("EnhancedVolcano")
library("here")
library("sessioninfo")
library("ggrepel")
library("jaffelab")
library("UpSetR")
#### Set up ####
## dirs
plot_dir <- here("plots", "10_bulk_vs_sn_DE", "04_DREAM_plots_sn")
if(!dir.exists(plot_dir)) dir.create(plot_dir, re... |
b5ba4ad9e11f2740ce2c2c31a8cc79951b23aa8ddf131790f51b5e3fe1eba8d6 | R | 7,727 | 155 | # ============================================================================ #
# Script: PCA and Correlation Analysis for MOCA and IADL Data (IDoct Version)
#
# Description:
# This script performs a principal component analysis (PCA) on cognitive data
# extracted from healthy and patient imaging records (IDoct ve... |
53691eed39b2e69cc3dd591f64467540756cd8ab9e75ddb6de2965461eed8bef | R | 7,748 | 170 | ---
output:
html_document: default
pdf_document:
latex_engine: xelatex
---
########################################################################################################
# Combining projects with overlapping barcodes
If you have a large project or are combining multiple runs, you might encounter ov... |
8ca018cc861abf338ea3df161f4ce4d6dfe4b5746f13ec93655be03e6b9551da | R | 7,752 | 230 | # ʹÓÃggmagnify»æÖÆÉ¢µãͼ£¨³ý´ËÖ®Í⣬ÀàËƵķ½·¨»¹ÓÐggforce°ü£©
install.packages("ggmagnify", repos = c("https://hughjonesd.r-universe.dev",
"https://cloud.r-project.org"))
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggmagnify)
library(g... |
e65e78326527fba6ea3828ffa9fe98590f860aa8b2fc13d4e49ff2f2e66fd6d9 | R | 7,785 | 160 | #' Compute seismic specificity score for each gene and cell type.
#'
#' @param sce A SingleCellExperiment object. This object needs to include the
#' assay_name specified and a ct_label_col column corresponding to cell type labels
#' for the granularity of interest. Row names are used as the gene name identifiers.
#' @... |
4d66cb00e1aa102edf64b0d3f773a844699ea8eb968b17ca4dbf03a91174c2d8 | R | 7,787 | 149 | library(Seurat)
library(Signac)
library(patchwork)
library(ggplot2)
atoms_merge <- readRDS("atoms_merge.rds")
atoms_merge <- FindNeighbors(atoms_merge, dims = 1:30, reduction = "integrated_dr")
atoms_merge <- FindClusters(atoms_merge, resolution = 0.8)
atoms_merge$cell_type_l1 <- Idents(atoms_merge)
Dim... |
04d43a714b38edd2f4c9b8f1fa07ec5b4c3a0cd6f56df0e19663e73faad0a474 | R | 7,792 | 225 | library(data.table)
library(dplyr)
library(TwoSampleMR)
#============= PSY Data Preparation ================================================================
# Read data
PSY_data <- fread("[Please replace with path to combined_hg38.txt.gz file]")
# Filter out invalid rows with OR <= 0
PSY_data <- PSY_data[OR ... |
a650a1ce9da6406d5d0487c5112012cb77d4167104ab72eaa61a4d0f702ec519 | R | 7,809 | 196 | #----03_auto_metadata_v01_single_experiments------------------------------------
#-------------------------------------------------------------------------------
# Locomotor activity analysis for Reinhard et al. 2025 (10.1073/pnas.2506164122)
# Requirements:
# 1)scripts:
# 01_setup_v01
# 02_variables_an... |
cfccf239da6455c4cd2712b5a12dd59e3969d9ea8038df8395ae8b4b0c3dd664 | R | 7,825 | 173 | library(rtracklayer)
# directory with bw files
dir<-"../example_data/"
cat("workdir: ", dir, "\n")
cat("Enter the number of BigWigs: ")
num_bw <- readLines("stdin", n=1)
num_bw <- as.integer(num_bw)
samples <- c()
for (i in 1 : num_bw)
{
cat("Enter BigWig number ", i, " without .bw : ")
samples <- append(samples, re... |
e6e247791340996b6b2193b1769be19cbf0de3c3b9244d09094d4b85d395bf06 | R | 7,831 | 183 | install.packages("readxl")
library(readxl)
library(ggplot2)
library(PMCMRplus)
data <- read_excel("V:/tallab/Experiments_new/PFAS2/PARC_IS080_hMNR_raw_data.xlsx")
# Step 1: Select the specific columns in the desired order
filtered_data <- data[, c("concate", "cellcondition", "treatment",
... |
a2d50f8f8b40ca775773da53b6c1cec4ca3b2a011d0bcfa0e3d357fb2d1a9d1b | R | 7,833 | 178 | #----05_actograms_v01_single_experiments----------------------------------------
#-------------------------------------------------------------------------------
# Locomotor activity analysis for Reinhard et al. 2025 (10.1073/pnas.2506164122)
# Requirements:
# 1)scripts:
# 01_setup_v01
# 02_variables_an... |
8bf7227fb74b8b2d7b485ce95c49a13932fb3ae91e147722c36ad0fc4b544519 | R | 7,834 | 203 | ## 01_read_matrices.R
library(Seurat)
library(scCustomize)
library(Matrix)
set.seed(1234)
# Read Li2023 dataset (frontal cortex)
li_ID <- c("A1","A2","A3","A4","A5","A6",
"F1","F3","F4","F5",
"C1","C2","C3","C4","C5","C6")
li_sample <- c(paste0("C9ALS", 1:6), "C9FTD1","C9FTD3","C9FTD4","C9FTD5", ... |
23f44ac322b0a2ed896ae453378dee0e4f3f55d39a6abd5ddb13e201e0f19cd0 | R | 7,840 | 225 | #OPEN LIBRARIES
```{r}
library(kronos)
library(ggplot2)
library(gridExtra)
library(tidyverse)
library(corrplot)
library(gprofiler2)
library(outliers)
library(dplyr)
```
# IMPORT DATASET
```{r}
library(readxl)
bigdata_pd60_HPC <- read_excel("/Users/mariareinacampos/Documents/00_MASTER/TFM/Resultats/Results_PD60_HPC/b... |
0192354e6445e45a82b86ec81d4baefbb159d3f98c5e33e63b3880d86f3cee3f | R | 7,858 | 171 | library(tidyverse)
#' helper function to collapse duplicated values in a delimited string to non-redundant values
#' I wasn't very smart with gene_name column in PAPA output - often get duplicated gene name values...
collapse_names <- function(col, split=",") {
apply(str_split(col, split, simplify = T), # genera... |
599d010739296c43f76288b86c654097c0ce64bb26ad1b66eb1d95394b707313 | R | 7,861 | 178 | #' Recover intra-sample doublets
#'
#' Recover intra-sample doublets that are neighbors to known inter-sample doublets in a multiplexed experiment.
#'
#' @param x A log-expression matrix for all cells (including doublets) in columns and genes in rows.
#' If \code{transposed=TRUE}, this should be a matrix of low-dimensi... |
168886c02af3f7509abab1fe4963d244cfbd5aaed273c8a2a50a874b599aedde | R | 7,865 | 155 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
#### PTA Indel burden vs. age (with AD) ####
df <- read.table("data/TableS3_PTA_burden.tsv", header=T, sep="\t")
df$Case_ID <- as.character(df$Case... |
07d12a3acbf05b7a2f64076c4d15910fd8186afe317c7e4dbb5cc2ca9739cab4 | R | 7,866 | 149 | #collect data, convert to Seurat objects.
#collect meta data for QC filtering
library(Seurat)
library(Signac)
library(dplyr)
library(ggplot2)
library(cowplot)
library(patchwork)
library(EnsDb.Hsapiens.v86)
library(hdf5r)
library(biovizBase)
#load command line parameter for what datasets to process
args ... |
5913e88f2af91bba70eceff000704d9846f183f2f73f3e6c964dd54411dd2df1 | R | 7,871 | 40 | ---
title: "Mouse ChIP-seq data from ENCODE"
author: "lecook"
date: "2022-02-23"
output: workflowr::wflow_html
editor_options:
chunk_output_type: console
---
# Mouse ChIP-seq data from ENCODE
Unfiltered aligned reads downloaded from https://www.encodeproject.org/search/?type=Experiment&control_type!=*&status=release... |
684a6370490400cfa4d8d9155efe26da161918809114adb9d89064d6fd185e64 | R | 7,874 | 168 | # ============================================================================ #
# Script: Correlation Analysis for Patient Imaging and Cognitive Data
#
# Description:
# This script performs a principal component analysis (PCA) on cognitive data
# derived from patient imaging records and investigates the correlatio... |
00353ebb9054926a4e9b65acd56d2cf88378cca1750d58f64a59bc0851728919 | R | 7,875 | 181 | # explore exp pattern of coding UCR genes using datasets data
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(dbplyr)
library(pheatmap)
library(readxl)
library(stringr)
library(ggview)
library(ComplexHeatmap)
# human rpkm
human_rpkm <- read.table(... |
17ac421f254421f9426efb43a62d29a5483622de11f003c953871811ba872baf | R | 7,877 | 172 | library(tidyverse)
library(writexl)
datasets <- read_tsv("data/2023-11-22_paper_tdp43_collection_library_statistics.tsv")
# df containing cleaned cooridnate columns extracted from quant GTF
le_id_coords <- read_tsv("processed/le_id_collapsed_coords.quant.last_exons.tsv")
# yes/no binding within plotting windows at rep... |
50072bf90b586a5ba1bc7ba178d0843a5ac6c9be7323b704c2a95d6ae49769c9 | R | 7,887 | 174 | #' Script: De-Novo Clustering
#' Author: Ilias Lagkouvardos
#'
#' Calculate beta-diversity for microbial communities
#' based on permutational mulitvariate analysis of variances (PERMANOVA) using multiple distance matrices
#' computed from phylogenetic distances between observed organisms
#'
#' Input:
#' 1. Set the pat... |
00b09aca985954317d964379777ad3200c791a035347ed2f93308be200d3cf0b | R | 7,890 | 218 | library(tidyverse)
library(tidytext)
dbrn_tbl <- read_tsv("processed/peka/papa/2023-11-03_papa_cryptics_kmer6_window_250_distal_window_500.cleaned_6mer_distribution_genome.tsv")
simp_dbrn_tbl <- read_tsv("processed/peka/papa/2023-11-03_papa_cryptics_kmer6_window_250_distal_window_500.cleaned_6mer_distribution_genome_s... |
4eec3aed5915f631b533e95371cbb2955c3fc2e6573a858fe06d485d47681620 | R | 7,891 | 161 | library("SingleCellExperiment")
library("rafalib")
library("iSEE")
library("pryr")
library("here")
library("whisker")
library("usethis")
library("withr")
library("sessioninfo")
load(here("rdas", "ztemp_NAc-ALL-n5_SCE-with-tSNEon15-10PCs_MNT.rda"), verbose = TRUE)
source(here("shiny_apps", "00_clean_functions.R"))
ex... |
80cdf7b596cd2632d9dd960daed0b6071c5313c5c30d3ceb3d92c20b81ae51a9 | R | 7,893 | 185 | install.packages("readxl")
library(readxl)
library(ggplot2)
library(PMCMRplus)
data <- read_excel("V:/tallab/Experiments_new/PFAS2/PARC_IS080_hMNR_raw_data.xlsx")
# Step 1: Select the specific columns in the desired order
filtered_data <- data[, c("concate", "cellcondition", "treatment",
... |
bfa5796ab71c30626e2040e2088c4f6b1abfb2a2d76ac28b8d068f93705f1840 | R | 7,917 | 160 | #----significance_test----------------------------------------------------------
#-------------------------------------------------------------------------------
#-------------------------------------------------------------------------------
# This script tests the experimental group against the control groups for ... |
b659762c254f23f834b96de49133c79aa00076fd8af44e75927d04da00fd594e | R | 7,935 | 163 | sem <- function(data) {
# Проверяем, что данные не пустые
if (length(data) == 0) {
stop("Входные данные не могут быть пустыми")
}
# Вычисляем стандартную ошибку
se <- sd(data) / sqrt(length(data))
return(se)
}
# Загрузка данных
SerpinE1_data <- read.table("C:/Anna_27.08.2021/PiloLatLenaP... |
fed7f9265c990b478217bf7235e0954115a7b24fad7458c069211a81190f6560 | R | 7,940 | 273 |
#----------------------------------------------------------------- QC UI ----
qc_ui = function(id){
ns = shiny::NS(id)
shiny::fluidPage(
# shiny::fluidRow(
# shiny::p("QC page imported data wil be shown here!"),
# ),
waiter::autoWaiter(html = waiter::spin_fading_circles(),
... |
733cc06f9905d3f4018b639882af06c22faa018153eb589732aac51fd69ed5f8 | R | 7,946 | 161 | # function for plotting
plot_rs10792832 <- function(chr, start, end, gene_name = "",
SNPname = "", SNPposition = 1L,
mcols = 100, strand = "+",
GWASTrack = "",
x_offset_1 = 0, x_offset_2 = 0, ylimit = 800) {
cell_t... |
5dd4aeae7f7b9af4ec0e18fe6868c12aa874a589beb432d75b212d744cb2e646 | R | 7,952 | 205 | #' Bayesian inference
#'
#' This script estimate the individual FC mean and standard error maps of a subject, based on a previous calculated population prior on DHCP data (2nd release)
#' It is written to be run in parallelized fashion
#' *warning*: Each thread will allocate ~100 GB of memory.#'
#' 125 s per subject ... |
637cc85f2e4ab2c858111f30770f743b9e680777efa70fe222bddf4517f5fe1e | R | 7,960 | 226 | setwd("~/Documents/mixOmics/")
########################## PRE PROCESSING OF DATA ##########################
# Upload libraries
library(readxl)
library(readr)
library(mixOmics)
library(dplyr)
library(ggplot2)
# Import dataset of groups -> Sample ID, Genotype, Diet, and Genotype-Diet interaction and define rownames
... |
7d8ef36701322aa8d71d84861741d96ae29c9fb67b40614d4080de03cdd62fc8 | R | 7,960 | 257 | # Siwei 20 Jun 2023 #####
# plot a large PCA include MG, Ast, GA, and possibly NGN2
# ATAC-Seq data using the count matrix of Kosoy et al. (syn26207321)
# (microglia regulome)
# init #####
library(readr)
library(edgeR)
library(Rfast)
library(factoextra)
library(Rtsne)
library(irlba)
library(stringr)
library(dplyr)... |
f814f837eade4d3f6921baa87f7dd5ac9a3b2d19fef21d15587a8efc18c2e57c | R | 7,978 | 237 | #OPEN LIBRARIES
```{r}
library(kronos)
gg_kronos_sinusoid_noNA <- function(kronosOut, fill = "unique_group"){
requireNamespace("ggplot2")
d <- merge(kronosOut@input, kronosOut@to_plot, by="row.names", all=TRUE)[,-1]
d_noNA <- na.omit(d)
x_obs <- paste0(kronosOut@plot_info$time, ".x")
x_pred = paste0(k... |
6b8abdca43d2fe5556e336f94d35246b503d0a6de2a7069e891628ecde0c695e | R | 7,987 | 216 | output$regionTimeSeries <- renderEcharts4r({
total <- colSums(byDate[, 2:ncol(byDate)])
totalOver0 <- names(total[total > 0])
dt <- cumsum(byDate[, 2:ncol(byDate)])
dt$date <- byDate$date
dt <- melt(dt, measure.vars = 1:50, variable.name = "region")
dt2show <- dt[!region %in% lang[[langCode]][35:36]]
dt2s... |
dc0425ec699af77e04d12c5846ba38fbd970e4a2462438b7fe5b35bb4a899dcd | R | 7,990 | 217 |
# OPEN LIBRARIES
```{r}
library(kronos)
#Change sinusoid function to omit NA in the plot
gg_kronos_sinusoid_noNA <- function(kronosOut, fill = "unique_group"){
requireNamespace("ggplot2")
d <- merge(kronosOut@input, kronosOut@to_plot, by="row.names", all=TRUE)[,-1]
d_noNA <- na.omit(d)
x_obs <- paste0... |
2a8a9c8bb4c44ca18f35bd16c1061158261838d3dd43ec35f8e52e4e688fbf7c | R | 7,991 | 237 | #OPEN LIBRARIES
```{r}
library(kronos)
gg_kronos_sinusoid_noNA <- function(kronosOut, fill = "unique_group"){
requireNamespace("ggplot2")
d <- merge(kronosOut@input, kronosOut@to_plot, by="row.names", all=TRUE)[,-1]
d_noNA <- na.omit(d)
x_obs <- paste0(kronosOut@plot_info$time, ".x")
x_pred = paste0(k... |
dcfd5c096bf5794194ddc564450e5929695cdd457636231916c039a464deac31 | R | 7,993 | 235 | rm(list = ls())
library(PanomiR)
library(RColorBrewer)
library(igraph)
library(qvalue)
fdr.paths <- 0.1
edge.fdr <- 0.05
cor.thresh <- sqrt(0.1)
PLOT.META <- T
# if(path.source == "MSigDBV7"){
# pcxn.dir <- "Data/GeneSets/improved_PCxN_MSigDBV7_Canonical.RDS"
# PCxN <- readRDS(pcxn.dir)
# ... |
e010d9172136bb095b2f370ac43c0fb2b3488a848d2965b432994fea0877cc69 | R | 8,005 | 218 |
# OPEN LIBRARIES
```{r}
library(kronos)
#Change sinusoid function to omit NA in the plot
gg_kronos_sinusoid_noNA <- function(kronosOut, fill = "unique_group"){
requireNamespace("ggplot2")
d <- merge(kronosOut@input, kronosOut@to_plot, by="row.names", all=TRUE)[,-1]
#d_noNA <- na.omit(d)
d_noNA <- d
x... |
7e4c22bd91e120e73757d7e16550cfabda2c82295b080dc16170eb28c4cb370c | R | 8,021 | 221 |
#OPEN LIBRARIES
```{r}
#SCN CLOCK GENES
#OPEN LIBRARIES
library(kronos)
#Change sinusoid function to omit NA in the plot
gg_kronos_sinusoid_noNA <- function(kronosOut, fill = "unique_group"){
requireNamespace("ggplot2")
d <- merge(kronosOut@input, kronosOut@to_plot, by="row.names", all=TRUE)[,-1]
d_noNA ... |
d37d704b49775a0c7bf412d2ac00469e536b85ca5bd604f6467bbcd2ba181eec | R | 8,057 | 164 | sem <- function(data) {
# Проверяем, что данные не пустые
if (length(data) == 0) {
stop("Входные данные не могут быть пустыми")
}
# Вычисляем стандартную ошибку
se <- sd(data) / sqrt(length(data))
return(se)
}
# Загрузка данных
SerpinE1_data <- read.table("C:/Anna_27.08.2021/PiloLena2023... |
f08e3c852c19232468d32c5524ba3a661e1ca3d6799f6055a7b82b35558a0d12 | R | 8,072 | 190 | #This script is similar to src/causal_sim.R
#Only save the results of the target cell type
#For each time it will first generate the background
#1: parameter df file path
#2: summarized output file header
#3: which column (name) indicates the final output file header?
#4: regular expression contains the pattern of ta... |
b82905916850a904eb7ff66c36f547b4ba873893fc576676789d0c91d4a3db5f | R | 8,083 | 259 | # run Fig5.R before this
# rm(list=ls())
source('load_libraries.R')
source('functions_for_network_analysis.R')
library(GSEABase)
library(dplyr)
library(purrr)
library(fgsea)
library(tibble)
library(rlang)
library(dplyr)
library(stringr)
library(ggnewscale)
get_fc_and_ks_by_celltype = function(degdf, q_geneset) {
... |
43480903acf25507e12c00e55fe866e326f0cef455c2f46c318a47bdefb44ad2 | R | 8,089 | 219 |
#OPEN LIBRARIES
```{r}
library(kronos)
#Change sinusoid function to omit NA in the plot
gg_kronos_sinusoid_noNA <- function(kronosOut, fill = "unique_group"){
requireNamespace("ggplot2")
d <- merge(kronosOut@input, kronosOut@to_plot, by="row.names", all=TRUE)[,-1]
d_noNA <- na.omit(d)
x_obs <- paste0(... |
c531eb637cbb0249859f334003367eeb4cf9353177f714b0a34a052aad69e6cd | R | 8,089 | 219 |
#OPEN LIBRARIES
```{r}
library(kronos)
#Change sinusoid function to omit NA in the plot
gg_kronos_sinusoid_noNA <- function(kronosOut, fill = "unique_group"){
requireNamespace("ggplot2")
d <- merge(kronosOut@input, kronosOut@to_plot, by="row.names", all=TRUE)[,-1]
d_noNA <- na.omit(d)
x_obs <- paste0(... |
1b0b545037f9298822a4ca844cdbcf305d00d1b11e68ae3f28eecdfcd0984b57 | R | 8,097 | 217 | # ----------------------------------------------------------------------------
# Libraries and setup ----
library("argparse")
library("ComplexHeatmap")
library("DESeq2")
library("ggplot2")
library("tidyverse")
library("Seurat")
# Command line arguments ----
parser <- ArgumentParser(description = "Differential express... |
472aa4bb8d6710971d78e34ba6cc44044729ef45c18d482062e9af13380b2341 | R | 8,117 | 215 | #actual RNAseq QC
# library(cqn)
# library(sva)
# library(biomaRt)
# library(preprocessCore)
# library(Hmisc)
library(CovariateAnalysis) # get the package from devtools::install_github('th1vairam/CovariateAnalysis@dev')
library(data.table)
library(plyr)
library(tidyverse)
# library(psych)
# library(limma)
library(edge... |
c53b70faae15b63cc709bb35dce1732bcf5c57d88946bda746f330fd3836aef7 | R | 8,118 | 222 | # ----------------------------------------------------------------------------
# Libraries and setup ----
library("argparse")
library("ggplot2")
library("janitor")
library("tidyverse")
library("Seurat")
# Command line arguments ----
parser <- ArgumentParser(description = "Differential expression analyses")
parser$ad... |
1864d5a28b0666ba02c61d80f9ba9f78ad37139878bab03cac0b4152dbf6779f | R | 8,126 | 235 | # Figure 4: Sex-specific cognition associations
# 4a: Stratified forest plot for sex-specific flux associations with cognition
# 4b: Stratified forest plot for Butyrivibrio crossotus and Bacteroides vulgatus
# Requirements:
# Flux regression results
# Microbe regression results
# 4a: stratified forest plot on sex-sp... |
4d582e6425be791251831d6c20ae3690c80a53ba1675fe4899db52a25b7e44fc | R | 8,163 | 174 | # Run CellChat to explore cell-cell communication - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/C... |
4cc0578de503ce395e8b1a4a4aa786e23f6803d79236f063b634a58fe39f5ba0 | R | 8,168 | 174 | # Run CellChat to explore cell-cell communication - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/C... |
b292ce6f6743af8b96ef985e50608bebad635fa9766e9a6b36bda3ee18902c09 | R | 8,173 | 174 | # Run CellChat to explore cell-cell communication - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/C... |
50881396344f0c3e966c892e20db2f1534d0893a560ea53ce60f0028fab96fb7 | R | 8,175 | 235 | # ICC Analysis with Dialog File Selection
# Load required libraries
if (!require("irr")) install.packages("irr")
library(irr)
# ===== FILE SELECTION USING DIALOG =====
cat("\nPlease select the CT data file in the dialog box that appears...\n")
ct_file <- file.choose() # Opens file selection dialog
cat("\nPl... |
5c6cb4b718a0a58d3d40c2099ece9bdb0883f0e1273eacd1375d1c5fa88a3c8a | R | 8,177 | 285 | #' Plot_Zygosity_Blocks
#'
#' Plot blocks of heterozygous and homozygous SNPs
#' @param Table The deletion table containing the output of the DeletionTable function
#' @param window the block size in bp, usually 1500000
#' @param Max How many Heterozygouse SNP need to be in a block to get the full color, usually 6
#' @... |
f7794d0c8464744ac1caeee43b6b15b771e3de0157d52c65799370cc077b3d18 | R | 8,179 | 204 | library("DeconvoBuddies")
library("SingleCellExperiment")
library("tidyverse")
library("ggrepel")
library("here")
library("sessioninfo")
library("UpSetR")
## prep plot_dir
plot_dir <- here("plots", "06_marker_genes", "04_marker_gene_plots")
if (!dir.exists(plot_dir)) dir.create(plot_dir, recursive = TRUE)
#### load s... |
5b6e79f8540ee1e73a88f46bec75709aac5e155d5c3281b736bfaa70a2257bde | R | 8,194 | 174 | # Run CellChat to explore cell-cell communication - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/C... |
4c354bf0620b8774ac751308b64463ad9b8bfa3b326b290e8a9e9f54f73b7620 | R | 8,199 | 174 | # Run CellChat to explore cell-cell communication - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/C... |
eb0ee3db86c944da695f16cfcf6acbbe7b19e9b905df355f7d5a13fdf54ae446 | R | 8,201 | 158 | ---
title: "EIB_behavioural_accuracy"
author: "Barbara Cassone"
date: "AUTOMATIC"
output: html_document
---
```{r echo=FALSE, message=FALSE}
if(!require("pacman")) install.packages("pacman")
library(pacman)
p_load("reshape2","ez","dplyr","lme4","lmerTest", "rmarkdown", "lattice", "ggplot2", "DACF")
select <... |
754ae0cf9521d0240d772116dd4e59592884832e7e4e251f7f0be9246ad1463a | R | 8,204 | 174 | # Run CellChat to explore cell-cell communication - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/C... |
a64f10cc4f0518aed9a88ecbc9791b65cc328f11e2885f15063a9a774e458b72 | R | 8,207 | 203 | observeEvent(input$sideBarTab, {
if (input$sideBarTab == "ecmo" && is.null(GLOBAL_VALUE$ECMO[[1]])) {
GLOBAL_VALUE$ECMO <- list(
ecmoUising = fread(paste0(DATA_PATH, "Collection/ecmoUsing.", languageSetting,".csv")),
ecmo = fread(paste0(DATA_PATH, "Collection/ecmo.csv")),
artificialRespirators =... |
401d76053a9cb916ab73dabee60401e8d56cd5722ef236a5a76b8326c65eea25 | R | 8,266 | 159 | # function for plotting BCL11B site
# SNPname = "rs12895055", chr = 14, SNPposition = 99246457,
# revised from plot_anywhere
# Use OverlayTrack to combine data tracks
plot_AsoC_BCL11B_2sites <- function(chr, start, end, gene_name = "",
mcols = 100, strand = "+",
... |
e13cdbd4801d37ade2efad219d18b30605d7850206dedefcbccc84a88b7a681d | R | 8,269 | 215 | is.nan.data.frame <- function(x)
do.call(cbind, lapply(x, is.nan))
#' Estimate sigma
#'
#' This function estimates the standard deviation sigma of the noise of the model where the data are generated from a signal of rank k corrupted by homoscedastic Gaussian noise.
#' Two estimators are implemented. The first one, ... |
a4c0ad782a3a271580aa6afc6910d87a01256c5e9c8b8a3b98cd1a2a4955d03c | R | 8,283 | 235 | # get other UCRs nearest upstream and downstream protein coding genes
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(readxl)
library(stringr)
library(ggplot2)
library(ggview)
library(cowplot)
otherUCRsNearestPCGsINFO <- read.table(
file = "01-d... |
2e5618bae77196b7cecc8c67f6e7d2211d4bc069d4761a8f866707b63d6bb881 | R | 8,284 | 273 | ---
title: "Meta-analysis using dmrff"
output:
pdf_document: default
word_document:
highlight: tango
html_document:
toc: true
---
# Meta-analysis using dmrff
## Download and prepare DNA methylation datasets
We'll use a couple of small cord blood DNA methylation datasets
available on GEO: GSE79056, GSE... |
1fcc58cdfa95bcf18871d81f98a830b0b72c829982981236d13501acac374ec1 | R | 8,288 | 179 | library(tidyverse)
library(data.table)
library(arrow)
library(here)
create_formated_metadata = function(dt){
dt[,disease := ifelse(disease == "FTD" & pathology %in% c("FTD-TAU","FTD-FUS"),"FTD-non-TDP",disease)]
dt[,disease := ifelse(disease == "FTD","FTD-TDP",disease)]
dt[,disease := ifelse(grepl("ALS",dise... |
3c0680dfbdf6e9e49edb823b621b84184489fefb588d0cbe97861bf37173623d | R | 8,296 | 175 | # Run CellChat to explore cell-cell communication - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/C... |
6d0910f8146ce11e766ab5fa4631099b92c51747a8d051db94c30932322839cf | R | 8,301 | 175 | # Run CellChat to explore cell-cell communication - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/C... |
48a7e2e7ff8ad6db459bfbc6bc0e05e1e82849dcfccd0d342b621c0f95d419e1 | R | 8,306 | 175 | # Run CellChat to explore cell-cell communication - cell subtypes
# CIRCUITS Multiregion single-nucleus RNA-seq data - single cell resilience project
# Isabel Castanho (icastanh@bidmc.harvard.edu)
# April 2024
# https://github.com/sqjin/CellChat
## Tutorial: https://htmlpreview.github.io/?https://github.com/jinworks/C... |
7f80e41bafac44c6fd85064c5e8a8af7faa4714c6b2e9222a7867e33ab1132bb | R | 8,313 | 217 | ---
title: '%'
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library(dplyr)
library(Seurat)
library(cowplot)
library(patchwork)
library(ggpubr)
library(stringr)
library(tidyverse)
```
1. WT only
2. analysis
3. WT and MUT
4. analysis
#2 cluster annotation study... |
252e29899256dff9a7b7cf1fda4ba9fed60bcfa5cbd5e8c73b3f0b4c8a2061ff | R | 8,322 | 221 | show.tree = function(tree, names) {
named.tree = tree
for (i in 1:dim(tree)[1]) {
for (j in 1:dim(tree)[2]) {
if (tree[i,j] < 0) {
t = -1*tree[i,j]
named.tree[i,j] <- names[t]
}
}
}
return(named.tree)
}
SelectIntegrationFeaturesWeighted = function (object.list, w= NULL... |
f54085b09369492241189a35309100da8d4523df8cfd370350c13be986e58e4b | R | 8,326 | 148 | ---
title: Detecting clusters of doublet cells with DE analyses
package: scDblFinder
author:
- name: Aaron Lun
email: infinite.monkeys.with.keyboards@gmail.com
date: "`r Sys.Date()`"
output:
BiocStyle::html_document
vignette: |
%\VignetteIndexEntry{3_findDoubletClusters}
%\VignetteEngine{knitr::rmarkdown}
%... |
82761e8a68c3897614cc42dee03ac73abc13f29c074ef9b00f04818a9ae0b29c | R | 8,329 | 217 | # Extracts information from the downloaded abstracts
# Adds for each gene in which cell type it is a diff. expressed or a signature gene
# Searches keywords tumor and melanoma and gene symbols and their synonyms (Ensembl/BioMart)
# in the abstracts to highlight them
library(xlsx)
library(stringr) # split abstracts
li... |
d329c234652a657317ed46af1022590d36800f897ecb6b9422757c36e1b082e4 | R | 8,339 | 250 |
#----------------------------------------------------------------- Start UI ----
start_ui = function(id){
ns = shiny::NS(id)
shiny::tagList(
shiny::fluidRow(
shiny::h1('SODA - Simple Omics Data Analysis')
),
shiny::fluidRow(
shiny::column(
shiny::hr(style = "border-top: 1px solid #7... |
b9db6904466f89f1530bbf7339453dc539a7b9bbdabb430fd0c7cae56c729224 | R | 8,342 | 205 | library(dmrff)
options(mc.cores=4)
source("functions.r")
## generate datasets
set.seed(20180220)
n.datasets <- 10
n.sites <- 1000
n.samples <- 100
manifest <- generate.manifest(n.sites)
datasets <- lapply(1:n.datasets, function(i) {
dataset <- generate.dataset(n.samples, manifest)
var <-... |
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