sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
da70cb38445835d1ac577498167473c4297e28b8819addb04c419dc6430d5489 | R | 3,325 | 44 |
gene.band = read.table("E:/project/embryo/data/database/gencode.v19.chr_patch_hapl_scaff.annotation_gene_info.add.UCSC.hg19.chromsome.band.txt",header = F,sep = "\t",stringsAsFactors = F,fill = T)
gene.band = gene.band[grep("chr",gene.band$V1),]
colnames(gene.band) = c("chr","star","end","ensembl.id","gene.name","band... |
ed8424b7f3aab4fc0b5a80ef7585d1afa23fa3ff7ebfe95ca658ab649927767d | R | 3,326 | 81 | #!/usr/bin/env Rscript
#### Generate non-tumor subsets for cell type annotation
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
cohort <- commandArgs()[6]
# Read-in object
seu <- readRDS(paste0('data/MBPM/', cohort, '/data_', cohort, '_anchor2000_dims30.rds'))
seu <- ... |
ae5162ae1df62993e68129a9671dd27688893b6114a8d2dad07dfe828bc360b2 | R | 3,329 | 88 | # Siwei 21 Mar 2019
# init
library(Gviz)
# data("cpgIslands")
library(rtracklayer)
library(BSgenome)
library(BSgenome.Hsapiens.UCSC.hg38)
library(TxDb.Hsapiens.UCSC.hg38.knownGene)
library(ensembldb)
library(org.Hs.eg.db)
library(grDevices)
library(gridExtra)
library(GenomicRanges)
###
options(ucscChromosomeNames = F)... |
37295f456fb3c8c38e08fdb142418ff2f5c74449c57643720652395e4883c497 | R | 3,331 | 136 | # Siwei 02 Feb 2025
# plot new Ex 9e
# init ####
{
library(readxl)
library(stringr)
library(ggplot2)
library(scales)
library(reshape2)
library(RColorBrewer)
library(ggpubr)
library(dplyr)
library(data.table)
library(DescTools)
library(multcomp)
library(gridExtra)
}
# load raw data ####
s... |
01c53c081ea940510430ff30466a62b089e32559026562481724a49d8c0136c7 | R | 3,338 | 64 | options(stringsAsFactors = FALSE)
library(ggplot2)
library(reshape2)
library(dplyr)
library(stringr)
library(lme4)
library(lmerTest)
library(RColorBrewer)
library(ggpubr)
#### PTA burden association analysis ####
# 1. Playing years, Age at death, Age Sx onset ####
meta <- read_table("data/sample_info.tsv", header=T, ... |
b454da102f865ec03a1cb4e146425d5081f1ad8ec18f97d5d7bd8c8d32247f57 | R | 3,347 | 91 | # ****************************************************
# Preprocessing steps for Tabula sapiens dataset
# ****************************************************
if(!require("Seurat")){
install.packages("Seurat")
library("Seurat")
}
if (!require("here")){
install.packages("here")
library("here")
}
if (!require("m... |
7826952b04db337df0476f73015ca9b3094b3b02292f4d4d7aa5d81c9d9c9f24 | R | 3,350 | 132 | #This file contains the processing functions which are common to all Figures
require(sjemea)
require(rhdf5)
library(gdata)
dyn.load("correlation_index.so")
dyn.load("spike_time_tiling_coefficient.so")
#This reads in the hdf5 file and calculates the ci and sttc for each pair of electrodes
run_measures_on_hdf5=functi... |
f569dea58f29f52d879ccb84ca0c91bae6afa03e22dad0a7f3bfbb4f38b37a4e | R | 3,350 | 87 | #!/usr/bin/env Rscript
#### Fine cell type annotation of B cell subset for MPM_sn
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(viridis)
'%notin%' <- Negate('%in%')
path.ct <- 'data/cell_type_DEG/MPM_sn/bcells/'
filename <- 'MPM_sn_bcells'
seu <- readRDS('da... |
9237129aec3169df383f6befcc63ad4bb4a90eb98c3703c2ccbd759cfb0b170a | R | 3,365 | 89 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(qs)
library(future)
library(foreach)
library(dplyr)
library(AnnotationDbi)
library(EnsDb.Hsapiens.v86)
library(gprofiler2)
objects_folder <- file.path(project_fol... |
7fbb521e53ea351d69cc2d7b9ea6e68d9d66cfafd7c39f998d079ae8ddd2eaf2 | R | 3,366 | 84 | #Run differential gene expression analyses between cell populations ####################################################
#Load libraries
library(Seurat)
library(tidyverse)
#Create directory to store plots
dir.create("5_diff_exp_mixed_1.5_2M", showWarnings=T)
#Load mapped Seurat data with reductions
df = LoadSeuratRd... |
f2a613bced454ea49d84980d1dda9fc485d0f58b44b3d99208c15f867963e1e9 | R | 3,372 | 80 | ```{r}
# load packages
suppressPackageStartupMessages(library(xfun))
pkgs = c("SingleCellExperiment","tidyverse","data.table","dendextend","fossil","gridExtra","gplots","metaSEM","foreach","Matrix","grid","spdep","diptest","ggbeeswarm","Signac","metafor","ggforce","anndata","reticulate","scales",
"matrixStats... |
0852fe888615c5b45e84c348fc54782f0d94bf2c963c74ca2339854541547ae5 | R | 3,384 | 59 | #generate plots for results of the varied window size plot
##### load packages and results #####
if (!require("here")){
install.packages("here")
library("here")
}
if (!require("tidyverse")){
install.packages("tidyverse")
library("tidyverse")
}
if (!require("magrittr")){
install.packages("magrittr")
library(... |
90e6850094a420ee1288041b5547374e66ce4d9941ea3c5df589890fb0084dd2 | R | 3,384 | 78 | #' SpliceFlow calculate splicing efficiencies
#'
#' @param se_counts : counts from exons and introns
#' @param regionsFlow: regionFlow-object containing exons and pairs
#'
#' @return SpliceFlow calculations
#' @export
#' @importFrom DEXSeq DEXSeqDataSetFromSE
#' @importFrom DEXSeq DEXSeq
#' @import dplyr
#' @examples
S... |
39905f20bfbb0d0ddc5d6f96da9424177719f9f9b5325fdc4ce7d827199527a3 | R | 3,385 | 110 | library("SummarizedExperiment")
library("here")
library("tidyverse")
library("jaffelab")
library("sessioninfo")
#### Round 2 version 40 ####
output_dir <- here("processed-data", "01_SPEAQeasy", "round2_v40_2023-04-05")
rse_fn_v40 <- list.files(here(output_dir, "count_objects"),
pattern = "rse*", full.names = TRUE... |
d15d0f8801f0ac003401e443621799ba42d996027dbd6af503e4297e2f023c38 | R | 3,386 | 86 | #' dmrff.cohort
#'
#' Identify differentially methylated regions
#' within an individual dataset with a `pre` object.
#'
#' @param object Object generated by \code{\link{dmrff.pre}} for the dataset.
#' @param p.cutoff Unadjusted p-value cutoff for membership in a candidate DMR
#' (Default: 0.05).
#' @param maxgap Maxi... |
3d6194dd8c4ec6fe388170f82b889ca693427167a0ab042966378fa4ff13d87e | R | 3,388 | 104 | # Siwei 14 Mar 2022
# make .gene_loc files
# init
library(stringr)
library(readr)
df_NCBI_37.3 <-
read_delim("~/NVME/VPS45/organoids_SETD1A_hg19/MAGMA_annotation/NCBI37.3/Rev.NCBI37.3.gene.loc",
delim = "\t", escape_double = FALSE,
col_names = FALSE, trim_ws = TRUE)
df_NCBI_37.3$X6 <- NU... |
7594588ff408c8e082fca6610a30fa2c628ed7f96613ca63158360ba18f4f14e | R | 3,390 | 102 | library(Seurat)
library(patchwork)
library(ggplot2)
sample_info <- read.delim("../COGA_Caudate_HT_scRNA-seq_Pool-sample_infor.txt")
pool_line <- "start"
pool_num <- 0
skipped_samples <- c("BTRC_288_BTRC_288", "BTRC_140_BTRC_140", "BTRC_68_BTRC_68", "BTRC_141_BTRC_141",
"BTRC_23_BTRC_23", ... |
32ec4020d45afc73a722db619b2b8a884db30c1090fc71d952d3118254906da2 | R | 3,395 | 104 | # Load packages
library(tidyverse)
# clear global environment
rm(list=ls())
setwd("/home/emba/Documents/EMBA/VMM_analysis/00_input")
dir.in = getwd()
# get all directories
subs = list.files(pattern = "sub-*")
# loop through them
for (subID in subs) {
tryCatch(
{
# check if the file exists
... |
7de4203a70d3af02f46b1fa924899129410b4aa04d7b1abb0fd79e41912d74b1 | R | 3,397 | 151 |
#library(remotes)
library(Seurat)
library('glmGamPoi')
source("IKAP_function.R")
#renv::status()
#renv::snapshot()
#renv::activate()
#renv::hydrate()
#devtools::install_github('immunogenomics/presto')
setwd('data_AD_organoid_GSE164089/')
files = sapply(dir()[grep("mtx",dir())], function(x) strsplit(x,"matrix... |
7db4194ed4286225ffad33c030947d4dc9ece16f522978e630413c0e1a1cf332 | R | 3,401 | 108 | library(magrittr)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(Hmisc)
library(cowplot)
library(pROC)
library(stringr)
library(RColorBrewer)
library(netresponse)
library(igraph)
#genes that are relevant across drugs? sensitivity genes? essentiality?
setwd(dirname(rstudi... |
8b99f9b043fd58d401ff2c4cdb0f229a9d8a1011278fce24a9f9886dbb6f2340 | R | 3,402 | 89 | library("DeconvoBuddies")
library("SingleCellExperiment")
library("tidyverse")
library("ggrepel")
library("here")
library("sessioninfo")
#### prep dirs ####
data_dir <- here("processed-data", "13_PEC_deconvolution", "01_find_markers_PEC")
if(!dir.exists(data_dir)) dir.create(data_dir)
plot_dir <- here("processed-data... |
99e3275f9f6dec212a780a7ac551d96d969d1f0242f6dccdafafed16948d1aa8 | R | 3,403 | 116 | # fig 3C
# Jaccard Index Statistical test comparing with ChIP-Exo
library(dplyr)
library(twice)
library(ggplot2)
library(ggpubr)
data("hmKZNFs337")
data("hg19rmsk_info")
# chipexo
chipexo <- read.csv("data/kznfs_TEs_ChIP_exo_modified.csv")
chipexo <- chipexo %>%
mutate(pair=paste(teName, ":", geneName))
# clust... |
c42ad961375095385ef1fa3ab501a22381c7230ec42c0c0677e2e2b153cc38dd | R | 3,404 | 91 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(qs)
library(future)
library(foreach)
library(dplyr)
library(AnnotationDbi)
library(EnsDb.Hsapiens.v86)
library(gprofiler2)
library(ComplexHeatmap)
objects_folder ... |
310b5c71b80f6834c6cde05e5929476857c057ae2eadbc4da4365b336bf38317 | R | 3,419 | 106 | library(magrittr)
library(data.table)
library(dplyr)
library(tidyr)
library(ggplot2)
library(ggrepel)
library(Hmisc)
library(cowplot)
library(DescTools)
setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
read_dat_no_embedding <- function() {
files <- list.files(paste0('../results_without_compound_embeddin... |
edeac447e4559ca5990ccc59ac0bb05742892aa282b561e6f128babc23b12dc7 | R | 3,420 | 80 | #' Calculate cell type-trait associations
#'
#' @param sscore A dgeMatrix of seismic specificity scores where
#' each column is a cell type and row names are gene identifiers.
#' (Note: the identifiers used should match those used in the MAGMA input)
#' @param magma A data.frame or file path to MAGMA output for a parti... |
a258b2daef01073a72fcfddf08b30ff9bde1c48431e84b0562326793027f9f3a | R | 3,423 | 87 | #' Jiayi rewrite Sep 2024
#' CreateVCF
#'
#'
#' Create the VCF (Variant Call Format) file
#' @param bamdir The Path of to the BAM Directory
#' @param Genome_Fa Path for whole genome FASTQ file
#' @param DIR_Genome_DICT
#' @param Picard_Path Path to the Picard directory, with all the JAR files
#' @param CMD_gatk
#' @par... |
9b5bc032a7bf4d245a33d0978b85e630427dd8a2cb6e0a564c8c4d78791cc671 | R | 3,424 | 102 | #!/usr/bin/env Rscript
# =========================================================================
# Script: atac_te_diff_analysis_in_r.R
# Author: Alireza Ghahramani
# Contact: aghahram@uwo.ca
#
# Purpose:
# Quantify transposable elements (TEs) in ATAC-seq BAMs using featureCounts
# (with a RepeatMasker SAF annota... |
7d7e5495700a4c7238e820a63d6963448c57be3aebf1a3a2eec291e6ab5d7e3d | R | 3,425 | 87 | ## Singh AK et al: Proteins with amino acid repeats constitute a rapidly evolvable and human-specific essentialome
##This file contains all scripts for statistical analysis.
#1. Computing enrichment using permutation testing: 10000 random sampling
##Note: The following example is to test whether human essential genes ... |
6909177e2aecd0eade655b1311378103103aefc15079730ad3d122c75e84bb74 | R | 3,427 | 99 |
## Heatmap of ATAC-seq peak enrichment for each nonlinear module ##
library(reshape2) # for melt()
library(ggplot2)
library(RColorBrewer)
#1. Load enrichment results - nonlinear =========================================================================================
modules <- c('All', 'turquoise', 'blue', 'brown'... |
1501e272a9205b8afbd94d8c7c2bbd3507fec7fb51910a0e644949a02b15a1d9 | R | 3,430 | 91 | library(tidyverse)
library(ggridges)
library(EnhancedVolcano)
#data <- read_csv('xpore_out_fb/diffmod.table')
######################################### load data
data_maj <- read_csv('xpore_out_fb/majority_direction_kmer_diffmod.table')
mod_key <- read_csv('mod_key.csv')#load in modifcation key
mod_key <- mod_key %>%... |
07bee560e4ac72b50500ea90176d401eedb9f8cd4045ee91cdb9e53aad808e43 | R | 3,432 | 91 | #-----------------------------
# get top 1000 markers by type
#-----------------------------
markers <- get(load("markerlist_celltypebroadk_dlpfc-ro1.rda"))
names(markers)
# [1] "Astro" "EndoMural" "Excit" "Inhib" "MicroOligo" "Oligo" "OPC"
n <- 1000 # get top n marker genes by type
genev <- uniqu... |
38e9aaabb2e189f6dfd18c911bb05ee8760f7127dbd7aa6c035b7e5ec208afdb | R | 3,433 | 100 | fluidPage(
fluidRow(
column(
width = 5, style = "padding:0px;",
userBox(
title = userDescription(
title = i18n$t("福岡県"),
subtitle = i18n$t("九州地方"),
type = 2,
image = "Pref/fukuoka.png"
),
width = 12,
status = "navy",
colla... |
978b6c56778a3a2d97276f0c3b9955da2ecea25532256284e8dadb6aa24dcc9a | R | 3,440 | 95 | #!/usr/bin/env Rscript
# ========================================================================
# Script: te_featurecounts_differential_expression.R
# Author: Alireza Ghahramani
# Contact: aghahram@uwo.ca
#
# Purpose:
# Differential expression analysis of transposable elements (TEs) using
# featureCounts (SAF, Re... |
40b6d90fa84c53b81e1ddd3c7feb4acc1be15a3bd4f8748029b8475f2dbd5dbf | R | 3,446 | 90 | #### Figure S5A ####
#### Upset plot for astrocytes ####
library(stringr)
library(ggplot2)
library(UpSetR)
library(here)
# pairwise comparisons:
# human vs chimp:
h_c <- read.table(here("data/astrocytes", "Astro_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h_c <- h_c[h_c$padj<0.01 & (h_c$log2FoldChange<(-0.... |
95fc0830fbd8b3c13bcdf82599a432dcd9de16ad947437be544de9c5ae763df3 | R | 3,450 | 91 | #' Save and Display a ggplot Object in Both PNG and PDF Formats
#'
#' Saves a ggplot object to both PNG and PDF formats and displays the PNG version inline.
#' Useful for analysis workflows where both high-quality vector output (PDF) and inline visualization (PNG)
#' are desired.
#'
#' @param filename A character strin... |
e7caa316bdaeab71a3027ba85a4369f08d3e488790de0069caa577ad5a718440 | R | 3,455 | 97 | # =============================================================================
# 正常样本质控脚本
# =============================================================================
# 功能：读取正常样本（normal_fascia）的10X数据，创建Seurat对象，进行质控过滤
print("Hello world!")
rm(list = ls())
# 加载配置文件
source("../config.R")
# 设置工作目录
setwd(ENV_DIR)
lf... |
fec5b1076a53200b2cea754c06dc642103d40e02af0c1d28bf98ba06b3ef2419 | R | 3,456 | 97 | library(data.table)
# ====準備部分====
source(file = "01_Settings/Path.R", local = T, encoding = "UTF-8")
# 国内の日報
domesticDailyReport <- fread(paste0(DATA_PATH, "domesticDailyReport.csv"))
domesticDailyReport$date <- as.Date(as.character(domesticDailyReport$date), "%Y%m%d")
setnafill(domesticDailyReport, type = "locf")
... |
71883b44839e3d3cd665d1d5d87987b5530e3bb2dd28678ce6f32a342e933f26 | R | 3,465 | 93 | # =====================================================================
# For compatibility with Rscript.exe:
# =====================================================================
if(length(.libPaths()) == 1){
# We're in Rscript.exe
possible_lib_paths <- file.path(Sys.getenv(c('USERPROFILE','R_USER')),
... |
08f499ea5e6109023afe379b4c77e207132b90bce4b9c562f0175e76a12c6ee2 | R | 3,466 | 115 | # Siwei 14 May 2021
# ASoC analysis of 17 Microglia lines ATAC data
# init
library(readr)
library(plyr)
library(dplyr)
library(stringr)
library(Rfast)
library(ggplot2)
library(RColorBrewer)
# load data
ASoC_df_raw <- read_table2("DP20_data_files/non_500bp_intersected/MG_28_lines_merged_peaks_filtered_03Jun2022_DP_... |
7e2f01ed6c6f77d29ad5793d9b91209bc197ff58cb8395810b46ff77b16bf87e | R | 3,472 | 73 | #' Adversarial Debiasing
#' @description Adversarial debiasing is an in-processing technique that learns a classifier to maximize prediction accuracy
#' and simultaneously reduce an adversary's ability to determine the protected attribute from the predictions
#' @param unprivileged_groups A list with two values: the c... |
1aa6bb197a3508913a505364f97e6133f7fce347df6ef422b1bf006f382d5b8e | R | 3,479 | 86 |
## Barplots of DMP distribution within genelist genes ##
library(viridis)
epicAnnotGeneList <- read.csv(paste0(refPath, "EPIC_annot_SFARI_SCHEMA.csv"), row.names=1)
glistFile <- read.csv(paste0(refPath, "GeneList_SFARI_SCHEMA.csv"), row.names=1)
#1. Load EWAS results =============================================... |
54fe7c88791b8049139776d9bd55b1be5cc5e15d9b76e846bdeefbaa817cc2a0 | R | 3,484 | 112 | # 実効再生産数
tabPanel(
title = tagList(
icon("chart-line"),
i18n$t("実効再生産数"),
boxLabel("New", status = "warning")
),
value = "rt_line",
fluidRow(
style = "margin-top:10px;",
column(
width = 8,
pickerInput(
inputId = "regionRtLinePicker",
label = i18n$t("地域選択"),
... |
fd4e9dad99d39a4549c8d4201e81ceba8ebd711bc76e4482373ae1da6634df8a | R | 3,487 | 139 | output$RtLine <- renderEcharts4r({
input$generateRtLine
isolate({
# parameters
mean_si <- input$RtLineMeanSi
std_si <- input$RtLineStdSi
selectedPref <- input$regionRtLinePicker
incid <- as.incidence(rowSums(byDate[, selectedPref, with = FALSE]),
dates = byDate$date
)
# handling ... |
cdd279a301e3a57c0dc08d057b33bc47fad2decdbe9af0890264ff97bbbb6511 | R | 3,494 | 90 | #### Figure S6A ####
#### Upset plot for microglia - primates ####
library(stringr)
library(ggplot2)
library(UpSetR)
library(here)
# pairwise comparisons:
# human vs chimp:
h_c <- read.table(here("data/microglia", "Micro-PVM_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h_c <- h_c[h_c$padj<0.01 & (h_c$log2F... |
4538cb1784428f3d2982a3ce01ae585f6b12a50b323bed4a9eb23638715de694 | R | 3,495 | 59 | context("compareRhythms_deseq2")
load("test_data_rnaseq.rda")
exp_design_batch <- cbind(exp_design, batch, stringsAsFactors=TRUE)
test_that("DESeq2 analysis works for default params", {
results <- compareRhythms(countsFromAbundance, exp_design, method = "deseq2")
expect_s3_class(results, "data.frame")
expect_n... |
bd397c389c020af28547c260f6c8fd48ec5be0999ec7af53b2229c7ba54e1fdc | R | 3,508 | 88 | # Describe microbiome abundances and create figure
# Figure 1a: boxplots of phylum abundances
# Figure 1b: Ordered boxplots of the most abundant species
# Figure 1b:
# 1: Calculate the mean abundance of each species
# 2: Sort by abundance
# 3: Filter on the top X species
# 4: Visualise
# Save species abundances
sp... |
114d30a782780902c92989ee331b40c42585664f71ca342e1eb4842c29755d29 | R | 3,510 | 124 | # get and analyse how many cCREs overlap with coding UCRs and ncRNA UCRs
setwd(dir = "D:/R_project/UCR_project/")
options(stringsAsFactors = FALSE)
rm(list = ls())
library(tidyverse)
library(stringr)
library(VennDiagram)
# cCREsOverlapCodingUCRs --------------------------------------------------
cCREsOverlapCodingU... |
05b51e5a4c40574decdea050699dafc9107cd805bd317d6e2bf96461b1419e84 | R | 3,515 | 119 | # exonicµÄgo¸»¼¯·ÖÎö½á¹û
# barplotչʾ¸»¼¯·ÖÎö½á¹û£¬ÍøÖ·ÈçÏÂ
# https://mp.weixin.qq.com/s/fVIRX8ieyWRRVdArwOHQvg
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(readxl)
library(stringr)
library(dbplyr)
intronic_UCR <- read_xlsx(path = "01-data/10-GO_enrichment_ana... |
32bd3f236099b4df8960bfd85925f9731bdc15a4f714ef6081d648dcebde840f | R | 3,519 | 80 | # load packages
require(tidyverse)
require(monocle3)
require(Seurat)
# load cell_data_set objects
cds <- readRDS('cds_downsampled_all.rds')
# subset CDS by time point
cds_E12 <- cds[, cds$time_point == 'E12.5']
cds_E14 <- cds[, cds$time_point == 'E14.5']
cds_E16 <- cds[, cds$time_point == 'E16.0']
cds_E17 <- cds[, cd... |
a0623d2068de84d7dd13f09de8edadd78add659762109d3d04bd0104146db823 | R | 3,531 | 90 | #### Figure S6H ####
#### Upset plot for oligodendrocytes - primates ####
library(stringr)
library(ggplot2)
library(UpSetR)
library(here)
# pairwise comparisons:
# human vs chimp:
h_c <- read.table(here("data/oligodendrocytes", "Oligo_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
h_c <- h_c[h_c$padj<0.01 & ... |
0836ddc3a626083027ddf18871e75b065495352363d6403ff20098776b936dc8 | R | 3,533 | 80 | # Run CellChat to explore cell-cell communication - cell subtypes (continued)
# 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.co... |
da1c4d02c5123b9e8df0897a7d38af13397f585750e9a07cff2bf1e2e447028d | R | 3,533 | 80 | # Run CellChat to explore cell-cell communication - cell subtypes (continued)
# 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.co... |
deeccf3bd926891d467e79b292549cb082216d87425786ebea394d584767fdf1 | R | 3,533 | 91 | #!/usr/bin/env Rscript
#### Fine cell type annotation of CNS and stromal cell subset for MBM_sc
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(viridis)
path.ct <- 'data/cell_type_DEG/MBM_sc/cns/'
filename <- 'MBM_sc_cns'
seu <- readRDS('data/cell_type_DEG/MBM... |
edd53045e3c478cfa3071b4b561cf905ff43064c5f7799aad0b03c7a331d9d3e | R | 3,534 | 73 | res <- read.csv(file="CrossData_exp2bulk (1).csv")
Mathys_Fujita <- res[1:5,-1]
colnames(Mathys_Fujita)[1] <- "Method"
Fujita_Mathys <- res[6:10,-1]
colnames(Fujita_Mathys)[1] <- "Method"
method_colors <- c("#e56b6f", "#fec89a", "#895737", "#0077b6", "#9f86c0" )
names(method_colors) <- c("BLEND", "BayesPrism", "MuSiC",... |
2b1bb1a9072499d6efaa47f6a785a7772197076ed6f0852b081650b60c7a49f8 | R | 3,536 | 80 | # Run CellChat to explore cell-cell communication - cell subtypes (continued)
# 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.co... |
bd1440b0ff9518c9003d73df4ce10027e364f422a3c5ff4d1a4147246db29275 | R | 3,536 | 80 | # Run CellChat to explore cell-cell communication - cell subtypes (continued)
# 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.co... |
4c538d60f9829d25416f9ea10478d8988c0ad5c1a5f402b9ea5eef50033f7b9d | R | 3,538 | 79 | # Run CellChat to explore cell-cell communication - cell subtypes (continued)
# 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.co... |
2edce6c8f7a593eabf5cef728ee4a409d2752a23938c05dad68d5c809227e1a2 | R | 3,539 | 86 |
library("tidyverse")
library("sessioninfo")
library("here")
## prep dirs ##
data_dir <- here("processed-data", "08_bulk_deconvolution", "12_get_est_prop_MuSiC_cell_size")
if (!dir.exists(data_dir)) dir.create(data_dir, recursive = TRUE)
#### data details ####
## dataset properties
dataset_lt <- tibble(Dataset = c("2... |
6cf9e7b04fe5c17df7026ce9024e02e698e7c1ac3423427efeb2b82847e0317c | R | 3,539 | 80 | # Run CellChat to explore cell-cell communication - cell subtypes (continued)
# 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.co... |
ef7511f3e7c66e23e00173dfce3200f4fae26eec96580343d2954e9df8c91cfb | R | 3,540 | 80 | # Run CellChat to explore cell-cell communication - cell subtypes (continued)
# 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.co... |
bf4f9ffd51414b539f96c011569a1fe2331e8c1bcac2b747a9ca264aa4af3de6 | R | 3,541 | 88 | #!/usr/bin/env Rscript
#### Fine cell type annotation of stromal cell subset for MPM_sn
#### Author: Jana Biermann, PhD
library(dplyr)
library(Seurat)
library(ggplot2)
library(gplots)
library(viridis)
path.ct <- 'data/cell_type_DEG/MPM_sn/stromal/'
filename <- 'MPM_sn_stromal'
seu <- readRDS('data/cell_type_DEG/MPM... |
dd1fe7442599fb81859620d34250a36fb54cc537a380c814146749d0f72e0045 | R | 3,543 | 80 | # Run CellChat to explore cell-cell communication - cell subtypes (continued)
# 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.co... |
9be83ae1685655f93e1cea2ef6524895de0d3ed84491b907372142fb65bf197b | R | 3,545 | 89 | #Code to split Allen Brain atlas transcriptomic data from excitatory neurons.
library(stringr)
library(dplyr)
samps = readRDS("samps.RDS")
prot_df = readRDS("prot_PSD_df.RDS")
annot = readRDS("annot.RDS")
annot_CA1 = annot[grep("CA1$",annot$cluster_label),]
samples_CA1 = as.vector(annot_CA1$sample_name)
samps_CA1 = ... |
92facb8d14f50326ee05ceacb88d1e3ae477f71ed643ec2906b58dc28690ff3e | R | 3,546 | 80 | # Run CellChat to explore cell-cell communication - cell subtypes (continued)
# 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.co... |
9bbb3dfc32702e4f7d04d6644c5bf59fc163c53e1614d96d1e81564788a67c61 | R | 3,547 | 63 |
## Leave-one-out (LOO) Z-score outlier removal ##
# Calculate Z-score for each sample by removing the sample from the dataset and calculating a Z-score for the remaining samples.
# Samples that create a large Z-score when they are removed from the dataset suggest they have a large influence on the distribution.
# O... |
85bb3c6a0ae54809e262aaff67dbd5c2c5a91f38ac6c602c320a6b40d1f2fc94 | R | 3,552 | 60 | #### Figure 4A - inset plot ####
#### Highly divergent genes in astrocytes ####
library(stringr)
library(dplyr)
library(here)
library(ggplot2)
# astrocyte DEG data:
astro_h_c <- read.table(here("data/Astrocytes", "Astro_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
astro_h_c <- astro_h_c[astro_h_c$padj<0.01 ... |
665e1a2ef32317673c58477a936a27fd7f015a035024fa7347f95dcfdffa868f | R | 3,569 | 58 | library(RIdeogram)
library(rCGH)
library(Biobase)
human_karyotype.hg19 = hg19
human_karyotype.hg19$star = 0
human_karyotype.hg19 = human_karyotype.hg19[,c(1,6,2,3,4)]
colnames(human_karyotype.hg19) = c("Chr","Start","End","CE_start","CE_end")
human_karyotype.hg19[23:24,1] = c("X","Y")
human_karyotype.hg19$Chr = paste0... |
a7b65641dbae981c8cccf312870b334496417b735a534b9034791dbb20b73acf | R | 3,574 | 111 |
```{r}
library(kronos)
library(tidyverse)
library(ggplot2)
```
Import dataset
```{r}
library(readxl)
bigdata_yjl_ht_new_othergenes <- read_excel("C:/Users/U177202/PhD/Experiments_thesis/Youth_Jet_Lag_Experiment_2/YJL_Exp2_Microarrays/YJL_Exp2_KRONOS/YJL_Exp2_circadian_clock/YJL_Exp2_circadian_clock_ht_extra... |
c138b7ab9464dbe763260d29c54f5791979b74caef4ff0eb2241a17cbe6c764c | R | 3,574 | 62 | #### Figure S6B - inset plot ####
#### Highly divergent genes in microglia ####
library(stringr)
library(dplyr)
library(here)
library(ggplot2)
# microglia DEG data
micro_h_c <- read.table(here("data/microglia", "Micro-PVM_human_vs_chimp_sig_genes.csv"), sep=",", header=TRUE)
micro_h_g <- read.table(here("data/microg... |
bd86866f06037f038bd1476516440ef597333d128b9049bf9d611bf5caa7ba28 | R | 3,584 | 89 | regressFluxesAgainstMicrobes <- function(fluxesPruned,microbiome) {
#' regressFluxesAgainstMicrobes - Regresses microbe abundances against metabolic fluxes.
#' This function performs linear regressions between microbe abundances and metabolic
#' fluxes for each microbe and each flux. The R-squared values from these... |
e7430f4e74a664abe3010b48b542d2dff9c5cebb8c7b927e78113f5474dce909 | R | 3,584 | 77 | RelRankEnrich <- function(exp, genelist){
if (!is.matrix(exp) || is.null(rownames(exp)) || length(genelist) == 0) {
stop("Invalid input. 'exp' must be a non-empty matrix and 'genelist' must be a non-empty list.")
}
row_names = rownames(exp)
num_genes = nrow(exp)
num_samples = ncol(exp)
R = matrixStat... |
1c2813e91df67c28b5f80eb69c598fb4e2295c5c00817f22c154cb4588af1817 | R | 3,585 | 105 |
## Loading libraries
library(tidyverse)
library(MatchIt)
library(optmatch)
library(gridExtra)
## Loading data
## There are 3 tissues
amy_base.a <- readRDS(paste0(directory, "ROSMAP_DLPFC_PhenoCov_Cross_070524.rds"))
## complete data for variables that are being used to match
nomiss <- amy_base.a %>%
select(proji... |
3e8125380d159ed7094042f3ff2a0cfb34a99a53fb507db2bd47d2b06559ac09 | R | 3,591 | 80 | # function for plotting rs2027349 site
# revised from plot_anywhere
# Use OverlayTrack to combine data tracks
plot_AsoC_peaks_rev <- function(chr, start, end, gene_name = "",
mcols = 100, strand = "+",
x_offset_1 = 0, x_offset_2 = 0, ylimit = 400,
... |
15cf1e18a265e7e2eeddf8cfb596f4604570b9d286d7326102a4e600c5b0ab7f | R | 3,599 | 95 | microbiomePhylumSummaryStats <- function(rawMicrobes, fluxAll){
## Step 1: Process the raw read counts
# Filter on flux samples
processedTotalReads <- rawMicrobes %>%
# Convert wide format data to long format
# Retains all columns except 'Taxon', pivoting others into 'ID' and 'value' columns.
pivot_longer(co... |
1c305dfeb3a55e15ce1fee0efceed8a60ea55223dae2cc697aa2ce2e5cf557e5 | R | 3,601 | 91 | library(tidyverse)
# Create a final table of manually validated cryptic bleedthrough events
i3_mv <- read_tsv("data/PAPA/riboseq_manual_verification_of_i3_cortical_cryptic_bleedthroughs.tsv")
other_mv <- read_tsv("data/PAPA/cryptics_summary_bleedthrough_manual_validation.tsv")
complex_mv <- read_tsv("data/PAPA/crypt... |
8ef1010e1abedb70a1c71a9998cec71f19a69adfa9af9ed23c3082251f73151b | R | 3,608 | 82 | setwd(dirname(rstudioapi::getActiveDocumentContext()$path))
list2env(rjson::fromJSON(file = "00_configs.json"), envir = .GlobalEnv)
library(Seurat)
library(ClustAssess)
library(qs)
library(ggplot2)
options(future.globals.maxSize = 2 * 1024^9)
metadata_folder <- file.path(project_folder, "metadata")
seurat_folder <- f... |
ccdd2f8591cccea265a744f6fc37f2848a58fcba72ce1f737fc9b594257be4fb | R | 3,611 | 99 | #!/usr/bin/env R
# Author: Sean Maden
#
# Compare parameter priorities from HALO settings files, across slides/samples.
#
#
#----------
# load data
#----------
# source helper functions
fpath <- file.path("read-halo-settings_functions.R")
source(fpath)
# concat settings flat tables
dpath <- file.path("HALO", "Export... |
b935c1b929e0d6b1115c0cf36966350c54f86e64a43f57d298efa8c8ceee4dbb | R | 3,622 | 52 | fluidRow(
box(
width = 12, closable = F,
title = tagList(icon("chart-line"), i18n$t("COVID-19 重症患者状況 日本COVID-19対策ECMOnet集計")),
tags$p(i18n$t("このページは、"),
tags$a(
icon("external-link-alt"),
i18n$t("COVID-19 重症患者状況 日本COVID-19対策ECMOnet集計"),
href = "https://... |
1866616a12b9d67cabd86b36ca6bcd414654e060df4e4fe243be906f80e73f7e | R | 3,625 | 75 | #!/usr/bin/env Rscript
## Modified version of perm.t.test from GmAMisc
## Original version: https://CRAN.R-project.org/package=GmAMisc
## Modified by: Jana Biermann, PhD
perm.t.test.mod <- function(data, format, sample1.lab = NULL, sample2.lab = NULL, B = 999, pathway,plot) {
#options(scipen = 999)
if (format == ... |
275e7fc658ace08d3f1612f87d4d78d2e39a9c1ca833f417d14b33eb074577ae | R | 3,626 | 111 | # ÓÃmetascapeÍøÕ¾×öµÄGO¸»¼¯·ÖÎö½á¹û»æͼ
setwd(dir = "D:/R_project/UCR_project/")
rm(list = ls())
library(tidyverse)
library(ggplot2)
library(readxl)
GO_result <- read_xlsx(path = "03-results/10-GO_enrichment_analysis/meatscape_coding_UCR_genes_GO/metascape_result.xlsx",
sheet = 2)
GO_result <... |
74fbeeeb5ad7fe3a3f24fe5fd460476423db7c4106f3bd9047517affb725a73f | R | 3,626 | 120 | #!/usr/bin/env Rscript
print("################################################################################################################")
print("# Estimation and removal of cell free mRNA contamination in droplet based single cell RNA-seq data with SoupX #")
print("#############################################... |
95b5bb6a7bf87c35ef8af1e56b950b3b1826b423f5e54942f81a0dcc2c587137 | R | 3,633 | 79 | ---
title: "Lvar Cell Culture Growth Plots"
output: html_document
date: "2024-01-03"
---
```{r }
# Load Libraries
library(ggplot2)
library(dplyr)
```
```{r }
##Make line/scatter plot for cell culture population doubling (PDL) and cell viability (CV)
##Figure 1
PDL <- read.table("PDL.txt", sep="\t", heade... |
0c231d867df1518053887ae414f683f2434046658b412f6944fbda9cd7634b83 | R | 3,637 | 126 | #rm(list=ls())
source('load_libraries.R')
library(GSEABase)
# for ex neur
Exsub=c('L2.3.IT', 'L4.IT', 'L5.IT', 'L5.ET', 'L5.6.NP','L6b','L6.IT','L6.CT','L6.IT.Car3')
# DI scores
#scz=read.csv("tables/Urban_DLPFC_BPD_delta_influence_gt3.csv")
scz=read.csv("tables/CMC_SCZ_delta_influence_gt3.csv")
#scz=read.csv("tables... |
a7eca65d98957db8da66844f0c84da2f1b8c69797f9afb7fc8e6db605386d1f4 | R | 3,659 | 115 | # run magma before this and store all .out files to data/
rm(list=ls())
source('load_libraries.R')
source('aux_functions.R')
safri=read.table("data/UCLA_ASD_ASD_micro_safri_enrichment_results.txt",header=T)
deg1=read.table("data/gandal_asd.fc-UCLA_ASD_ASD_micro.zscore.mat",header=T)
deg2=read.table("data/41586_2022_5... |
5ecdeaf735aac1349605de5705739486ac7a99e549ff7240fd826178fbdd7483 | R | 3,663 | 90 | suppressPackageStartupMessages(library(optparse))
option_list = list(
make_option(c("-b", "--iclip_bedgraph"), type='character',
help="iCLIP bedgraph (iCount)"),
make_option(c("-g", "--intron_set"), type='character',
help='mapped introns'),
make_option(c("-i", "--intron_db"), type... |
8f44904a805bd21668508ba63faab62261798a44c6f91e39d3e92a12996135f4 | R | 3,667 | 68 |
## Filter non-variable probes and probes with constant DNAm >0.9 or <0.1 ##
#1. Load data ===================================================================================================================
load(paste0(PathToBetas,"fetalBulk_EX3_23pcw_n91.rdat")) #pheno fil... |
3f004c5b27e943003a5edc3370ad4f948c3b633d1ceecacb6ac79cba6a19c840 | R | 3,672 | 79 | #' Map query dataset to the fetal midbrain subatlas
#'
#' Wrapper function to map a query dataset onto the fetal midbrain subatlas.
#' This function uses the Seurat algorithm to project the query dataset onto
#' the reference. Upon projection, the midbrain detailed celltype annotation is
#' predicted. Furthermore, a ve... |
9de9ac8d2ba8f66190fb33d3e7d90192c883eac15e1183f12ab716da4135b397 | R | 3,684 | 145 | ---
title: "QC & filtering"
author: "Anne Hoffrichter, Eric Poisel, Lea Zillich"
date: "2023/02/23"
output:
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE, tidy.opts=list(width.cutoff=80),tidy=TRUE, fig.asp=0.5, fig.width=12, warning = FALSE)
knitr::opts_knit$set... |
b967b497f85befd5be276efc78d2bd8d1fe122c5a47d42f179e3439d86c3ed19 | R | 3,693 | 76 |
###
library(Seurat)
setwd("/projects/ps-renlab/chz272/transposon/05.Paired-ChIP/20.NovaSeq/04.Single_cell_All/04.RNA_Proc/01.RNA_seurat")
#pt.seu<-Read10X(data.dir="/projects/ren-transposon/home/chz272/transposon/05.Paired-ChIP/20.NovaSeq/04.Single_cell_All/03.filtered_matrices/RNA_filtered_matrix")
#pt.seu<-Read10X(d... |
cff7382ef1f6d1eeb48a05df3c6fdadfb780ee489d02fcab271e08be5b2422c9 | R | 3,693 | 69 | context("compareRhythms_cosinor")
load("test_data_ma.rda")
test_that("cosinor analysis works for default params and independent sampling", {
results <- compareRhythms(expr, exp_design, method = "cosinor")
expect_s3_class(results, "data.frame")
expect_named(results,
c("id", "category", "rhythmic_i... |
9dac81da7b42e09e05352f514cf99d0f6c93d9be14d5b0c60ab703f2d2c209b3 | R | 3,696 | 85 | seurat_process <- function(seurat_object, workers1 = 5, workers2 = 10, maxmem = 32, do_harmony = TRUE){
library(Seurat)
#library(monocle3)
library(tidyverse)
library(patchwork)
library(harmony)
library("parallel")
makecore <- function(workcore, memory){
if(!require(Seurat)) install.packages('Seurat... |
8395372aff0719a6a2b20d97791e631d47a2f930413bf0a3ca6c0732cf616d8e | R | 3,700 | 94 | pred.vizA <- function (cur_pat){
# Design A: No ystar distribution, only error bar.
# Arguments:
# <cur_pat> an integer specifying the patient number
# Requires:
# <pred_xgb> data frame with bootstrap prediction results (output predict.XGB)
# <dat_plot> long format data frame with all available me... |
838803270eb0ba9948a364170cf257a8dcf948b4175c647a0b21cbe4898fc456 | R | 3,703 | 93 | library(tidyverse)
library(data.table)
source("/mnt/data/lijincheng/mGWAS/result/02MRBMA/get_each_MVinput.R")
#--------------------{1.getMVinput}-----------------------
##-------------{01 MiBioGen}---------------------------
get_MVinput_for_MRBMA(exposure="mibio",
outcome="LOAD",
... |
f98298292bf57c0785352cb5f16c83baec55ed73626693069fbb3e8300296227 | R | 3,707 | 99 | #!/usr/bin/env Rscript
#### Title: Scoring markers from our MBM/ECM tumor signatures on bulk patient samples
#### Author: Jana Biermann, PhD
library(dplyr)
library(ggplot2)
library(gplots)
library(singscore)
library(DESeq2)
'%notin%' <- Negate('%in%')
colBP <- c('#A80D11', '#008DB8')
colSCSN <- c('#E1AC24', '#288F5... |
2edb63ce2ed93d76bb3620f8102194577e4499c8c37a27ba3a25704ef3e7dc79 | R | 3,716 | 76 | #### Data analysis - Xuan's EM data ####
## Loading required info
library(ggplot2)
library(car)
library(openxlsx)
library(dplyr)
#devtools::install_github("coolbutuseless/ggpattern")
library(ggpattern)
library(coin)
set.seed(0)
#### Comparison of myelinated axons per area - CA1 ####
as.data.frame(read.xlsx... |
8b89b90b999367bdc260d8f193802a760cad16d481aeab8d22d16ac3d6922ec5 | R | 3,723 | 94 | #' Prepare PCA Data from LFQ or LBQ Dataset
#'
#' This function prepares the PCA input data from either LFQ or LBQ quantitative proteomics data.
#' It reshapes the data, performs PCA, and merges sample-level annotation metadata.
#'
#' @param df A data frame containing the input proteomics data.
#' @param annotation A d... |
662be0ac518aabf27439cf0492924b53e8229a3c66d7ada345ae9b0b273c0247 | R | 3,729 | 75 | # Load libraries ####
library(DESeq2)
library(plotrix)
library(ggplot2)
library(beeswarm)
# Load DESeq2 results ####
load("../data/chu/chu_deseq2_results.RData")
# Generate plots foldchange plots ####
# Panels A and B
pdf(useDingbats = F, "../figs/Fig5_A_B.pdf", width = 8, height = 4.5)
par(mfrow = c(1, 2... |
b97a117965eec622b0e5daa71f6f6d4aa98727ef3178c92b76efa4072b345414 | R | 3,736 | 108 | # Get pubmed abstracts for DEGs and signature genes with the keywords "Melanoma" and "Tumor"
# This script is just for the download, processing is done in extract-pubmed-abstracts.r.ipynb
# We set timeouts to not overload NCBI servers
library(rentrez)
library(dplyr)
dataPath = "data/"
# DEGs:
DEGres = readRDS(paste0... |
b0fffe5d98145c00b4f22aefeecc412ac35e81af42f65da98553f1cf2fb7bf3c | R | 3,741 | 86 | ## 03_doublet_removal.R
library(Seurat)
library(scDblFinder)
library(BiocParallel)
set.seed(1234)
# Process Li2023 dataset
li_obj <- readRDS("li2023_integrated.RDS")
li_sce <- as.SingleCellExperiment(li_obj)
li_sce_rand <- scDblFinder(li_sce, samples="sample", BPPARAM=MulticoreParam(3))
li_rand <- as.Seurat(li_sce_ra... |
17e7637a1a1cdaf652f432311b738d77c557a5c9c5f241fcede3d2672743aa00 | R | 3,744 | 113 | library(tidyverse)
library(MAST)
library(Seurat)
library(magrittr)
library(future)
set.seed(1234)
# Function to run MAST analysis
run_MAST <- function(snRNA, selected_cluster, selected_disease_1, selected_disease_2,
output_dir = "results/MAST") {
message(sprintf("Processing %s: %s vs %s", sele... |
Subsets and Splits
No community queries yet
The top public SQL queries from the community will appear here once available.