sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
c857fbe00dbd9e6f5802c112b5fbbeca6ff8252ec028e251d943309fdbe42dd2 | R | 490 | 21 | ### MOTIMUS PARAMETERS
# General
nb_participant = 36
nb_condition = 3
# HRV
path_raw = gsub("1_code/", "", here("0_data/hrv/test/"))
# Headset
headset_fs = 100 # sampling frequency
headset_time2average = 30 * headset_fs
headset_nb_markers = 4
# NIRS
nirs_fs = 10 # sampling frequency
path_processed = gsub("1_code/",... |
4965a7b932986b17e7a54a38c65469a6c65b7fcdc0825194ae06aaeec13cf04d | R | 494 | 16 | setwd('~/Documents/work/PTSD_2023/round3_medi/interaction_withMiRNA_NOV/code_for_share/Base_model/FACTORwave2and3_PTSSwave3//Life_Current_Factor_PRS//')
df <- readRDS('df.rds')
fomular <- as.formula(sprintf("%s ~ %s", colnames(df)[1],
paste(paste(colnames(df)[-c(1)],
... |
aa3014a88a95a56e85ecaf73d2ccc36580c75a203f0ff54f6f851214f0265fa7 | R | 503 | 16 | setwd('~/Documents/work/PTSD_2023/round3_medi/interaction_withMiRNA_NOV/code_for_share/Base_model/FACTORwave2_PTSSwave3/Life_Current_Factor_PRS_Cellproportion/')
df <- readRDS('df.rds')
fomular <- as.formula(sprintf("%s ~ %s", colnames(df)[1],
paste(paste(colnames(df)[-c(1)],
... |
62ce2b673a40c5f9ecac4072ba6c8b27df2499b7fc3f710581ba2dd5cb6d0773 | R | 505 | 23 | ##-------------------------------------##
## TSNE TAB ##
##-------------------------------------##
getTSNE <- function(mat, subset_row, perplexity, token, session_obj, ID){
tsne <- scater::calculateTSNE(x = mat,
ncomponents = 3,
subset_row = subset_r... |
e888d8104e02e75606adb9ee3d44db3c65b775ff8e9a9a52bf9e78b609939084 | R | 507 | 23 | if (!require("BiocManager", quietly = TRUE))
install.packages("BiocManager")
if (!require("cBioPortalData", quietly = TRUE))
install.packages("cBioPortalData")
BiocManager::install("cBioPortalData")
require(cBioPortalData)
require(BiocManager)
require(AnVIL)
install.packages("mice",'GGally','grpreg'... |
433e8c90f3e6e42ebac8ef9836a28ec802931e598cc08ac89c7f9c7d3040ba2b | R | 518 | 13 | test_that("count_keyvec counts genes overlapping the key vector", {
df <- data.frame(
pathway = c("p1", "p2"),
genes = c("TP53;EGFR;MYC", "KRAS;BRAF"),
stringsAsFactors = FALSE
)
out <- count_keyvec(df, cols = "genes", separator = ";",
genes_vec = c("TP53", "MYC", "KRAS"))
#... |
e21fe91cd1c90db968d6bce8a7a35697132e857c79d14b38396ccfe03cb89f42 | R | 521 | 8 | # Generate a consensus clustering model with given arguments
CC_modelization <- function(datasets, title_model, cluster_alg = "hc", distance = "euclidean"){
CC_model = ConsensusClusterPlus(datasets, maxK = 6, reps = 50, pItem = 0.8, pFeature = 1,
clusterAlg = cluster_alg, distance... |
fe1bb22e3275e5875a37a40281e437fd072c94de9bd7235862b3150144f69fd5 | R | 523 | 16 | source("../shinytest_helpers.R")
set_window()
app$uploadFile(layout_table_file = demo_path("MRA_LayoutAndReadouts_BT-40_ST04.xlsx"))
app$setInputs(upload_reference_samples = TRUE)
app$uploadFile(reference_samples_file = demo_path("Controls_DKFZ_ST10-min.xlsx"))
app$setInputs(start_oca = "click", timeout_ = 1500e3)
ap... |
c0be82e1714bb47c3d7a66fec928bcfccdf966d59ab5c643ca082a02b0f5c1e1 | R | 534 | 24 | messageU <- function(..., underline = "=", overline = "=") {
x <- paste0(..., collapse = "")
if (!is.null(overline)) {
message(rep(overline, nchar(x)))
}
message(x)
if (!is.null(underline)) {
message(rep(underline, nchar(x)))
}
}
startTimedMessage <- function(...) {
x <- pa... |
2e53bd0e70a6dd1b17901cb753dc7b07bd87d26d37f08163000a2da0045e7480 | R | 545 | 15 | # various helper functions for working with BOLD niftis in targets pipeline ----
get_bold_gz <- function (subject, task, run) {
inject(here::here(!!!path_here_derivatives, subject, "func",
paste(subject, task, run, "space-MNI152NLin2009cAsym_res-2_desc-preproc_bold.nii.gz", sep = "_")))
}
gunzip... |
dd5f9964b3a9e3c8726ae87e76371e1622f4099d71fd631d35273c6132a91643 | R | 546 | 27 | library(tidyverse)
library(ggpubr)
library(lme4)
library(lmerTest)
library(MASS)
library(dplyr)
library(sjPlot)
library(sjmisc)
library(ggplot2)
library(report)
library(dplyr)
library(rstatix)
R2_cat <- readxl::read_xlsx('anova_df_04022025.xlsx',sheet='cat_long')
# category one-way anova
model <- aov(... |
505297f0e592311337b16c63caebad76d3d4addf71d7a61ed28b896e8af90b37 | R | 553 | 18 | library(pROC)
data(aSAH)
a.ndka <- auc(aSAH$outcome, aSAH$ndka)
test_that("can convert auc to numeric", {
expect_is(a.ndka, "auc") # a.ndka is not a numeric to start with
expect_equal(as.numeric(a.ndka), 0.611957994579946)
})
test_that("can do math on an AUC", {
expect_equal(sqrt(a.ndka), 0.782277440924859)
... |
6451cd11212748a3d0f2219eba26e580e1322208cb367709133ecaf6287f6f30 | R | 556 | 20 | args <- commandArgs(TRUE)
name <- as.character(args[1])
cap <- as.numeric(args[2])
index <- as.character(args[3])
if (!dir.exists(name)) {
dir.create(name)
}
setwd(name)
dir.create("DDD_TES")
setwd("DDD_TES")
ddd_tes_list <- replicate(500, eveGNN::dd_sim_fix_n(200, pars = c(c(0.6, 0.1), cap), 10, 1), simplify = F... |
94a5696e35f528dd16a3cc6db92939dcb074f49eb64bbd25cccc414b7282d3d0 | R | 559 | 15 | test_that("coll_reduction replaces coll cells whose COUNT exceeds the cutoff", {
df <- data.frame(
Term = c("OXPHOS", "TCA cycle", "Apoptosis"),
genes_coll = c("NDUFS2;SDHA;NDUFV1", "IDH1", "BID;BBC3"),
genes_COUNT = c(100, 5, 60),
stringsAsFactors = FALSE
)
out <- coll_reduction(df, cu... |
da03303d5c505084ad4b31c755295682589c77147c2098a80305a0f182664786 | R | 561 | 16 | #######################################
#
# This function is used to obtain the
# time delay projecton maps from inputs
# while matching the selected participants
#
# This function can load the data of TDp and
# ETS. Indicates what kinds of data you want to
# load in [type] parameter
ObtainBrainData_indivi... |
19449f666c1e7a27724e2a42959d8715ead71dc9347c548db9b7b46324a9eba6 | R | 563 | 20 | ##
## Set up common variables across scripts
##
RDS.DIR <- file.path( ".", "RDS" ) ## Where output files are saves
RAW.DIR <- file.path( ".", "CSV" )
##
## The switch below will not work on Windows, and may have some issues.
## NOTE: It is used in novel-script, to define a 'choosen' expanded-fit for the longitudinal... |
18cd4d890aaa7ac39056e54fcd1cb4ed97bcf0296283bd88e5e832b3fb20b37f | R | 566 | 18 | source("100.common-variables.r")
source("101.common-functions.r")
source("300.variables.r")
source("301.functions.r")
FIT <- readRDS("Share/FIT_GMV.rds")
POP.CURVE.LIST <- list(AgeTransformed=seq(log(90),log(365*95),length.out=2^4),sex=c("Female","Male"))
POP.CURVE.RAW <- do.call( what=expand.grid, args=POP.CURVE.LI... |
d23e052527214bbdfefc6aeed574af4d25db960b9156dc00665ae5aa3a370476 | R | 566 | 17 | setwd('~/Documents/work/PTSD_2023/round3_medi/interaction_withMiRNA_NOV/code_for_share/Base_model/FACTORwave2_PTSSwave3/Life_Current_Factor/')
df <- readRDS('df.rds')
fomular <- as.formula(sprintf("%s ~ %s", colnames(df)[1],
paste(paste(colnames(df)[-c(1)],
... |
bfe23b6d2035ce452e8ae37e40792b1b663f48023d8092eef30fe5cc29aadc4d | R | 569 | 13 | ################################# ED.Fig.1k
#Read in the reduced_data R data from Fig.1i and All_VNG R data
DimPlot(All_VNG,label = T)
DefaultAssay(All_VNG)
DimPlot(reduced_all)
DefaultAssay(reduced_all)
anchors <- FindTransferAnchors(reference = All_VNG, query = reduced_all,
dims = 1:3... |
efa8abbd0684ce9ec0c4e8759a0dd336062c0041e16a2b5fe4038518a9070c46 | R | 570 | 17 | setwd('~/Documents/work/PTSD_2023/round3_medi/interaction_withMiRNA_NOV/code_for_share/Base_model/FACTORwave2and3_PTSSwave3/Life_Current_Factor/')
df <- readRDS('df.rds')
fomular <- as.formula(sprintf("%s ~ %s", colnames(df)[1],
paste(paste(colnames(df)[-c(1)],
... |
968503bf8fb96aff446d068236c2429a186f21eab54af9333a83bbdbffbea8e3 | R | 576 | 19 | library(data.table)
library(R.matlab)
mvgwas <- fread('/path/to/sumstats')
# 3) make sure vars_qc SNP ids are in the same order you’ll as the SNP correlation matrix
extract_rs <- scan("/path/to/vars_qc.txt",
what = character())
snps9 <- mvgwas[match(extract_rs, mvgwas$SNP), ]
# 4) pull out the z ... |
cdd505ff5cb320d01c86a11c59cd249004f817f9dddde3d614cf8065923ec73f | R | 577 | 15 | # ------ Function to compute PACES score
compute_paces_score <- function(data, paces_row, col_num) {
# Extract the relevant rows and convert to numeric
paces <- as.data.frame(data[ (paces_row + 1):(paces_row + 10), col_num ])
paces <- as.numeric(paces[, 1])
# Define rows to reverse
rows_to_reverse <- c(1, ... |
406d905a7bce864b1c31fddc97bddaf81a6eef4e735bb37bdac2ff6127e7df3b | R | 578 | 17 | setwd('~/Documents/work/PTSD_2023/round3_medi/interaction_withMiRNA_NOV/code_for_share/Base_model/FACTORwave4_PTSSwave4//Life_Current_Factor/')
df <- readRDS('df.rds')
fomular <- as.formula(sprintf("%s ~ %s", colnames(df)[1],
paste(paste(colnames(df)[-c(1)],
... |
cd5112c836d99572721b065d23e69a7802f558851b99420289437fad1da7fd09 | R | 580 | 19 | source("../shinytest_helpers.R")
set_window()
app$setInputs(layout_and_readouts = "separate_files")
app$uploadFile(layout_table_file = demo_path("MRA_Layout.xlsx"))
app$uploadFile(readout_matrices_file = demo_path("MRA_1ReadoutTxt_Flat.zip"))
app$setInputs(start_oca = "click", timeout_ = 1500e3)
app$setInputs(iTReX =... |
166f76febff8f6429e2516324d9a7e404c2492e6484603cde69c446390ccfea4 | R | 581 | 12 | library(org.Hs.eg.db)
library(AnnotationDbi)
library(tidyverse)
setwd("/Users/melis/Documents/MRI_cortical_layers/MRI_layers/code")
dirs <- list.files(path = "../data/GO pathways/GO_pathways_with_offspring/.", pattern = "\\.csv$")
for (i in 1:6) {
data = read.csv(paste0("../data/GO pathways/GO_pathways_with_offsprin... |
638d55ce72793bc465cf0ba21ba5b75ec5d40beb9da73a67a03c59636556c71d | R | 584 | 17 | setwd('~/Documents/work/PTSD_2023/round3_medi/interaction_withMiRNA_NOV/code_for_share/Base_model/FACTORwave2and3_PTSSwave3//Life_Current_Factor_PRS_Cellproportion/')
df <- readRDS('df.rds')
fomular <- as.formula(sprintf("%s ~ %s", colnames(df)[1],
paste(paste(colnames(df)[-c(1)],
... |
577f93d3dac2cbc21ad826789a582fc5f5fba8e2e46527786a7c327da0e445a7 | R | 586 | 37 | # Hua Sun
library(Seurat)
library(monocle3)
library(SeuratWrappers)
library(dplyr)
library(data.table)
set.seed(42)
outdir <- 'out_monocle3'
dir.create(outdir)
rds <- 'seurat5.1_v6.2/multiome_integrated.plus.rds'
seu <- readRDS(rds)
DefaultAssay(seu) <- 'SCT'
Idents(seu) <- 'cell_type2'
seu[["UMAP"]] <- seu[['wnn... |
0f8fb91d0ec33f597fa5185ff96c80ac44314ee77bd87b05686231d9bd0a521b | R | 589 | 25 | # Load necessary libraries
library(ggplot2)
# Using built-in dataset 'mtcars'
data(mtcars)
# Print the first few rows of the dataset
head(mtcars)
# Basic summary statistics
summary(mtcars)``
# Create a new column 'kpl' (kilometers per liter) for fuel efficiency
mtcars$kpl <- mtcars$mpg * 0.425144
# Simple plot: Mi... |
aafd113d1d96cc0b986d2cac2a88ec7593e32a5eb53b40adaf6891c3f3542afd | R | 590 | 12 | library(data.table)
INPUTFILE="output.paf" #mapping output in paf format
READIDS="readids.repetitive.txt" #read id, one per line
OUTPUTFILE="output.repetitive.paf" #filtered paf output with specified read ids
allMappings = read.table(INPUTFILE, header=F, fill=TRUE)
selectedReadIds = read.table(READIDS, header=F, fill... |
d89c54b6bf060ca9feb776cd1d1896c7bfd45e67b36e5f5ab510bbc734374495 | R | 592 | 17 | setwd('~/Documents/work/PTSD_2023/round3_medi/interaction_withMiRNA_NOV/code_for_share/Base_model/FACTORwave4_PTSSwave4///Life_Current_Factor_PRS_Cellproportion/')
df <- readRDS('df.rds')
fomular <- as.formula(sprintf("%s ~ %s", colnames(df)[1],
paste(paste(colnames(df)[-c(1)],
... |
b4a7727abe775ef766df184fe6e9986edbd39737a293c6a95493c20c659955a3 | R | 594 | 12 | library(org.Hs.eg.db)
library(AnnotationDbi)
library(tidyverse)
setwd("/Users/melis/Documents/GitHub/MRIxST/code")
dirs <- list.files(path = "../processed_data/00-prepare_GO/00-fetch_GO_offspring/.", pattern = "\\.csv$")
for (i in 1:6) {
data = read.csv(paste0("../processed_data/00-prepare_GO/00-fetch_GO_offspring/"... |
f08edf221012ea52eb454e3e2a9c0ade43f300c27e4df5af4b5a10a5c595bc71 | R | 594 | 19 | source("../shinytest_helpers.R")
set_window()
app$setInputs(layout_and_readouts = "separate_files")
app$uploadFile(layout_table_file = demo_path("MRA_Layout-Imaging-3Plates.xlsx"))
app$uploadFile(readout_matrices_file = demo_path("MRA_1ReadoutTxt_NA.zip"))
app$setInputs(start_oca = "click", timeout_ = 1500e3)
app$set... |
1ba7f501a5d5f1706cc62bb689ceb80fe311f27c449ede3fc731bfb9b1ef9fd4 | R | 596 | 14 | require(GenomicSEM)
library(data.table)
library(devtools)
ld <- "/path/to/eur_w_ld_chr/"
wld <- "/path/to/eur_w_ld_chr/"
traits <- c("/path/to/ADHD.sumstats.gz", "/path/to/BPD.sumstats.gz", "/path/to/MDD.sumstats.gz" )
sample.prev <- c(0.20708, 0.07055, 0.20608)
population.prev <- c(0.028, 0.018, 0.064)
trait.names<-c... |
760aaf15c0f9abcad6f254f144173e027470a53b17654dbbf71f24fa17362477 | R | 599 | 18 | setwd("/data/wuqinhua/phase/covid19/datasets")
library(Seurat)
library(SeuratDisk)
pbmc <- readRDS('./pre_data/6_Liu_2021/GSE161918_AllBatches_SeuratObj.rds')
sce <- UpdateSeuratObject(object = pbmc)
DefaultAssay(sce)
DefaultAssay(sce) <- "RNA"
DefaultAssay(sce)
sce@meta.data <- as.data.frame(sce@meta.data)
sce@met... |
f5c4edac9f8c8e97f4f6c49866164455943b545c57327cf0095256943df2cdb2 | R | 599 | 22 | # Title : HTML Functions
# Objective : Functions for creating pretty HTML links
# Created by: mumichae
# Created on: 6/21/21
build_link_list <- function(file_paths, captions=NULL) {
if (is.null(captions)) {
captions <- file_paths
}
file_link <- paste0('\n* [', captions , '](', file_paths,
... |
bf884eb3eef2e98c77211bd63ea84548cecb0a9a0aa9204ebb895e9155049b68 | R | 601 | 16 | setwd("/data/wuqinhua/phase/covid19/datasets")
library(Seurat)
library(SeuratDisk)
sce <- readRDS('./pre_data/20_Zhu_2020/Final_nCoV_0716_upload.RDS')
sce <- UpdateSeuratObject(object =sce)
DefaultAssay(sce) <- "RNA"
rna_counts =sce@assays$RNA@counts
colnames(sce@meta.data)[colnames(sce@meta.data) == 'batch'] <- 's... |
0c62b8360ba4cfbcaabde72e07f4bbbb3e8080ba4ab3a98370de247517340e45 | R | 603 | 28 | beta0=-0.0439 # rs10512201 association with AD in Bellenguez et al (2022)
se0=0.0103
n0=450000
sigmaj=sqrt(n0)*se0
beta=beta0/sigmaj
se=1/sqrt(n0)
# now beta~N(a,1/n)
ns=seq(500000,1.5e6,1e4)
alpha=5e-8
q0=qchisq(1-5e-8,1)
power=c()
for(i in 1:length(ns)) {
lambda=beta^2*ns[i]
power[i]=pchisq(q0,1,lambda,lower.tail=F... |
917116b9c85cde1d20443d3daa07fbbb883ba806ab2467d7f8408935a1d3b163 | R | 603 | 21 | ##-------------------------------------##
## UMAP TAB ##
##-------------------------------------##
getUMAP <- function(mat, subset_row, min_dist, nneigh, token, session_obj, ID){
umap <- scater::calculateUMAP(x = mat,
ncomponents = 10,
... |
9ba3a3729e771417b730e60190a084cee1059616a803e388e6275e37682da106 | R | 603 | 23 | # Load packages ----
library(shiny)
library(shinydashboard)
library(dplyr)
# Load datasets of AIRE dependant genes
AIREdep = read.csv2("data/TRA_AIRE_dependency.csv")
# Load datasets of gene expression in mouse and human
gene_keys = read.delim2("data/Mouse_Human_merged_expression_data.txt", sep = " ")
colnames(gene_ke... |
e755cbecdaf78258a93055c09f20f9280ea4dc365bfa3983a6a1ac4d264a7291 | R | 603 | 18 | source("../shinytest_helpers.R")
set_window()
app$setInputs(layout_and_readouts = "separate_files")
app$uploadFile(layout_table_file = demo_path("CRA_Layout.xlsx"))
app$uploadFile(readout_matrices_file = demo_path("CRA_1ReadoutXlsx_INF-R-1632.zip"))
app$setInputs(type_of_analysis = "StepA")
app$snapshot(filename = "00... |
8934d06c7a82b98d47a2d3faeae2f63c5ac1cd52017b1c2e37abec9b1bd2527d | R | 606 | 16 | # version of write_csv that returns the file path as expected when called by targets
write_csv_target <- function (x, file, ...) {
write_csv(x = x, file = file, ...)
return (file)
}
# primarily for derivatives that are getting re-written into a copy folder that mirrors bids structure
# thus default 2 to get the su... |
be81017cd5c1efbcb637d8388afae0c6d075616f9c65577da60d1a5ca223cd6b | R | 618 | 19 | source("../shinytest_helpers.R")
set_window()
app$setInputs(layout_and_readouts = "separate_files")
app$uploadFile(layout_table_file = demo_path("MRA_Layout-Imaging-1Plate_ST06.xlsx"))
app$uploadFile(readout_matrices_file = demo_path("MRA_Readout-Imaging_BT-40-V3-DS1_ST05.xlsx"))
app$setInputs(start_oca = "click", tim... |
279182e1632a3ee35fe8179acae1a0a5d2427c8daf83f4797c5500d5f506b86d | R | 619 | 18 | setwd('~/Documents/work/PTSD_2023/round3_medi/interaction_withMiRNA_NOV/code_for_share/Base_model/FACTORwave4_PTSSwave4///Life_Current_Factor_PRS//')
df <- readRDS('df.rds')
fomular <- as.formula(sprintf("%s ~ %s", colnames(df)[1],
paste(paste(colnames(df)[-c(1)],
... |
2c93577a0572387c9f935fed15e2cc80bb8557b1f4603059ff2f24ff8b632e13 | R | 629 | 24 | # Hua Sun
library(TFBSTools)
library(JASPAR2022)
library(Signac)
library(BSgenome.Mmusculus.UCSC.mm10)
library(BSgenome.Hsapiens.UCSC.hg38)
multiome <- readRDS('multiome.rds')
ref <- 'hg38'
genome <- BSgenome.Mmusculus.UCSC.mm10
if (ref == 'hg38'){ genome <- BSgenome.Hsapiens.UCSC.hg38 }
pfm <- getMatrixSet(x = J... |
909eb68aec12c99e136e097962610f6dd5d1614684ae91f54443462163f9750f | R | 636 | 30 | test_that(
"Olink color palette",
{
expect_equal(
object = olink_pal()(n = 5L),
expected = c("#00C7E1FF", "#FE1F04FF", "#00559EFF",
"#FFC700FF", "#077183FF")
)
expect_equal(
object = olink_pal(
coloroption = c("teal", "pink")
)(n = 2L),
expected ... |
d9404159b771f38c1d787f493430ee9882ff9da0f1d42f9f9cfe0d4450b1ab6b | R | 639 | 19 | source("../shinytest_helpers.R")
set_window()
app$uploadFile(layout_table_file = demo_path("MRA_LayoutAndReadouts_BT-40_ST04.xlsx"))
app$setInputs(upload_reference_samples = TRUE)
app$uploadFile(reference_samples_file = demo_path("Controls_DKFZ_ST10-min.xlsx"))
app$setInputs(type_of_analysis = "StepA")
app$setInputs(... |
2c7bcbbe74aad52e9ea09f624c6fafbe39264371e6816631d3118d5d08dbe9d6 | R | 643 | 19 | source("../shinytest_helpers.R")
set_window()
app$uploadFile(layout_table_file = demo_path("MRA_LayoutAndReadouts_INF-R-153_ST12.xlsx"))
app$setInputs(upload_reference_samples = TRUE)
app$uploadFile(reference_samples_file = demo_path("Controls_DKFZ_ST10-min.xlsx"))
app$setInputs(type_of_analysis = "StepA")
app$setInp... |
a6966f9033083e1ce7940a243e4d83f87e059e2ccad094145b37696100f7f7e5 | R | 646 | 26 | library("UMI4Cats")
library(parallel)
bcpath = snakemake@params[['barcodes']]
fqdir = snakemake@params[['fqdir']]
ofqdir = snakemake@params[['ofqdir']]
nthreads = snakemake@threads
bc <- read.table(
bcpath,
sep=',', header=TRUE
)
cl <- makeCluster(nthreads)
clusterExport(cl, varlist = c("fqdir", "bc", "ofqdir", ... |
5428c814a6af4044b89d6a2d922a43a69ff841e3c92c511b7aa3b9cfade63d18 | R | 660 | 21 | source("../shinytest_helpers.R")
set_window()
app$setInputs(layout_and_readouts = "separate_files")
app$uploadFile(layout_table_file = demo_path("CRA_Layout.xlsx"))
app$uploadFile(readout_matrices_file = demo_path("CRA_1ReadoutXlsx_INF-R-1632.zip"))
app$setInputs(start_oca = "click", timeout_ = 1500e3)
for (module in... |
026b79b34f43383b859056eed7f7f1cbc1ed38cee246356b5ce6953e1a1c1167 | R | 661 | 19 |
# OR gene filtering
dplyr::filter(substring(gene_symbol,1,2) == "OR" & substring(gene_symbol,3,3) %in% seq(1,9,1))
# bulk RNA Consensus clustering
ConsensusClusterPlus(KIRC_FPKM_df_OR_tumor_filtered_for_consensus,
maxK = 6,
reps = 500,
pItem = 0.9,
... |
b9694da4935ce2250ae6b5172b7f4761a5add9dfdd20c7240d42885446ac64b1 | R | 661 | 28 | #' lnc_RNARNA_scanner_output
#'
#'
#' example output from lnc_RNARNA_scanner function, tables are truncated to reduced size of the file
#'
#'
#'
#'
#' @format a list of data frames
#' \describe{
#' }
#'
#'
#' @references
#'RNARNA.db creators: Terai G, Iwakiri J, Kameda T, Hamada M, Asai K. Comprehensive prediction of ... |
d906844a001c01f98ea6834715be74c0e043002c274518c65dafc4a437a19ffd | R | 661 | 29 | ## code to prepare internal dataset goes here
## based on https://r-pkgs.org/data.html#sec-data-sysdata
## Specifications for Olink parquet files ----
olink_parquet_spec <- list(
parquet_metadata = c(
product = "Product",
data_file_type = "DataFileType"
),
optional_metadata = c(
ruo = "RUO",
fil... |
aa285efd0287a01b51b4c3019991138d8a11a32ead1ae2979d85c0388372ee9b | R | 671 | 26 | library( rmarkdown )
library( ggplot2 )
stitchedFile <- "stitchedGrantBlurb.md"
rmdFiles <- c( "format.md",
"grantBlurb.md",
"references.md"
)
for( i in 1:length( rmdFiles ) )
{
cat( rmdFiles[i] )
if( i == 1 )
{
cmd <- paste( "cat", rmdFiles[i], ">", stitchedF... |
5d386bec579e4ae0c9c4e3bca6ae003d94ece572daf05c58ee5abc54ab05ebec | R | 675 | 16 | library(tidyverse)
# set directories
root_dir <- rprojroot::find_root(rprojroot::has_dir(".git"))
cnv_gatk_dir <- file.path(root_dir, "analyses", "copy_number_consensus_call", "results")
cnv_manta_dir <- file.path(root_dir, "analyses", "copy_number_consensus_call_manta", "results")
output_file <- file.path(cnv_gatk_di... |
835e465296a62390c30fe6aadd0a1bab0a48f9319893d54ac6cb3ec155d2d33f | R | 678 | 24 | args <- commandArgs(TRUE)
name <- as.character(args[1])
if (!dir.exists(name)) {
dir.create(name)
}
setwd(name)
dir.create("DDD_LAMU_TES")
setwd("DDD_LAMU_TES")
dists <- list(
list(distribution = "uniform", n = 1, min = 0.5, max = 1.0),
list(distribution = "uniform", n = 1, min = 0, max = 0.4)
)
ddd_lamu_tes... |
3adb0f2489f80ff8de518bcddb4208566caa58af31d010e6c04e36d274d1f36e | R | 681 | 23 | plot.randomForest <- function(x, type="l", main=deparse(substitute(x)), ...) {
if(x$type == "unsupervised")
stop("No plot for unsupervised randomForest.")
test <- !(is.null(x$test$mse) || is.null(x$test$err.rate))
if(x$type == "regression") {
err <- x$mse
if(test) err <- cbind(err, x$test$mse)
} els... |
532071b984c21320847abf9614f15b7901338121a543fd596faa5d057a77b435 | R | 682 | 30 | slim_seurat <- function(seurat_obj, keep_counts = FALSE, keep_scale = FALSE, keep_reductions = FALSE){
if (!"SCT" %in% names(seurat_obj@assays)){
return(seurat_obj)
showNotification("SCT assay not found in Seurat object")
}
if (!keep_counts && length(seurat_obj@assays$SCT@counts) > 0) {
seurat_ob... |
bfe46d33f576a97b2a94a5987658fbe0a35d039b02165bb2007be9291d57b684 | R | 683 | 29 | library(Seurat)
library(reticulate)
ad <- import("anndata")
sc <- import("scanpy")
library(HumanLiver)
viewHumanLiver()
seurat_object = HumanLiverSeurat
variable_genes = VariableFeatures(seurat_object)
seurat_object <- seurat_object[variable_genes]
count_matrix = seurat_object@assays$RNA@counts
meta_data = seurat_ob... |
1fdc658a983f71ab2eb05d3ebe9ca1a9602b11d0d3766eb86aaa148c4e1a2a3a | R | 685 | 23 | # PageRank scores ----
getPageRank <-
function(disease, intGraph, geneVariant) {
variantList <-
unique(geneVariant[geneVariant$diseaseId == paste0(disease), 'targetId'])
if (length(variantList) < 2 || sum(V(intGraph)$name %in% variantList) < 2) {
pageRankRes = NA
} else {
V(intGrap... |
4d82998376ad354ba7aa2f478f7ec6398e2133b515edef5dd375ba786f550aa6 | R | 687 | 21 | guess_counts <- function(vect, lowestCount = 1){
# Try to restore a compositional vector to its former count glory.
# *WARNING*
# In the rare case that the lowest count wasn't zero, this will fail.
if(min(vect != 0)){
output <- round(vect*lowestCount/min(vect))
cat("There don't seem to be any ze... |
4893bd41fdfa287a5bb316314b60cd6df68d0b043fec9b2e2defa6b2b20b57c2 | R | 698 | 35 | #' Launch scFOCAL
#' @examples
#' runscFOCAL()
#' @import shiny
#' @import Seurat
#' @import ggplot2
#' @import ggpubr
#' @import tibble
#' @import cowplot
#' @import viridis
#' @import dplyr
#' @import ggsci
#' @import ggrepel
#' @import tidyverse
#' @import plotly
#' @import htmlwidgets
#' @import reshape2
#' @import... |
9dfc5a5e5cf604891685aac168562ea2faec1f152796c4d18a2a26c79d3d02ec | R | 699 | 24 | #' Convert logical TRUE/FALSE values to 1/0
#'
#' Replaces every \code{TRUE} with \code{1} and every \code{FALSE} with \code{0}
#' across a data frame. Convenient for turning logical membership matrices into
#' numeric ones for summing or clustering.
#'
#' @param inputDF A \code{data.frame} of logical (or coercible) va... |
2bff76da254403b00c9799da6eeb0e976607dcbacccb2f1aa86437affc9ef332 | R | 708 | 23 | #' @keywords internal
n2ndis = function(x, atlas, groups){
pat = x; rownames(pat)=groups; colnames(pat)=groups
pat = pat[order(groups),order(groups)]
pat[upper.tri(pat)]=NA
pat = melt(pat, na.rm=T)
pat$Var = paste0(pat$Var2, "_to_",pat$Var1)
rownames(atlas)=groups; colnames(atlas)=groups
atlas = atlas[o... |
3dc77c59aed36c6bf8ca41b764cbb97b5e8da9b038fcf0598203bc89f0a41623 | R | 713 | 29 | ##-------------------------------------##
##### REPORT #####
##-------------------------------------##
tab_SESSIONINFO <- tabItem(
tabName = "Session Information",
#sidebarLayout(
#sidebarPanel(width = 2,
... |
3a28f5cf6bb0e7ae7e918ba8658e477f382206e2f89482c77716859c524d7e2a | R | 714 | 22 | #Author: Kim Kundert-Obando for questions please reach out to me at k.rogge.obando@gmail.com
#Example on how to do FDR corrections with one output file from the mixed model code "net_regress_model"
install.packages("FDRestimation")
library(FDRestimation)
#load files
df<-read.csv("/Users/roggeokk/Desktop/Projects/nk... |
d8e563bc7c3083386515b45f52619e1313a96084cb6094523612ea4b7bb3257a | R | 715 | 15 | test_that("miR_mature expands a precursor into three rows and keeps a mature miRNA", {
df <- data.frame(mir = c("hsa-mir-21", "hsa-miR-21-5p"),
stringsAsFactors = FALSE)
out <- miR_mature(df, col = "mir")
# precursor -> precursor + 5p + 3p (3 rows); mature -> 1 row
expect_equal(nrow(out), 4)... |
c1aa2fb375dfd037bd72466f2883e9f5d28952bad881293ccf4cc9db3325d0c5 | R | 718 | 40 | #' Help function checking if a variable is a list.
#'
#' @inherit .check_params params author return
#'
#' @keywords internal
#' @noRd
#'
check_is_list <- function(x,
error = FALSE) {
# check if input error is boolean vector of length 1
check_is_scalar_boolean(x = error,
... |
1d18b4fec8031aedc018f25d33c6e8dedc0a2a88cbb831bea74c3ff47dbda299 | R | 722 | 17 | # Smoke test: col_agrecounter_log has intricate merge/aggregate logic. We check
# that it runs on a minimal input and produces the expected COUNT/coll columns,
# rather than asserting every aggregated value.
test_that("col_agrecounter_log runs and adds coll/COUNT columns", {
df <- data.frame(
mir = c("miR-... |
feed35042f35b15d9705b73d811d2be1fd0445f0e9ab0995fba62cb6097b6434 | R | 726 | 34 | genome_browser_links <- function(setbp1_metadata, gene) {
if (gene == "") return()
position <- gene_position(setbp1_metadata, gene)
url_encoded_position <- paste0(
position$chromosome,
"%3A",
position$beginning,
"%2D",
position$ending
)
div(
h5("Genome Browsers", tags$small(gene)),
... |
09e2f346690756e26f3c3da485737120656fb2e9c2da728625c1a1f87a4b3f76 | R | 730 | 31 | ##-------------------------------------##
# VAREXPLAINED TAB ##
##-------------------------------------##
plot_variance_explained <- function(mat, vars, df_metadata){
df_metadata <- df_metadata[,vars]
mat <- log2(mat+1)
print("Calculating variance explained by each variable.")
... |
4a2ca376664892d01bc5dbcf876bbcfd08fa1ed749488c71f1356fee4c1a2aad | R | 733 | 26 | #'---
#' title: FRASER counting analysis over all datasets
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "AS" / "CountingOverview.Rds")`'
#' input:
#' - counting_summary: '`sm expand(config["htmlOutputPath"] +
#' "/AberrantSplicing/{dataset}_countSummary.html",
#' dat... |
89b816cb43c673563c362d5cf98e29ace73feb9b32245a65de2dd60554bf1b17 | R | 733 | 8 | bookdown::render_book("index.Rmd", "bookdown::gitbook", clean = TRUE, preview = TRUE)
bookdown::render_book("00-FAQ.Rmd", "bookdown::gitbook", clean = TRUE, preview = TRUE)
bookdown::render_book("01-gettingstarted.Rmd", "bookdown::gitbook", clean = TRUE, preview = TRUE)
bookdown::render_book("02-workflow.Rmd", "bookdow... |
9cc8876dffab7392671f2c550bde43381808498d4b9c32b6f6bdf1e9a284be6c | R | 735 | 20 | test_that("cbind_filler binds unequal-length inputs and pads with NA", {
a <- data.frame(x = 1:3, stringsAsFactors = FALSE)
b <- data.frame(y = 1:5, stringsAsFactors = FALSE)
out <- cbind_filler(list(a, b))
expect_equal(nrow(out), 5)
expect_equal(ncol(out), 2)
expect_equal(as.numeric(out$x), c(1, 2, 3, NA,... |
b3fe3bd9395c95bba6435c77966fd810782b9c65afeb051f2251b6d8f07a68a7 | R | 737 | 29 | #'---
#' title: VCF-BAM Matching Analysis over All Datasets
#' author:
#' wb:
#' log:
#' - snakemake: '`sm str(tmp_dir / "MAE" / "QC_overview.Rds")`'
#' input:
#' - html: '`sm expand(
#' config["htmlOutputPath"] + "/MonoallelicExpression/QC/{dataset}.html",
#' dataset=cfg.MAE.qcGroups
#' ... |
4245f65e5dc988096551927935665ca6adf14dfc3092a8ad5c05c1f23d6e95bb | R | 738 | 10 | source('src/transcriptomics/helpers.R')
stopifnot(identical(names(make_rank(c(2,2,NA,-1),c('B','A','C','D'))),c('A','B','D')))
stopifnot(inherits(try(make_rank(c(1,2),c('A','A')),silent=TRUE),'try-error'))
stopifnot(all.equal(family_bh(c(.01,.04,NA),3),c(.03,.06,1)))
stopifnot(classify_robustness(.01,1,c(1,2,3),c(1,1,1... |
e4e0198e49d1dfb3b076f3ef719ca62f0793746a114cfaf669f3e7ad779377af | R | 741 | 36 |
#function for selecting/ reordering columns with similar names within a dataframe
# use special characters to extract key_words: *=any character; ^=block beginning of the string , $=block end of the string
#example###
# inputDF<-iris
# key_words<-c("Species", "*Width", "*Length")
# temp<-col_selecto... |
f17f30e436a22565bb343c590e03e496b2fd20922f38df1f586dd4edfd45107f | R | 742 | 30 | library("UMI4Cats")
library(BSgenome.mm10.ensembl.local)
library(stringr)
fqdir = snakemake@params[['fqdir']]
wk_dir = snakemake@params[['wk_dir']]
dig_genome = snakemake@params[['dig_genome']]
btix = snakemake@params[['btix']]
bc = snakemake@params[['bcs']]
sample = snakemake@params[['sample']]
threads = snakemake@th... |
97a34541172ad1d2c636f4518eefe55972face4fdb13350455f587f4fd445212 | R | 744 | 43 | ---
title: "02_Neural"
output: html_document
date: "2024-08-29"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
library("Matrix")
library("readr")
library(Seurat)
library(tidyverse)
library(harmony)
library(cowplot)
library(patchwork)
```
```{r}
merged<-readRDS("Ctrl_FoxA_merged_082924.... |
3b841bc35e0e0b43d98041e1e16f7035bdd6a5d70b981675975f68137f33bc32 | R | 745 | 28 | #' flashpcaR
#' An R interface to FlashPCA for fast principal component analysis and
#' related analyses.
#'
#' \tabular{ll}{
#' Package: \tab flashpcaR\cr
#' Type: \tab Package\cr
#' Version: \tab 2.0.1\cr
#' Date: \tab 2017-01-19\cr
#' License: \tab GPL (>= 3)\cr
#' }
#'
#' @name flashpcaR-package
#' @aliases flashpc... |
249d4a9c712f50c656f50e4f768f50efeda38b50095b1503e3b09cefda3371e3 | R | 750 | 23 | test_that("log_to_NR converts TRUE/FALSE to 1/0", {
df <- data.frame(a = c(TRUE, FALSE, TRUE),
b = c(FALSE, FALSE, TRUE))
out <- log_to_NR(df)
expect_equal(as.numeric(out$a), c(1, 0, 1))
expect_equal(as.numeric(out$b), c(0, 0, 1))
})
test_that("countgenes encodes the number of unique non-mi... |
c42573b6f534466af8b85ec832379e6c7d11e2c14868d9baf09f218e21af7fcd | R | 751 | 33 | args <- commandArgs(TRUE)
lambda <- as.numeric(args[1])
mu <- as.numeric(args[2])
cap <- as.numeric(args[3])
ntip <- as.numeric(args[4])
family_name <- as.character(args[5])
tree_name <- as.character(args[6])
path <- as.character(args[7])
pars <- c(lambda, mu, cap)
meta <- c("Family" = family_name, "Tree" = tr... |
2ccd1b858a8717673ba146091ea78e813ffdf1adfa6bea074032a847da04c90b | R | 753 | 34 | #' Output a vector of colours based on the ggplot colour scheme
#'
#' This function takes as input the number of colours the user would like, and
#' outputs a vector of colours in the ggplot colour scheme.
#'
#' @param g the number of colours to be generated.
#'
#' @return a vector with the names of the colours.
#' @ex... |
386beb432d6715bd500414abe0a41e2bd84323c2c2a0e473d0ca7b008a3f799c | R | 757 | 30 | args <- commandArgs(TRUE)
file_name <- as.character(args[1])
family_name <- as.character(args[2])
tree_name <- as.character(args[3])
tree_brts <- readRDS(file_name)
ml <- DDD::dd_ML(
brts = tree_brts,
idparsopt = c(1, 2, 3),
btorph = 0,
soc = 2,
cond = 1,
ddmodel = 1,
num_cycles = 1
)
df_ddd_results... |
5a2f3e1d08b1aed6dcb1873be010912b0ada3b34b467c335df2e3bcf398cbfe7 | R | 763 | 31 | library(pROC)
context("large data sets")
test_that("roc can deal with 1E5 data points and many thresholds", {
response <- rbinom(1E5, 1, .5)
predictor <- rnorm(1E5)
# ~ 0.6s
r <- roc(response, predictor)
ci(r)
expect_is(auc(r, partial.auc = c(0.9, 1)), "auc")
})
test_that("roc can deal with 1E6 data poin... |
5f4d60b5423d373eac35d22f4e0d5227c60a97429f050fef0fcb3d95fca6b61f | R | 763 | 24 | #################################
#
# This function is used to obtain
# the TD for intra-SMN and inter-SMN-DMN
#
# the input should be the time delay matrix
#
ObtainTDinSMN <- function(dat_TD, net_anna){
dat_test <- rio::import(dat_TD) %>% as.matrix()
sbj_name <- stringr::str_extract(dat_TD, pattern = "sub-[0-9... |
9f0b2cae4af99403b1993a0843c4a73beaad2ff4aae8eca60c9e3999a053c407 | R | 766 | 23 | #' Random feature selection
#'
#' Perform feature selection on a single-cell feature matrix (e.g., gene
#' expression) by randomly removing a specified proportion of features.
#'
#' @param mat a single-cell matrix to be filtered, with features (genes) in rows
#' and cells in columns
#' @param feature_perc percenta... |
ab4d5e40d9951e077fce2cf903858f214bfa26d7cdf2e8d6f4234e85e721e4c1 | R | 772 | 22 | ############################
#
# Identify statistic function
# to test the difference in
# demographic information
#
pvalue <- function(x, ...){
# construct vectors of data y, and groups (strata) g
y <- unlist(x)
g <- factor(rep(1:length(x), times = sapply(x, length)))
if(is.numeric(y)){
# f... |
2cdab950f9ae231810c7479196c20c7fd5146571ad96447542f7ea90c520aeaa | R | 774 | 35 |
library(readxl)
tt <- read_excel("metadata/metadata_mid_organoids_10x_sample.xlsx", sheet = "invitro")
tt <- as.data.frame(tt)
list_perFile <- split(tt$Donors, tt$Sample)
tt2 <- read_excel("metadata/metadata_mid_organoids_10x_sample.xlsx", sheet = "chipInfo")
tt2 <- as.data.frame(tt2)
tt2 <- subset(tt2, sampleOrig... |
7284bfcebf467dcd57dada1fa62457aa66901163c4d66c3cdfef77a858d4d7cf | R | 774 | 21 |
# File: /scripts/visualization/raincloud_RMS.R
# Purpose: Generate raincloud plot for RMS_post by Group
# Input: ../../data/processed/EMG_EEG_strength.csv
# Output: ../../results/figures/raincloud_RMS.png
library(tidyverse)
library(ggdist)
data <- read_csv('../../data/processed/EMG_EEG_strength.csv')
p <- data %>%
... |
46dce4a9091e468171878932601d98ad8c68e59940d62ca6558933086a74bd21 | R | 779 | 27 | library(targets)
library(dplyr)
Sys.setenv(TAR_PROJECT='naturalistic')
tar_prune()
network_edges <- tar_network()$edges
final_target <- c("rmd_ms_stats", "rmd_ms_supp")
all_upstream_targets <- c()
i <- 1
targets_to_check <- final_target
repeat {
these_upstream_targets <- network_edges %>%
filter(to %in% targe... |
dd0875ae1dd0d6bd17f96437949506eb1bfeba1267b843db6bc6fa136ae4a927 | R | 785 | 42 |
#Source the respective user interface and server R files.
source("UI.R")
source("server.R")
#Load in necessary packages.
library(shiny)
library(EBImage)
library(jpeg)
library(ggplot2)
library(shinydashboard)
library(dplyr)
library(tibble)
library(ComplexHeatmap)
library(grid)
library(gridExtra)
library(cowplot)
libra... |
0e3b719b64c0b9c946b80aee18e700433215697f71b661bc1a1fc46c22a50728 | R | 795 | 28 |
# Generate some data
nsamp <- 10
# True cell type proportions
p <- c(0.05, 0.15, 0.35, 0.45)
# Parameters for beta distribution
a <- 40
b <- a*(1-p)/p
set.seed(986)
# Sample total cell counts per sample from negative binomial distribution
numcells <- rnbinom(nsamp,size=20,mu=5000)
true.p <- matrix(c(rbeta(nsamp,a,b[... |
6802ab40b8c3457e8459e8a2994de78d10e9fce37b6e69a453064f63472e057d | R | 803 | 25 | #' @export correct_bias_coef
correct_bias_coef <- function(data, task = NULL) {
pars_list <- NULL
diff_list <- NULL
if (task == "BD") {
pars_list <- c("lambda", "mu")
diff_list <- c("lambda_pred", "mu_pred")
} else if (task == "DDD") {
pars_list <- c("lambda", "mu", "cap")
diff_list <- c("lambda... |
cb6a36ccac46b42e3d6d3572b4f0e845de1402ba250b62e62ee52126b1e85ff1 | R | 805 | 34 | #' Homo Sapiens annotation file
#'
#' Data downloaded from ftp.ncbi.nlm.nih.gov/gene/DATA/GENE_INFO/Mammalia/Homo_sapiens.gene_info.gz"
#' data were pre-processed by uncollapsing the rows. Created Jun. 2020
#'
#'
#'
#'
#' @format A data frame with columns:
#' \describe{
#' \item{rows}{row number before uncollapsing}
#... |
030b1d08baaf5b8a263354f2d69a221a781b37e0367a79337b1db5747b8c732f | R | 817 | 29 | #' @keywords internal
#' @importFrom neurobase readnii writenii
util_get_coords=function(cfg){
# load parcels
my_img = readnii(cfg$parcel_path)
# get unique values
unique_v = unique(as.vector(my_img));
unique_v = unique_v[unique_v > 0]
# loop through unique values and get coordinates in MNI
get.coords=... |
3b0cf232053168cafc8924ca4dd0a276da1ce8c14d6614e0e42dc240f2fc353b | R | 821 | 48 | #INFORMATION-----------------------------
#LOAD LIBRARIES ------------------------
library(shiny)
library(shinyjs)
print("Sucessfully loaded libraries.")
#LOAD TABS-------------------------------
source("UI.R")
print("Successfully loaded tabs.")
#USER INFERFACE ----------------------------
ui <- fluidPage(
## t... |
0241fd972a5520a7c0cdc70588526a52b86b5f5ecb6cd1cf0a2a469c79f4ff24 | R | 836 | 26 | setwd("/data/wuqinhua/phase/covid19/datasets")
library(Seurat)
library(SeuratDisk)
sbj <- list()
data_folder <- "./pre_data/17_Wilk_2021/data"
file_list <- list.files(data_folder, pattern=".rds", full.names=FALSE)
for (file_name in file_list) {
sample_id <- substr(file_name, 1, 10)
sample_data <- readRDS(f... |
3a609499f51b4591b1244c8621e038f89505af00b767cc782ee51164642c3162 | R | 841 | 26 | source("../shinytest_helpers.R")
set_window()
app$setInputs(layout_and_readouts = "separate_files")
app$uploadFile(layout_table_file = demo_path("CRA_Layout.xlsx"))
app$uploadFile(readout_matrices_file = demo_path("CRA_1ReadoutXlsx_INF-R-1632.zip"))
app$setInputs(type_of_analysis = "StepA")
app$setInputs(iTReX = "QCN... |
db96348fab1858946d04bcc18d9500e1078674137c981be62d2bcf8a4302e13a | R | 844 | 30 | #' Estimate the parameters of a Beta distribution
#'
#' This function estimates the two parameters of the Beta distribution, alpha
#' and beta, given a vector of proportions. It uses the method of moments to
#' do this.
#'
#' @param x a vector of proportions.
#'
#' @return a list object with the estimate of alpha in \c... |
fee9af6d7d98ed8c4b077ab0efc0da89b2f1458092f1619ec3aeed6a67375e58 | R | 846 | 47 | ---
title: "00_Preprocess Fincher"
output: html_document
date: "2023-07-06"
---
```{r setup, include=FALSE}
# Set global chunk options
knitr::opts_chunk$set(echo = TRUE)
```
```{r}
# Load required packages
library(Seurat)
library(tidyverse)
```
```{r}
# Load Seurat object (raw or previously processed)
Fincher.orig <... |
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