sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 17k | content stringlengths 1 200k |
|---|---|---|---|---|
75b2b5253b4f2ac1d33f4b17abdd041effbc2d24ccab25b332242ae1f545d428 | R | 5,551 | 120 | context("Testing calcNhoodExpression function")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
library(miloR)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen valu... |
b7c73fa5d2da4e8fab7d2e8de2343b31590148f7c0ecb93b812f9606555afb74 | R | 5,597 | 197 | # some functions ||
encode_1mer <- function(seq, map){
features <- c()
n <- nchar(seq)
seq <- toupper(seq)
for (i in 1:n)
features <- c(features, map[substr(seq, i, i),])
return(features)
}
complDNA <- function(seq){
tbl <- list()
tbl[["a"]] <- "t"
tbl[["c"]] <- "g"
tbl[["g"]] <- "c"
... |
1aec75a9173d04ec93297856f08117ae44559f08c054adefaee5e0db535af6a5 | R | 5,600 | 161 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y <- scale(ref$GrainYield[ref$Set == "Train"])
yc <- attr(y, "scaled:center")
ys <- attr(y, "scaled:scale")
rm(ref, y); gc()
ref_pcs <- read_csv("processed/reference_sort... |
e7c566db1d0fcb0cc1e5311dfc9ecafec40e0b0cd1ef83455ebb49992ca7a6d2 | R | 5,607 | 162 | library(ggplot2)
library(dplyr)
library(cowplot)
library(nlme)
library(tidyr)
data <- read.csv("Analysis_Files/Spontaneous/SLC Spontaneous Gastrointestinal Motility - Analysis_Wide.csv",header=TRUE)
data_long <- pivot_longer(data,
cols = starts_with("X"),
names_to... |
469f7688ee8e25b1befb5ead1d1696fec310f674877cc23e87d114f546ab6d75 | R | 5,630 | 106 | # Step 1: Split Visium histology whole slide image into individual capture area images
The Visium histology whole slide image from the imaging system (slide scanner) is a multiplane tif image (~20GB). The sample image [Lieber_Institute_OTS-20-7690_rush_anterior.tif](https://visium-libd.s3.amazonaws.com/Lieber_Institut... |
39875e79eecd41df1cc116d10cdf33c43ffacf306e06447918bfb6fc59e53468 | R | 5,649 | 121 | context("Testing fitGLMM function")
### Set up a mock data set using simulated data
suppressWarnings({
library(miloR)
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
})
##### ------- Simulate data ------- #####
data(sim_family)
sim.df <- sim_family$DF
set.seed(42)
rando... |
6c4ac99fbf619334856a27d4fb55c991ea984e727a37fdc4b19540de11fbe343 | R | 5,676 | 169 | ---
title: "Hemisphere x Age Bin Interactions"
output: html_notebook
---
Version 1.0, July 2025, SA
This script plots hippocampal connectivity with neocortical clusters identified by a significant hemisphere x age-bin interaction.
Input: Hippo_BinxHem_F_betas.txt
Output: Plots in Fig S4C
# Packages and functions
... |
32ffff19a7d2b82fb2a151f575fb1600a8d604fc64ddbf53959192de02acb2fb | R | 5,686 | 193 | # merge peak -------------------------------------------------------------------------
source('/cluster/home/chencheng/Mac_gaba/prepare.R')
library(future.apply)
library(argparse)
parser <- ArgumentParser(description='merge peaks to union peakSet')
parser$add_argument('-I', '--input', help='cluster peak information')
a... |
d8a4e45411fabfc754739feda9bdc853987002a88c9cab6e538c3ed4b82b22b2 | R | 5,712 | 174 | setClassUnion("matrixOrNULL", c("matrix", "NULL"))
setClassUnion("arrayOrNULL", c("array", "NULL"))
setClassUnion("numericOrNULL", c("numeric", "NULL"))
setClassUnion("data.frameOrNULL", c("data.frame", "NULL"))
#' @title Epoch Class
#' @description S4 class to handle epoch data with electrodes and time points
#' @slo... |
56f6b1a5eb1e23b66f6f47a4941b919c2579738388226ccfae0bb72fd5ccbcbf | R | 5,731 | 121 | ## List of all drugs and their short names
DRUG_LIST = c("Amikacin", "Bedaquiline", "Capreomycin", "Delamanid", "Ethambutol", "Ethionamide", "Isoniazid", "Kanamycin",
"Levofloxacin", "Linezolid", "Moxifloxacin", "Pyrazinamide", "Rifampicin", "Streptomycin", "Clofazimine", "Pretomanid")
SHORT_NAMES =... |
7ed5e8a2d9c7d225822d1c84bd850270d619f5daed4256c67e925e9ed8bcbff7 | R | 5,734 | 146 | #if (!requireNamespace("BiocManager", quietly = TRUE))
# install.packages("BiocManager")
#BiocManager::install("limma")
#install.packages("reshape2")
#install.packages("ggpubr")
#install.packages("ggExtra")
#ÒýÓðü
library(limma)
library(reshape2)
library(ggpubr)
library(ggExtra)
gene="KLHL21" ... |
059392cff24c205ba528f49a9ae2d406a977becf23bb0bec3d4e4a2ba03d29f5 | R | 5,738 | 129 | context("Testing annotateNhoods function")
### Set up a mock data set using simulated data
suppressWarnings({
library(miloR)
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
})
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 50... |
2e2daacab041813df616c952440b09ebfeeb90205ef3bc96dcc2893fc7a30a1d | R | 5,744 | 274 |
# ---- libs ----
library("dplyr")
library("tidyr")
library("tictoc")
library("foreach")
library("EmpiricalCalibration")
library("Sequential")
library("arrow")
library("ggplot2")
# ---- consts ----
min_event <- 1
alpha <- 0.05
cv_df_0 <-
expand_grid(
cntl_to_case_ratio = seq(0.5, 5, by = 0.5),
max_n = ... |
526c22e6b99618da202ffc9dace593ace1091150f6563e07b8cb9f959fffc1b5 | R | 5,773 | 167 | library(ggplot2)
library(dplyr)
library(cowplot)
library(nlme)
library(tidyr)
data <- read.csv("Analysis_Files/PFF/PFF Gastrointestinal Motility - FP_output.csv",header=TRUE)
data_long <- pivot_longer(data,
cols = starts_with("X"),
names_to = "timepoint",
... |
e760cb178cc5c02b3efe3b2df1ada2ae19fc106c1e27ddf35eb5ce76827acfbb | R | 5,783 | 154 | #' This function trains, fits and evaluates a deep neural network (DNN) model.
#'
#' This function creates a DNN model from the reference dataset (scRNA-seq) by using the keras package.
#' @param out The output of the ds_split_data_encoder function. The input data used to train the DNN model.
#' @param hnodes The numbe... |
0e1d9ed7220f8e5ba1b76d5dcde0fe48a6dadd177279c7703872478a08f6eb44 | R | 5,788 | 157 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data")
library(tidyverse)
source("src/kernel_functions.R")
source("src/scale2.R")
### The reference table has the pedigree and environment of each data record --
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE) %>%
mutate(yNA = if_else(Set == "... |
6071f61343d9176bfa7f2d6c535b69e501ad1b6423f1ad95e92fb3c788f31f0e | R | 5,796 | 195 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(robustlmm)
library(glmmTMB)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\i... |
4ccee37001ae3f9e8587a45bf54e113840d24d2e09480dcbd36d6af16ee6cfb3 | R | 5,832 | 238 | library("readr")
library("stringr")
# raw data
rawd <-
read_csv(
### previously
# "https://raw.githubusercontent.com/curtis-murray/MedicalDevicesNLP/master/data/all_reports/all_reports_df.csv"
### now saved locally for snapshot
"dat/all_reports_df.csv"
)
# rawd <- vroom::vroom("dat/all_reports_df... |
3f65a782866e6564cfcbc341fb84ab3058288282e8ac612ace67fef92e5faefd | R | 5,853 | 108 | # ------------- Figure S1 --------------
# -------- Path settings --------
project_dir <- "./NSCLC-DMSPsig"
data_dir <- file.path(project_dir, "data")
results_dir <- file.path(project_dir, "results")
figure1_dir <- file.path(results_dir, "Figure1")
figure2_dir <- file.path(results_dir, "Figure2")
dir.create(figu... |
75385b0e12ae1ad25cebd5761c0617ea8aff837df7c85350d1e7d410b6efeee6 | R | 5,860 | 104 | ---
output:
rmarkdown::github_document:
html_preview: true
toc: false
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
fig.path = "images/dea-",
comment = "#>"
)
```
```{r setup, include = FALSE}
devtools::load_all()
```
# Differential Expression Analysis
In this vignette we wil... |
15c79e9e534ee0626a01efd8fd29348d3702297eeb909abd16a132507445e269 | R | 5,861 | 160 | ---
title: "tSNR analyses"
output: html_notebook
---
Version 1.0, July 2025, SA
This script plots mean and standard deviation of connectivity across the age bins examined.
Input: Mean_and_SD_cortical_conn_Schaefer2018_200Parcels.csv
Output: Figs S12 and S13
# Read in packages
```{r}
library(ggplot2)
library(dpl... |
fbf14174e91598a3fe5abb96343822a36d1959bbf1c73fa6eae0d93345dba7f9 | R | 5,886 | 223 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(robustlmm)
library(glmmTMB)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\i... |
cc5443823539ef67476a7e9a23b340ca5753d9cfe6eccc6305e5a50af2898bab | R | 5,903 | 175 | # Spec[tagram].r
# Smooth with blur() from library(spatstat) !!
SHOW_PALETTE = T
BANDS.USE = 16:100 # 16:100 (10:48)
BANDS.OUT = 54:66 # 54:66 (52:68 or more for small windows) # stop band
if (CC_INACT) stop("Not written!\n")
# FROM http://sebastianraschka.com/Articles/heatmaps_in_r.html
if (!require("RColorBre... |
e5b6e00c121cb8bea2300ee969f47669c14e6986a3b0485a1d10d3862fafdb9a | R | 5,913 | 181 | ---
title: "MNI Space CSV"
output: html_document
date: "2023-11-07"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(
echo = FALSE, # don't print the code chunk
warning = FALSE, # don't print warnings
message = FALSE, # don't print messages
fig.width = 5, # set default width of figures
fig.height = ... |
90f22033b1656688d422f2feae8482fcbe51db07bded73589fcfe52c5bccbe32 | R | 5,915 | 176 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("src/kernel_functions.R")
### Load the hybrid marker matrix ---------------------------------------------
# From Lopez-Cruz et al. (2023)
X <- read_csv("../../genomes2fields_curated/data/data_g2f/GENO.csv", show_col_types = FALSE)
x_taxa ... |
b2818a5dbf67744933f5de0befa0c54f4eb09a942f0cd5ef5aef673e7e9195b0 | R | 5,927 | 149 | #By Library
lst=readRDS("/cluster/home/mengjuan/project/snATAC_snRNA/5.WB_mafas5/5.merge2macaque/snATAC.AnnoColor.V1105.rds")
MonkeyCols=lst$MonkeyCols
region_L0_cols=lst$region_L0_cols
outdir=getOutputDirectory(proj)
outdir
samples=proj$Sample %>% unique
FragmentSizes=list()
TSSEnrichment=list()
FeaturePlot=l... |
62155b0d464d5d61e26ebacfe9d24742494bd010604cd5169cc6d042135e57b9 | R | 5,936 | 203 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\isttc\\results\\mice\\dataset\\cut_30... |
98c6bbfff91a458ce9700eff2a7808b79af4d75bd92d0b9d5de18ea91381f8c9 | R | 5,937 | 206 |
library(tidyr)
library(dplyr)
library(ggplot2)
df<- read.table("radj_ados.txt", header = TRUE, sep = "\t" )
# 假设数据框名为df，包含variable、mri、ec等列
df_long <- df %>%
pivot_longer(
cols = c(mri, ec, pathway, taxon, met), # 选择需要可视化的指标列
names_to = "metric",
values_to = "value"
)
p<... |
7a9867c4db62da0283894f367bf7b6aa79d0551cb906d0f1ca90dd91b42bd91b | R | 6,006 | 101 | library(sceasy)
library(reticulate)
library(SingleCellExperiment)
library(org.Hs.eg.db)
library(org.Mm.eg.db)
library(scater)
setwd("/scr1/users/manchela/Data")
#change to ENSEMBL ID for cellxgene
# convert rds to h5ad for human objects
seu <- readRDS("human_face_no-neuro_clustering_cellrangerARC-raw_emptyDrops_sing... |
b4eb156500804f945ba172535192de69e7884a91d20a0535851ac57fc073f078 | R | 6,008 | 147 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(RSpectra)
library(tensorEVD)
source("src/bigLK.R") # Needed for the soil kernel
### BGLR uses an internal tolerance of 1e-10 (see setLT.RKHS in BGLR.R)
### For our purposes, we keep any eigenvalue > 0 for completeness.
### The reference... |
b778e430de810b6a2774639bb3ba00213b82b12a4fb54acb9620d4310bd29976 | R | 6,028 | 130 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(multcomp)
res <- read_csv("results/metrics_all_env.csv", show_col_types = FALSE) |>
filter(Data %in% c("GSW", "ASW")) |>
dplyr::select(-Data) |>
mutate(Model = if_else(Model == "DNN-CO", "ReLU", Model))
res2 <- list.files("10.in... |
65c01ce8e2d3893dfaf4a58f482f020ffacdd75b951a9f4293b96f7cabc71026 | R | 6,063 | 162 | # doIndiPower.r
###########################################################################
# Set basic parameters #
########################
MONK = "zen" # zen, tyr, both (don't use both: R calls will merge monks)
AREA = "LIP"
PRINT = F # Print the calls?
PRINT.BRIEF = T # Just the names of the cells
EXECUTE = T ... |
bd3133a638b572c5d941be073fe217cb9164261940066036e0e200493dbfe373 | R | 6,063 | 216 | #' CIBERSORT R script v1.03
#' Note: Signature matrix construction is not currently available; use java version for full functionality.
#' Author: Aaron M. Newman, Stanford University (amnewman@stanford.edu)
#' Requirements:
#' R v3.0 or later. (dependencies below might not work properly with earlier versions... |
3a86f25a25126c656c96daac49fd88ddedcce34bed8d907582986350d706de0f | R | 6,090 | 125 | library(Maaslin2)
library(funrar)
library(plyr)
library(dplyr)
library(ggplot2)
library(cowplot)
library(readr)
#### JEJUNUM ####
setwd("/Users/rochellelai/Documents/JacobsGit/slcproject/PFF_Microbiome/differential_taxa/Jejunum/")
### Run Maaslin2 and get table of relative abundances
input_data <- read.csv("export_s... |
de4c7bd65659dba6dec24a882de47e9d5f75f873cb2867cb79e1d9902498213c | R | 6,099 | 139 | context("Testing class instantiation and methods")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
library(miloR)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen v... |
e9dc8829e17fbec84c452d17d67a9023f34a34ad7e20479d2c6748d3f5f991ff | R | 6,112 | 148 | #####################################
# Estimate metacognitive efficiency (Mratio) at the group level
#
# Adaptation in R of matlab function 'fit_meta_d_mcmc_groupCorr.m'
# by Steve Fleming
# for more details see Fleming (2017). HMeta-d: hierarchical Bayesian
# estimation of metacognitive efficiency from confidence ... |
113d10268ca19e66699c30644eb5beb54b883b21fa3b362e866c7bcbe8d50812 | R | 6,129 | 180 | # doConcat.r
FROM.TIME = 800 # [ 650] [-300 100 800 -100 100]
END.TIME = 1050 # [1150] [ -50 350 1050 -100 100] [go:maxi 160]
# NOTE: intervals are centered on these times
ALIGN.OVERRIDE = ""
NEAR.OVERRIDE = 2
AREA.OVERRIDE = "LIP"
MONK.OVERRIDE = "both"
CLASS.OVERRIDE = "all8"
INACT_INTERVAL.OVERRIDE = ... |
50e4dc032d0a1a51cde53ebbc81d68291d8e16c93c5bc9c3f0ee30eea2eb6c75 | R | 6,131 | 226 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
library(moments)
library(ggeffects)
library(robustlmm)
library(glmmTMB)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\is... |
b4aaa54a1e766d5a76622814bdfd555d299719759fe119eaacf3a5efdf144963 | R | 6,147 | 125 | context("Test function countCells")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
library(miloR)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen values for the c... |
33aa1dff298a86a3d004e6b1f15b7178fa2e6ac405f351f641ac78201b4e340c | R | 6,167 | 145 | #' Calculates pseudocells from a Seurat object
#'
#' This function calculates pseudocells from a Seurat object, based on pre-calculated cell clusters and dimentionality reduction. WARNING: This might be time consuming, depending on the size of the dataset.
#' @param s.cells The seurat object, with pre-computed PCA or ... |
f66d544fa68c8daf5044d942607766033fe7598a739b1c1fc6f27f88efe3ff2b | R | 6,179 | 201 | ---
title: "Hierarchical Clustering"
output: html_notebook
---
Version 1.0, July 2025, SA
This script clusters the clusters resulting from a long-axis x age bin interaction into superclusters that share similar profiles of connectivity with the anteroposterior hippocampus (as visualized in Figure 4A)
It also plots s... |
95dc23096ca5aa12586ee1d451b19aaecb89e279f7ddac4592e7f76d31dbe4e4 | R | 6,190 | 165 | ---
output:
github_document:
html_preview: true
toc: false
---
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
# fig.path = "man/figures/README-",
message = FALSE
)
```
# Installing the MS-DAP R package
This guide helps you install the MS-DAP R package and all of its s... |
f64fdab5a1bec8dd7a046d5cc7eac02879a3395642655a221a9e04d5c4bde972 | R | 6,199 | 249 | library(tidyverse)
library(synExtra)
library(qs)
library(data.table)
library(powerjoin)
library(ggbeeswarm)
synapser::synLogin()
syn <- synDownloader("~/data", .cache = TRUE)
rosmap_quants <- syn("syn43841162") %>%
fread()
msbb_quants <- syn("syn50920431") %>%
fread()
msbb_clinical <- syn("syn6101474") %>%
re... |
b0158aad8cab1829e60c9aab26112931706b30d5ddb4e70e82505d44147284e6 | R | 6,206 | 180 | library(Seurat)
library(SeuratObject)
library(DESeq2)
library(ggplot2)
library(scales)
library(qs)
library(dplyr)
library(zebrafishRNASeq)
library("biomaRt")
#'#######################################################################
# Load method functions ----
#'#######################################################... |
687b139af7251d95dead0b166e8713f5e455ee4362756d95eb3b27acd8a9b36b | R | 6,229 | 165 | library(ggplot2)
library(vegan)
library(dplyr)
library(rlang)
library(cowplot)
library(viridis)
## Environment --
here::i_am("Rscripts/Figure_Correlate_PFF_Puncta.R")
metadata <- read.csv("Analysis_Files/PFF/PFF_Microbiome/starting_files/PFF_Mapping.csv",header=TRUE)
counts <- read.table("Analysis_Files/PFF/PFF_Micro... |
a53dcb57a3849717f9b1cc4e4564f99d3acdad70338da0a3aa1a6141731a1739 | R | 6,239 | 192 | #' @title
#' The Milo constructor
#'
#' @description
#' The Milo class extends the SingleCellExperiment class and is designed to
#' work with neighbourhoods of cells. Therefore, it inherits from the
#' \linkS4class{SingleCellExperiment} class and follows the same usage
#' conventions. There is additional support for ce... |
7da37c1028a2bb991a7b5ac86cab27eac08abe2dabdea8b72c1808f59b432512 | R | 6,244 | 176 | # =============================================================
# ELASTIC NET-PENALISED COX PH MODEL FOR SOMASCAN 11K PLASMA PROTEINS
# =============================================================
# COHORT: Lothian Birth Cohort 1936 (LBC1936)
# ELASTIC-NET PENALISED COX MODEL:
# Surv(time_to_death, dead) ~ protei... |
4501d142ae02504db75479830ec49aff7c71383fdf4fd0e0a54238dff914d0ec | R | 6,262 | 140 | context("Testing checkSeparation function")
### Set up a mock data set using simulated data
suppressWarnings({
library(miloR)
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
})
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 5... |
8c685498dcc6a3927bd9226e5c5f8aca1276e54315aef6721c1d5d5b5bbf728b | R | 6,338 | 113 | # set threat function
threat_function <- distributions3::Beta(2, 5.5)
create_distance_df_bci <- function(df, ghost = TRUE){
if(ghost){
distance_df <- df %>%
filter(Trial != "ITI") %>%
filter(TrialType <= 16) %>% # ghost trials only
group_by(trial_numeric) %>%
# user movement and dista... |
654ff19c3b059b355af429f698356e3475d7e53c6ece81f849b2e3f8ae4607cd | R | 6,348 | 237 | ```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
library(data.table)
library(SCAVENGE)
library(chromVAR)
library(gchromVAR)
library(BuenColors)
library(SummarizedExperiment)
library(data.table)
library(dplyr)
library(BiocParallel)
library(BSgenome.Hsapiens.UCSC.hg38)
library(igraph)
```
```{r}
set.see... |
d547cdec906338d1a24602ad87a45dc1b0f93b7d1f9181a6218e3bcd72e75e3f | R | 6,409 | 130 | #05_mediation.R
rm(list=ls())
library(mediation)
library(dplyr)
meta=read.csv('./01_taxa/all/state_table/00_meta_all.csv')
Topic=read.table('./01_taxa/all/Topic/pheno.txt',header = T)
datas=merge(meta,Topic,by=c('FID','IID'),sort=F)
datas=datas%>%mutate(
C1.sd = C1/sd(C1, na.rm = T),
C2.sd = C2/sd(C2, na.rm = T)... |
ad568801d7e31457ce4791bdc4ae8fc0f810b7e0fdaa62a87251576d7a720f78 | R | 6,424 | 168 | # install.packages("BiocManager")
# BiocManager::install("clusterProfiler")
# BiocManager::install("org.Hs.eg.db")
# BiocManager::install("AnnotationDbi")
# BiocManager::install("rrvgo")
library(clusterProfiler)
library(org.Hs.eg.db)
library(AnnotationDbi)
library(rrvgo)
library(purrr)
library(stringr)
library(ggplo... |
d52c8e94b5911483e6f8fb40b5d551cdb843c3503725ab142550f54e4e61ebd5 | R | 6,449 | 232 | library(dplyr)
library(tidyr)
df<- read.table("taxon_beh_vf_sel.tsv", header = TRUE, sep = "\t" )
df_processed <- df %>%
mutate(across(c(X1812935,X2052056,X2714945,X33887,X37919), ~ ifelse(is.na(.), "NA", "non-NA"))) %>%
pivot_longer(
cols = c(X1812935,X2052056,X2714945,X33887,X37919),
names_to = "... |
ffcc593a64d6965bf5e63eb837a765ec4f4ffc9697019ccfa9b1cd878eba70a4 | R | 6,490 | 178 | # 1. °²×°²¢¼ÓÔرØÒªµÄ°ü
if (!require("sva")) install.packages("sva")
if (!require("limma")) install.packages("limma")
if (!require("kBET")) {
if (!require("devtools")) install.packages("devtools")
devtools::install_github("theislab/kBET")
}
if (!require("ggplot2")) install.packages("ggplot2")
if (!require("... |
04d3a6cbdd0249af63d328302f16fa7054c23341295c10f5b7e62360c7c66941 | R | 6,505 | 170 | library(ggplot2)
library(vegan)
library(dplyr)
library(rlang)
library(cowplot)
library(viridis)
## Environment --
here::i_am("Rscripts/Figure_Correlate_PFF_Puncta.R")
metadata <- read.csv("Analysis_Files/PFF/PFF_Microbiome/starting_files/PFF_Mapping.csv",header=TRUE)
counts <- read.table("Analysis_Files/PFF/PFF_Micro... |
c328ef598825decb90b215b93298732082c260817140e024d2a95f50509f0529 | R | 6,534 | 200 | library(here)
library(tidyverse)
library(cowplot)
library(ggplot2)
### Establish location ---
here::i_am("src/ASO/ASO_Correlate_DAT_with_GFAP.R")
### Read in input files ---
ASO_lum_col_counts <- read.delim(here("data/ASO/Microbiome/differential_taxa/ASO_L6_Subset_Luminal_Colon.tsv")) %>%
t() %>% as.data.frame()... |
904f820e0a77085f735fe9f8bee4634fdf8e2bc23bac3e9076f4a9f00736c9ce | R | 6,556 | 173 | ##' Implementation of probabilistic PCA (PPCA). PPCA allows to perform
##' PCA on incomplete data and may be used for missing value
##' estimation. This script was implemented after the Matlab version
##' provided by Jakob Verbeek ( see
##' \url{http://lear.inrialpes.fr/~verbeek/}) and the draft \emph{``EM
##' Algorit... |
1fb555348f5b23c0bd188fe9c03d93977920fa99e999293e63b73987f89d9f6d | R | 6,583 | 197 | library(Seurat)
library(SeuratObject)
library(DESeq2)
library(ggplot2)
library(scales)
library(qs)
library(dplyr)
library(zebrafishRNASeq)
library("biomaRt")
#'#######################################################################
# Load method functions ----
#'#######################################################... |
b501353e59e854937bd9cbd3c215b4a5fa81d8409c33e34a457aff2b2bebe644 | R | 6,592 | 289 | ```{r}
library(tidyverse)
library(synExtra)
synapser::synLogin()
syn <- synDownloader(normalizePath("~/data"), .cache = TRUE)
```
```{r}
dge_gmt <- map(
c("syn20820056", "syn20820060"),
~syn(.x) %>%
DRIAD::read_gmt()
) %>%
enframe("experiment", "gene_sets") %>%
mutate(
gene_sets = map(gene_sets, ~enf... |
e6e9e5f832d391b0bf1cf402d3a727120effac9e3846c31424347078064c6694 | R | 6,597 | 189 | # Taken from the supplementary code file of
# 'Characterizing noise structure in single-cell RNA-seq distinguishes genuine from technical allelic expression'
# by Jong Kyoung Kim
# published in Nature Communications
# DOI: 10.1038/ncomms9687
# Estimate gamma and theta
#' @importFrom stats lm coefficients nls.control
#... |
02215e38ec2fc62bd31c1684177529dcc29f6d4af621320004e2e08ed30f0e76 | R | 6,613 | 168 | library(SeuratDisk)
library(Seurat)
library(tidyverse)
library(dplyr)
library(patchwork)
#E15.5 spatial plots
#loading mouse facial transcriptomics and E15 spatial data from Pina et al
cds <- readRDS("face_mouse_final.rds")
cds1 <- readRDS("data/combined_HQ.rds")
cds2 <- subset(cds,cells = rownames(cds@meta.data[cds$s... |
41ffc908a44add586d48c824fb1dd6a1a21683949564d9da0a377f01fd711787 | R | 6,659 | 180 | library(rhdf5)
# Load Python and R functions for basic Wilcoxon Tests, DESeq2, and CocoA-Diff
# and causarray, cinemaot
require(reticulate)
use_condaenv('renv')
path_base = '/home/jinandmaya/'
setwd(paste0(path_base, 'methods'))
source('R_functions.R')
path_base = '/home/jinandmaya/simu_nb/'
setwd(path_base)
library(... |
3c9bdd9164ca1354cacca802a1a84f54285940f7a8d3b4b814df9f714b0980f1 | R | 6,676 | 209 | # set LFC -----------------------------------------------------------------
#' @importFrom methods is
.setFC <- function(input, nDEgenes, k) {
# two groups
if(k==2) {
if (is.vector(input)) { ## vector
if (length(input) == 1) { ## constant
lfc = rep(input, nDEgenes)
} else if (length(input) ... |
0d4ee780913a4b5c23ccd83731e8dcd2dd4b078d02026b5ecd54034255c5a658 | R | 6,696 | 165 | library(data.table)
library(infercnv)
library(tidyverse)
library(crayon)
library(ape)
library(readxl)
options(scipen = 100)
base_dir <- "/n/scratch/users/s/sad167/EPN/scRNAseq"
resources_dir <- file.path(base_dir, 'scripts/resources')
ext_ctrl <- file.path(resources_dir, 'ctrl_data')
source(file.path(resources_dir, ... |
7f20220ed362c06de786e6e53936702bd7c0c7ff7a20f963150558120094113d | R | 6,723 | 144 | context("Test calcNhoodDistance function")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen values for the covarianc... |
be2a54ef00a4c52af1b8f77adf64df2e2991bf34413cf3f440b57689b8ed7136 | R | 6,750 | 174 | ##' PCA by non-linear iterative partial least squares
##'
##' Can be used for computing PCA on a numeric matrix using either the
##' NIPALS algorithm which is an iterative approach for estimating the
##' principal components extracting them one at a time. NIPALS can
##' handle a small amount of missing values. It is no... |
1c87173688a3fa20259ed5b5efacfd277b0d1bc910e3b1197b4349088af46da4 | R | 6,800 | 207 | # abs_power.r Plot power spectra for 5 stacks, ONE class (Larry)
LINES = T # Solid line to show mean
RIBBONS = F # Ribbon of +/- 1 SEM (can have both)
NAME.PDF = F # [F] 'T' if concat two monkeys in SCATTER=2
X.int = 250 # [250] Spacing between tics on X axis
if (!exists("OVERRIDE.OVERRIDE")) {
AREA.OVE... |
f4d1a43c475f2ec7f70b9c1428d75d60171c8aa84a373668d33cd27ab9a8f996 | R | 6,824 | 166 | # On-Demand Seizures Facilitate Rapid Screening of Therapeutics for Epilepsy
# Authors: Yuzhang Chen, Brian Litt, Flavia Vitale, Hajime Takano
# DOI: https://doi.org/10.7554/eLife.101859
# Step 1: Import Libraries and Generate List of SubFolders
library(ggplot2)
library(lmerTest)
library(dplyr)
# KEY: Change to loca... |
e3ef5e1f543527d11292095707827e721ed9793d6d300d4ef89a3aacf4d0eb01 | R | 6,886 | 142 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
source("src/scales.R")
res <- read_csv("results/metrics_all_env.csv") %>%
filter(Model %in% c("Linear", "ReLU")) %>%
filter(Data %in% c("G", "S", "W", "GSW"))
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y <- s... |
2430741736b27b287e33e70768eb86f70ec39cd7530922c6f1df1bfedf32ddac | R | 6,895 | 188 | ## This script imports two developmental references from the original publications, and prepares them for developmental mapping
# Load packages -----------------------------------
rm(list = ls())
library(data.table)
library(tidyverse)
library(qs)
library(Seurat)
library(data.table)
library(SeuratData)
library(SeuratD... |
72184dc1d196fb292730fe31186fef3e3c028ac0038058dd097cda448dd1d075 | R | 6,897 | 180 | # install.packages("BiocManager")
# BiocManager::install("clusterProfiler")
# BiocManager::install("org.Hs.eg.db")
# BiocManager::install("AnnotationDbi")
# BiocManager::install("rrvgo")
library(clusterProfiler)
library(org.Hs.eg.db)
library(AnnotationDbi)
library(rrvgo)
library(purrr)
library(stringr)
library(ggplo... |
1f861dba8d438305d347da796e2c3ff5012c5331d0981a4e6430a61adbc17c62 | R | 6,899 | 166 | # On-Demand Seizures Facilitate Rapid Screening of Therapeutics for Epilepsy
# Authors: Yuzhang Chen, Brian Litt, Flavia Vitale, Hajime Takano
# DOI: https://doi.org/10.7554/eLife.101859
# Step 1: Import Libraries and Generate List of SubFolders
library(ggplot2)
library(lmerTest)
library(dplyr)
# KEY: Change to loca... |
dab0cf44ff1091ee17d80da3351793e9daf226dc604e7be47cb96bf7ca8ea47a | R | 6,939 | 186 | ###############################################################################
# PAKETE LADEN
###############################################################################
library(readxl)
library(lmerTest) # für LMM
library(ggpubr) # für Boxplots
library(glmmTMB) # für Beta- (und andere GLMMs)
# -> Optio... |
b69cad0e00b240dae74e368f85c93747ae018c02d0e9ebefcdecd28d205b3667 | R | 6,946 | 188 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(tensorflow)
tf$config$list_logical_devices()
library(keras3)
library(abind)
source("src/scale2.R")
### Reproducibly select sub-model runs ----------------------------------------
seeds <- c(758069L, 929940L, 591511L, 882475L, 961222L, ... |
bb43808817f3eb43b0ab6b66ed969dac0b313e6cc9e3a73548d58913ff80919f | R | 6,973 | 176 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data")
library(tidyverse)
source("src/kernel_functions.R")
source("src/scale2.R")
### The reference table has the pedigree and environment of each data record --
ref <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE) %>%
mutate(yNA = if_else(Set == "... |
ac0d9796325510eb92529ef576291ef9a8011ae3d4fcf9938ead9dfb24c0bc54 | R | 7,018 | 256 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
#library(moments)
library(ggeffects)
#library(robustlmm)
#library(glmmTMB)
library(patchwork)
library(forcats)
library(scales)
df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\... |
1365663738b2857524f987ca7f786f2ac52465df79e5b365103f80e7ffeda43b | R | 7,038 | 157 | ---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, echo = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "README-"
)
```
# `powsimR` <br/> Power analysis for bulk and <br/> single cell RNA-seq experiments <img src="vignettes/... |
9ef245e94ea209a63fc8078543677218f7d5ce0a034611328a0e1d1a3e3fbff8 | R | 7,080 | 184 | library(tidyverse)
library(ggsignif)
df<- read.table("7Networks_LH_Default_Par_3_MeanCurv_lh.Schaefer2018_200Parcels_7Networks_order_taxon.tsv", header = TRUE, sep = "\t" )
df$mri_d<-df$X7Networks_LH_Default_Par_3_MeanCurv_lh.Schaefer2018_200Parcels_7Networks_order
new_df <- subset(df, select = c('X2665642',... |
236396c56496fac51af8f232836a753895bba1317d8d715a7b04f7edbf25247f | R | 7,083 | 178 |
#' extract the protein identifier from a fasta header
#'
#' the first set of non-whitespace characters on the row is assumed to be the protein ID.
#' the leading '>' is not included (if present).
#'
#' this regex should be robust for all sorts of input, including those not following official standards.
#' should be ">... |
d0925bee70603c2ae21d18f2758aacd7477a87f3b2fd179c39d899aece5b3078 | R | 7,140 | 165 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
ref2 <- read_csv("processed/reference_sorted.csv", show_col_types = FALSE)
y <- scale(ref2$GrainYield[ref2$Set == "Train"])
yc <- attr(y, "scaled:center")
ys <- attr(y, "scaled:scale")
rm(ref2, y); gc()
ref <- read_csv("processed/reference_gene... |
231cfa29362cf1559f7952a4a92ced7b2e4928723a2f9d1721bec215eff11687 | R | 7,163 | 111 | # Step 4: GUI to count nuclei in a Visium spot
## spotspotcheck
`spotspotcheck` is a graphical user interface (GUI) designed for:
1. Obtaining the nuclei count per Visium spot and saving it to a `.csv` file.
2. Performing quality check on the nuclei segmentations (from [Step2](http://research.libd.org/VistoSeg/ste... |
b909cc47da6c806a4ca9c7d18a6c6cd6807b063c0eebd593fafb5d094f8a41a1 | R | 7,164 | 181 | library(rhdf5)
# Load Python and R functions for basic Wilcoxon Tests, DESeq2, and CocoA-Diff
# and causarray, cinemaot
require(reticulate)
use_condaenv('renv')
path_base = '/home/jinandmaya/'
setwd(paste0(path_base, 'methods'))
source('R_functions.R')
path_base = '/home/jinandmaya/simu_poi/'
setwd(paste0(path_base, '... |
9db72c7c3349b6935ffa44b3fcffb48b7fb4bc64198889fdc3a4a6e89de2595a | R | 7,170 | 155 | library(dplyr)
library(Seurat)
library(ggplot2)
## Style plots
# color palettes
col_sample = c('#370617',
'#e01e37',
'#f6cacc',
'#ffba08',
'#ffa200',
'#d4d700',
'#55a630',
'#8be8d7',
'#2fb5c7',
'#0377a8',
'#002855',
... |
6567df7739c773141956c000bb4c843eeeb5d06abd0ab76197f56df1f15d8c6a | R | 7,184 | 258 | library(sjPlot)
library(broom.mixed) # for tidy()
library(dplyr)
library(emmeans)
library(lme4)
library(lmerTest)
library(ggplot2)
#library(moments)
library(ggeffects)
library(patchwork)
library(forcats)
library(scales)
#df <- read.csv("E:\\work\\q_backup_06_03_2025\\projects\\isttc\\results\\synthetic\\results\... |
d0b366fdd3399149e03de9cbf8626e015afe81fd0a1e0f0a72d7ee1563326791 | R | 7,184 | 237 | ---
title: "Getting Started with the Epoch Package"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{vignette}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
eval = TRUE,
fig.align =... |
c07cb24e1e59074c21047d6730cb494f8aa1aaafb5e065590d2355135cda02ad | R | 7,207 | 206 | ---
title: "Fig 4 HFA/Theta Combined Plot"
output: html_document
date: "2025-02-20"
---
```{r setup, include=FALSE}
## libraries ##
library(tidyverse)
library(ggplot2)
library(lmerTest)
library(doParallel)
library(parallel)
library(foreach)
library(here)
library(fs)
library(lmtest)
library(scales)
library(ggthemr)
lib... |
a22bd8d1f63fed82ed7c1bb9ad5dda9afa0bd772294c498929831a4d8e73ab6b | R | 7,258 | 188 | if(!require(devtools)) install.packages("devtools")
devtools::install_github("kassambara/ggpubr")
setTimeLimit(100000); setSessionTimeLimit(10000)
library(ggplot2)
library("cowplot")
library("gridExtra")
library(ggpubr)
library(plyr)
# load data
data <- read.csv(file="//lexport/iss01.charpier/analyses/stephen.whitma... |
fbb05c8caa10238b13d8a72b8fa6fd5f745de7e7f465cbd628bad12e1f1c1d93 | R | 7,273 | 190 |
# census mode function
#' @importFrom stats density
.dmode <- function(x, breaks="Sturges") {
if (length(x) < 2) return (0);
den <- stats::density(x, kernel=c("gaussian"), na.rm = T)
( den$x[den$y==max(den$y)] )
}
# census estimate_t function
.estimate_t <- function(relative_expr_matrix,
... |
dc81c16494c6f05d4161e0a01898c24065032edec089e21ce5758baead363c10 | R | 7,277 | 198 | #' Runs a comparative scWGCNA analysis
#'
#' This function runs a WGCNA analysis adapted for single cells. Based on single-cell or pseudocell data.
#' @param scWGCNA.data The WGCNA data to use as reference for comparative analysis. Output from run.scWGCNA
#' @param test.list List of Seurat objects. The samples to test... |
20592f2ee0ee6ffc4131a3350e5325a7a2f8fe99dfcad3b2e4c2fc3e74395f74 | R | 7,278 | 158 | ##' Implements a Bayesian PCA missing value estimator. The script
##' is a port of the Matlab version provided by Shigeyuki OBA. See
##' also \url{http://ishiilab.jp/member/oba/tools/BPCAFill.html}.
##' BPCA combines an EM approach for PCA with a Bayesian model. In
##' standard PCA data far from the training set but... |
e12c39c81535ad86d0d1fd98fb80021e04285906cf2f08e81ffd9ce72428d97c | R | 7,288 | 223 | require(parallel)
require(optimParallel)
source("~/Documents/yu_lab/kernels_in_GP/Kick_data/src/margh.fun.R")
source("~/Documents/yu_lab/kernels_in_GP/Kick_data/src/margl.fun.R")
### N.B.: Optimization of the Gaussian and deep kernels should be done using
### the *training* phenotypic data to reduce information leak... |
afc522927730b3d0df04abfe95181c8cabbc9aff14df9c1ac3d57313d2800181 | R | 7,333 | 159 | context("Testing nhood grouping function")
library(miloR)
### Set up a mock data set using simulated data
library(SingleCellExperiment)
library(scran)
library(scater)
library(irlba)
library(MASS)
library(mvtnorm)
set.seed(42)
r.n <- 1000
n.dim <- 50
block1.cells <- 500
# select a set of eigen values for the covarianc... |
5a2fa85d341a520fc1476004122dbefcfc58efdbc46ba9c01c2926935954c06d | R | 7,361 | 179 | library(Seurat)
library(tidyverse)
library(cluster)
#library(factoextra)
library(dendextend)
library(weights)
library(ggpubr)
library(matrixStats)
library(readxl)
base_dir <- "/n/scratch/users/s/sad167/EPN/scRNAseq"
resources_dir <- file.path(base_dir, 'scripts/resources')
source(file.path(resources_dir, "single_cell... |
f1eeffacae76efb2e0d1ee73574dcc2f6d1ddf75dab7faafc8f31beae0863e57 | R | 7,393 | 199 | library(Seurat)
library(SeuratObject)
library(DESeq2)
library(ggplot2)
library(scales)
library(qs)
library(dplyr)
library(zebrafishRNASeq)
#'#######################################################################
# Load method functions ----
#'#######################################################################
pa... |
f0bbb9c88a0f3613561b1a3c194ec2142e1d48c00a0646fd2a3a0e150939c883 | R | 7,399 | 216 | # PowerVsFreq_baseline.r (Seul Ah)
# ----------------------------------------------------
# Plot LFPeffect as a function of frequency bands
# Power vs. 1 variable (freq)
rm(list=ls())
NEAR.OVERRIDE = 2 # [2] good value
ALIGN.OVERRIDE = "" # [""] Other aligns cannot be normalized (w/o writing
# much more c... |
64d3ab9697eb8189b93eb4af93d46e04e79201bb825e7babc84a40056c487419 | R | 7,414 | 178 | #install.packages("R.utils")
#devtools::install_github("mrcieu/gwasglue",force = TRUE)
#BiocManager::install("VariantAnnotation")
#install.packages("devtools")
#devtools::install_github("mrcieu/gwasglue", force = TRUE)
#install.packages("remotes")
#remotes::install_github("MRCIEU/TwoSampleMR")
#引用包
li... |
6b2fcecdc94bd83b5a97cf12c595aeb63491d6d97b5fdde319352145c05b0ab3 | R | 7,418 | 194 | ###############################################################################
# PAKETE LADEN
###############################################################################
library(readxl)
library(lmerTest) # für LMM
library(ggpubr) # für Boxplots
library(glmmTMB) # für Beta- (und andere GLMMs)
# -> Optio... |
9bf724d1f45f03831eb50f8463ce90b6710b2374f6d35cce34997a0f00de5c52 | R | 7,422 | 222 | setwd("~/Documents/yu_lab/kernels_in_GP/Kick_data/")
library(tidyverse)
library(abind)
# For reproducibility:
set.seed(840509)
### Load the hybrid marker matrix ---------------------------------------------
# From Lopez-Cruz et al. (2023)
X <- read_csv("../../genomes2fields_curated/data/data_g2f/GENO.csv", show_co... |
35300c5cb32437a73da2f2d0e6101dab057d2aa8ff2c534e97b0b2feb327dd5b | R | 7,470 | 176 | #' Build a k-nearest neighbour graph
#'
#' This function is borrowed from the old buildKNNGraph function in scran.
#' Instead of returning an igraph object it populates the graph and distance
#' slots in a Milo object. If the input is a SingleCellExperiment object or
#' a matrix then it will return a de novo Milo objec... |
eeed29146d5b2c4b0a76cfe657731ad456aab9828485b6112252d2af2cc17e4d | R | 7,481 | 220 | ---
title: "synapses_by_roi"
output: html_document
date: "2024-05-31"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## R Markdown
This is an R Markdown document. Markdown is a simple formatting syntax for authoring HTML, PDF, and MS Word documents. For more details on using R Markdown see <h... |
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