sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
cce2304e09f0953adc115436cd3325ecb6574fb147e887e518df87dc74d933a5 | Quarto | 3,559 | 73 | # cmd { #kimmdy.cmd }
`cmd`
Functions for starting KIMMDY either from python or the command line.
Other entry points such as `kimmdy-analysis` also live here.
## Functions
| Name | Description |
| --- | --- |
| [entry_point_kimmdy](#kimmdy.cmd.entry_point_kimmdy) | Run KIMMDY from the command line. |
| [get_cmdline... |
c03a829094c66bf746a81f64b7bf83ffe8ca32caa132b56e4d3400fec82a8e7f | Quarto | 3,582 | 98 | # plugins { #kimmdy.plugins }
`plugins`
Plugin base classes and basic instances thereof.
Also discovers and loads KIMMDY plugins.
## Classes
| Name | Description |
| --- | --- |
| [BasicParameterizer](#kimmdy.plugins.BasicParameterizer) | reconstruct base force field state |
| [ReactionPlugin](#kimmdy.plugins.Reac... |
f7fec0bc8a7b07c773072b7e13f3c81c9ca453f17674c77ccd973c22ebcc21fb | Quarto | 3,583 | 103 | # utils { #kimmdy.utils }
`utils`
Utilities for building plugins, shell convenience functions and GROMACS related functions
## Attributes
| Name | Description |
| --- | --- |
| [TopologyAtomAddress](#kimmdy.utils.TopologyAtomAddress) | Address to an atom in the topology. |
## Functions
| Name | Description |
| --... |
6fcecfa3c1916f89b3f51e689fea9d99d6c8ec5cf65f026190b8ee6f80ebfc64 | Quarto | 3,893 | 121 | ---
title: "Contribute"
subtitle: "How to contribute to KIMMDY"
---
## Setup
See [Installation](./install-kimmdy.qmd).
For new plugins, make a separate repo, and add your entrypoint in the
`pyproject.toml` to the `kimmdy.reaction_plugins` entrypoint:
```pyproject.toml
[project.entry-points."kimmdy.reaction_plugin... |
49d2d998cb909a7af4d33df3874a1c4f33900499c60c8b4bb4579c9b2151d78b | Quarto | 4,363 | 83 | # runmanager { #kimmdy.runmanager }
`runmanager`
The Runmanager is the main entry point of the program.
It manages the queue of tasks, communicates with the
rest of the program and keeps track of global state.
## Classes
| Name | Description |
| --- | --- |
| [RunManager](#kimmdy.runmanager.RunManager) | The Runma... |
42b1f7f1f8df044bc90b1fa00e5852a749ea2724c0db650238d7657b028e4a78 | Quarto | 4,926 | 103 | # tasks { #kimmdy.tasks }
`tasks`
The tasks module holds the TaskFiles class which organizes input and
output paths and the Task class for tasks in the runmanager queue.
## Classes
| Name | Description |
| --- | --- |
| [AutoFillDict](#kimmdy.tasks.AutoFillDict) | Dictionary that gets populated by calling get_missi... |
134a4cd4bdff4f935d5c66ee662aedc6964400a4fc35fa08eee7a42fa30049bf | Quarto | 5,534 | 158 | # coordinates { #kimmdy.coordinates }
`coordinates`
coordinate, topology and plumed modification functions
## Classes
| Name | Description |
| --- | --- |
| [AffectedInteractions](#kimmdy.coordinates.AffectedInteractions) | keeping track of affected interactions during the merge process to add the correct helper pa... |
dfe87d9e4e0946496c8725f3710e8e1dc8840e338d193e2cadd449ab719b4582 | Quarto | 5,588 | 206 | ---
title: Get Started
resources:
- './getting-started-files/*'
image: ./img/getting-started-vmd-radicals.png
author: Jannik Buhr
categories:
- user
---
In this tutorial we will be simulating hydrogen atom transfer in a simple ACE/NME-capped Alanine molecule in a box of water.
## Installation
### Prerequisites
... |
5ad1be3dcb0c11349fe72a241ad67274fb3fe7a60f8f7a7c36aa06500c756a18 | Quarto | 6,064 | 241 | ---
title: |
Release Notes: \
Omics Playground v3.5.0
subtitle: "`r readLines('../VERSION')`"
author: "BigOmics Analytics Inc."
date: today
date-format: "MMMM YYYY"
license: "Dual Licensed"
copyright:
holder: BigOmics Analytics
year: 2024
format:
pdf:
documentclass: scrreprt
papersize: a4
fontsi... |
35700000814fa4bf4e6c479ebe364d3717f39b0cec2c8c421710e6683719d9ae | Quarto | 6,429 | 181 | ---
title: Write a Reaction Plugin
description: In this tutorial, you will learn how to create your own reaction plugin in a GitHub repository.
execute:
eval: false
code-fold: true
author: Eric Hartmann
categories:
- developer
---
## Creating a GitHub repository
By creating a GitHub repository, you have version c... |
9f3d7b6b7ea4c1401b74529b4129d6f10ff6ba0f28924baec24819ee43488670 | Quarto | 7,186 | 120 | ---
title: Modify a Topology
resources:
- './modify-topology-files/*'
image: ./img/getting-started-vmd-radicals.png
author: Eric Hartmann
categories:
- user
---
In this tutorial we will check out different ways to process GROMACS topology files with the command-line interface `kimmdy-modify-top`. Use `kimmdy-modif... |
e5b9b784dea7593d0a597d50bc43c73550df704166f288ca1d5a9af7415ccd8f | Quarto | 7,577 | 146 | # tools { #kimmdy.tools }
`tools`
Standalone tools that are complementary to KIMMDY.
## Functions
| Name | Description |
| --- | --- |
| [build_examples](#kimmdy.tools.build_examples) | Build example directories for KIMMDY from integration tests. |
| [edgelist_to_dot_graph](#kimmdy.tools.edgelist_to_dot_graph) | Co... |
cfeeb0dd6f6fc9a617bb2cbd67d9ebcdff275433169a4994a5056f8eb59ac6c0 | Quarto | 7,821 | 167 | # topology.utils { #kimmdy.topology.utils }
`topology.utils`
## Functions
| Name | Description |
| --- | --- |
| [get_is_reactive_predicate_f](#kimmdy.topology.utils.get_is_reactive_predicate_f) | Returns whether a moleculetype name is configured to be recognized as reactive. |
| [get_is_reactive_predicate_from_co... |
f80e2d1c6cbbb1d306effb8c54f3c4942169faf2726fc584c80b32a940450070 | Quarto | 8,416 | 234 | ---
title: Run KIMMDY from Colbuilder fibril
author: Eric Hartmann
image: ./img/colbuilder.png
categories:
- user
---
In this tutorial we will download a collagen fibril from [colbuilder](https://colbuilder.h-its.org/)
and run a KIMMDY simulation on it.
## Preparation
- Download the desired model and the ff parame... |
a33e2922380d618ec0a937e4cb71a567d8331f655bc46b5a3a64b782ec612838 | Quarto | 9,905 | 169 | # kmc { #kimmdy.kmc }
`kmc`
Kinetic Monte Carlo (KMC) classes and functions.
In our system, the reaction rate r = (deterministic) reaction constant k
= stochastic reaction constant c (from gillespie 1977)
= propensity a (from Anderson 2007)
because of the fundamental premise of chemical kinetics
and because we have ... |
031b964cac8876737651cd7a885a13bb9bd7dc58e75bbe81d12e6108c7cb2cca | Quarto | 11,139 | 453 | # topology.atomic { #kimmdy.topology.atomic }
`topology.atomic`
Atomic datatypes for the topology such as Atom, Bond, Angle, Dihedral, etc.
The order of the fields comes from the gromacs topology file format.
See [gromacs manual](https://manual.gromacs.org/current/reference-manual/topologies/topology-file-formats.htm... |
f2d079feec1f58c64ca01e63a35a8e53917fe39755ee165359a47bb4baeb08aa | Quarto | 11,371 | 317 | # parsing { #kimmdy.parsing }
`parsing`
All read_<...> and write_<...> functions.
## Attributes
| Name | Description |
| --- | --- |
| [TopologyDict](#kimmdy.parsing.TopologyDict) | A raw representation of a topology file returned by [](`~kimmdy.parsing.read_top`). |
## Classes
| Name | Description |
| --- | --- ... |
43ca4b041aae08cf4a4aa191755f04c29b6c89c334e370bb4cc60686d72e4a96 | Quarto | 11,787 | 198 | # analysis { #kimmdy.analysis }
`analysis`
Analysis tools for KIMMDY runs.
For command line usage, run `kimmdy-analysis -h`.
## Functions
| Name | Description |
| --- | --- |
| [concat_traj](#kimmdy.analysis.concat_traj) | Find and concatenate trajectories (.xtc files) from a KIMMDY run into one trajectory. |
| [en... |
aae180597e6a8cf7b013233977db6f4a96382fbac913d0e5f5007a82d3ed7229 | Quarto | 14,882 | 249 | # topology.topology { #kimmdy.topology.topology }
`topology.topology`
## Classes
| Name | Description |
| --- | --- |
| [MoleculeType](#kimmdy.topology.topology.MoleculeType) | One moleculetype in the topology |
| [Topology](#kimmdy.topology.topology.Topology) | Smart container for parsed topology data. |
### Mol... |
3a1f48eab9a02ff4dff9ebf884dca4178fb872d3578685163dcd7a022b9e13b1 | Quarto | 15,407 | 202 | ---
title: "Supplementary Material"
date: 2026-04-15
engine: knitr
bibliography: bibliography.json
csl: styles/nature-brackets.csl
css: styles/styles.css
format:
html:
toc: true
code-fold: true
embed-resources: true
execute:
echo: false
warning: false
message: false
crossref:
... |
49574ab153fa374883829f708d474935c4ee45f6a42b41d9ba0a93fdb682654f | Quarto | 16,338 | 337 | # recipe { #kimmdy.recipe }
`recipe`
Contains the Reaction Recipe, RecipeStep and RecipeCollection.
## Classes
| Name | Description |
| --- | --- |
| [Bind](#kimmdy.recipe.Bind) | Change topology to form a bond |
| [BondOperation](#kimmdy.recipe.BondOperation) | Handle a bond operation on the recipe step. |
| [Brea... |
fb07c7fbcf2d7a29530bb48a555c3f8ab237aa91a150a80be559f55630291919 | Quarto | 37,578 | 218 | ---
title: "Predicting Post-Stroke Functional Outcome Using Explainable Machine Learning and Integrated Data"
date: 2026-04-15
author:
- name: Jesper Olsson
orcid: 0009-0000-3920-3874
id: jo
email: jesper.olsson@gu.se
affiliation: Department of Laboratory Medicine, Institute of Biomedicine, Sa... |
ac35ac26d20723e1cc7fb07edea10b364d0839a3dd60c29303bd32b0a1e7f488 | Quarto | 51,080 | 29 | ---
title: Components of KIMMDY
author: Kai Riedmiller
---
Here is an overview of the most important components of KIMMDY, and how they interact with each other:
<div class="mxgraph" style="max-width:100%;border:1px solid transparent;" data-mxgraph="{"highlight":"#0000ff","nav":true,&quo... |
9fefac2965046d6e39894147df3ef32cee41742b1605a93f8e5220a7a5d177d4 | R | 53 | 1 | shiny::runApp("/home/s215v/Workspace/HTGTS/QCReport") |
dc7c6f2eb0bb5b6d7130f30c66a89990e6e37868a3dac747241da1e411975f56 | R | 59 | 1 | ## See .../board.clustering/R/__init.R (for the moment..)
|
f7f72cbcd480dc1a7f91846dbdad06e3cd6102e1c0d8bdd054fab77658fd9f4a | R | 85 | 2 | # Load application support files into testing environment
shinytest2::load_app_env()
|
70c787ab6cce44cf2a406dbf9c20319bfeea4279d585590ba12914d6a49c4830 | R | 87 | 6 | #' @keywords internal
"_PACKAGE"
#' @useDynLib rddm
#' @importFrom Rcpp sourceCpp
NULL |
29eabaf5a65eae4760fd500e7e3bd45ec6867317a8bd4c02cc23677f06b35aa6 | R | 152 | 9 | library(ggplot2)
library(stringr)
library(R.matlab)
library(cowplot)
library(expm)
library(viridis)
library(RColorBrewer)
library(lm.beta)
library(mgcv) |
dae888f1108f5c98c2ffc66a23411daa88e851cbccc01e070525e916248e6a3e | R | 154 | 6 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
shinytest2::test_app() |
3b35d8f7c0c926fbdafe0a90b9d45b948ee4d70266ac2cbedefc97bf296329b2 | R | 208 | 7 | #log <- file(snakemake@log[[1]], open='wt')
#sink(file=log, type='message')
#sink(file=log, type='output')
source("utils/count_add_peakind.R")
count_add_peakind(inputfile = snakemake@input[["count_sort"]])
|
f22519470a5b9ceddff1f40069b034fa259c9bdac77abb3956e85a9938850e5b | R | 221 | 8 | auto_R <- function(x){
library(matrixStats)
m <- nrow(x); n <- ncol(x);
mx = colMeans(x);
stdx = colSds(x);
ax = (x-t(matrix(1, ncol(x), nrow(x))*mx)) / t(matrix(1, ncol(x), nrow(x))*stdx);
return(ax)
} |
a2fde9d17a9bf5436cc18e7d6a4b7398cf789cbbc160a2b3a25b271b5c345fdc | R | 233 | 8 | vip_R_v2 <- function(w, r2) {
k <- nrow(w)
a <- ncol(w)
# library(SuperPCA)
source("workflow/scripts/scNOVA_scripts/script_PLSDA/normc.R")
vip <- sqrt(rowSums((normc(w)^2) %*% diag(r2)) / sum(r2) * nrow(w))
return(vip)
}
|
f2f46533ae53b57cd70291f540b99e627798e460ea9b8d7c50ad0a890ea7e8ba | R | 238 | 5 | data_r <- as.matrix(read.csv("data/testdata_combat_r.csv"))
data_matlab <- as.matrix(read.csv("data/testdata_combat_matlab.csv", head=FALSE))
tol <- 10e-14
similarity <- sum(data_r-data_matlab<tol)/ncol(data_r)/nrow(data_r)*100
similarity |
f81aad1266ebd3dca0ee063316d941b3f3959e5c1434ef97ea4bf04d0278f297 | R | 247 | 7 | # Generated by using Rcpp::compileAttributes() -> do not edit by hand
# Generator token: 10BE3573-1514-4C36-9D1C-5A225CD40393
asMatrix <- function(rp, cp, z, nrows, ncols) {
.Call('_SCP_asMatrix', PACKAGE = 'SCP', rp, cp, z, nrows, ncols)
}
|
acec508b73b63382d7f140a7a0be797119c0e941a7ab0adc343a42b05d0e4ee0 | R | 258 | 14 | setwd('components/app/R')
source('global.R')
source('ui.R')
source('server.R')
shinyApp(
ui = app_ui,
server = app_server,
uiPattern = '.*',
options = list(
launch.browser = TRUE,
host = "0.0.0.0",
port = 3838
)
)
|
7b8aec563958e20a7b93315c8ad86282286a81f05598d47e4b71270ba5ba786a | R | 259 | 14 | setwd('components/app/R')
source('global.R')
source('ui.R')
source('server.R')
shinyApp(
ui = app_ui,
server = app_server,
uiPattern = '.*',
options = list(
launch.browser = FALSE,
host = "0.0.0.0",
port = 3838
)
)
|
ee92bcded41a27e74694f18dd15b82b9f6e070f6d5d08297d022bf1d212e524f | R | 281 | 6 | if (!require("BiocManager", quietly = TRUE)) {
install.packages("BiocManager", repos = "http://cran.us.r-project.org")
}
BiocManager::install("BSgenome.Hsapiens.UCSC.hg38", update = FALSE)
# BiocManager::install("BSgenome.Hsapiens.UCSC.hg19", update = FALSE)
quit(save = "no")
|
d1d321bc9fa388ba3bc3150deb00d106073cabda9ecf6d99f130615ef35fa7e3 | R | 293 | 8 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
if(requireNamespace('spelling', quietly = TRUE))
spelling::spell_check_test(vignettes = TRUE, error = FALSE,
skip_on_cran = TRUE)
|
011e2d80411e6093ab1e59089311f3848aa0a8f703eacee0d0ee2afc1f2f23c6 | R | 294 | 8 | nrow <- 10000
ncol <- 10
data <- matrix(rnorm(nrow*ncol), nrow, ncol)
# Let's introduce a biological effect:
data[,c(1,3,5,7,9)] <- data[,c(1,3,5,7,9)]+3
# Let's introduce a batch effect:
data[,6:10] <- (data[,6:10]+5)*1.5
write.csv(data, quote=FALSE, file="data/testdata.csv", row.names=FALSE) |
8c0c9bc6bd282dd447ab210dbb15fd7fed949bcb703abf6ae94e710e6c9c16cd | R | 328 | 8 | library(scRNAseq)
tasic = TasicBrainData(ensembl = FALSE)
tasic$study_id <- 'tasic'
Matrix::writeMM(Matrix::Matrix(counts(tasic),sparse=T),'tasic_counts.mtx')
write.table(rownames(tasic), 'tasic_genes.csv',row.names = F,col.names = F,quote=F)
write.csv(colData(tasic)[,c('study_id','primary_type')],'tasic_col.csv',quot... |
33598933841d47aaac7a2b64593701a20dcfcd1c519ad50eaae6613f22881066 | R | 332 | 12 | package <- snakemake@input[["tarball"]]
is_package_available <- require(package)
if (!isTRUE(is_package_available)) {
if (!require("BiocManager", quietly = TRUE)) {
install.packages("BiocManager")
}
BiocManager::install("GenomeInfoDbData", update = FALSE)
install.packages(package)
quit(sav... |
6372f757e4e00b6a96d38b07ea717ec45e77744a57e1af473f5bcbc826122458 | R | 333 | 10 | sink(snakemake@log[[1]])
library(data.table)
d <- fread(snakemake@input[["states"]])
e <- fread(snakemake@input[["info"]])
e$bam <- basename(e$bam)
f <- merge(d, e, by = c("sample", "cell"))[class == "WC", .(chrom, start, end, bam)]
write.table(f, file = snakemake@output[[1]], quote = F, row.names = F, col.names = F... |
3a0adebc131e055a160db898e7f44d4f7f5dfc4f58c5c76f01fc55f51330e809 | R | 335 | 15 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
output <- sass::sass(
sass::sass_file(
'scss/main.scss'
),
cache = NULL,
options = sass::sass_options(
output_style = "compressed"
),
output = 'components/app/R/www/sty... |
1f90bc0a11cdb4eb40ef17999c076a7ea4d4f985aee87cd398b0754b655f4fa6 | R | 341 | 7 | log <- file(snakemake@log[[1]], open = "wt")
sink(file = log, type = "message")
sink(file = log, type = "output")
source("workflow/scripts/plotting/sv_consistency_barplot.R")
SVplotting(inputfile = snakemake@input[["sv_calls"]], outputfile.byPOS = snakemake@output[["barplot_bypos"]], outputfile.byVAF = snakemake@outp... |
e9a8fb48e06101a52dd569d93d4091ea1fa7b8675ad6d35918a88ef540a76300 | R | 367 | 8 | args=commandArgs(trailingOnly=TRUE)
NO_table <- read.table(args[1], header=T, sep ='\t', comment.char = "")
GB_matrix <- read.table(args[2], header=T, sep ='\t', comment.char = "")
NO_table_annot <- cbind(NO_table[,1:3], GB_matrix$name, NO_table[,4:ncol(NO_table)])
write.table(NO_table_annot, args[3], row.names = TRU... |
cf67b7d2c1cae630c5332bddca93bc5ab7809d99e63d385e243467420265e6f6 | R | 374 | 13 | #!/usr/bin/Rscript
options(error = traceback)
args <- commandArgs(TRUE)
# add user defined path to load needed libraries
# .libPaths(c(.libPaths(), args[6]))
suppressPackageStartupMessages(library(breakpointR))
breakpointr(
inputfolder = args[1], outputfolder = args[2], configfile = args[3]
# WCregions = arg... |
eed539bee67bfbb388823a4d2e252737ae78482de0d10ecb04854599f7d65771 | R | 382 | 8 | source("/g/korbel2/weber/workspace/mosaicatcher-update/workflow/scripts/plotting/plot-clustering.R")
traceback()
plot.clustering(
inputfile = ".tests/data_CHR17_NEW/RPE-BM510/mosaiclassifier/sv_calls/stringent_filterTRUE.tsv",
bin.bed.filename = "workflow/data/bin_200kb_all.bed",
position.outputfile = "TEST... |
04f0d1b83ddab7cbddd2455b5a85062773a9f1e1eb24801105404f7b4ab3634a | R | 386 | 15 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
library(shiny)
## RUN FROM root folder!
##setwd(pkgload::pkg_path())
setwd("~/Playground/omicsplayground")
source("components/00SourceAll.R",chdir=TRUE) ## global variable
load("data/ex... |
be866ff2bca29e5f245654f8eed7c0e49724043bbed76e7774697fd4bd70bc9f | R | 392 | 21 | library(scTRIPmultiplot)
args <- commandArgs(TRUE)
counts_path <- args[1]
haplo_path <- args[2]
sv_path <- args[3]
chromosome <- args[4]
cell_id <- args[5]
savepath <- args[6]
scTRIPmultiplot::generate_multiplot(
counts_path = counts_path,
haplo_path = haplo_path,
chromosome = chromosome,
cell_id = ... |
a96ab98a5be730b935f3efc783c00a8724ee44dccf8548aa284fa8e99249edf6 | R | 396 | 12 | log <- file(snakemake@log[[1]], open='wt')
sink(file=log, type='message')
sink(file=log, type='output')
#library(data.table)
source("workflow/scripts/arbigent_utils/mosaiclassifier_scripts/mosaiClassifier/makeSVcalls.R")
probs = readRDS(snakemake@input[["probs"]])
probs <- makeCNcall(probs)
write.table(probs, file ... |
fdac6bb1604cbfc44db68e7eb2d3060efeea36ba17a2471a942e45b48bcc5593 | R | 401 | 8 |
options(Ncpus = 8L)
options(timeout = 99999) ## download time.out
options(HTTPUserAgent = sprintf("R/%s R (%s)", getRversion(), paste(getRversion(), R.version["platform"], R.version["arch"], R.version["os"])))
source("https://docs.rstudio.com/rspm/admin/check-user-agent.R")
options(repos = c(REPO_NAME = "https://pac... |
ca1f5411c9a3697d68cda4d3999a5a0a2ce1d0c34d053c9dd74a5682c43a464e | R | 403 | 20 | library(readr)
library(stringr)
library(dplyr)
library(shinyjs)
library(GenomeInfoDb)
library(shiny)
library(BSgenome)
devtools::load_all('breaktools')
source("graphics.R")
options(shiny.maxRequestSize=5*1024^3)
options(shiny.sanitize.errors = TRUE)
# Remove all VennDiagram report logs
for(p in list.files(pattern="^... |
7caee0890313105906196718fa87e945835e9ae15088f8b14c29a9deea62b49d | R | 426 | 13 | library(data.table)
qu = 0.4
print(qu)
d = fread("/g/korbel2/weber/MosaiCatcher_output/segmentation/ERR2940607/100000.txt")
print(d)
print('\n')
print(1:max(70))
print(quantile(1:max(70), qu, type = 1))
stop()
# type = 1 is important to get discrete values!
d = d[, .SD[k == quantile(1:max(k), qu, type = 1)], by=chrom][... |
c206942479935b3924c0e7fc8884b87dc76344925bac1b3c14abac5060935977 | R | 440 | 17 | source("../R/scripts/utils.R")
source("../R/scripts/combat.R")
data <- read.csv("data/testdata.csv")
batch = c(1,1,1,1,1,2,2,2,2,2)
pheno <- rep(0, ncol(data))
pheno[c(1,3,5,7,9)] <- 1
mod=model.matrix(~pheno)
norm <- combat(data, batch=batch, mod=mod)
norm <- norm$dat.combat
write.csv(norm, quote=FALSE, file="data/tes... |
50613b8de2c20f90f176aeb1d4293d469b73b55f9f44e51ba00273d717e39f88 | R | 443 | 13 | library(NMF)
expr=read.table("dir/results/1711_ASDs_gene_counts_normed_by_limma_686genes_matrix.txt",header=T)
print(dim(expr))
expr<-data.frame(expr)
rank<-c(2:10)
nmf.results<- nmf(expr,rank,nrun=30)
save(nmf.results,file="dir/results/1711_686genes_NMF_results_rank_2_10_nrun50_bootstraped_overlapgenes_asd.Rdata")
p... |
4a3a76880b4d3336626749e1c00d9c5d5b605727a6a2b7027f0edf0f530084d5 | R | 452 | 28 | # %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
# Functions and Generics
# %%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%%
#' @importFrom rlang %||%
#' @export
#'
rlang::`%||%`
#' @importFrom dplyr %>%
#' @export
#'
dplyr::`%>%`
#' @importFrom grid un... |
c924db4ee5fcc82f814e2ec356f9ffe219daecbd272ea96ad8db9f6d8add7adb | R | 452 | 12 | log_input = function(input) {
vals = shiny::reactiveValuesToList(input)
vals_filter = !grepl("shinyActionButtonValue", sapply(vals, function(z) {paste(class(z), collapse="|")}))
vals = vals[vals_filter]
vals_pretty = sapply(vals, function(z) {
if(is.data.frame(z) & "name" %in% colnames(z)) z = paste(z$name,... |
d8aa87cf652c45d0af3822f965012cafaf2519aaeedb7910c66132044c054fcd | R | 456 | 25 | # library(pracma)
#' Normaliz the columns of x to a length of 1.
#'
#' @param x n*p matrix
#'
#' @return xn normalized result
#' @export normc
#'
#' @examples
#' #ex1.
#' m <- matrix(1:4,2,2,byrow=TRUE)
#' normc(m)
#' #ex2.
#' n <- matrix(rnorm(100,10,1),10,10)
#' normc(n)
normc <- function(x){
n <- nrow(x)
p <- nc... |
8a001851465bee0814eb0993400b5f16b577377e0fb22b9227fbc918e9571ed9 | R | 458 | 21 | # For calculating Fraction of Reads in Peaks
library(GenomicAlignments)
library(rtracklayer)
args <- commandArgs(trailingOnly = TRUE)
bam_file <- args[1]
peaks_file <- args[2]
output_file <- args[3]
# Read BAM file
reads <- readGAlignments(bam_file)
# Read peaks
peaks <- import(peaks_file)
# Calculate FRIP
reads_in... |
5a448e60e2cbbf3c67cdc443cc959c144f207c2da80aa74cdb27f567add0c3d9 | R | 463 | 24 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
withTooltip <- function(
el,
title,
placement = "bottom",
trigger = NULL,
options = NULL
) {
if (!is.null(trigger)) {}
if (!is.null(options)) {}
htmltools::tagAppendAttri... |
8b2448dbc1cf1770d4c7c2da2a80f2ff80b6fa9725c2bccdab6f3b2da45cf273 | R | 474 | 21 | connect_db <- function(database_path) {
connection <- DBI::dbConnect(RSQLite::SQLite(), dbname = database_path)
return(connection)
}
disconnect_db <- function(connection) {
DBI::dbDisconnect(connection)
}
query_by_email <- function(email, connection) {
query_result <- DBI::dbGetQuery(connection, paste0("
... |
40abe105822eb6d1aaee4051ad6856487c05aaa7d704ae7844852abf92743eb9 | R | 481 | 13 | start_inactivityControl <- function(session, timeout, inactivityCounter) {
reactive({
invalidateLater(timeout / 6 * 1000)
ia_counts <- isolate(inactivityCounter())
shiny::isolate(inactivityCounter(ia_counts + 1)) ## increase counter
## If >30 min inactivity, close session
if (ia_counts == 6) {
... |
366bd5ee003b35f634e44b4a86a290604724c0bedaa85400ab60668be201a5d9 | R | 493 | 20 | library(data.table)
library(dplyr)
library(LDlinkR)
gwas_risk_variants <- fread("$PATH/AppDataProcessing/gwas_risk_variants.csv")
dim(gwas_risk_variants)
head(gwas_risk_variants)
snps <- gwas_risk_variants$RSID
for( snp in snps)
{
print(snp)
proxies <- LDproxy(snp=snp,pop="EUR",r2d="r2",token="sometoken")
... |
6f79b7170482aab8284a006745f0e4012fd52b9effaa4ac5bca6f2b294ba208c | R | 496 | 12 | library(data.table)
source("workflow/scripts/mosaiclassifier_scripts/mosaiClassifier/makeSVcalls.R")
probs <- readRDS(snakemake@input[["probs"]])
llr <- as.numeric(snakemake@wildcards[["llr"]])
bin_size <- as.numeric(snakemake@wildcards[["window"]])
probs <- mosaiClassifierPostProcessing(probs)
probs <- forceBialleli... |
0bd493bbcb417862c3ccdf8ffcfe8df659074c79fd2ad1adc4b603797ab23530 | R | 504 | 15 | Pred_PLS_R <- function(xtrain1, ytrain, xtest1, lv) {
source("workflow/scripts/scNOVA_scripts/script_PLSDA/pls_R_scNOVA.R")
result_pls <- pls_R(xtrain1, ytrain, lv)
B <- matrix(0, lv, lv)
that1 <- matrix(0, 1, lv)
for (l in 1:lv) {
B[l, l] <- result_pls$pls_b[l]
that1[1, l] <- t(as.m... |
75f1ed121c1142a4bab93e2cb63ba7b3a59a181229f77fca4717828ac7dd6eea | R | 517 | 9 |
options(Ncpus = 8L)
options(timeout = 99999) ## download time.out
options(HTTPUserAgent = sprintf("R/%s R (%s)", getRversion(), paste(getRversion(), R.version["platform"], R.version["arch"], R.version["os"])))
source("https://docs.rstudio.com/rspm/admin/check-user-agent.R")
options(repos = c(REPO_NAME = "https://pac... |
5b6a159de2353733780058212a9e6fc1c36a2433f42a5d76cbd42aba87e2a1d0 | R | 532 | 25 | #' Parameter Trasnform functions
#'
#' @description Parameter Transform Functions
#'
#' @param x numeric; vector of parameter values
#' @param lower numeric; parameter lower bound
#' @param upper numeric; parameter upper bound
#'
#' @return transformed parameter vector
#'
#' @name transform
#' @rdname transform
#' ... |
1d1d9606d82838dd6409c551ed2df46a0d797fd48937c736e85b1df8f73f3cd6 | R | 535 | 29 | #run on server as very slow
library(iNEXT)
setwd("/data2/rawdata/thomaz/coffee/reads/merged/woltka_o/R_alpha/")
counts <- read.delim(file = "species.tsv", row.names = 1)
row.names(counts) = counts$Name
counts <- counts[,-276]
print('running Chao1')
chao1 <- ChaoRichness(counts)
write.csv(chao1, "chao1.csv")
pr... |
1a1bc65eb9cabcac6d95094649faa68347ca105111644575b02f1f43cd6eadd4 | R | 544 | 23 | options(error = traceback)
args <- commandArgs(TRUE)
# add user defined path to load needed libraries
.libPaths(c(.libPaths(), args[6]))
# library(StrandPhaseR)
library(devtools)
# source("/g/korbel2/weber/Gits/StrandPhaseR/R/StrandPhase.R")
load_all("/g/korbel2/weber/Gits/StrandPhaseR/")
print("/g/korbel2/weber/Git... |
9e6afd9690733ed5c248ff23d9ab1a94e5dcd1c4aadfffdfdbc80de763d2b0b1 | R | 553 | 16 | output$aboutOutput <- renderUI({
#get the about file from google drive and then download it
aboutFile <- drive_get(id = "1uuE9SF805HDqcCTUQCZhGJZ2IjOXlMfVaAb6B5X_2Qo")
data <- drive_download(aboutFile, path = "about.html", overwrite = T, verbose = F)
#read the lines from the file and format them
file... |
5a0902b2020eed0e4f7691bb322355673415b6b72b2aa8e8ce3c711a8d6e033e | R | 563 | 17 | start_time <- Sys.time()
library(NMF)
library(data.table)
expr=read.table("dir/results/1711_ASDs_gene_counts_normed_by_limma_686genes_matrix.txt",header=T)
print(dim(expr))
expr<-data.frame(expr)
rank=3
nmf.results<- nmf(expr,rank,nrun=300)
nmf.fit<-fit(nmf.results)
w <- basis(nmf.results)
h <- coef(nmf.results)
featur... |
258bee69c963d96b2cc852f142f9e91272ae845dc7050169ceebe6d4c1530010 | R | 600 | 22 | # For filtering peaks based on quality metrics
library(rtracklayer)
args <- commandArgs(trailingOnly = TRUE)
peaks_file <- args[1]
frip_file <- args[2]
output_file <- args[3]
min_frip <- as.numeric(args[4])
min_score <- as.numeric(args[5])
# Read FRIP score
frip <- as.numeric(readLines(frip_file)[1])
# Filter peaks ... |
55876a9d82f8311e09756359ac1f529f7d8e1d9d9a919308f5e94a6ba5b11814 | R | 606 | 23 | options(scipen = 999) # turn-off scientific notation like 1e+48
library(ggplot2)
theme_set(theme_bw()) # pre-set the bw theme.
data("midwest", package = "ggplot2")
# midwest <- read.csv("http://goo.gl/G1K41K") # bkup data source
print(midwest)
# Scatterplot
gg <- ggplot(midwest, aes(x = area, y = poptotal)) +
geom... |
6d252c8d835a2ec3552e4dac8732c7a131167da5e46375a7c91c71859845c374 | R | 615 | 16 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
record_UPGRADE <- function(reportName = "UPGRADE_button") {
if (reportName %in% names(UPGRADE_LOGGER$log)) {
UPGRADE_LOGGER$log[[reportName]] <- UPGRADE_LOGGER$log[[reportName]] + 1... |
caca71970bd3b42a7ff331f6396d88a0f96f5d86f8a3e16891260edbe9ea15d0 | R | 632 | 14 | library(devtools)
load_all("/g/korbel2/weber/Gits/Rsamtools/")
load_all("/g/korbel2/weber/Gits/GenomicAlignments/")
load_all("/g/korbel2/weber/Gits/StrandPhaseR/")
bamfile <- "/g/korbel2/weber/MosaiCatcher_files/tmp/h/RPE1WTPE20401.sort.mdup.bam"
bamindex <- "/g/korbel2/weber/MosaiCatcher_files/tmp/h/RPE1WTPE20401.so... |
46c4085400bf11f5a214a82dc49cb3b60f767445630ca7a2f28d3a73d502208c | R | 646 | 16 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
record_plot_download <- function(plotName) {
if (plotName %in% names(PLOT_DOWNLOAD_LOGGER$log)) {
PLOT_DOWNLOAD_LOGGER$log[[plotName]] <- PLOT_DOWNLOAD_LOGGER$log[[plotName]] + 1
... |
99e896bdab2e2030ba8ba4982d9eaf6921130bd9204136fe7143b7a5f07d9455 | R | 661 | 21 | log <- file(snakemake@log[[1]], open = "wt")
sink(file = log, type = "message")
sink(file = log, type = "output")
source("workflow/scripts/plotting/plot-clustering.R")
plot.clustering(
inputfile = snakemake@input[["sv_calls"]],
bin.bed.filename = snakemake@input[["binbed"]],
position.outputfile = snakemake... |
c037c4c97e8dd0d103cfe1941d20c30ec1a7a331113b6a0852ac879de946ee2d | R | 672 | 31 | ---
title: "Agepredictor_neurodev"
output: html_document
date: "2025-01-26"
---
```{r}
library(caret)
library(Seurat)
library(ggplot2)
library(ggpubr)
```
```{r}
load("code/celltypeinvariantmodels_19082024.Rdata")
load("code/common_genes_allfetalorganoid.Rdata")
fetaldata=readRDS("data/Wang2024_humanfetal.rds")
```
... |
bc7abd829c07b7393eb86307a10c48eefa9edf0a8d486dd975964cea2dcaeff5 | R | 674 | 22 | #
plot_adjust <- function(plots){
library(gridExtra)
# Your code, but using p1 and p2, not the plots with adjusted margins
gl <- lapply(plots, ggplotGrob)
widths <- do.call(grid::unit.pmax, lapply(gl, function (x) x$widths)) #"[[", "widths"))
heights <- do.call(grid::unit.pmax, lapply(gl, function (x) x$heig... |
38d29275195b6a373cbb2c91d157a96c54e0f7489841f82da7846ede3d2dfb43 | R | 676 | 16 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
record_report_download <- function(reportName) {
if (reportName %in% names(REPORT_DOWNLOAD_LOGGER$log)) {
REPORT_DOWNLOAD_LOGGER$log[[reportName]] <- REPORT_DOWNLOAD_LOGGER$log[[rep... |
fd499df72638a631ec763eca72560274805c4d13cbae312d73c0a7b2412d91f7 | R | 685 | 13 | log <- file(snakemake@log[[1]], open = "wt")
sink(file = log, type = "message")
sink(file = log, type = "output")
system("LC_MEASUREMENT=C")
source("workflow/scripts/haplotagging_scripts/haplotagTable.R")
paired_end <- sub("\n", "", readChar(snakemake@input[["paired_end"]], file.info(snakemake@input[["paired_end"]])... |
82195dacb31d05360a2b1d0bab3052eedfc206a6d4870c7a969c110aa96d9765 | R | 686 | 30 | ## For normalizing the raw data matrix from CAMERA
rm(list=ls())
library(preprocessCore)
library(ggplot2)
library(reshape2)
setwd("D://work//skoltech//lipid//writing//GitHub//data")
## for milk ##
DATA <- read.csv("milk_FA.rawdata.csv", header= TRUE, row.names = 1)
data <- normalize.quantiles(as.mat... |
614715c9e4067af7fc2dedde19ab4ca449087e6310caffa610bb04bf71c97d31 | R | 693 | 19 | library(ggplot2)
library(reshape2)
args = commandArgs(trailingOnly=TRUE)
x = read.table(args[1], header=T)
x$gc = x$gcsum / (nrow(x)-1)
x$fractionSample = x$fractionSample * 100
x$fractionReference = x$fractionReference * 100
df = melt(x[,c("gc","fractionSample","fractionReference")], id.vars=c("gc"))
# Whole genome
... |
30490da4bb0d4c0bee754439ded7fb2b9472668674ae045d91e4f5a4b4b062ef | R | 710 | 27 | conpred_R <- function(b,w,p,q,lv){
mq <- nrow(q); nq <- ncol(q);
mw <- nrow(w); nw <- ncol(w);
if (nw != lv){
if (lv > nw){
cat(paste0('Original model has a maximum of ', nw,' LVs Calculating vectors for ', nw, ' LVs only'))
lv <- nw;
} else {
w <- w[,1:lv];
q <- q[,1:lv];
p <- p[,1:lv];
b <- b... |
a94926188a32efd14a6ecc11008175e3a60dc50568ff00119de2d9137339517e | R | 712 | 30 | dist_matrix = function(x, by.row = FALSE) {
x = as.matrix(x);
if (by.row == FALSE) {
x = t(x)
}
m = matrix(nrow=nrow(x), ncol=nrow(x));
diag(m) = 0;
colnames(m) = rownames(x);
rownames(m) = rownames(x);
# operates by rows.
for (i in 1:(nrow(x)-1)) {
me... |
b7c0ddceb713ce1f626624dd85ebf6df789b552e002a0b6f0b65ab6d28fd228f | R | 722 | 32 | rm(list = ls())
library(harmony)
library(Seurat)
library(data.table)
set.seed(1234)
# load data
setwd("res_spatialglue")
latent <- read.csv("latent_spatialglue.csv", row.names = 1)
meta <- read.csv("meta.csv", row.names = 1)
latent <- as.matrix(latent)
obj <- CreateSeuratObject(counts = t(latent*0), meta.data = meta... |
c63d7961ae31222f7cb972943dce9322e906cb2eef32b0ee07bfdc672a063747 | R | 734 | 32 | rm(list = ls())
library(harmony)
library(Seurat)
library(data.table)
set.seed(1234)
# load data
setwd("res_spagcn_latent")
latent <- read.csv("res_spagcn_sharednet.csv", row.names = 1)
meta <- read.csv("meta.csv", row.names = 1)
latent <- as.matrix(latent)
obj <- CreateSeuratObject(counts = t(latent*0), meta.data = ... |
17b09e6dd4834f1062272bb3fe51bc54fe176bd193b292ca785ef71ece639d59 | R | 736 | 14 | args=commandArgs(trailingOnly=TRUE)
Deeptool_result <- read.table(args[1], header=TRUE, sep ='\t', comment.char = "")
Deeptool_result_new <- Deeptool_result[,4:ncol(Deeptool_result)]
Ref_bed <- read.table(args[2], header=F, sep ='\t', comment.char = "")
if (ncol(Deeptool_result)==4){Deeptool_result_new <- as.matrix(D... |
7ea6d0d6b7024e57bcaded5c55c2c8ba3b7b65db5f7852d4d2c871159846ffa2 | R | 740 | 21 | log <- file(snakemake@log[[1]], open = "wt")
sink(file = log, type = "message")
sink(file = log, type = "output")
source("workflow/scripts/mosaiclassifier_scripts/haplotagProbs.R")
haplotagCounts <- fread(snakemake@input[["haplotag_table"]])
probs <- readRDS(snakemake@input[["sv_probs_table"]])
# FIXME : tmp solutio... |
a6580ff67bfd468e59353031e4297300d92ef236993867a31e6ce3798eac9bd6 | R | 752 | 27 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
library(shiny)
## RUN FROM root folder!
setwd(pkgload::pkg_path())
source("shiny/global.R") ## global variable
source("R/00Headers.R") ## global variable
load("data/example-data.pgx",v... |
f9f12f6b36551c7eba7fda86e6368192106ae1edf5b082779d3f5489aea22047 | R | 760 | 32 | rm(list = ls())
library(harmony)
library(Seurat)
library(data.table)
set.seed(1234)
# load data
setwd("run_stagate_harmony")
latent <- read.csv("latent_stagate_sharednet.csv", row.names = 1)
meta <- read.csv("meta_stagate_sharednet.csv", row.names = 1)
latent <- as.matrix(latent)
obj <- CreateSeuratObject(counts = t... |
c25d569fad88c2f011b182ccbbc5a1a1b05da40940e8081d8698913ffcc46a4d | R | 785 | 28 | # QQ plots
## load packages
library(ggplot2)
library(ggrepel)
library(CMplot)
data <- $path_to_TWAS
setwd("/Users/leofanfever/Documents/Biostatistics/TIGAR/Thesis/Manuscript/Figures/Supplementary/QQplots/")
columns <- c("p_DPR", "p_EN", "p_FUSION", "p_ACAT")
for (i in 1:4) {
p_values <- as.numeric(na.omit(data[[co... |
be4214165a6d780eaed3da328896ce105149a823bbe4a4d072dfc28698b03a50 | R | 840 | 34 | AD_DPR <- $path_to_TWAS_DPR
AD_EN <- $path_to_TWAS_EN
AD_FUSION <- $path_to_TWAS_FUSION
AD_DPR <- AD_DPR[,c(1:5,10)]
AD_EN <- AD_EN[,c(1:5,10)]
AD_FUSION <- AD_FUSION[,c(1:5,10)]
colnames(AD_DPR)[6] <- "p_DPR"
colnames(AD_EN)[6] <- "p_EN"
colnames(AD_FUSION)[6] <- "p_FUSION"
library(dplyr)
result <- AD_DPR %>%
ful... |
da7de85865160a143277fd8ef3430fde1334bfc84b5bad5c80d559bf0eb79ab6 | R | 844 | 22 | # log <- file(snakemake@log[[1]], open = "wt")
# sink(file = log, type = "message")
# sink(file = log, type = "output")
args <- commandArgs(trailingOnly = T)
source("utils/haplotagProbs.R")
haplotagCounts <- fread(args[1])
probs <- readRDS(args[2])
# FIXME : tmp solution to fix error : Segments must covered all bins... |
d443a27e71df82ff2c33305d7fade8aa1ace9fc92b99aae34234c2cd41016fee | R | 865 | 39 | ## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
MODULE.epigenomics <- list(
module_menu = function() {
c(
ideograms = "Beta Ideograms"
)
},
module_ui = function() {
list(
bigdash::bigTabItem(
"ideograms-t... |
a741a3f83e5bdf7086efe5f24d55e727f611c77ad8fdd4d50e7090345d2ed72a | R | 884 | 17 | # sink(snakemake@log[[1]])
library(data.table)
d = fread("/g/korbel2/weber/MosaiCatcher_output/Mosaicatcher_output_HGSVC_correct/segmentation/HTN7CAFXY_HG02492x02_19s003809-1-1/Selection_initial_strand_state")
print(d)
print(unique(d$cell))
print(unique(d$sample))
e = fread("/g/korbel2/weber/MosaiCatcher_output/Mosaica... |
d4edec32e223f2aa0b51032834b1ef3025c0269155db70e864ef1e9e10f47c17 | R | 889 | 28 | library(scRNAseq)
my_data <- list(
baron = BaronPancreasData(),
lawlor = LawlorPancreasData(),
seger = SegerstolpePancreasData(),
muraro = MuraroPancreasData()
)
rownames(my_data$muraro) <- rowData(my_data$muraro)$symbol
my_data$muraro <- my_data$muraro[!duplicated(rownames(my_data$muraro)),]
library(org.Hs.e... |
100d9645e47ce845884038c1a56fe65e9394f146c1b11648cba484bc3fec9ac1 | R | 895 | 25 | library(PubChemR)
library(RefMet)
library(tidyverse)
res <- get_aids(urmets, namespace = "name")
a <- AIDs(res)
setdiff(urmets, unique(a$NAME))
write.csv(data.frame(NAME = urmets %>%
str_remove("Paraxanthine / ") %>%
str_replace_all(pattern = "`|′|’", replacement = "'"... |
d0c7c0e9ce48dc7d243709588ed9af83833f2e82ecd12cc7e3506ec379402e6c | R | 910 | 35 | ##
## Initialize (prepopulate) AnnotationHub cache with main species
##
##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
if(1) {
if(!require("org.Hs.eg.db")) BiocManager::install("org.Hs.eg.db")
if(!require("org.Mm.eg.db")) BiocManag... |
171e3ec161f7c31e53a59df4871fe29e7ea40bdcbe2edfa78717020a0c2bb725 | R | 960 | 25 | rm(list=setdiff(ls(),c('params','grp')))
basedir <- params$basedir
setwd(basedir)
savedir <- paste(params$opdir,'G20vsNTG/',sep='')
tp <- c(1,3,6)
tf = 0.5
load(paste(savedir,'NTGSyntimeconstantsTF',tf,'.RData',sep=''))
c.train.NTG <- c.train.Grp
r.NTG <- r.Grp
load(paste(savedir,'G20SyntimeconstantsTF',tf,'.RData',s... |
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