sha256 stringlengths 64 64 | language stringclasses 27
values | size int32 1 491k | lines int32 1 21.8k | content stringlengths 1 200k |
|---|---|---|---|---|
f40df4027100d6d6d927193ccadabb571efcb73d12883dd125029cb24c162f54 | R | 28,704 | 819 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
##' Clustering board server module
##'
##' .. content for \details{} ..
##' @title
##' @param id
##' @param pgx
##' @return
##' @author kwee
ClusteringBoard <- function(id, pgx, labeltype... |
feb4caa118acfd2ae96b175339c136005a59ff508af41876fb0d9e636d500bb8 | R | 28,936 | 661 | ---
title: "Analyse_Exome_Variants_Tiers1_2_2plus"
author: "Christelle Tesson - christelle.tesson@icm-institute.org"
date: "`r format(Sys.time(), '%B, %Y')`"
output: html_document
---
# Packages R utilisé pour l'analyse
* Les versions sont indiquées dans la partie SessionInfo().
```{r, pakage_loading, mes... |
4a55bf18310053a077aab8ca705677debf2c9fbadb25b9c35ea0a4440161580f | R | 29,067 | 746 | ---
title: "Comparison of In Vitro and In Vivo GPC4s"
author: "John Mariani"
date: "10/30/2025"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```
```{r, echo = TRUE, message=FALSE, warning=FALSE}
librar... |
22a09a260ed859edd31946c8878d2cfb513fe4319348d6f5d8c36b93be466b75 | R | 30,147 | 589 | Variant_anno_Exome_V9_T1 = function(){
#load library
library(xlsx)
library(myvariant)
library(plyr)
library(stringr)
library(rtracklayer)
library(kableExtra)
library(dplyr)
library(tidyverse)
#load file
Gene_Panel <- read.csv(file = "File/Gene_Panel.csv", header = TRUE, sep = ",")... |
595d43621007af2c1564f79cb637097b869c54549e7ec4cb691dbe992f64d614 | R | 30,284 | 677 | # simulations using Science paper's data for data-driven sims
# makes figures showing individual observations as examples
# this R code is saved in: /gpfs/gsfs8/users/loewingergc/photometry_fglmm/code
library(lme4) ## mixed models
library(refund) ## fpca.face
library(dplyr) ## organize lapply results
library(progress)... |
2d93c99ce747b49d8eaddca672655ea559106d6ed90d20907519688556fbeb5c | R | 30,529 | 559 | #!/usr/bin/env Rscript
library( colorRamps )
library( RColorBrewer )
library( ggplot2 )
library( optparse )
# TAB file columns indices
C_I_PRIMARY_POS = 1
C_I_PRIMARY_REF = 2
C_I_PRIMARY_GT = 3
C_I_PRIMARY_DEPTH = 4
C_I_SECONDARY_POS = 5
C_I_SECONDARY_REF = 6
C_I_SECONDARY_GT = 7
C_I_SECONDARY_DEPTH = 8
#C_STR_DETAI... |
bf7c81c99eca5df2182f226136007f42e23510ca6204be7d35c9d985dc3be1dc | R | 30,731 | 586 | # DATA ANALYSIS
# EVOKED NEURAL ACTIVITY IN CHORUS FROGS REVEALS CANDIDATE MECHANISMS OF ENAHANCED SPECIES RECOGNITION
##### PART 1: FUNCTIONAL SPECIALIZATION OF BRAIN REGIONS
##### Load packages
library(readxl)
library(MASS)
library(lme4)
library(ggplot2)
library(emmeans)
#### Import data
area_data <- read_excel("R... |
6e055424469753660b9730bcdb109b76c40b772f7c833861938ad4d02473abb0 | R | 31,696 | 718 | # FLMM Explanation Inset for explanation
# Final Version
library(data.table)
library(dplyr)
library(parallel)
library(lme4)
library(mgcv)
# fGLMM code
source("/Users/loewingergc/Desktop/NIMH Research/Photometry/code/existing_funs.R") # for time align functions
source("~/Desktop/NIMH Research/git_repos/fast_univ_fda/co... |
a4e2c51e88956c8ee84666c6fb3351ed392d2ac0e77dfe0c1faa4f847c9a8083 | R | 32,173 | 1,074 | ---
title: "BCR analysis - IgLON5 - donor D1"
author: "Mathilde Foglierini"
output: html_document
date: "`r format(Sys.time(), '%a %d %B %Y %X')`"
---
# Analysis script for BCR repertoire study in anti-IgLON5 disease
Publication: "Structural basis for antibody-mediated IgLON5 receptor clustering and endocytosis in... |
7977b5be775db4b6c475abed69936bde84fbfc3f4f30c3d949f8449d229a710a | R | 32,536 | 1,004 | # A modied version of ggsankey(https://github.com/davidsjoberg/ggsankey)
utils::globalVariables(c(".", ".data", "x", "node", "next_node", "next_x", "..r"))
# importFrom(ggplot2, "%+replace%")
#' @importFrom ggplot2 %+replace%
# ** Support functions ----------
prepare_params <- function(...) {
# Prepare aesthics for... |
4e04656a8ae735e93e05ee32a92070198fd3a3ffac71aa421ef564d173cabff8 | R | 32,616 | 851 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
upload_table_preview_counts_ui <- function(id) {
ns <- shiny::NS(id)
uiOutput(ns("table_counts"), fill = TRUE)
}
upload_table_preview_counts_server <- function(id,
... |
834ab3642fa61adc4e37196c10fcdef6b2e68cf5b00be96e7747b2b0ffd2ef77 | R | 32,641 | 718 | devtools::load_all('breaktools/')
library(dplyr)
library(shiny)
library(shinyjs)
library(readr)
library(GenomeInfoDb)
library(BSgenome)
library(Gviz)
library(stringr)
library(ggplot2)
library(forcats)
source("logs.R")
source("graphics.R")
download_link = function(data, file) {
if(!is.null(data) && nrow(data)>0) {
... |
6e04d2d551f51a3ade94b03a279052098aa4b1ad61cbf66d5d9d9aa331918041 | R | 34,131 | 795 | # Lick aligned Experiment 1
# Final Version
library(data.table)
library(dplyr)
library(parallel)
library(lme4)
library(mgcv)
# fGLMM code
source("/Users/loewingergc/Desktop/NIMH Research/Photometry/code/existing_funs.R") # for time align functions
source("~/Desktop/NIMH Research/git_repos/fast_univ_fda/code/fui.R") #... |
7ff56559d27a94e3ff28228ea1790c2468a8c3d6fae05381c69ce93de4d10fa4 | R | 34,517 | 942 | #' This function prepares the SCP Python environment by installing the required dependencies and setting up the environment.
#'
#' @param miniconda_repo Repositories for miniconda. Default is \code{https://repo.anaconda.com/miniconda}
#' @param force Whether to force a new environment to be created. If \code{TRUE}, th... |
b2f2f9896c1b04d595ca174b17b2916059ac438a9b6dea6be20939360f8e1291 | R | 34,783 | 591 | set.seed(1)
##################GSN part
###500 epoch
#read in the data & ###calculate the within-person vs. between-person correlation
setwd("./GSNResult/500/cor_output_CSV/")
require(magic)
##construct diagnal matrix
dia_matrix<-matrix(T, 4, 4)
dia_matrix_block<-adiag(dia_matrix,dia_matrix,dia_matrix,dia_matrix,dia_ma... |
ee4b7a139c10eff25e1454e613b92e5d81cbfd8a7982a4ff45bfdfa9a63bcb45 | R | 36,593 | 610 | set.seed(1)
##################EIB part
###500 epoch
#read in the data & ###calculate the within-P vs. between-P correlation
setwd("./EIBResult/500/cor_output_CSV/")
require(magic)
##construct diagnal matrix
dia_matrix<-matrix(T, 4, 4) ## 4 images for training
dia_matrix_block<-adiag(dia_matrix,dia_matrix,dia_matrix,di... |
aadef9895f5711821b4d907a5111205d54564a0cd13cb2e28ac76e715b94d845 | R | 36,910 | 833 | ---
title: "Differential NicheNet Analysis of in vivo and in vitro hGPCs"
author: "John Mariani"
date: "3/6/2023"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```
## Load in Libraries
```{r}
library... |
b3400f1c3279a9e69a886f6db967a4aa5db74d3c7e1eb64db7ce30f03d0e33f1 | R | 37,963 | 1,020 | library(xlsx)
#COGNITION----
measurement_type <- "cognition"
df_long_stats_input <- df_long_cog
#Exp 1 Baseline
df_long_stats_input %>%
filter(Visit == "Baseline") %>%
mutate(Coffee_Type = factor(Coffee_Type, levels = c("Coffee", "NCD"))) %>%
group_by(name) %>%
reframe(
lm(value ~ Coffee_Type, data = ... |
4bdf0a291afec7217e09df016384128a855bdf2ac4ded5e210c091c687124044 | R | 38,186 | 787 | # StemID
NULL
#' RunKNNPredict
#'
#' This function performs KNN prediction to annotate cell types based on reference scRNA-seq or bulk RNA-seq data.
#'
#' @param srt_query An object of class Seurat to be annotated with cell types.
#' @param srt_ref An object of class Seurat storing the reference cells.
#' @param bulk_... |
ed458cd6246e973269239e27d75c09405f1a230c3100c27953bc71f3b3c39d4a | R | 39,668 | 811 | # scArches
NULL
#' Single-cell reference mapping with KNN method
#'
#' This function performs single-cell reference mapping using the K-nearest neighbor (KNN) method. It takes two single-cell datasets as input: srt_query and srt_ref. The function maps cells from the srt_query dataset to the srt_ref dataset based on th... |
71601a73cfa8381744bb9b9dc58d88365fd205e681a52088b37b344d663e43f0 | R | 40,318 | 1,108 | ---
title: "Figure 3F-J: IT Subclass PC Gradients in Opossum and Mouse"
output:
---
```{r setup, include=FALSE}
# This sets the project root based on the repo structure.
# If you move this file, you may to set the root manually to find config.R
knitr::opts_knit$set(root.dir = dirname(dirname(rstudioapi::getSourceEdito... |
2bbda4e7c463217ab20ce20a9ab1cd24fa03f981a16c8781bb7ab68cb5eebde0 | R | 40,692 | 1,121 | ---
title: "Analysis of Human Cells out of Shiverer Chimeras"
author: "John Mariani"
date: "12/6/2022"
output: github_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
knitr::opts_knit$set(root.dir = rprojroot::find_rstudio_root_file())
```
```{r, echo = TRUE, message=FALSE, warning=FALSE... |
7bb8d6f2eedae9842d64ba83e336acac14f355603639dad9381316e242f7167d | R | 44,473 | 903 | # written by Gabe Loewinger 4/4/23
# simulations using Science paper's data for data-driven sims
# this R code is saved in: /gpfs/gsfs8/users/loewingergc/photometry_fglmm/code
# reward period lengthened
list.of.packages <- c("Rfast")
new.packages <- list.of.packages[!(list.of.packages %in% installed.packages()[,"Packag... |
79634235ce8f3aaa012add57f4d1ec9a3cb57aec2577bd617f966f21ce7a0202 | R | 46,248 | 1,435 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
AuthenticationUI <- function(id) {
ns <- shiny::NS(id) ## namespace
}
NoAuthenticationModule <- function(id,
show_modal = TRUE,
... |
8540ef5cf62af1a3271e93208bdb0241f9fc30e0a7efcaf7955ddd39d7670e77 | R | 47,194 | 1,278 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
## Wrap an editor modal body with a top "Reset to defaults" button and a
## namespaced container div. The button id and container id are placed in
## the *parent* namespace because the edi... |
f5229386f41e57e04f708b11923cd902d58095f38bf91c5651bfd55385422b37 | R | 47,222 | 1,391 | library(ggplot2)
library(heemod)
pacman::p_load(data.table, dplyr)
par_mod <- define_parameters(
c_3rdOP = 1165,
c_4thOP = 1341,
c_5thOP = 1686,
c_3rdIP = 405,
c_4thIP = 405,
c_5thIP = 572,
c_AD = 12.5,
c_COM = 25,
c_AUG = 109,
c_PSY = 12600,
c_rTMS1 = 60000,
c_rTMS2 = 30000,
c_ECT1 = 77760,
... |
f6fcad57373f2616fe8d3cb293408041c9458df8ee9bcba357c278ba1e9c7e45 | R | 47,539 | 1,326 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
loading_table_datasets_ui <- function(
id,
title,
info.text,
caption,
height,
width
) {
ns <- shiny::NS(id)
## Datatype filter (always present)
## Metadata filters are r... |
e331214c677aca9925bf21823352066faa97ad7656340c3f4a27f2854548a6b7 | R | 49,487 | 753 | TAMPOR <- function (dat, traits, noGIS = FALSE, useAllNonGIS = FALSE, batchPrefixInSampleNames = FALSE, GISchannels = "GIS", iterations = 250, skipMDS = FALSE, sampleMedianRows = "ALL", fractionNAmax = 0.500,
samplesToIgnore = FALSE, meanOrMedian = "median", removeGISafter = FALSE, minimumBatchSize = 5, parallelThr... |
d1ff0fb9689f865453c60545fb1392de0ccdcfede567cd46a87825ae8cfbfcb6 | R | 49,831 | 1,381 | ## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
UploadBoard <- function(id,
pgx_dir,
pgx,
auth,
reload_pgxdir,
load_upload... |
ebc97eb293043fa4de0f6ec873492e211991e1f8f3c4c17dba04f4c60584d769 | R | 51,376 | 1,518 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
#' The main application Server-side logic
#'
#' @param input,output,session Internal parameters for {shiny}.
#' DO NOT REMOVE.
#' @export
app_server <- function(input, output, session... |
cde95f5d3497c65af05dc715c17d404e5195b95a3ba376d454975e3b279e2095 | R | 51,966 | 1,135 | ---
title: "Transcriptomic Analyses"
output:
pdf_document: default
html_notebook: default
html_document:
df_print: paged
---
Transcriptomic Analyses for Deng et al. 2026
This R notebook contains the code used for performing differential expression analysis as well as for creating the figures and ... |
32ee1858cd2b4b013142c0b11a3a149c5fb5d71fa7dbb86a3465dcfe4518d578 | R | 52,899 | 1,375 | ## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
upload_module_normalization_ui <- function(id, height = "100%") {
ns <- shiny::NS(id)
uiOutput(ns("normalization"), fill = TRUE)
}
upload_module_normalization_server <- function(
id,
r... |
5138101bfd67f4ca9545a7b30fa995a1ae487ec46b377d671a8ca13efb2e0e45 | R | 52,948 | 1,188 | ---
title: "Proteomic Analyses"
output:
pdf_document: default
html_notebook: default
html_document:
df_print: paged
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(warning = FALSE, message = FALSE)
```
Proteomic Analyses for Deng et al. 2024
This R notebook contains the code used for... |
9f2209bc94d70bc37600aec7126047af22dab9b3ada3c52be2db9d9b44461127 | R | 54,801 | 1,295 | ---
title: "Serum ferritin and delirium risk: Integrative genomic analysis identifies locus-specific signals at 19q13"
subtitle: "Ferritin and Delirium: A genetic dissection"
date: "2026-01-02"
output:
html_document:
toc: true
toc_depth: 3
toc_float: true
number_sections: true
code_foldi... |
8c235bf65a0d580ac483de2daf37d074697ec90d081b5372c1eb2e9e8f2e4bb7 | R | 56,276 | 1,573 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
#' Parse label_features input into feature names
#'
#' Universal parser: splits on newlines first, then for each line
#' tries exact match against known names. If no exact match, tries
#'... |
ab6749c3a514a101ab0efbc9dffad4ecf11b768fc154d70bd3fcad61d5fc6fce | R | 56,338 | 1,107 | PCA_YM_fviz <- function(Data,
color = c("#FF3300", "#660099", "#FFCC00", "#99CC00", "#0066CC", "#FF6600"),
legend_position = "none",
fig_width = 24,
fig_height = 20,
components = c(1, 2),
... |
bfc052b648401e98507f04cca697968e8001f5a18596ed50f3c3bb50b8ef6f9e | R | 60,457 | 1,552 | ## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
upload_module_computepgx_ui <- function(id) {
ns <- shiny::NS(id)
shiny::uiOutput(ns("UI"), fill = TRUE)
}
upload_module_computepgx_server <- function(
id,
countsRT,
countsX,
norm_m... |
c5b5aae96d2162d13a307d4de9165a05bc18afbebe9c74f096f8f7565b41e38b | R | 67,164 | 1,485 | #' Fast Univariate Inference for Longitudinal Functional Models
#'
#' Fit a longitudinal function-on-scalar regression for longitudinal
#' functional outcomes and scalar predictors using the Fast Univariate
#' Inference (FUI) approach (Cui et al. 2022).
#'
#' The FUI approach comprises of three steps:
#' 1. At each loc... |
c1be053ebef1579a1ea76731a87f36f720c22365fd60f8b05109d0a6ddd0439c | R | 67,551 | 1,957 | ##
## This file is part of the Omics Playground project.
## Copyright (c) 2018-2026 BigOmics Analytics SA. All rights reserved.
##
## just to list functions in this file
viz.ClusterMarkers <- function(pgx) {}
viz.PhenoMaps <- function(pgx) {}
viz.PhenoStats <- function(pgx) {}
viz.PhenoStatsBy <- function(pgx, by.phe... |
8b4b3bd9abc773136a39fcbcf02bb9dec84a7b08b31ba84e4a4022090b817509 | R | 73,409 | 2,042 | ---
title: "Longitudinal protein profiling of blood during childhood into early adulthood"
output: html_document
author: "Sofia Bergström and Simon Kebede Merid"
date: "`r format(Sys.time(), '%Y-%m-%d')`"
editor_options:
chunk_output_type: console
---
# Set up
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo ... |
960b4042bed4567dd7fb9917df36c0267ccdfd97cef5ff9ddaccadb5d6e916d6 | R | 77,476 | 1,642 | suppressPackageStartupMessages({
library(SingleCellExperiment)
library(tidyverse)
library(ggplot2)
#library(GGally)
#library(GSEABase)
library(limma)
library(reshape2)
library(data.table)
library(knitr)
library(stringr)
library(NMF)
library(rsvd)
library(RColorBrewer)
library(MAST)
library... |
0cb7ef844827c207276456d5b14fbdc0841853db2b600f8339e05dd20b776464 | R | 85,413 | 1,857 | #' Run NMF (non-negative matrix factorization)
#'
#' @param object An object. This can be a Seurat object, an Assay object, or a matrix-like object.
#' @param assay A character string specifying the assay to be used for the analysis. Default is NULL.
#' @param slot A character string specifying the slot name to be used... |
f399ad506ec2d086fc083533edac19ece6670434159cf24aa9c727cc00ec8159 | R | 88,767 | 2,299 | #' CreateDataFile
#'
#' Creates a data file in HDF5 format from a Seurat object.
#'
#' @param srt The Seurat object.
#' @param DataFile Path to the output data file. If not provided, the file will be named "Data.hdf5" in the current directory.
#' @param name Name of the dataset. If not provided, the name will default t... |
0fa34cd4a4295c292d4d1e4530b5a9a99afc3257249b1955a6f848cf2cdd478f | R | 106,974 | 2,241 |
# eNeuro paper code
##########################################################################################################
## Eric Dammer - adapted code for WGCNA from Neelroop Parikshak, Vivek Swarup, and Divya Nandakumar
## SeyfriedLab&ProteomicsCorePipeline.R
##
## Applied to Emory 41 BULK Data fro... |
d1da1bca1a9242c83c4746aec54495acb296011db34324ad3da7a431924db79d | R | 178,021 | 3,959 | #' Check and report the type of data
#'
#' This function checks the type of data and returns a string indicating the type of data. It checks for the presence of infinite values, negative values, and whether the values are floats or integers.
#'
#' @param srt An object of class 'Seurat'.
#' @param data The input data. I... |
2833b4482e61f6079e7e6997ae89578cf23b932a462e2652665a3388398507e9 | R | 183,288 | 6,077 | ---
title: "Figures_MSA-PD"
author: "Rasmus Rydbirk"
date: "04-12-2024"
output:
html_document:
toc: yes
toc_float: yes
---
# Setup
```{r setup, message = F}
library(conos)
library(magrittr)
library(dplyr)
library(cacoa) # github.com/kharchenkolab/cacoa
library(sccore)
library(scHelper) # github.com/rrydbir... |
1ddf60086b251e603d50fb4fb7a464d2714fb644084b52b224be4e4281e9790e | R | 185,628 | 5,753 | ---
title: "Figures_MSA-PD"
author: "Rasmus Rydbirk"
date: "04-12-2024"
output:
html_document:
toc: yes
toc_float: yes
---
# Setup
```{r setup, message = F}
library(conos)
library(magrittr)
library(dplyr)
library(cacoa) # github.com/kharchenkolab/cacoa
library(sccore)
library(scHelper) # github.com/rrydbir... |
9198bd90a6b0da7c2966349151f53820675a796f3441d189d27545fc1a899023 | R | 200,000 | 4,088 | #' Gene ID conversion function using biomart
#'
#' This function can convert different gene ID types within one species or between two species using the biomart service.
#'
#' @param geneID A vector of the geneID character.
#' @param geneID_from_IDtype Gene ID type of the input \code{geneID}. e.g. "symbol", "ensembl_id... |
1ee0ec0c3ed12acbbbc98ba3600edcf8368e018ea51a34400c1ed6d897d43d49 | R | 200,002 | 3,700 | #' @import Biobase limma tximport igraph biomaRt openxlsx msigdbr ConsensusClusterPlus kableExtra
#' @importFrom GEOquery getGEO
#' @importFrom RColorBrewer brewer.pal
#' @importFrom plot3D scatter3D
#' @importFrom plotrix draw.ellipse draw.circle
#' @importFrom impute impute.knn
#' @importFrom umap umap umap.defaults
... |
a7db507500a1a6aae95547d4042eafb170e1019b80f3eb7ce9468ec1265d2dfe | R | 200,002 | 4,237 | #' SCP theme
#'
#' The default theme for SCP plot function.
#'
#' @param aspect.ratio Aspect ratio of the panel.
#' @param base_size Base font size
#' @param ... Arguments passed to the \code{\link[ggplot2]{theme}}.
#'
#' @examples
#' library(ggplot2)
#' p <- ggplot(mtcars, aes(x = wt, y = mpg, colour = factor(cyl))) +... |
31ee7c6de71c8f903c8c6ec7d1a2623a2e96408a7d805754f36ba11c0675022f | R | 200,125 | 6,024 |
# Customized R functions
# Author= Yvon Mbouamboua (yvon.mbouamboua@inserm.fr)
# Utility: %||%
`%||%` <- function(a, b) if(!is.null(a)) a else b
#' Remove duplicated cell barcodes within and/or across Seurat objects
#'
#' This function cleans duplicated cell barcodes in a list of Seurat objects.
#' It can remove dup... |
289058c57ae8b8e71b813055aacb33b1902a2a8e629bbd3f7d4a4dbf40d6d413 | Rust | 4,131 | 92 | /// phenotype permutation procedure
/// shuffling group labels and calculate the new ranking metric
/// return shuffled metric (not sorted)
pub fn phenotype_permutation(data: &[Vec<f64>], group: &[bool], method: Metric) -> Vec<Vec<f64>> {
//let mut indices: Vec<Vec<usize>> = Vec::new();
let mut arr: Vec<Vec<f64... |
da90739a5c220f358fb2dccc91f009c9fcc8f4dc094fbe2ab0775d52878b5ec9 | Rust | 4,552 | 125 | //! Translated from `fgsea/src/fgseaMultilevel.cpp` + `fgsea/src/fgseaMultilevel.h`.
//!
//! `ranks` arrives as an R integer vector (`INTSXP`); modelled as `&[i32]`.
//! The Rcpp `DataFrame` return becomes the `FgseaMultilevelResult` struct.
//!
//! Vendored from the faithful fgsea-rs translation.
use crate::fgsea::fg... |
3848cc3f7e4788d7b6be1fc0a877a2acd830722cfbe4e2894aa45256502fab13 | Rust | 5,073 | 124 | //! Faithful Rust translation of the `fgsea` C++ core (multilevel p-value +
//! calcGseaStat batch), vendored from the standalone fgsea-rs port.
//!
//! Source: `fgsea/src/*.cpp` + `*.h` (alserglab/fgsea). Each original C++ function
//! maps to exactly one Rust function; original camelCase names are preserved to keep
/... |
2e5b2ace41fe7e8246447cdec3500c192f8a96d1adf5cbd54c25a76748e8efac | Rust | 5,484 | 150 | //! Translated from `fgsea/src/util.cpp` + `fgsea/src/util.h`.
//!
//! Only the active (`#ifndef USE_STD_UID`) branch of `uid_wrapper` is translated;
//! the `USE_STD_UID` branch is dead under the default build.
//!
//! Vendored from the faithful fgsea-rs translation (boost::mt19937 bit-for-bit).
use special::Gamma;
... |
4b8392851e61bf15fa5f2caa290ac6b17940512f93f45d5835afb47733ad9363 | Rust | 6,847 | 204 | //! Translated from `fgsea/src/esCalculation.cpp` + `fgsea/src/esCalculation.h`.
//!
//! `int128` (boost::multiprecision or `__int128`) maps to native `i128`.
//!
//! Vendored from the faithful fgsea-rs translation.
/// An exact (rational) enrichment-score value, where
/// `score = coef_NS / NS - coef_const / diff`.
/... |
e854a6e8cd5ca6011812e9aec100744c65dd3ac586c2d3ec71e3b76444f934a5 | Rust | 8,793 | 195 | //! End-to-end bit-exactness validation of the vendored fgsea core against
//! ground-truth outputs of the ORIGINAL fgsea C++ (generated via `Rcpp::sourceCpp`
//! with `boost::mt19937`). Inputs and reference outputs live under
//! `tests/data/fgsea/` (copied verbatim from the fgsea-rs `validation/` tree).
//!
//! Toler... |
e89598e75f1964f049357a986db02f1540ced80c6edf3769baf5848c9e1b5f34 | Rust | 9,443 | 312 | #![allow(dead_code, unused)]
use pyo3::prelude::*;
use std::error::Error;
use std::fs::File;
use std::io::{BufRead, BufReader};
// use std::path::{Path};
use csv;
use itertools::Itertools;
use rand::seq::SliceRandom;
use rand::Rng;
use std::collections::HashMap;
use std::hash::Hash;
#[pyclass(eq, eq_int, from_py_obje... |
c5f262bf8cd93a567fa28d8070e668c95b0acde08d9c83eaaaee52b8c890a3ce | Rust | 14,380 | 405 | #![allow(dead_code, unused)]
use crate::stats::{GSEAResult, GSEASummary};
use crate::utils::{DynamicEnum, Statistic};
use rayon::prelude::*;
use statrs::distribution::{ContinuousCDF, DiscreteCDF, Normal, Poisson};
use std::collections::BTreeMap;
pub struct GSVA {
genes: DynamicEnum<String>,
kcdf: bool,
ta... |
5bae6d32750009b666b82a3f61a3412371228793780b6a85e9a9f5b05c737519 | Rust | 14,754 | 428 | use pyo3::exceptions::PyRuntimeError;
use pyo3::prelude::*;
use std::collections::BTreeMap;
// import own modules
mod algorithm;
mod fgsea;
mod gsva;
mod stats;
mod utils;
// export module fn, struct, trait ...
use algorithm::GseaStatResult;
use gsva::gsva;
use stats::{GSEAResult, GSEASummary};
use utils::{CorrelType, ... |
20f07a7e41b59e59bd906db687f9bd1ee7ce58fe5f9771e72638370e4721fafd | Rust | 16,435 | 530 | //! Translated from `fgsea/src/fastGSEA.cpp` + `fgsea/src/fastGSEA.h`.
//!
//! C++ templates become Rust generics:
//! * `SegmentTree<T>` -> `SegmentTree<T>`
//! * `order<T>` -> `order<T>` (the `IndirectCmp<T>` functor is folded into
//! `order`'s sort comparator — it has no standalone Rust counterpart).
//!
//... |
4555553739d285e2063ff9de8f6ed52cf1ac41c2cadb1202c988696ac668bd93 | Rust | 29,038 | 765 | //! Translated from `fgsea/src/fgseaMultilevelSupplement.cpp` + `fgsea/src/fgseaMultilevelSupplement.h`.
//!
//! `std::function<bool(int,int)>` in `perturbate_until` becomes a generic closure
//! parameter `F: Fn(i32, i32) -> bool`. The C++ `check` lambda inside
//! `perturbate_until` is kept as a local closure (a fait... |
53e8eedba91c343e4fafac70c5b2165c07cf3a5c76ea5a14c656bddaea398d63 | Rust | 40,966 | 1,007 | #![allow(dead_code, unused)]
use crate::utils::DynamicEnum;
use crate::utils::{Metric, ScoreType, Statistic};
use pyo3::prelude::*;
use rand::rngs::SmallRng;
use rand::seq::SliceRandom;
use rand::SeedableRng;
use rayon::prelude::*;
/// Result of `calc_gsea_stat()`, mirroring fgsea's `calcGseaStat()` return value.
///... |
2ec53b1bf7a35be8eaa9ef99c475968afe7d64083a4f0f3239827b84dec9f738 | Rust | 74,290 | 1,713 | #![allow(dead_code, unused)]
use crate::algorithm::{EnrichmentScore, EnrichmentScoreTrait};
use crate::fgsea::util::multilevelError;
use crate::fgsea::{calcGseaStatCumulativeBatch, compute_pvalue_multilevel, scale_ranks};
use crate::utils::{CorrelType, DynamicEnum, Metric, Statistic};
use itertools::{izip, Itertools};... |
f9e238267b432b16d1d18d0f845733a1032bd2e632800b8ac08cdcf0b846c322 | Shell | 28 | 2 | #!/bin/sh
./run.sh make all
|
98342dd0767876d7c2ec0f098f6b45ea124e92f8df40ac7fe8f326bb5a66a60e | Shell | 29 | 2 | #!/bin/sh
./run.sh make test
|
60238dff6cc393b4077c6a442873d5a69fbace32accb233badf1b32313bbb1eb | Shell | 32 | 1 | docker build -t netbid2:2.0.1 .
|
9c825857fbd9e048565162965a796e0c96c7843b080d2e2aa90bc1b72981717a | Shell | 33 | 2 | #!/bin/sh
./run.sh make patterns
|
71fc2d6f1514215fcf34df5b3ed20c51aa838b41452a09db8b9b986a02b89c35 | Shell | 34 | 1 | docker push jyyulab/netbid2:2.0.1
|
d0e5c346f42b79efbe4c9947eb43d698b079f27b84bcc36e44dd26afa35236fd | Shell | 40 | 2 | #!/bin/sh
./run.sh make prepare_release
|
59befc38718d3ad7a9dcc3aeaf5415f27455cdc844ca8ca7d181880dad2e92f4 | Shell | 57 | 3 | #python3 parse.py
bundle update
bundle exec jekyll serve
|
479d7e68588e6d9d1dc1a39636b177a27d151528361b7ff5c54b9830ddbfe139 | Shell | 70 | 7 | #!/bin/bash
set -e
docker push trinityctat/ctat_mutations:latest
|
14cde2e4a758e885c93d1aec82543c527c5a9613941d0b4d0dee1e2962d62a43 | Shell | 80 | 8 | #! /bin/sh
set -e
aclocal -I m4
autoheader
automake --add-missing
autoreconf
|
56cf584424e5319529373db164155c2c0d1b5d4782acfc69e1a387885b258b8e | Shell | 81 | 1 | $PYTHON setup.py install --single-version-externally-managed --record=record.txt
|
d62a06a3b077c647d9f4aeada5a2698e34b17ae55dc251605e4a313da8efac9a | Shell | 82 | 5 | #!/usr/bin/env bash
rm -rv dist
python3 setup.py bdist_wheel
twine upload dist/*
|
7abf87352c89b2cbe8294f3129d586e979bf77d8e8f39a7fba95c347a857c2d8 | Shell | 87 | 4 | rm skimpy.rst skimpy.*.rst
rm modules.rst
sphinx-apidoc -o . ../skimpy
#rm modules.rst
|
e263d934db40438ecf790155a678799096d9a17a0e84dacc959dd523d1eabc70 | Shell | 88 | 6 | #!/bin/sh
eval "$(conda shell.bash hook)"
eval "conda activate mocohealthy"
eval "$@"
|
6bd9db3dfea656916dfd66834c345f3e43913a44edc8ba79982a2b4a649bc327 | Shell | 101 | 9 | #!/bin/bash
set -e
VERSION=`cat VERSION.txt`
docker push trinityctat/ctat_mutations:${VERSION}
|
3e501c16944ce76025f2f5b9c2a3bee15a49b456d70960016fba3d9b6faf85ab | Shell | 103 | 8 | #!/bin/bash
set -x
LOG="logs/fcn_`date +%Y-%m-%d_%H-%M-%S`.txt"
exec &> >(tee -a "$LOG")
./solve.py
|
036b0573dc7beb9f2032a5de8353890b87aa4276120ab53d27389e6188a3a063 | Shell | 105 | 4 | #!/bin/env bash
source /home/tconstab1/kg98_scratch/Toby/python_venv/bin/activate
python CombineFigs.py
|
122aac175f082c83a0367467c8c484e6519918fa892a22fac6b76d626bf03ef6 | Shell | 108 | 4 | for cwl_file in $(git diff --staged --name-only | grep '.*\.cwl$')
do
cwltool --validate "$cwl_file"
done
|
e5356fb28cb77017fc949b34e7dd8dddcf3508c5857d3ec9105c74f3aa939f1b | Shell | 108 | 4 | #!/bin/env bash
source /home/tconstab1/kg98_scratch/Toby/python_venv/bin/activate
python Step2.KRR_Plot.py
|
a511ad4da679168b3a3e27d5c74bb2a9a8bdac8a589aa143bcf0d23ae8f988c5 | Shell | 110 | 5 | #!/bin/bash
ln -fs ../../bin/* .
ln -fs ../../forcebalance/* .
./CallGraph.py | dot -Tpng > ../CallGraph.png
|
354543e49c723d12ee290acc81a5ae5c82d91beffc0da4a7ec72cdaf97b979a5 | Shell | 111 | 9 | #!/bin/bash
echo "Running isort..."
isort .
echo "Running black..."
black .
echo "Running flake..."
flake8 .
|
41ea44f3a74f85cd41a80c4f347dec3de1e108e499e50318fbc2253c9148fd60 | Shell | 113 | 2 | ./force-xvg-xyz.py gmx-all.gro gmx-f.xvg 10
echo "Now run: vmd -e drawforces.vmd -args gmx-all.gro gmx-grad.xyz"
|
956eb42af86a54000c6dc5b251c2f3b567ee47d7c4b990fa94073fef1e230a16 | Shell | 142 | 5 | DIRECTORY="experiments/Fig4_populations"
for i in $(seq 0 160)
do
python runner.py --params $DIRECTORY/runs/lr$i/params.json &
done
|
348f7efa039c2f5e403212019c7cf6119d46bfa3dc110d67898eb62c7453de63 | Shell | 156 | 10 | #!/bin/bash
set -e
VERSION=`cat VERSION.txt`
docker build -t trinityctat/ctat_mutations:$VERSION .
docker build -t trinityctat/ctat_mutations:latest .
|
c90f4f8d8999b063676b9f6441b40e98843cc8baf5ca5c4311f3b1a1d749b14c | Shell | 166 | 6 | #!/usr/bin/bash
# edit part of the line below to reflect where you git cloned rd_filters
export FILTERS_RULES_DATA=~/src/rd_filters/rd_filters/data
rd_filters "$@"
|
ec524a3626a8aa6f5456f4ad61abf4a62f0512dddd7bfb43e5a94904d329be5f | Shell | 176 | 5 | #!/usr/bin/env bash
if [[ ! -f "WDL/cromwell-58.jar" ]]; then
wget https://github.com/broadinstitute/cromwell/releases/download/58/cromwell-58.jar -O WDL/cromwell-58.jar
fi
|
9eed03487a04becb694d8ee6f1e9b1eb975254b06f96642ec51254782107ad6d | Shell | 178 | 5 | #
# Copyright (c) 2018 German Cancer Research Center (DKFZ).
#
# Distributed under the MIT License (license terms are at https://github.com/DKFZ-ODCF/AlignmentAndQCWorkflows).
#
|
45b8c29ed4dde2360767a5949c70c56b9588a562a9344a9e8ffe430bee8ac9e2 | Shell | 183 | 4 | #!/usr/bin/env bash
python ../../circleseq/circleseq.py all --manifest ../CIRCLEseq_MergedTest.yaml
python ../../circleseq/circleseq.py all --manifest ../CIRCLEseq_StandardTest.yaml
|
263bf8a3027abdd378ff062edaf83663b7887809c258514a0a11dba08468e52c | Shell | 184 | 9 | #!/bin/bash
#SBATCH --time=72:00:00
#SBATCH --nodes=1
#SBATCH --mem=247g
#SBATCH --cpus-per-task=24
#SBATCH --job-name=buildDataset
module load python
python -m src.utils.buildDataset |
0450980f40870ee7dbd8dd7b9933305bbe4963f0a07e5db34d19bd0889fd3305 | Shell | 186 | 4 | #!/bin/sh
ODK_DEBUG_FILE=${ODK_DEBUG_FILE:-debug.log}
echo "Command: sh $@" >> $ODK_DEBUG_FILE
/usr/bin/time -a -o $ODK_DEBUG_FILE -f "Elapsed time: %E\nPeak memory: %M kb" /bin/sh "$@"
|
8a3ef7b8e1460bdbcc51319d160ab461cc7a4016c70e7a3831865277a3ceed3f | Shell | 190 | 7 | #! /bin/bash
cmd='ccl_nmf_prediction -i ../input/IXI/dlmuse/IXI_dlmuse.csv -d ../input/IXI/lists/IXI_demog_n10.csv -o ../output/IXI/IXI_CCL-NMF_Scores.csv'
echo "About to run: $cmd"
$cmd
|
08ce09326053e4f9f55c52b9f2a2e946bfcb6b759bf64a5190e33055a2aeea6f | Shell | 193 | 11 | #!/bin/bash
# configure the project
BASEDIR=$(dirname $0)
source $BASEDIR/defaults.sh
if ! $WITH_CMAKE ; then
source $BASEDIR/configure-make.sh
else
source $BASEDIR/configure-cmake.sh
fi
|
0be095e797d1c86114d16110258e591e6188cafac18a8cb8c0fb4e6cef3a2098 | Shell | 198 | 9 | #!/bin/bash
#SBATCH --time=72:00:00
#SBATCH --nodes=1
#SBATCH --mem=247g
#SBATCH --cpus-per-task=24
#SBATCH --job-name=convertPetToNifti
module load python
python -m src.utils.PET_ConvertToNifti.py |
a50a0543daa06e0460d74a85c9e8fb8dbff012412bdfa4a832c81e4761e4e2dc | Shell | 203 | 10 | #!/bin/bash
for i in *.sam
do
echo "Making paired end tag dir: tags/${i%Aligned.out.sam}"
makeTagDirectory tags/${i%Aligned.out.sam} $i -unique -sspe 2> ${i%Aligned.out.sam}.tagDirectory.log
done
|
c1e64f3e9a0ae5b5df7bf75daf753b653b932242043576b4c4adb228baa43f71 | Shell | 205 | 10 | #!/bin/bash
#SBATCH --time=2:00:00
#SBATCH --nodes=1
#SBATCH --mem=16g
#SBATCH --cpus-per-task=12
#SBATCH --job-name=scalePretrain
#SBATCH --array=0-25
module load python
python -m src.utils.scalePretrain |
6e43dc90e720d57025f3cdc064804b8bb8589b0f49895681c5e4494899bdaceb | Shell | 208 | 10 | #!/bin/bash
#SBATCH --time=1:00:00
#SBATCH --nodes=1
#SBATCH --mem=16g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=mriPreprocess
#SBATCH --array=0-25
module load python
python -m src.utils.MRI_PreProcessing |
b945317cc58fcb7967804d56260baf59934df633a42bc6e7c20bedb7b9c68a7c | Shell | 208 | 10 | #!/bin/bash
#SBATCH --time=2:00:00
#SBATCH --nodes=1
#SBATCH --mem=16g
#SBATCH --cpus-per-task=8
#SBATCH --job-name=petPreprocess
#SBATCH --array=0-25
module load python
python -m src.utils.PET_PreProcessing |
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