sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
1b943a0f4b872b2d5dca6f67feab6df01495f7abe6a1868e7d8f05949191562e | R | 17,830 | 623 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# plot_confusion_matrix (generic) ----------------------------------------------
#' @title Plot confusion matrix.
#'
#' @description This method creates confusion matrices based on data in a
#' familiarCollection object.
#'
#' @param dir_path (*... |
71ed92f33f52cb14789e15663a086e111e4cdb751ef8e21af536d76ab9857687 | R | 17,882 | 489 | #' Split \code{TreeSummarizedExperiment} column-wise or row-wise based on
#' grouping variable
#'
#' @inheritParams agglomerate-methods
#'
#' @param group \code{Character scalar}, \code{character vector} or
#' \code{factor vector}. A column name from \code{rowData(x)} or
#' \code{colData(x)} or alternatively a vector s... |
d7d4eb39cf32f226c4093bda38415973236370132c495123f2e84488f497063e | R | 18,020 | 315 | #' get_genomes_NCBI(organisms = "bacteria", assembly_summary = NULL, outputdir = NULL, assembly_accession = NULL, organism_name = NULL, infraspecific_name = NULL, taxid = NULL, species_taxid = NULL, asm_name = NULL, as_general_expression = FALSE, assembly_level = NULL, assembly_upto = NULL, bioproject = NULL, biosample... |
fcf5adf877d20bba70ec317c0f50acceab45cd572bb5fdaa84223bba007d9233 | R | 18,322 | 622 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarNaiveModel objects ---------------------------------------------------
setClass(
"familiarNaiveModel",
contains = "familiarModel"
)
setClass(
"familiarNaiveCoxModel",
contains = "familiarNaiveModel"
)
setClass(
"familiarNaiveSu... |
8b67a2a16e85022055161e1d7e0bc72cf07712fddac949fe2ee3034a88403a33 | R | 18,328 | 629 | .required_packages_backend <- function(backend_type) {
backend_packages <- NULL
if (backend_type == "socket_server") {
backend_packages <- "callr"
}
return(backend_packages)
}
.get_available_backend_types <- function() {
# All types of backend.
all_backend_types <- c("none", "socket_server")
... |
8e6771e3ef5ea5ba269bbd1b7410fe30a35b408a7abe0c2752793a0115d5d698 | R | 18,494 | 308 | #' plot_correlation_heatmap(ExpObj = NULL, glomby = NULL, stattype = "spearman", subsetby = NULL, maxnumfeatallowed = 10000, minabscorrcoeff = NULL, ntopvar = NULL, featuresToKeep = NULL, only_allow_CSBs = FALSE, samplesToKeep = NULL, applyfilters = NULL, featcutoff = NULL, GenomeCompletenessCutoff = NULL, show_GenomeC... |
70849ef60ea203b4916ec296dd08e9ed03aadaf315dc8a381820ed45a2cc7943 | R | 18,578 | 630 | ---
title: "Manuscript 1 from Fernanda Hansen P. de Moraes Thesis - Cortical folding alterations in humans due to aging and diseases"
author: "Fernanda Hansen Pacheco de Moraes"
date: "1 dec 2021"
output:
html_document:
fig_caption: yes
fig_width: 8
number_sections: yes
theme: paper
toc: yes
... |
28b9387251c7074fb2d261e16d25f1b9568d01540a1606a91310aa6ce314a3b5 | R | 18,600 | 481 | ---
title: "mini_starmap"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{mini_starmap}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup, eval=FALSE}
library(Giotto)
```
### In... |
c6f751622552fba5789645418e50e9b2c0b366edcb5e8cb8a9d8ab85250e3181 | R | 18,672 | 580 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
#' @include Normalisation.R
NULL
# Parametric ComBat object -----------------------------------------------------
setClass(
"featureInfoParametersNormalisationParametricCombat",
contains = "featureInfoParametersNormalisationShiftScale",
slots = l... |
a7e67092975e54b64d2da783983096ca84e13207314a6184645e48e181dd1308 | R | 18,686 | 648 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarTaskTrain -------------------------------------------------------------
setClass(
"familiarTaskTrain",
contains = "familiarTask",
slots = list(
"vimp_method" = "character",
"learner" = "character",
"vimp_table_file" = "... |
b96973166daff91046f1cd3f74cf95c5d7655fb67f4f67ae2c11ed4bf66459da | R | 18,706 | 392 | #' add_interpro_to_featuredata
#'
#' JAMSalpha function
#' @export
add_interpro_to_featuredata <- function(opt = NULL, doinparallel = FALSE){
#Define which interpro analyses I have
iproanalyses <- sort(unique(opt$interproscanoutput$Analysis))
if ("IproAcc" %in% colnames(opt$interproscanoutput)){
i... |
e4dd44b2429310fa1d6f27c14857c8309439763066b0b918b4c46a6cd0d8303b | R | 18,824 | 478 | ##############
### Script ###
##############
# Run BLESS (= Bayesian Lesion Estimation for a Structured Spike-and-Slab Prior) with MCAR prior over sparsity parameters.
# (3D lesion masks with parameter estimation and inference for masked version of 3D scan)
# (Code for UK Biobank application)
#################
### Li... |
fdd9b3aa0ff4918754bf8d93266d30abe9928329e11caf34513ab7c626b5b635 | R | 18,935 | 549 | # Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_ci()
# Survival data with multiple instances ----------------------------------------
# Create a dataset using the good dataset.
data <- familiar:::test_create_good_data("survival")
# Trai... |
85a5ed6dcdbc79264996e618a13d60ea067385a944814ee7baf97714667d6c32 | R | 19,011 | 426 | #!/usr/bin/env Rscript
###############################
###############################
#"
## Fingerprinting - CAMCAN Cohort
## Data Analysis - Polynomial (Quadratic) regression
#Script - Data Analysis - Quadratic regression - FPC/ASC
#Author: Frederic St-Onge
#Date created: 2020-12-23
#Date modified: 2021-12-15
#Ve... |
f93f9637e8abaadc8f451b20a9dcb40478934ecb02c7560b36d96c07fea22d75 | R | 19,048 | 389 | #!/usr/bin/env Rscript
# =============================================================================
# make_visiumhd_mini_crc.R
#
# Build a small, format-faithful Visium HD fixture by SPATIALLY CROPPING a
# Space Ranger 4 `outs/` directory (default: 10x's Visium HD human colorectal
# cancer FFPE run, Space Ranger 4.0... |
43631691788d5ead6e689888e0fb191a066b89f19b42ca43b43197989eede8f7 | R | 19,078 | 654 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# Cluster parameter object -----------------------------------------------------
setClass(
"featureInfoParametersCluster",
contains = "featureInfoParameters",
slots = list(
"type" = "character",
"method" = "ANY",
"weight" = "numeri... |
8c9ce149c75d85af4cf92c65480ec2893a8f8d38385b7bbc070aabb9367e294c | R | 19,108 | 332 | ---
title: "Untitled"
author: "Joke Deschildre"
date: "10 februari 2020"
output: html_document
version: "R version 4.0.3 (2020-10-10) -- Bunny-Wunnies Freak Out"
---
```{r}
library(data.table)
library(stringr)
```
# Sample info
Table with the general sample info 20Q4 v2 downloaded 10/03/2021 from the Dep... |
fbe7c0b43b6b32dbfb5bfc356c19d913a62b3d4d4a5a1a8e63e0bb9e6bb550c9 | R | 19,121 | 717 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarDataElementSampleSimilarity object -----------------------------------
setClass(
"familiarDataElementSampleSimilarity",
contains = "familiarDataElement",
slots = list(
"similarity_metric" = "character",
"cluster_method" = "... |
096aa2bbd43fdb01e8ab1828e6a7275db069ca0d22d7f827733ac4d6458405de | R | 19,127 | 720 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# export_all methods -----------------------------------------------------------
## export_all (generic) --------------------------------------------------------
#'@title Extract and export all data.
#'
#'@description Extract and export all data f... |
014b456941c3777a382ec7778ce968703127bdf302711dd522e9a81a0b58130a | R | 19,269 | 467 | ---
title: "TIME_Behavioural_Analysis"
output:
pdf_document:
date: "`r Sys.Date()`"
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Confirmatory Analysis
```{r Participant Exclusion Analysis with BootStrapping,message=FALSE, warning=FALSE, out.width="55%"}
# Load libraries and set path -... |
dd01d80c4f6b846174b8b2b1309ee965932e044d5c07defebd1fcea254acae0b | R | 19,297 | 804 | ..warning <- function(
...,
warning_class = NULL,
call = rlang::caller_env()
) {
# Generic error function.
message_string <- paste0(list(...), collapse = "")
rlang::warn(
message = message_string,
class = union("familiar_warning", warning_class),
call = call
)
}
..warning_missing_cohor... |
ea581294d10a91bb99fedcbaec5d5e03f583ac58c583a19405fe66182cf6de66 | R | 19,327 | 278 | #' evaluate_LKTs(opt = opt, contigsdata = "contigsdata", output_list_name = "taxonomic_completeness_and_counts")
#'
#' JAMSalpha function
#' @export
evaluate_LKTs <- function(opt = opt, contigsdata_name = "contigsdata", output_list_name = "taxonomic_completeness_and_counts", taxlvls = c("LKT", "Species")){
setwd(... |
ef737f08f7536d972ec3f069969d8693b96ff49c56b4928d1a7f05967132f25d | R | 19,396 | 742 | ---
title: "Setbp1_CellTypesKidney_05"
author: "Jordan Whitlock"
date: '2022-09-14'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
---
# *Single-nuclei analysis: Identify Cell Types Kidney*
* Loading libraries
* Selecting resolution
* Investigating top markers for Kidney Cortex
... |
8d4fb33af270ea41e55247ecb58c9f132fe7497eebd1a37f752c9c3db31c2a3c | R | 19,414 | 402 | # This code was used for statistical analyses involving behavioral data and brain region immediate early gene data for the following manuscript:
# Morgan L. Gustison, Rodrigo Muñoz Castañeda, Pavel Osten, Steven M. Phelps (2023) Sexual coordination in a whole-brain map of prairie vole pair bonding. eLife. DOI: https:/... |
4393eb709e0f58267c31fd903ee9762c9efadeb58351de51fa4c4b7c381edb8d | R | 19,503 | 562 | ---
_title: "Setbp1_ELISA_BCA"
author: "Jordan Whitlock"
date: '2023-03-14'
output: html_document
---
```{r library-seed-setup}
set.seed(2178)
library(here)
library(readr)
library(ggplot2)
library(ggpubr)
library(styler)
library(lintr)
library(tidyr)
```
```{r load-exp-results}
ELISA <- read_csv(here("results/ELISA/2... |
d5a6315ee498cee0a941144dd6afed76a25059716a1240b55f2c9d33c3af270f | R | 19,513 | 492 | ##############
### Script ###
##############
# Run BB-BLESS (= Bayesian Bootstrap - Bayesian Lesion Estimation for a Structured Spike-and-Slab Prior) with MCAR prior over sparsity parameters.
# (Code for Simulation Study)
#################
### Libraries ###
#################
library(LaplacesDemon)
library(brglm2)
li... |
32debd459538d640c60386694ba6cb32503ad3ac5ab1c37e824358739b1535f5 | R | 19,636 | 471 |
library(Giotto)
# createGiottoInstructions(python_path = '/your/path')
## Giotto 0.3.5 ##
## mini-test seqFish Giotto 0.3.5 ##
#temp_dir = '/your/path/'
temp_dir = getwd()
temp_dir = '~/Temp/'
## 1. giotto object ####
expr_path = system.file("extdata", "seqfish_field_expr.txt", package = 'Giotto')
loc_path = syst... |
10d30b8357f9937244b950df05ed157d05e419525b95578e8d464cfbcbfd2bf9 | R | 19,743 | 659 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# batch normalisation container ------------------------------------------------
setClass(
"featureInfoParametersBatchNormalisationContainer",
contains = "featureInfoParameters",
slots = list(
"method" = "character",
"batch_parameters... |
dc850c0e1e77a4f9c9ab940a53b1855cb6d257584f91b2b81472d5f1b5c07bd1 | R | 19,816 | 443 | ---
title: "Setbp1_DGEcortex_06"
author: "Jordan Whitlock"
date: '2022-09-08'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
---
This analysis comes after 'Setbp1_CellTypesCortex_05.Rmd' and precedes 'Setbp1_PathwayAnalysisCortexKidney_07.Rmd'
# *Single-nuclei analysis: DGE cor... |
48a53b39282c3b80acfd4429c66a55a9bd1effff7fa5253253ce5a5c794b370a | R | 20,017 | 611 | .determine_preprocessing_parameters <- function(
cl = NULL,
data,
feature_info_list,
settings,
message_indent = 0L,
verbose = FALSE
) {
if (!is(data, "dataObject")) {
..error_reached_unreachable_code(
".determine_preprocessing_parameters: data is not a dataObject."
)
}
... |
d3dfda26a186e0183d06d86d66dbf44844cc78c12ab91eb75de8ce252b96e9dd | R | 20,070 | 704 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
setClass(
"familiarMutualInformationVimp",
contains = "familiarVimpMethod"
)
setClass(
"familiarUnivariateMutualInfoVimp",
contains = "familiarMutualInformationVimp"
)
setClass(
"familiarMultivariateMutualInfoVimp",
contains = "familia... |
bd1f40c217b04c14cf8e099bda2803984d147342c8019db5055515b042b7a42a | R | 20,313 | 627 | ---
title: "Setbp1_QC_postSoup_01"
author: "Jordan Whitlock"
date: '2023-04-13'
output: html_document
---
# *Single-nuclei analysis: Quality Control*
* load in data and create Seurat objects
* check proportion of mitochondrial and ribosomal genes
* visualize data feature distribution
* filter nFeatureRNA and mt
* check... |
a97610449c71a7d028d5c2de2332d7f3525d1df7d6dc1e4a6a9cc4ce1af8a9f8 | R | 20,330 | 519 | ---
title: "mini_seqfish"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{mini_seqfish}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup, eval=FALSE}
library(Giotto)
```
### I... |
5fda6edced95d00757deaf1b6359ddef4dc1549fbfee1efbf251054890bea1c9 | R | 20,335 | 428 | #################Comparison of spatial analysis methods to construct topographic cell densities maps. Xml version.
################# Garza-Gisholt, E., Hemmi, J.M., Hart, N.S. and Collin, S.P.
###This version extracts the information from an xml file exported from StereoInvestigator. If you have your information ... |
6381db5d742ae11cd19944de3cad5adca69160ff74e0c74cbd76063629e774c7 | R | 20,604 | 524 | if (!rlang::is_installed("power.transform")) testthat::skip()
# Generic test -----------------------------------------------------------------
outcome_type <- "survival"
for (n_numeric_features in c(4, 3, 2, 1, 0)) {
data <- familiar:::test_create_synthetic_series_data(
outcome_type = outcome_type,
n_numeri... |
a92aa199674576be11b9d3d3fb6308758f14cedc0a8d3f36e6db8f7729f9ac21 | R | 20,796 | 473 | #Maria Di Biase, Feb 6th 2023
#This code will optimise a gamlss model and compute deviations based on the optimised model
#Two models are optimised: the first incorporates batch and the second incorporates batch and bmi
#The code can be split into 4 steps:
#1) GAMLSS optimisation, which uses all the samples
#2) Runnin... |
03ab4aff1bfc175ab98079dd17b40028532a965f6232e9fc57b9ddc231b84ed0 | R | 20,819 | 474 | #Maria Di Biase, Feb 6th 2023
#This code will optimise a gamlss model and compute deviations based on the optimised model
#Two models are optimised: the first incorporates batch and the second incorporates batch and bmi
#The code can be split into 4 steps:
#1) GAMLSS optimisation, which uses all the samples
#2) Runnin... |
81b6934950106c212f16b9382ae9feffa59f868b4054699566c7a9f5b7c18346 | R | 20,840 | 473 | #Maria Di Biase, Feb 6th 2023
#This code will optimise a gamlss model and compute deviations based on the optimised model
#Two models are optimised: the first incorporates batch and the second incorporates batch and bmi
#The code can be split into 4 steps:
#1) GAMLSS optimisation, which uses all the samples
#2) Runnin... |
97de8e40237001b8cba0e855ab02dea62a67b7b379ccbf9c0d0643f0d4dc6b1e | R | 20,841 | 473 | #Maria Di Biase, Feb 6th 2023
#This code will optimise a gamlss model and compute deviations based on the optimised model
#Two models are optimised: the first incorporates batch and the second incorporates batch and bmi
#The code can be split into 4 steps:
#1) GAMLSS optimisation, which uses all the samples
#2) Runnin... |
34dafe075da9bd3dcd5bd97f22394f647547b5fee2c793aeb88aebffbd8fc9a5 | R | 20,883 | 475 | #Maria Di Biase, Feb 6th 2023
#This code will optimise a gamlss model and compute deviations based on the optimised model
#Two models are optimised: the first incorporates batch and the second incorporates batch and bmi
#The code can be split into 4 steps:
#1) GAMLSS optimisation, which uses all the samples
#2) Runnin... |
5afbd3eb6f13d446d265f374d14c9daf63f9ee4524b3c4b7b7e56623f73fe12f | R | 20,889 | 475 | #Maria Di Biase, Feb 6th 2023
#This code will optimise a gamlss model and compute deviations based on the optimised model
#Two models are optimised: the first incorporates batch and the second incorporates batch and bmi
#The code can be split into 4 steps:
#1) GAMLSS optimisation, which uses all the samples
#2) Runnin... |
cf9972f2559cbfbf1ba64373f37779ab9ee18b19f450191597db2cfdcde5d850 | R | 21,010 | 780 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
.is_available_prediction_type <- function(type) {
return(type %in% c(.get_available_prediction_type_arguments()))
}
.get_available_prediction_type_arguments <- function() {
return(c(
.get_available_novelty_prediction_type_arguments(),
... |
e22536df268491d2db86c3b3c60707473a485fdd248c6e6798119c328858f5e2 | R | 21,018 | 360 | ---
title: "paper_spearman_corr"
author: "Joke Deschildre"
date: "19 april 2021"
output: html_document
---
---
title: "spearman correlation"
author: "Joke Deschildre"
date: "26 mei 2020"
output: html_document
---
```{r message=F}
library(data.table)
library(gtools)
library(reshape2)
library(ggplot2)... |
50f9e2f41410e3a4743b183bb87d7f5b593388a5242c7aeeddf9a6e0faa5c5c5 | R | 21,059 | 550 |
context("getCrossAssociation")
test_that("getCrossAssociation", {
# Try 5 times to fetch the data
for(i in seq_len(5) ){
mae <- tryCatch(
{
# Try to fetch the data
microbiomeDataSets::peerj32()
},
error = function(cond) {
... |
76031ed7460b1e8cdd9a55e49f05d6e4adf1b976d64e941685db3d3547ae9adb | R | 21,264 | 325 | #' contextualize_taxonomy(LKTdosesall = LKTdosesall, list.data = list.data, normalize_length = FALSE, dissimilarity_cutoff = 0.15, threads = 1, totmembytes = NULL)
#'
#' Internal function used exclusively within make_SummarizedExperiments to cluster taxonomic
#' entities belonging to the same species taxid into functio... |
0641e20d1d6f56e4017d6474c526d5903127b53b65bd3a784fdbe0fbd0a26ea6 | R | 21,300 | 443 |
rm(list = ls())
options(stringsAsFactors = F)
options(BioC_mirror="https://mirrors.ustc.edu.cn/bioc/")
# options(BioC_mirror="https://anaconda.org/bioconda/")
options("repos" = c(CRAN="https://mirrors.tuna.tsinghua.edu.cn/CRAN/"))
getwd()
setwd('C:/Users/LWH/Downloads/bioinfo/IPF&CHD&SSc')
library(sva)
library(limma... |
d3c4b52808d6971f78a386e67ae96e03ad5626751e60e6638559c5e7f31b224b | R | 21,772 | 384 | #!/usr/bin/Rscript
############################################################################################################################
#### Goal: PCA on (ranked) HumanNet networks -> can we see a subtype clustering in certain methods?
###########################################################################... |
9b9a235284a7553ad0ab3eba297c26b4450548e2ddad59deed42856d0b5409bb | R | 21,837 | 528 | ##############################################
# Generate a tiled heatmap for GLASS methylation data
# Updated: 2020.06.07
# Author: Kevin J.
##################################################
## Working directory for this analysis.
mybasedir = "/Users/johnsk/github/"
setwd(mybasedir)
################################... |
a2d263c9f580da1eaa8daf0bdd3b6cfa76e756f554686b1116cd8dcb780fbec8 | R | 22,000 | 625 | # Skip on CRAN as this test takes some time.
testthat::skip_on_cran()
testthat::skip_on_ci()
outcome_type <- "survival"
n_numeric_features <- 1
imputation_method <- "lasso"
# Generic test -----------------------------------------------------------------
for (n_numeric_features in c(4, 3, 2, 1, 0)) {
data <- familia... |
bc0209e9dac3c65115b139bb68d5249468e407920cb9844339ffe7995f66006c | R | 22,183 | 807 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# plot_decision_curve (generic) ------------------------------------------------
#' @title Plot decision curves.
#'
#' @description This method creates decision curves based on data in a
#' familiarCollection object.
#'
#' @param dir_path (*opt... |
b037f247dcc36d9951fab2f65757160cbc365061bdda98d1525370462193b3ca | R | 22,265 | 468 | # produces
# Figure 5: circos plots of source and target genes
# SupplTable S10 target genes mean impact and expr. ratios per group
# SupplFigure S8 target gene candidates scatterplot ompact vs. expression
library(reshape2)
library(ggplot2)
library(circlize)
library(scales) # for alpha semi-transparency
library(parall... |
d20b68671c9ce99eff4150e959840e03f5ef3230c8b8ee89fa8140f9266e56f5 | R | 22,414 | 407 |
evaluate_events = function() {
events = NULL # list()
if (length(input$events_dataset2) > 0) {
f = input$events_dataset2
if (input$events_type == "binary") {
events = identify.binary.events(data$raw[[f]], data$meta[[f]]$time, input$events_binary_variable)
} else i... |
7ad04d2a8751ce2b6d18bff03f33127476e787e465e899638d82c3d885967c72 | R | 22,603 | 612 | ---
title: "MouseSetbp1_PANDA_expression"
output: html_document
date: '2022-10-27'
---
This script extracts all processed count matrices from the Seurat object and pseudobulked them to create sample x gene matrices for each cell type as the expression input for PANDA.
```{r load-libraries}
set.seed(2178)
library(Sing... |
3c99b8b41744b9e62d7d8dda77e7549339d850f5c86ea7ab069435556cf2f42b | R | 22,616 | 746 | # Final plots for ms
First, I downloaded the output csv files from the [Google Drive](https://drive.google.com/drive/u/2/folders/1G_ied9kMbcJTt_ZNWWN67SAkggFISNGT) and put inside a folder `phylo-nn-googledrive/outputs/PlotsData/`: `EpochAccuracy1300.csv`, `SixMethodsComparison.csv`, `trainLoss1300.csv`, `testLoss1300.... |
e67713b3c038f4bae47337916838b2a7e37e67d36c81306db048cedfa96e4579 | R | 22,702 | 412 | # import-spectra.R
spectra.reference = reactiveValues()
spectra.preview = reactiveValues()
shinyFileChoose(input, "data_import_spectrometry_file", root=c(directories, `Working Directory`='.', getVolumes()()), filetypes=c("txt"))
shinyFileChoose(input, "data_import_spectrometry_reference", root=c(directories, `W... |
0fe4030af11c5322e03447030cc9ef08ff9c9ce3f71bf0e5f2c8e355091e4d04 | R | 22,761 | 512 | context("prevalence")
test_that("getPrevalence", {
data(GlobalPatterns, package="mia")
expect_error(getPrevalence(GlobalPatterns, detection="test"),
"'detection' must be a single numeric value or coercible to one")
expect_error(getPrevalence(GlobalPatterns, include.lowest="test"),
... |
eb555be0595a921a333f20c39a9e042d9adba7620f4b6c4451a21a1b83710257 | R | 22,800 | 767 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
create_outcome_info <- function(settings) {
outcome_info <- methods::new(
"outcomeInfo",
name = settings$data$outcome_name,
outcome_type = settings$data$outcome_type,
outcome_column = settings$data$outcome_col
)
if (outcome_info@o... |
c267bf1f7eb5fffa11026e9f44cfdc21ee5208d149ea5498c8a17e57746523a7 | R | 22,915 | 666 | #' Calculation prevalence information for features across samples
#'
#' These functions calculate the population prevalence for taxonomic ranks in a
#' \code{\link[SummarizedExperiment]{SummarizedExperiment}} object.
#'
#' @inheritParams getDissimilarity
#'
#' @param x
#' \code{\link[TreeSummarizedExperiment:TreeSummar... |
2758c21ffb6ed09320525a2793442b1ff9c97f596158dfdcafe4771287f42a2a | R | 23,214 | 770 | ---
title: "Manuscript 1 from Fernanda Hansen P. de Moraes Thesis - Cortical folding alterations in humans due to aging and diseases"
author: "Fernanda Hansen Pacheco de Moraes"
date: "1 dec 2021"
output:
html_document:
fig_caption: yes
fig_width: 8
number_sections: yes
theme: paper
toc: yes
edit... |
f524d01be5d1960a0789ae99a21b6236fb24a0b886408414e9e56c739566a4ea | R | 23,285 | 881 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
#' @include PredictionTable.R
NULL
# familiarDataElementAUCCurve object definition --------------------------------
setClass(
"familiarDataElementAUCCurve",
contains = "familiarDataElement",
prototype = methods::prototype(
value_column = "y",
... |
af0c5edc001b3abde08a1a5a8b45d992a9101a0b36a54c48279ccdd52909745e | R | 23,358 | 438 | #!/usr/bin/env Rscript
# =============================================================================
# make_xenium_mini_lung_cancer.R
#
# Build a small, format-faithful Xenium test fixture by SPATIALLY CROPPING the
# full 10x Xenium human lung cancer FFPE run.
#
# SOURCE_DIR -> OUT_DIR in one run. No manual steps.... |
149a94ed85b079bfdb320f52e2a5e37bfd59086413257eb820b957ffb34d889b | R | 23,503 | 901 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarMetricCM -------------------------------------------------------------
setClass(
"familiarMetricCM",
contains = "familiarMetric"
)
# familiarMetricCMAveraging ----------------------------------------------------
setClass(
"familia... |
9bf2eb3d23cc60ee2733591271bb0b8edbcdee4f46bc8e4db46f6766c91158fc | R | 23,544 | 440 | # mutRate_vs_epimutRate.R - Correlates cell DNAme disorder with bulk mutation rates
require(VariantAnnotation)
require(genomation)
require(GenomicRanges)
require(plyr)
require(tidyverse)
require(RColorBrewer)
require(ggplot2)
### Input arguments ###
# Input directory for variant calls
variantDir
# Input directory ... |
5b178166bf58ac1b6acb63f485301dc118736616716944e1957d6e4a3f416dae | R | 23,575 | 786 | #' Parse experimental design
#'
#' @param message_indent Spacing inserted before messages.
#' @param verbose Sets verbosity.
#' @inheritParams .parse_experiment_settings
#'
#' @details This function converts the experimental_design string
#'
#' @return data.table with subsampler information at different levels of the
#... |
715b0a03ac82677814335f802606870ec0081efaa1d04c0dea25f0d3cab97a2e | R | 23,890 | 829 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# plot_shap_dependence (generic) -----------------------------------------------
#' @title Create SHAP dependence plot.
#'
#' @description This method creates a SHAP dependence plot that shows the
#' dependence of the SHAP value of a feature ag... |
114712b90e2b24960f40b89b18081f0a9ad602f3a4621d211a420ea841330742 | R | 23,966 | 803 | .create_hyperparameter_challenger_sets <- function(
score_table,
parameter_list,
parameter_table,
time_optimisation_model,
score_optimisation_model,
acquisition_function,
n_max_bootstraps,
n_challengers = 20L,
smbo_iter,
measure_time,
random_admixture_fraction = 0.20
) {
# ... |
0e02260d81d3bc68b9ec4b9498304ce28b8749b183a9f731bc57c2e347ab26c0 | R | 24,090 | 568 | ---
title: "04_Setbp1_modulescore"
author: "Jordan Whitlock"
date: '2023-06-19'
output: html_document
---
This analysis serves as a targeted investigation of cell-specific pathway involvement using [VISION](https://yoseflab.github.io/VISION/articles/VISION-vignette.html) and the Seurat 'AddModuleScore' function.
```{... |
9966127554cc6eb75f978325185abb98511a678585643a762fe2e0ace171c037 | R | 24,214 | 452 | #!/usr/bin/Rscript
################################################################################################################
#### Top n connected genes as hubs + check enrichment for driver genes
################################################################################################################
li... |
e04c3703737f3c837fa8faa469b4bae65b0174d740d15929bce7bf0794ab4e4d | R | 24,281 | 412 | #' make_colour_dictionary(variable_list = NULL, pheno = NULL, phenolabels = NULL, columnsToUse = NULL, columnsToIgnore = NULL, class_to_ignore = "N_A", colour_of_class_to_ignore = "#bcc2c2", legacy_ctable = NULL, colour_table = NULL, within_variable_palette = TRUE, diverging_palette = "Spectral", binary_positive_colour... |
9bbb65e6b1487b38af421fc9a7031fbaa510e6b59d52d71271e5c64cb0ff43ca | R | 24,567 | 589 |
test_that("Importing biom files yield SummarizedExperiment objects", {
skip_if_not(require("biomformat", quietly = TRUE))
rich_dense_file = system.file("extdata", "rich_dense_otu_table.biom",
package = "biomformat")
me <- importBIOM(rich_dense_file)
expect_s4_class(... |
b58253b44b608efbff060b3325182c63b14e78ef6061a0c232ef0dba99128d35 | R | 24,624 | 1,052 | .all_gtable_guide_names <- function(type = "all") {
if (type == "all") {
return(c(
"guide-box-right", "guide-box-left",
"guide-box-top", "guide-box-bottom", "guide-box-inside"
))
} else if (type == "right") {
return("guide-box-right")
} else if (type == "left") {
return("guid... |
7f513fbd24dac6722342096b1656ecd5f61282fbf57636769df75c5ab50fd111 | R | 24,913 | 460 | #!/usr/bin/env Rscript
#"
## Fingerprinting - CAMCAN Cohort
## Data Analysis - Objective 1 - Describing the fingerprinting across the lifespan
#Script - Data Analysis - Objective 1
#Author: Frédéric St-Onge
#Date created: 2020-12-17
#Date modified: 2022-01-26
#Updates:
### 2022-01-26: Added compatibility to generat... |
80f90e8c72af240083e1c27351b604a2717139bf0340c7629f5ba1dde4b667be | R | 25,325 | 648 | # Rhddmjagsutils.R - Functions for simulation, model diagnostics, and parameter recovery
#
# Copyright (C) 2022 Kianté Fernandez, <kiantefernan@gmail.com>
#
# This program is free software: you can redistribute it and/or modify
# it under the terms of the GNU General Public License as published by
# the Free Software F... |
e6a4a6237487160777274a5c28488399554124388df4dc885e02fb0d7262be41 | R | 25,327 | 578 | ---
title: "VDJdb summary statistics"
author: "Mikhail Shugay"
date: "26-02-2022"
output:
html_document:
code_folding: hide
params:
legacy: "../out/legacy"
reference_years: "reference_years.tsv"
annotations: "annotations.tsv"
---
<!--
The release dashboard. Its published fragment -- everything between `!su... |
b223836364d4911f011514b266387c19c1421d7baef4cf60a41cc11902041b91 | R | 25,514 | 706 | ---
title: 'Neonatal brain dynamic functional connectivity and its association with 18-month neurodevelopment'
authors:
- Lucas França, Institution One
- Dafnis Batallé, Institution Two
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
bookdown::html_document2:
number_sections: false
toc: true
toc_f... |
868a88a257f12a6f1f563cc04db1973f2e6a1533628e1de59996916ab21b87e8 | R | 25,571 | 901 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# plot_shap_waterfall (generic) ------------------------------------------------
#' @title Create SHAP waterfall plot
#'
#' @description This method creates plots that show a waterfall of SHAP values
#' obtained from the data stored in a famili... |
0a7a62510e965f5a9c102af5307f1a712891ee11574676faef79a17d0e300120 | R | 25,670 | 383 | ############################
#
# Script for cleaning ABCD baseline hormone data
# Created by MEAB, last edited 2023-2-14
#
############################
#STEPS:
#Follow decision tree in Herting et al. 2021
#Clean confounds: Find nonsensical values in time confounds and set these to NA
#Add in medication use ... |
9c8a6f8ad349b8fcbcb97e8babeb9e40c6b5efab12711a9257038267ee6449d7 | R | 25,690 | 650 | ---
title: "Setbp1_PathwayAnalysisCortexKidney_07"
date: '2022-09-21'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
editor_options:
chunk_output_type: inline
---
This analysis comes after 'Setbp1_CellTypesDGEcortex_06.Rmd', 'Setbp1_CellTypesDGEkidney_06.Rmd'.
## Code:
###... |
ce55bad3ecd9e63be36803cc5dbc4eb536c97112e0b3d4825bdce6a9b647861f | R | 25,708 | 536 | # predicts expression of the test data with the trained and random networks
# predicts expression of MelBrainSys data
# creates correlation plots of these predicted and original expression:
# Figure-2-gene-expr-prediction-correl.png
library(ggplot2)
library(gridExtra)
library(parallel)
basePath = "/data/bcu_project... |
2cfa278357a581ca916db5fafc3287c2abe0ebfb2e2bd7bb9d6f91f74ec6ae04 | R | 25,739 | 957 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# plot_shap_force (generic) ----------------------------------------------------
#' @title Create SHAP force plot
#'
#' @description This method creates plots that show stacked SHAP force values
#' obtained from the data stored in a familiarCol... |
141259be4ac3263520441ddabd49049f38e47ef62bee58321f2c065c0845ca90 | R | 25,902 | 939 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# set_data_set_names -----------------------------------------------------------
#' @title Name datasets for plotting and export
#'
#' @description Tabular exports and figures created from a familiarCollection
#' object can be customised by set... |
b7333de121fcdd81b72c0d31f9df8a4567301594230e958d0dea949ee7b52b6f | R | 26,438 | 639 | ---
title: 'Neonatal brain dynamic functional connectivity and its association with 18-month neurodevelopment'
authors:
- Lucas França, Institution One
- Dafnis Batallé, Institution Two
date: "`r format(Sys.time(), '%d %B, %Y')`"
output:
bookdown::html_document2:
number_sections: false
toc: true
toc_f... |
86f5d237e9023b95c7e03b07f272b49de4e399b9d80518ae8c0b4a190891ff8f | R | 26,450 | 721 | testthat::skip_on_cran()
normalisation_test <- function(
data_list,
norm_method,
expected_shift = NULL,
expected_scale = NULL,
expected_min,
expected_max,
expected_none) {
# Iterate over the data sets.
for (ii in seq_along(data_list)) {
# Get values
x <- data_list[[ii]]
# C... |
f4610e09b84bee4a0b331d40bbaa65cab7bde363c737bfb13dfe5c68c291ea2b | R | 26,482 | 928 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarTaskCollect ----------------------------------------------------------
setClass(
"familiarTaskCollect",
contains = "familiarTask",
slots = list(
"data_file" = "character"
),
prototype = methods::prototype(
data_file = NA_... |
2a20faf6f7f766473ae5fa852ec39e2225fbb87cd4cf2419703ce98aa59c57ec | R | 26,484 | 1,027 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
.get_available_imputation_methods <- function() {
return(c(
.get_available_simple_imputation_methods(),
.get_available_lasso_imputation_methods()
))
}
.get_available_simple_imputation_methods <- function() {
return("simple")
}
.get_a... |
9bd0a58bf063ab1a3547dadacd61f2a6b94dca5cfd117c11a14fd6babab16bc5 | R | 26,486 | 727 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# extract_data (generic) -------------------------------------------------------
setGeneric(
"extract_data",
function(object, ...) standardGeneric("extract_data")
)
# extract_data (familiarEnsemble) ------------------------------------------... |
1a19c0302b1a7ebe635287145d716df7029d5475f6283e76ea02e3de51bd2d44 | R | 26,506 | 413 | #' compare_within_subsets(ExpObj = NULL, compareby = NULL, subsetby = NULL, ...)
#'
#' Orchestrates the three core JAMS comparison plots (ordination, relative abundance
#' heatmap, and alpha diversity) for a SINGLE analysis space, organised subset-by-subset.
#' For each subset tier produced by multiple_subsetting_sampl... |
d5bae24e9444b42b0979894766120a4c359156b0810c9e562ce6274c1c65412d | R | 26,616 | 905 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarGLM object -----------------------------------------------------------
setClass("familiarGLM",
contains = "familiarModel",
slots = list(
"encoding_reference_table" = "ANY",
"feature_order" = "character"
),
prototype = list... |
4902f7609d2d6cbfc65ae8432ef2061809559fedb02c0c52f2a2173bbe7cdcf5 | R | 26,754 | 925 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarDataElementVimpData object -------------------------------------------
setClass(
"familiarDataElementVimpData",
contains = "familiarDataElement",
slots = list(
"rank_threshold" = "numeric",
"rank_aggregation_method" = "chara... |
7bfd7f48515ddad0731bf06b151e04cd4b11715453b5a55ae0d785a5974175cc | R | 26,797 | 591 | ---
title: "Introducing familiar"
author: "Alex Zwanenburg"
date: "`r Sys.Date()`"
output:
rmarkdown::html_vignette:
includes:
in_header: familiar_logo.html
after_body: license.html
toc: TRUE
rmarkdown::github_document:
html_preview: FALSE
includes:
in_header: familiar_logo.html
... |
ffc2c831c1ed87d92ddbe6a20d50b08c236dfb32d55d5448380a6d4e6c347951 | R | 26,961 | 835 | ###################################################################
## PLOT FUNCTIONS
###################################################################
requireNamespace("graphics", quietly = TRUE)
requireNamespace("grid", quietly = TRUE)
requireNamespace("rgl", quietly = TRUE)
requireNamespace("ggplot2", quietly = TR... |
58a482f138ece617047ec9348992f691fd6725b75c4745b9543ddc4ff5809d45 | R | 26,978 | 596 | #transform_avgnets, uses a transformation often referred to as the softplus function. This transformation is useful when you want to ensure that all values in your data are positive between 0 and 1, but you want to preserve more of the relative differences between values
transform_avgnets<-function(n) {
n<-log(exp(1)... |
40b81c4d0c87c673c062185d3247df376562ee484fbc5325a20db9564c5d2b6d | R | 27,070 | 910 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# plot_univariate_importance (generic) -----------------------------------------
#' @title Plot univariate importance.
#'
#' @description This function plots the univariate analysis data stored in a
#' familiarCollection object.
#'
#' @param dra... |
bf76a735209420e8508a41537cf7829b29cf8804037948394a212db19bae6967 | R | 27,379 | 1,029 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# plot_auc_roc_curve (generic) -------------------------------------------------
#' @title Plot the receiver operating characteristic curve.
#'
#' @description This method creates receiver operating characteristic curves
#' based on data in a fam... |
a81d718a18cec1381ce59e0820bccbe7af037862f9a71cde863725dba484d411 | R | 27,393 | 630 | ---
title: "Manuscript 1 from Fernanda Hansen P. de Moraes Thesis - Cortical folding alterations in humans due to aging and diseases"
author: "Fernanda Hansen Pacheco de Moraes"
date: "1 dec 2021"
output:
html_document:
fig_caption: yes
fig_width: 8
number_sections: yes
theme: paper
toc: yes
edit... |
fbfff8591049ad2cd35262ab93dd81a0e6db82192172e7fbf7dbbb81ceb61fc9 | R | 27,926 | 999 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# plot_variable_importance (generic) -------------------------------------------
#' @title Plot variable importance scores of features during feature selection
#' or after training a model.
#'
#' @description This function plots variable importan... |
653afaca6ba41af6cead02cb841b0d28f94f2340544e9cb3d975636909ba8d36 | R | 27,965 | 621 | ---
title: "Introducing familiar"
author: "Alex Zwanenburg"
date: "2026-08-21"
output:
rmarkdown::html_vignette:
includes:
in_header: familiar_logo.html
after_body: license.html
toc: TRUE
rmarkdown::github_document:
html_preview: FALSE
includes:
in_header: familiar_logo.html
... |
95e9856f3b3a0ce5c5398b5ec8fb5acea3fc19ae4a27c81b4d8784342fd4b251 | R | 28,258 | 978 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarRanger object --------------------------------------------------------
setClass(
"familiarRanger",
contains = "familiarModel"
)
# familiarRangerDefault object --------------------------------------------------
setClass(
"familiarR... |
93c57daab86e145f90d1949e14a00348add991f6e166bd2c33163d738dbddedc | R | 28,437 | 702 | #' @name
#' agglomerate-methods
#'
#' @title
#' Agglomerate data using taxonomic information or other grouping
#'
#' @description
#' Agglomeration functions can be used to sum-up data based on specific criteria
#' such as taxonomic ranks, variables or prevalence.
#'
#' \code{agglomerateByRank} can be used to sum up dat... |
10565ef497730ead0ee84785cdc30211c6d46a4d9e4d9183234a722fbc24e0f6 | R | 28,454 | 572 | # This code was used for statistical analyses involving behavioral data for the following manuscript:
# Morgan L. Gustison, Rodrigo Muñoz Castañeda, Pavel Osten, Steven M. Phelps (2023) Sexual coordination in a whole-brain map of prairie vole pair bonding. eLife. DOI: https://doi.org/10.7554/eLife.87029.1
#=========... |
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