sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
a0daed89fd9cefce094609acf75aa298dfc3ba1f1c97759367cf5efe4fa780fc | R | 12,192 | 225 | #!/usr/bin/env Rscript
# =============================================================================
# make_stereoseq_mini_mouse_eyeball.R
#
# Build a small, format-faithful Stereo-seq test fixture by SPATIALLY CROPPING
# the full SAW 8.2.0 run for chip C04687E314 (mouse eyeball).
#
# SOURCE_DIR (a SAW `outs/`) -> ... |
a4431cb725ef94a1b356f44f7faf65a976f409e36672ad8231c994f2a7292870 | R | 12,203 | 412 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarTaskVimpHyperparameters ----------------------------------------------
setClass(
"familiarTaskVimpHyperparameters",
contains = "familiarTask",
slots = list(
"vimp_method" = "character",
"feature_info_file" = "character"
)... |
c7bd87c866ba766f4296ef23c6c1a127406341c45631d5c78cc14fe25a9d14a2 | R | 12,258 | 356 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# .file_exists (generic task) --------------------------------------------------
setMethod(
".file_exists",
signature(object = "familiarTask"),
function(object, ...) {
if (is.na(object@file) || is.null(object@file)) return(FALSE)
... |
8ce44b5b0128ff965153e03109deba42ab6970d567339c15f34425d82df6a9b4 | R | 12,317 | 374 | # regression_test.R - Testing JAGS fits of HDDM models with participant-level regressors in JAGS using Rjags in R
#
# 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... |
06c77e5901dd06d911ec7220c6ed4d2b2732839866cd6de8b1183eb9bef64745 | R | 12,332 | 382 | # Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_ci()
debug_flag <- FALSE
# Generic test.
familiar:::test_plots(
plot_function = familiar:::plot_model_performance,
data_element = "model_performance",
not_available_all_prospective = ... |
769c618ebf32341c447266770885a81d3d1cdcbddaa9b68ef69a9284d376dd04 | R | 12,403 | 352 | #blocked_exp_conds.R - Code to fit a HDDM with fixed start point from a blocked condition experiment using R2JAGS in R
# 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 publis... |
19088a10c45061487877232a7f2e35edef2c78814b8ff3a406efe71f2e50a668 | R | 12,459 | 274 | ##############################################
# Investigate DNAme disorder and gene expression levels.
# Updated: 2021.05.13
# Author: Kevin J.
##################################################
# Working directory for this analysis in the SCGP project.
mybasedir = "/Users/johnsk/github/"
setwd(mybasedir)
#########... |
f704dfd6a088349ca70ef955d99304fbb92a8dcf1ce754670361c8c84a1087c4 | R | 12,528 | 380 | #' @name
#' getAbundant
#'
#' @title
#' Determine abundant and rare taxa. Rare taxa can be further classified
#' to conditionally rare and permanently rare.
#'
#' @description
#' These functions determine abundant and rare taxa based on the abundances
#' of taxa. Compared to \code{\link[=getPrevalence]{getPrevalent}} a... |
8c6f11a46b6fce4cdf43ca16dcc40ed5837bd4a975afd0132316ae1d5201860e | R | 12,532 | 283 | test_that("Estimate Alpha Diversity Indices with Rarefaction", {
data(GlobalPatterns, package="mia")
tse <- GlobalPatterns
## Testing diversity
# Calculate the default Shannon index with no rarefaction with 3 different
# ways: default, niter=NULL, niter=0
tse <- addAlpha(tse, assay.type = "count... |
a26a9b12321d12f5c611b8fbe8edfb23571452234bddfb303829b1d32620219a | R | 12,662 | 293 | library(parallel)
library(pheatmap)
library(xlsx)
basePath = "/data/bcu_projects/MelBrainSys_PostdocProject_Gruetzmann/publications/2022-my-MelBrainSys-paper/scripts-etc-for-publication/"
myPath = paste0(basePath,"regNet/validation-cohort/")
setwd(myPath)
localRlibs = paste0(basePath,"conda/lib/R/library/")
library(r... |
f4b9844c25db6d26abdb7c0cc6b149c0d31c4a8b5ac254109fb176b97962ad68 | R | 12,727 | 307 | #################################################################
###################### ABCD: code for figures ###################
#################################################################
# These are the code for the figures used in the manuscript
########## load all the packages
packages <- c('psych','doPa... |
417337238917f66160c097aa104a30725d6080ade18cd1a1849a40860b3e87b8 | R | 12,732 | 379 | ##############
### 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... |
d6432f9d0b725f00fd9ed66515fbd480199eb3a549beb923d181bc966c2f59da | R | 12,747 | 302 | ##################################
# Visualize the DNAme disorder and DNAme values across promoters
# Updated: 2020.04.30
# Author: Kevin J.
###################################
# Working directory for this analysis in the SCGP project.
mybasedir = "/Users/johnsk/github/"
setwd(mybasedir)
############################... |
e7ebe652a7a610dc9f46db5cec4cd17915372663d8d897902e0c988347db5a4e | R | 12,787 | 485 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarDataElementFeatureExpression object ----------------------------------
setClass(
"familiarDataElementFeatureExpression",
contains = "familiarDataElement",
slots = list(
"feature_info" = "ANY",
"evaluation_time" = "ANY"
),
... |
f87d4837f52432113640e652c8ebe80cc52d1f1aa38c89eff9b2eb835e51aa3b | R | 12,852 | 472 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# extract_predictions (generic) ------------------------------------------------
#'@title Internal function to extract predicted values from models.
#'
#'@description Collects predicted values from models in a `familiarEnsemble`.
#'
#'@inheritPar... |
7d9d808ad7672137c004ddfeebc44988d5f3f9e0c9a8386b1ba1044f91a01783 | R | 12,920 | 204 |
analysis.ui = navbarMenu("Analysis",
"Visualizations",
tabPanel("dataset traces",
fluidRow(
column(5, pickerInput("heatmap2_dataset", NULL, c(), width = "100%", multiple = T, options = list(title="Select dataset(s)", `actions-box`=T))),
column(4, pickerInput("heatmap2_se... |
e3018b63eff0a529f682c844c46c0349117da85badeaceb7f3db20a41e43c5ae | R | 12,991 | 293 | ##################################
# Determine + visualize TFs that regulate cell states (IDHwt samples).
# Updated: 2021.05.16
# Author: Kevin J.
###################################
## Working directory for this analysis.
mybasedir = "/Users/johnsk/github/"
setwd(mybasedir)
###################################
# Nece... |
296934e66b8046696c2f0e04f16fe9226fc6013a3997f2c16eb8a4d4d4d8aa5f | R | 12,997 | 229 | #' check_for_banked_samples(search_path = NULL, query_samples = NULL, mode = c("inventory", "query"), recursive = TRUE, flag_duplicates = TRUE)
#'
#' Scans a directory tree for banked JAMS samples and reports what is available.
#'
#' Unlike the previous version, which required the EXACT path to a single JAMSarchive
#' ... |
6481d5ef339d5f18f38536258cedb1035250e2b6b97292346e0369633a8a3668 | R | 13,020 | 297 | ##################################
# Determine + visualize TFs that regulate cell states (IDHmut samples as example).
# Updated: 2021.05.16
# Author: Kevin J.
###################################
## Working directory for this analysis.
mybasedir = "/Users/johnsk/github/"
setwd(mybasedir)
#############################... |
8a8c58ae16443df99bd47666d1e6c07477a6470db3ef7e0e962d7caad6ec28f4 | R | 13,036 | 316 | ###########################
## Retinol PGS tuning and association
## William Reay (2023)
###########################
library(dplyr)
library(data.table)
library(ggplot2)
library(lmvar)
library(ComplexHeatmap)
library(circlize)
library(ggpubr)
library(caret)
setwd("~/Desktop/Retinol_GWAS/TwinsUK_app/E1205_20.01.2023... |
6018827db1b4b285dd5b234e14aa044b6695388255fe769d4821f5a2a32b168b | R | 13,387 | 404 | ---
title: "03_Setbp1_DiffTargeting_FEA"
author: "Jordan Whitlock"
date: '2023-05-24'
output: html_document
---
Goal: Perform Functional Enrichment Analysis (FEA) on heterozygous enriched, differentially targeted genes from the Setbp1 gene set using Gene Ontology and Human Phenotype from gprofiler.
```{r libraries-pr... |
abccc286e1d78409eb22fe8e8b9a5635f984943ac04a6a2dc76515a00811d32c | R | 13,408 | 365 |
#' @title createNearestNetwork
#' @name createNearestNetwork
#' @description create a nearest neighbour (NN) network
#' @param gobject giotto object
#' @param type sNN or kNN
#' @param dim_reduction_to_use dimension reduction method to use
#' @param dim_reduction_name name of dimension reduction set to use
#' @param... |
f559fcae1865621d133b2d03da6635a205b18c1647f86414fedc3b64a930e6ee | R | 13,408 | 499 | ---
title: "Setbp1_CellTypesKidney_SoupX_06"
author: "Jordan Whitlock"
date: '2023-04-12'
output: html_document
---
# *Single-nuclei analysis: Identify Cell Types Kidney for SoupX*
* Loading libraries
* Selecting resolution
* Investigating top markers for Kidney Cortex
* ID cell types for SoupX
## Code:
This analysi... |
82f3a24d5b0e6be53ea84cc9168364eb1208661be8f5c4af260d6fc492036cab | R | 13,482 | 274 | #!/usr/bin/Rscript
##################################################################################################################
##### Limma analysis on expression data
##################################################################################################################
library(data.table)
library(s... |
2b1a58f24dbc0008aafb787098e4ba38d54ece28e49038f5008eb843f9339586 | R | 13,485 | 377 | # import tdt
.tdt.temp = reactiveValues()
shinyDirChoose(input, "data_import_tdt_dir", root=c(directories, `Working Directory`='.', getVolumes()()), filetypes=c("tsq","tev"))
observeEvent(input$data_tdt, {
.tdt.temp$headers = NULL
output$data_import_tdt_path = renderText("select a folder that contain... |
07c1304c11f3a3225ce07de5667fda56e5b055554e263afc3c292ba566cf7ca8 | R | 13,510 | 375 |
#' @title checkGiottoEnvironment
#' @name checkGiottoEnvironment
#' @param verbose be verbose
#' @details Checks if a miniconda giotto environment can be found.
#' Can be installed with \code{\link{installGiottoEnvironment}}.
#' @export
checkGiottoEnvironment = function(verbose = TRUE) {
## get operating system
... |
3a0deb0b396eb99dc0de6685f8ab068f12e5bd7769f3ebd30027bcff735c4200 | R | 13,652 | 226 | ####################################################################################
## Max Gold
## Code to generate figures from data
####################################################################################
#############################################
## Import packages
##################################... |
6f788d5e273b9d47c2f3c6e4910c3d16bec6bed7748d171211d74161a90371e5 | R | 13,747 | 339 |
#quantWorkFlow preprocess############
# extract data
input = "20210219_Maxquant_proteinGroups.txt"
output = "PCx"
blast_path = "ncbi-blast-2.7.1+/bin/"
db1_name = "human-uniprot-20181022.fasta"
db2_path = "mouse-all-20210202-APP added.fasta"
source("proteomic_data_extract_R")
#remove human APP expression
rm... |
d69b764e56855d4e48cd0fe9d066c01df71c542b627cda5138c5b0155f2f0acf | R | 13,786 | 411 |
#' @title convert_mgImage_to_array_DT
#' @name convert_mgImage_to_array_DT
#' @description converts a magick image object to a data.table
#' @param mg_object magick image or Giotto image object
#' @return data.table with image pixel information
#' @keywords internal
convert_mgImage_to_array_DT = function(mg_object) {... |
2d6a94c8ee1629cba967f531ae87bfc51d8a41fa7d7c44c18e0ecfbda6d72a37 | R | 13,854 | 498 | # This file contains all the generics
#' @rdname addAlpha
#' @export
setGeneric(
"addAlpha", signature = c("x"), function(x, ...)
standardGeneric("addAlpha"))
#' @rdname addAlpha
#' @export
setGeneric(
"getAlpha", signature = c("x"), function(x, ...)
standardGeneric("getAlpha"))
#' @rdname getDissimi... |
c6fe67aed221deb054da7f0a96d7220de096a89e49db1492b692182816bf3bd4 | R | 13,877 | 346 |
#' @title write_giotto_viewer_annotation
#' @description write out factor-like annotation data from a giotto object for the Viewer
#' @param annotation annotation from the data.table from giotto object
#' @param annot_name name of the annotation
#' @param output_directory directory where to save the files
#' @retur... |
b406fa6838145f62d264e9fd739a0b3d10d33134e13c42762e138e66843a889b | R | 13,892 | 274 | #' tweak_ctable(ctable = NULL, pheno = NULL, outfile = NULL, ncol = 3, launch_browser = TRUE)
#'
#' Interactive Shiny colour editor for a JAMS colour table (ctable), e.g. the output of
#' make_colour_dictionary(..., object_to_return = "ctable"). Presents one colour-wheel
#' picker per class and returns the edited ctabl... |
eedea79b69e63c52c708108fc5605b02baf0d64e30cfbd7f358a77b388391835 | R | 13,918 | 258 | #' generate_JAMS16_methods(opt = NULL, rawfastqsdf = NULL, trunclen_used = NULL,
#' samples_requested = NULL, samples_used = NULL,
#' subsampling_triggered = FALSE, outfile = NULL)
#'
#' Synthesises a prose Methods paragraph describing the choices applied in a
#' JAMS16 r... |
ece7983eb6cb51276b481bb41c2d2af92802b65c64c0184f2a0b58bc59f7545a | R | 13,939 | 498 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
# promote_learner (hyperparameter learner) -------------------------------------
setMethod(
"promote_learner",
signature(object = "familiarHyperparameterLearner"),
function(object) {
learner <- object@learner
if (learner %in% .get_availa... |
2cd15366ba48d0e1071239854561230bd45d5ef09740e14641bb0d5a6d46eb99 | R | 13,986 | 432 | ---
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
... |
287e16131eba9a811a140e7a2ae5b58a6d18424952525259345ef67f1cdaa3cf | R | 13,995 | 480 | #' Internal checks on common plot input arguments
#'
#' @param x_range (*optional*) Value range for the x-axis.
#' @param y_range (*optional*) Value range for the y-axis.
#' @param x_n_breaks (*optional*) Number of breaks to show on the x-axis of the
#' plot. `x_n_breaks` is used to determine the `x_breaks` argument ... |
9b74883637b5ce7fe2eb7bb2fc52d96f5995415d8bac5b1e558357d97e555686 | R | 14,016 | 306 | # process_RRBS.R - this script assesses quality of RRBS data generated using bismark methylation extractor
require(methylKit)
require(genomation)
require(GenomicRanges)
require(GenomicFeatures)
##### Input arguments #####
# Sample linker file
#linker <- read.delim("~/sumner/verhaak-lab/scgp/data/RRBS/20-verhaak-006/... |
e91cbb99020bec01bb61a8c3a0062f444189a240b9f563e939b61d9289f5c940 | R | 14,040 | 503 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
.parse_hyperparameters <- function(
data,
parameter_list,
outcome_type,
vimp_method = NULL,
learner = NULL,
detector = NULL
) {
# Check if any hyperparameters have been set
if (length(parameter_list) == 0L) return(para... |
7435cbd0b9b055886f5c4455ee445883dd30b762da6243cdbfe15b52ca595336 | R | 14,060 | 326 |
#' Network inference module: Define sub-network inference problems
#'
#' This is an internal function that divides the total number of genes (nrResponses) in a data set into a fixed number (cores) of equal-sized intervals of genes. This is required to separate the global network inference problem into independent sub-... |
f3f024bb4b52111c260daf9ea315e28f3f523c4e53af400a3d22036f85cc5bbe | R | 14,087 | 261 | #' calculate_matrix_stats(countmatrix = NULL, uselog = NULL, statsonlog = TRUE, stattype = NULL, classesdf = NULL, invertbinaryorder = FALSE, fun_for_l2fc = "geom_mean", threshPA = 0, numthreads = 1, nperm = 10000, paired = FALSE)
#'
#' Returns a data frame with the statistics for a feature count matrix ordered by high... |
7be5c90df111b5d6239a3bbda2edec40ef9cb33180208f0933333ca69df5d69b | R | 14,220 | 523 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
setClass(
"featureInfoParametersTransformationPowerTransform",
contains = "featureInfoParameters",
slots = list(
"method" = "character",
"transformer" = "ANY",
"fitting_parameters" = "ANY"
),
prototype = list(
"method" = "n... |
3bc42477da35582110391b6aa25ade73578bf848bd3b3d31558e92aea1081e4b | R | 14,260 | 339 | #' plot_taxsplit_tree()
#'
#' Helper plotting function for use exclusively within plot_relabund_features (q.v.). Do not attempt to use this outside of this context, or you will find yourself in a frightful muddle.
#' @export
plot_taxsplit_tree <- function(taxsplit_df = NULL,
strat_tt = N... |
a5bd9d92ce4edae998afa5bf3da79b6d057125be5d2f9d9f51f86ff9206e2d80 | R | 14,352 | 303 | #!/usr/bin/Rscript
##################################################################################################################
##### Limma analysis on expression data
##################################################################################################################
library(data.table)
library(s... |
6f23f5a4ad5f04d159c547f90c8c4c9a201aa2a94beeb6f257da954953cac797 | R | 14,477 | 453 | # Don't perform any further tests on CRAN due to time of running the test.
testthat::skip_on_cran()
testthat::skip_on_ci()
verbose <- FALSE
# Test acquisition functions for all hyperparameter learners -------------------
for (hyperparameter_learner in familiar:::.get_available_hyperparameter_learners()) {
for (acqu... |
ac596293920199cb5509c3d866e8b0d05c233d9cb8f7760b24418fbe4a5ff8ac | R | 14,490 | 514 | ---
title: "Setbp1_Integration_02"
author: "Jordan Whitlock"
date: '2022-09-06'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
---
This analysis follows 'Setbp1_QC_01.Rmd'.
### Loading in all required libraries
```{r}
set.seed(2178)
library(devtools)
library(magrittr)
library(... |
dd020e5b03471cc7310dbdeb3d04dfade0a00c7a1ace13ba1e4b50cfe2a02b1d | R | 14,540 | 242 | #!/usr/bin/Rscript
library(data.table)
library(dplyr)
library(limma)
library(stringr)
library(ggplot2)
library(tidyr)
library(EnhancedVolcano)
library(biomaRt)
library(edgeR)
library(ggpubr)
library(parallel)
library(tidyverse)
library(reshape2)
library(purrr)
setwd('/home/boris/Documents/PhD/single_sample/')
#######... |
40bfd9f48875d79c3c548947fadbb07bc284473adb5027d2c77b94b89750a083 | R | 14,554 | 526 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarSVM ------------------------------------------------------------------
setClass(
"familiarSVM",
contains = "familiarModel"
)
setClass("familiarSVMC", contains = "familiarSVM")
setClass("familiarSVMNu", contains = "familiarSVM")
setCla... |
aed40eda413302f83d564b3f6abe2900cc7cd6b925d8ff698c2ef5b8a5ec4b1f | R | 14,576 | 373 | testthat::skip_on_cran()
# power.transform is also required for transformation_method = "none".
if (!rlang::is_installed("power.transform")) testthat::skip()
# Basic tests ------------------------------------------------------------------
data <- familiar:::test_create_synthetic_correlated_data(
outcome_type = "co... |
8047d144519687dce65732a43fe29c4c562d31fa525533342ac8984d77f6cdc6 | R | 14,684 | 285 | ---
title: "Preprocessing DepMap data"
author: "Joke Deschildre"
date: "17 maart 2021"
output: html_document
version: "R version 4.0.3 (2020-10-10) -- Bunny-Wunnies Freak Out"
---
```{r}
library(data.table)
library(limma)
library(stringr)
library(edgeR)
library(ggplot2)
```
#Preprocessing of the neede... |
222ca04cdd87347d8ccfa3093f77bff3fbf56ec9189177e621a44d58f5915b7d | R | 14,746 | 435 | #' Summarizing microbiome data
#'
#' To query a \code{SummarizedExperiment} for interesting features, several
#' functions are available.
#'
#' @inheritParams getPrevalence
#'
#' @param top \code{Numeric scalar}. Determines how many top taxa to return.
#' Default is to return top five taxa. (Default: \code{5})
#'
#' @p... |
da4bdbe657f24bb80d6656fb17eedfa1fa5500f0568cf1247f653ceb301ca47a | R | 14,941 | 356 | ##################################
# Generate ranked and annotated scatterploot
# of subtype-averaged TFBS motif-specific DNAme disorder
# Updated: 2021.05.15
# Author: Kevin A.
###################################
### Preprocessing
#### Load data
library(topGO)
library(tidyverse)
library(openxlsx)... |
63bd1d31ac1468ec51f54ec5c985c4a1530006576754b5d3830f4867187b01b7 | R | 15,073 | 417 | ########################
### Acknowledgements ###
########################
# Code for plotting 2D slices of MRI scans has kindly been provided by Petya Kindalova! Many thanks!
#################################
### Slice plots: MRI template ###
#################################
#-----------------
## set constants
#... |
1d15545d02f41018e95f1d89a8fa365161edaa2901dec684295ccdf6afd6fe7a | R | 15,106 | 565 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# .train -----------------------------------------------------------------------
setMethod(
".train",
signature(
object = "familiarNoveltyDetector",
data = "dataObject"
),
function(
object,
data,
get_additional_info = FAL... |
d5288d0b3b6a34f58b00dc9be30ca98dd2fa9379400ed1cb128757d33d77a3d6 | R | 15,161 | 408 | ---
title: "mini_visium"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{mini_visium}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r setup, eval=FALSE}
library(Giotto)
```
### Inst... |
5105f9e5dc50b4973c234b4f5bac3ec306193795e9de4ad9b7e19a756b03f8ed | R | 15,230 | 518 | familiar:::test_all_vimp_methods_available(
familiar:::.get_available_ranger_vimp_methods(show_general = TRUE))
familiar:::test_all_vimp_methods_available(
familiar:::.get_available_ranger_default_vimp_methods(show_general = TRUE))
# Don't perform any further tests on CRAN due to time of running the complete test.... |
e21b404781f0f94c8e37c362d5a1eae527d0d42d2bd5d417e9d169c1cef98c18 | R | 15,467 | 298 | #!usr/bin/Rscript
library(data.table)
library(stringr)
library(limma)
library(EnhancedVolcano)
library(plyr)
library(edgeR)
library(ggplot2)
library(dplyr)
library(viridis)
library(jamba)
library(data.table)
library(ggplot2)
library(ggfortify)
library(ggpubr)
library(biomaRt)
library(clusterProfiler)
library(enrichplo... |
843e5d854c38e0071155a80d9c676d159fb217b940de399b0a29de6f2d6f1487 | R | 15,474 | 302 | #!/usr/bin/Rscript
################################################################################################################
#### Top n connected genes as hubs + check enrichment for driver genes
################################################################################################################
l... |
ba6f9f376c137b16ecd3688272be77a0b8f2261e2d175c030ba86a6df2baebc0 | R | 15,528 | 170 | #Script to clean and categorize medication use data in ABCD baseline dataset.
#Focusing on medication use in last 2 weeks (medsy01.txt)
#Created by MEAB on 2021-10-29
#last edited 2022-01-31
#Libraries and set folders
library(dplyr)
library(psych)
library(tidyr)
library(stringr)
origdatadir <- 'C:/path/to/A... |
0ec1f20fc3ff7ec851953efde33834a0e7f58bf40a4d3e27c15e323e81323df3 | R | 15,567 | 399 | #' Converting a
#' \code{\link[SummarizedExperiment:SummarizedExperiment-class]{SummarizedExperiment}}
#' object into a long data.frame
#'
#' \code{meltSE} Converts a
#' \code{\link[SummarizedExperiment:SummarizedExperiment-class]{SummarizedExperiment}}
#' object into a long data.frame which can be used for \code{tidyv... |
564a401384fe8d55a3877b87a584a83ac052a4a051dc4ebdb289b34eefbbff27 | R | 15,655 | 315 | #!/usr/bin/env Rscript
###############################
###############################
#"
## Fingerprinting - CAMCAN Cohort
## Data Analysis - Polynomial (Quadratic) regression - Variability in FC and HCTSA
#Script - Data Analysis - Quadratic regression - Variability
#Author: Frederic St-Onge
#Date created: 2020-12-... |
93e3583e472398de6338677083953a03f119461335351295a0a35556915c50b7 | R | 15,695 | 296 | # - Generate mutational signatures from vcf data
require(VariantAnnotation)
require(MutationalPatterns)
### Input arguments ###
# Input directory for variant calls
variantDir <- "~/Documents/scgp/mutect2/post_processing"
# Reference genome
ref_genome <- "BSgenome.Hsapiens.UCSC.hg19"
library(ref_genome, character.o... |
6f7d0b8f312ed55e3f61703976508e822022984e830ec2225effe54126b3bff5 | R | 15,705 | 461 | library(shiny)
library(ggplot2)
library(ggpubr)
library(RColorBrewer)
library(Matrix)
choice.gene <- readRDS('choice.gene.rds')
meta.pdx <- readRDS('PDX.Meta.rds')
Matrix.PDX <- readRDS('PDX.Counts.rds')
meta.patient <- readRDS('Patient.Meta.rds')
Matrix.Patient <- readRDS('Patient.Counts.rds')
ind <-... |
a1e55bc65b22776be72610567e8002d9b7c778bd49988a308ecc595d79f64b6f | R | 15,732 | 533 | ---
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... |
8ea77a91c1165de796bbeb4057ae75ba664665d26f43718ec2eef4a90b3ed4b7 | R | 15,780 | 625 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarTaskGenericFeatureInfo -----------------------------------------------
setClass(
"familiarTaskGenericFeatureInfo",
contains = "familiarTask",
prototype = methods::prototype(
task_name = "create_generic_feature_info",
data_id... |
18c7067f3bd0b73cdf6dc3a036e2625f3392c913480b3487b5b686502a88b786 | R | 15,822 | 401 | #' EEGfMRIPav_5_StayBehavior_Neural
#'
#' Perform mixed-effects linear and logistic regression testing how
#' trial-by-trial BOLD amplitude and EEG power predict stay behavior
#' as reported behavioral results section and in Supplementary Material S15.
#' Interactive script, run step-by-step, NOT in one Go.
#'
#' Re... |
4c51c469480d03d8fc65ee3e73860ed5142fb2bbd9bd93e0ce6c425063aea90d | R | 15,858 | 270 | #' retrieve_features_by_taxa(FuncExpObj = NULL, glomby = NULL, only_allow_CSBs = FALSE, LKTsToKeep = NULL, assay_for_matrix = "BaseCounts", wantedfeatures = NULL, wantedsamples = NULL, asPPM = TRUE, PPM_normalize_to_bases_sequenced = TRUE, PPMthreshold = 0, append_metatada = TRUE, include_samples_with_zero = TRUE, retu... |
02217185189537401ef6d895899137bda7aa26081b4b448a08e35524b792b6b4 | R | 15,911 | 282 | ---
title: "Setbp1_AmbientRNA_SoupX_02"
author: "Jordan Whitlock"
date: '2023-04-12'
output: html_document
---
# *Single-nuclei analysis: Run SoupX*
* Loading libraries
* Selecting resolution
* Investigating top markers for Kidney Cortex
* ID cell types for SoupX
All resources for running SoupX were found in the foll... |
a33918a2864b51320156c6ebdc5c62e0ef37fc25062ba85b519de103aee76b13 | R | 15,916 | 337 | ---
title: "03_alpaca_network_analysis"
output: html_document
date: "2023-06-20"
---
Goal: Identify the overlap between all community and cell type pairings for each tissue as well as only between the Communities containing TF Setbp1.
* Cerebral cortex: TF Setbp1 is in community 1
* Kidney: TF Setbp1 is in community ... |
a2da8e967daf1d3380e3b0aef138105fcdf1b0925a4d30fd57c28a187b737710 | R | 15,942 | 429 | #' Calculate dissimilarities
#'
#' These functions are designed to calculate dissimilarities on data stored
#' within a
#' \code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-class]{TreeSummarizedExperiment}}
#' object. For overlap, Unifrac, and Jensen-Shannon Divergence (JSD)
#' dissimilarities, the function... |
af4b91540d69772abc9c61ba82fe2eff7e98f4f7b9d3822396acb64d0cb4d599 | R | 15,946 | 395 | # creates
# - connectivity table for paper - SupplTable-S5-connectivity-stats.xls
# - DotPlot of connections - SupplFigure-S5-source-target-interactions.png
# - known melanoma driver gene ranking in networks - SupplTable-S6-key-driver-ranks.xls
library(ggplot2)
library(gridExtra)
library(parallel)
library(xlsx)
li... |
954076c04e8bec805d72b00155dd6089573995fb8d1e79d62f9c961f97e1a5e3 | R | 15,988 | 338 |
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(biomaRt)
library(cl... |
52b156cb63bfaf63a20990e1798e4e17a7a09dd07b8545f66b1f758df3490f83 | R | 16,010 | 630 | # Don't perform any further tests on CRAN due to parallelisation issues.
testthat::skip_on_cran()
testthat::skip_on_ci()
apply_vector_fun <- function(x) {
Sys.sleep(x)
return(x)
}
apply_vector_fun_2 <- function(x, y) {
Sys.sleep(x)
return(y)
}
apply_list_fun <- function(x) {
Sys.sleep(x)
return(list("... |
58255d0c5738d958e5d4e959cbb00b828b76d6a16070d94ce6597c9e5e271b89 | R | 16,026 | 419 | #' Convert a \code{TreeSummarizedExperiment} object to/from \sQuote{BIOM}
#' results
#'
#' For convenience, a few functions are available to convert BIOM, DADA2 and
#' phyloseq objects to
#' \code{\link[TreeSummarizedExperiment:TreeSummarizedExperiment-class]{TreeSummarizedExperiment}}
#' objects, and
#' \code{\link[Tr... |
97c291c4d5139295f00b353e1f3946c3030f3da4ba733cebc94c93501813541c | R | 16,274 | 411 | #' Import Metaphlan results to \code{TreeSummarizedExperiment}
#
#' @param file \code{NULL} or \code{DataFrame}-like object. Defines the file
#' path of the Metaphlan file. The file must be in merged Metaphlan format.
#'
#' @param col.data a DataFrame-like object that includes sample names in
#' rownames, or a sing... |
f828bd83e1f03a27e9708aedd789fd693df76f1e5ddac059146d9ae1ef63c650 | R | 16,317 | 307 | #' check_resources(opt = opt, application = "WGS")
#'
#' JAMSalpha function
#' @export
check_resources <- function(opt = opt, applications_to_check = c("base", "reads", "assembly", "kraken", "host", "annotation", "16S", "blast")){
opt$abort <- FALSE
deplist <- list(base = c("pigz", "wget"), reads = c("sratool... |
46533534c37c89ff5df03c57aeaae54a1dd7a27caae46033a2bd117d10ee1576 | R | 16,329 | 245 | #' titrate_feature_counts(ExpObj = NULL, glomby = NULL, only_allow_CSBs = FALSE, samplesToKeep = NULL, PPM_thresholds = NULL, completeness_thresholds = NULL, n_ppm = 10, n_comp = 10, prevalence_pct = 5, PPM_normalize_to_bases_sequenced = FALSE, ignoreunclassified = TRUE, class_to_ignore = "N_A", return_plots = TRUE, ve... |
cc9967d889b0869df3462aa4bdc70844e8f14592d631b7906ab35b2192525af2 | R | 16,477 | 296 | ---
title: "rev_plot_icc_measures"
output: html_document
date: "2022-10-10"
---
```{r}
library(tidyverse)
library(glue)
library(ggnewscale)
options(scipen = 999)
```
```{r}
icc_sub_meas <- read_csv("/PATH/TO/INPUT/icc_metrics_sub_100_40.csv")
icc_task_meas <- read_csv("/PATH/TO/INPUT/icc_mertics_task_100_40.csv")
icc... |
45419634ccb906b50028c678992e487647eb7303085bf01615899554adba8e61 | R | 16,484 | 580 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarSurvRegr -------------------------------------------------------------
setClass(
"familiarSurvRegr",
contains = "familiarModel",
slots = list("encoding_reference_table" = "ANY"),
prototype = list("encoding_reference_table" = NULL)
... |
5445de192adc1e58dc1c93fbddc2b270d6d9d12de84068f71ba79d5c3d72d607 | R | 16,522 | 466 | ##############
### Script ###
##############
# Run Gibbs sampler for BLESS model (= 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... |
5768e4d479d6d701c9da6f9c382dc5ce67fc3aec9ed7c43bfeb628ae15305e9a | R | 16,599 | 571 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
setClass(
"familiarRegressionVimp",
contains = "familiarVimpMethod"
)
setClass(
"familiarUnivariateRegressionVimp",
contains = "familiarRegressionVimp"
)
setClass(
"familiarMultivariateRegressionVimp",
contains = "familiarRegressionVim... |
d17ee2c50e62e6f8578a1b3e077ce01268d0e5bf3e45e12100c2eac7ba85b883 | R | 16,678 | 410 | ##############################################
# Generate a heatmap with bulk and single-cell RNA data
# Updated: 2020.05.31
# Author: Kevin J.
##################################################
## Project working directory
mybasedir = "/Users/johnsk/github/"
setwd(mybasedir)
########################################
... |
13f33f85a09ab8ab3b2c335fb5a502476ed77338811fcbf26c39695f3df6a55e | R | 16,826 | 555 | testthat::skip_on_cran()
testthat::skip_on_ci()
# Train a model to transfer settings.
data <- familiar:::test_create_good_data("survival")
# Train a simple linear GLM using the good dataset.
fam_model <- familiar:::test_train(
data = data,
cluster_method = "none",
imputation_method = "simple",
hyperparameter_... |
8f3c6d03cea852cbdeb205d19b85b19beac775b97c1f472a6e1062be82b055e0 | R | 16,829 | 309 | # export.R
shinyFileSave(input, "export_r", root=c(`Working Directory`='.', getVolumes()()), filetypes=c("rds"))
shinyFileSave(input, "export_excel", root=c(`Working Directory`='.', getVolumes()()), filetypes=c("xlsx"))
observeEvent(input$export_format, {
shinyjs::hide("export_excel_opts")
shinyjs::h... |
bf760c70ee0a9f1f417e77cece7fdd85d0482aec332d05cec1b7705581e5c2ff | R | 16,849 | 316 | ##################################
# Investigate relationships between promoter DNAme and other variables (Klughammer et al. Nature Medicine).
# Updated: 2021.05.12
# Author: Kevin J.
###################################
# Working directory for this analysis.
mybasedir = "/Users/johnsk/github/"
setwd(mybasedir)
######... |
40ba8adeca82987f8f736405d5ee0e5fe653776e6e7b6bb5d78dc365ee02758b | R | 16,905 | 374 | ---
title: "Performance metrics"
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
a... |
c1ba64e931210fbc2f48619ad4b18083b3aaa8a31f80f4103855bcd10e4b7df0 | R | 16,975 | 376 | ---
title: "Performance metrics"
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
... |
dd683e9be41a9eccf727690d0088ffb89a372ed4f8abc36f55f9af00e01640d1 | R | 17,009 | 398 | ---
title: "Setbp1_CellTypesCortex_05"
author: "Jordan Whitlock"
date: '2022-09-08'
output:
html_document:
toc: true
toc_depth: 2
toc_float: true
---
# *Single-nuclei analysis: Identify Cell Types Cerebral Cortex*
* Loading libraries
* Selecting resolution
* Investigating top markers for Cereb... |
15fbf205e471f5ddd62525a4803567e68c83cbbb04a3c735c7ce281f307a55cb | R | 17,011 | 454 | #' Exporters to common formats for microbiome data outside of R
#'
#' @name export-methods
#' @aliases exportRaw exportQIIME2 exportMothur
#'
#' @description
#' There are a few very popular external tools for microbiome analysis,
#' including QIIME2 and mothur. However, R does not currently provide any class
#' to acco... |
b0671c52c2f9bc6d1518466a52b31e93f171733e17dcb1fb32ab3a4741e53322 | R | 17,022 | 490 | familiar:::test_all_metrics_available(
metrics = familiar:::.get_available_confusion_matrix_metrics()
)
# Don't perform any further tests on CRAN due to time of running the complete
# test.
testthat::skip_on_cran()
testthat::skip_on_ci()
# power.transform and other packages are required.
if (!rlang::is_installed("p... |
d03f7191c4162fef947a6ac8560cc2fa7532242fe3ee826c582504a4c692ddde | R | 17,174 | 323 | #' EEGfMRIPav_4_StayBehavior()
#'
#' Perform mixed-effects linear and logistic regression testing effects of
#' outcome valence, outcome salience, and performed action on stay behavior.
#' as reported in behavioral results section.
#' Interactive script, run step-by-step, NOT in one Go.
#'
#' Requires that .csv file... |
b031c34b9038bf3c14dd9adce19409f23cb20396a6273796fdad3cbcff51cc7e | R | 17,341 | 300 | #' filter_experiment(SEobj = NULL, samplesToKeep = NULL, featuresToKeep = NULL, featmaxatleastPPM = 0, featcutoff = c(0, 0), GenomeCompletenessCutoff = NULL, GenomeContaminationCutoff = NULL, applyfilters = NULL, discard_SDoverMean_below = NULL, normalization = NULL, PPM_normalize_to_bases_sequenced = TRUE, only_allow_... |
d369965f2439f4d3d9553f866b9b019b720a72a36903f8a5aacbc74310d19875 | R | 17,350 | 694 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarMetricRegression -----------------------------------------------------
setClass(
"familiarMetricRegression",
contains = "familiarMetric",
slots = list("robust" = "character"),
prototype = list("robust" = "none")
)
# initialize ... |
8daf0252a386b763180694b838dbc82c7df8bf57a9818cebc8b4da137c0b3834 | R | 17,395 | 650 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarDataElementModelPerformance object -----------------------------------
setClass(
"familiarDataElementModelPerformance",
contains = "familiarDataElement",
prototype = methods::prototype(value_column = "value")
)
# extract_performanc... |
4d3206be44d154e7fdeb1ca6af39b654b989f08277810b04e8a46ce61d1d30b0 | R | 17,416 | 340 | ##################################
# Determine the DNAme differences between cell states (separately for IDHmut & IDHwt)
# Updated: 2021.05.16
# Author: Kevin J.
###################################
# Working directory for this analysis.
mybasedir = "/Users/johnsk/github"
setwd(mybasedir)
#############################... |
c94e03e6c44aa3d87c8b9f60b60576f5d47eaccf5187b0613d20311ccde57b27 | R | 17,489 | 387 | # Save method for model saving. The save function appears in base and is always
# explicity imported.
setGeneric("save")
setGeneric(".complete", function(object, ...) standardGeneric(".complete"))
setGeneric(".copy", function(object, ...) standardGeneric(".copy"))
setGeneric(".predict", function(object, data, ...) s... |
f40590a2331144dd6c22558ed44e955ba9b2225eb26de236ed1e5d08cbf14255 | R | 17,537 | 396 | context("mergeSEs")
test_that("mergeSEs", {
# Load data
data(GlobalPatterns, package="mia")
data(esophagus, package="mia")
data(enterotype, package="mia")
tse1 <- GlobalPatterns[1:50, ]
tse2 <- esophagus[1:50, ]
tse3 <- enterotype[1:50, ]
# Expect errors
expect_error( merge... |
69ff0441aae1d007e7cd6fc4b0e75df6cc201999d692aa6d6803792d149770d9 | R | 17,640 | 467 | #' Perform mediation analysis
#'
#' \code{getMediation} and \code{addMediation} provide a wrapper of
#' \code{\link[mediation:mediate]{mediate}} for
#' \code{\link[SummarizedExperiment:SummarizedExperiment-class]{SummarizedExperiment}}.
#'
#' @param x a
#' \code{\link[SummarizedExperiment:SummarizedExperiment-class]{Su... |
fb6ea2eb8bb4f70d51046edd398a757ad06173bb77fe18c58c16679c55aa5bc2 | R | 17,713 | 618 | #' @include FamiliarS4Generics.R
#' @include FamiliarS4Classes.R
NULL
# familiarTaskVimp -------------------------------------------------------------
setClass(
"familiarTaskVimp",
contains = "familiarTask",
slots = list(
"vimp_method" = "character",
"hyperparameter_file" = "character",
"feature_in... |
c68a0a579aaaa1a1e626092de93a1be5848016857d460b160df091f1f4aa0f8e | R | 17,789 | 434 | #' \code{mia} Package.
#'
#' \code{mia} implements tools for microbiome analysis based on the
#' \code{SummarizedExperiment}, \code{SingleCellExperiment} and
#' \code{TreeSummarizedExperiment} infrastructure. Data wrangling and analysis
#' in the context of taxonomic data is the main scope. Additional functions for
#' ... |
ee04fcf6e0756d426b1192444b68a3e779f3f4819136e4de09af05c7383b7d86 | R | 17,799 | 338 | #!/usr/bin/env Rscript
###############################
###############################
#"
## Fingerprinting - CAMCAN Cohort
## Data Analysis - Regression - FPC
#Script - Data Analysis - Regression - Var ~ ICA
#Author: Frederic St-Onge
#Date created: 2021-12-21
#Date modified:
#Version: 2.0
#--------
#Purpose:
#--... |
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