sha256 stringlengths 64 64 | language stringclasses 28
values | size int32 1 491k | lines int32 1 23.2k | content stringlengths 1 200k |
|---|---|---|---|---|
33e51e8010f97c60794963ad40129e8b7e5181fa8f520c127d17ac5d5990026c | R | 17,578 | 376 | ---
title: "CBDA with SuperLearner"
author: "Simeone Marino and Ivo Dinov"
date: "June 13, 2016"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Introduction
This is a R Markdown document describing the first steps of **Compressive Big Data Analytics-CBDA** implementat... |
bd6376f4b398e11e25062eb4f3a2b0f4173857459b8ef7135c2c231c0c868205 | R | 17,681 | 486 | ##########################
##### READ LIBRARIES #####
##########################
library(crayon, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(backports, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES/")
library(withr, lib.loc = "/Shared/IVR/apvoigt/programs/R-4.1.0/bin/PACKAGES... |
765914256538a5e8edc5dfc1325e37428e1b2ac2720df7c5243989ebdfe1573f | R | 17,690 | 459 | ---
title: "Statistical analysis ADC Study"
author: "Vitus Gosch"
date: '2022-11-17'
output: html_document
---
Df "Xrepro_annonymized" was created by reading in text file output out of shell scripts written to analyze image data. The Df is available in annonymized form, meaning all possible identifiers are removed.
N... |
a0c2dbe495e7da9b0a6459df50aefeff96c9116cf59b9dcccef3e798f03e3ba4 | R | 17,764 | 342 | ---
title: "Analysis of human embryo"
output:
rmarkdown::html_vignette:
df_print: "kable"
vignette: >
%\VignetteIndexEntry{Analysis of human embryo}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{... |
e59ffd436c079619a796233fb6ec4e53e85a9d7e08c77e3d87900a15f9ce4672 | R | 17,792 | 383 | # # Data pre-processing for significance tests for shifts in psychometric curves
# For each subject and condition:
# - obtain the predicted value for probability 0.5
# - conditioned on the previous choices
library(tidyverse)
source("functions/CountConditionalProbabilitiesExtended.R")
load("processed_data/allValues/... |
d6ec42321ef53ceecaaad2ecb6ca24372b27b370eea08d009fbcd1fff8567ffc | R | 17,940 | 407 | library(tidyverse)
library(here)
library(sjPlot) #plot_model
library(cowplot) #arrange plots
#Prepare buzz and bray datasets: ----
cpodbrayti=read.table(here::here("AI-ARS_ToDryad","OFB_VarForARS_AI_CPOD_Encounter10Buzz5_PropForagingMinutes_Tides.txt"),
sep = "\t" ,he... |
be22fb7c11860e05417ac2251942af5f06a7aa4a0d03033babc0a8480315ae24 | R | 17,951 | 473 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
job_id=as.numeric(args[1])
label=as.array(args[2])
workspace_directory=as.array(args[3])
M= as.numeric(args[4])
top= as.numeric(args[5])
max_covs= as.numeric(args[6])
#dataset_file = as.array(args[2])
#alpha=as.numeric(args[3])
# i_exp=as.numeric(args[5])
pr... |
bd8f25d010bebda383277b3e117dc5d0771a4276316bc28e0c2735932f0fcf70 | R | 18,003 | 423 | ---
title: "SBR2 - Bayou Shifts Code 042722"
output: html_notebook
editor_options:
chunk_output_type: console
---
New Categories based on Masahito's suggestion - Frug/Non-Frug, Fol/Non-Fol - using the % time spent feeding as a resource
All Primates Analysis
```{r}
rm(list=ls())
#devtools::install_github("kopperud/... |
99719dafbc3b6c49228168bb08d2d8b4a420186133cf55639ad28d3070b18963 | R | 18,208 | 405 | #!/usr/bin/env Rscript
load("~/Info_for_consolidation.RData")
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.nu... |
ff748323d4c2f3aff91d8e664aa72b223a0e08da5f882c53fafeee08207fa839 | R | 18,282 | 436 | ---
title: "SB R2 BDS ms Intercept Plots - Platyrrhines"
output: html_notebook
editor_options:
chunk_output_type: console
---
Platyrrhines Analysis
Run: SIMMAPS 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
Fol... |
82e49ea1a1a86bd0a278693520675e3502c53685d3830c38f10a0057eda3f42e | R | 18,364 | 428 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(args)
print(label)
... |
016536ace7e9549a77437270d7ba0cfa069482bb8b3eadad2fb8eb827bdcee6f | R | 18,527 | 439 | ---
title: "SB R2 BDS ms Intercept Plots - Haplorhines"
output: html_notebook
editor_options:
chunk_output_type: console
---
Haplorhines Analysis
Run: SIMMAPS 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
Fol.N... |
27de7968e63e891787fe30ca1ab55fdc277b8ca2a1a7a1ee9520a524fe1981bc | R | 18,552 | 374 | # Test significance between learning models
# Purpose: For significance test
# Script for plot preparation: data_preprocessing_LLhood_absolute.R
library(fs)
library(tidyverse)
### === LOAD DATA === ###
file_list <- dir_ls("processed_data/LLhood_Easy_vs_Difficult/") |>
path_filter(" |Stim_loglik_easy.Rda|Stim_l... |
cd9ea656760b033dc93f06928316df45f3f72cc1b5e386cbaa987993fd4c2bd3 | R | 18,896 | 403 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
dataset_file = as.array(args[3])
workspace_directory=as.array(args[4])
i_exp=as.numeric(args[5])
# label to append to the RData workspaces as soon as they are created
label... |
7afbdc8368b0e01bfd04e644dc81dc1483432cf75d5e35122bf7439da89470de | R | 19,072 | 443 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.numeric(args[7])
print(args)
print(label)... |
e33d462caac426a079a676f9637ee6c905c3cd865ce4264d38e0dc666fff79b3 | R | 19,099 | 440 | ---
title: "SB R2 BDS ms Intercept Plots - Strepsirrhines"
output: html_notebook
editor_options:
chunk_output_type: console
---
Strepsirrhines Analysis
Run: SIMMAPS 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
... |
c6c54388132e8f8e3f80ab2584919b52f01cbdcd83f72b147c3cbe18ded1543b | R | 19,359 | 524 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
job_id=as.numeric(args[1])
label=as.array(args[2])
workspace_directory=as.array(args[3])
#M= as.array(args[4])
#top= as.array(args[5])
#max_covs= as.array(args[6])
#dataset_file = as.array(args[2])
#alpha=as.numeric(args[3])
# i_exp=as.numeric(args[5])
print... |
3495c2bf2ac7ebf8d60fc674d390d3886ffd4145bdee6795c6103b1a3076f9cb | R | 19,368 | 517 | ---
title: "SiT - figures"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression isoforms in tissu... |
ecc67068e422e697b67fa344ed9510733787faf8cccd31a61199ec9ae2dbfa69 | R | 19,411 | 373 | # Test significance between learning models and Stim-Only Model
# Purpose: For significance test (mainly for reply letter)
# Script for plot preparation: data_preprocessing_LLhood_absolute_vs_Stim.R
library(fs)
library(tidyverse)
### === LOAD DATA === ###
file_list <- dir_ls("processed_data/LLhood_Easy_vs_Difficul... |
56bfca2de9507159faae4fa98ee3f26fbe3f1f29006a076eb9abe5a75f850318 | R | 19,434 | 288 | library(data.table)
library(dplyr)
library(ggrepel)
library(ggplot2)
library(RNOmni)
library(ggpubr)
library(ensembldb)
library(EnsDb.Hsapiens.v86)
library(cowplot)
load("/mnt/depts/dept04/compbio/human_genetics/users/yhuang5/muscle_targets/regenie/gencode.v38.annotation.exon.rda")
plot.gene.name <- "TTN"
chr <- uniq... |
1fd93ae4f365faf0ff46257a5232907da511eed108cda422030f8d77926537d6 | R | 19,463 | 410 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
dataset_file = as.array(args[3])
workspace_directory=as.array(args[4])
i_exp=as.numeric(args[5])
# label to append to the RData workspaces as soon as they are created
label... |
494d574ec2672bc17f6edca99e4f3ed38b1187375f6da2af60e0819042e31431 | R | 19,913 | 484 | # Plot GLM Coefficients for all modalities and learning models.
library(tidyverse)
library(patchwork)
library(ggtext)
load("processed_data/Results/df_GLM_absolute.Rdata")
# Filter for "auditory", "vestibular", and "visual" modalities separately
sub_auditory_TP <- dfabs_GLM |>
filter(modality == "auditory",
... |
8286054585e7fd64185e2ca0d26e0a509eeb305c94ec55335cb2e184b40132ea | R | 19,965 | 416 | # working directory where the files are retrieved from and where they are saved
workspace_directory <- setwd("~/Downloads")
# specs for the experiments to run
arg_file <- c("arg_Binomial_new_Test.txt")
# dataset to analyze
dataset_file <- c("Binomial_dataset_new_sd.txt")
# label to append to the RData workspaces as soo... |
4b902a0029ae9d2cacdcd6cd82b720bb2bf7b19dd9e9add3da69873580ceacea | R | 19,975 | 499 | # Conditional probabilities and corresponding correlation matrices for conditions
library(tidyverse)
library(patchwork)
library(ggcorrplot)
library(ggtext)
### === OVERVIEW === ###
# First part of the script creates the plots for the psychometric curves
# Second part of the script creates corresponding correlation m... |
2d0a453cc22a2da6370869b0e4cf491469a8a5e88112e2cbadf53612010b4d07 | R | 20,027 | 456 | #!/usr/bin/env Rscript
load("~/Info_for_consolidation.RData")
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
alpha=as.numeric(args[3])
dataset_file = as.array(args[4])
workspace_directory=as.array(args[5])
label=as.array(args[6])
i_exp=as.nu... |
038cbc77971b4983d57339d55aed2b8291542e272e1e2ff92f37628c30c1632e | R | 20,038 | 417 | #!/usr/bin/env Rscript
args = commandArgs(trailingOnly=TRUE)
print(args)
arg_file = as.array(args[1])
print(arg_file)
j_global=as.numeric(args[2])
dataset_file = as.array(args[3])
workspace_directory=as.array(args[4])
i_exp=as.numeric(args[5])
# label to append to the RData workspaces as soon as they are created
label... |
e88059a6a2ca47f4b47d86d2bddc32498f417c64d1a16376fdcd55f29afbcdff | R | 20,039 | 448 | context("Test data management")
## .get_SVD is correct
test_that(".get_SVD works for matrix", {
mat <- matrix(1:50, 10, 5)
rownames(mat) <- paste0("c", 1:10)
colnames(mat) <- paste0("g", 1:5)
res <- .get_SVD(mat, center = T, dims = 1:2, scale = T, scale_max = NULL)
expect_true(inherits(res, "svd"))
exp... |
9d12e52a93ee63bd1adba31d03c900a145a4c6205e957745707398875aa8b84d | R | 20,070 | 435 | ---
title: "CBDA-SL and Knockoff Filter Results on the ADNI Dataset - 9000 jobs x 9 experiments - MULTINOMIAL CASE"
author: "Simeone Marino"
date: "January 12, 2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some useful information
This is a summary of a set of 9... |
6ce184ac2f6a72ab5df92cafd3c70a13649bb586672fcb94d294a7878ed99ca4 | R | 20,231 | 529 | # SuperLearner Introduction
September 16, 2016
Instructor: Chris Kennedy
Assumed prior training:
* D-Lab's R Intro (12 hours) or Chris Paciorek's R Bootcamp (14 hours)
* Evan's caret training (yesterday)
## Outline
* Background
* Installing
* Create dataset
* Review available models
* Fit single models
* Fit ense... |
a033b968bed60416d0d6d876e6ae3cf360dd4f918da32202db7538fbadbc6ecf | R | 20,447 | 484 | # Log-Likelihood Analysis for TP, IF and AF when Omega is fixed
# Data preprocessing to plot LLhood, when Omega is fixed across learning models
library(tidyverse)
library(ggtext)
library(patchwork)
library(DescTools)
# Source functions
source("functions/function_LogLikelihood.R")
# Load data
load("processed_data/al... |
c6867d16b1832052fa344ac00d54d072ad601133b0a6c372077e92528e3bee66 | R | 20,481 | 318 | ---
title: "Guide to Compressive Big Data Analytics [CBDA]"
date: "`r Sys.Date()`"
author: "Simeone Marino, Jiachen Xu, Yi Zhao,
Nina Zhou, Yiwang Zhou, Ivo D. Dinov.
University of Michigan, Ann Arbor"
output:
prettydoc::html_pretty:
#theme: cayman
theme: hpstr
highlight: githu... |
f2e3151d6e0e1b12360e8768ba0439c644f5646c0ddfea57ccd0153a4d896468 | R | 20,490 | 431 | library(plyr)
library(dplyr)
library(pROC)
library(PRROC)
#Load packages
#Import files
setwd("/home/repositories/git/nkmj/topicc-local/data")
hospadmit <- read.csv("HOSPITALADMIT.csv")
picudischarge <- read.csv("PICUDISCHARGE.csv")
picuadmit <- read.csv("PICUADMIT.csv")
labsvitals <- read.csv("PHYSIOSTATUS.csv")
hosp... |
56a8721c811903d6ece9c5cdb67d90433e81c3335ca32f849a32505fa75f756f | R | 20,557 | 445 | ---
title: "CBDA-SL and Knockoff Filter Results on the ADNI Dataset - 9000 jobs x 9 experiments - MULTINOMIAL CASE"
author: "Simeone Marino"
date: "January 12, 2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some useful information
This is a summary of a set of 9... |
3b35c334ecf25169285654e6c8f4f97e0ccf78f22ce1628a76a1940fe21034a4 | R | 20,662 | 447 | ---
title: "CBDA-SL and Knockoff Filter Results on the ADNI Dataset - 9000 jobs x 9 experiments - MULTINOMIAL CASE"
author: "Simeone Marino"
date: "January 12, 2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## Some useful information
This is a summary of a set of 9... |
1fcaea0d2b1aad139cc3c1c1b7fbbc45880ef9c35a90b00cf35bca08bad6d1c3 | R | 20,725 | 481 |
# #' Functions for other matching methods: scmap, Seurat
# #'
# #' @return A list of:
# #' \item{method}{Call of matching method used.}
# #' \item{pctmat}{A matrix of % matched cells. Rows are reference clusters, and columns are query clusters.}
# #' \item{prediction.scores}{A matrix of Seurat prediction scores. Rows ... |
44d2dad881540e86c6ace2d1817ca154d893b729b0bff7b25075770bfb19c446 | R | 20,735 | 324 | ---
title: "v_1"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
#SuperLearner Function
##SL.library
### Generalized linear model (GLM)
GLM is fitting generalized linear models, specified by giving a symbolic description of the linear predictor and a description of the err... |
d0e63c6cc466a3394807d0f8d117bb117cd1481cb9adb146b128aca808a571a2 | R | 20,756 | 697 | # Vendored from https://github.com/CahanLab/cancerCellNet (MIT License),
# commit dc29a8e7982461416a12fd3372bb844d35bd0ee0, by tools/vendor_cnlib.R.
# Only the functions the PACNet web app calls are included. Local changes
# are marked with "# PACNet-web:" comments.
addRandToSampTab<-function(classRes, sampTab, desc, ... |
b06a4fde360c631900618994a4aa50db280d8b509312ee84df907c26c13b236b | R | 21,245 | 505 | ---
title: "Grabowski et al. - SBR2 - Bayou Analysis - log Brain Size ~ log Body Size"
output: html_notebook
editor_options:
chunk_output_type: console
---
Setup for Bayou with Only log BoM as a predictor
From: Bayou - SBR2 - All Species 042722.Rmd
Species Only Analysis
```{r}
#Clear workspace
closeAllConnections()... |
b982d410192286a858fac97ca9ac829b29b2aba9be511887eae876d0b26abd12 | R | 21,369 | 482 | #' @title
#' CBDA Validation function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function generates *max_covs - min_covs* nested models based on the ranking
#' returned by the *Consolidation* function. It also consolidates all the *max_covs - min_covs*
#' workspaces into a single one.
# @par... |
fecf2b7901794fa8dae04038ed7d73b588a81a2b0a8f3e2cb4ddd85b1e044c9a | R | 21,692 | 576 | ---
title: "SBR2 - BDS ms All Primates Make SIMMAPs Code - Univariate - 062722 v1"
output: html_notebook
editor_options:
chunk_output_type: console
---
All Primates Analysis
Run: SIMMAPS 062722 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is f... |
aebfc2cba4e2ef4d21155120a1503b51720dff6730dbf5a54f7449883550dc92 | R | 21,851 | 482 | #' @title
#' CBDA Validation function for Compressive Big Data Analytics
#'
#' @description
#' This CBDA function generates *max_covs - min_covs* nested models based on the ranking
#' returned by the *Consolidation* function. It also consolidates all the *max_covs - min_covs*
#' workspaces into a single one.
... |
a9585d43b10a35c231effc0264bd7a5eb9e0c21dc259c7fd9fa0d12e9620efde | R | 22,278 | 764 |
```{r}
source("~/data2/rstudio/birds/utils/seq_packages.R")
source("~/data2/rstudio/birds/utils/seq_analysis.R")
source("~/data2/rstudio/birds/utils/db.R")
#source("~/data2/rstudio/birds/utils/go.R")
source("~/data2/rstudio/birds/utils/ggplot.R")
#source("~/data2/rstudio/birds/utils/voom.R")
source("~/data2/rstudio/bi... |
ab6f1babaf40af613e29cc9d9263a96e210893da89eb93bbaba8235f99ca22c1 | R | 23,711 | 531 | # Plot all Likelihood Plots
# Initial comparison (i.e. response-based model vs. generative-based model)
# 1. delta in LLhood
# 2. absolute in LLhood
# 3. constantOmega in LLhood
library(tidyverse)
library(patchwork)
library(ggpubr)
library(rstatix)
### === DELTA LL HOOD === ###
load("processed_data/Results/df_LLh... |
9c47d12b14d0431b2691fc2fb87068108297cebca44fc842d95c6a37b2cf4349 | R | 24,132 | 524 | ---
title: "MS – Metabolomics Analysis"
author: "Abishek Arora"
date: "23/26/2022"
---
# Mass Spectrometry
Mass spectrometry was performed for cell lines exposed to fluoxetine (3 µM) for 5 days. The cell lines that were a part of this study were CTRL9II, ASD12BI, ASD17AII and AF22. This analysis pipeline utilities me... |
8d0aa9c25aaae59f72d62cb44fc5bdcf0c87e6fb0fec7d3a1342ca698fbc8f18 | R | 24,362 | 841 |
library(data.table)
library(stringr)
library(parallel)
library(monocle)
#monocle2, https://www.bioconductor.org/packages/release/bioc/html/monocle.html
library(cicero)
#cicero for monocle2, https://cole-trapnell-lab.github.io/cicero-release/docs/
library(glassoFast)
library(hash)
inferloop.bed2granges<-function(bed)... |
a550d3c67673b8b6d55d10b078f945f84f699e324381a5c3269889e8e19c3150 | R | 24,489 | 652 | ---
title: "NMR Data Analysis Tutorial"
author: "Nightingale Health Ltd."
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{Blood Biomarker Analysis Tutorial}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r setup, include = FALSE}
knitr::opts_chunk$se... |
04758ca211b5fdf16bab05f85e0e83f6bd79da3935391c3df07506bb7f384dd3 | R | 24,709 | 604 | # Conditional probabilities and corresponding correlation matrices for conditions
# Note: # factor levels are crucial for correct plotting!
library(tidyverse)
library(patchwork)
library(ggtext)
source("functions/CountConditionalProbabilitiesExtended.R")
# Data pre-processing
load("processed_data/allValues/allValu... |
a19b2314503da7a0d1edf2e325dec30c27e9e8949dbf57502bf347ed7f8dcee6 | R | 24,730 | 812 | ---
title: "KPR_Network_Analysis.Rmd"
author: "Whitney Friedman"
date: "`r Sys.Date()`"
output:
rmdformats::robobook:
self_contained: true
thumbnails: false
lightbox: false
gallery: false
highlight: tango
---
# KPR Network Analysis
This RMD describes and generates the KPR network analysis that is... |
ce20834ab079fab5d8b5e2c11d5d9aa46065083f6ae732f4d3735ca90b230d61 | R | 25,173 | 999 |
library(dplyr)
library(foreign)
library(kableExtra)
library(pROC) ## ROC
library(caret) ## ROC
library(rpart)
library(rpart.plot)
library(gtsummary)
library(multicon)
library("scales")
# library(multicon) # scale2
hex_codes1 <- hue_pal()(6)
# Data Preparation
ad.raw = read.csv("../data/ad_full_d... |
df02fb8623f1de8b92f442ad06308dd692598a006d9a2bd373186ffaa41a0e1b | R | 25,780 | 544 | #' @title
#' Training/Learning Compressive Big Data Analytics - CBDA.training function
#'
#' @description
#' This CBDA function comprises all the input specifications to run a set M of subsamples
#' from the Big Data [Xtemp, Ytemp]. We assume that the Big Data is already clean and harmonized.
#' This version 1.0.0 ... |
6dc2d0895c262b94c20400c5c64e42f5f95e38bc4a3ad283a3928b9f7d17b133 | R | 25,978 | 491 | library("VennDiagram")
library("RUVSeq")
library("RColorBrewer")
library("pheatmap")
library("ggplot2")
library("topGO")
library("sleuth")
library("biomaRt")
library(WGCNA)
library("ggbiplot")
library(reshape)
library(gplots)
library(ops)
library(calibrate)
library(biomaRt)
library(sva)
library(ggplot2)
library("corrpl... |
b2356ab3b5f95af746255bab0e66c4e6bb4512403c973d116ef1ce4f0c46da36 | R | 26,049 | 591 | ---
title: "CBDA-SL and Knockoff Filter Results on the ADNI Dataset, 9000 jobs x 2 experiment - Accuracy as ranking metric"
author: "Simeone Marino, Ivo Dinov, Jiachen Xu"
date: "`r format(Sys.time(), '%b %d %Y')`"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = FALSE)
```
## Some u... |
175f7f3eec8eb8fc19c069cc7230bd12fb5d3f33960bd18fe59c011db621cc39 | R | 26,324 | 544 | #' @title
#' Training/Learning Compressive Big Data Analytics - CBDA.training function
#'
#' @description
#' This CBDA function comprises all the input specifications to run a set M of subsamples
#' from the Big Data [Xtemp, Ytemp]. We assume that the Big Data is already clean and harmonized.
#' This version ... |
464f540bfe74f1c1187f0113bada4ec1db11ebd514ce1b94ad9139a67ce39193 | R | 26,545 | 631 | ---
title: "SB R2 SIMMAP_ACE - All Primates - 062522 v1"
output: html_notebook
editor_options:
chunk_output_type: console
---
All Primates Analysis
Run: SIMMAPS ACE 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
... |
de0955c69d1bb4319470086d28743e1bb52d8844693e856c7346f6e60c170534 | R | 26,654 | 600 | # GLM Analysis Easy Vs. Difficult
# Either TP, IF, or AF are tested as predictors for participants' responses
# Here, stimulus intensity is NOT included in an interaction
# Instead, the analysis is performed for easy and difficult trials separately
library(tidyverse)
library(ggtext)
library(patchwork)
library(DescToo... |
ac7de63b26fc8261ca21619f461e5888ecf8d54c7c9f673df01264c0c32af4f3 | R | 26,753 | 666 | # Details ---------------------------------------------------------------
# AUTHOR: James Foster DATE: 2023 03 29
# MODIFIED: James Foster DATE: 2023 03 31
#
# DESCRIPTION: Compare slopes and offsets between two groups of animals,
# one of which was walking and one of ... |
ee4ad4e29985b6e34085424fbd2d4749662816ccd91169c7616e6738d161ed6a | R | 26,773 | 611 | ---
title: "Guide to SuperLearner"
date: "March 16, 2017"
author: "Chris Kennedy, University of California, Berkeley"
output:
prettydoc::html_pretty:
#theme: cayman
theme: hpstr
highlight: github
toc: TRUE
number_sections: TRUE
css: styles.css
rmarkdown::html_vignette:
toc: ... |
2dad8fceaee3be58881a4039004ccf7c94b12ece504d0ee266938e395383519d | R | 26,867 | 503 | setwd(dirname(rstudioapi::getSourceEditorContext()$path))
library(ggplot2)
library(dplyr)
library(scales)
library(tibble)
library(ggrepel)
library(ggpubr)
library(gtable)
library(grid)
library(readxl)
png("Fig. 1.png", width = 2000, height = 2000)
############NOVOPlasty###############
table1 <- read_excel("Summary ... |
8e96f26f5a683af23f7bec18bbc5f0b3147c64230e71fc7691fcb3154b7bb72a | R | 27,137 | 537 | ---
title: "Analysis of CITE-seq"
output:
rmarkdown::html_vignette:
df_print: "kable"
vignette: >
%\VignetteIndexEntry{Analysis of CITE-seq}
%\VignetteEngine{knitr::rmarkdown}
%\VignetteEncoding{UTF-8}
---
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
```
```{r, eval ... |
ebb9cf58a5b8e1b24dd594332640f97941601c20f97b6f4e7b628eeb6fc4cd21 | R | 27,244 | 445 | ################################
## Exome-wide burden analysis ##
## Author: Yunfeng Huang ##
## Date: Sep-20-2021 ##
################################
library(data.table)
library(dplyr)
library(ggplot2)
library(ggpubr)
library(ggrepel)
library(forestplot)
pheno <- fread("/mnt/depts/dept04/compbio/human_... |
54f357a23527549866e992d020e31de4bf3c1894d078a31e558016e32d3598cf | R | 27,330 | 571 | #' @title
#' Main Compressive Big Data Analytics - CBDA function
#'
#' @description
#' This CBDA function comprises all the input specifications to run a set M of subsamples
#' from the Big Data [Xtemp, Ytemp]. We assume that the Big Data is already clean and harmonized.
#' This version 1.0.0 is fully tested ONLY o... |
b09931d703559960806fb8223dab5ff1371c5028565b7b581de48d8bc8ed5e2c | R | 27,640 | 614 | # Loglikelihood approach easy vs. difficult (generative)
# What is done:
# --> loglik <- pbinom(1, prob = probs, q = y, log.p = TRUE) |> sum()
# NOTE: Data is backtransformed within the function with plogis()
# Because for the GLM-Analyses, data was transformed using qlogis()
library(tidyverse)
# Load data
load("pr... |
9ecc0a7afe08bc610df3921bd8d5e90cd9033937017e22e009d335ae02e0d6c3 | R | 27,901 | 571 | #' @title
#' Main Compressive Big Data Analytics - CBDA function
#'
#' @description
#' This CBDA function comprises all the input specifications to run a set M of subsamples
#' from the Big Data [Xtemp, Ytemp]. We assume that the Big Data is already clean and harmonized.
#' This version 1.0.0 is fully tested ... |
c10f440ee754a94a8b0cb7c1571aba9dda39f65f3d9664a3869bc704411d9336 | R | 28,699 | 603 | # GLM Analysis Easy Vs. Difficult Generative
# Either TP, IF, or AF are tested as predictors for participants' responses
# Here, stimulus intensity is NOT included in an interaction
# Instead, the analysis is performed for easy and difficult trials separately
library(tidyverse)
library(ggtext)
library(patchwork)
libr... |
3b4c392b806bedcd005a3c04b2ccf83028b10f947614c16a841312f9ef9a07b6 | R | 28,795 | 908 | library(shape)
library(randomcoloR)
library(DescTools)
setwd(dirname(rstudioapi::getSourceEditorContext()$path))
linMap <- function(x)
(x*3/max(x))
colorize <- function(x)
{
if(x == "A"){return("#007FFF")}
else if(x == "T"){return("#FFF700")}
else if(x == "G"){return("green")}
else if(x == "C"){return("#96... |
69d879058f5673a1bda3f6a5fed219303da4b6d065661461c8df5dc48f0f594f | R | 29,401 | 867 | # Euclidean distances from Vp_Extract.m parameters
# packages & functions ----------------------------------------------------
library(here)
library(tidyr)
library(dplyr)
library(tibble)
library(data.table)
library(ggplot2)
library(scales)
library(ggbeeswarm)
source('~/Dropbox/phd/framebyframe/genotypeUtilities.R')
... |
e6ce89c4820ae712c48a625358c117fedcf3c6c85ab7abc799739c66ca854cc0 | R | 29,638 | 719 | # Loglikelihood approach easy vs. difficult
# What is done:
# --> loglik <- pbinom(1, prob = probs, q = y, log.p = TRUE) |> sum()
# In this script: Stimulus-Only Models are computed too
# NOTE: Data is backtransformed within the function with plogis()
# Because for the GLM-Analyses, data was transformed using qlogis... |
52e02f5932bc45a8b09caaaf856486b66a1af6a6f9279aaf1235834ddbe0ad53 | R | 29,810 | 730 | #!/usr/bin/env Rscript
## GenomeScope: Fast Genome Analysis from Unassembled Short Reads
##
## This is the automated script for computing genome characteristics
## from a kmer histogram file, k-mer size, and readlength
## Number of rounds before giving up
NUM_ROUNDS=4
## Coverage steps to trim off between rounds
ST... |
ac7ee1f22cacd094f3eaf243d1cbcb01c082b68d295fa7880215a2f546f2f224 | R | 31,047 | 723 | # CellNet WebApp server for top-pairs, rank-based agnostic analysis of expression profiles
# Packages and helper sources are loaded in global.R.
server <- function(input, output, session) {
# The validation tab only applies after a retrain; hide it until then.
hideTab(inputId="analysis_tabs", target="newTrainingV... |
7f9d458ca40747cf4c133835c9b46897a83930b7012e680dee240e0473bead82 | R | 31,189 | 811 | ---
title: "SiT - Figures"
author: '[Kevin Lebrigand](mailto:lebrigand@ipmc.cnrs.fr)§, Joseph Bergenstråhle §, Kim Thrane §, Annelie Mollbrink, Pascal Barbry, Rainer Waldmann and Joakim Lundeberg'
date: 'Compiled: `r format(Sys.Date(), "%B %d, %Y")`'
abstract: 'The spatial landscape of gene expression isoforms in tissu... |
0a470b53af3648b307699765796aa50d761dbf60a1a9d1e6efbcce251ddd0c9a | R | 32,584 | 797 | #Calculate some quality control measures
setwd("/Volumes/Daniel_Lukens/HannahData/")
library(lattice)
library(DESeq2)
library(pheatmap)
library(GSA)
library(seq2pathway)
library(ggplot2)
library(dplyr)
library(tidyverse)
library(EnhancedVolcano)
library(pals)
qc <- read.table('qc_metrics.txt',sep='\t',header=TRUE)... |
5f375661acf5ef49f018179b07c750ef0b8087b1d38f14c6698c8fce6ecb8a44 | R | 33,097 | 773 | library(dplyr)
library(readr)
library(mvtnorm) # For multivariate Gaussian distribution
library(moments) # For generating moments
GenerateTrainingData <- function(m, Nk, training_prefix, logThetaMin, logThetaMax, logAlphaMin, logAlphaMax, logSigmaSqMin, logSigmaSqMax) {
scenarios <- c("Conserved", "Dive... |
d4cb259d551918309b00dff40851edda8160dcdb0bbd318fa1c884146675b282 | R | 34,297 | 792 | ---
title: "CBDA Code R Markdown"
author: "Andrew Soncrant"
date: "March 23, 2017"
output: html_document
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
```
## CBDA R Script
Before we can begin to do any real data analysis, we first need to do some setup work. Here we load the dataset to be examin... |
f0144aa3532c9fbca932d3b654e854479ebfc5337db59e3afc8f1dbf360abcee | R | 35,741 | 927 | ---
title: "A Principal Odor Map Unifies Diverse Tasks in Olfactory Perception"
author: "Brian K. Lee†, Emily J. Mayhew†, Benjamin Sanchez-Lengeling, Jennifer N. Wei, Wesley Qian, Kelsie Little, Matthew Andres, Britney B. Nguyen, Theresa Moloy, Jacob Yasonik, Jane Parker, Richard C. Gerkin, Joel D. Mainland, Alex Wil... |
2302164d6f7fbad7b6947ee1843371879251cd485143064790ff9b3707b3a858 | R | 36,576 | 961 | ---
title: "SBR2 - Fig 2 Plots - mean centered data"
output: html_notebook
editor_options:
chunk_output_type: console
---
Run: Using SIMMAPS 062522 v1 data originally
Mean centered brain and body size, then ran the best model for each group in Slouch
```{r}
rm(list=ls())
library(ggplot2)
library(phytools)
library(... |
6b0911822cdafd623398c27a09b8324480761b7a60fe2026eb4506416ba401f6 | R | 38,423 | 491 | ---
title: "Algorithm Description V_2"
author: "SOCR CBDA Team"
date: "August 1, 2016"
output: pdf_document
csl: apa.csl
bibliography: algorithmdescription.bib
---
#SuperLearner Function
##SL.library: Algorithms description
###Notations:
* X: a predictors column-joined.
* Y: the response variable.
* k: the number ... |
d2ec91d0101721f928531bd9c612a49574a39e5ed98a22bc00a41279e28333a2 | R | 41,451 | 700 | # data preprocessing loglikelihood plots (delta LLhood)
# Purpose: Both, preparing for plot & significance testing
library(fs)
library(tidyverse)
library(patchwork)
### === LOAD DATA === ###
# Create file list (space is needed if you use path_filter())
file_list <- dir_ls("processed_data/LLhood_Easy_vs_Difficult/"... |
3e73e5ea127bb020d124b4877e5990f2ce21760dd427d1c66d71fed4fcad8081 | R | 42,027 | 897 | ---
title: "BigDots"
author: "Daniel Newman"
date: "12 June 2016"
output:
html_document:
fig_width: 8
keep_md: yes
word_document: default
---
```{r Load and Pre-Process the single trial Data, echo=FALSE, include=FALSE}
####Which computer/directory is this being run on?
location<-"Monash"
# location<-"Gers... |
68710f4166522353c3a0f159151f446fb898b769a5a918df186648acdb20d2d1 | R | 42,448 | 687 | ---
title: "Algorithm Description V_2"
author: "SOCR CBDA Team"
date: "August 1, 2016"
output: word_document
csl: apa.csl
bibliography: algorithmdescription.bib
---
```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)
#library(devtools)
require(knitcitations)
#library(bibtex)
#library("pandocfilters")
setwd(... |
6ce3a391931112d5c02af502f18c486fededc6b6811c18ac8fe721ea15b42e25 | R | 43,682 | 906 | # [O.Penacchio, March 23]
# Code for statistical analysis of paper 'A mechanistic account for visual discomfort'
# O.Penacchio, X.Otazu, A.J. Wilkins, S.M.Haigh, Journal, issue, year <to be completed>
# set working directory
directory <- "D:/MATLAB old/21 sparse coding visual dicomfort/code and outputs for public... |
25de14bec9b1d34d855d94c5b433b1976a2d41ab3e6329303094d23977692723 | R | 43,809 | 1,301 | ```{r}
#source("~/data2/rstudio/birds/utils/seq_analysis.R")
#source("~/data2/rstudio/birds/utils/colors.R")
#source("~/data2/rstudio/birds/utils/network.R")
#source("~/data2/rstudio/birds/utils/db.R")
#source("~/data2/rstudio/birds/utils/stats.R")
#source("~/data2/rstudio/birds/utils/scde.R")
#source("~/data2/rstudi... |
7d61dd309e5236a062adbfedfd50e6b0c74d20655ba1c05c75f999f16bec4751 | R | 45,993 | 1,117 | ################"Stats_NCOMMS-22-47025" #################
###Author: Daria Antonenko, Anna E. Fromm, Ulrike Grittner
#R version 4.1.2 (2021-11-01)
#Windows 7 x64 (build 7601) SP 1system x86_64, mingw32ui
rm(list=ls())
library(openxlsx)
library(rstatix)
library(dplyr)
#library(mice)
#library(lme4)
... |
2cba82c1c8e0ac22b1cf13a4252d23edf4100660e4a8e773b1f06cccf3357fea | R | 49,837 | 1,491 | ### Uploading packages-----
library(EnvStats)
require(ggplot2)
require(car)
require(ggeffects)
require(blme)
require(emmeans)
require(lme4)
require(MuMIn)
require(coefplot)
require(nortest)
require(broom.mixed)
require(fitdistrplus)
require(glmmTMB)
require(ggplot2)
require(corrplot)
require(patchwork)
require(gridExt... |
6adb08d0ac7c01f5a532ae0fe4feeb9a1fcc7e7005ca60d7ae1005ce248eefda | R | 51,636 | 780 | #This is a copy of the code used to train and test ANNs that classify stimuli as 'animals' or 'leaves'.
#This code can be edited to replicate that used to create ANNs that classify stimuli as 'shaded' versus 'unshaded' (find and replace 'Animal' with 'Shaded').
#We acknowledge use of the following resources in develo... |
25f90113268f91595e6a3bbdab8bab79156cd1580f0aef50fbfb8ce764afe5a6 | R | 61,024 | 1,468 | ---
title: "Sulcal morphology of posteromedial cortex substantially differs between humans and chimpanzees"
author: "EH Willbrand et al."
date: "4/17/2023"
output: pdf_document
---
# Libraries
## Requirements: packages below must be installed to load.
```{r setup, include=FALSE}
# Set working directory to "morph_csv" ... |
581337f1b75f298662fa846715c43435f01305ade9e64a440e754f74931ae914 | R | 96,029 | 2,127 | ---
title: "BigDots"
author: "Daniel Newman"
output:
html_document:
fig_width: 8
keep_md: yes
---
```{r Load and Pre-Process the single trial Data, echo=FALSE, include=FALSE, warning=FALSE}
####Which computer/directory is this being run on?
# location<-"Monash"
location<-"DansLaptop"
if (location=="Monash"... |
c6ab84d03ac79e599c119789d4f87048cca8c514c80cccb55d41839abf033ba0 | R | 99,711 | 1,933 | ---
title: "SB R2 SIMMAP ACE - Haplorrhines - 062522 v1"
output: html_notebook
editor_options:
chunk_output_type: console
---
Haplorrhine Analysis
Run: SIMMAPS 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
Fol... |
8d9afd38a32606c6f8e43552ff703966fc3a4e6eae8f4e633cda5cbfcd264106 | R | 99,745 | 1,933 | ---
title: "SB R2 SIMMAP ACE - Platyrrhines - 062522 v1"
output: html_notebook
editor_options:
chunk_output_type: console
---
Platyrrhine Analysis
Run: SIMMAPS 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
Fol... |
c128be6caa52621ab3d1c8bbf1e7eace767c03c2732fd5db3a62ff0beb76c4db | R | 100,149 | 1,943 | ---
title: "SB R2 SIMMAP ACE - Haplorrhines - 062522 v1 Males"
output: html_notebook
editor_options:
chunk_output_type: console
---
Haplorrhine Analysis
Run: SIMMAP ACE 062522 v1 Males
Running 062522 v1 SIMMAPS
Testing revised data from R2 but running original fitMk program, not parallel
Predictors:
Fol.Non.F... |
fb3ff13c88e18235f622992d37f18680cd0f1e0a8c502bfcfab0f2a6659e776f | R | 100,166 | 1,943 | ---
title: "SB R2 SIMMAP ACE - Haplorrhines - 062522 v1 Females"
output: html_notebook
editor_options:
chunk_output_type: console
---
Haplorrhine Analysis
Run: SIMMAP ACE 062522 v1 Feales
Running 062522 v1 SIMMAPS
Testing revised data from R2 but running original fitMk program, not parallel
Predictors:
Fol.No... |
b8f4d642bb93a2cf53b3dd50ba613f5eb6d8b9dcfdb582620760b2215729adbd | R | 100,185 | 1,945 | ---
title: "SB R2 SIMMAP ACE - Platyrrhines - 062522 v1 Males"
output: html_notebook
editor_options:
chunk_output_type: console
---
Platyrrhine Analysis
Run: SIMMAP ACE 062522 v1 Males
Running 062522 v1 SIMMAPS
Testing revised data from R2 but running original fitMk program, not parallel
Predictors:
Fol.Non.F... |
9e6e446a7bd597ade7f657f892ddd47dbff7e2ee3aba39f773d8c7acf366e6cf | R | 100,204 | 1,946 | ---
title: "SB R2 SIMMAP ACE - Platyrrhines - 062122 v1 Females"
output: html_notebook
editor_options:
chunk_output_type: console
---
Platyrrhine Analysis
Run: SIMMAP ACE 062522 v1 Females
Running 062122 v1 SIMMAPS
Testing revised data from R2 but running original fitMk program, not parallel
Predictors:
Fol.N... |
608e423da22d9a8a7335d17342eb03de9d2f2b4362b9d5a5c9eec7c37a86755d | R | 104,454 | 2,009 | ---
title: "SBR2 - BDS ms Haplorrhine Analysis for SIMMAPs Plot Code - 062722"
output: html_notebook
editor_options:
chunk_output_type: console
---
Haplorhine Analysis
Run: SIMMAPS ACE 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorillas in strict classification is foli... |
c8ffafdd794f51eadebf0ffe0b4b863f9cf088597f2b432a4c82147a735eaacc | R | 104,458 | 2,009 | ---
title: "SBR2 - BDS ms Platyrrhine Analysis for SIMMAPs Plot Code - 062722"
output: html_notebook
editor_options:
chunk_output_type: console
---
Haplorhine Analysis
Run: SIMMAPS ACE 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorillas in strict classification is foli... |
2c4a342a761e76f504fa507fc54b8dd91169cd94dcaa1554fbae5e70c637a2f7 | R | 120,663 | 3,684 | ---
title: "Neural indicators of gut-brain mechanosensation in humans "
output:
Author: Ahmad Mayeli
pdf_document: default
html_notebook: default
---
# Libraries
```{r}
library(ggplot2)
# install("ggplot2")
# install.packages("rstatix")
library(rstatix)
library(optLog)
library(RColorBrewer)
librar... |
f871bec3c312d2838d14f61b436c6274b6bb7bbf129db26522f8d6a4fe6e5e49 | R | 153,789 | 3,932 | ###################################################################################################
##### Merged Analyses #####
###################################################################################################
### !!! Before running the code below, 'analysis.01.w... |
919c077c0ecbd6de15392cd1147c432b6d981d115477abffb27801b8ea22849b | R | 196,603 | 3,530 | ---
title: "SB R2 SIMMAP_ACE - All Primates - 062522 v1"
output: html_notebook
editor_options:
chunk_output_type: console
---
All Primates Analysis
Run: SIMMAPS ACE 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel
Gorilla g in strict classification is folivore
Predictors:
... |
2f78dcf4fc8fa06a2b541df2d277f59384df456eeb622f744273dc6a8ab94480 | R | 197,240 | 3,542 | ---
title: "SB R2 SIMMAP_ACE - All Primates - 062522 v1 - Males"
output: html_notebook
editor_options:
chunk_output_type: console
---
All Primates Analysis
Run: SIMMAP ACE 062522 v1 Males
Running 062522 v1 SIMMAPS
Testing revised data from R2 but running original fitMk program, not parallel
Predictors:
Fol.Non... |
4970848565615b7ba487870a4131f854b885e643f1c3b98929b76d970a2c1a95 | R | 197,260 | 3,544 | ---
title: "SB R2 SIMMAP_ACE - All Primates - 062522 v1 Females"
output: html_notebook
editor_options:
chunk_output_type: console
---
All Primates Analysis
Run: SIMMAP ACE 062522 v1 Females
Running 062522 v1 SIMMAPS
Testing revised data from R2 but running original fitMk program, not parallel
Predictors:
Fol.N... |
6b25343b883934dbe7e50c4f35dfe3b9a8e66436c5a1b53640eb8bae371ea0a8 | R | 200,000 | 3,667 | ---
title: "Grabowski et al. - SBR2 - Create SIMMAPs"
output: html_notebook
editor_options:
chunk_output_type: console
---
All Primates Analysis
Formerly: "SBR2 - BDS ms All Primates Make SIMMAPs Code - 062522 v1"
Run: SIMMAPS 062522 v1
Testing revised data from R2 but running original fitMk program, not parallel... |
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