DANA paper supplementary materials

Overview

DANA Supplements

This repository stores the data, results, and R scripts to generate these reuslts and figures for the corresponding paper Depth Normalization of Small RNA Sequencing: Using Data and Biology to Select a Suitable Method. The DANA package is available on github: https://github.com/LXQin/DANA

DANA is an approach for assessing the performance of normalization for microRNA-Seq data based on biology-motivated and data-driven metrics. Our approach takes advantage of well-known biological features of microRNAs for their expression pattern and polycistronic clustering to assess (1) how effectively normalization removes handling effects and (2) how normalization biases true biological signals. DANA is implemented in R and can be used for assessing any normalization method (under minimal assumptions) for any microRNA-Seq data set and only requires additional information on polycistronic clustering, which is typically readily available.

Installation

This repository is not a package for DANA. It stores the R scripts and data to generate the results and figures in the paper. For simplicity, this package contains a "snapshot" of the DANA implementation as includable R code. This way you don't need to install the DANA package to run the analysis and future updates of the DANA package do not affect the results generated here. You can install the released version of DANA directly from github using devtools.

Dependencies

To run the R code, you need to install the following packages: ggplot2, gridExtra, ggnewscale, corrplot, stargazer, plotly, ggrepel, glmnet, huge, Rcpp, FastGGM, edgeR, DESeq, PoissonSeq, sva, RUVSeq, vsn, DescTools, ffpe. Please make sure to install all dependencies prior to running the code. The code presented here was implemented and tested in R version 4.0.2.

Usage

  1. Download this repository.
  2. Set your R working directory to the root directory of the project.
  3. Run or knit any of the following R markdowns
    • MSK_Data_Analysis.Rmd to generate the DANA results for the paired MSK sarcoma data sets
    • TCGA_UCEC_Data_Analysis.Rmd to generate the DANA results for the single-batch and mixed-batch data sets from the TCGA-UCEC project.
    • TCGA_BRCA_UCS_Data_Analysis.Rmd to generate the DANA results for the combined TCGA-BRCA and TCGA-UCS data set.

All of these markdowns were previously run (so you don't have to) and the resulting knitted html files can be found in the directory docs/

Neural Articulated Radiance Field

Neural Articulated Radiance Field NARF Neural Articulated Radiance Field Atsuhiro Noguchi, Xiao Sun, Stephen Lin, Tatsuya Harada ICCV 2021 [Paper] [Co

Atsuhiro Noguchi 144 Jan 03, 2023
Synthetic structured data generators

Join us on What is Synthetic Data? Synthetic data is artificially generated data that is not collected from real world events. It replicates the stati

YData 850 Jan 07, 2023
Unofficial pytorch implementation for Self-critical Sequence Training for Image Captioning. and others.

An Image Captioning codebase This is a codebase for image captioning research. It supports: Self critical training from Self-critical Sequence Trainin

Ruotian(RT) Luo 906 Jan 03, 2023
Powerful unsupervised domain adaptation method for dense retrieval.

Powerful unsupervised domain adaptation method for dense retrieval

Ubiquitous Knowledge Processing Lab 191 Dec 28, 2022
Malware Bypass Research using Reinforcement Learning

Malware Bypass Research using Reinforcement Learning

Bobby Filar 76 Dec 26, 2022
Unofficial keras(tensorflow) implementation of MAE model from Masked Autoencoders Are Scalable Vision Learners

MAE-keras Unofficial keras(tensorflow) implementation of MAE model described in 'Masked Autoencoders Are Scalable Vision Learners'. This work has been

Yewon 11 Jun 12, 2022
Text and code for the forthcoming second edition of Think Bayes, by Allen Downey.

Think Bayes 2 by Allen B. Downey The HTML version of this book is here. Think Bayes is an introduction to Bayesian statistics using computational meth

Allen Downey 1.5k Jan 08, 2023
CLDF dataset derived from Robbeets et al.'s "Triangulation Supports Agricultural Spread" from 2021

CLDF dataset derived from Robbeets et al.'s "Triangulation Supports Agricultural Spread" from 2021 How to cite If you use these data please cite the o

Digital Linguistics 2 Dec 20, 2021
My implementation of transformers related papers for computer vision in pytorch

vision_transformers This is my personnal repo to implement new transofrmers based and other computer vision DL models I am currenlty working without a

samsja 1 Nov 10, 2021
Official implementation of "Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks", NeurIPS 2021.

PHDimGeneralization Official implementation of "Intrinsic Dimension, Persistent Homology and Generalization in Neural Networks", NeurIPS 2021. Overvie

Tolga Birdal 13 Nov 08, 2022
Code for Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021)

Pose-Controllable Talking Face Generation by Implicitly Modularized Audio-Visual Representation (CVPR 2021) Hang Zhou, Yasheng Sun, Wayne Wu, Chen Cha

Hang_Zhou 628 Dec 28, 2022
This repository contains the code used for the implementation of the paper "Probabilistic Regression with HuberDistributions"

Public_prob_regression_with_huber_distributions This repository contains the code used for the implementation of the paper "Probabilistic Regression w

David Mohlin 1 Dec 04, 2021
Face detection using deep learning.

Face Detection Docker Solution Using Faster R-CNN Dockerface is a deep learning face detector. It deploys a trained Faster R-CNN network on Caffe thro

Nataniel Ruiz 181 Dec 19, 2022
simple artificial intelligence utilities

Simple AI Project home: http://github.com/simpleai-team/simpleai This lib implements many of the artificial intelligence algorithms described on the b

921 Dec 08, 2022
Tensorflow 2 implementation of the paper: Learning and Evaluating Representations for Deep One-class Classification published at ICLR 2021

Deep Representation One-class Classification (DROC). This is not an officially supported Google product. Tensorflow 2 implementation of the paper: Lea

Google Research 137 Dec 23, 2022
Unofficial PyTorch Implementation of "Augmenting Convolutional networks with attention-based aggregation"

Pytorch Implementation of Augmenting Convolutional networks with attention-based aggregation This is the unofficial PyTorch Implementation of "Augment

DK 20 Sep 09, 2022
A simple python library for fast image generation of people who do not exist.

Random Face A simple python library for fast image generation of people who do not exist. For more details, please refer to the [paper](https://arxiv.

Sergei Belousov 170 Dec 15, 2022
Mememoji - A facial expression classification system that recognizes 6 basic emotions: happy, sad, surprise, fear, anger and neutral.

a project built with deep convolutional neural network and ❤️ Table of Contents Motivation The Database The Model 3.1 Input Layer 3.2 Convolutional La

Jostine Ho 761 Dec 05, 2022
PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks

Dynamic Data Augmentation with Gating Networks This is an official PyTorch implementation of the paper Dynamic Data Augmentation with Gating Networks

九州大学 ヒューマンインタフェース研究室 3 Oct 26, 2022
Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

Accompanying code for the paper Sub-Cluster AdaCos: Learning Representations for Anomalous Sound Detection.

Kevin Wilkinghoff 6 Dec 01, 2022