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/

Code for the paper: Audio-Visual Scene Analysis with Self-Supervised Multisensory Features

[Paper] [Project page] This repository contains code for the paper: Andrew Owens, Alexei A. Efros. Audio-Visual Scene Analysis with Self-Supervised Mu

Andrew Owens 202 Dec 13, 2022
Easy way to add GoogleMaps to Flask applications. maintainer: @getcake

Flask Google Maps Easy to use Google Maps in your Flask application requires Jinja Flask A google api key get here Contribute To contribute with the p

Flask Extensions 611 Dec 05, 2022
Image-to-image regression with uncertainty quantification in PyTorch

Image-to-image regression with uncertainty quantification in PyTorch. Take any dataset and train a model to regress images to images with rigorous, distribution-free uncertainty quantification.

Anastasios Angelopoulos 25 Dec 26, 2022
State-Relabeling Adversarial Active Learning

State-Relabeling Adversarial Active Learning Code for SRAAL [2020 CVPR Oral] Requirements torch = 1.6.0 numpy = 1.19.1 tqdm = 4.31.1 AL Results The

10 Jul 14, 2022
Data and Code for ACL 2021 Paper "Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning"

Introduction Code and data for ACL 2021 Paper "Inter-GPS: Interpretable Geometry Problem Solving with Formal Language and Symbolic Reasoning". We cons

Pan Lu 81 Dec 27, 2022
Code and data for ACL2021 paper Cross-Lingual Abstractive Summarization with Limited Parallel Resources.

Multi-Task Framework for Cross-Lingual Abstractive Summarization (MCLAS) The code for ACL2021 paper Cross-Lingual Abstractive Summarization with Limit

Yu Bai 43 Nov 07, 2022
Built a deep neural network (DNN) that functions as an end-to-end machine translation pipeline

Built a deep neural network (DNN) that functions as an end-to-end machine translation pipeline. The pipeline accepts english text as input and returns the French translation.

Afropunk Technologist 1 Jan 24, 2022
🔥🔥High-Performance Face Recognition Library on PaddlePaddle & PyTorch🔥🔥

face.evoLVe: High-Performance Face Recognition Library based on PaddlePaddle & PyTorch Evolve to be more comprehensive, effective and efficient for fa

Zhao Jian 3.1k Jan 04, 2023
[CVPR2021 Oral] UP-DETR: Unsupervised Pre-training for Object Detection with Transformers

UP-DETR: Unsupervised Pre-training for Object Detection with Transformers This is the official PyTorch implementation and models for UP-DETR paper: @a

dddzg 430 Dec 23, 2022
Unified MultiWOZ evaluation scripts for the context-to-response task.

MultiWOZ Context-to-Response Evaluation Standardized and easy to use Inform, Success, BLEU ~ See the paper ~ Easy-to-use scripts for standardized eval

Tomáš Nekvinda 38 Dec 13, 2022
Neural style in TensorFlow! 🎨

neural-style An implementation of neural style in TensorFlow. This implementation is a lot simpler than a lot of the other ones out there, thanks to T

Anish Athalye 5.5k Dec 29, 2022
PyTorch code for 'Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning'

Efficient Single Image Super-Resolution Using Dual Path Connections with Multiple Scale Learning This repository is for EMSRDPN introduced in the foll

7 Feb 10, 2022
Minimal deep learning library written from scratch in Python, using NumPy/CuPy.

SmallPebble Project status: experimental, unstable. SmallPebble is a minimal/toy automatic differentiation/deep learning library written from scratch

Sidney Radcliffe 92 Dec 30, 2022
Codebase for the Summary Loop paper at ACL2020

Summary Loop This repository contains the code for ACL2020 paper: The Summary Loop: Learning to Write Abstractive Summaries Without Examples. Training

Canny Lab @ The University of California, Berkeley 44 Nov 04, 2022
Testing and Estimation of structural breaks in Stata

xtbreak estimating and testing for many known and unknown structural breaks in time series and panel data. For an overview of xtbreak test see xtbreak

Jan Ditzen 13 Jun 19, 2022
lightweight python wrapper for vowpal wabbit

vowpal_porpoise Lightweight python wrapper for vowpal_wabbit. Why: Scalable, blazingly fast machine learning. Install Install vowpal_wabbit. Clone and

Joseph Reisinger 163 Nov 24, 2022
Discriminative Condition-Aware PLDA

DCA-PLDA This repository implements the Discriminative Condition-Aware Backend described in the paper: L. Ferrer, M. McLaren, and N. Brümmer, "A Speak

Luciana Ferrer 31 Aug 05, 2022
Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness

Computationally Efficient Optimization of Plackett-Luce Ranking Models for Relevance and Fairness This repository contains the code used for the exper

H.R. Oosterhuis 28 Nov 29, 2022
A cross-lingual COVID-19 fake news dataset

CrossFake An English-Chinese COVID-19 fake&real news dataset from the ICDMW 2021 paper below: Cross-lingual COVID-19 Fake News Detection. Jiangshu Du,

Yingtong Dou 11 Dec 01, 2022
Public Code for NIPS submission SimiGrad: Fine-Grained Adaptive Batching for Large ScaleTraining using Gradient Similarity Measurement

Public code for NIPS submission "SimiGrad: Fine-Grained Adaptive Batching for Large Scale Training using Gradient Similarity Measurement" This repo co

Heyang Qin 0 Oct 13, 2021