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+## A Denoised Multi-omics Integration Framework for Cancer Subtype Classification and Survival Prediction
+
+---
+
+### What we do?
+
+- We developed a new feature selection method, Feature Selection with Distribution (FSD), for multi-omics data denosing and feature selection.
+
+- We developed a biologically informed deep learning algorithm for multi-omics integration to predict cancer subtypes and patient survival. 
+
+- Commonly used feature selection methods, ANOVA, RFE, LASSO, PCA, were incorporated for comparison.
+
+- Several machine learning and deep learning algorithms, including Random Forest, XGboost, SVM, DNN, MOGONET<sup>1</sup>, Moanna<sup>2</sup>, were integrated for multi-omics integration for cpmparison. MOGONET used graph convolutional networks for multi-omics integration, and Moanna is a Autoencoder-based neural network.
+
+---
+
+<div align=center>
+<img src="https://github.com/BioAI-kits/AttentionMOI/blob/master/img/Figure1.jpg" />
+</div>
+
+**Introduction of project**. The availability of high-throughput sequencing data create opportunities to comprehensively understand human diseases as well as challenges to train machine learning models using such high dimensions of data. Here, we propose a denoised multi-omics integration framework for cancer subtype classification and survival prediction. Firstly, a distribution based feature denosing algorithm, Feature Selection with Distribution (FSD), were designed to reduce dimensions of omics features. Secondly, we introduced a a multi-omics integration framework, Attention Multi-Omics Integration (AttentionMOI), which is inspired by the central dogma of biology. We demonstrated that FSD improved model performance either using single omics data or multi-omics data in 13 TCGA cancers for survival prediction and kidney cancer subtype identification. And our integration framework outperformed traditional artificial intellegnce models current multi-omics integration algorithms under high dimensions of features. Furthermore, FSD identisied features were related to cancer prognosis and could be considered as biomarkers. 
+
+---
+
+### Install
+
+You can install programs and dependencies via pip. We recommend using conda to build a virtual environment with python version 3.9 or higher.
+
+(optional) Create a virtual environment
+
+```bash
+conda create -n env_moi python=3.9
+
+conda activate env_moi  # Activate the environment
+```
+
+Install
+
+```bash
+pip install AttentionMOI
+```
+
+### Parameters
+ 
+After your installation is complete, your computer terminal will contain a `moi` command. This is the only interface to our program. You will use this command to build an omics model.
+
+First, you can execute the following command line to get detailed help information.
+
+```
+moi -h
+```
+
+Then, we also introduce these parameters in the following documents: 
+
+
+**1. Input**
+
+The input file format is described below, or you can refer to the reference data we provide (https://github.com/BioAI-kits/AttentionMOI/tree/master/AttentionMOI/example).
+
+f | omic_file
+
+> REQUIRED: File path for omics files (should be matrix)
+
+**NOTE:The file must be in csv format, such as rna.csv. Of course, it can be compressed with gz, such as rna.csv.gz.**. Example: The first line is the header, patient_id and gene (features) names.
+
+>  patient_id,A1BG,A1CF,A2BP1,A2LD1,....
+>
+>  TCGA.KL.8323,3.3491,0.0,0.0,5.8939,....
+>
+>  TCGA.KL.8324,2.922,0.5557,0.5557,6.4226,....
+
+n | omic_name
+
+> REQUIRED: Omic names for omics files, should be the same order as the omics file
+
+l | label_file
+
+> REQUIRED: File path for label file
+
+**NOTE:The file must be in csv format, such as label.csv. Of course, it can be compressed with gz, such as label.csv.gz.**. Example: The first line is the header, patient_id and label represent the sample name and sample classification label respectively. 
+
+> patient_id,label
+>
+> TCGA.KL.8328,0
+>
+> TCGA.KL.8339,0
+>
+> TCGA.KM.8439,1
+>
+> TCGA.KM.8441,1
+>
+> TCGA.KM.8442,1
+
+
+**2. Output**
+
+o | outdir
+
+> OPTIONAL: Setting output file path, default=./output
+
+
+**3. Feature selection**
+
+method
+
+> OPTIONAL: Method of feature selection, choosing from ANOVA, RFE, LASSO, PCA, default is no feature selection
+
+percentile
+
+> OPTIONAL: Percent of features to keep for ANOVA (integer between 1-100), only used when using ANOVA, default=30
+
+num_pc
+
+> OPTIONAL: Number of PCs to keep for PCA (integer), only used when using PCA, default=50
+
+FSD
+
+> OPTIONAL: Whether to use FSD to mitigate noise of omics. Default is not using FSD, and set --FSD to use FSD
+
+i | iteration
+
+> OPTIONAL: The number of FSD iterations (integer), default=10
+
+s | seed
+
+> OPTIONAL: Random seed for FSD (integer), default=0
+
+threshold
+
+> OPTIONAL: FSD threshold to select features (float), default=0.8 (select features that are selected in 80 percent FSD iterations)
+
+
+**4. Building Model**
+
+m | model 
+
+> OPTIONAL: Model names, choosing from DNN, Net (Net for AttentionMOI), RF, XGboost, svm, mogonet, moanna, default=DNN.
+
+t | test_size
+
+> OPTIONAL: Testing dataset proportion when split train test dataset (float), default=0.3 (30 percent data for testing)
+
+b | batch
+
+> OPTIONAL: Mini-batch number for model training (integer), default=32
+
+e | epoch
+
+> OPTIONAL: Epoch number for model training (integer), default=300
+
+r | lr
+
+> OPTIONAL: Learning rate for model training(float), default=0.0001
+
+w | weight_decay
+
+> OPTIONAL: weight_decay parameter for model training (float), default=0.0001
+
+---
+
+### Example
+
+Example (Data can be downloaded from https://github.com/BioAI-kits/AttentionMOI ): 
+```
+moi -f GBM_exp.csv.gz -f GBM_met.csv.gz -f GBM_logRatio.csv.gz -n rna -n met -n cnv -l GBM_label.csv --FSD -m Net -o GBM_Result
+```
+
+---
+
+### Ref.
+
+1. MOGONET integrates multi-omics data using graph convolutional networks allowing patient classification and biomarker identification
+
+2. Moanna: Multi-Omics Autoencoder-Based Neural Network Algorithm for Predicting Breast Cancer Subtypes 
+
+
+---
+
+All rights reserved.
+
+
+