UMINT

Maitra, C., Seal, D.B., Das, V. and De, R.K., 2022. UMINT: unsupervised neural network for single cell multi-omics integration. BioRxiv, pp.2022-04. https://www.biorxiv.org/content/10.1101/2022.04.21.489041v1

Now published at Frontiers in Molecular Biosciences

Maitra, C., Seal, D.B., Das, V. and De, R.K., Unsupervised neural network for single cell Multi-omics INTegration (UMINT): An application to health and disease. Frontiers in Molecular Biosciences, 10, p.335. https://www.frontiersin.org/articles/10.3389/fmolb.2023.1184748/full

Architecture of UMINT

Running UMINT

To run UMINT, import umint.py from the Proposed directory and run the function CombinedEncoder. All the parameters are mentioned below for better understanding. One can also follow a .ipynb file from the Proposed directory.

Requirements

To run umint, one needs to install tensorflow, sklearn, scipy and pandas packages. Installation codes are as follows:

• pip install tensorflow
• pip install scikit-learn
• pip install scipy
• pip install pandas

Parameters

All input parameters are as follows: layer_neuron, mid_neuron, seed, lambda_act, lambda_weight, epoch, bs

• data: List of input data matrices for training. [The input data matrices should be in the form of cells x features.]
• val: List of data matrices for validation. [The validation data matrices also should be in the same format as input data matrices i.e., cells x features.]
• layer_neuron: List of neurons in the modality encoding layer [modality wise].
• mid_neuron: Dimension onto which the data is being projected.
• seed: To reproduce the results set a seed value.
• lambda_act: Activity regularizer parameter.
• lambda_weight: Kernel regularizer parameter.
• epoch: Total number of iteration for training.
• bs: Training batch size.

Demo

Code to run UMINT

To run UMINT, one needs to import the script umint (within the Proposed directory) first. An example is provided below. Let x1_train [cells x features] and x2_train [cells x features] be two training datasets, coming from two different omics modalities, and x1_test [cells x features], x2_test [cells x features] be their respective counterparts for validation.

import umint
MyEncoder, MyAE = umint.CombinedEncoder(data=[x1_train, x2_train], val=[x1_test, x2_test],
                                        layer_neuron=[128, 10], mid_neuron=64, seed=98,
                                        lambda_act=0.0001, lambda_weight=0.001, epoch=25, bs=16)

Code to find the lower dimensional embedding.

Once UMINT is trained, to find the latent lower dimensional embedding produced by UMINT, run the code below.

low = MyEncoder.predict([x1, x2]) 

To integrate multiple modalities please change the input accordingly. The sizes of data, val and layer_neuron must match in order to run umint.py successfully.

Authors' Information

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Chayan Maitra

Machine Intelligence Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700108, India

E-mail: [email protected]

Dibyendu B. Seal

Tatras Data Services Pvt. Ltd., E64, Vasant Marg, Vasant Vihar, New Delhi 110057, India.

E-mail: [email protected]

Vivek Das

Novo Nordisk A/S, Novo Nordisk Park 1, 2760 M ̊aløv, Denmark

E-mail: [email protected]

Rajat K. De

Machine Intelligence Unit, Indian Statistical Institute, 203 Barrackpore Trunk Road, Kolkata 700108, India

E-mail: [email protected]

Dataset Source

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https://doi.org/10.5281/zenodo.7723340

Graphical abstract

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