Human Cell Atlas: An innovative initiative & vision to change the course of Biomedicine with Data Science & more precisely using Deep Learning
By: Vivek Das
Tags: Rna Seq, Machine Learning, Data Science, Genomics, Deep Learning

Human Cell Atlas: An innovative initiative & vision to change the course of Biomedicine with Data Science & more precisely using Deep Learning Vivek Das Follow Jun 23, 2019 · 6 min read

This week’s post, I will shed some information based on my understanding of the future of DeepLearning in an era of single-cell omics. This is in reference to the Human Cell Atlas project, that recently obtained Seed Network funding from Chan Zuckerberg Science Initiative and the way I see it will change the course of understanding human biology. To begin with:

What is Human Cell Atlas initiative?

To my knowledge, this is one of the most comprehensive initiatives that has been taken by various leading labs, institutes to envision the dream to build a reference map of human cells. This will help us understand the fundamentals of human life systems, starting from a big map that can be broken down to the granular single unit of a cell. Thus it aims at addressing the complexity of the human system or tissues via building a multi-reference map from one-cell at a time. This will enable us to get a comprehensive understanding of human health via the basic understanding of both development and disease biology. The promise of such understanding can already help us to connect the dots of how various cells in higher dimensional space cross-communicate with each other to build the human tissue system thus providing deeper insights into health, disease onset, and progression. More of its information can be found here and in this paper Human Cell Atlas.

How do I see this vision through my lens?

The vision that HCA wants to reach is built on addressing some of the biggest challenges that we have in age single cell omics. It is not only one of the most promising revolution of our time but it also opens up a lot of complexities like that of “Pandora's box”. The challenge is not only solving human biological complexity but a broad spectrum that addresses aspects of technology, the computational infrastructure needed not only for making the complex data analysis but also in handling the massive amount of data. Such data handling needs efficient means of storage, privacy, security & sharing. This also means there has to be massive quality control(QC) and quality assurance(QA) in terms of delivering such advancements with precision. One of the advancements that we will witness through HCA will be the development of amazing computational methods or algorithms to handle, interrogate, analyze and visualize the big data that single cell will generate. This will also bring in the incremental usage of Machine Learning and Deep Learning. One of my favorite read in this space is a Medium post titled: Deep Learning for Single Cell Biology published in Towards Data Science by Nikolay Oskolkov. It is an amazing read to check out if you haven't done it yet.

Here are some of my thoughts that I echo with this post:

This post provides a projection of how the field will move ahead with more data generation& acquisition: HCA is already trying to build a map of billions or trillions of cells that characterizes the human development & disease biology. This will use massive amounts of single cell technologies to build those cell maps that will span addressing complexities of DNA, RNA, Protein, layered with Epigenomics data, Digital Pathology via Imaging, Phenotypic measures. In short, it will lead to the generation of multi-omics and multi-modal data to connect genotype to phenotype understanding. The post also provides a thorough analogy of upcoming need for usage of dimension reduction algorithms to better delineate complexity/heterogeneity of cells or cellular morphology: In biological data, one of the many ways of understanding data is via visualizing them in 2D. Now with such enormous volumes, we need to get better with our algorithms to capture the dimensional space for better elucidating the biological problem we want to address or at least provides first-hand testing of our initial hypothesis. To get some more understanding I would redirect to one of my previous posts that sheds some information of dimension reduction. Current dimension reduction in forms of t-SNE, UMAP will be soon complemented with deep learning Autoencoder based projections for complexity breakdowns to tackle non-linearity of dimensions. One of the most important aspects of dimension reduction is to capture the non-linearity of gene expression via single-cell RNA-Seq data. This non-linearity is in a way addressing the complex stochastic events that happen during the transcriptional process. This also represents cell space that is constituted of the cell states in multi-dimension to capture intricacies of human development and disease biology. More detailed information of this representation of cell space through HCA can be found in the Chris P. Ponting review.

Image from Chris P. Ponting “The Human Cell Atlas: making ‘cell space’ for disease” https://dmm.biologists.org/content/12/2/dmm037622 Disease Models & Mechanisms 2019 12: dmm037622 doi: 10.1242/dmm.037622 Published 1 February 2019

4. This post already gives us some information about data-sets already in order of ~1.3 or 2 Million cells that have been generated via 10x genomics platform. These are mostly done in mice but the projection of HCA aims at establishing such enormous amounts of data. With such enormous amounts of data, one also needs to address these massive sparse datasets we see in single-cell data. One of the fine reads of addressing such sparsity in single cell space is by using denoising of single cell data via Deep Autoencoder model can be found here. Autoencoder has earlier been used in the prediction of gene expression dynamics in yeast a using MultiLayer Perceptron and Stacked Denoising Auto-encoder (MLP-SAE) that relies on the regression-based prediction model. More on the work can be found here. Currently, the scope of it will be more realized in predicting the cell states & cell fates in the context of single-cell.

5. The post also addresses the growing needs for computational infrastructure & expertise to help resolve the ever-growing atlas of single-cell data & putting an architectural framework. Having said that, such can be done when we can bring in more computational, mathematical, statistical, data science, epidemiologists to join the league of extraordinary physicians, geneticists, molecular and developmental biologists. This will help us realize the potential of interdisciplinary biology or biomedicine. Chan Zuckerberg Science Initiative is putting in massive efforts in this space. Some of its new efforts and initiatives to accelerate the space can be found here.

6. The Medium post of Deep Learning for Single Cell also touches upon innovations that will be in place with usage of machine learning applications in training & testing sets. To my understanding, this will also assist in the understanding of cellular diversity both in cell type identification space & in mapping cell lineage or trajectory in developmental &/or disease biology. Two powerful & comprehensive state of art workflows in single-cell space are Seurat (currently version 3 including new integrative models & label transfers via anchoring) & Monocle (currently Monocle3 owing to its latest upgraded version).

A representative image of various functionalities that can be obtained from newly upgraded Monocle3. Image sourced from https://cole-trapnell-lab.github.io/monocle3/

7. Finally, something it touches upon for interoperability & scalability to implement Deep Autoencoders for massive 10X Genomics e.g. ~1.3M mouse brain cells single cell RNAseq data set using Keras and Tensor Flow. Earlier works of single-cell using R, Keras & TensorFlow have already been shown by Jean Fen & Kieran R Campbell.

I thank & acknowledge all the authors whose work I have cited in this story as they provided me enough food for thought to start with & pen down my understanding. This is how I envision the evergrowing field of single-cell and HCA based on my learning and understanding. I would be happy to add more information for clarity if needed. I also thank Nikolay Oskolkov for the wonderful post that gave me the foundation for this write-up to dig deeper and put my readings in a collective manner.

Edit 1: I have added images sourced from various articles and websites & linked them to their origin.