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Article: MULTIMODAL DATA VISUALIZATION AND DENOISING WITH INTEGRATED DIFFUSION.

Kuchroo, Manik / Godavarthi, Abhinav / Tong, Alexander / Wolf, Guy / Krishnaswamy, Smita

IEEE International Workshop on Machine Learning for Signal Processing : [proceedings]. IEEE International Workshop on Machine Learning for Signal Processing

2021 Volume 2021

Abstract: We propose a method called integrated diffusion for combining multimodal data, gathered via different sensors on the same system, to create a integrated data diffusion operator. As real world data suffers from both local and global noise, we introduce ... ...

Abstract	We propose a method called integrated diffusion for combining multimodal data, gathered via different sensors on the same system, to create a integrated data diffusion operator. As real world data suffers from both local and global noise, we introduce mechanisms to optimally calculate a diffusion operator that reflects the combined information in data by maintaining low frequency eigenvectors of each modality both globally and locally. We show the utility of this integrated operator in denoising and visualizing multimodal toy data as well as multi-omic data generated from blood cells, measuring both gene expression and chromatin accessibility. Our approach better visualizes the geometry of the integrated data and captures known cross-modality associations. More generally, integrated diffusion is broadly applicable to multimodal datasets generated by noisy sensors collected in a variety of fields.
Language	English
Publishing date	2021-11-15
Publishing country	United States
Document type	Journal Article
ISSN	2161-0363
ISSN	2161-0363
DOI	10.1109/mlsp52302.2021.9596214
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article: Manifold Interpolating Optimal-Transport Flows for Trajectory Inference.

Huguet, Guillaume / Magruder, D S / Tong, Alexander / Fasina, Oluwadamilola / Kuchroo, Manik / Wolf, Guy / Krishnaswamy, Smita

Advances in neural information processing systems

2022 Volume 35, Page(s) 29705–29718

Abstract: We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) that learns stochastic, continuous population dynamics from static snapshot samples taken at sporadic timepoints. MIOFlow combines dynamic models, manifold learning, and ... ...

Abstract	We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) that learns stochastic, continuous population dynamics from static snapshot samples taken at sporadic timepoints. MIOFlow combines dynamic models, manifold learning, and optimal transport by training neural ordinary differential equations (Neural ODE) to interpolate between static population snapshots as penalized by optimal transport with manifold ground distance. Further, we ensure that the flow follows the geometry by operating in the latent space of an autoencoder that we call a geodesic autoencoder (GAE). In GAE the latent space distance between points is regularized to match a novel multiscale geodesic distance on the data manifold that we define. We show that this method is superior to normalizing flows, Schrödinger bridges and other generative models that are designed to flow from noise to data in terms of interpolating between populations. Theoretically, we link these trajectories with dynamic optimal transport. We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment.
Language	English
Publishing date	2022-08-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	1012320-9
ISSN	1049-5258
ISSN	1049-5258
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Book ; Online: Multimodal Data Visualization and Denoising with Integrated Diffusion

Kuchroo, Manik / Godavarthi, Abhinav / Tong, Alexander / Wolf, Guy / Krishnaswamy, Smita

2021

Abstract: We propose a method called integrated diffusion for combining multimodal datasets, or data gathered via several different measurements on the same system, to create a joint data diffusion operator. As real world data suffers from both local and global ... ...

Abstract	We propose a method called integrated diffusion for combining multimodal datasets, or data gathered via several different measurements on the same system, to create a joint data diffusion operator. As real world data suffers from both local and global noise, we introduce mechanisms to optimally calculate a diffusion operator that reflects the combined information from both modalities. We show the utility of this joint operator in data denoising, visualization and clustering, performing better than other methods to integrate and analyze multimodal data. We apply our method to multi-omic data generated from blood cells, measuring both gene expression and chromatin accessibility. Our approach better visualizes the geometry of the joint data, captures known cross-modality associations and identifies known cellular populations. More generally, integrated diffusion is broadly applicable to multimodal datasets generated in many medical and biological systems.
Keywords	Computer Science - Machine Learning ; Computer Science - Human-Computer Interaction
Subject code	004
Publishing date	2021-02-12
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: linc-mipep

Tornini, Valerie A / Miao, Liyun / Lee, Ho-Joon / Gerson, Timothy / Dube, Sarah E / Schmidt, Valeria / Kroll, François / Tang, Yin / Du, Katherine / Kuchroo, Manik / Vejnar, Charles E / Bazzini, Ariel Alejandro / Krishnaswamy, Smita / Rihel, Jason / Giraldez, Antonio J

eLife

2023 Volume 12

Abstract: Thousands of long intergenic non-coding RNAs (lincRNAs) are transcribed throughout the vertebrate genome. A subset of lincRNAs enriched in developing brains have recently been found to contain cryptic open-reading frames and are speculated to encode ... ...

Abstract	Thousands of long intergenic non-coding RNAs (lincRNAs) are transcribed throughout the vertebrate genome. A subset of lincRNAs enriched in developing brains have recently been found to contain cryptic open-reading frames and are speculated to encode micropeptides. However, systematic identification and functional assessment of these transcripts have been hindered by technical challenges caused by their small size. Here, we show that two putative lincRNAs (
MeSH term(s)	Animals ; Humans ; RNA, Long Noncoding/genetics ; Chromatin ; Zebrafish/genetics ; Zebrafish/metabolism ; Cell Differentiation/genetics ; Micropeptides
Chemical Substances	RNA, Long Noncoding ; Chromatin
Language	English
Publishing date	2023-05-16
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2687154-3
ISSN	2050-084X ; 2050-084X
ISSN (online)	2050-084X
ISSN	2050-084X
DOI	10.7554/eLife.82249
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Article ; Online: Diffusion Earth Mover's Distance and Distribution Embeddings.

Tong, Alexander / Huguet, Guillaume / Natik, Amine / MacDonald, Kincaid / Kuchroo, Manik / Coifman, Ronald / Wolf, Guy / Krishnaswamy, Smita

ArXiv

2021

Abstract: We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mover's Distance (EMD). We model the datasets as distributions supported on common data graph that is derived from ... ...

Abstract	We propose a new fast method of measuring distances between large numbers of related high dimensional datasets called the Diffusion Earth Mover's Distance (EMD). We model the datasets as distributions supported on common data graph that is derived from the affinity matrix computed on the combined data. In such cases where the graph is a discretization of an underlying Riemannian closed manifold, we prove that Diffusion EMD is topologically equivalent to the standard EMD with a geodesic ground distance. Diffusion EMD can be computed in $\tilde{O}(n)$ time and is more accurate than similarly fast algorithms such as tree-based EMDs. We also show Diffusion EMD is fully differentiable, making it amenable to future uses in gradient-descent frameworks such as deep neural networks. Finally, we demonstrate an application of Diffusion EMD to single cell data collected from 210 COVID-19 patient samples at Yale New Haven Hospital. Here, Diffusion EMD can derive distances between patients on the manifold of cells at least two orders of magnitude faster than equally accurate methods. This distance matrix between patients can be embedded into a higher level patient manifold which uncovers structure and heterogeneity in patients. More generally, Diffusion EMD is applicable to all datasets that are massively collected in parallel in many medical and biological systems.
Language	English
Publishing date	2021-02-25
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Book ; Online: Manifold Interpolating Optimal-Transport Flows for Trajectory Inference

Huguet, Guillaume / Magruder, D. S. / Tong, Alexander / Fasina, Oluwadamilola / Kuchroo, Manik / Wolf, Guy / Krishnaswamy, Smita

2022

Abstract	We present a method called Manifold Interpolating Optimal-Transport Flow (MIOFlow) that learns stochastic, continuous population dynamics from static snapshot samples taken at sporadic timepoints. MIOFlow combines dynamic models, manifold learning, and optimal transport by training neural ordinary differential equations (Neural ODE) to interpolate between static population snapshots as penalized by optimal transport with manifold ground distance. Further, we ensure that the flow follows the geometry by operating in the latent space of an autoencoder that we call a geodesic autoencoder (GAE). In GAE the latent space distance between points is regularized to match a novel multiscale geodesic distance on the data manifold that we define. We show that this method is superior to normalizing flows, Schr\"odinger bridges and other generative models that are designed to flow from noise to data in terms of interpolating between populations. Theoretically, we link these trajectories with dynamic optimal transport. We evaluate our method on simulated data with bifurcations and merges, as well as scRNA-seq data from embryoid body differentiation, and acute myeloid leukemia treatment. Comment: Presented at NeurIPS 2022, 24 pages, 7 tables, 14 figures
Keywords	Computer Science - Machine Learning
Publishing date	2022-06-29
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: linc-mipep and linc-wrb encode micropeptides that regulate chromatin accessibility in vertebrate-specific neural cells

Valerie A Tornini / Liyun Miao / Ho-Joon Lee / Timothy Gerson / Sarah E Dube / Valeria Schmidt / François Kroll / Yin Tang / Katherine Du / Manik Kuchroo / Charles E Vejnar / Ariel Alejandro Bazzini / Smita Krishnaswamy / Jason Rihel / Antonio J Giraldez

eLife, Vol

2023 Volume 12

Abstract	Thousands of long intergenic non-coding RNAs (lincRNAs) are transcribed throughout the vertebrate genome. A subset of lincRNAs enriched in developing brains have recently been found to contain cryptic open-reading frames and are speculated to encode micropeptides. However, systematic identification and functional assessment of these transcripts have been hindered by technical challenges caused by their small size. Here, we show that two putative lincRNAs (linc-mipep, also called lnc-rps25, and linc-wrb) encode micropeptides with homology to the vertebrate-specific chromatin architectural protein, Hmgn1, and demonstrate that they are required for development of vertebrate-specific brain cell types. Specifically, we show that NMDA receptor-mediated pathways are dysregulated in zebrafish lacking these micropeptides and that their loss preferentially alters the gene regulatory networks that establish cerebellar cells and oligodendrocytes – evolutionarily newer cell types that develop postnatally in humans. These findings reveal a key missing link in the evolution of vertebrate brain cell development and illustrate a genetic basis for how some neural cell types are more susceptible to chromatin disruptions, with implications for neurodevelopmental disorders and disease.
Keywords	micropeptides ; neurodevelopment ; behavior ; single cell analyses ; cell identity ; gene regulation ; Medicine ; R ; Science ; Q ; Biology (General) ; QH301-705.5
Subject code	612
Language	English
Publishing date	2023-05-01T00:00:00Z
Publisher	eLife Sciences Publications Ltd
Document type	Article ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Book ; Online: Time-inhomogeneous diffusion geometry and topology

Huguet, Guillaume / Tong, Alexander / Rieck, Bastian / Huang, Jessie / Kuchroo, Manik / Hirn, Matthew / Wolf, Guy / Krishnaswamy, Smita

2022

Abstract: Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifold learning, denoising, clustering, and visualization of high-dimensional ... ...

Abstract	Diffusion condensation is a dynamic process that yields a sequence of multiscale data representations that aim to encode meaningful abstractions. It has proven effective for manifold learning, denoising, clustering, and visualization of high-dimensional data. Diffusion condensation is constructed as a time-inhomogeneous process where each step first computes and then applies a diffusion operator to the data. We theoretically analyze the convergence and evolution of this process from geometric, spectral, and topological perspectives. From a geometric perspective, we obtain convergence bounds based on the smallest transition probability and the radius of the data, whereas from a spectral perspective, our bounds are based on the eigenspectrum of the diffusion kernel. Our spectral results are of particular interest since most of the literature on data diffusion is focused on homogeneous processes. From a topological perspective, we show diffusion condensation generalizes centroid-based hierarchical clustering. We use this perspective to obtain a bound based on the number of data points, independent of their location. To understand the evolution of the data geometry beyond convergence, we use topological data analysis. We show that the condensation process itself defines an intrinsic condensation homology. We use this intrinsic topology as well as the ambient persistent homology of the condensation process to study how the data changes over diffusion time. We demonstrate both types of topological information in well-understood toy examples. Our work gives theoretical insights into the convergence of diffusion condensation, and shows that it provides a link between topological and geometric data analysis.
Keywords	Computer Science - Machine Learning ; Statistics - Machine Learning
Subject code	514
Publishing date	2022-03-28
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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Article ; Online: Single-cell analysis reveals inflammatory interactions driving macular degeneration.

Kuchroo, Manik / DiStasio, Marcello / Song, Eric / Calapkulu, Eda / Zhang, Le / Ige, Maryam / Sheth, Amar H / Majdoubi, Abdelilah / Menon, Madhvi / Tong, Alexander / Godavarthi, Abhinav / Xing, Yu / Gigante, Scott / Steach, Holly / Huang, Jessie / Huguet, Guillaume / Narain, Janhavi / You, Kisung / Mourgkos, George /

Dhodapkar, Rahul M / Hirn, Matthew J / Rieck, Bastian / Wolf, Guy / Krishnaswamy, Smita / Hafler, Brian P

Nature communications

2023 Volume 14, Issue 1, Page(s) 2589

Abstract: Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared ... ...

Abstract	Due to commonalities in pathophysiology, age-related macular degeneration (AMD) represents a uniquely accessible model to investigate therapies for neurodegenerative diseases, leading us to examine whether pathways of disease progression are shared across neurodegenerative conditions. Here we use single-nucleus RNA sequencing to profile lesions from 11 postmortem human retinas with age-related macular degeneration and 6 control retinas with no history of retinal disease. We create a machine-learning pipeline based on recent advances in data geometry and topology and identify activated glial populations enriched in the early phase of disease. Examining single-cell data from Alzheimer's disease and progressive multiple sclerosis with our pipeline, we find a similar glial activation profile enriched in the early phase of these neurodegenerative diseases. In late-stage age-related macular degeneration, we identify a microglia-to-astrocyte signaling axis mediated by interleukin-1β which drives angiogenesis characteristic of disease pathogenesis. We validated this mechanism using in vitro and in vivo assays in mouse, identifying a possible new therapeutic target for AMD and possibly other neurodegenerative conditions. Thus, due to shared glial states, the retina provides a potential system for investigating therapeutic approaches in neurodegenerative diseases.
MeSH term(s)	Humans ; Mice ; Animals ; Macular Degeneration/metabolism ; Retina/metabolism ; Neuroglia/metabolism ; Neurodegenerative Diseases/metabolism ; Single-Cell Analysis
Language	English
Publishing date	2023-05-05
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-37025-7
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Book ; Online: Embedding Signals on Knowledge Graphs with Unbalanced Diffusion Earth Mover's Distance

Tong, Alexander / Huguet, Guillaume / Shung, Dennis / Natik, Amine / Kuchroo, Manik / Lajoie, Guillaume / Wolf, Guy / Krishnaswamy, Smita

2021

Abstract: In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observations in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We ...

Abstract	In modern relational machine learning it is common to encounter large graphs that arise via interactions or similarities between observations in many domains. Further, in many cases the target entities for analysis are actually signals on such graphs. We propose to compare and organize such datasets of graph signals by using an earth mover's distance (EMD) with a geodesic cost over the underlying graph. Typically, EMD is computed by optimizing over the cost of transporting one probability distribution to another over an underlying metric space. However, this is inefficient when computing the EMD between many signals. Here, we propose an unbalanced graph EMD that efficiently embeds the unbalanced EMD on an underlying graph into an $L^1$ space, whose metric we call unbalanced diffusion earth mover's distance (UDEMD). Next, we show how this gives distances between graph signals that are robust to noise. Finally, we apply this to organizing patients based on clinical notes, embedding cells modeled as signals on a gene graph, and organizing genes modeled as signals over a large cell graph. In each case, we show that UDEMD-based embeddings find accurate distances that are highly efficient compared to other methods. Comment: 5 pages, 5 figures, ICASSP 2022
Keywords	Computer Science - Machine Learning ; Electrical Engineering and Systems Science - Signal Processing
Subject code	511
Publishing date	2021-07-26
Publishing country	us
Document type	Book ; Online
Database	BASE - Bielefeld Academic Search Engine (life sciences selection)

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