scholarly journals Learning Single-Cell Perturbation Responses using Neural Optimal Transport

2021 ◽  
Author(s):  
Charlotte Bunne ◽  
Stefan G Stark ◽  
Gabriele Gut ◽  
Jacobo Sarabia del Castillo ◽  
Kjong-Van Lehmann ◽  
...  
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
State Of The Art ◽  
Cell Level ◽  
Cellular Behavior ◽  
External Perturbations ◽  
Current State ◽  
Treatment Responses ◽  
Cell Data ◽  
Resolution Single

Understanding and predicting molecular responses towards external perturbations is a core question in molecular biology. Technological advancements in the recent past have enabled the generation of high-resolution single-cell data, making it possible to profile individual cells under different experimentally controlled perturbations. However, cells are typically destroyed during measurement, resulting in unpaired distributions over either perturbed or non-perturbed cells. Leveraging the theory of optimal transport and the recent advents of convex neural architectures, we learn a coupling describing the response of cell populations upon perturbation, enabling us to predict state trajectories on a single-cell level. We apply our approach, CellOT, to predict treatment responses of 21,650 cells subject to four different drug perturbations. CellOT outperforms current state-of-the-art methods both qualitatively and quantitatively, accurately capturing cellular behavior shifts across all different drugs.

scholarly journals 12 Grand Challenges in Single-Cell Data Science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single-Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single-Cell Data Science' for the coming years.

scholarly journals 12 Grand challenges in single-cell data science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szcureck ◽  
Davis McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.

scholarly journals 12 Grand Challenges in Single-Cell Data Science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single-Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single-Cell Data Science' for the coming years.

scholarly journals 12 Grand challenges in single-cell data science

2019 ◽  
Author(s):  
David Laehnemann ◽  
Johannes Köster ◽  
Ewa Szczurek ◽  
Davis J McCarthy ◽  
Stephanie C Hicks ◽  
...  
Keyword(s):  
Single Cell ◽  
Cell Biology ◽  
Data Science ◽  
Single Cell Analysis ◽  
State Of The Art ◽  
Cell Analysis ◽  
Open Problems ◽  
Single Cell Sequencing ◽  
Current State ◽  
Cell Data

The recent upswing of microfluidics and combinatorial indexing strategies, further enhanced by very low sequencing costs, have turned single cell sequencing into an empowering technology; analyzing thousands—or even millions—of cells per experimental run is becoming a routine assignment in laboratories worldwide. As a consequence, we are witnessing a data revolution in single cell biology. Although some issues are similar in spirit to those experienced in bulk sequencing, many of the emerging data science problems are unique to single cell analysis; together, they give rise to the new realm of 'Single Cell Data Science'. Here, we outline twelve challenges that will be central in bringing this new field forward. For each challenge, the current state of the art in terms of prior work is reviewed, and open problems are formulated, with an emphasis on the research goals that motivate them. This compendium is meant to serve as a guideline for established researchers, newcomers and students alike, highlighting interesting and rewarding problems in 'Single Cell Data Science' for the coming years.

scholarly journals Gromov-Wasserstein optimal transport to align single-cell multi-omics data

2020 ◽  
Author(s):  
Pinar Demetci ◽  
Rebecca Santorella ◽  
Björn Sandstede ◽  
William Stafford Noble ◽  
Ritambhara Singh
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
Learning Algorithm ◽  
State Of The Art ◽  
Single Cells ◽  
Wasserstein Distance ◽  
Cell Alignment ◽  
Shared Space ◽  
Real World Datasets ◽  
Unsupervised Algorithms

AbstractData integration of single-cell measurements is critical for understanding cell development and disease, but the lack of correspondence between different types of measurements makes such efforts challenging. Several unsupervised algorithms can align heterogeneous single-cell measurements in a shared space, enabling the creation of mappings between single cells in different data domains. However, these algorithms require hyperparameter tuning for high-quality alignments, which is difficult in an unsupervised setting without correspondence information for validation. We present Single-Cell alignment using Optimal Transport (SCOT), an unsupervised learning algorithm that uses Gromov Wasserstein-based optimal transport to align single-cell multi-omics datasets. We compare the alignment performance of SCOT with state-of-the-art algorithms on four simulated and two real-world datasets. SCOT performs on par with state-of-the-art methods but is faster and requires tuning fewer hyperparameters. Furthermore, we provide an algorithm for SCOT to use Gromov Wasserstein distance to guide the parameter selection. Thus, unlike previous methods, SCOT aligns well without using any orthogonal correspondence information to pick the hyperparameters. Our source code and scripts for replicating the results are available at https://github.com/rsinghlab/SCOT.

scholarly journals Optimal transport analysis reveals trajectories in steady-state systems

2021 ◽  
Vol 17 (12) ◽  
pp. e1009466
Author(s):  
Stephen Zhang ◽  
Anton Afanassiev ◽  
Laura Greenstreet ◽  
Tetsuya Matsumoto ◽  
Geoffrey Schiebinger
Keyword(s):  
Single Cell ◽  
Optimal Transport ◽  
Single Cell Analysis ◽  
Simulated Data ◽  
Unified Approach ◽  
Transport Analysis ◽  
Time Courses ◽  
Cell Trajectories ◽  
Cell Data ◽  
Natural Way

Understanding how cells change their identity and behaviour in living systems is an important question in many fields of biology. The problem of inferring cell trajectories from single-cell measurements has been a major topic in the single-cell analysis community, with different methods developed for equilibrium and non-equilibrium systems (e.g. haematopoeisis vs. embryonic development). We show that optimal transport analysis, a technique originally designed for analysing time-courses, may also be applied to infer cellular trajectories from a single snapshot of a population in equilibrium. Therefore, optimal transport provides a unified approach to inferring trajectories that is applicable to both stationary and non-stationary systems. Our method, StationaryOT, is mathematically motivated in a natural way from the hypothesis of a Waddington’s epigenetic landscape. We implement StationaryOT as a software package and demonstrate its efficacy in applications to simulated data as well as single-cell data from Arabidopsis thaliana root development.

scholarly journals Biocompatibility assay of cellular behavior inside a leaf-inspired biomimetic microdevice at the single-cell level

RSC Advances ◽  
2017 ◽  
Vol 7 (52) ◽  
pp. 32710-32720 ◽  
Author(s):  
Wenming Wu ◽  
Andreas Manz
Keyword(s):  
Single Cell ◽  
Lab On A Chip ◽  
Single Cell Level ◽  
Cell Level ◽  
Cellular Behavior ◽  
Biomimetic Method ◽  
Natural Leaf

Inspired by recent studies, we created a biomimetic method to replicate the veinal microvasculature from a natural leaf into a lab-on-a-chip system, which could be further utilized as a biomimetic animal vessel as well as in vessel-derived downstream applications.

scholarly journals Scalable batch-correction method for integrating large-scale single-cell transcriptomes

2021 ◽  
Author(s):  
Xiangchun Li ◽  
Xilin Shen
Keyword(s):  
Single Cell ◽  
Expression Profile ◽  
Large Scale ◽  
State Of The Art ◽  
Correction Method ◽  
Feature Representation ◽  
Batch Correction ◽  
Data Alignment ◽  
Current State ◽  
Core Idea

Integration of the evolving large-scale single-cell transcriptomes requires scalable batch-correction approaches. Here we propose a simple batch-correction method that is scalable for integrating super large-scale single-cell transcriptomes from diverse sources. The core idea of the method is encoding batch information of each cell as a trainable parameter and added to its expression profile; subsequently, a contrastive learning approach is used to learn feature representation of the additive expression profile. We demonstrate the scalability of the proposed method by integrating 18 million cells obtained from the Human Cell Atlas. Our benchmark comparisons with current state-of-the-art single-cell integration methods demonstrated that our method could achieve comparable data alignment and cluster preservation. Our study would facilitate the integration of super large-scale single-cell transcriptomes. The source code is available at https://github.com/xilinshen/Fugue.

scholarly journals Optimal transport- and kernel-based early detection of mild cognitive impairment patients based on magnetic resonance and positron emission tomography images

2022 ◽  
Vol 14 (1) ◽  
Author(s):  
Ziyu Liu ◽  
Travis S. Johnson ◽  
Wei Shao ◽  
Min Zhang ◽  
Jie Zhang ◽  
...  
Keyword(s):  
Positron Emission Tomography ◽  
Cognitive Impairment ◽  
Mild Cognitive Impairment ◽  
Transfer Learning ◽  
Optimal Transport ◽  
State Of The Art ◽  
Emission Tomography ◽  
Brain Scans ◽  
Current State ◽  
Positron Emission

Abstract Background To help clinicians provide timely treatment and delay disease progression, it is crucial to identify dementia patients during the mild cognitive impairment (MCI) stage and stratify these MCI patients into early and late MCI stages before they progress to Alzheimer’s disease (AD). In the process of diagnosing MCI and AD in living patients, brain scans are collected using neuroimaging technologies such as computed tomography (CT), magnetic resonance imaging (MRI), or positron emission tomography (PET). These brain scans measure the volume and molecular activity within the brain resulting in a very promising avenue to diagnose patients early in a minimally invasive manner. Methods We have developed an optimal transport based transfer learning model to discriminate between early and late MCI. Combing this transfer learning model with bootstrap aggregation strategy, we overcome the overfitting problem and improve model stability and prediction accuracy. Results With the transfer learning methods that we have developed, we outperform the current state of the art MCI stage classification frameworks and show that it is crucial to leverage Alzheimer’s disease and normal control subjects to accurately predict early and late stage cognitive impairment. Conclusions Our method is the current state of the art based on benchmark comparisons. This method is a necessary technological stepping stone to widespread clinical usage of MRI-based early detection of AD.

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