Allen Cell Explorer
  • About
    • What We Do
    • FAQs
    • Videos & Tutorials
    • Site Updates
    • < Forum >
    • < AllenInstitute.org >
  • Animated Cell
    • Educational Resources
    • Visual Guide to Human Cells
    • Visual Guide Tutorial
    • Research Projects >
      • Pathtrace rendering
  • Cells & Biology
    • Cell Catalog
    • About our Cells >
      • hiPS Cell Biology Overview
      • Cell Structure Observations
      • Cell Catalog QuickView
    • Genomics
    • Methods & SOPs >
      • Methods for Cells in the Lab
      • Cell Methods: Videos from the Lab
      • Methods for Microscopy
    • Research Projects >
      • hiPS Cells During Mitosis
      • Drug Perturbation Pilot
      • Differentiation Into Cardiomyocytes
      • Why Endogenous Tagging?
  • Data & Tools
    • Cell Catalog
    • 3D Cell Viewer
    • Cell Feature Explorer
    • Integrated Mitotic Stem Cell Z-stack Viewer
    • Deep Cell Zoom
    • Segmenter
    • Data Downloading
    • Software & Code
    • Simularium
    • Research Projects >
      • Extracting Information
  • Modeling & Analysis
    • Integrated Mitotic Stem Cell
    • Allen Integrated Cell
    • Label-free Determination
    • Data Notebook Exploration >
      • Cell Shape Analysis
      • Programmatic Data Access
    • Research Projects >
      • 3D Probabilistic Modeling
  • News & Publications
    • News Feed
    • Publications
    • Archived Content

3D Probabilistic Modeling

Visualize variability in cell structure shape, localization, and quantification

3D Probabilistic Modeling of Human Stem Cell Organization

As part of the Allen Integrated Cell, we have developed and implemented a state-of-the-art machine learning model, the 3D Probabilistic Cell Model, which captures the relative variations in cell and organelle morphologies and locations for all components studied. Like traditional probabilistic approaches, this model allows us to analyze heterogeneity in our cell population – with a powerful difference.
Picture
The model can capture and analyze all of the variation among components of our cells and then use this information to predict the locations of structures not observed in any particular sample, given the location and morphology of the cell boundary and the nucleus. In addition, the model allows us to both predict how cells and their components will look given certain conditions, and to integrate cells with components observed in different measurements (see <archived page: Modeling: Integrated Cells>; modeling publications).
 
Explore how organelles are likely shaped and where these are most probably located, as well as probabilistic spatial distributions of these structures in the 3D viewer below:
 

3D Probabilistic Cell Structure Viewer

Picture
Open Viewer

How does the 3D probabilistic cell structure model work?

Recent machine learning methods based on deep neural networks (deep learning) are a powerful approach for encoding and integrating large sets of diverse images, and then generating integrated photorealistic outputs. Here, we have developed and applied a novel computational method (see Figure), to predict the location and morphologies of key organelles in our cells, given an observed cell (plasma membrane) and nuclear (DNA) morphology.
Picture
Figure. Autoencoders can learn conditional models. Overview of the deep learning model used to encode cell variance and predict key proteins in a new cell. The model captures the variance between cell and nucleus (DNA) location and shape, and also serves to capture covariance between these (cell and nucleus) as well as observed structures, e.g., nuclear membrane (LaminB1), endoplasmic reticulum (Sec61b), mitochondria (Tom20), microtubules (alpha-tubulin), actin (alpha-actinin; beta-actin), tight junctions (ZO-1), (arXiv:1511.05644v2 [cs.LG]. The model latent spaces, as encapsulated in the blue text boxes above, encode, from top, a learned cell and nuclear shape representation, a structure label (class, e.g. mitochondria (Tom20)), and all other variation in the structure shape and location, e.g. possible morphology and location of mitochondria (Tom20).

Other resources

Modeling Publications
The latest publications & preprints related to the 3D Statistical Cell Model
Methods for Modeling
Detailed and technical methods from the publications provided here
Software & Code
Links to our repositories, where all our open-source tools and code can be found.

Home

Terms of Use

Citation Policy

Privacy Policy

FAQs

Help

Send us a message

Copyright © 2020 Allen Institute. All Rights Reserved.
cellscience.alleninstitute.org
  • About
    • What We Do
    • FAQs
    • Videos & Tutorials
    • Site Updates
    • < Forum >
    • < AllenInstitute.org >
  • Animated Cell
    • Educational Resources
    • Visual Guide to Human Cells
    • Visual Guide Tutorial
    • Research Projects >
      • Pathtrace rendering
  • Cells & Biology
    • Cell Catalog
    • About our Cells >
      • hiPS Cell Biology Overview
      • Cell Structure Observations
      • Cell Catalog QuickView
    • Genomics
    • Methods & SOPs >
      • Methods for Cells in the Lab
      • Cell Methods: Videos from the Lab
      • Methods for Microscopy
    • Research Projects >
      • hiPS Cells During Mitosis
      • Drug Perturbation Pilot
      • Differentiation Into Cardiomyocytes
      • Why Endogenous Tagging?
  • Data & Tools
    • Cell Catalog
    • 3D Cell Viewer
    • Cell Feature Explorer
    • Integrated Mitotic Stem Cell Z-stack Viewer
    • Deep Cell Zoom
    • Segmenter
    • Data Downloading
    • Software & Code
    • Simularium
    • Research Projects >
      • Extracting Information
  • Modeling & Analysis
    • Integrated Mitotic Stem Cell
    • Allen Integrated Cell
    • Label-free Determination
    • Data Notebook Exploration >
      • Cell Shape Analysis
      • Programmatic Data Access
    • Research Projects >
      • 3D Probabilistic Modeling
  • News & Publications
    • News Feed
    • Publications
    • Archived Content