Related Experiment Video

Updated: Nov 24, 2025

Author Spotlight: Advancing Organoid Generation for Drug Development Using iPSCs
06:38

Author Spotlight: Advancing Organoid Generation for Drug Development Using iPSCs

Published on: March 15, 2024

1.8K

Single-cell systems analysis: decision geometry in outliers

Lianne Abrahams1

  • 1Ronin Institute, Montclair, NJ 07043-2314, USA.

Bioinformatics (Oxford, England)
|December 28, 2020
PubMed
Abstract

Insights

Single-cell systems biology offers a novel approach to combatting cancer relapse by analyzing intratumoral heterogeneity. This method utilizes advanced mathematical models to identify biomarkers for evolution-resistant cancer therapies.

Area of Science:

  • Computational Biology
  • Systems Biology
  • Cancer Research

Background:

  • Current anti-cancer therapies target specific oncoproteins and tumor suppressors.
  • Intratumoral heterogeneity drives resistance and clinical relapse in cancer patients.

Purpose of the Study:

  • To advocate for single-cell systems biology as the optimal analysis level for addressing clinical relapse.
  • To explore mathematical abstractions for defining evolution-resistant cancer biomarkers.

Main Methods:

  • Applying graph theory to understand single-cell decision-making.
  • Abstracting cellular decision-making to the geometry of outlier cells.
  • Utilizing phase portrait analysis as a bridge between graph theory and deep learning.

Main Results:

  • Single-cell systems biology provides a framework for understanding cancer evolution and resistance.
  • Geometric analysis of outlier cell decision-making can define novel biomarkers.
  • Phase portrait analysis integrates diverse computational approaches for cancer research.

Conclusions:

  • Higher-level mathematical abstractions in cancer biology are crucial for clinical needs.
  • Single-cell analysis is key to overcoming therapeutic resistance and improving patient outcomes.
  • Interdisciplinary approaches combining systems biology, graph theory, and deep learning hold promise for future cancer treatments.

More Related Videos

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline10:44

Inherent Dynamics Visualizer, an Interactive Application for Evaluating and Visualizing Outputs from a Gene Regulatory Network Inference Pipeline

Published on: December 7, 2021

2.5K
The HoneyComb Paradigm for Research on Collective Human Behavior06:48

The HoneyComb Paradigm for Research on Collective Human Behavior

Published on: January 19, 2019

9.6K

Related Concept Videos

Outliers and Influential Points01:08

Outliers and Influential Points

An outlier is an observation of data that does not fit the rest of the data. It is sometimes called an extreme value. When you graph an outlier, it will appear not to fit the pattern of the graph. Some outliers are due to mistakes (for example, writing down 50 instead of 500), while others may indicate that something unusual is happening. Outliers are present far from the least squares line in the vertical direction. They have large "errors," where the "error" or residual is the...
5.3K
Quantifying and Rejecting Outliers: The Grubbs Test01:02

Quantifying and Rejecting Outliers: The Grubbs Test

Sometimes, a data set can have a recorded numerical observation that greatly  deviates from the rest of the data. Assuming that the data is normally distributed, a statistical method called the Grubbs test can be used to determine whether the observation is truly an outlier.  To perform a two-tailed Grubbs test, first, calculate the absolute difference between the outlier and the mean. Then, calculate the ratio between this difference and the standard deviation of the sample. This...
3.2K
Detection of Gross Error: The Q Test01:00

Detection of Gross Error: The Q Test

When one or more data points appear far from the rest of the data, there is a need to determine whether they are outliers and whether they should be eliminated from the data set to ensure an accurate representation of the measured value. In many cases, outliers arise from gross errors (or human errors) and do not accurately reflect the underlying phenomenon. In some cases, however, these apparent outliers reflect true phenomenological differences. In these cases, we can use statistical methods...
6.6K
What Are Outliers?01:12

What Are Outliers?

Outliers are observed data points that are far from the least squares line. They have unusual values and need to be examined carefully. Though an outlier may result from erroneous data, at other times, it may hold valuable information about the population under study and should be included in the data. Hence, it is crucial to examine what causes a data point to be an outlier.
The z score is used to find outliers or unusual values. It should be noted that any values beyond -2 and +2 are...
4.7K
Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving01:29

Mechanistic Models: Compartment Models in Algorithms for Numerical Problem Solving

Mechanistic models play a crucial role in algorithms for numerical problem-solving, particularly in nonlinear mixed effects modeling (NMEM). These models aim to minimize specific objective functions by evaluating various parameter estimates, leading to the development of systematic algorithms. In some cases, linearization techniques approximate the model using linear equations.
In individual population analyses, different algorithms are employed, such as Cauchy's method, which uses a...
179
Mechanistic Models: Compartment Models in Individual and Population Analysis01:23

Mechanistic Models: Compartment Models in Individual and Population Analysis

Mechanistic models are utilized in individual analysis using single-source data, but imperfections arise due to data collection errors, preventing perfect prediction of observed data. The mathematical equation involves known values (Xi), observed concentrations (Ci), measurement errors (εi), model parameters (ϕj), and the related function (ƒi) for i number of values. Different least-squares metrics quantify differences between predicted and observed values. The ordinary least...
154