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Variance01:15

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The deviations show how spread out the data are about the mean. A positive deviation occurs when the data value exceeds the mean, whereas a negative deviation occurs when the data value is less than the mean. If the deviations are added, the sum is always zero. So one cannot simply add the deviations to get the data spread. By squaring the deviations, the numbers are made positive; thus, their sum will also be positive.
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To achieve precise distance measurements, especially in surveying and construction, certain corrections must be applied to account for potential sources of error like the standardization errors, temperature variations, and slope adjustments.Standardization error emerges when measurement equipment undergoes changes, such as wear, repairs, or weather impacts. To address this, surveyors compare the equipment’s readings to a standard. This process identifies any deviation that might lead to...
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Correcting the Mean-Variance Dependency for Differential Variability Testing Using Single-Cell RNA Sequencing Data.

Nils Eling1, Arianne C Richard2, Sylvia Richardson3

  • 1European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge CB10 1SD, UK; Cancer Research UK Cambridge Institute, University of Cambridge, Li Ka Shing Centre, Cambridge CB2 0RE, UK.

Cell Systems
|September 3, 2018
PubMed
Summary
This summary is machine-generated.

This study introduces a new method to accurately measure cell-to-cell gene expression variability, crucial for understanding tissue function. The approach helps distinguish biological differences from technical noise in single-cell RNA sequencing data.

Keywords:
Bayesianimmune activationsingle-cell RNA sequencingstatisticstranscriptional noisevariability

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Area of Science:

  • Genomics
  • Computational Biology
  • Molecular Biology

Background:

  • Cell-to-cell transcriptional variability is vital for tissue function and development.
  • Single-cell RNA sequencing (scRNA-seq) is a powerful tool for characterizing this variability.
  • Technical noise and mean expression confounding limit scRNA-seq comparisons of variability.

Purpose of the Study:

  • To develop a robust statistical framework for analyzing cell-to-cell expression variability.
  • To create a method for quantifying technical noise without spike-in controls.
  • To provide a residual measure of variability independent of mean expression levels.

Main Methods:

  • Extension of the BASiCS statistical framework.
  • Development of a procedure for estimating technical noise.
  • Derivation of a mean-independent residual measure of transcriptional variability.

Main Results:

  • The new method accurately quantifies cell-to-cell expression variability, separating biological from technical sources.
  • Identified synchronized upregulation of biosynthetic machinery in activated immune cells.
  • Revealed heterogeneous gene upregulation in CD4+ T cells during activation and differentiation.

Conclusions:

  • The enhanced BASiCS framework offers a reliable approach to analyze transcriptional variability in scRNA-seq data.
  • The method provides novel biological insights into cell population dynamics.
  • Distinguishing biological variability is key for understanding immune cell activation and differentiation.