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Deconvolution

Deconvolution

Deconvolution, also known as inverse filtering, is the process of extracting the impulse response from known input and output signals. This technique is vital in scenarios where the system's characteristics are unknown, and they must be inferred from the observable signals.
Deconvolution involves several mathematical techniques to derive the impulse response. One common approach is polynomial division. In this method, the input and output sequences are treated as coefficients of...

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Related Experiment Video

Updated: Jan 8, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

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A robust workflow to benchmark deconvolution of multi-omic data.

Elise Amblard¹, Vadim Bertrand², Hugo Barbot³

¹TIMC, UMR 5525, Univ. Grenoble Alpes, CNRS, Grenoble, France. elise.amblard@univ-grenoble-alpes.fr.

|December 17, 2025

Summary

This summary is machine-generated.

This study introduces a framework to compare deconvolution algorithms for analyzing tumor heterogeneity from molecular data. It provides guidance on selecting the best methods for transcriptomic and methylomic analyses.

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Last Updated: Jan 8, 2026

Workflow for High-content, Individual Cell Quantification of Fluorescent Markers from Universal Microscope Data, Supported by Open Source Software

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Published on: December 16, 2014

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Published on: July 29, 2022

Leveraging CyVerse Resources for De Novo Comparative Transcriptomics of Underserved Non-model Organisms

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Published on: May 9, 2017

Area of Science:

Computational biology
Genomics
Cancer research

Background:

Tumor heterogeneity impacts cancer progression and treatment efficacy.
Quantifying tumor heterogeneity from bulk molecular data is challenging.
Deconvolution algorithms estimate cell type proportions but lack consensus for transcriptomic/methylomic data.

Purpose of the Study:

To develop an unbiased evaluation framework for deconvolution algorithms.
To comprehensively compare deconvolution methods across transcriptomic and methylomic data.
To provide practical guidance for selecting optimal algorithms.

Main Methods:

Developed a reproducible evaluation framework using containerization.
Compared reference-based and reference-free deconvolution algorithms.
Assessed algorithm performance, stability, and efficiency across diverse datasets and conditions.

Main Results:

Presented the first comprehensive comparison of deconvolution algorithms for both omics types.
Evaluated algorithm performance under various conditions, including gene dependencies and sample composition.
Utilized benchmark datasets and a novel multi-omics dataset for validation.

Conclusions:

Highlighted the strengths and limitations of different deconvolution algorithms.
Provided practical guidance for algorithm selection based on data type and context.
Established a new standard for evaluating deconvolution methods for tumor heterogeneity analysis.