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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: Dec 29, 2025

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

3D Imaging of Soft-Tissue Samples using an X-ray Specific Staining Method and Nanoscopic Computed Tomography

Published on: October 24, 2019

DTD: An R Package for Digital Tissue Deconvolution.

Marian Schön¹, Jakob Simeth¹, Paul Heinrich¹

¹Department of Statistical Bioinformatics, Institute of Functional Genomics, University of Regensburg, Regensburg, Germany.

Journal of Computational Biology : a Journal of Computational Molecular Cell Biology

|January 30, 2020

Summary

This summary is machine-generated.

Digital tissue deconvolution (DTD) estimates cellular makeup from gene expression. Loss-function learning tailors DTD models to specific tissues like tumors, improving accuracy with provided software.

Keywords:

R package cell-type deconvolution loss-function learning model adaptation

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Area of Science:

Computational biology
Genomics
Bioinformatics

Background:

Digital tissue deconvolution (DTD) infers cellular composition from bulk tissue gene expression.
DTD models bulk expression as a mixture of cell-specific profiles.
Tissue context, including cell states and environments, influences deconvolution accuracy.

Purpose of the Study:

To adapt DTD models to specific tissue contexts using loss-function learning.
To enhance the accuracy of DTD for applications in diverse tissues, such as blood and tumors.
To provide accessible software for loss-function learning, validation, visualization, and application of DTD models.

Main Methods:

Implemented loss-function learning to tailor DTD algorithms.
Developed and validated DTD models for specific tissue types.
Created software tools for the entire DTD workflow, from learning to application.

Main Results:

Demonstrated that loss-function learning effectively adapts DTD to specific tissue contexts.
Achieved improved deconvolution accuracy by tailoring models to tissue-specific characteristics.
Successfully applied the developed DTD models to new gene-expression datasets.

Conclusions:

Loss-function learning is a powerful approach for context-specific DTD.
Tailored DTD models offer more accurate cellular composition estimation.
The provided software facilitates the practical implementation of advanced DTD methods.