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Metal ions can be separated from one another by complexation with organic ligands–the chelating agent– to form uncharged chelates. Here, the chelating agent must contain hydrophobic groups and behave as a weak acid, losing a proton to bind with the metal. Since most organic ligands used in this process are insoluble or undergo oxidation in the aqueous phase, the chelating agent is initially added to the organic phase and extracted into the aqueous phase. The metal-ligand complex is formed in...

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Updated: May 31, 2026

Microfluidic Platform with Multiplexed Electronic Detection for Spatial Tracking of Particles

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Published on: March 13, 2017

BLEND: Probabilistic Cellular Deconvolution with Automated Reference Selection.

Penghui Huang¹, Manqi Cai¹, Chris McKennan²

¹Department of Biostatistics, University of Pittsburgh, De Soto St, Pittsburgh, 15261, PA, USA.

Biorxiv : the Preprint Server for Biology

|August 16, 2024

Summary

This summary is machine-generated.

BLEND, a new Bayesian method, accurately estimates cell type fractions from bulk omics data by integrating multiple references. It overcomes limitations of existing methods, showing superior performance in simulations and real-world applications like Alzheimer's disease research.

Keywords:

Bayesian estimation Cellular deconvolution EM algorithm Gibbs sampling Maximum a posteriori estimation Single-cell RNA sequencing

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Area of Science:

Computational biology
Bioinformatics
Genomics

Background:

Cellular deconvolution estimates cell type proportions in bulk omics data.
Existing methods struggle with cell type-specific expression heterogeneity and reference data discrepancies.
Guidance on reference selection and integration is often lacking.

Purpose of the Study:

Introduce BLEND, a novel hierarchical Bayesian method for cellular deconvolution.
Address limitations of current methods by leveraging multiple reference datasets and accounting for heterogeneity.
Provide a robust framework for cell type fraction estimation from omics data.

Main Methods:

BLEND utilizes a hierarchical Bayesian approach with multiple reference datasets.
It employs a "bag-of-words" model for bulk count data and explores reference convex hulls.
An efficient EM algorithm is used for parameter estimation, requiring no data transformation or normalization.

Main Results:

BLEND demonstrates superior performance in deconvolution across simulated and real human brain data.
The method effectively handles cell type expression heterogeneity and reference discrepancies.
No prior cell type marker selection or reference quality evaluation is needed.

Conclusions:

BLEND offers a robust and efficient solution for cellular deconvolution, improving accuracy and applicability.
It facilitates the integration of diverse reference resources for enhanced deconvolution.
The method shows promise for applications in complex diseases, such as Alzheimer's disease research.