PubMed Health⌕ Search

SEARCH · PubMed Health

Results for “deconvolution”

Explore indexed PubMed citations for clinical trials, systematic reviews and public health research. Read source abstracts and follow each citation to its original PubMed record.

Quote a phrase for an exact phrase match. Source license links do not imply unrestricted reuse.

At least 19 recordsLinked to original sources
PubMed1979

Deconvolution analysis in radionuclide quantitation of left-to-right cardiac shunts.

A poor bolus injection results in an unsatisfactory quantitative radionuclide angiocardiogram in as many as 20% of children with possible, left-to-right (L-R) cardiac shunts. Deconvolution analysis was applied to similar studies in experimental animals to determine whether dependence on the input bolus could be minimized. Repeated good-bolus, prolonged (greater than 2.5 sec), or multiple-peak injections were made in four normal dogs and seven dogs with surgically created atrial septal defects (ASD). QP/QS was determined using the gamma function. The mean QP/QS from ten good-bolus studies in each animal was used as the standard for comparison. In five trials in normal animals, where a prolonged or double-peak bolus led to a shunt calculation (QP/QS greater than 1.2 : 1), deconvolution resulted in QP/QS = 1.0. Deconvolution improved shunt quantitation in eight of ten trials in animals that received a prolonged bolus. The correlation between the reference QP/QS and the QP/QS calculated from uncorrected bad bolus studies was only 0.39 (p greater than 0.20). After deconvolution using a low pass filter, the correlation improved significantly (r = 0.77, p less than 0.01). The technique gave inconsistent results with multiple-peak bolus injections. Deconvolution analysis in these studies is useful in preventing normals from being classified as shunts, and in improving shunt quantitation after a prolonged bolus. Clinical testing of this technique in children with suspected L-R shunts seems warranted.

Angiocardiography↗
PubMed2025

OmicsTweezer: A distribution-independent cell deconvolution model for multi-omics Data.

Cell deconvolution estimates cell type proportions from bulk omics data, enabling insights into tissue microenvironments and disease. However, practical applications are often hindered by batch effects between bulk data and referenced single-cell data, a challenge that is frequently overlooked. To address this discrepancy, we developed OmicsTweezer, a distribution-independent cell deconvolution model. By integrating optimal transport with deep learning, OmicsTweezer aligns simulated and real data in a shared latent space, effectively mitigating data shifts and inter-omics distribution differences. OmicsTweezer is versatile, capable of deconvolving bulk RNA-seq, bulk proteomics, and spatial transcriptomics. Extensive evaluations on simulated and real-world datasets demonstrate its robustness and accuracy. Furthermore, applications in prostate and colon cancer showcase OmicsTweezer's ability to identify biologically meaningful cell types. As a unified deconvolution framework for multi-omics data, OmicsTweezer offers an efficient and powerful tool for studying disease microenvironments.

Humans↗
PubMed2025

Enhancing and accelerating cell type deconvolution of large-scale spatial transcriptomics slices with dual network model.

MOTIVATION: Cell type deconvolution deciphers spatial distribution of mRNA transcripts at single cell level by integrating single-cell RNA sequencing (scRNA-seq) and spatial transcriptomics data to infer mixture of cell types of spots in slices. Current algorithms are criticized for neglecting connection between scRNA-seq and spatial transcriptomics data, as well as time-consuming, hampering their application to large-scale datasets. RESULTS: In this study, we propose a joint learning nonnegative matrix factorization algorithm for fast cell type deconvolution (aka jMF2D), which integrates scRNA-seq and spatial transcriptomics data with network models. To bridge scRNA-seq and spatial transcriptomics data, jMF2D jointly learns cell type similarity network to enhance quality of signatures of cell types, thereby promoting accuracy and efficiency of deconvolution. Experiments demonstrate that jMF2D outperforms state-of-the-art baselines in terms of accuracy by saving about 90% running time on various datasets generated by different platforms. Furthermore, it can also facilitates the identification of spatial domains and bio-marker genes, providing an efficient and effective model for analyzing spatial transcriptomics data. AVAILABILITY AND IMPLEMENTATION: The software is coded using python, and is free available for academic https://github.com/xkmaxidian/jMF2D.

Algorithms↗
PubMed2025

Continuous DNA Methylation Deconvolution-Based Surrogate for B-Cell Differentiation State in CLL.

Chronic Lymphocytic Leukemia (CLL) is clinically divided into IGHV mutated (M-CLL) and IGHV unmutated (U-CLL) subtypes, which are thought to arise from distinct cells of origin along the B-cell differentiation pathway. We measured genome-scale DNA methylation in purified CLL samples ( n = 89) and utilized reference-based cell deconvolution techniques to develop a continuous metric of epigenetic similarity across a B-naive-like to B-memory-like scale (B-Index). B-Index accurately classifies CLL into clinical subtypes (98.8%), has a stronger epigenetic signal than IGHV gene percent identity, and demonstrates additional epigenetic signal within the M-CLL subgroup. We demonstrate that U-CLL is epigenetically more similar to B-memory than B-naive cells and reconcile previous reports of a B-naive-like epigenetic signal. The B-memory-like program of U-CLL is enriched for binding sites of transcription factors related to the germinal center activation pathway. Our findings provide epigenetic evidence for discerning CLL mechanisms of initiation and cell of origin. We also identified an epigenetic signal associated with tumor burden, which may have some relation to viral infections such as Epstein-Barr-Virus. Our cell-type deconvolution-based approach to developing a continuous metric for CLL epigenetic differentiation state can be applied to other tumors with multiple subtypes across differentiation stages.

B-memory-like↗
PubMed2026

Cell cycle-dependent protein dynamics in budding yeast resolved by deconvolution of bulk proteomics.

The cell division cycle is characterised by oscillatory dynamics in regulatory mechanisms and biosynthesis, coordinated with genome replication and segregation. To understand these dynamics, quantitative cell cycle-dependent protein concentration data are essential. Unfortunately, accurately resolving cell cycle-dependent protein dynamics is challenging because single-cell proteomics is currently infeasible and bulk proteomics requires - inherently imperfect - cell synchronisation. Here, we developed a computational method to deconvolve cell cycle-dependent protein concentration dynamics and applied it to new budding yeast bulk proteome data. Key to this method was a yeast population model, parameterised with experimental cell cycle progression and volume growth data, for quantifying the desynchronisation in sampled populations. We performed deconvolution on 3272 proteins, using cross-validation to determine regularisation parameters, and identified 539 proteins with cell cycle-dependent dynamics. Many of these dynamics were consistent with known yeast biology and dynamic proteins were enriched for several metabolic process, extending previous observations and supporting the emerging picture of metabolic activity as varying substantially over cell cycle phases. We consider the generated cell cycle-resolved budding yeast proteome data a key resource.

Journal Article↗
PubMed1979

A study of the application of a deconvolution method to scintigraphy.

It is shown that an Anger-type gamma camera can be considered as a linear filter. The image is therefore the convolution of the object by the camera point spread function. An important property of the objects is the fact that they are basically positive (count-rates as a function of space variables). The proposed deconvolution method (due to Biraud) is shown to work satisfactorily on a 1-D scintigraphic signal which is a particular cross-section of a 2-D image. This is a preliminary study of the enhancement of real scintigraphic images.

Image Enhancement↗
PubMed1979

On the deconvolution of exponential response functions.

The deconvolution or unfolding of exponential response functions from experimental data has been examined through the use of a Bayesian based algorithm. The algorithm, which is founded upon the concepts of probability, ensures positivity of solution. This constraint leads to a significant reduction in the growth of statistical noise in deconvolved data when compared with the more common linear unfolding techniques. The algorithm is an iterative procedure which, in the absence of statistical noise, can ultimately result in complete signal recovery. When noise is present one must balance the degree with which the response function is removed against the growth in the noise and, at some point, terminate the iterative process. Criteria for determining the point at which this 'best estimate' is attained are examined and an operationally realisable test is given. Comparison of results is made with the inverse filter solution which, for an exponential response function, is shown to consist of the sum of the observed data and its first derivative.

Mathematics↗
PubMed2025

Sparse deconvolution of cell type medleys in spatial transcriptomics.

Mapping cell distributions across spatial locations with whole-genome coverage is essential for understanding cellular responses and signaling However, current deconvolution models aim to estimate the proportions of distinct cell types in each spatial transcriptomics spot by integrating reference single-cell data. These models often assume strong overlap between the reference and spatial datasets, neglecting biology-grounded constraints such as sparsity and cell-type variations, as well as technical sparsity. As a result, these methods rely on over-permissive algorithms that ignore given constraints leading to inaccurate predictions, particularly in heterogeneous or unmatched datasets. We introduce Weight-Induced Sparse Regression (WISpR), a machine learning algorithm that integrates spot-specific hyperparameters and sparsity-driven modeling. Unlike conventional approaches that neglect biology-grounded constraints, WISpR accurately predicts cell-type distributions while preserving biological coherence, i.e., spatially and functionally consistent cell-type localization, even in unmatched datasets. Benchmarking against five alternative methods across ten datasets, WISpR consistently outperformed competitors and predicted cellular landscapes in both normal and cancerous tissues. By leveraging sparse cell-type arrangements, WISpR provides biologically informed, high-resolution cellular maps. Its ability to decode tissue organization in both healthy and diseased states highlights WISpR's practical utility for spatial transcriptomics, particularly in challenging settings involving noise, sparsity, or reference mismatches.

Humans↗
PubMed1979

[Evaluation of isotope nephrograms by the deconvolution method (author's transl)].

By means of deconvolution, which can be preformed simply with a minicomputer, a new function is derived from the circulation activity time function and the renal activity time function. This function describes the impulse response of the system "Kidney". The activity time functions are determined with a four-probe counter. The impulse response allows statements about the time during which the activity remains in the kidney, the relative blood flow of both kidneys and a so-called passage time distribution. It was also attempted to substitute the circulation activity time function by a derivative of the bladder activity time function.

Humans↗
PubMed2026

Palaeoproteomic Deconvolution of Physical and Genetic Collagen Mixtures.

Species identification in palaeoproteomics relies on genome-derived protein sequences which are often poor-quality, and lacks tools to cope with multi-species samples. Here, we address both challenges through the analysis of "physical and genetic mixtures". Species that are absent from our database are considered a "genetic mixture", i.e. a patchwork of peptides from closely related species. Inversely, various overlapping peptide stretches allow us to resolve complex "physical mixtures". This is benchmarked by analysing physical mixtures of modern bone fragments, including genetic mixtures. We illustrate the impact of our approach via a rapid and high-throughput analysis of >2500 bone fragments, revealing the Eemian-era faunal environment around Scladina Cave, including the first Palaeoloxodon antiquus identified at this site.

bioarchaeology↗