deconvolution

Robust decomposition of cell type mixtures in spatial transcriptomics

This paper introduces RCTD, a method to decompose cell type mixtures in spatial transcriptomics, improving cell type assignment and enabling discovery of spatially dependent gene expression.

Thresholded Partial Least Squares: Fast Construction of Interpretable Whole-brain Decoders

This paper introduces a method to build interpretable whole-brain neural decoders that are computationally efficient and scalable.

Estimation of Laminar BOLD Activation Profiles using Deconvolution with a Physiological Point Spread Function

This paper deconvolves laminar BOLD activation profiles using a physiological PSF to remove signal propagation effects from intracortical veins, achieving high laminar specificity.

ASIGNTF: AGNOSTIC SIGNATURE USING NTF: A UNIVERSAL AGNOSTIC STRATEGY TO ESTIMATE CELL-TYPES ABUNDANCE FROM TRANSCRIPTOMIC DATASETS

The paper introduces ASigNTF, a method to estimate cell-type abundance from transcriptomic data using non-negative tensor factorization and the Herfindahl-Hirschman index, and provides two new immune cell-type signatures that outperform existing methods.

The Gene Expression Deconvolution Interactive Tool (GEDIT): Accurate Cell Type Quantification from Gene Expression Data

GEDIT estimates cell type abundances from gene expression data, outperforming existing tools across multiple platforms and tissue types.

Single-cell deconvolution of 3,000 post-mortem brain samples for eQTL and GWAS dissection in mental disorders

The paper deconvolves 3,387 post-mortem brain samples using SPLITR to identify cell-type-specific eQTLs and genetic effects in mental disorders.

ViralRecall: A Flexible Command-Line Tool for the Detection of Giant Virus Signatures in Omic Data

ViralRecall detects NCLDV signatures in omic data using a library of Giant Virus Orthologous Groups, effectively identifying sequences and removing contaminants.

SPOTlight:Seeded NMF regression to Deconvolute Spatial Transcriptomics Spots with Single-Cell Transcriptomes

SPOTlight integrates spatial transcriptomics with single-cell RNA-seq to deconvolute cell types and states in tissues, showing accurate predictions in mouse brain and human pancreatic cancer.

DeCompress: tissue compartment deconvolution of targeted mRNA expression panels using compressed sensing

DeCompress improves deconvolution of targeted mRNA panels by expanding feature space with reference data, achieving better cell-type proportion estimation and identifying a tumor-specific cis-eQTL for CCR3.

COT: an efficient Python tool for detecting marker genes among many subtypes

COT detects marker genes across subtypes using cosine similarity in a one-sample test, applicable to multi-omics data.

BELLA: Berkeley Efficient Long-Read to Long-Read Aligner and Overlapper

BELLA improves long-read sequencing overlap detection and assembly by balancing precision and recall, achieving high F1 scores on real and synthetic data.

DSTG: Deconvoluting Spatial Transcriptomics Data through Graph-based Artificial Intelligence

DSTG deconvolutes spatial transcriptomics data to reveal cell compositions and spatial architecture of cellular heterogeneity in tissues.

AdRoit: an accurate and robust method to infer complex transcriptome composition

AdRoit infers cell composition from bulk or spatial transcriptome data using single-cell references, showing improved sensitivity and speed over existing methods.

REGALS: a general method to deconvolve X-ray scattering data from evolving mixtures

REGALS deconvolves X-ray scattering data from evolving mixtures by incorporating prior knowledge and regularization, enabling analysis of complex datasets with unknown components.

Deconvolution of cellular subsets in human tissue based on targeted DNA methylation analysis at individual CpG sites

The paper demonstrates that targeted DNA methylation analysis at individual CpG sites can accurately deconvolve cellular composition in human tissues.

Long read metagenomics, the next step?

This study compares third-generation long-read sequencing technologies with short-read sequencing in metagenomics, finding that long reads improve gene and genome reconstruction, especially for microbes with high microdiversity.

Copy number-aware deconvolution of tumor-normal DNA methylation profiles

This paper introduces CAMDAC, a method to deconvolve tumor-normal DNA methylation profiles while accounting for copy number changes, revealing epigenetic instability and distinct patterns in lung cancer.

Isolation of high yield and quality RNA from human precision-cut lung slices for RNA-sequencing and computational integration with larger patient cohorts

The paper presents a method for isolating high-quality RNA from human precision-cut lung slices, enabling RNA-seq analysis and computational integration with larger patient cohorts.

Symbiont-Screener: a reference-free filter to automatically separate host sequences and contaminants for long reads or co-barcoded reads by unsupervised clustering

Symbiont-Screener separates host sequences from contaminants in long reads or co-barcoded reads using unsupervised clustering without reference alignment, achieving high accuracy in simulated and real data.

Strain-level sample characterisation using long reads and MAPQ scores

The paper presents a method for strain-level microbial sample characterization using long reads and MAPQ scores, achieving better strain differentiation than existing tools.

SSMD: A semi-supervised approach for a robust cell type identification and deconvolution of mouse transcriptomics data

SSMD improves cell type identification and deconvolution in mouse transcriptomics by addressing variations in genetics, experimental platforms, and small sample sizes.

A novel algorithm to optimize generalized gamma distributed multiplicative noise with implications on speckle removal from OCT images

This paper introduces an algorithm to optimize generalized gamma distributed multiplicative noise in OCT images, improving speckle removal and contrast-to-noise ratio.

I-CONVEX: Fast and Accurate de Novo Transcriptome Recovery from Long Reads

I-CONVEX improves de novo transcriptome recovery from long reads by providing fast, alignment-free isoform clustering with high consensus accuracy.

Reference-free deconvolution of complex DNA methylation data - a systematic protocol

This paper introduces a reference-free deconvolution protocol for DNA methylomes to identify hidden cell populations in complex samples, applied to lung cancer data from TCGA.

Converting Microwave Ovens into Plasma-Generating Decontamination Units for N-95 Respirators

The paper demonstrates a method to decontaminate N-95 respirators using a modified microwave oven, achieving significant viral and bacterial reduction without damaging the respirators.

Tracking strains predicts personal microbiomes and reveals recent adaptive evolution

The paper introduces StrainTrack, a method to track bacterial strains in metagenomes and identifies shared strains between twins.

AFid: A tool for automated identification and exclusion of autofluorescent objects from microscopy images

AFid identifies and removes autofluorescent signals from multi-channel microscopy images to improve protein expression analysis.

3D Deconvolution Processing for STEM Cryo-Tomography

This paper applies deconvolution algorithms from fluorescence microscopy to improve 3D reconstruction in cryo-scanning transmission electron tomography, enhancing contrast and reducing noise in cellular structures.

Mixed-stock analyses among migratory, non-native Chinook salmon at-sea and assignment to natal sites in freshwater at their introduced range in South America

The paper uses genetic analyses to determine the natal origins of non-native Chinook salmon caught at-sea and in estuaries in South America.

Advances in mixed cell deconvolution enable quantification of cell types in spatially-resolved gene expression data

SpatialDecon quantifies cell populations in spatially-resolved gene expression data, showing spatially heterogeneous immune responses in lung tumors.