Single-cell and spatial transcriptomics

The paper evaluates discrepancies in dimensionality reduction techniques for time-series scRNA-seq data using synthetic data and finds that different methods may not reliably represent dynamics when used alone.

scValue improves efficiency of large scRNA-seq datasets for ML/DL by prioritizing high-value cells, outperforming existing methods in cell-type annotation tasks.

This paper introduces GIANT, a method for integrating atlas-scale single cell data from multiple modalities and tissues, creating a unified gene embedding space that reflects gene functions and regulations.

lute improves cell composition estimation in bulk RNA-seq by accounting for varying cell sizes, using simulated and real datasets to demonstrate its accuracy.

This paper creates a human endoderm organoid cell atlas by integrating single-cell transcriptomes from multiple organoid types to compare cell states with primary tissues and disease models.

This study compares the transcriptomic landscapes of Alzheimer's disease, Dementia with Lewy bodies, and Parkinson's disease using single-nucleus RNA sequencing to identify shared and distinct molecular features.

MEA-seqX integrates electrophysiological and transcriptional data to reveal coordinated spatiotemporal dynamics in hippocampal circuits and identify cell types based on bimodal profiles.

The paper introduces Kasumi, a method to identify persistent local patterns in spatial omics data for improved patient stratification in cancer.

HERGAST identifies spatial structures and amplifies gene expression signals in ultra-large spatial transcriptomics data, outperforming existing methods and revealing novel biological insights in cancer.

TissueViewer is a web-based tool for viewing large multiplexed image datasets from spatial biology techniques efficiently over the internet.

StereoMM integrates spatial transcriptomic data and histological images to improve spatial domain recognition and tumor heterogeneity analysis in cancer tissues.

The paper introduces STIM, a framework for visualizing and aligning spatial transcriptomics data, demonstrating its use on mouse and human tissue samples.

The paper introduces TRADE, a statistical framework to detect transcriptome-wide differential expression effects from noisy Perturb-seq data, showing that many true effects are not statistically significant but detectable in aggregate.

This paper introduces iSORT, a transfer learning method that integrates single-cell and spatial transcriptomics data to reconstruct spatial organization and identify key genes in multicellular systems.

Stereopy provides a framework for analyzing multi-sample spatial transcriptomics data to model comparative and spatiotemporal cellular heterogeneity through three key algorithms.

The paper integrates seven single-cell transcriptomic datasets to generate 225 meta-modules, identifying key gene networks involved in human cortical cell subtype specification and validating their roles in neurodevelopment.

The paper identifies reproducible transcription regulator coexpression patterns using single-cell RNA-seq data across human and mouse, showing consistency with ChIP-seq results.

SPECTRE-Plex improves speed, automation, and resolution for cyclic multiplex immunofluorescence imaging to better map tissue spatial organization.

CAFE is a web app for analyzing and visualizing high-dimensional spectral flow cytometry data, enabling interactive exploration without coding expertise.

scPRINT pre-trains on 50 million cells to predict gene networks, outperforming existing methods in inference and zero-shot tasks.

This study benchmarks five scCNV inference methods across multiple scRNA-seq platforms and mixed samples, finding variations in performance based on data characteristics and batch effects.

This study identifies molecular signatures of ancestral cell types in mammalian olfactory cortex neurons by comparing transcriptomes across species and cortical layers.

The paper benchmarks cellular deconvolution methods using a multi-assay dataset from human prefrontal cortex, finding Bisque and hspe to be the most accurate.

The paper examines how missing cell types in single-cell references affect deconvolution of bulk data and shows that missing cell-type profiles can be detected using non-negative matrix factorization.

CHOIR improves cell type and state detection in single-cell data by using statistical inference to assess clustering results, outperforming existing methods across multiple data types.

This study uses spatial single-cell analysis to identify 10 cell phenotypes in stage III colorectal cancers and links specific spatial features to recurrence risk.

This study identifies fourteen enterochromaffin cell clusters in the gut using single-cell transcriptomics and spatial analysis, revealing subtypes with distinct functional properties and a Piezo2+ population involved in colon motility.

MIST is a deep learning framework that integrates single-cell transcriptomic and TCR data to analyze T cell function and antigen specificity, revealing heterogeneity in lung cancer CD8+ T cells and their response to immunotherapy.

The paper introduces scCRAFT, an autoencoder for scRNA-seq batch integration that outperforms existing methods by preserving biological heterogeneity and handling unbalanced batches and rare cell types.