Single-cell and spatial transcriptomics

pyALRA provides a Python implementation of ALRA for imputing dropouts in single-cell RNA-seq with improved speed and memory efficiency.

ScaleSC provides a GPU-accelerated single-cell RNA-seq analysis pipeline with 20x speedup and scalability for large datasets.

scEGOT is a single-cell trajectory inference framework that identifies cell populations and segregation events in time-series scRNA-seq data, revealing key transcription factors involved in PGCLC differentiation.

This study maps the transcriptional landscape of the rat cardiovascular system at single-cell resolution, identifying 26 cell types and revealing tissue-specific molecular diversity and communication networks.

This paper introduces GSPA, a method to map gene space at single-cell resolution, enabling gene characterization and analysis of gene expression patterns across biological tasks.

The paper introduces cyCONDOR, a computational framework for high-dimensional cytometry data analysis, offering scalable and user-friendly tools for preprocessing, clustering, and downstream biological interpretation.

This paper introduces VeloVGI, a method for accurate RNA velocity estimation in batch scRNA-seq data, outperforming existing methods in various datasets.

HIPSD&R-seq profiles genomic copy number and transcriptomes in thousands of cells, enabling rare clone detection in human cell models and primary tissue.

This paper proposes a biophysical model for analyzing ATAC-seq data and evaluates its benefits for integrating multiome data with single-cell RNA-seq.

NLSDeconv is a cell-type deconvolution method for spatial transcriptomics data that offers competitive performance and superior computational efficiency compared to existing methods.

The paper introduces a method to produce monoclonal antibodies from single B cells using FB5P-seq libraries for integrative analysis of B cell phenotype, transcriptome, and antigen receptor sequence.

The paper introduces Multiome Perturb-seq to simultaneously measure gene expression and chromatin accessibility in CRISPR screens, revealing regulatory programs in human cells.

The paper defines the transcriptional taxonomy of ventral pallidum cell types across mice, macaques, and baboons using snRNA-seq and in situ hybridization, finding transcriptional conservation and unique subclusters in primates.

The paper develops and validates multiplexing strategies for brain organoids combined with single cell RNA-seq to study neurodevelopmental trajectories across multiple genetic lines.

SuperSpot reduces spatial transcriptomic data size and noise by grouping similar spots into metaspots using graph-based clustering.

NiCo identifies extrinsic drivers of cell state modulation by analyzing niche covariation in spatial transcriptomics data.

This paper introduces Annotatability, a method that uses deep network training dynamics to identify and correct ambiguous cell annotations in single-cell and spatial omics data.

This study characterizes peripheral glia in the lung and other organs using scRNA-seq, identifying molecularly distinct populations and conserved gene expression across species.

IOBR2 is a tool for analyzing tumor microenvironment using multi-omics data, offering modules for preprocessing, estimation, interaction analysis, and visualization.

The paper benchmarks TCR reconstruction methods from scRNA-seq data, finding TRUST4 performs best, while sequencing depth limits accuracy.

SpaCCC uses a fine-tuned LLM and gene network to infer cell-cell communications from spatial transcriptomic data, outperforming existing methods and revealing hidden ligand-receptor pairs.

The paper introduces MIST, a cost-effective spatial transcriptomics method combining tissue microarrays with Visium, and presents AnnotateMap for analyzing multiple ROIs.

The paper presents a pipeline using scRNA-seq data to deconvolute and estimate cell type fractions in bulk transcriptome data from organoids.

A TCR-first approach to single-cell data analysis improves identification of disease-specific TCR features and reveals previously unknown T-cell clusters and arrangements.

PanSci, a single-cell transcriptome atlas of over 20 million cells throughout the mouse lifespan, unveils the temporal architecture of aging-associated cellular population dynamics, organ-specific immune cell shifts, and the lymphocytes role in organismal aging.

EfNST improves spatial domain identification in spatial transcriptomics by using a composite scaling network of EfficientNet, outperforming existing methods on three datasets.

The paper introduces tissueResolver, an algorithm that infers single-cell type information from bulk RNA-seq data, revealing cell-type-specific regulatory differences in DLBCL subtypes.

fast-scBatch corrects batch effects in scRNA-seq data using a neural network-driven approach, outperforming existing methods in simulation and real datasets.

scMoE learns interpretable cell-type-specific representations from heterogeneous scRNA-seq data, improving clustering and spatial deconvolution across multiple datasets.

scMitoMut is an R package that improves mitochondrial mutation calling in single cells by using statistical methods, showing better sensitivity and precision than existing tools.