Fourteen years of cellular deconvolution: methodology, applications, technical evaluation and outstanding challenges
Single-cell RNA sequencing (scRNA-Seq) is a recent technology that allows for the
measurement of the expression of all genes in each individual cell contained in a sample …
measurement of the expression of all genes in each individual cell contained in a sample …
Challenges and best practices in omics benchmarking
TG Brooks, NF Lahens, A Mrčela, GR Grant - Nature Reviews Genetics, 2024 - nature.com
Technological advances enabling massively parallel measurement of biological features—
such as microarrays, high-throughput sequencing and mass spectrometry—have ushered in …
such as microarrays, high-throughput sequencing and mass spectrometry—have ushered in …
A comparison of marker gene selection methods for single-cell RNA sequencing data
JM Pullin, DJ McCarthy - Genome Biology, 2024 - Springer
Background The development of single-cell RNA sequencing (scRNA-seq) has enabled
scientists to catalog and probe the transcriptional heterogeneity of individual cells in …
scientists to catalog and probe the transcriptional heterogeneity of individual cells in …
DELVE: feature selection for preserving biological trajectories in single-cell data
Single-cell technologies can measure the expression of thousands of molecular features in
individual cells undergoing dynamic biological processes. While examining cells along a …
individual cells undergoing dynamic biological processes. While examining cells along a …
Single-cell sequencing analysis within biologically relevant dimensions
R Kousnetsov, J Bourque, A Surnov, I Fallahee… - Cell Systems, 2024 - cell.com
The currently predominant approach to transcriptomic and epigenomic single-cell analysis
depends on a rigid perspective constrained by reduced dimensions and algorithmically …
depends on a rigid perspective constrained by reduced dimensions and algorithmically …
Gene Regulatory Network Inference in the Presence of Dropouts: a Causal View
Gene regulatory network inference (GRNI) is a challenging problem, particularly owing to
the presence of zeros in single-cell RNA sequencing data: some are biological zeros …
the presence of zeros in single-cell RNA sequencing data: some are biological zeros …
An integrated Bayesian framework for multi‐omics prediction and classification
With the growing commonality of multi‐omics datasets, there is now increasing evidence that
integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers …
integrated omics profiles lead to more efficient discovery of clinically actionable biomarkers …
Semi-supervised integration of single-cell transcriptomics data
Batch effects in single-cell RNA-seq data pose a significant challenge for comparative
analyses across samples, individuals, and conditions. Although batch effect correction …
analyses across samples, individuals, and conditions. Although batch effect correction …
Evaluating the Utilities of Foundation Models in Single-cell Data Analysis
Abstract Foundation Models (FMs) have made significant strides in both industrial and
scientific domains. In this paper, we evaluate the performance of FMs in single-cell …
scientific domains. In this paper, we evaluate the performance of FMs in single-cell …
Simulating multiple variability in spatially resolved transcriptomics with scCube
A pressing challenge in spatially resolved transcriptomics (SRT) is to benchmark the
computational methods. A widely-used approach involves utilizing simulated data. However …
computational methods. A widely-used approach involves utilizing simulated data. However …