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Single-Cell Analysis

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The multimodality cell segmentation challenge: toward universal solutions.

Nature methods
Cell segmentation is a critical step for quantitative single-cell analysis in microscopy images. Existing cell segmentation methods are often tailored to specific modalities or require manual interventions to specify hyper-parameters in different exp...

DANCE: a deep learning library and benchmark platform for single-cell analysis.

Genome biology
DANCE is the first standard, generic, and extensible benchmark platform for accessing and evaluating computational methods across the spectrum of benchmark datasets for numerous single-cell analysis tasks. Currently, DANCE supports 3 modules and 8 po...

Machine learning aided single cell image analysis improves understanding of morphometric heterogeneity of human mesenchymal stem cells.

Methods (San Diego, Calif.)
The multipotent stem cells of our body have been largely harnessed in biotherapeutics. However, as they are derived from multiple anatomical sources, from different tissues, human mesenchymal stem cells (hMSCs) are a heterogeneous population showing ...

scFSNN: a feature selection method based on neural network for single-cell RNA-seq data.

BMC genomics
While single-cell RNA sequencing (scRNA-seq) allows researchers to analyze gene expression in individual cells, its unique characteristics like over-dispersion, zero-inflation, high gene-gene correlation, and large data volume with many features pose...

Quantitative image analysis pipeline for detecting circulating hybrid cells in immunofluorescence images with human-level accuracy.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Circulating hybrid cells (CHCs) are a newly discovered, tumor-derived cell population found in the peripheral blood of cancer patients and are thought to contribute to tumor metastasis. However, identifying CHCs by immunofluorescence (IF) imaging of ...

Distribution-Agnostic Deep Learning Enables Accurate Single-Cell Data Recovery and Transcriptional Regulation Interpretation.

Advanced science (Weinheim, Baden-Wurttemberg, Germany)
Single-cell RNA sequencing (scRNA-seq) is a robust method for studying gene expression at the single-cell level, but accurately quantifying genetic material is often hindered by limited mRNA capture, resulting in many missing expression values. Exist...

SLIDE: Significant Latent Factor Interaction Discovery and Exploration across biological domains.

Nature methods
Modern multiomic technologies can generate deep multiscale profiles. However, differences in data modalities, multicollinearity of the data, and large numbers of irrelevant features make analyses and integration of high-dimensional omic datasets chal...

Convolutional neuronal network for identifying single-cell-platelet-platelet-aggregates in human whole blood using imaging flow cytometry.

Cytometry. Part A : the journal of the International Society for Analytical Cytology
Imaging flow cytometry is an attractive method to investigate individual cells by optical properties. However, imaging flow cytometry applications with clinical relevance are scarce so far. Platelet aggregation naturally occurs during blood coagulati...

scMGCN: A Multi-View Graph Convolutional Network for Cell Type Identification in scRNA-seq Data.

International journal of molecular sciences
Single-cell RNA sequencing (scRNA-seq) data reveal the complexity and diversity of cellular ecosystems and molecular interactions in various biomedical research. Hence, identifying cell types from large-scale scRNA-seq data using existing annotations...

The impacts of active and self-supervised learning on efficient annotation of single-cell expression data.

Nature communications
A crucial step in the analysis of single-cell data is annotating cells to cell types and states. While a myriad of approaches has been proposed, manual labeling of cells to create training datasets remains tedious and time-consuming. In the field of ...