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RNA-seq

RNA-seq

RNA sequencing, or RNA-Seq, is a high-throughput sequencing technology used to study the transcriptome of a cell. Transcriptomics helps to interpret the functional elements of a genome and identify the molecular constituents of an organism. Additionally, it also helps in understanding the development of an organism and the occurrence of diseases.
Before the discovery of RNA-seq, microarray-based methods and Sanger sequencing were used for transcriptome analysis. However, while...

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Timestamp calibration for time-series single cell RNA-seq expression data.

Xiran Chen¹, Sha Lin², Xiaofeng Chen¹

¹School of Mathematics and Statistics, Chongqing Jiaotong University, Chongqing, China.

Journal of Molecular Biology

|February 26, 2025

Summary

This summary is machine-generated.

ScPace effectively calibrates noisy timestamps in time-series single-cell RNA sequencing (scRNA-seq) data. This novel method improves downstream analysis by accurately identifying and handling unreliable manual timestamps, enhancing biological discovery.

Keywords:

adasampling psupertime self-paced learning support vector machine time-series scRNA-seq

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Area of Science:

Genomics
Computational Biology
Bioinformatics

Background:

Timestamp automatic annotation (TAA) is vital for time-series scRNA-seq data analysis.
Current TAA methods are hindered by unreliable manual timestamps, impacting accuracy.
Existing noise handling strategies often neglect critical samples for calibration.

Purpose of the Study:

To develop a robust timestamp calibration model for noisy time-series scRNA-seq data.
To improve the reliability of timestamp automatic annotation.
To enhance downstream analyses like pseudotime ordering.

Main Methods:

Introduced ScPace, a novel timestamp calibration model.
Incorporated a latent variable indicator within a base classifier to detect noisy samples.
Validated ScPace on simulated and real time-series scRNA-seq datasets.

Main Results:

ScPace significantly outperforms existing methods across various mislabeling rates.
Timestamp calibration with ScPace enhances supervised pseudotime analysis performance.
The model effectively reclassifies and deletes noisy labeled samples.

Conclusions:

ScPace offers an effective solution for timestamp calibration in time-series scRNA-seq data.
The method demonstrates robustness across diverse datasets.
ScPace improves the accuracy and reliability of dynamic biological analyses.