Bioinformatics Methods for Single Cell Sequencing Data Analysis

Dear Colleagues,

As an emerging biotechnology, single cell sequencing has become widely used. However, single cell data analysis is still challenging. Compared with traditional bulk sequencing, single cell sequencing data is sparse. There are many genes in cells that cannot be measured. Another difference is that the sample size of single cell data is usually much larger than that of bulk sequencing data, enabling the applications of lasted deep learning methods, which require large sample size. Furthermore, the QC (quality control) of single cell sequencing data is different from that of bulk sequencing. There are many new issues. For example, a cell with a unique barcode may be doublet, which leads to different data processing methods.

To address these challenges in single cell sequencing data analysis, new bioinformatics methods are needed. Therefore, we would like to organize a Special Issue of Life to introduce the latest methods in single cell data analysis. Potential topics include, but are not limited to:

1. Quality control methods of single cell data;
2. Missing value imputation for single cell data;
3. Normalization methods for single cell data;
4. Clustering of single cell data;
5. Cell type annotation methods;
6. Cell regulatory network construction and analysis;
7. Cell–cell communication analysis;
8. Trajectory analysis of single cells.

Relevant webinar is available at, https://life-1.sciforum.net/.

Prof. Dr. Yudong Cai
Prof. Dr. Tao Huang
Guest Editors

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