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Life
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Bioinformatics Methods for Single Cell Sequencing Data Analysis
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Bioinformatics Methods for Single Cell Sequencing Data Analysis

A special issue of Life (ISSN 2075-1729). This special issue belongs to the section "Biochemistry, Biophysics and Computational Biology".

Deadline for manuscript submissions: closed (15 June 2022) | Viewed by 5718

Special Issue Editors

Prof. Dr. Yudong Cai

E-Mail Website
Guest Editor
College of Life Science, Shanghai University, Shanghai 200244, China
Interests: systems biology; bioinformatics; protein sequence; machine learning
Special Issues, Collections and Topics in MDPI journals
Dr. Tao Huang

E-Mail Website
Guest Editor
Shanghai Institute of Nutrition and Health, Chinese Academy of Sciences, 320 Yue Yang Road, Shanghai 200031, China
Interests: bioinformatics; genetics; genomics; machine learning; ceRNA network; predictive modeling
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

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

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Life is an international peer-reviewed open access monthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • single cell
  • omics data
  • bioinformatics
  • clustering
  • cell type annotation
  • regulatory network
  • cell–cell communication
  • cell evolution

Published Papers (2 papers)

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Research

6 pages, 1885 KiB  
Communication
Y/X-Chromosome-Bearing Sperm Shows Elevated Ratio in the Left but Not the Right Testes in Healthy Mice
by Chengqing Hu, Jiangcheng Shi, Yujing Chi, Jichun Yang and Qinghua Cui
Life 2021, 11(11), 1219; https://0-doi-org.brum.beds.ac.uk/10.3390/life11111219 - 11 Nov 2021
Viewed by 1971
Abstract
The sex chromosomes play central roles in determining the sex of almost all of the multicellular organisms. It is well known that meiosis in mammalian spermatogenesis produces ~50% Y- and ~50% X-chromosome-bearing sperm, a 1:1 ratio. Here we first reveal that the X-chromosome-encoded [...] Read more.
The sex chromosomes play central roles in determining the sex of almost all of the multicellular organisms. It is well known that meiosis in mammalian spermatogenesis produces ~50% Y- and ~50% X-chromosome-bearing sperm, a 1:1 ratio. Here we first reveal that the X-chromosome-encoded miRNAs show lower expression levels in the left testis than in the right testis in healthy mice using bioinformatics modeling of miRNA-sequencing data, suggesting that the Y:X ratio could be unbalanced between the left testis and the right testis. We further reveal that the Y:X ratio is significantly elevated in the left testis but balanced in the right testis using flow cytometry. This study represents the first time the biased Y:X ratio in the left testis but not in the right testis is revealed. Full article
(This article belongs to the Special Issue Bioinformatics Methods for Single Cell Sequencing Data Analysis)
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14 pages, 2151 KiB  
Article
Single-Cell Transcriptome Profiling Simulation Reveals the Impact of Sequencing Parameters and Algorithms on Clustering
by Yunhe Liu, Aoshen Wu, Xueqing Peng, Xiaona Liu, Gang Liu and Lei Liu
Life 2021, 11(7), 716; https://0-doi-org.brum.beds.ac.uk/10.3390/life11070716 - 19 Jul 2021
Viewed by 2194
Abstract
Despite the scRNA-seq analytic algorithms developed, their performance for cell clustering cannot be quantified due to the unknown “true” clusters. Referencing the transcriptomic heterogeneity of cell clusters, a “true” mRNA number matrix of cell individuals was defined as ground truth. Based on the [...] Read more.
Despite the scRNA-seq analytic algorithms developed, their performance for cell clustering cannot be quantified due to the unknown “true” clusters. Referencing the transcriptomic heterogeneity of cell clusters, a “true” mRNA number matrix of cell individuals was defined as ground truth. Based on the matrix and the actual data generation procedure, a simulation program (SSCRNA) for raw data was developed. Subsequently, the consistency between simulated data and real data was evaluated. Furthermore, the impact of sequencing depth and algorithms for analyses on cluster accuracy was quantified. As a result, the simulation result was highly consistent with that of the actual data. Among the clustering algorithms, the Gaussian normalization method was the more recommended. As for the clustering algorithms, the K-means clustering method was more stable than K-means plus Louvain clustering. In conclusion, the scRNA simulation algorithm developed restores the actual data generation process, discovers the impact of parameters on classification, compares the normalization/clustering algorithms, and provides novel insight into scRNA analyses. Full article
(This article belongs to the Special Issue Bioinformatics Methods for Single Cell Sequencing Data Analysis)
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