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细胞质小分子RNA序列数据的填充方法综述[Appl. Sci.专栏第四篇发表论文]

已有 1450 次阅读 2022-11-5 01:44 |系统分类:论文交流

我在Applied Sciences(综合性、交叉性期刊,CiteScore=3.70IF=2.84)组织了一个Special Issue,大题目是“大数据分析进展”,比较宽泛。该专栏的推出主要是为了回应因为可获取数据和数据分析的平台、工具的快速增长给自然科学和社会科学带来的重大影响。我们特别欢迎(但不限于)下面四类稿件:(1)数据分析中的基础理论分析,例如一个系统的可预测性(比如时间序列的可预测性)、分类问题的最小误差分析、各种数据挖掘结果的稳定性和可信度分析;(2)数据分析的新方法,例如挖掘因果关系的新方法(这和Topic 1也是相关的)、多模态分析的新方法、隐私计算的新方法等等;(3)推出新的、高价值的数据集、数据分析平台、数据分析工具等等;(4)把大数据分析的方法用到自然科学和社会科学的各个分支(并获得洞见),我们特别喜欢用到那些原来定量化程度不高的学科。

投稿链接:https://www.mdpi.com/journal/applsci/special_issues/75Y7F7607U 

投稿截止时期为20221220日,我们处理稿件非常快,欢迎大家投稿支持。


其中第四篇论文已经正式发表:

Imputation Methods for scRNA Sequencing Data

Abstract

More and more researchers use single-cell RNA sequencing (scRNA-seq) technology to characterize the transcriptional map at the single-cell level. They use it to study the heterogeneity of complex tissues, transcriptome dynamics, and the diversity of unknown organisms. However, there are generally lots of technical and biological noises in the scRNA-seq data since the randomness of gene expression patterns. These data are often characterized by high-dimension, sparsity, large number of “dropout” values, and affected by batch effects. A large number of “dropout” values in scRNA-seq data seriously conceal the important relationship between genes and hinder the downstream analysis. Therefore, the imputation of dropout values of scRNA-seq data is particularly important. We classify, analyze and compare the current advanced scRNA-seq data imputation methods from different angles. Through the comparison and analysis of the principle, advantages and disadvantages of the algorithm, it can provide suggestions for the selection of imputation methods for specific problems and diverse data, and have basic research significance for the downstream function analysis of data.

论文免费下载链接:

https://www.mdpi.com/2076-3417/12/20/10684    




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