Python 用于基因表达。Python for gene expression.-医云文献数字医云科研云海量医学决策数据服务

Abstract：

Genome biology shows substantial progress in its analytical and computational part in the last decades. Differential gene expression is one of many computationally intense areas; it is largely developed under R programming language. Here we explain possible reasons for such dominance of R in gene expression data. Next, we discuss the prospects for Python to become competitive in this area of research in coming years. We indicate that Python can be used already in a field of a single cell differential gene expression. We pinpoint still missing parts in Python and possibilities for improvement.

摘要：

在过去的几十年中，基因组生物学在其分析和计算方面取得了重大进展。差异基因表达是许多计算密集领域之一；它主要是在R编程语言下开发的。在这里，我们解释了基因表达数据中R优势的可能原因。接下来,我们讨论了Python在未来几年在这一研究领域具有竞争力的前景。我们指出Python已经可以用于单细胞差异基因表达领域。我们确定了Python中仍然缺少的部分以及改进的可能性。

Python for gene expression.

1 Opportunities and obstacles for deep learning in biology and medicine.

2 Spatial reconstruction of single-cell gene expression data.

3 Harnessing QbD, Programming Languages, and Automation for Reproducible Biology.

4 Synthetic data sets for the identification of key ingredients for RNA-seq differential analysis.

5 A comparison of common programming languages used in bioinformatics.

6 Linear models and empirical bayes methods for assessing differential expression in microarray experiments.

7 edgeR: a Bioconductor package for differential expression analysis of digital gene expression data.

8 Exploration, normalization, and summaries of high density oligonucleotide array probe level data.

9 Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R.

10 Bioconda: sustainable and comprehensive software distribution for the life sciences.

11 A scalable SCENIC workflow for single-cell gene regulatory network analysis.

12 How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use?

13 Next-generation sequencing in the clinic: promises and challenges.

14 In Papyro Comparison of TMM (edgeR), RLE (DESeq2), and MRN Normalization Methods for a Simple Two-Conditions-Without-Replicates RNA-Seq Experimental Design.

15 Insights about genome function from spatial organization of the genome.

16 An open RNA-Seq data analysis pipeline tutorial with an example of reprocessing data from a recent Zika virus study.

17 Modeling genomic regulatory networks with big data.

18 Orchestrating high-throughput genomic analysis with Bioconductor.

19 limma powers differential expression analyses for RNA-sequencing and microarray studies.

20 NCBI GEO: archive for functional genomics data sets--update.

21 The characteristic direction: a geometrical approach to identify differentially expressed genes.

22 A comparison of normalization methods for high density oligonucleotide array data based on variance and bias.

23 An intuitive Python interface for Bioconductor libraries demonstrates the utility of language translators.

24 Snakemake--a scalable bioinformatics workflow engine.

25 The Impact of Normalization Methods on RNA-Seq Data Analysis.

26 limmaGUI: a graphical user interface for linear modeling of microarray data.

27 Bioinformatics analysis of microarray data.

28 Orchestrating single-cell analysis with Bioconductor.

29 Synthetic Biology Open Language Visual (SBOL Visual) Version 2.1.

30 Differential Expression Analysis in RNA-seq Data Using a Geometric Approach.

31 Human genomics projects and precision medicine.

32 SCANPY: large-scale single-cell gene expression data analysis.

33 Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.

34 A step-by-step workflow for low-level analysis of single-cell RNA-seq data with Bioconductor.