scAN1.0：用于处理10X单细胞RNAseq数据的可复制和标准化管道。

Q2 Medicine

In Silico Biology Pub Date : 2023-01-01 DOI:10.3233/ISB-220252

Maxime Lepetit, Mirela Diana Ilie, Marie Chanal, Gerald Raverot, Philippe Bertolino, Christophe Arpin, Franck Picard, Olivier Gandrillon

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引用次数: 0

摘要

单细胞转录组学最近大受欢迎，这导致需要在不同系统和机构之间具有可复制性、模块化和互操作性的数据分析管道。为了满足这一需求，我们引入了scAN1.0，这是一种用于分析10X单细胞RNA测序数据的处理管道。scAN1.0是使用Nextflow DSL2构建的，可以在大多数计算系统上运行。Nextflow管道的模块化设计使不同区块能够轻松集成和评估特定分析步骤。我们通过显示scAN1.0在分析两个数据集期间检测映射步骤的影响的能力来证明其有用性：（i）人类垂体促性腺激素肿瘤数据集的10X scRNAseq和（ii）免疫反应期间在CD8 T细胞上获得的小鼠10X scRNA seq。

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scAN1.0: A reproducible and standardized pipeline for processing 10X single cell RNAseq data.

Single cell transcriptomics has recently seen a surge in popularity, leading to the need for data analysis pipelines that are reproducible, modular, and interoperable across different systems and institutions.To meet this demand, we introduce scAN1.0, a processing pipeline for analyzing 10X single cell RNA sequencing data. scAN1.0 is built using the Nextflow DSL2 and can be run on most computational systems. The modular design of Nextflow pipelines enables easy integration and evaluation of different blocks for specific analysis steps.We demonstrate the usefulness of scAN1.0 by showing its ability to examine the impact of the mapping step during the analysis of two datasets: (i) a 10X scRNAseq of a human pituitary gonadotroph tumor dataset and (ii) a murine 10X scRNAseq acquired on CD8 T cells during an immune response.

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来源期刊

In Silico Biology Computer Science-Computational Theory and Mathematics

CiteScore

2.20

自引率

0.00%

发文量

期刊介绍： The considerable "algorithmic complexity" of biological systems requires a huge amount of detailed information for their complete description. Although far from being complete, the overwhelming quantity of small pieces of information gathered for all kind of biological systems at the molecular and cellular level requires computational tools to be adequately stored and interpreted. Interpretation of data means to abstract them as much as allowed to provide a systematic, an integrative view of biology. Most of the presently available scientific journals focus either on accumulating more data from elaborate experimental approaches, or on presenting new algorithms for the interpretation of these data. Both approaches are meritorious.