Ghosh Arijit, Sheeba Vasu
Chronobiology and Behavioral Neurogenetics Laboratory, Neuroscience Unit, Jawaharlal Nehru Centre for Advanced Scientific Research, Bangalore, India.
J Biol Rhythms. 2022 Apr;37(2):222-231. doi: 10.1177/07487304221077662. Epub 2022 Feb 25.
Chronobiologists and sleep researchers often need to estimate various rhythm and sleep parameters from locomotor activity data from different organisms. The available open-source or expensive paid tools do not offer consolidated analysis and visualization options in one bundle, are often cumbersome for users unfamiliar with coding, offer very low customization options, introduce sources of human errors by requiring users to manually pick period and power values from periodogram plots, and do not generate reproducible reports. We present VANESSA, a family of cross-platform apps written in R, which, in our opinion, have several advantages compared with available tools-(a) open-source; (b) automatic period-power detection; (c) time-series filtering and smoothing; (d) high-resolution publication-quality figures with dynamic coloring, resizing, and light/dark shading; (e) reproducible code-report generation; (f) analysis and visualization of multiple monitor files, defining genotypes and replicates separately; and (g) sleep profile analysis, various sleep parameter estimations, quantification, bout analysis, and latency analysis. The current version of the app is for data acquired through Activity Monitors (DAM, TriKinetics) but can be easily extended to that from other data acquisition systems and from other organisms. We will continue to develop VANESSA with more useful features and version control will be done via archiving versions with significant changes on GitHub (https://github.com/orijitghosh/VANESSA-DAM) and Zenodo.
时间生物学家和睡眠研究人员经常需要从不同生物体的运动活动数据中估计各种节律和睡眠参数。现有的开源工具或昂贵的付费工具都没有在一个软件包中提供综合分析和可视化选项,对于不熟悉编码的用户来说通常很麻烦,提供的定制选项非常少,要求用户从周期图中手动选取周期和功率值会引入人为错误源,并且不会生成可重复的报告。我们展示了VANESSA,这是一个用R编写的跨平台应用程序家族,我们认为与现有工具相比它有几个优点:(a)开源;(b)自动周期-功率检测;(c)时间序列滤波和平滑;(d)具有动态着色、调整大小和明暗阴影的高分辨率可用于发表的图形;(e)可重复的代码-报告生成;(f)多个监测文件的分析和可视化,分别定义基因型和重复样本;以及(g)睡眠概况分析、各种睡眠参数估计、量化、发作分析和潜伏期分析。该应用程序的当前版本适用于通过活动监测器(DAM,TriKinetics)获取的数据,但可以轻松扩展到来自其他数据采集系统和其他生物体的数据。我们将继续开发具有更多有用功能的VANESSA,并通过在GitHub(https://github.com/orijitghosh/VANESSA-DAM)和Zenodo上存档有重大变化的版本来进行版本控制。