Software

Docker的安装使用和配置

将自己安装docker,使用以及配置记录下来,主要还包括常用的命令总结以及一些bug的处理,以及一些常见资源.

网络分析之igraph

0.0.1 安装 0.0.2 常用功能总结 0.0.3 从data frame转变为``igraph对象 igraph是一些列网络分析工具的集合.开源且免费,拥有R, Python

LipidCCS Prediction of Collision Cross-Section Values for Lipids with High Precision To Support Ion Mobility–Mass Spectrometry-Based Lipidomics

The use of collision cross-section (CCS) values derived from ion mobility–mass spectrometry (IM–MS) has been proven to facilitate lipid identifications. Its utility is restricted by the limited availability of CCS values. Recently, the machine-learning algorithm-based prediction (e.g., MetCCS) is reported to generate CCS values in a large-scale. However, the prediction precision is not sufficient to differentiate lipids due to their high structural similarities and subtle differences on CCS values. To address this challenge, we developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values.

LipidIMMS Analyzer integrating multi-dimensional information to support lipid identification in ion mobility—mass spectrometry based lipidomics

Ion mobility—mass spectrometry (IM-MS) has showed great application potential for lipidomics. However, IM-MS based lipidomics is significantly restricted by the available software for lipid structural identification. Here, we developed a software tool, namely, LipidIMMS Analyzer, to support the accurate identification of lipids in IM-MS. For the first time, the software incorporates a large-scale database covering over 260 000 lipids and four-dimensional structural information for each lipid [i.e. m/z, retention time (RT), collision cross-section (CCS) and MS/MS spectra]. Therefore, multi-dimensional information can be readily integrated to support lipid identifications, and significantly improve the coverage and confidence of identification. Currently, the software supports different IM-MS instruments and data acquisition approaches.

Large-Scale Prediction of Collision Cross-Section Values for Metabolites in Ion Mobility-Mass Spectrometry

The use of collision cross-section (CCS) values derived from ion mobility–mass spectrometry (IM–MS) has been proven to facilitate lipid identifications. Its utility is restricted by the limited availability of CCS values. Recently, the machine-learning algorithm-based prediction (e.g., MetCCS) is reported to generate CCS values in a large-scale. However, the prediction precision is not sufficient to differentiate lipids due to their high structural similarities and subtle differences on CCS values. To address this challenge, we developed a new approach, namely, LipidCCS, to precisely predict lipid CCS values.

Normalization and integration of large-scale metabolomics data using support vector regression

Untargeted metabolomics studies for biomarker discovery often have hundreds to thousands of human samples. Data acquisition of large-scale samples has to be divided into several batches and may span from months to as long as several years. The signal drift of metabolites during data acquisition (intra- and inter-batch) is unavoidable and is a major confounding factor for large-scale metabolomics studies. We aim to develop a data normalization method to reduce unwanted variations and integrate multiple batches in large-scale metabolomics studies prior to statistical analyses. We developed a machine learning algorithm-based method, support vector regression (SVR), for large-scale metabolomics data normalization and integration. An R package named MetNormalizer was developed and provided for data processing using SVR normalization. After SVR normalization, the portion of metabolite ion peaks with relative standard deviations (RSDs) less than 30 % increased to more than 90 % of the total peaks, which is much better than other common normalization methods. The reduction of unwanted analytical variations helps to improve the performance of multivariate statistical analyses, both unsupervised and supervised, in terms of classification and prediction accuracy so that subtle metabolic changes in epidemiological studies can be detected. SVR normalization can effectively remove the unwanted intra- and inter-batch variations, and is much better than other common normalization methods.