# statTarget ============== Description ----------------- An easy to use tool provides graphical user interface for quality control based shift signal correction, integration of metabolomic data from multi-batch experiments, and the comprehensive statistic analysis in non-targeted or targeted metabolomics. Link to CRAN: https://cran.r-project.org/web/packages/statTarget/ The main GUI of statTarget has two basic components. The first is shift correction. It includes quality control-based robust LOESS signal correction (QC-RLSC) that is a widely accepted method for quality con trol based signal correction and integration of metabolomic data from multiple analytical batches (Dunn WB., et al. 2011; Luan H., et al. 2015). statTarget - Shift Correction provide QC-RLSC algorithm that fit the QC data, and each metabolites in the true sample will be normalized to the QC sample. Additionally, LOESS based generalised cross-validation (GCV) would be automatically applied to avoid overfitting of the observed data, when the QCspan was set at 0 (Default value). statTarget - Statistical Analysis provide features including Data preprocessing, Data descriptions, Multivariate statistics analysis and Univariate analysis. Data preprocessing : 80-precent rule, glog transformation, KNN imputation, Median imputation and Minimum values imputation. Data descriptions : Mean value, Median value, Sum, Quartile, Standard derivatives, etc. Multivariate statistics analysis : PCA, PLSDA, VIP, Random forest. Univariate analysis : Student T-test, Shapiro-Wilk normality test and Mann-Whitney tests. Biomarkers analysis : ROC, Odd ratio, P-value, and Volcano plot. Download the [statTarget tutorial](https://github.com/13479776/Picture/blob/master/work flow.pptx "statTarget tutorial .pptx") and [example data](https://github.com/13479776/Picture/blob/master/Data_example.zip "Data_example.zip") . Requirements ----------------- Dependent on R (>= 3.3.0) Packages should be installed: randomForest,plyr,pROC,rrcov,RGtk2,pls,gWidgets2,gWidgets2RGtk2,pdist,impute Steps and Data Frame -----------------  Usage ----------------- 1 If you did not install the R software yet,you can download R >= 3.3.0 from https://www.r-project.org 2 Install the package "statTarget" at the CRAN For Windows PC, copy this code into R > source("https://bioconductor.org/biocLite.R") > biocLite("impute") > install.packages("statTarget") > library(statTarget) ## Execute statTarget For mac PC, the package "statTarget" requires X11 support being installed. XQuartz could be installed. Download it from https://www.xquartz.org Copy this code into R > source("https://bioconductor.org/biocLite.R") > biocLite("impute") > install.packages("statTarget") > library(statTarget) ## Execute statTarget 4 Input data and run (See the [example data](https://github.com/13479776/statTarget/blob/master/Data_example.zip "Data_example.zip")) Author ----------------- Hemi Luan, hemi.luan@gmail.com Citation ------------------ Hemi Luan (2016). statTarget: Statistical Analysis of Metabolite Profile. R package version 1.2.2. References ----------------- Dunn, W.B., et al.,Procedures for large-scale metabolic profiling of serum and plasma using gas chromatography and liquid chromatography coupled to mass spectrometry. Nature Protocols 2011, 6, 1060-83. Luan H., LC–MS-Based Urinary Metabolite Signatures in Idiopathic Parkinson’s Disease. J Proteome Res., 2015, 14 (1),467–478. Luan H., Non-targeted metabolomics and lipidomics LC–MS data from maternal plasma of 180 healthy pregnant women. GigaScience 2015 4:16