Update README.rst

computational-metabolomics · Apr 1, 2021 · 641fb00 · 641fb00
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-========================================================
 SBCMS: Signal and Batch Correction for Mass Spectrometry
-========================================================
+===========================================================
 
-|Git| |Bioconda| |Build Status (Travis)| |License| |Coverage| |mybinder|
-
-.. contents::
-  :local:
-
--------------
-Installation
--------------
-
-Github
-------------
-
-.. code-block:: r
-
-  install.packages("devtools")
-  install.packages("ggplot2")
-  install.packages("gridExtra")
-  install.packages("reshape2")
-
-  devtools::install_github("computational-metabolomics/pmp", build_vignettes=TRUE)
-  devtools::install_github('computational-metabolomics/sbcms',
-    build_vignettes=TRUE)
-
-Conda
-------------
-
-.. code-block:: command
-
-   conda create -n sbcms bioconductor-sbcms -c conda-forge -c bioconda
-    -c computational-metabolomics
-  source activate sbcms
-
-
-------------
-Example
-------------
-
-.. image:: html_docs/pca_scores.png
-  :width: 300
-
-.. code-block:: r
-
-  library(sbcms)
-  library(pmp)
-
-  # DIMS data set is included in 'sbcms' package
-  # (MTBLS79) [https://www.ebi.ac.uk/metabolights/MTBLS79]
-  data <- sbcdata$data
-  class <- sbcdata$class
-  batch <- sbcdata$batch
-  sample_order <- c(1:nrow(data))
-
-  # Input data frame or matrix should have features in rows and samples in columns
-  data <- t(data)
-
-  corrected_data <- sbcms::QCRSC(df=data, order=sample_order, batch=batch,
-    classes=class, spar=0, minQC=4)
-
-  # Create PDF file comparing data before and after correction fo the first 100 features
-  sbcms::plot_sbc(df=data, corrected_df=corrected_data, classes=class,
-    batch=batch, order=order, output="Correction_summary.pdf")
-
-  #Principal components analysis (PCA) before and after drift correction
-
-  library(ggplot2)
-  library(gridExtra)
-
-  manual_color=c("#386cb0", "#ef3b2c", "#7fc97f", "#fdb462", "#984ea3",
-    "#a6cee3", "#778899", "#fb9a99", "#ffff33")
-
-  # PQN used to normalise data
-  # KNN for missing value imputation
-  # glog scaling
-  # A more detailed overview is detailed in Di Guida et al, Metabolomics, 12:93, 2016
-  # https://dx.doi.org/10.1007/s11306-016-1030-9
-
-  pca_data <- pmp::pqn_normalisation(data, classes=class, qc_label="QC")[[1]]
-  pca_data <- pmp::mv_imputation(pca_data, method="KNN", k=5, rowmax=5, colmax=5, check_df=F)
-  pca_data <- pmp::glog_transformation(pca_data, classes=class, qc_label="QC")
-
-  pca_corrected_data <- pmp::pqn_normalisation(corrected_data, classes=class, qc_label="QC")[[1]]
-  pca_corrected_data <- pmp::mv_imputation(pca_corrected_data, method="KNN", k=5, rowmax=5, colmax=5, check_df=F)
-  pca_corrected_data <- pmp::glog_transformation(pca_corrected_data, classes=class, qc_label="QC")
-
-  pca_data <- prcomp(t(pca_data), center=T, scale=F)
-  pca_corrected_data <- prcomp(t(pca_corrected_data), center=T, scale=F)
-
-  exp_var_pca <- round(((pca_data$sdev^2)/sum(pca_data$sdev^2)*100)[1:2],2)
-  exp_var_pca_corrected <- round(((pca_corrected_data$sdev^2)/sum(pca_corrected_data$sdev^2)*100)[1:2],2)
-
-  plots <- list()
-
-  plotdata <- data.frame(PC1=pca_data$x[, 1], PC2=pca_data$x[, 2], batch=as.factor(batch),
-    class=class)
-
-  plots[[1]] <- ggplot(data=plotdata, aes(x=PC1, y=PC2, col=batch))+ geom_point()+
-    theme(panel.background=element_blank())+
-    scale_color_manual(values=manual_color)+
-    ggtitle("PCA scores, before correction")+
-    xlab(paste0("PC1 (", exp_var_pca[1] ," %)"))+
-    ylab(paste0("PC2 (", exp_var_pca[2] ," %)"))
-
-  plots[[2]] <- ggplot(data=plotdata, aes(x=PC1, y=PC2, col=class))+ geom_point()+
-    theme(panel.background=element_blank())+
-    scale_color_manual(values=manual_color)+
-    ggtitle("PCA scores, before correction")+
-    xlab(paste0("PC1 (", exp_var_pca[1] ," %)"))+
-    ylab(paste0("PC2 (", exp_var_pca[2] ," %)"))
-
-  plotdata_corr <- data.frame(PC1=pca_corrected_data$x[, 1], PC2=pca_corrected_data$x[, 2],
-  batch=as.factor(batch), class=class)
-
-  plots[[3]] <- ggplot(data=plotdata_corr, aes(x=PC1, y=PC2, col=batch))+ geom_point()+
-    theme(panel.background=element_blank())+
-    scale_color_manual(values=manual_color)+
-    ggtitle("PCA scores, after correction")+
-    xlab(paste0("PC1 (", exp_var_pca_corrected[1] ," %)"))+
-    ylab(paste0("PC2 (", exp_var_pca_corrected[2] ," %)"))
-
-  plots[[4]] <- ggplot(data=plotdata_corr, aes(x=PC1, y=PC2, col=class))+ geom_point()+
-    theme(panel.background=element_blank())+
-    scale_color_manual(values=manual_color)+
-    ggtitle("PCA scores, after correction")+
-    xlab(paste0("PC1 (", exp_var_pca_corrected[1] ," %)"))+
-    ylab(paste0("PC2 (", exp_var_pca_corrected[2] ," %)"))
-
-  grid.arrange(ncol=2, plots[[1]], plots[[2]], plots[[3]], plots[[4]])
-
-------------
-Tutorials
-------------
-
-For step by step tutorial on how to assess and/or correct signal drift and batch
-effects within/across a multi-batch liquid chromatography mass
-spectrometry (LCMS) dataset using SBCMS please check R package vignettes.
-
-.. code-block:: r
-
-  browseVignettes("sbcms")
-
-------------
-References
-------------
-
-
-.. |Build Status (Travis)| image:: https://img.shields.io/travis/computational-metabolomics/sbcms/master.svg?label=Travis
-   :target: https://travis-ci.org/computational-metabolomics/sbcms
-
-.. |Build Status (AppVeyor)| image:: https://ci.appveyor.com/api/projects/status/github/computational-metabolomics/sbcms?branch=master&svg=true
-   :target: https://ci.appveyor.com/project/computational-metabolomcis/sbcms
-
-.. |Git| image:: https://img.shields.io/badge/repository-GitHub-blue.svg?style=flat&maxAge=3600
-   :target: https://github.com/computational-metabolomics/sbcms
-
-.. |Bioconda| image:: https://img.shields.io/badge/install%20with-bioconda-brightgreen.svg?style=flat&maxAge=3600
-   :target: https://bioconda.github.io/recipes/bioconductor-sbcms/README.html
-
-.. |License| image:: https://img.shields.io/badge/licence-GNU_v3-teal.svg?style=flat&maxAge=3600
-   :target: https://www.gnu.org/licenses/gpl-3.0.html
-
-.. |Coverage| image:: https://codecov.io/gh/computational-metabolomics/sbcms/branch/master/graph/badge.svg
-   :target: https://codecov.io/github/computational-metabolomics/sbcms?branch=master
-
-.. |mybinder| image:: https://mybinder.org/badge_logo.svg
-   :target: https://mybinder.org/v2/gh/computational-metabolomics/sbcms/master?filepath=notebooks
+\:warning\: \:no_entry_sign\: This repository has been deprecated. Please use **stuctToolbox** (`github <https://github.com/computational-metabolomics/structToolbox>`__ | `bioconductor <http://www.bioconductor.org/packages/release/bioc/html/structToolbox.html>`__) or **pmp** (`github <https://github.com/computational-metabolomics/pmp>`__ | `bioconductor <http://www.bioconductor.org/packages/release/bioc/html/pmp.html>`__) packages instead.