R/pbcmcPackage.R

In pbcmc: Permutation-Based Confidence for Molecular Classification

#'Permutation-Based Confidence for Molecular Classification (pbcmc)   
#'
#'Gene expression-based classifiers, known as molecular signatures (MS), 
#'are a set of genes coordinately expressed and an algorithm that use these
#'data to predict disease subtypes, response to therapy, disease risk or  
#'clinical outcome (Andre et al. 2006). They are especially important in  
#'breast cancer (BC) where several MS are currently on the market like PAM50
#'(Perou et al. 2000 & 2010), Prosigna \url{www.prosigna.com}, Oncotype DX   
#'\url{www.oncotypedx.com}, MammaPrint \url{www.agendia.com}, etc. 
#'As far as the authors know, these classifiers do not give a real
#'uncertainty of the classification at all. This package characterizes MS   
#'classification uncertainty. In order to achieve this goal, synthetic
#'simulated subjects are obtained by permutations of gene labels. Then,   
#'each synthetic subject is tested against the classifier corresponding
#'subtype to build the null distribution, thus, classification confidence
#'measurement can be provided for each subject. In this context, subjects  
#'belonging to the null distribution (random or noisy individuals) are not  
#'assigned (NA) to any class. On the contrary, if reliable results are 
#'obtained, subjects could be either assigned (A) to the more reliably  
#'subtype or marked as ambiguous (AMB) if proximal to two or more reliable
#'subtypes. In the later, the combinations of classes are given.   
#'At present, it is only implemented for genefu's PAM50 package   
#'(Haibe-Kains et al. 2014) but it can easily be extended to other 
#'MS. This package includes the following features:  
#'    \itemize{
#'        \item Implemented classifier:  
#'            \enumerate{
#'                \item PAM50.   
#'            }
#'        \item Single subject classification: 
#'            \enumerate{
#'                \item No pilot study needs to be carried out to obtain  
#'                    classification uncertainty.   
#'                \item No normalization is required. If required, external 
#'                    database normalization, genefu normalization 
#'                    alternatives (scale/robust) or even gene median can  
#'                    be applied before simulations. 
#'            }
#'        \item Classification:   
#'            \enumerate{
#'                \item The original PAM50 calls obtained by genefu. 
#'                \item The proposed classification scheme: Assigned   
#'                    (PAM50 call), Not Assigned (NA) or Ambiguous (reliable 
#'                    PAM50 class combinations).  
#'                \item Classification significance p-value or False   
#'                    Discovery Rate (FDR).  
#'                \item Observed subject Spearman's correlation for each  
#'                    breast cancer subtype.  
#'            }
#'        \item Physician treatment decision support:
#'            \enumerate{
#'                \item A friendly subject report is provided which includes
#'                    summary data such as subtype centroid Spearman's  
#'                    correlation, p-value and FDR for each subtype,  
#'                    original PAM50 classification and the recommended   
#'                    strategy (assigned, not assigned or ambiguous   
#'                    classes).
#'                \item Scatter plot of the observed gene-expression  
#'                    (subject) versus PAM50 centroids panel, plus the  
#'                    corresponding linear regression fit. 
#'                \item Null distribution boxplot, plus observed (subject)  
#'                    value.
#'            }
#'    }
#'
#'@docType package   
#'@name pbcmcPackage   
#'@author Cristobal Fresno \email{cfresno@@bdmg.com.ar}, German A. Gonzalez  
#'    \email{ggonzalez@@bdmg.com.ar}, Andrea S. Llera 
#'    \email{allera@@leloir.org.ar} and Elmer Andres Fernandez
#'    \email{efernandez@@bdmg.com.ar}
#'@keywords Molecular Signature PAM50 
#'@references    
#'    \enumerate{
#'        \item Andre F, Pusztai L, 2006, Molecular classification of
#'             breast cancer: implications for selection of adjuvant 
#'             chemotherapy. Nature Clinical Practice Oncology 3(11),  
#'              621-632.  
#'        \item Haibe-Kains B, Schroeder M, Bontempi G, Sotiriou C and   
#'             Quackenbush J, 2014, genefu: Relevant Functions for Gene
#'             Expression Analysis, Especially in Breast Cancer. R package
#'             version 1.16.0, \url{www.pmgenomics.ca/bhklab/} 
#'        \item Perou CM, Sorlie T, Eisen MB, et al., 2000, Molecular  
#'             portraits of human breast tumors. Nature 406:747-752 
#'        \item Perou CM, Parker JS, Prat A, Ellis MJ, Bernard PB., 2010, 
#'             Clinical implementation of the intrinsic subtypes of breast
#'             cancer, The Lancet Oncology 11(8):718-719   
#'    }
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