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Mi-kyoung Seo

miniABS

miniABS: an absolute, single-sample subtype classifier of breast cancer with 11 functional genes

Outstanding features of miniABS

  • miniABS is an absolute, single-sample subtype classifier that can assign subtypes of patients with only the gene expression profile of the patient without relative expression inference (which can ensure reproducibility of subtype assignments).

  • miniABS has high accuracy without bias for various gene expression quantification technologies.

  • miniABS predicts subtypes with only 11 biologically meaningful genes in the breast cancer subtype.

  • The miniABS uses pairwise gene expression ratios (PGER), which is statistically significantly different between subtypes than the expression of a single gene, allowing subtypes to be well classified.

  • The miniABS does not lose information when subtyping is classified using actual ratio values ​​rather than the magnitude of the expression of the two genes.

How to install

Download the source and install it as shown below.

install.packages("miniABS_0.1.1.tar.gz", repos = NULL, type = "source")
library(miniABS)

How to use

The Input should be a log2 transformed expression matrix of 11 genes.

data(marker) # list of 11 genes used in miniABS

MYBL2 SFRP1 CEP55 ESR1 FOXA1 MKI67 MLPH PGR ERBB2 FGFR4 KRT17

data(exprRNAseq) # example RNA-seq expression data (eg. log2(FPKM+1))

  • Set the directory where the output file will be created to ratioDir.

ratioDir = "directory"
createMatrix(exprRNAseq, ratioDir) #generate a ratio matrix
classifierMiniABS(ratioDir) # classify using 7 models and report final predicted subtypes

A description of the function can be found by entering :

help(package="miniABS")