Accurate characterization of pathogenic microbes that may be present in food or clinical samples is essential in the design of appropriate intervention strategies. Inherent genomic patterns (codon-biases and rate of evolution) do simplify the classification of microbes at most taxonomic levels (genus and above), but mostly blur classification at Species/Strain levels. Hence, their classification at these finer taxonomic levels requires high-resolution genomic-data that provide SNP (Single Nucleotide Polymorphism) level precision. Existing classification methods involve either targeted amplification of sero-specific genes (serotyping and MLST) or sequencing of the entire microbial genome, both of which require extra time and resources. We present a computational approach, which harnesses the power of the metagenomic NGS-data and object-detection abilities of Convolutional Neural Networks (CNN)(Inception V3), for precise classification of pathogens by converting genomic-data (NGS-reads) into images (nucleotide-by-color). A small scale retraining (<;50 images/class) of `Inception V3' resulted in a classifier with 100% and 96% validation and test accuracies, respectively, when classifying pathogens such as Campylobacter coli/jejuni and Escherichia coli (O157:H7 and Non O157-STECs). We aim to extend this protocol to the detection of several microbes (multiple-objects) in a metagenomic image (genomic image of an entire microbial community).

BACKGROUND: Clustering is a widely used collection of unsupervised learning techniques for identifying natural classes within a data set. It is often used in bioinformatics to infer population substructure. Genomic data are often categorical and high dimensional, e.g., long sequences of nucleotides. This makes inference challenging: The distance metric is often not well-defined on categorical data; running time for computations using high dimensional data can be considerable; and the Curse of Dimensionality often impedes the interpretation of the results. Up to the present, however, the literature and software addressing clustering for categorical data has not yet led to a standard approach. RESULTS: We present software for an ensemble method that performs well in comparison with other methods regardless of the dimensionality of the data. In an ensemble method a variety of instantiations of a statistical object are found and then combined into a consensus value. It has been known for decades that ensembling generally outperforms the components that comprise it in many settings. Here, we apply this ensembling principle to clustering. We begin by generating many hierarchical clusterings with different clustering sizes. When the dimension of the data is high, we also randomly select subspaces also of variable size, to generate clusterings. Then, we combine these clusterings into a single membership matrix and use this to obtain a new, ensembled dissimilarity matrix using Hamming distance. CONCLUSIONS: Ensemble clustering, as implemented in R and called EnsCat, gives more clearly separated clusters than other clustering techniques for categorical data. The latest version with manual and examples is available at https://github.com/jlp2duke/EnsCat .

MOTIVATION: Global expression patterns within cells are used for purposes ranging from the identification of disease biomarkers to basic understanding of cellular processes. Unfortunately, tissue samples used in cancer studies are usually composed of multiple cell types and the non-cancerous portions can significantly affect expression profiles. This severely limits the conclusions that can be made about the specificity of gene expression in the cell-type of interest. However, statistical analysis can be used to identify differentially expressed genes that are related to the biological question being studied. RESULTS: We propose a statistical approach to expression deconvolution from mixed tissue samples in which the proportion of each component cell type is unknown. Our method estimates the proportion of each component in a mixed tissue sample; this estimate can be used to provide estimates of gene expression from each component. We demonstrate our technique on xenograft samples from breast cancer research and publicly available experimental datasets found in the National Center for Biotechnology Information Gene Expression Omnibus repository. AVAILABILITY: R code (http://www.r-project.org/) for estimating sample proportions is freely available to non-commercial users and available at http://www.med.miami.edu/medicine/x2691.xml.