GD-RDA: A New Regularized Discriminant Analysis for High-Dimensional Data

16S rDNA analysis of the intestinal microbes in osteoporotic rats

This study aimed to reveal the differences in intestinal microbes in osteoporotic rats. The rats were divided into two groups: the control and OP (osteoporosis) groups (n=6). Days 0 and 70 were set as the time points. The rats in the OP group underwent bilateral ovariectomy (OVX). Differences between the control and OP groups were determined by 16S rDNA analysis. The relative abundances of OTUs and alpha/beta diversities were determined at days 0 days and 70. The abundances of Verrucomicrobia at the phylum level and Aerococcus, Coprobacillus, Veillonella, Anaerobiospirillum, Flavobacterium, Comamonadaceae, Ohtaekwangia, etc., at the genus level were found to be different between the control_70d and OP_70d groups. KEGG ontology analysis showed that the function of lipid metabolism could be related to OP. The 16S rDNA analysis in the OP rats revealed that intestinal microbes take part in the processes of OP and could affect lipid metabolism. Further study of the relationship between OP and intestinal microbes is necessary, and the prospect for intestinal microbes is a potential treatment for OP.

Automated syndrome diagnosis by three-dimensional facial imaging

Purpose

Deep phenotyping is an emerging trend in precision medicine for genetic disease. The shape of the face is affected in 30–40% of known genetic syndromes. Here, we determine whether syndromes can be diagnosed from 3D images of human faces.

Methods

We analyzed variation in three-dimensional (3D) facial images of 7057 subjects: 3327 with 396 different syndromes, 727 of their relatives, and 3003 unrelated, unaffected subjects. We developed and tested machine learning and parametric approaches to automated syndrome diagnosis using 3D facial images.

Results

Unrelated, unaffected subjects were correctly classified with 96% accuracy. Considering both syndromic and unrelated, unaffected subjects together, balanced accuracy was 73% and mean sensitivity 49%. Excluding unrelated, unaffected subjects substantially improved both balanced accuracy (78.1%) and sensitivity (56.9%) of syndrome diagnosis. The best predictors of classification accuracy were phenotypic severity and facial distinctiveness of syndromes. Surprisingly, unaffected relatives of syndromic subjects were frequently classified as syndromic, often to the syndrome of their affected relative.

Conclusion

Deep phenotyping by quantitative 3D facial imaging has considerable potential to facilitate syndrome diagnosis. Furthermore, 3D facial imaging of “unaffected” relatives may identify unrecognized cases or may reveal novel examples of semidominant inheritance.

Classifying next-generation sequencing data using a zero-inflated Poisson model

Motivation

With the development of high-throughput techniques, RNA-sequencing (RNA-seq) is becoming increasingly popular as an alternative for gene expression analysis, such as RNAs profiling and classification. Identifying which type of diseases a new patient belongs to with RNA-seq data has been recognized as a vital problem in medical research. As RNA-seq data are discrete, statistical methods developed for classifying microarray data cannot be readily applied for RNA-seq data classification. Witten proposed a Poisson linear discriminant analysis (PLDA) to classify the RNA-seq data in 2011. Note, however, that the count datasets are frequently characterized by excess zeros in real RNA-seq or microRNA sequence data (i.e. when the sequence depth is not enough or small RNAs with the length of 18-30 nucleotides). Therefore, it is desired to develop a new model to analyze RNA-seq data with an excess of zeros.

Results

In this paper, we propose a Zero-Inflated Poisson Logistic Discriminant Analysis (ZIPLDA) for RNA-seq data with an excess of zeros. The new method assumes that the data are from a mixture of two distributions: one is a point mass at zero, and the other follows a Poisson distribution. We then consider a logistic relation between the probability of observing zeros and the mean of the genes and the sequencing depth in the model. Simulation studies show that the proposed method performs better than, or at least as well as, the existing methods in a wide range of settings. Two real datasets including a breast cancer RNA-seq dataset and a microRNA-seq dataset are also analyzed, and they coincide with the simulation results that our proposed method outperforms the existing competitors.

Availability and implementation

The software is available at http://www.math.hkbu.edu.hk/∼tongt.

Contact

xwan@comp.hkbu.edu.hk or tongt@hkbu.edu.hk.

Supplementary information

Supplementary data are available at Bioinformatics online.