arXiv subjects

Baolin Wu

Publications and source records attributed to Baolin Wu.

2 recordsLinked to original sources

arXiv2019

Efficient and powerful equivalency test on combined mean and variance with application to diagnostic device comparison studies

In medical device comparison studies, equivalency test is commonly used to demonstrate two measurement methods agree up to a pre-specified performance goal based on the paired repeated measures. Such equivalency test often involves controlling the absolute differences that depend on both the mean and variance parameters, and poses some challenges for statistical analysis. For example, for the oximetry comparison study that motivates our research, FDA has clear guidelines approving an investigational pulse oximeter in comparison to a standard oximeter via testing the root mean squares (RMS), a composite measure of both mean and variance parameters. For the hypothesis testing of this composite measure, existing methods have been either exploratory or relying on the large-sample normal approximation with conservative and unsatisfactory performance. We develop a novel generalized pivotal test to rigorously and accurately test the system equivalency based on RMS. The proposed method has well-controlled type I error and favorable performance in our extensive numerical studies. When analyzing data from an oximetry comparison study, aiming to demonstrate performance equivalency between an FDA-cleared oximetry system and an investigational system, our proposed method resulted in a highly significant test result strongly supporting the system equivalency. We also provide efficient R programs for the proposed method in a publicly available R package. Considering that many practical equivalency studies of diagnostic devices are of small to medium sizes, our proposed method and software timely bridge an existing gap in the field.

stat.ME↗

arXiv2015

Network-based Isoform Quantification with RNA-Seq Data for Cancer Transcriptome Analysis

High-throughput mRNA sequencing (RNA-Seq) is widely used for transcript quantification of gene isoforms. Since RNA-Seq data alone is often not sufficient to accurately identify the read origins from the isoforms for quantification, we propose to explore protein domain-domain interactions as prior knowledge for integrative analysis with RNA-seq data. We introduce a Network-based method for RNA-Seq-based Transcript Quantification (Net-RSTQ) to integrate protein domain-domain interaction network with short read alignments for transcript abundance estimation. Based on our observation that the abundances of the neighboring isoforms by domain-domain interactions in the network are positively correlated, Net-RSTQ models the expression of the neighboring transcripts as Dirichlet priors on the likelihood of the observed read alignments against the transcripts in one gene. The transcript abundances of all the genes are then jointly estimated with alternating optimization of multiple EM problems. In simulation Net-RSTQ effectively improved isoform transcript quantifications when isoform co-expressions correlate with their interactions. qRT-PCR results on 25 multi-isoform genes in a stem cell line, an ovarian cancer cell line, and a breast cancer cell line also showed that Net-RSTQ estimated more consistent isoform proportions with RNA-Seq data. In the experiments on the RNA-Seq data in The Cancer Genome Atlas (TCGA), the transcript abundances estimated by Net-RSTQ are more informative for patient sample classification of ovarian cancer, breast cancer and lung cancer. All experimental results collectively support that Net-RSTQ is a promising approach for isoform quantification.

cs.CE↗