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author:("Yu, guoyang")
1.  Diffuse optical monitoring of repeated cerebral ischemia in mice 
Optics Express  2011;19(21):20301-20315.
Occlusions of bilateral common carotid arteries (bi-CCA) in mice are popular models for the investigation of transient forebrain ischemia. Currently available technologies for assessing cerebral blood flow (CBF) and oxygenation in ischemic mice have limitations. This study tests a novel near-infrared diffuse correlation spectroscopy (DCS) flow-oximeter for monitoring both CBF and cerebral oxygenation in mice undergoing repeated transient forebrain ischemia. Concurrent flow measurements in a mouse brain were first conducted for validation purposes; DCS measurement was found highly correlated with laser Doppler measurement (R2 = 0.94) and less susceptible to motion artifacts. With unique designs in experimental protocols and fiber-optic probes, we have demonstrated high sensitivities of DCS flow-oximeter in detecting the regional heterogeneity of CBF responses in different hemispheres and global changes of both CBF and cerebral oxygenation across two hemispheres in mice undergoing repeated 2-minute bi-CCA occlusions over 5 days. More than 75% CBF reductions were found during bi-CCA occlusions in mice, which may be considered as a threshold to determine a successful bi-CCA occlusion. With the progress of repeated 2-minute bi-CCA occlusions over days, a longitudinal decline in the magnitudes of CBF reduction was observed, indicating the brain adaptation to cerebral ischemia through the repeated preconditioning.
PMCID: PMC3495871  PMID: 21997041
(170.0170) Medical optics and biotechnology; (170.3660) Light propagation in tissues; (170.3880) Medical and biological imaging; (170.6480) Spectroscopy, speckle
2.  BACOM: in silico detection of genomic deletion types and correction of normal cell contamination in copy number data 
Bioinformatics  2011;27(11):1473-1480.
Motivation: Identification of somatic DNA copy number alterations (CNAs) and significant consensus events (SCEs) in cancer genomes is a main task in discovering potential cancer-driving genes such as oncogenes and tumor suppressors. The recent development of SNP array technology has facilitated studies on copy number changes at a genome-wide scale with high resolution. However, existing copy number analysis methods are oblivious to normal cell contamination and cannot distinguish between contributions of cancerous and normal cells to the measured copy number signals. This contamination could significantly confound downstream analysis of CNAs and affect the power to detect SCEs in clinical samples.
Results: We report here a statistically principled in silico approach, Bayesian Analysis of COpy number Mixtures (BACOM), to accurately estimate genomic deletion type and normal tissue contamination, and accordingly recover the true copy number profile in cancer cells. We tested the proposed method on two simulated datasets, two prostate cancer datasets and The Cancer Genome Atlas high-grade ovarian dataset, and obtained very promising results supported by the ground truth and biological plausibility. Moreover, based on a large number of comparative simulation studies, the proposed method gives significantly improved power to detect SCEs after in silico correction of normal tissue contamination. We develop a cross-platform open-source Java application that implements the whole pipeline of copy number analysis of heterogeneous cancer tissues including relevant processing steps. We also provide an R interface, bacomR, for running BACOM within the R environment, making it straightforward to include in existing data pipelines.
Availability: The cross-platform, stand-alone Java application, BACOM, the R interface, bacomR, all source code and the simulation data used in this article are freely available at authors' web site:
Supplementary Information: Supplementary data are available at Bioinformatics online.
PMCID: PMC3102226  PMID: 21498400
3.  Direct measurement of tissue blood flow and metabolism with diffuse optics 
Diffuse optics has proven useful for quantitative assessment of tissue oxy- and deoxyhaemoglobin concentrations and, more recently, for measurement of microvascular blood flow. In this paper, we focus on the flow monitoring technique: diffuse correlation spectroscopy (DCS). Representative clinical and pre-clinical studies from our laboratory illustrate the potential of DCS. Validation of DCS blood flow indices in human brain and muscle is presented. Comparison of DCS with arterial spin-labelled MRI, xenon-CT and Doppler ultrasound shows good agreement (0.50
PMCID: PMC3263785  PMID: 22006897
diffuse correlation spectroscopy; blood flow; cerebral blood flow; oxygen metabolism; brain; cancer
BMC Genomics  2011;12:344.
Interactions among genetic loci are believed to play an important role in disease risk. While many methods have been proposed for detecting such interactions, their relative performance remains largely unclear, mainly because different data sources, detection performance criteria, and experimental protocols were used in the papers introducing these methods and in subsequent studies. Moreover, there have been very few studies strictly focused on comparison of existing methods. Given the importance of detecting gene-gene and gene-environment interactions, a rigorous, comprehensive comparison of performance and limitations of available interaction detection methods is warranted.
We report a comparison of eight representative methods, of which seven were specifically designed to detect interactions among single nucleotide polymorphisms (SNPs), with the last a popular main-effect testing method used as a baseline for performance evaluation. The selected methods, multifactor dimensionality reduction (MDR), full interaction model (FIM), information gain (IG), Bayesian epistasis association mapping (BEAM), SNP harvester (SH), maximum entropy conditional probability modeling (MECPM), logistic regression with an interaction term (LRIT), and logistic regression (LR) were compared on a large number of simulated data sets, each, consistent with complex disease models, embedding multiple sets of interacting SNPs, under different interaction models. The assessment criteria included several relevant detection power measures, family-wise type I error rate, and computational complexity. There are several important results from this study. First, while some SNPs in interactions with strong effects are successfully detected, most of the methods miss many interacting SNPs at an acceptable rate of false positives. In this study, the best-performing method was MECPM. Second, the statistical significance assessment criteria, used by some of the methods to control the type I error rate, are quite conservative, thereby limiting their power and making it difficult to fairly compare them. Third, as expected, power varies for different models and as a function of penetrance, minor allele frequency, linkage disequilibrium and marginal effects. Fourth, the analytical relationships between power and these factors are derived, aiding in the interpretation of the study results. Fifth, for these methods the magnitude of the main effect influences the power of the tests. Sixth, most methods can detect some ground-truth SNPs but have modest power to detect the whole set of interacting SNPs.
This comparison study provides new insights into the strengths and limitations of current methods for detecting interacting loci. This study, along with freely available simulation tools we provide, should help support development of improved methods. The simulation tools are available at:
PMCID: PMC3161015  PMID: 21729295
Biomedical Optics Express  2011;2(7):1969-1985.
In this study we evaluate the influences of optical property assumptions on near-infrared diffuse correlation spectroscopy (DCS) flow index measurements. The optical properties, absorption coefficient (µa) and reduced scattering coefficient (µs′), are independently varied using liquid phantoms and measured concurrently with the flow index using a hybrid optical system combining a dual-wavelength DCS flow device with a commercial frequency-domain tissue-oximeter. DCS flow indices are calculated at two wavelengths (785 and 830 nm) using measured µa and µs′ or assumed constant µa and µs′. Inaccurate µs′ assumptions resulted in much greater flow index errors than inaccurate µa. Underestimated/overestimated µs′ from −35%/+175% lead to flow index errors of +110%/−80%, whereas underestimated/overestimated µa from −40%/+150% lead to −20%/+40%, regardless of the wavelengths used. Examination of a clinical study involving human head and neck tumors indicates up to +280% flow index errors resulted from inter-patient optical property variations. These findings suggest that studies involving significant µa and µs′ changes should concurrently measure flow index and optical properties for accurate extraction of blood flow information.
PMCID: PMC3130582  PMID: 21750773
(170.0170) Medical optics and biotechnology; (170.3660) Light propagation in tissues; (170.3880) Medical and biological imaging; (170.6480) Spectroscopy, speckle

Results 1-5 (5)