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Model-based Analysis of Oligonucleotide Arrays: Model Validation, Design Issues and Standard Error Application
Background: A model-based analysis of oligonucleotide expression arrays we developed previously uses a probe-sensitivity index to capture the response characteristic of a specific probe pair and calculates model-based expression indexes (MBEI). MBEI has standard error attached to it as a measure of accuracy. Here we investigate the stability of the probe-sensitivity index across different tissue types, the reproducibility of results in replicate experiments, and the use of MBEI in perfect match (PM)-only arrays. Results: Probe-sensitivity indexes are stable across tissue types. The target gene's presence in many arrays of an array set allows the probe-sensitivity index to be estimated accurately. We extended the model to obtain expression values for PM-only arrays, and found that the 20-probe PM-only model is comparable to the 10-probe PM/MM difference model, in terms of the expression correlations with the original 20-probe PM/MM difference model. MBEI method is able to extend the reliable detection limit of expression to a lower mRNA concentration. The standard errors of MBEI can be used to construct confidence intervals of fold changes, and the lower confidence bound of fold change is a better ranking statistic for filtering genes. We can assign reliability indexes for genes in a specific cluster of interest in hierarchical clustering by resampling clustering trees. A software dChip implementing many of these analysis methods is made available. Conclusions: The model-based approach reduces the variability of low expression estimates, and provides a natural method of calculating expression values for PM-only arrays. The standard errors attached to expression values can be used to assess the reliability of downstream analysis
Downlink and Uplink Intelligent Reflecting Surface Aided Networks: NOMA and OMA
Intelligent reflecting surfaces (IRSs) are envisioned to provide
reconfigurable wireless environments for future communication networks. In this
paper, both downlink and uplink IRS-aided non-orthogonal multiple access (NOMA)
and orthogonal multiple access (OMA) networks are studied, in which an IRS is
deployed to enhance the coverage by assisting a cell-edge user device (UD) to
communicate with the base station (BS). To characterize system performance, new
channel statistics of the BS-IRS-UD link with Nakagami- fading are
investigated. For each scenario, the closed-form expressions for the outage
probability and ergodic rate are derived. To gain further insight, the
diversity order and high signal-to-noise ratio (SNR) slope for each scenario
are obtained according to asymptotic approximations in the high-SNR regime. It
is demonstrated that the diversity order is affected by the number of IRS
reflecting elements and Nakagami fading parameters, but the high-SNR slope is
not related to these parameters. Simulation results validate our analysis and
reveal the superiority of the IRS over the full-duplex decode-and-forward
relay.Comment: Accepted for publication in the IEEE Transactions on Wireless
Communication
A generalized Gaussian process model for computer experiments with binary time series
Non-Gaussian observations such as binary responses are common in some
computer experiments. Motivated by the analysis of a class of cell adhesion
experiments, we introduce a generalized Gaussian process model for binary
responses, which shares some common features with standard GP models. In
addition, the proposed model incorporates a flexible mean function that can
capture different types of time series structures. Asymptotic properties of the
estimators are derived, and an optimal predictor as well as its predictive
distribution are constructed. Their performance is examined via two simulation
studies. The methodology is applied to study computer simulations for cell
adhesion experiments. The fitted model reveals important biological information
in repeated cell bindings, which is not directly observable in lab experiments.Comment: 49 pages, 4 figure
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