A Correlation-Based Optical Flowmeter for Enclosed Flows

A low-cost flowmeter would be very useful in a wide variety of monitoring situations. This article discusses the development of such a flowmeter based on optical components and its testing with water in an enclosed flow system. The sensor consisted of two sets of LEDs and phototransistors spaced 4 cm apart, monitoring the optical properties of the fluid at upstream and downstream locations, respectively. A small amount of dye was injected into the flow, which caused a change in the optical properties of the fluid at both locations. The time required for this change to move from the upstream to the downstream locations was determined using the biased estimate of the cross-covariance between the upstream and downstream signals. The velocity was then calculated using this time difference and the known distance between the locations. Tests were conducted at fluid velocities from 0.125 to 4.5 m s-1, and separate results were calculated using phototransistors located 45° and 180° from the LEDs. The mean percent error was between 5% and 0% for individual measurements using the 180° phototransistors at velocities from 0.5 to 4.5 m s-1 and between 2% and -8% for measurements using the 45° phototransistors in the same velocity range. Error increased when the velocity was reduced to 0.5 m s-1 and was greater than 20% at 0.125 m s-1 for both sets of phototransistors. A regression model was developed to correct the velocity estimate. This regression model was validated by conducting an independent test of the sensor under the same conditions. After using the regression model for calibration, errors in the validation set were between 9.1% and -5% for the 180° phototransistors and between 10.5% and -3.6% for the 45° phototransistors for the entire velocity range tested (0.125 to 4.5 m s-1). Finally, the cross-correlation coefficient for each measurement was calculated to determine the degree of similarity between the signals recorded by the phototransistors at the upstream and downstream locations. The cross-correlation coefficient was higher at lower velocities and higher for measurements using the 180° phototransistors

Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies

We present a set of statistical methods for the analysis of DNA methylation microarray data, which account for tumor purity. These methods are an extension of our previously developed method for purity estimation; our updated method is flexible, efficient, and does not require data from reference samples or matched normal controls. We also present a method for incorporating purity information for differential methylation analysis. In addition, we propose a control-free differential methylation calling method when normal controls are not available. Extensive analyses of TCGA data demonstrate that our methods provide accurate results. All methods are implemented in InfiniumPurify. Electronic supplementary material The online version of this article (doi:10.1186/s13059-016-1143-5) contains supplementary material, which is available to authorized users

DriverRWH: discovering cancer driver genes by random walk on a gene mutation hypergraph

Abstract Background Recent advances in next-generation sequencing technologies have helped investigators generate massive amounts of cancer genomic data. A critical challenge in cancer genomics is identification of a few cancer driver genes whose mutations cause tumor growth. However, the majority of existing computational approaches underuse the co-occurrence mutation information of the individuals, which are deemed to be important in tumorigenesis and tumor progression, resulting in high rate of false positive. Results To make full use of co-mutation information, we present a random walk algorithm referred to as DriverRWH on a weighted gene mutation hypergraph model, using somatic mutation data and molecular interaction network data to prioritize candidate driver genes. Applied to tumor samples of different cancer types from The Cancer Genome Atlas, DriverRWH shows significantly better performance than state-of-art prioritization methods in terms of the area under the curve scores and the cumulative number of known driver genes recovered in top-ranked candidate genes. Besides, DriverRWH discovers several potential drivers, which are enriched in cancer-related pathways. DriverRWH recovers approximately 50% known driver genes in the top 30 ranked candidate genes for more than half of the cancer types. In addition, DriverRWH is also highly robust to perturbations in the mutation data and gene functional network data. Conclusion DriverRWH is effective among various cancer types in prioritizes cancer driver genes and provides considerable improvement over other tools with a better balance of precision and sensitivity. It can be a useful tool for detecting potential driver genes and facilitate targeted cancer therapies

Additional file 3: Tables S1 – S4. of Estimating and accounting for tumor purity in the analysis of DNA methylation data from cancer studies

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DISIS: Prediction of Drug Response through an Iterative Sure Independence Screening

<div><p>Prediction of drug response based on genomic alterations is an important task in the research of personalized medicine. Current elastic net model utilized a sure independence screening to select relevant genomic features with drug response, but it may neglect the combination effect of some marginally weak features. In this work, we applied an iterative sure independence screening scheme to select drug response relevant features from the Cancer Cell Line Encyclopedia (CCLE) dataset. For each drug in CCLE, we selected up to 40 features including gene expressions, mutation and copy number alterations of cancer-related genes, and some of them are significantly strong features but showing weak marginal correlation with drug response vector. Lasso regression based on the selected features showed that our prediction accuracies are higher than those by elastic net regression for most drugs.</p></div

Predicting Anticancer Drug Responses Using a Dual-Layer Integrated Cell Line-Drug Network Model

<div><p>The ability to predict the response of a cancer patient to a therapeutic agent is a major goal in modern oncology that should ultimately lead to personalized treatment. Existing approaches to predicting drug sensitivity rely primarily on profiling of cancer cell line panels that have been treated with different drugs and selecting genomic or functional genomic features to regress or classify the drug response. Here, we propose a dual-layer integrated cell line-drug network model, which uses both cell line similarity network (CSN) data and drug similarity network (DSN) data to predict the drug response of a given cell line using a weighted model. Using the Cancer Cell Line Encyclopedia (CCLE) and Cancer Genome Project (CGP) studies as benchmark datasets, our single-layer model with CSN or DSN and only a single parameter achieved a prediction performance comparable to the previously generated elastic net model. When using the dual-layer model integrating both CSN and DSN, our predicted response reached a 0.6 Pearson correlation coefficient with observed responses for most drugs, which is significantly better than the previous results using the elastic net model. We have also applied the dual-layer cell line-drug integrated network model to fill in the missing drug response values in the CGP dataset. Even though the dual-layer integrated cell line-drug network model does not specifically model mutation information, it correctly predicted that BRAF mutant cell lines would be more sensitive than BRAF wild-type cell lines to three MEK1/2 inhibitors tested.</p></div

Comparison of predicted and observed activity areas using the dual-layer integrated cell line-drug network model for BRAF mutant and wild-type cell lines for which experimental activity areas was missing from the CGP dataset for three MEK1/2-inhibitors, including (A) AZD6244, (B) RDEA119 and (C) PD-0325901.

<p>Comparison of predicted and observed activity areas using the dual-layer integrated cell line-drug network model for BRAF mutant and wild-type cell lines for which experimental activity areas was missing from the CGP dataset for three MEK1/2-inhibitors, including (A) AZD6244, (B) RDEA119 and (C) PD-0325901.</p

Consistence between correlations of true and predicted drug responses by elastic net and ISIS.

<p>Dotted line indicates equal predictions.</p

Scatter plots of the true and predicted sensitivities for some drugs.

<p>Scatter plots of the true and predicted sensitivities for some drugs.</p