Biomedical subjects

Hui-Rong Qian

Publications and source records attributed to Hui-Rong Qian.

5 recordsLinked to original sources

PubMed2006

Identification of blood biomarkers of rheumatoid arthritis by transcript profiling of peripheral blood mononuclear cells from the rat collagen-induced arthritis model.

Rheumatoid arthritis (RA) is a chronic debilitating autoimmune disease that results in joint destruction and subsequent loss of function. To better understand its pathogenesis and to facilitate the search for novel RA therapeutics, we profiled the rat model of collagen-induced arthritis (CIA) to discover and characterize blood biomarkers for RA. Peripheral blood mononuclear cells (PBMCs) were purified using a Ficoll gradient at various time points after type II collagen immunization for RNA preparation. Total RNA was processed for a microarray analysis using Affymetrix GeneChip technology. Statistical comparison analyses identified differentially expressed genes that distinguished CIA from control rats. Clustering analyses indicated that gene expression patterns correlated with laboratory indices of disease progression. A set of 28 probe sets showed significant differences in expression between blood from arthritic rats and that from controls at the earliest time after induction, and the difference persisted for the entire time course. Gene Ontology comparison of the present study with previous published murine microarray studies showed conserved Biological Processes during disease induction between the local joint and PBMC responses. Genes known to be involved in autoimmune response and arthritis, such as those encoding Galectin-3, Versican, and Socs3, were identified and validated by quantitative TaqMan RT-PCR analysis using independent blood samples. Finally, immunoblot analysis confirmed that Galectin-3 was secreted over time in plasma as well as in supernatant of cultured tissue synoviocytes of the arthritic rats, which is consistent with disease progression. Our data indicate that gene expression in PBMCs from the CIA model can be utilized to identify candidate blood biomarkers for RA.

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PubMed2005

Comparison of false discovery rate methods in identifying genes with differential expression.

Current high-throughput techniques such as microarray in genomics or mass spectrometry in proteomics usually generate thousands of hypotheses to be tested simultaneously. The usual purpose of these techniques is to identify a subset of interesting cases that deserve further investigation. As a consequence, the control of false positives among the tests called "significant" becomes a critical issue for researchers. Over the past few years, several false discovery rate (FDR)-controlling methods have been proposed; each method favors certain scenarios and is introduced with the purpose of improving the control of FDR at the targeted level. In this paper, we compare the performance of the five FDR-controlling methods proposed by Benjamini et al., the qvalue method proposed by Storey, and the traditional Bonferroni method. The purpose is to investigate the "observed" sensitivity of each method on typical microarray experiments in which the majority (or all) of the truth is unknown. Based on two well-studied microarray datasets, it is found that in terms of the "apparent" test power, the ranking of the FDR methods is given as Step-down<Step-up: dependent<Step-up: one-stage (BH95)<Step-up adaptive<qvalue. The BH95 method shows the best control of FDR at the target level. It is our hope that the observed results could provide some insight into the application of different FDR methods in microarray data analysis.

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PubMed2004

SUM: a new way to incorporate mismatch probe measurements.

Affymetrix's high-density oligonucleotide arrays offer an exciting technology in biomedical research. With more and more statistical involvement in every step of the process, there has been a constant effort to make sure that the expression data are appropriately extracted in the first place. According to Affymetrix GeneChip technology, each gene is represented by 11-20 oligo probe pairs; the challenge is how to extract one meaningful number, expression, from the 11-20 pairs of numbers. More specifically, there is first a need to differentiate the components of specific binding, nonspecific binding, and optical background noise in both PM and MM probes, and then an expression measure that is proportional to the true abundance of transcripts is to be derived. A new method, SUM, which sums up PM and MM values and then follows a process similar to that of RMA, is considered. The performance of SUM is investigated and compared to the three most popular methods, MAS5, dChip, and RMA. The assessments are based on a well-controlled experiment dataset that is publicly available. The results show that in several respects the performance of SUM is comparable to that of RMA and dChip, and all three of these methods show some advantages over MAS5. There is some evidence showing that SUM has higher differential sensitivity than other methods in certain situations.

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PubMed2003

Assessing the variability in GeneChip data.

INTRODUCTION: Oligonucleotide and cDNA microarray experiments are now common practice in biological science research. The goal of these experiments is generally to gain clues about the functions of genes by measuring how their expression levels rise and fall in response to changing experimental conditions. Measures of gene expression are affected, however, by a variety of factors. This paper introduces statistical methods to assess the variability of Affymetrix GeneChip data due to randomness. METHODS: The variation of Affymetrix's GeneChip signal data are quantified at both chip level and individual gene level, respectively, by the agreement study method and variance components method. Three agreement measurement methods are introduced to assess the variability among chips. Variation sources for gene expression data are decomposed into four categories: systematic experiment variation, treatment effect, biological variation, and chip variation. The focus of this paper is on evaluating and comparing the last two kinds of variations. RESULTS: Measurement of agreement and variance components methods were applied to an experimental data, and the calculation and interpretation were exemplified. The variability between biological samples were shown to exist and were assessed at both the chip level and individual gene level. Using the variance components method, it was found that the biological and chip variation are roughly comparable. The Statistical Analysis System (SAS) program for doing the agreement studies can be obtained from the correspondence author.

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PubMedDate not supplied

Optimization and validation of small quantity RNA profiling for identifying TNF responses in cultured human vascular endothelial cells.

INTRODUCTION: Affymetrix oligonucleotide microarrays are widely used in basic and applied research (Lander, E.S., (1999). Array of hope. Nature Genetics 21, 3-4; Lockhart, D.J. & Winzeler, E.A. (2000) Genomics, gene expression and DNA arrays. Nature 405, 827-836.) The need for a significant amount of starting RNA has limited its use in applications where the amount of RNA is limiting, such as with Laser Captured Microdissection (LCM), small biopsies, or peripheral blood in rodent models. To overcome this limitation, various RNA amplification and labeling methods have been described, however, further optimization and validation of these methods are needed. METHODS: Here we reported using the Arcturus technology to optimize amplification and labeling of small amounts of RNA for Affymetrix microarray studies. We assessed the technical feasibility and variation introduced by differences in starting RNA quantity and differences in technical performance by microarray hybridization. RESULTS: We demonstrated that the current approach is reliable to amplify as little as 40 ng total RNA, and it is suitable for Affymetrix studies yielding satisfactory quantitative chip performance. We also showed that differences in labeling methods contribute more to variation than the differences in starting RNA quantity per se. As a model, we studied the well-documented TNF-induced inflammatory responses in cultured human vascular endothelial cells. We were able to recapitulate the TNF-induced responses using small RNA sample profiling.

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