Biomedical subjects

Marina Bakay

Publications and source records attributed to Marina Bakay.

3 recordsLinked to original sources

PubMed2004

Interactively optimizing signal-to-noise ratios in expression profiling: project-specific algorithm selection and detection p-value weighting in Affymetrix microarrays.

MOTIVATION: The most commonly utilized microarrays for mRNA profiling (Affymetrix) include 'probe sets' of a series of perfect match and mismatch probes (typically 22 oligonucleotides per probe set). There are an increasing number of reported 'probe set algorithms' that differ in their interpretation of a probe set to derive a single normalized 'signal' representative of expression of each mRNA. These algorithms are known to differ in accuracy and sensitivity, and optimization has been done using a small set of standardized control microarray data. We hypothesized that different mRNA profiling projects have varying sources and degrees of confounding noise, and that these should alter the choice of a specific probe set algorithm. Also, we hypothesized that use of the Microarray Suite (MAS) 5.0 probe set detection p-value as a weighting function would improve the performance of all probe set algorithms. RESULTS: We built an interactive visual analysis software tool (HCE2W) to test and define parameters in Affymetrix analyses that optimize the ratio of signal (desired biological variable) versus noise (confounding uncontrolled variables). Five probe set algorithms were studied with and without statistical weighting of probe sets using the MAS 5.0 probe set detection p-values. The signal-to-noise ratio optimization method was tested in two large novel microarray datasets with different levels of confounding noise, a 105 sample U133A human muscle biopsy dataset (11 groups: mutation-defined, extensive noise), and a 40 sample U74A inbred mouse lung dataset (8 groups: little noise). Performance was measured by the ability of the specific probe set algorithm, with and without detection p-value weighting, to cluster samples into the appropriate biological groups (unsupervised agglomerative clustering with F-measure values). Of the total random sampling analyses, 50% showed a highly statistically significant difference between probe set algorithms by ANOVA [F(4,10) > 14, p < 0.0001], with weighting by MAS 5.0 detection p-value showing significance in the mouse data by ANOVA [F(1,10) > 9, p < 0.013] and paired t-test [t(9) = -3.675, p = 0.005]. Probe set detection p-value weighting had the greatest positive effect on performance of dChip difference model, ProbeProfiler and RMA algorithms. Importantly, probe set algorithms did indeed perform differently depending on the specific project, most probably due to the degree of confounding noise. Our data indicate that significantly improved data analysis of mRNA profile projects can be achieved by optimizing the choice of probe set algorithm with the noise levels intrinsic to a project, with dChip difference model with MAS 5.0 detection p-value continuous weighting showing the best overall performance in both projects. Furthermore, both existing and newly developed probe set algorithms should incorporate a detection p-value weighting to improve performance. AVAILABILITY: The Hierarchical Clustering Explorer 2.0 is available at http://www.cs.umd.edu/hcil/hce/ Murine arrays (40 samples) are publicly available at the PEPR resource (http://microarray.cnmcresearch.org/pgadatatable.asp http://pepr.cnmcresearch.org Chen et al., 2004).

Cluster Analysis↗

PubMed2002

Sources of variability and effect of experimental approach on expression profiling data interpretation.

BACKGROUND: We provide a systematic study of the sources of variability in expression profiling data using 56 RNAs isolated from human muscle biopsies (34 Affymetrix MuscleChip arrays), and 36 murine cell culture and tissue RNAs (42 Affymetrix U74Av2 arrays). RESULTS: We studied muscle biopsies from 28 human subjects as well as murine myogenic cell cultures, muscle, and spleens. Human MuscleChip arrays (4,601 probe sets) and murine U74Av2 Affymetrix microarrays were used for expression profiling. RNAs were profiled both singly, and as mixed groups. Variables studied included tissue heterogeneity, cRNA probe production, patient diagnosis, and GeneChip hybridizations. We found that the greatest source of variability was often different regions of the same patient muscle biopsy, reflecting variation in cell type content even in a relatively homogeneous tissue such as muscle. Inter-patient variation was also very high (SNP noise). Experimental variation (RNA, cDNA, cRNA, or GeneChip) was minor. Pre-profile mixing of patient cRNA samples effectively normalized both intra- and inter-patient sources of variation, while retaining a high degree of specificity of the individual profiles (86% of statistically significant differences detected by absolute analysis; and 85% by a 4-pairwise comparison survival method). CONCLUSIONS: Using unsupervised cluster analysis and correlation coefficients of 92 RNA samples on 76 oligonucleotide microarrays, we found that experimental error was not a significant source of unwanted variability in expression profiling experiments. Major sources of variability were from use of small tissue biopsies, particularly in humans where there is substantial inter-patient variability (SNP noise).

Animals↗

PubMed2002

A web-accessible complete transcriptome of normal human and DMD muscle.

We present an assessment of the complete transcriptome of human skeletal muscle in Duchenne muscular dystrophy patient muscle and non-dystrophic controls (36 RNAs analyzed from ten Duchenne dystrophy and eight controls; approximately 65,000 gene/expressed sequence tag/probe sets queried on U95 five-GeneChip series and MuscleChip). The use of the multiple chip types allowed us to compare results from different probe sets for the same gene: we found excellent concordance between different probe sets on different microarrays. We found 30% of human genes expressed in muscle at detectable levels. Three percent of these showed differential regulation in dystrophin deficiency. Among 1,882 dysregulated probe sets, 1,324 corresponded to characterized genes/proteins (891 non-redundant transcript units), and 588 to expressed sequence tags or predicted genes. Data interpretation was limited to the insulin-like growth factor pathway members, an investigation of possible de-regulation towards a cardiac lineage, and identification of male- and female-specific transcripts. We found transcriptional upregulation of both IGF-I and IGF-II in dystrophic muscle, however the possible beneficial effects of the growth factors appear offset by transcriptional upregulation of inhibitory IGF-binding proteins and regulators (IGFBP-2, -4, -6 and -7; and PRSS11 [IGFBP-5 protease]). We hypothesize that the beneficial effects of IGF-I or IGF-II supplementation in dystrophic muscle may be the result of dose-dependent sequestration of inhibitory IGF-binding proteins. We also focused on six 'cardiac' genes expressed in muscle (alpha-cardiac actin, CARP, CASQ2, troponin T2 cardiac [TNNT2], CUGBP2, and connexin 43). Comparison to a 27 time point murine muscle regeneration series and mdx muscle profiles showed that CARP and Cx43 were macrophage-associated, and TNNT2 activated-myoblast-associated. Upregulation of cardiac actin and CUGBP2 was not associated with muscle regeneration profiles, suggesting a more specific dysregulation induced by dystrophin deficiency. We found two Y-linked genes expressed solely in male muscle (RPS4Y, DDX3Y), and two autosomal genes expressed much more highly in female muscle (GRO2, ZNF91) (all comparisons P<0.01). Finally, we present the first web-accessible expression profiling database for all data, including image files (.dat), processed image files (.cel), and complete comparison files which are publicly available through a novel queriable web site, that permits query-by-gene across all profiles (http://microarray.cnmcresearch.org/pga). These data enumerate the full range of molecular changes associated downstream of dystrophin deficiency, and provide a web-accessible platform to study the specificity of transcriptional pathway alterations in muscle disease.

Animals↗