A review of statistical methods for preprocessing oligonucleotide microarrays

Wu, Zhijin

doi:10.1177/0962280209351924

Cited by 32 publications

(12 citation statements)

References 30 publications

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“…The aim of this study was to use machine learning techniques to build CC and MGC transcriptomic classifiers to predict oocyte competence. Several combinations of microarray data pre-processing (RMA normalization) and feature selection were tested during cross validation rounds to derive the best performing algorithm and classifier gene signature [ 27 – 30 ].…”

Section: Discussionmentioning

confidence: 99%

Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age

et al. 2016

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ObjectiveBy focussing on differences in the mural granulosa cell (MGC) and cumulus cell (CC) transcriptomes from follicles resulting in competent (live birth) and non-competent (no pregnancy) oocytes the study aims on defining a competence classifier expression profile in the two cellular compartments. Design: A case-control study. Setting: University based facilities for clinical services and research. Patients: MGC and CC samples from 60 women undergoing IVF treatment following the long GnRH-agonist protocol were collected. Samples from 16 oocytes where live birth was achieved and 16 age- and embryo morphology matched incompetent oocytes were included in the study.MethodsMGC and CC were isolated immediately after oocyte retrieval. From the 16 competent and non-competent follicles, mRNA was extracted and expression profile generated on the Human Gene 1.0 ST Affymetrix array. Live birth prediction analysis using machine learning algorithms (support vector machines) with performance estimation by leave-one-out cross validation and independent validation on an external data set.ResultsWe defined a signature of 30 genes expressed in CC predictive of live birth. This live birth prediction model had an accuracy of 81%, a sensitivity of 0.83, a specificity of 0.80, a positive predictive value of 0.77, and a negative predictive value of 0.86. Receiver operating characteristic analysis found an area under the curve of 0.86, significantly greater than random chance. When applied on 3 external data sets with the end-point outcome measure of blastocyst formation, the signature resulted in 62%, 75% and 88% accuracy, respectively. The genes in the classifier are primarily connected to apoptosis and involvement in formation of extracellular matrix. We were not able to define a robust MGC classifier signature that could classify live birth with accuracy above random chance level.ConclusionWe have developed a cumulus cell classifier, which showed a promising performance on external data. This suggests that the gene signature at least partly include genes that relates to competence in the developing blastocyst.

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Section: Discussionmentioning

confidence: 99%

Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age

et al. 2016

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“…There are fewer false positive transcripts identified with RNA-Seq than with microarrays[8]. The underlying sequencing platforms display excellent within- and between-platform reproducibilities[7], [9], without the ad hoc corrections common with microarrays[10].…”

Section: Introductionmentioning

confidence: 99%

ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq

et al. 2013

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Experimental variance is a major challenge when dealing with high-throughput sequencing data. This variance has several sources: sampling replication, technical replication, variability within biological conditions, and variability between biological conditions. The high per-sample cost of RNA-Seq often precludes the large number of experiments needed to partition observed variance into these categories as per standard ANOVA models. We show that the partitioning of within-condition to between-condition variation cannot reasonably be ignored, whether in single-organism RNA-Seq or in Meta-RNA-Seq experiments, and further find that commonly-used RNA-Seq analysis tools, as described in the literature, do not enforce the constraint that the sum of relative expression levels must be one, and thus report expression levels that are systematically distorted. These two factors lead to misleading inferences if not properly accommodated. As it is usually only the biological between-condition and within-condition differences that are of interest, we developed ALDEx, an ANOVA-like differential expression procedure, to identify genes with greater between- to within-condition differences. We show that the presence of differential expression and the magnitude of these comparative differences can be reasonably estimated with even very small sample sizes.

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“…The original (raw) data in X. laevis GeneChip® CEL files acquired from three replicate arrays were preprocessed [80] using GeneChip robust multichip analysis (GCRMA, [63,81]) methods to produce a single log 2 transformed measure for the intensity level of every Xl-PSID on each replicate array. Intensity values are reported in arbitrary units (a.u.)…”

Section: Methodsmentioning

confidence: 99%

Probing the Xenopus laevis inner ear transcriptome for biological function

et al. 2012

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BackgroundThe senses of hearing and balance depend upon mechanoreception, a process that originates in the inner ear and shares features across species. Amphibians have been widely used for physiological studies of mechanotransduction by sensory hair cells. In contrast, much less is known of the genetic basis of auditory and vestibular function in this class of animals. Among amphibians, the genus Xenopus is a well-characterized genetic and developmental model that offers unique opportunities for inner ear research because of the amphibian capacity for tissue and organ regeneration. For these reasons, we implemented a functional genomics approach as a means to undertake a large-scale analysis of the Xenopus laevis inner ear transcriptome through microarray analysis.ResultsMicroarray analysis uncovered genes within the X. laevis inner ear transcriptome associated with inner ear function and impairment in other organisms, thereby supporting the inclusion of Xenopus in cross-species genetic studies of the inner ear. The use of gene categories (inner ear tissue; deafness; ion channels; ion transporters; transcription factors) facilitated the assignment of functional significance to probe set identifiers. We enhanced the biological relevance of our microarray data by using a variety of curation approaches to increase the annotation of the Affymetrix GeneChip® Xenopus laevis Genome array. In addition, annotation analysis revealed the prevalence of inner ear transcripts represented by probe set identifiers that lack functional characterization.ConclusionsWe identified an abundance of targets for genetic analysis of auditory and vestibular function. The orthologues to human genes with known inner ear function and the highly expressed transcripts that lack annotation are particularly interesting candidates for future analyses. We used informatics approaches to impart biologically relevant information to the Xenopus inner ear transcriptome, thereby addressing the impediment imposed by insufficient gene annotation. These findings heighten the relevance of Xenopus as a model organism for genetic investigations of inner ear organogenesis, morphogenesis, and regeneration.

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A review of statistical methods for preprocessing oligonucleotide microarrays

Cited by 32 publications

References 30 publications

Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age

Competence Classification of Cumulus and Granulosa Cell Transcriptome in Embryos Matched by Morphology and Female Age

ANOVA-Like Differential Expression (ALDEx) Analysis for Mixed Population RNA-Seq

Probing the Xenopus laevis inner ear transcriptome for biological function

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