Metabolomics,Unknown,Transcriptomics,Genomics,Proteomics

Dataset Information

Transcription profiling of mouse metaphase II oocytes collected from 5-6 week old mice with those collected from 40-42 week old mice

ABSTRACT: Decreasing oocyte competence with maternal aging is a major factor in human infertility. To investigate the age-dependent molecular changes in a mouse model, we compared the expression profiles of metaphase II oocytes collected from 5-6 week old mice with those collected from 40-42 week old mice using the NIA 22K 60-mer oligo microarray.

ORGANISM(S): Mus musculus

SUBMITTER: Carole Stagg

PROVIDER: E-MEXP-150 | biostudies-arrayexpress |

REPOSITORIES: biostudies-arrayexpress

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Publications

Age-associated alteration of gene expression patterns in mouse oocytes.

Hamatani Toshio T Falco Geppino G Carter Mark G MG Akutsu Hidenori H Stagg Carole A CA Sharov Alexei A AA Dudekula Dawood B DB VanBuren Vincent V Ko Minoru S H MS

Human molecular genetics 20040818 19

Decreasing oocyte competence with maternal aging is a major factor in human infertility. To investigate the age-dependent molecular changes in a mouse model, we compared the expression profiles of metaphase II oocytes collected from 5- to 6-week-old mice with those collected from 42- to 45-week-old mice using the NIA 22K 60-mer oligo microarray. Among approximately 11,000 genes whose transcripts were detected in oocytes, about 5% (530) showed statistically significant expression changes, excludi ...[more]

PMID: 15317747

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Project description:Feature Extraction 5.1.1 software (Agilent Technologies) performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). Details are described in the publication (Hamatani T. et al., Developmental Cell. Published online Dec. 18, 2003).

Project description:Microarray data analysis Intensity of 21,939 gene features per array was extracted from scanned microarray images using Feature Extraction 5.1.1 software (Agilent Technologies), which performs background subtractions and dye normalization. This normalization method is targeted at detecting changes in relative expression of individual genes rather than global expression. Global expression change would require external normalization controls (van de Peppel et al., 2003). Text output was processed using an application developed in-house to perform ANOVA analysis (http://lgsun.grc.nia.nih.gov/ANOVA/). Intensity of features measured with a >50% error were replaced with missing values except features with very low intensity. Surrogate values equal to mean error were inserted for values that were negative or less than the probe error. Data were analyzed using ANOVA with embryonic stage as a factor. The small number of biological replications typical in expression profiling experiments results in a highly variable error variance, and this problem is usually addressed by log-ratio thresholds (Schena et al., 1995) that require subjective decisions about biological significance, or by Bayesian adjustment of error variance (Baldi and Long, 2001), which may still underestimate error variance and result in false positive results. To reduce false-positives, we opted for a very conservative error model in which error variance that is used for estimating F-statistics is the maximum of the actual error variance for this gene and the average error variance in 500 genes with similar average intensity. Statistical significance was determined using the False Discovery Rate (FDR = 10%) method (Benjamini and Hochberg, 1995). Pair-wise mean comparison was done with t-statistics and FDR=10%. Further data processing including scatter plots, hierarchical clustering, and principal component analysis (PCA) were also performed through NIA microarray analysis tool (http://lgsun.grc.nia.nih.gov/ANOVA/). The input file for NIA microarray analysis tool is available at http://lgsun.grc.nia.nih.gov/microarray/data.html

Dataset Information

Transcription profiling of mouse metaphase II oocytes collected from 5-6 week old mice with those collected from 40-42 week old mice

Publications

Age-associated alteration of gene expression patterns in mouse oocytes.

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