Gender: Female Age: 7 months Tissue: Left Ventricle
Extracted molecule
total RNA
Extraction protocol
Tissue samples (20 mg) were homogenized in 100 µL TRIzol (Life Technologies, Gaithersburg, US) using a Mixer Mill MM301 at 20-25 Hz. RNA clean-up was performed using RNA Mini kit (Qiagen, Germantown, US). Total RNA was isolated and RNA clean-up was performed according to the manufacturer's instructions. RNA integrity, purity and quantity were assessed by Bioanalyzer (Agilent Technologies, Santa Clara, US) and Nanodrop (NanoDrop Technologies, Baltimore, US). The concentration of total RNA was measured by Nanodrop with ultraviolet spectrophotometry at 260/280 nm. RNA quality was assessed by electrophoresis on Bioanalyzer chips (Agilent Technologies, Santa Clara, US).
High quality RNA was classified as a 260/280 ratio above 1.8. Only samples with a 260/280 ratio of more than 1.8 and no signs of degradation based on Bioanalyzer results were used for analysis.
Label
Biotin
Label protocol
Commercial method by Affymetrix
Hybridization protocol
Commercial method by AffymetrixCommercial method by Affymetrix
Scan protocol
Commercial method by Affymetrix
Description
Commercial method by Affymetrix
Data processing
Gene expression were analyzed on whole-genome RAE 230 2.0 chip from Affymetrix GeneChip (Affymetrix, Santa Clara, US) comprised of 31,042 probe sets, analyzing over 30,000 transcripts and variants from over 28,000 substantiated rat genes. On the Affymetrix GeneChip arrays, each gene is represented by a set of 11-20 probe pairs consisting of a perfect match (PM) and a mismatch (MM) probe. The statistical analysis is based on summary expression measures for each probe set, RMA.
The arrays also include a set of rat maintenance genes to facilitate the normalization and scaling of array experiments. These probe sets serve as a tool to normalize or scale your data prior to performing data comparison. All normalization genes show consistent levels of expression over defined sample sets.
Statistical analysis for finding differential expressed genes
For each gene (probeset), a linear regression model, including parameters representing the effect of aerobe capacity is specified. Based on the estimated effects, tests for significant differential expression are performed using T-tests (22). However, to improve the power of the tests, the T-tests are modified by replacing the gene-specific variance estimates by estimates found by borrowing strength from data on the remaining genes (35). The software package Limma implementing the method is available as part of the Bioconductor project (11).
To account for multiple testing, we calculate adjusted p-values controlling the False Discovery Rate (FDR) (5). Consequently, selecting differentially expressed genes based on a threshold of 0.05 on the adjusted FDR p-values means that the expected proportion of genes falsely classified as differential expressed should be below 0.01 or 0.05. The model is fitted using the R statistical package (R Development Core Team, 2004).
RMA using the Bioconduction libraries
The summary measures are computed based on a linear statistical model for background-corrected, normalized and log-transformed PM values for each probe pair denoted the robust multiarray average (RMA) method (17). The PM values are normalized using the quantile normalization method (7), normalizing the arrays such that the empirical distribution of the expression measures is equal across arrays.
