Bioinformatics - Data Analysis
The final step in microarray experiments is the data analysis step, which should answer the biological question asked at the beginning. This is absolutely vital for a successful study. A standard array analysis is included with each microarray processing. Advanced array analysis is optional on a per study basis. Said this, the analysis strategy should be carefully considered during the planning phase of the experiment. Depending on your specific type of experiment and biological question, our scientific staff offers advice in experimental design and statistical issues. We offer a steadily growing palette of tools to extract meaningful results from your experiment, regardless of whether your data were generated in our laboratory or on another array platform.
Preprocessing and statistical methods:
- Quality control, normalization, summarization
- Parametric tests (t-Test, ANOVA, limma)
- Non-parametric tests(Mann-Whitney, Wilcoxon, Rank Products)
- Supervised (class prediction) and unsupervised machine learning (clustering)
- Principal Component Analysis
- Gene set enrichment analysis
- Gene ontology and pathway analysis
- Building interaction networks
- Literature mining
Basic genotyping analysis:
- Experimental data processing
- Data filtering (for example using Hardy-Weinberg equilibrium, missing genotypes)
- Genotype calling
Additional and customized services:
- Statistical tests for association (using various models, e.g. dominant / recessive) and sample clustering.
- Visualization and determination of the Linkage Disequilibrium (LD) structure within the genotype data.
- Identify genes and gather all forms of annotation in the genomic regions of interest.
- Integrate genotype data with expression data to identify genomic regions regulating expression.
- Haplotype estimation and haplotype association analysis.
- Genomic structural variation identification, CNV and LOH analysis of SNP microarray data.