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Synopis

Yes, we are still doing Microarray analysis. They had been a valid and successful tool for biologist and medical researchers for more than a decade and we have not counted the thousands of arrays we had in our hands. By doing carefully designed microarray experiments, researchers in all over the world have been collecting insight in all kind of biological and clinical topics, very often with very deep impact on the knowledge about distinct genes, pathways or phenotypes. We think, that although Next-Generation-Sequencing has mayor advantages for many biological questions one has in mind, microarray technology has still a big potential in helping us, explaining life.

Microarray types and formats

We analysed and analyse following microarray types:

Affymetrix

  • 3´IVT Expression Arrays (all kinds, all orgranisms)
  • Gene ST Arrays
  • Exon ST Arrays
  • Transcriptome Arrays (Gene, Exons, Junctions), for example HTA 2.0
  • miRNA Arrays (micro RNAs)
  • Genome-Wide Genotyping Arrays (e.g. SNP 5.0, SNP 6.0 for both SNP and Copy-Number analysis)
  • CytoScan R Arrays
  • OncoScan R FFPE Arrays

Illumina

  • Genotyping BedChip Arrays (e.g. HumanOmni2.5)
  • Expression BeadChip Arrays (e.g. HumanHT-12)
  • Methylation BeadChip Arrays (e.g. Illumina 450K BeadChips )

Agilent Technologies

  • Gene expression Arrays (all organisms)
  • miRNA expression Arrays
  • CGH+SNP Microarrays (Copy Number detection and SNP analysis)
  • CGH Microarrays (Copy Number only)

Nanostring

Although actually count data, Nanostrings nCounter technology uses hybridization to specify gene expression. We therefore decided to put it in the microarray section. We analyse Nanostring data from any organism and assay.

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Expression analysis portfolio

Quality Control

Normalization

Statistics
Differential Expression

Biological Interpretation

Visualizations

  • Visual expection of original scans
  • Spike-in controls
  • Endogenous controls
  • Background estimation
  • MA Plots
  • Unsupervised unfiltered clustering
  • Princal component analysis (PCA)
  • Boxplots
  • Robust multi-array average (RMA)
  • Invariant set
  • Loess methods
  • Variance stabilization and normalization
  • Limma
  • Analysis of variance (ANOVA)
  • Mixed-model ANOVA
  • Non-parametric tests
  • Multiple test correction
  • Post-hoc tests
  • Batch effect correction
  • Empirical bayes methods
  • Power analysis
  • Gene Ontolgy (GO) enrichment
  • Pathway enrichment
  • Pathway / GO linear modelling
  • Rank procedures (GSE)
  • Correlation analysis
  • Cluster to pathway
  • Transcription Factor binding site enrichment
  • Micro RNA target site enrichment
  • Partioning clustering (SOM, Kmeans…)
  • Hierarchical clustering
  • Scatter plots
  • Interaction plots
  • Barplots
  • Correlation plots
  • Pathway plots
  • Gene network graphs
  • Highly customized graphs
  • Integrated graphs (e.g. expression versus methylation)

Micro RNA Portfolio

In addition to the shared points with standard gene expression arrays, we will apply:

  • miRNA target RNA detection using various databases and confident levels
  • Further investigation of RNA targets by means of biological interpretation (Pathways, GO involvement)
  • Integration with gene expression data (if available)
  • Correlation analysis (miRNAs vs target RNAs)

Copy Number Portfolio

  • Quality control of copy number / SNP microarrays
  • GC bias removal
  • Normalization with internal or external (Hapmap) control arrays
  • Copy Number estimation (paired or unpaired)
  • Breakpoint determination
  • Detection of amplification and deletions
  • Plotting of detected regions (focused, chromosomal and genomic level)
  • Detection of shared deletions and amplifications in the samples
  • Filtering by regions of interest, amplification or deletion status
  • Overlap with annotation: genes, snps, known Syndroms
  • Integration with gene expression data (if available)

Methylation Array Portfolio

  • Array quality control
  • Background correction
  • Probe specific quality control, filtering
  • Normalization (Illumina default, SWAN)
  • Principal component analysis of CpG islands, imprinted regions and core gene regions
  • Batch effect analysis / correction
  • Differential methylation detection on probe or region level (e.g. CpG island, N-shore, N-shelf, S-shore, S-shelf)
  • P-value adjustment
  • Annotation of differential methylation events with corresponding genes
  • Biological interpretation of target genes (Pathway / Gene Ontology Enrichment)

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