Explore data

Make the journey from data to knowledge with renewed assurance. From the first daunting leap into newly acquired results to the painstaking refinement of gene lists, Precision Biomarker Resources can illuminate your pathway to meaningful findings.

Let us help you design experiments that deliver comparable datasets and adequate replicates. Or if you already have your data, let us help you:

  • Normalize data: Screen out noise and outliers to give your results more clarity
  • Find similarities: Sift through multiple variables to identify which ones unify datasets
  • Find differences: Quantify expression differences between genes
  • Generate gene lists: Confidently identify significant biomarkers


Microarray data exploration

Normalize data

To ensure your datasets are comparable, we check that chips are performing consistently from one sample to the next. Changes in expression intensity can scatter false outliers through your results. We make it simple to identify suspect datasets or double-check normalization by plotting chip intensity in easy-to-read graphs.

Histograms show the frequency with which probes record a given intensity. For comparable datasets, there should be minimal deviation between samples, as in this graph.
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After normalization, we check to make sure chip readings are comparable. A box and whisker plot should show minimal deviation in mean signal intensity, as in this graph.
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Find similarities between samples

Organize your data into a framework that highlights important relationships. Even when multiple variables threaten to cloud the lines between cause and effect, tools such as principal components analysis (PCA) will identify and visualize similarities between samples.

This PCA plot reveals samples clustering into four groups, each defined by a boundary of two standard deviations.
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Find differences in expression

Gain a fuller understanding of gene behavior by visualizing changes in expression levels. Our heat maps reveal a hierarchy of effects at a glance.

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Gene lists

After highlighting the most relevant genes, we can organize the list however you like, including by chromosomal location.

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