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Gene Profiling and Microarrays

Purity for clarity: the need for purification of tumor cells in DNA microarray studies

Leukemia volume 19, pages 618–627 (2005)Cite this article

Abstract

It is now well established that gene expression profiling using DNA microarrays can provide novel information about various types of hematological malignancies, which may lead to identification of novel diagnostic markers. However, to successfully use microarrays for this purpose, the quality and reproducibility of the procedure need to be guaranteed. The quality of microarray analyses may be severely reduced, if variable frequencies of nontarget cells are present in the starting material. To systematically investigate the influence of different types of impurity, we determined gene expression profiles of leukemic samples containing different percentages of nonleukemic leukocytes. Furthermore, we used computer simulations to study the effect of different kinds of impurity as an alternative to conducting hundreds of microarray experiments on samples with various levels of purity.

As expected, the percentage of erroneously identified genes rose with the increase of contaminating nontarget cells in the samples. The simulations demonstrated that a tumor load of less than 75% can lead to up to 25% erroneously identified genes. A tumor load of at least 90% leads to identification of at most 5% false-positive genes. We therefore propose that in order to draw well-founded conclusions, the percentage of target cells in microarray experiment samples should be at least 90%.

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Acknowledgements

We thank Dr E van Wering for providing T-ALL samples.

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Author notes

  1. D de Ridder and C E van der Linden: These authors contributed equally.

Authors and Affiliations

  1. Department of Immunology, Erasmus MC, University Medical Center Rotterdam, Rotterdam, The Netherlands

    D de Ridder, C E van der Linden, T Schonewille, W A Dik, J J M van Dongen & F J T Staal

  2. Information and Communication Theory Group, Faculty of Electrical Engineering, Mathematics and Computer Science, Delft University of Technology, Delft, The Netherlands

    D de Ridder & M J T Reinders

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Correspondence to F J T Staal.

Additional information

Supplementary Information

Supplementary Information accompanies the paper on the Leukemia website (http://www.nature.com/leu).

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Appendix A

Appendix A

Microarray data were simulated50 as follows. For n={10, 20, 40, 100} microarrays, two groups of n/2 arrays each, A and B, were simulated. Each array contained 1000 genes, of which 50 were set to be truly expressed in B only. The base log2-expression of a gene g in array a was simulated as follows:

  • calculate an average expression over all arrays mg=log2(mg′) with (mg′)−1∼Γ(1,1)

  • true differential expression: dg∈{0,1} with P(dg=1)=0.05

  • sign of expression difference: sg∈{−1,1} with P(sg=1)=0.5

  • amplitude of expression: cg∼U(1.4, 1.5)

  • expression: ○ log2(ea,g)∼N(mg,1) ∀a∈A ○ log2(ea,g)∼N(mg+dgsgcg, 1) ∀a∈B

where the subscript g indicates values for gene g over all arrays, subscript a,g denotes values for gene g in array a, P(x) indicates the probability of x occurring, Γ(α,θ) is the Gamma distribution, U(l,r) is the uniform distribution on [l,r], and N(μ,σ) is the Gaussian distribution with mean μ and standard deviation σ.

Next to the 50 truly expressed genes, either 50 random impurity genes or 50 group-specific impurity genes were set to be differentially expressed in arrays, expressed at a fraction f of the level of a truly expressed gene. Random impurity genes g were added as follows:

  • presence of differential expression: da,g∈{0,1} with P(da,g=1)=0.05

  • sign of expression difference: sa,g∈{−1,1} with P(sa,g=1)=0.5

  • amplitude of expression difference:

  • ca,g∼U(1.4,1.5)

  • differential expression: log2(ea,g)=log2(ea,g)+f da,g sa,g ca,g ∀a∈B

Group-specific impurity genes g were added as follows:

  • presence of differential expression: dg∈{0,1}, with P(dg=1)=0.05

  • sign of expression difference: sg∈{0,1}, with P(sg=1)=0.5

  • amplitude of expression difference: ca,g∼U(1.4,1.5)

  • differential expression: log2(ea,g)=log2(ea,g)+f dg sg ca,g ∀a∈B

In the simulations, the impurity fraction f was varied between 0.0 and 1.0.

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de Ridder, D., van der Linden, C., Schonewille, T. et al. Purity for clarity: the need for purification of tumor cells in DNA microarray studies. Leukemia 19, 618–627 (2005). https://doi.org/10.1038/sj.leu.2403685

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