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. 2020 Jun 2;22(3):bbaa072. doi: 10.1093/bib/bbaa072

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© The Author(s) 2020. Published by Oxford University Press.

This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted reuse, distribution, and reproduction in any medium, provided the original work is properly cited.

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Abundance and change as gene filters. Analogously to Figure 2, the Pearson correlation as a similarity function is run on different subsets of genes. Genes are filtered for their maximal abundance, selecting the 5, 10, 100 and 10 000 most expressed (‘Abs ’) or most changing genes (‘FC ’) in comparison with the performance of the complete set (‘all’). With either filter, at around 1000 genes is a maximum of the performance.

Inline graphic — Abundance and change as gene filters. Analogously to Figure 2, the Pearson correlation as a similarity function is run on different subsets of genes. Genes are filtered for their maximal abundance, selecting the 5, 10, 100 and 10 000 most expressed (‘Abs ’) or most changing genes (‘FC ’) in comparison with the performance of the complete set (‘all’). With either filter, at around 1000 genes is a maximum of the performance.