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. 2018 Apr;15(4):233-234.
doi: 10.1038/nmeth.4642. Epub 2018 Apr 3.

Statistics versus machine learning

Danilo Bzdok  1 , Naomi Altman  2 , Martin Krzywinski  3
Affiliations

Statistics versus machine learning

Danilo Bzdok et al. Nat Methods. 2018 Apr.
No abstract available

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Figures

Figure 1
Figure 1
Simulated expression and RNA-seq read counts for 40 genes in which the last 10 genes (A–J) are differentially expressed across two phenotypes (−/+). Simulated quantities and heat maps are log-scaled. (a) Simulated log mean expression levels for the genes generated by sampling from the normal distribution with mean 4 and s.d. 2. In the + phenotype the differential expression of genes A–J was created by the addition of a standard normal to each mean expression in the – phenotype. (b) The simulated RNA-seq read counts for ten subjects in each phenotype generated from an overdispersed Poisson distribution based on mean expression in a with biological variation. The heat map shows z-scores of the read counts normalized across all 20 subjects for a given gene.
Figure 2
Figure 2
Analysis of gene ranking by classical inference and ML. (a) Unadjusted log-scaled P values from statistical differential expression analysis as a function of effect size, measured by fold change in expression. (b) Log-scaled P values from a as a function of gene importance from random forest classification. In a and b, red circles identify the ten differentially expressed genes from Figure 1; the remaining genes are indicated by open circles. (c) Distribution of the number of dysregulated genes correctly identified in 1,000 simulations by inference (gray fill) and random forest (black line).
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References

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