A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory

doi:10.1371/journal.pcbi.1006794

A complete statistical model for calibration of RNA-seq counts using external spike-ins and maximum likelihood theory

Fig 5

Up-regulation of ribosomal RNA molecules by growth rate.

(A) Small subunit ribosomal RNA (SSUrRNA) molecules (GO:0015935). Normalized abundances (filled symbols) of significantly up-regulated SSUrRNAs, plotted as a function of growth rate on log-linear coordinates, and corresponding exponential-model values (solid lines), drawn from Eq (6), where γ is growth rate. Each filled symbol at a given growth rate is the normalized mean over 3 replicates at that growth rate; the normalization factor is the mean at the lowest growth rate, 0.12 h^-1. The determination of maximum likelihood parameters, ϕ₀ and ϕ₁, in Eq (6) was based on all replicates, so the model values (solid lines) are not constrained to go through the mean normalized value of 1 at the lowest growth rate. Mean ± sd for exponential constant ϕ₁, 8.0±1.5. (B) Large subunit ribosomal RNA (LSU rRNA) molecules (GO:0015934). Normalized abundances of significantly up-regulated LSU rRNAs. Symbols and lines as in panel A. The total mean ± sd for exponential constant ϕ₁ is 7.9±1.4.

doi: https://doi.org/10.1371/journal.pcbi.1006794.g005