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Fig S2 ā€“ Statistics of Absolute Normalization, related to Fig 1, Supplemental Experimental Procedures (A) Absolute normalization of RNA standards. Left: relative normalization in TPM on linear and log scale for the three datasets. The trend shared by all spikes is clearly visible and differs between PolyA+ and rdRNA samples. Right: absolute normalization in transcripts per embryo on log and linear scale. Trend in spikes is removed, the sample noise of the spikes is retained and does not contaminate the absolute normalization of native transcripts. (B) Absolute normalization factors. Circles mark per sample factors, lines mark Gaussian Process smoothed factors. Left: Estimates of š›½š‘— (Supplemental Experimental Procedures), for three datasets. Center-left: per dataset correction factors eāˆ’š›½š‘—, note discrepancy in magnitude but not in trend between datasets. Center-right: correction for PolyA+ bias, and averaging Clutch A and Clutch B spike levels results in excellent agreement between the correction factors. Right: Smoothed correction factors calculated with non-stationary Gaussian Process (Supplemental Experimental Procedures). (C) Absolute normalization consistency and accuracy. Scatter plots of ERCC standards spiked vs calculated for Clutch A PolyA+, Clutch A rdRNA, Clutch B PolyA+ and Clutch A/Clutch B PolyA+ combined. Points are averaged over all samples in time to remove spikein sample noise. ArrayControl spikes are given with fold change errors for Clutch B PolyA+, fold changes range from 1.11-1.25. ArrayControl spikes EC02 and EC12 have are spiked in with the same copy number and are marked with the same vertical line.Combined Clutch A/Clutch B gives 95% confidence interval for a linear model with Gaussan noise, with slope unity and with standard deviation šœŽš‘  = 0.25. Note that residuals do not depend on expression level. (D) Absolute normalization uncertainty model. Left: model as described in C (far-right), with 25%, 50%, 75%, 95% confidence intervals for 106 transcripts. Right: Propagation of uncertainty model to absolute normalization of eef1a1o Clutch A polyA+, again 25%, 50% 75% and 95% of confidence intervals for the true number of eef1a1o transcripts/embryo marked. (E) RNA-seq detection limits ā€“ Transcript abundance required to produce a single read with time, detection limits increase as RNA content in the embryo increases. Top row: Detection limits in kb, points give the per sample detection limits, lines and shaded area give Gaussian Process median and 95% confidence interval. Bottom: Detection limit in transcripts/embryo, calculated by averaging kb Gaussian Process distribution over all transcript lengths. (F) The depletion of non-ribosomal RNAs in rdRNA sequencing. Comparisons between Clutch A PolyA+ and rdRNA sequencing (top). Examples of extreme depletion: mixer and foxh1.2.

Image published in: Owens ND et al. (2016)

Copyright Ā© 2016. Image reproduced on Xenbase with permission of the publisher and the copyright holder. This is an Open Access article distributed under the terms of the Creative Commons Attribution License.

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