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

Abstract

A fundamental assumption, common to the vast majority of high-throughput transcriptome analyses, is that the expression of most genes is unchanged among samples and that total cellular RNA remains constant. As the number of analyzed experimental systems increases however, different independent studies demonstrate that this assumption is often violated. We present a calibration method using RNA spike-ins that allows for the measurement of absolute cellular abundance of RNA molecules. We apply the method to pooled RNA from cell populations of known sizes. For each transcript, we compute a nominal abundance that can be converted to absolute by dividing by a scale factor determined in separate experiments: the yield coefficient of the transcript relative to that of a reference spike-in measured with the same protocol. The method is derived by maximum likelihood theory in the context of a complete statistical model for sequencing counts contributed by cellular RNA and spike-ins. The counts are based on a sample from a fixed number of cells to which a fixed population of spike-in molecules has been added. We illustrate and evaluate the method with applications to two global expression data sets, one from the model eukaryote Saccharomyces cerevisiae, proliferating at different growth rates, and differentiating cardiopharyngeal cell lineages in the chordate Ciona robusta. We tested the method in a technical replicate dilution study, and in a k-fold validation study.

Gene regulatory network reconstruction using single-cell RNA sequencing of barcoded genotypes in diverse environments

“…However, the number of transcripts per cell is generally equivalent between strains even when they differ in representation within libraries (Supplemental Figure 2B). By contrast, we find that total transcript counts per cell are highly linked to environmental growth condition (Supplemental Figure 2C), which is consistent with decreased total transcriptome pool size in suboptimal conditions (Athanasiadou et al, 2019) .…”

The accuracy of absolute differential abundance analysis from relative count data

“…We acknowledge the difficulty in reconstructing absolute abundances and caution that these estimates are likely noisy. Rather than call them absolute abundances, we will adopt the helpful terminology of [ 9 ] and call these reconstructions “nominal abundances” to distinguish them from some theoretical ground truth. While these quantities are an abstraction, since they derive from real experiments, we believe they capture some of the real variation in composition and scale we could expect to see in data from typical experiments and should serve to ground expectations.…”

Nuclear oligo hashing improves differential analysis of single-cell RNA-seq

“…2d ). Importantly, such artifacts are characteristic library size-based normalization 29 . By contrast, hash ladder-based normalization yielded 406 more downregulated genes and 31 fewer upregulated genes (Supplementary Fig.…”