Normalizing single-cell RNA sequencing data with internal spike-in-like genes

Abstract

Normalization with respect to sequencing depth is a crucial step in single-cell RNA sequencing preprocessing. Most methods normalize data using the whole transcriptome based on the assumption that the majority of transcriptome remains constant and are unable to detect drastic changes of the transcriptome. Here, we develop an algorithm based on a small fraction of constantly expressed genes as internal spike-ins to normalize single-cell RNA sequencing data. We demonstrate that the transcriptome of single cells may undergo drastic changes in several case study datasets and accounting for such heterogeneity by ISnorm (Internal Spike-in-like-genes normalization) improves the performance of downstream analyses.

Library adaptors with integrated reference controls improve the accuracy and reliability of nanopore sequencing

“…Reference standards are needed to understand the sequencing accuracy and quantitative performance of NGS libraries. Currently available reference standards include both natural reference genome materials (such as the NA12878 genome) and synthetic spike-in controls (such as sequins, ERCC and SIRV controls) 6 , 11 , 14 , 16 , 42 , 44 . Although synthetic spike-ins have the advantage of measuring internal library variation, they must be precisely added to a sample during library preparation, must be bioinformatically calibrated, and risk overwhelming low input or degraded samples.…”

Anti-bias training for (sc)RNA-seq: experimental and computational approaches to improve precision

“…Correcting for technical variation in library size is therefore a crucial step in the pre-processing of scRNA-seq data. Similar to bulk RNAseq, this can be achieved by using an internal [ 79 ] (e.g. housekeeping genes) or external (RNA spike-ins) reference point [ 69 ], with respect to which the individual libraries can be scaled.…”

Editorial: Compositional data analysis and related methods applied to genomics—a first special issue fromNAR Genomics and Bioinformatics

“…Lin et al. ( 27 ) propose one such alternative, an algorithm that seeks to identify genes that are stably expressed in single-cell RNA-seq data, and to use them as an internal reference to normalize the data. Two more articles discuss normalization-free alternatives to the clr that aim to learn interpretable log ratios directly from the data.…”