Sources of variation in cell-type RNA-Seq profiles

Abstract

Cell-type specific gene expression profiles are needed for many computational methods operating on bulk RNA-Seq samples, such as deconvolution of cell-type fractions and digital cytometry. However, the gene expression profile of a cell type can vary substantially due to both technical factors and biological differences in cell state and surroundings, reducing the efficacy of such methods. Here, we investigated which factors contribute most to this variation. We evaluated different normalization methods, quantified the variance explained by different factors, evaluated the effect on deconvolution of cell type fractions, and examined the differences between UMI-based single-cell RNA-Seq and bulk RNA-Seq. We investigated a collection of publicly available bulk and single-cell RNA-Seq datasets containing B and T cells, and found that the technical variation across laboratories is substantial, even for genes specifically selected for deconvolution, and this variation has a confounding effect on deconvolution. Tissue of origin is also a substantial factor, highlighting the challenge of using cell type profiles derived from blood with mixtures from other tissues. We also show that much of the differences between UMI-based single-cell and bulk RNA-Seq methods can be explained by the number of read duplicates per mRNA molecule in the single-cell sample. Our work shows the importance of either matching or correcting for technical factors when creating cell-type specific gene expression profiles that are to be used together with bulk samples.

Learning the Regulatory Code of Gene Expression

“…With the latter, unsupervized training is used with approaches including: 1) autoencoders, which learn efficient representations of the training data, typically for dimensionality reduction ( Way and Greene, 2018 ) or feature selection ( Xie et al, 2017 ), 2) generative adversarial networks, which learn to generate new data with the same statistics as the training set ( Wang Y. et al, 2020 ; Repecka et al, 2021 ), and 3) deep belief networks, which learn to probabilistically reconstruct their inputs, acting as feature detectors, and can be further trained with supervision to build efficient classifiers ( Bu et al, 2017 ). Moreover, the advent of single-cell HTS technologies such as single-cell RNA-seq will offer many novel research opportunities, including modeling of cell-type or cell-state specific enhancer or TFBS activations and chromatin changes ( Angermueller et al, 2017 ; Gustafsson et al, 2020 ; Kawaguchi et al, 2021 ).…”

A review of digital cytometry methods: estimating the relative abundance of cell types in a bulk of cells

“…Since the gene expression data sets can be collected through completely different experimental settings with the use of different experimentation plans, platforms and methodologies, there are undesired batch effects in the gene expression values. These technical variations can in some cases be as large as the biological variations between different cell types [23]. It has been shown that the ComBat algorithm [24] can effectively remove these unwanted variations from bulk RNA-Seq data [23].…”

“…These technical variations can in some cases be as large as the biological variations between different cell types [23]. It has been shown that the ComBat algorithm [24] can effectively remove these unwanted variations from bulk RNA-Seq data [23]. Newman et al [13] introduced CIBERSORTx, which extends CIBERSORT by adding batch correction using ComBat to address the possible cross-platform variations in gene expression data sets.…”

A Statistical Framework to Identify Cell Types Whose Genetically Regulated Proportions are Associated with Complex Diseases

“…First, high expense and technical noise (e.g., high sparsity of gene expression) limit the number of samples analyzed and quality of cell type composition estimation, leading to low power in association analysis. Second, cell type compositions measured in single cell experiments are highly dependent on the biopsy samples and do not necessarily reflect the true cell type compositions in the corresponding tissue 13 . Instead of directly calculating cell type proportions from scRNA-seq data, cell type proportions can also be inferred through deconvolution of bulk RNA-sequencing (RNA-seq) data available with larger sample sizes.…”