Network-Based Single-Cell RNA-Seq Data Imputation Enhances Cell Type Identification

Zand, Maryam; Ruan, Jianhua

doi:10.3390/genes11040377

Cited by 14 publications

(13 citation statements)

References 35 publications

(45 reference statements)

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“…Nevertheless, the use of "dropouts" in later papers became inconsistent and confusing: most papers meant non-biological zeros [20,36,40,50,53,93,94]; some meant non-biological zeros and low expression measurements [43,95]; some meant all zeros [44,45,96]. In addition,…”

Section: Clarification Of Zero-related Terminologymentioning

confidence: 99%

“…” Hence, dropouts, as a data-driven concept, are not equivalent to either biological or non-biological zeros. Nevertheless, the use of “dropouts” in later papers became inconsistent and confusing: most papers meant non-biological zeros [20, 36, 40, 52, 55, 93, 94]; some meant non-biological zeros and low expression measurements [45, 95]; some meant all zeros [46, 47, 96]. In addition, “dropout” was often used as an adjective to mean the existence of many zeros [97].…”

Section: Introductionmentioning

confidence: 99%

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Statistics or biology: the zero-inflation controversy about scRNA-seq data

Jiang

Sun

Song

et al. 2020

Preprint

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Single-cell RNA sequencing (scRNA-seq) technologies have revolutionized biomedical sciences by enabling genome-wide profiling of gene expression levels at an unprecedented single-cell resolution. A distinct characteristic of scRNA-seq data is the vast proportion of zeros unseen in bulk RNA-seq data. Researchers view these zeros differently: some regard zeros as biological signals representing no or low gene expression, while others regard zeros as false signals or missing data to be corrected. As a result, the scRNA-seq field faces much controversy regarding how to handle zeros in data analysis. In this paper, we first discuss the origins of biological and non-biological zeros in scRNA-seq data. Second, we clarify the definitions of several commonly-used but ambiguous terms, including “dropouts,” “excess zeros,” and “zero inflation.” Third, we evaluate the impacts of non-biological zeros on cell clustering and differential gene expression analysis. Fourth, we summarize the advantages, disadvantages, and suitable users of three input data types: original counts, imputed counts, and binarized counts. Finally, we discuss the open questions regarding non-biological zeros, the need for benchmarking, and the importance of transparent analysis.

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Section: Clarification Of Zero-related Terminologymentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Statistics or biology: the zero-inflation controversy about scRNA-seq data

Jiang

Sun

Song

et al. 2020

Preprint

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“…However, for the sake of completeness we also examined the possible role of "imputation" and using raw data instead of the binarized scRNA-seq data. We tried to impute the dropouts in scRNA-seq data using SAVER 19 and netImpute 20 , but no significant improvement was gained in terms of enhancing our metric scores. On the other hand, although our analysis indicated that using raw data instead of the binarized data can potentially increase the consistency between gene expression pattern similarity and cell proximity in this challenge (according to M 1 and M 2 metrics), we are limited by the fact that the true locations of the cells to be predicted are unknown, and prediction accuracy is at least partially defined by comparing to the “gold standard” location obtained from binarized data.…”

Section: Resultsmentioning

confidence: 99%

Spatial mapping of single cells in the Drosophila embryo from transcriptomic data based on topological consistency

Zand

Ruan

2021

F1000Res

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The advancement in single-cell RNA sequencing technologies allow us to obtain transcriptome at single cell resolution. However, the original spatial context of cells, a crucial knowledge for understanding cellular and tissue-level functions, is often lost during sequencing. To address this issue, the DREAM Single Cell Transcriptomics Challenge launched a community-wide effort to seek computational solutions for spatial mapping of single cells in tissues using single-cell RNAseq (scRNA-seq) data and a reference atlas obtained from in situ hybridization data. As a top-performing team in this competition, we approach this problem in three steps. The first step involves identifying a set of most informative genes based on the consistency between gene expression similarity and cell proximity. For this step, we propose two different approaches, i.e., an unsupervised approach that does not utilize the gold standard location of the cells provided by the challenge organizers, and a supervised approach that relies on the gold standard locations. In the second step, a Particle Swarm Optimization algorithm is used to optimize the weights of different genes in order to maximize matches between the predicted locations and the gold standard locations. Finally, the information embedded in the cell topology is used to improve the predicted cell-location scores by weighted averaging of scores from neighboring locations. Evaluation results based on DREAM scores show that our method accurately predicts the location of single cells, and the predictions lead to successful recovery of the spatial expression patterns for most of landmark genes. In addition, investigating the selected genes demonstrates that most predictive genes are cluster specific, and stable across our supervised and unsupervised gene selection frameworks. Overall, the promising results obtained by our methods in DREAM challenge demonstrated that topological consistency is a useful concept in identifying marker genes and constructing predictive models for spatial mapping of single cells.

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“…Thus, a particular strength of network propagation is the fact that prior knowledge is utilized for the analysis of new data, which potentially helps increasing the signal-to-noise ratio and which aids the mechanistic interpretation of results. Within the realm of molecular biology, network propagation has a wide range of applications such as imputation of missing values (van Dijk et al 2017; Ronen and Akalin 2018; Zand and Ruan 2020), protein function prediction (Warde-Farley et al 2010), inferring condition-specifically altered sub-networks (Hofree et al 2013), and prioritization of disease genes (Cowen et al 2017; Hadas, Kupiec, and Sharan 2019).…”

Section: Introductionmentioning

confidence: 99%

Optimizing Network Propagation for Multi-Omics Data Integration

Charmpi

Chokkalingam

Johnen

et al. 2021

Preprint

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Network propagation refers to a class of algorithms that integrate information from input data across connected nodes in a given network. These algorithms have wide applications in systems biology, protein function prediction, inferring condition-specifically altered sub-networks, and prioritizing disease genes. Despite the popularity of network propagation, there is a lack of comparative analyses of different algorithms on real data and little guidance on how to select and parameterize the various algorithms. Here, we address this problem by analyzing different combinations of network normalization and propagation methods and by demonstrating schemes for the identification of optimal parameter settings on real proteome and transcriptome data. Our work highlights the risk of a ‘topology bias’ caused by the incorrect use of network normalization approaches. Capitalizing on the fact that network propagation is a regularization approach, we show that minimizing the bias-variance tradeoff can be utilized for selecting optimal parameters. The application to real multi-omics data demonstrated that optimal parameters could also be obtained by either maximizing the agreement between different omics layers (e.g. proteome and transcriptome) or by maximizing the consistency between biological replicates. Furthermore, we exemplified the utility and robustness of network propagation on multi-omics datasets for identifying ageing-associated genes in brain and liver tissues of rats and for elucidating molecular mechanisms underlying prostate cancer progression. Overall, this work compares different network propagation approaches and it presents strategies for how to use network propagation algorithms to optimally address a specific research question at hand.

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Network-Based Single-Cell RNA-Seq Data Imputation Enhances Cell Type Identification

Cited by 14 publications

References 35 publications

Statistics or biology: the zero-inflation controversy about scRNA-seq data

Statistics or biology: the zero-inflation controversy about scRNA-seq data

Spatial mapping of single cells in the Drosophila embryo from transcriptomic data based on topological consistency

Optimizing Network Propagation for Multi-Omics Data Integration

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