Dataset Information

Unbiased visualization of single-cell genomic data with SCUBI.

ABSTRACT: Visualizing low-dimensional representations with scatterplots is a crucial step in analyzing single-cell genomic data. However, this visualization has significant biases. The first bias arises when visualizing the gene expression levels or the cell identities. The scatterplot only shows a subset of cells plotted last, and the cells plotted earlier are masked and unseen. The second bias arises when comparing the cell-type compositions across samples. The scatterplot is biased by the unbalanced total number of cells across samples. We developed SCUBI, an unbiased method that visualizes the aggregated information of cells within non-overlapping squares to address the first bias and visualizes the differences of cell proportions across samples to address the second bias. We show that SCUBI presents a more faithful visual representation of the information in a real single-cell RNA sequencing (RNA-seq) dataset and has the potential to change how low-dimensional representations are visualized in single-cell genomic data.

SUBMITTER: Hou W

PROVIDER: S-EPMC8871596 | biostudies-literature | 2022 Jan

REPOSITORIES: biostudies-literature

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Unbiased visualization of single-cell genomic data with SCUBI.

Hou Wenpin W Ji Zhicheng Z

Cell reports methods 20220104 1

Visualizing low-dimensional representations with scatterplots is a crucial step in analyzing single-cell genomic data. However, this visualization has significant biases. The first bias arises when visualizing the gene expression levels or the cell identities. The scatterplot only shows a subset of cells plotted last, and the cells plotted earlier are masked and unseen. The second bias arises when comparing the cell-type compositions across samples. The scatterplot is biased by the unbalanced to ...[more]

PMID: 35224531

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Project description:BackgroundTime-lapse microscopy live-cell imaging is essential for studying the evolution of bacterial communities at single-cell resolution. It allows capturing detailed information about the morphology, gene expression, and spatial characteristics of individual cells at every time instance of the imaging experiment. The image analysis of bacterial "single-cell movies" (videos) generates big data in the form of multidimensional time series of measured bacterial attributes. If properly analyzed, these datasets can help us decipher the bacterial communities' growth dynamics and identify the sources and potential functional role of intra- and inter-subpopulation heterogeneity. Recent research has highlighted the importance of investigating the role of biological "noise" in gene regulation, cell growth, cell division, etc. Single-cell analytics of complex single-cell movie datasets, capturing the interaction of multiple micro-colonies with thousands of cells, can shed light on essential phenomena for human health, such as the competition of pathogens and benign microbiome cells, the emergence of dormant cells ("persisters"), the formation of biofilms under different stress conditions, etc. However, highly accurate and automated bacterial bioimage analysis and single-cell analytics methods remain elusive, even though they are required before we can routinely exploit the plethora of data that single-cell movies generate.ResultsWe present visualization and single-cell analytics using R (ViSCAR), a set of methods and corresponding functions, to visually explore and correlate single-cell attributes generated from the image processing of complex bacterial single-cell movies. They can be used to model and visualize the spatiotemporal evolution of attributes at different levels of the microbial community organization (i.e., cell population, colony, generation, etc.), to discover possible epigenetic information transfer across cell generations, infer mathematical and statistical models describing various stochastic phenomena (e.g., cell growth, cell division), and even identify and auto-correct errors introduced unavoidably during the bioimage analysis of a dense movie with thousands of overcrowded cells in the microscope's field of view.ConclusionsViSCAR empowers researchers to capture and characterize the stochasticity, uncover the mechanisms leading to cellular phenotypes of interest, and decipher a large heterogeneous microbial communities' dynamic behavior. ViSCAR source code is available from GitLab at https://gitlab.com/ManolakosLab/viscar .

Project description:BackgroundSequencing data enable the detection of causal loci or single nucleotide polymorphisms (SNPs) highly linked to causal loci to improve genomic prediction. However, until now, studies on integrating such SNPs using a single-step genomic best linear unbiased prediction (ssGBLUP) model are scarce. We investigated the integration of sequencing SNPs selected by association (1262 SNPs) and bioinformatics (2359 SNPs) analyses into the currently used 54K-SNP chip, using three ssGBLUP models which make different assumptions on the distribution of SNP effects: a basic ssGBLUP model, a so-called featured ssGBLUP (ssFGBLUP) model that considered selected sequencing SNPs as a feature genetic component, and a weighted ssGBLUP (ssWGBLUP) model in which the genomic relationship matrix was weighted by the SNP variances estimated from a Bayesian whole-genome regression model, with every 1, 30, or 100 adjacent SNPs within a chromosome region sharing the same variance. We used data on milk production and female fertility in Danish Jersey. In total, 15,823 genotyped and 528,981‬ non-genotyped females born between 1990 and 2013 were used as reference population and 7415 genotyped females and 33,040 non-genotyped females born between 2014 and 2016 were used as validation population.ResultsWith basic ssGBLUP, integrating SNPs selected from sequencing data improved prediction reliabilities for milk and protein yields, but resulted in limited or no improvement for fat yield and female fertility. Model performances depended on the SNP set used. When using ssWGBLUP with the 54K SNPs, reliabilities for milk and protein yields improved by 0.028 for genotyped animals and by 0.006 for non-genotyped animals compared with ssGBLUP. However, with the SNP set that included SNPs selected from sequencing data, no statistically significant difference in prediction reliability was observed between the three ssGBLUP models.ConclusionsIn summary, when using 54K SNPs, a ssWGBLUP model with a common weight on the SNPs in a given region is a feasible approach for single-trait genetic evaluation. Integrating relevant SNPs selected from sequencing data into the standard SNP chip can improve the reliability of genomic prediction. Based on such SNP data, a basic ssGBLUP model was suggested since no significant improvement was observed from using alternative models such as ssWGBLUP and ssFGBLUP.

Dataset Information

Unbiased visualization of single-cell genomic data with SCUBI.

Publications

Unbiased visualization of single-cell genomic data with SCUBI.

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