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BEARscc determines robustness of single-cell clusters using simulated technical replicates.

ABSTRACT: Single-cell messenger RNA sequencing (scRNA-seq) has emerged as a powerful tool to study cellular heterogeneity within complex tissues. Subpopulations of cells with common gene expression profiles can be identified by applying unsupervised clustering algorithms. However, technical variance is a major confounding factor in scRNA-seq, not least because it is not possible to replicate measurements on the same cell. Here, we present BEARscc, a tool that uses RNA spike-in controls to simulate experiment-specific technical replicates. BEARscc works with a wide range of existing clustering algorithms to assess the robustness of clusters to technical variation. We demonstrate that the tool improves the unsupervised classification of cells and facilitates the biological interpretation of single-cell RNA-seq experiments.

SUBMITTER: Severson DT

PROVIDER: S-EPMC5864873 | biostudies-other | 2018 Mar

REPOSITORIES: biostudies-other

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BEARscc determines robustness of single-cell clusters using simulated technical replicates.

Severson D T DT Owen R P RP White M J MJ Lu X X Schuster-Böckler B B

Nature communications 20180322 1

Single-cell messenger RNA sequencing (scRNA-seq) has emerged as a powerful tool to study cellular heterogeneity within complex tissues. Subpopulations of cells with common gene expression profiles can be identified by applying unsupervised clustering algorithms. However, technical variance is a major confounding factor in scRNA-seq, not least because it is not possible to replicate measurements on the same cell. Here, we present BEARscc, a tool that uses RNA spike-in controls to simulate experim ...[more]

PMID: 29567991

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BEARscc: robustness of single-cell clusters determined using simulated technical replicates

Project description:Technical variance is a major confounding factor in single-cell RNA sequencing, not least because measurements on the same cell are not replicable. We developed BEARscc, a tool that simulates experiment-specific technical replicates based on a probabilistic model of technical variance trained on RNA spike-in measurements. We demonstrate that the tool improves the unsupervised classification of cells and aids the interpretation of single-cell RNA-seq experiments.

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Project description:BEARscc: robustness of single-cell clusters determined using simulated technical replicates

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Selecting single cell clustering parameter values using subsampling-based robustness metrics.

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Technical replicates to determine array-to-array reproducibility.

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High-resolution spatial normalization for microarrays containing embedded technical replicates.

Project description:Microarray data are susceptible to a wide-range of artifacts, many of which occur on physical scales comparable to the spatial dimensions of the array. These artifacts introduce biases that are spatially correlated. The ability of current methodologies to detect and correct such biases is limited.We introduce a new approach for analyzing spatial artifacts, termed 'conditional residual analysis for microarrays' (CRAM). CRAM requires a microarray design that contains technical replicates of representative features and a limited number of negative controls, but is free of the assumptions that constrain existing analytical procedures. The key idea is to extract residuals from sets of matched replicates to generate residual images. The residual images reveal spatial artifacts with single-feature resolution. Surprisingly, spatial artifacts were found to coexist independently as additive and multiplicative errors. Efficient procedures for bias estimation were devised to correct the spatial artifacts on both intensity scales. In a survey of 484 published single-channel datasets, variance fell 4- to 12-fold in 5% of the datasets after bias correction. Thus, inclusion of technical replicates in a microarray design affords benefits far beyond what one might expect with a conventional 'n = 5' averaging, and should be considered when designing any microarray for which randomization is feasible.CRAM is implemented as version 2 of the hoptag software package for R, which is included in the Supplementary information.

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Technical replicates to determine array-to-array reproducibility.

Project description:Technical replicates, consisting of four independent hybridizations of the same labelled cRNA, were performed to determine array-to-array reproducibility. After normalization, correlation index between all replicates exceeded 98%. In this study, we analyzed reproducibility of Agilent-016251 Sparus aurata Oligo Microarray based on single-colour detection (Cyanine-3 only). A RNA sample, prepared mixing four different larval stages of gilthead sea bream, was labelled in according to Agilent One-Color Microarray-Based Gene Expression Analysis protocol. The same labelled cRNA was used for four independent fragmentation reactions and then hybridized spanning all array positions of 4x44K platform. Microarrays are scanned with Agilent scanner G2565BA (barcode on the left, DNA on the back surface, scanned through the glass) at a resolution of 5 microns; all slides are scanned twice at two different sensitivity settings (XDRHi 100% and XDRLo 10%); the scanner software creates a unique ID for each pair of XDR scans and saves it to both scan image files. Feature Extraction 9.5 uses XDR ID to link the pairs of scans together automatically when extracting data. The signal left after all the FE processing steps have been completed is ProcessedSignal that contains the Multiplicatively Detrended Background-Subtracted Signal.

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