Signal recovery in single cell batch integration
Ontology highlight
ABSTRACT:
PROVIDER: PRJNA1180965 | ENA |
REPOSITORIES: ENA
Dataset's files
| Action | DRS | |||
|---|---|---|---|---|
| Other | ||||
| SRR31192642.fastq.gz | Fastqsanger.gz | |||
| SRR31192645.fastq.gz | Fastqsanger.gz | |||
| SRR31192647.fastq.gz | Fastqsanger.gz | |||
| SRR31192649.fastq.gz | Fastqsanger.gz |
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Project description:Data integration to align cells across batches has become a cornerstone of single cell data analysis, critically affecting downstream results. Yet, how much biological signal is erased during integration? Currently, there are no guidelines for when the biological differences between samples are separable from batch effects, and thus, data integration involves a lot of guesswork: Cells across batches should be aligned to be “appropriately” mixed, while preserving “main cell type clusters”. We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation and introducing distortion. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, that harnesses experimental design to explicitly recover biological signals that are erased during single cell data integration. CellANOVA utilizes a “pool-of-controls” design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered. These concepts are illustrated on studies of diverse designs, where the biological signals that are recovered by CellANOVA are validated by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nuclei data integration, where it recovered subtle biological signals are can be validated and replicated by external data.
2024-11-08 | GSE280883 | GEO
Project description:Data integration to align cells across batches has become a cornerstone of single cell data analysis, critically affecting downstream results. Yet, how much biological signal is erased during integration? Currently, there are no guidelines for when the biological differences between samples are separable from batch effects, and thus, data integration usually involve a lot of guesswork: Cells across batches should be aligned to be "appropriately" mixed, while preserving "main cell type clusters". We show evidence that current paradigms for single cell data integration are unnecessarily aggressive, removing biologically meaningful variation. To remedy this, we present a novel statistical model and computationally scalable algorithm, CellANOVA, to recover biological signal that is lost during single cell data integration. CellANOVA utilizes a "pool-of-controls" design concept, applicable across diverse settings, to separate unwanted variation from biological variation of interest. When applied with existing integration methods, CellANOVA allows the recovery of subtle biological signals and corrects, to a large extent, the data distortion introduced by integration. Further, CellANOVA explicitly estimates cell- and gene-specific batch effect terms which can be used to identify the cell types and pathways exhibiting the largest batch variations, providing clarity as to which biological signals can be recovered. These concepts are illustrated on studies of diverse designs, where the biological signals that are recovered by CellANOVA are shown to be validated by orthogonal assays. In particular, we show that CellANOVA is effective in the challenging case of single-cell and single-nuclei data integration, where the recovered biological signals are replicated in an independent study.
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Project description:A key challenge in single cell RNA-sequencing (scRNA-seq) data analysis are dataset- and batch-specific differences that can obscure the biological signal of interest. While there are various tools and methods to perform data integration and correct for batch effects, their performance can vary between datasets and according to the nature of the bias. Therefore, it is important to understand how batch effects manifest in order to adjust for them in a reliable way. Here, we systematically explore batch effects in scRNA-seq data from a variety of datasets according to magnitude, cell type specificity and complexity. We developed a cell-specific mixing score (\texttt{cms}) that quantifies how well cells from multiple batches are mixed. By considering distance distributions (in a lower dimensional space), the score is able to detect local batch bias and differentiate between unbalanced batches (i.e., when one cell type is more abundant in a batch) and systematic differences between cells of the same cell type. We implemented the \texttt{cms}, as well as related metrics to detect batch effects or measure structure preservation, in the CellMixS R/Bioconductor package. We systematically compare different metrics that have been proposed to quantify batch effects or bias in scRNA-seq data using real datasets with known batch effects and synthetic data that mimic various real data scenarios. While these metrics target the same question and are used interchangeably, we find differences in inter- and intra-dataset scalability, sensitivity and in a metric's ability to handle batch effects with differentially abundant cell types. We find that cell-specific metrics outperform cell type-specific and global metrics and recommend them for both method benchmarks and batch exploration.
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Project description:Lymphocyte activation and effector state differentiation critically depend on integration of multiple signals, including those from antigen, co-stimulatory ligand and cytokine receptors. Thus, discovery of molecular components and mechanisms underlying signal integration is vital for the understanding and control of adaptive immune responses. Stimulation of antigen receptors in lymphocytes leads to the activation of NFAT family transcription factors (TFs) as well as the inducible expression of the immune-selective IRFs, IRF4 and IRF8; the latter are differentially controlled by co-stimulatory ligand and cytokine receptor signaling. Despite their major overlapping functions in lymphocyte activation and differentiation, it has remained enigmatic if NFATs can perform signal integration with IRF4 and IRF8, via their cooperative assembly on composite genomic regulatory elements. Composite elements (CEs) provide a powerful means of signal integration by inducible TFs and despite their clear importance, CEs arguably remain the least explored elements within cis-regulomes. To test the above hypothesis, we deployed a generalizable computational strategy, which revealed a stereospecific NFAT-IRF composite element (NICE) motif that was enriched within active chromatin regions (H3K27Ac) of activated B and T lymphocytes. NICE containing DNA sequences promoted cooperative binding of NFATs with IRF4 or IRF8. Genomic analyses in activated B cells revealed co-dependent genes that undergo activation or repression and contain NICE motifs in their regulatory regions. Notably, NFATc2 and IRF8 form a positive feedforward loop and coordinately repress the Prdm1 gene by binding a NICE motif in a phylogenetically conserved region, thereby inhibiting extrafollicular plasma cell differentiation and promoting the germinal center fate of activated B cells. The NICE motif is also enriched in DNA sequences located in accessible chromatin of human lymphocytes, suggesting evolutionary conservation of its function. The cooperative assembly of NFAT family members with IRF4 and IRF8 on NICE motifs reveals a new genomic control mechanism that enables signal integration during lymphocyte activation and differentiation.
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