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Directly selecting cell-type marker genes for single-cell clustering analyses.

ABSTRACT: In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We present Festem, a statistical method for the direct selection of cell-type markers for downstream clustering. Festem distinguishes marker genes with heterogeneous distribution across cells that are cluster informative. Simulation and scRNA-seq applications demonstrate that Festem can sensitively select markers with high precision and enables the identification of cell types often missed by other methods. In a large intrahepatic cholangiocarcinoma dataset, we identify diverse CD8⁺ T cell types and potential prognostic marker genes.

SUBMITTER: Chen Z

PROVIDER: S-EPMC11294843 | biostudies-literature | 2024 Jul

REPOSITORIES: biostudies-literature

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Directly selecting cell-type marker genes for single-cell clustering analyses.

Chen Zihao Z Wang Changhu C Huang Siyuan S Shi Yang Y Xi Ruibin R

Cell reports methods 20240708 7

In single-cell RNA sequencing (scRNA-seq) studies, cell types and their marker genes are often identified by clustering and differentially expressed gene (DEG) analysis. A common practice is to select genes using surrogate criteria such as variance and deviance, then cluster them using selected genes and detect markers by DEG analysis assuming known cell types. The surrogate criteria can miss important genes or select unimportant genes, while DEG analysis has the selection-bias problem. We prese ...[more]

PMID: 38981475

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Project description:The mammalian brain contains numerous neurons distributed across forebrain, midbrain, and hindbrain that project axons to the lower spinal cord and work in concert to control movement and achieve homeostasis. Extensive work has mapped the anatomic location of supraspinal cell types and continues to establish specific physiological functions. The patterns of gene expression that typify and distinguish these disparate populations, however, are mostly unknown. Here, using adult mice of mixed sex, we combined retrograde labeling of supraspinal cell nuclei with fluorescence-activated nuclei sorting and single-nuclei RNA sequencing analyses to transcriptionally profile neurons that project axons from the brain to lumbar spinal cord. We identified 14 transcriptionally distinct cell types and used a combination of established and newly identified marker genes to assign an anatomic location to each. To validate the putative marker genes, we visualized selected transcripts and confirmed selective expression within lumbar-projecting neurons in discrete supraspinal regions. Finally, we illustrate the potential utility of these data by examining the expression of transcription factors that distinguish different supraspinal cell types and by surveying the expression of receptors for growth and guidance cues that may be present in the spinal cord. Collectively, these data establish transcriptional differences between anatomically defined supraspinal populations, identify a new set of marker genes of use in future experiments, and provide insight into potential differences in cellular and physiological activity across the supraspinal connectome.SIGNIFICANCE STATEMENT The brain communicates with the body through a wide variety of neuronal populations with distinct functions and differential sensitivity to damage and disease. We have used single-nuclei RNA sequencing technology to distinguish patterns of gene expression within a diverse set of neurons that project axons from the mouse brain to the lumbar spinal cord. The results reveal transcriptional differences between populations previously defined on the basis of anatomy, provide new marker genes to facilitate rapid identification of cell type in future work, and suggest distinct responsiveness of different supraspinal populations to external growth and guidance cues.

Project description:BackgroundClustering is a fundamental operation in the analysis of biological sequence data. New DNA sequencing technologies have dramatically increased the rate at which we can generate data, resulting in datasets that cannot be efficiently analyzed by traditional clustering methods.This is particularly true in the context of taxonomic profiling of microbial communities through direct sequencing of phylogenetic markers (e.g. 16S rRNA) - the domain that motivated the work described in this paper. Many analysis approaches rely on an initial clustering step aimed at identifying sequences that belong to the same operational taxonomic unit (OTU). When defining OTUs (which have no universally accepted definition), scientists must balance a trade-off between computational efficiency and biological accuracy, as accurately estimating an environment's phylogenetic composition requires computationally-intensive analyses. We propose that efficient and mathematically well defined clustering methods can benefit existing taxonomic profiling approaches in two ways: (i) the resulting clusters can be substituted for OTUs in certain applications; and (ii) the clustering effectively reduces the size of the data-sets that need to be analyzed by complex phylogenetic pipelines (e.g., only one sequence per cluster needs to be provided to downstream analyses).ResultsTo address the challenges outlined above, we developed DNACLUST, a fast clustering tool specifically designed for clustering highly-similar DNA sequences.Given a set of sequences and a sequence similarity threshold, DNACLUST creates clusters whose radius is guaranteed not to exceed the specified threshold. Underlying DNACLUST is a greedy clustering strategy that owes its performance to novel sequence alignment and k-mer based filtering algorithms.DNACLUST can also produce multiple sequence alignments for every cluster, allowing users to manually inspect clustering results, and enabling more detailed analyses of the clustered data.ConclusionsWe compare DNACLUST to two popular clustering tools: CD-HIT and UCLUST. We show that DNACLUST is about an order of magnitude faster than CD-HIT and UCLUST (exact mode) and comparable in speed to UCLUST (approximate mode). The performance of DNACLUST improves as the similarity threshold is increased (tight clusters) making it well suited for rapidly removing duplicates and near-duplicates from a dataset, thereby reducing the size of the data being analyzed through more elaborate approaches.

Dataset Information

Directly selecting cell-type marker genes for single-cell clustering analyses.

Publications

Directly selecting cell-type marker genes for single-cell clustering analyses.

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