从单细胞基因表达数据发现推定的细胞类型
Putative cell type discovery from single-cell gene expression data
摘要：
我们提出了一种从单细胞RNA-seq（scRNA-seq）数据自动识别推定细胞类型的新方法。通过迭代地将机器学习方法应用于给定一组细胞的基因表达谱的初始聚类，我们同时识别不同的细胞组和每组的特征基因的加权列表。在特定细胞群中差异表达的特征基因共同将给定细胞群与其他细胞区分开。每个这样的细胞群对应于推定的细胞类型或状态，其特征在于作为标记的特征基因。为了对这种方法进行基准测试，我们使用了来自一系列实验的专家注释的scRNA-seq数据集，并与现有的细胞注释方法进行了比较，这些方法都是基于预先存在的参考。我们表明，我们的方法自动识别的“地面真相”的细胞分配具有很高的准确性。此外，我们的方法，单细胞聚类评估框架（SCCAF）预测新的推定的生物学意义的细胞状态在已发表的数据造血和人类皮层。SCCAF作为GitHub（https：//github.com/SCCAF/sccaf）上的开源软件包和Python软件包索引提供，也已作为人类细胞图谱中的Galaxy工具实施。
英文摘要：
We present a novel method for automated identification of putative cell types from single-cell RNA-seq (scRNA-seq) data. By iteratively applying a machine learning approach to an initial clustering of gene expression profiles of a given set of cells, we simultaneously identify distinct cell groups and a weighted list of feature genes for each group. The feature genes, which are differentially expressed in the particular cell group, jointly discriminate the given cell group from other cells. Each such group of cells corresponds to a putative cell type or state, characterised by the feature genes as markers. To benchmark this approach, we use expert-annotated scRNA-seq datasets from a range of experiments, as well as comparing to existing cell annotation methods, which are all based on a pre-existing reference. We show that our method automatically identifies the 'ground truth' cell assignments with high accuracy. Moreover, our method, Single Cell Clustering Assessment Framework (SCCAF) predicts new putative biologically meaningful cell-states in published data on haematopoiesis and the human cortex. SCCAF is available as an open-source software package on GitHub (https://github.com/SCCAF/sccaf) and as a Python package index and has also been implemented as a Galaxy tool in the Human Cell Atlas.
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