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Haematopoietic stem cells (HSCs) are an essential source and reservoir for normal haematopoiesis, and their function is compromised in many blood disorders. HSC research has benefitted from the recent development of single-cell molecular profiling technologies, where single-cell RNA-sequencing (scRNA-seq) in particular has rapidly become an established method to profile HSCs and related haematopoietic populations. The classical definition of HSCs relies on transplantation assays, which have been used to validate HSC function for cell populations defined by flow cytometry. Flow cytometry information for single cells however is not available for many new high-throughput scRNA-seq methods, thus highlighting an urgent need for the establishment of alternative ways to pinpoint the likely HSCs within large scRNA-seq datasets. To address this, we tested a range of machine learning approaches and developed a tool, hscScore, to score single-cell transcriptomes from murine bone marrow based on their similarity to gene expression profiles of validated HSCs. We evaluated hscScore across scRNA-seq data from different laboratories, which allowed us to establish a robust method that functions across different technologies. To facilitate broad adoption of hscScore by the wider haematopoiesis community, we have made the trained model and example code freely available online. In summary, our method hscScore provides fast identification of mouse bone marrow HSCs from scRNA-seq measurements and represents a broadly useful tool for analysis of single-cell gene expression data.
This article was published in the following journal.
Name: Experimental hematology
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A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of unlabeled paired input-output training (sample) data.
A MACHINE LEARNING paradigm used to make predictions about future instances based on a given set of labeled paired input-output training (sample) data.
SUPERVISED MACHINE LEARNING algorithm which learns to assign labels to objects from a set of training examples. Examples are learning to recognize fraudulent credit card activity by examining hundreds or thousands of fraudulent and non-fraudulent credit card activity, or learning to make disease diagnosis or prognosis based on automatic classification of microarray gene expression profiles drawn from hundreds or thousands of samples.
A type of ARTIFICIAL INTELLIGENCE that enable COMPUTERS to independently initiate and execute LEARNING when exposed to new data.
Specialized stem cells that are committed to give rise to cells that have a particular function; examples are MYOBLASTS; MYELOID PROGENITOR CELLS; and skin stem cells. (Stem Cells: A Primer [Internet]. Bethesda (MD): National Institutes of Health (US); 2000 May [cited 2002 Apr 5]. Available from: http://www.nih.gov/news/stemcell/primer.htm)
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DNA sequencing is the process of determining the precise order of nucleotides within a DNA molecule. During DNA sequencing, the bases of a small fragment of DNA are sequentially identified from signals emitted as each fragment is re-synthesized from a ...