Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology.Meanwhile,pioneering explorations in spatial transcriptomics have opened up...Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology.Meanwhile,pioneering explorations in spatial transcriptomics have opened up avenues to address fundamental biological questions in health and diseases.Here,we review the technical attributes of single-cell RNA sequencing and spatial transcriptomics,and the core concepts of computational data analysis.We further highlight the challenges in the application of data integration methodologies and the interpretation of the biological context of the findings.展开更多
Identifying genes that define cell identity is a requisite step for characterising cell types and cell states and predicting cell fate choices.By far,the most widely used approach for this task is based on differentia...Identifying genes that define cell identity is a requisite step for characterising cell types and cell states and predicting cell fate choices.By far,the most widely used approach for this task is based on differential expression(DE)of genes,whereby the shift of mean expression are used as the primary statistics for identifying gene transcripts that are specific to cell types and states.While DE-based methods are useful for pinpointing genes that discriminate cell types,their reliance on measuring difference in mean expression may not reflect the biological attributes of cell identity genes.Here,we highlight the quest for non-DE methods and provide an overview of these methods and their applications to identify genes that define cell identity and functionality.展开更多
基金This work was supported in part by the National Key Basic Research and Development Program of China(Grant Nos.2019YFA0801402,2018YFA0107200,2018YFA0801402,2018YFA0800100,2018YFA0108000,and 2017YFA0102700)the“Strategic Priority Research Program”of the Chinese Academy of Sciences(Grant Nos.XDA16020501 and XDA16020404)+1 种基金the National Natural Science Foundation of China(Grant Nos.31630043 and 31900573)the China Postdoctoral Science Foundation Grant(Grant No.2018M642106).
文摘Recent advances of single-cell transcriptomics technologies and allied computational methodologies have revolutionized molecular cell biology.Meanwhile,pioneering explorations in spatial transcriptomics have opened up avenues to address fundamental biological questions in health and diseases.Here,we review the technical attributes of single-cell RNA sequencing and spatial transcriptomics,and the core concepts of computational data analysis.We further highlight the challenges in the application of data integration methodologies and the interpretation of the biological context of the findings.
基金This work is supported by a National Health and Medical Research Council(NHMRC)Investigator Grant(1173469)to P.Y.,a NHMRC Research Fellowship(1110751)to P.T.an Australian Research Council(ARC)Postgraduate Research Scholarship and Children’s Medical Research Institute Postgraduate Scholarship to H.J.K.
文摘Identifying genes that define cell identity is a requisite step for characterising cell types and cell states and predicting cell fate choices.By far,the most widely used approach for this task is based on differential expression(DE)of genes,whereby the shift of mean expression are used as the primary statistics for identifying gene transcripts that are specific to cell types and states.While DE-based methods are useful for pinpointing genes that discriminate cell types,their reliance on measuring difference in mean expression may not reflect the biological attributes of cell identity genes.Here,we highlight the quest for non-DE methods and provide an overview of these methods and their applications to identify genes that define cell identity and functionality.
