![]() ![]() We represent these overlaps in three different ways. ![]() Several themes and aspects recur across the boundaries of research communities and methodological approaches. For each challenge, we provide a thorough review of the status relative to existing approaches and point to possible directions of research to solve it. To make it accessible to these different communities, we categorize challenges into the following: transcriptomics (see “ Challenges in single-cell transcriptomics”), genomics (see the “ Challenges in single-cell genomics”), and phylogenomics (see “ Challenges in single-cell phylogenomics”). It shall serve as a compendium for researchers of various communities, looking for rewarding problems that match their personal expertise and interests. This catalog of SCDS challenges aims at focusing the development of data analysis methods and the directions of research in this rapidly evolving field. Here, we propose the data science challenges that we believe to be among the most relevant for bringing SCDS forward. Finally, no matter how varied the challenges are-by research goal, tissue analyzed, experimental setup, or just by whether DNA or RNA is sequenced-they are all rooted in data science, i.e., are computational or statistical in nature. Further, any increase in resolution results in another-rapidly growing-dimension in data matrices, calling for scalable data analysis models and methods. When amplification is used to generate more material, technical noise is added to the resulting data. Limited amounts of material available per cell lead to high levels of uncertainty about observations. SCDS exacerbates many of the data science issues arising in bulk sequencing, but it also constitutes a set of new, unique challenges for the SCDS community to tackle. As these aspects clearly match a recent definition of “Data Science”, we posit that we have entered the era of single-cell data science (SCDS). These vast quantities of data and the research hypotheses that motivate them need to be handled in a computationally efficient and statistically sound manner. Sc-seq datasets comprising very large cell numbers are becoming available worldwide, constituting a data revolution for the field of single-cell analysis. This development has even enabled a recent publication analyzing millions of cells at once. In particular, the emergence of microfluidics techniques and combinatorial indexing strategies has led to hundreds of thousands of cells routinely being sequenced in one experiment. A prominent example is the Human Cell Atlas, an initiative aiming to map the numerous cell types and states comprising a human being.Įncouraged by the great potential of investigating DNA and RNA at the single-cell level, the development of the corresponding experimental technologies has experienced considerable growth. It is therefore no surprise that enthusiasm about the possibility to screen the genetic material of the basic units of life has continued to grow. The opportunities arising from single-cell sequencing (sc-seq) are enormous: only now is it possible to re-evaluate hypotheses about differences between pre-defined sample groups at the single-cell level-no matter if such sample groups are disease subtypes, treatment groups, or simply morphologically distinct cell types. In a similar vein, analyses based on single-cell DNA sequencing (scDNA-seq) can highlight somatic clonal structures (e.g., in cancer, see ), thus helping to track the formation of cell lineages and provide insight into evolutionary processes acting on somatic mutations. This can lead to a much clearer view of the dynamics of tissue and organism development, and on structures within cell populations that had so far been perceived as homogeneous. Single-cell RNA sequencing (scRNA-seq) enables transcriptome-wide gene expression measurement at single-cell resolution, allowing for cell type clusters to be distinguished (for an early example, see ), the arrangement of populations of cells according to novel hierarchies, and the identification of cells transitioning between states. Single-cell measurements of both RNA and DNA, and more recently also of epigenetic marks and protein levels, can stratify cells at the finest resolution possible. Since being highlighted as “Method of the Year” in 2013, sequencing of the genetic material of individual cells has become routine when investigating cell-to-cell heterogeneity. Genome Biology volume 21, Article number: 31 ( 2020) Eleven grand challenges in single-cell data science
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