Eleven grand challenges in single-cell data science

By David Lähnemann, Johannes Köster, Ewa Szczurek, Davis J. McCarthy, Stephanie C. Hicks, Mark D. Robinson, Catalina A. Vallejos, Kieran R. Campbell, Niko Beerenwinkel, A. Mahfouz and 41 more. Genome Biology, 2020

1,198 citations recorded by the source corpus.

The recent boom in microfluidics and combinatorial indexing strategies, combined with low sequencing costs, has empowered single-cell sequencing technology. Thousands—or even millions—of cells analyzed in a single experiment amount to a data revolution in single-cell biology and pose unique data science problems. Here, we outline eleven challenges that will be central to bringing this emerging field of single-cell data science forward. For each challenge, we highlight motivating research questions, review prior work, and formulate open problems. This compendium is for established researchers, newcomers, and students alike, highlighting interesting and rewarding problems for the coming years.

Subfields: Biophysics, Molecular Biology

View Paper: https://www.semanticscholar.org/paper/370d248f97b75c4040e5828a658bbe4c3b80bf1e

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Biophysics

In Molecular Biology

Citations in the corpus

Computational principles and challenges in single-cell data integration (2021, background): “In particular, a key challenge in the analysis of single-cell multimodal data is to devise efficient computational strategies to integrate different data modalities 8,12–15 .”
Best practices for single-cell analysis across modalities (2023, neutral): “3).”
scDeepSort: a pre-trained cell-type annotation method for single-cell transcriptomics using deep learning with a weighted graph neural network (2021, background): “Still, the accurate cell-type annotation for single-cell transcriptomic data remains a great challenge (19).”
Robust mapping of spatiotemporal trajectories and cell–cell interactions in healthy and diseased tissues (2023, neutral): “Finding patterns in spatial gene expression data thus remains one of the grand challenges in omics data science today 8,18 .”
Gene regulatory network inference in the era of single-cell multi-omics (2023, background): “Although sparsity is a known property of single-cell technologies 151,152 , none of the GRN inference methods discussed here explicitly accounts for sparsity in its modelling.”
stLearn: integrating spatial location, tissue morphology and gene expression to find cell types, cell-cell interactions and spatial trajectories within undissociated tissues (2020, background): “Pseudo-space-time (PST) Intratumour heterogeneity (ITH) is a signiﬁcant problem in oncology and can lead to low treatment effectiveness and high tumour recurrence. scRNASeq has proved a useful technology for the study of ITH 53,54 and reconstruction of the evolutionary 5 trajectories of cancer cell ”
A new gene set identifies senescent cells and predicts senescence-associated pathways across tissues (2021, background): “However, this has been challenging, especially at the single cell level.(25)”