Thinking process templates for constructing data stories with SCDNEY

Yue Cao; Andy Tran; Hani Kim; Nick Robertson; Yingxin Lin; Marni Torkel; Pengyi Yang; Ellis Patrick; Shila Ghazanfar; Jean Yang

doi:10.12688/f1000research.130623.1

Thinking process templates for constructing data stories with SCDNEY

F1000Res. 2023 Dec 15:12:261. doi: 10.12688/f1000research.130623.1. eCollection 2023.

Authors

Yue Cao^{1

2

3

4}, Andy Tran^#^{1

2

3

4}, Hani Kim^#^{2

4

5}, Nick Robertson^{1

2

3

4}, Yingxin Lin^{1

2

3

4}, Marni Torkel^{1

2

3

4}, Pengyi Yang^{1

2

4

5}, Ellis Patrick^{1

2

3

4}, Shila Ghazanfar^{2

3

4}, Jean Yang^{1

2

3

4}

Affiliations

¹ Laboratory of Data Discovery for Health Limited (D24H), Science Park, Hong Kong SAR, China.
² Sydney Precision Data Science Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
³ Charles Perkins Centre, The University of Sydney, Sydney, NSW, 2006, Australia.
⁴ School of Mathematics and Statistics, The University of Sydney, Sydney, NSW, 2006, Australia.
⁵ Children's Medical Research Institute, The University of Sydney, Westmead, NSW, 2145, Australia.

^# Contributed equally.

Abstract

Background: Globally, scientists now have the ability to generate a vast amount of high throughput biomedical data that carry critical information for important clinical and public health applications. This data revolution in biology is now creating a plethora of new single-cell datasets. Concurrently, there have been significant methodological advances in single-cell research. Integrating these two resources, creating tailor-made, efficient, and purpose-specific data analysis approaches can assist in accelerating scientific discovery.

Methods: We developed a series of living workshops for building data stories, using Single-cell data integrative analysis (scdney). scdney is a wrapper package with a collection of single-cell analysis R packages incorporating data integration, cell type annotation, higher order testing and more.

Results: Here, we illustrate two specific workshops. The first workshop examines how to characterise the identity and/or state of cells and the relationship between them, known as phenotyping. The second workshop focuses on extracting higher-order features from cells to predict disease progression.

Conclusions: Through these workshops, we not only showcase current solutions, but also highlight critical thinking points. In particular, we highlight the Thinking Process Template that provides a structured framework for the decision-making process behind such single-cell analyses. Furthermore, our workshop will incorporate dynamic contributions from the community in a collaborative learning approach, thus the term 'living'.

Keywords: data analysis; data story; living workshop; single-cell analysis; thinking process template.

Grants and funding

This work was supported by the AIR@innoHK programme of the Innovation and Technology Commission of Hong Kong to all authors; Research Training Program Tuition Fee Offset and Stipend Scholarship to AT; Research Training Program Tuition Fee Offset and University of Sydney Postgraduate Award Stipend Scholarship to YC; Australian Research Council Discovery Early Career Researcher Awards (DE220100964, DE200100944) funded by the Australian Government to SG and EP; A National Health and Medical Research Council (NHMRC) Investigator Grant (1173469) to PY. The funding source had no role in the study design; in the collection, analysis, and interpretation of data, in the writing of the manuscript, and in the decision to submit the manuscript for publication.