| Title | Statistical and machine learning methods for spatially resolved transcriptomics data analysis |
| Authors | Zeng, Zexian Li, Yawei Li, Yiming Luo, Yuan |
| Affiliation | Peking Univ, Acad Adv Interdisciplinary Studies, Ctr Quantitat Biol, Beijing 100084, Peoples R China Peking Univ, Acad Adv Interdisciplinary Studies, Peking Tsinghua Ctr Life Sci, Beijing 100084, Peoples R China Harvard TH Chan Sch Publ Hlth, Dana Farber Canc Inst, Dept Data Sci, Boston, MA 02215 USA Northwestern Univ, Dept Prevent Med, Div Hlth & Biomed Informat, Feinberg Sch Med, Chicago, IL 60611 USA Northwestern Univ, Clin & Translat Sci Inst, Chicago, IL 60611 USA Northwestern Univ, Inst Augmented Intelligence Med, Chicago, IL 60611 USA Northwestern Univ, Ctr Hlth Informat Partnerships, Chicago, IL 60611 USA |
| Keywords | GENE-EXPRESSION CELL RNA REVEALS TISSUE SEQ IDENTIFICATION CHROMATIN ATLAS |
| Issue Date | 25-Mar-2022 |
| Publisher | GENOME BIOLOGY |
| Abstract | The recent advancement in spatial transcriptomics technology has enabled multiplexed profiling of cellular transcriptomes and spatial locations. As the capacity and efficiency of the experimental technologies continue to improve, there is an emerging need for the development of analytical approaches. Furthermore, with the continuous evolution of sequencing protocols, the underlying assumptions of current analytical methods need to be re-evaluated and adjusted to harness the increasing data complexity. To motivate and aid future model development, we herein review the recent development of statistical and machine learning methods in spatial transcriptomics, summarize useful resources, and highlight the challenges and opportunities ahead. |
| URI | http://hdl.handle.net/20.500.11897/641603 |
| ISSN | 1474-760X |
| DOI | 10.1186/s13059-022-02653-7 |
| Indexed | SCI(E) |
| Appears in Collections: | 前沿交叉学科研究院 |
