PMID- 34623423
OWN - NLM
STAT- MEDLINE
DCOM- 20230202
LR  - 20230923
IS  - 1367-4811 (Electronic)
IS  - 1367-4803 (Print)
IS  - 1367-4803 (Linking)
VI  - 38
IP  - 4
DP  - 2022 Jan 27
TI  - Clustering spatial transcriptomics data.
PG  - 997-1004
LID - 10.1093/bioinformatics/btab704 [doi]
AB  - MOTIVATION: Recent advancements in fluorescence in situ hybridization (FISH) 
      techniques enable them to concurrently obtain information on the location and 
      gene expression of single cells. A key question in the initial analysis of such 
      spatial transcriptomics data is the assignment of cell types. To date, most 
      studies used methods that only rely on the expression levels of the genes in each 
      cell for such assignments. To fully utilize the data and to improve the ability 
      to identify novel sub-types, we developed a new method, FICT, which combines both 
      expression and neighborhood information when assigning cell types. RESULTS: FICT 
      optimizes a probabilistic function that we formalize and for which we provide 
      learning and inference algorithms. We used FICT to analyze both simulated and 
      several real spatial transcriptomics data. As we show, FICT can accurately 
      identify cell types and sub-types, improving on expression only methods and other 
      methods proposed for clustering spatial transcriptomics data. Some of the spatial 
      sub-types identified by FICT provide novel hypotheses about the new functions for 
      excitatory and inhibitory neurons. AVAILABILITY AND IMPLEMENTATION: FICT is 
      available at: https://github.com/haotianteng/FICT. SUPPLEMENTARY INFORMATION: 
      Supplementary data are available at Bioinformatics online.
CI  - (c) The Author(s) 2021. Published by Oxford University Press. All rights reserved. 
      For permissions, please e-mail: journals.permissions@oup.com.
FAU - Teng, Haotian
AU  - Teng H
AUID- ORCID: 0000-0003-0337-8722
AD  - Department of Computational Biology, School of Computer Science, Carnegie Mellon 
      University, Pittsburgh, PA 15213, USA.
FAU - Yuan, Ye
AU  - Yuan Y
AUID- ORCID: 0000-0002-4270-8002
AD  - Institute of Image Processing and Pattern Recognition, Shanghai Jiao Tong 
      University, and Key Laboratory of System Control and Information Processing, 
      Ministry of Education of China, Shanghai 200240, China.
FAU - Bar-Joseph, Ziv
AU  - Bar-Joseph Z
AUID- ORCID: 0000-0003-3430-6051
AD  - Department of Computational Biology, School of Computer Science, Carnegie Mellon 
      University, Pittsburgh, PA 15213, USA.
LA  - eng
GR  - U24 CA268108/CA/NCI NIH HHS/United States
GR  - U54 AG075931/AG/NIA NIH HHS/United States
GR  - 1R01GM122096/NH/NIH HHS/United States
PT  - Journal Article
PT  - Research Support, N.I.H., Extramural
PT  - Research Support, Non-U.S. Gov't
PL  - England
TA  - Bioinformatics
JT  - Bioinformatics (Oxford, England)
JID - 9808944
SB  - IM
EIN - Bioinformatics. 2023 Sep 2;39(9):. PMID: 37738522
MH  - *Transcriptome
MH  - In Situ Hybridization, Fluorescence
MH  - *Gene Expression Profiling/methods
MH  - Algorithms
MH  - Cluster Analysis
PMC - PMC8796363
EDAT- 2021/10/09 06:00
MHDA- 2023/02/03 06:00
PMCR- 2022/10/08
CRDT- 2021/10/08 12:20
PHST- 2021/04/14 00:00 [received]
PHST- 2021/08/28 00:00 [revised]
PHST- 2021/10/06 00:00 [accepted]
PHST- 2021/10/09 06:00 [pubmed]
PHST- 2023/02/03 06:00 [medline]
PHST- 2021/10/08 12:20 [entrez]
PHST- 2022/10/08 00:00 [pmc-release]
AID - 6384569 [pii]
AID - btab704 [pii]
AID - 10.1093/bioinformatics/btab704 [doi]
PST - ppublish
SO  - Bioinformatics. 2022 Jan 27;38(4):997-1004. doi: 10.1093/bioinformatics/btab704.