| Title |
Spatially aware dimension reduction for spatial transcriptomics
|
|---|---|
| Published in |
Nature Communications, November 2022
|
| DOI | 10.1038/s41467-022-34879-1 |
| Pubmed ID | |
| Authors |
Lulu Shang, Xiang Zhou |
| Abstract |
Spatial transcriptomics are a collection of genomic technologies that have enabled transcriptomic profiling on tissues with spatial localization information. Analyzing spatial transcriptomic data is computationally challenging, as the data collected from various spatial transcriptomic technologies are often noisy and display substantial spatial correlation across tissue locations. Here, we develop a spatially-aware dimension reduction method, SpatialPCA, that can extract a low dimensional representation of the spatial transcriptomics data with biological signal and preserved spatial correlation structure, thus unlocking many existing computational tools previously developed in single-cell RNAseq studies for tailored analysis of spatial transcriptomics. We illustrate the benefits of SpatialPCA for spatial domain detection and explores its utility for trajectory inference on the tissue and for high-resolution spatial map construction. In the real data applications, SpatialPCA identifies key molecular and immunological signatures in a detected tumor surrounding microenvironment, including a tertiary lymphoid structure that shapes the gradual transcriptomic transition during tumorigenesis and metastasis. In addition, SpatialPCA detects the past neuronal developmental history that underlies the current transcriptomic landscape across tissue locations in the cortex. |
X Demographics
The data shown below were collected from the profiles of 63 X users who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| United States | 20 | 32% |
| United Kingdom | 7 | 11% |
| France | 3 | 5% |
| Japan | 2 | 3% |
| Canada | 2 | 3% |
| India | 2 | 3% |
| Hong Kong | 1 | 2% |
| Belgium | 1 | 2% |
| Italy | 1 | 2% |
| Other | 5 | 8% |
| Unknown | 19 | 30% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Scientists | 36 | 57% |
| Members of the public | 25 | 40% |
| Science communicators (journalists, bloggers, editors) | 1 | 2% |
| Practitioners (doctors, other healthcare professionals) | 1 | 2% |
Mendeley readers
The data shown below were compiled from readership statistics for 92 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 92 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Ph. D. Student | 24 | 26% |
| Researcher | 10 | 11% |
| Student > Bachelor | 5 | 5% |
| Student > Master | 4 | 4% |
| Professor | 4 | 4% |
| Other | 9 | 10% |
| Unknown | 36 | 39% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 22 | 24% |
| Mathematics | 6 | 7% |
| Computer Science | 6 | 7% |
| Agricultural and Biological Sciences | 6 | 7% |
| Engineering | 4 | 4% |
| Other | 11 | 12% |
| Unknown | 37 | 40% |
Attention Score in Context
This research output has an Altmetric Attention Score of 41. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 09 February 2023.
All research outputs
#992,339
of 25,245,273 outputs
Outputs from Nature Communications
#16,096
of 55,878 outputs
Outputs of similar age
#22,295
of 489,328 outputs
Outputs of similar age from Nature Communications
#526
of 2,013 outputs
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