| Title |
Defining cell identity beyond the premise of differential gene expression
|
|---|---|
| Published in |
Cell Regeneration, May 2021
|
| DOI | 10.1186/s13619-021-00083-7 |
| Pubmed ID | |
| Authors |
Hani Jieun Kim, Patrick P. L. Tam, Pengyi Yang |
| Abstract |
Identifying genes that define cell identity is a requisite step for characterising cell types and cell states and predicting cell fate choices. By far, the most widely used approach for this task is based on differential expression (DE) of genes, whereby the shift of mean expression are used as the primary statistics for identifying gene transcripts that are specific to cell types and states. While DE-based methods are useful for pinpointing genes that discriminate cell types, their reliance on measuring difference in mean expression may not reflect the biological attributes of cell identity genes. Here, we highlight the quest for non-DE methods and provide an overview of these methods and their applications to identify genes that define cell identity and functionality. |
X Demographics
The data shown below were collected from the profile of 1 X user who shared this research output. Click here to find out more about how the information was compiled.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| China | 1 | 100% |
Demographic breakdown
| Type | Count | As % |
|---|---|---|
| Science communicators (journalists, bloggers, editors) | 1 | 100% |
Mendeley readers
The data shown below were compiled from readership statistics for 14 Mendeley readers of this research output. Click here to see the associated Mendeley record.
Geographical breakdown
| Country | Count | As % |
|---|---|---|
| Unknown | 14 | 100% |
Demographic breakdown
| Readers by professional status | Count | As % |
|---|---|---|
| Student > Master | 4 | 29% |
| Student > Ph. D. Student | 2 | 14% |
| Student > Doctoral Student | 2 | 14% |
| Researcher | 1 | 7% |
| Student > Postgraduate | 1 | 7% |
| Other | 0 | 0% |
| Unknown | 4 | 29% |
| Readers by discipline | Count | As % |
|---|---|---|
| Biochemistry, Genetics and Molecular Biology | 7 | 50% |
| Mathematics | 1 | 7% |
| Nursing and Health Professions | 1 | 7% |
| Agricultural and Biological Sciences | 1 | 7% |
| Unknown | 4 | 29% |
Attention Score in Context
This research output has an Altmetric Attention Score of 1. 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 25 June 2021.
All research outputs
#18,807,229
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Outputs from Cell Regeneration
#116
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Outputs of similar age
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Outputs of similar age from Cell Regeneration
#5
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