Investigators
Qing Nie
Contact info (email)
qnie@uci.edu
1. Define context(s)
reveal new biological insights
Current Conformance Level / Target Conformance Level
Extensive
Primary goal of the model/tool/database
Distinguishing biological from technical variation is crucial when integrating and comparing single-cell genomics datasets across different experiments. Existing methods lack the capability in explicitly distinguishing these two variations, often leading to the removal of both variations. Here, we present an integration method scMC to remove the technical variation while preserving the intrinsic biological variation. scMC learns biological variation via variance analysis to subtract technical variation inferred in an unsupervised manner. Application of scMC to both simulated and real datasets from single-cell RNA-seq and ATAC-seq experiments demonstrates its capability of detecting context-shared and context-specific biological signals via accurate alignment.
Biological domain of the model
single-cell genomics datasets of various tissues
Structure(s) of interest in the model
Integration of multiple datasets
Spatial scales included in the model
N/A
Time scales included in the model
seconds to days
2. Data for building and validating the model
| Data for building the model | Published? | Private? | How is credibility checked? | Current Conformance Level / Target Conformance Level |
|---|---|---|---|---|
| in vitro (primary cells cell, lines, etc.) | ||||
| ex vivo (excised tissues) | ||||
| in vivo pre-clinical (lower-level organism or small animal) | Yes | No | The model was built in an unsupervised way on unbiased single-cell genomics data. | Extensive |
| in vivo pre-clinical (large animal) | ||||
| Human subjects/clinical | Yes | No | The model was built in an unsupervised way on unbiased single-cell genomics data. | Extensive |
| Other: ________________________ |
| Data for validating the model | Published? | Private? | How is credibility checked? | Current Conformance Level / Target Conformance Level |
|---|---|---|---|---|
| in vitro (primary cells cell, lines, etc.) | ||||
| ex vivo (excised tissues) | ||||
| in vivo pre-clinical (lower-level organism or small animal) | Yes | No | By comparing to existing knowledge and ground truth. | Extensive |
| in vivo pre-clinical (large animal) | ||||
| Human subjects/clinical | Yes | No | By comparing to existing knowledge and ground truth. | Extensive |
| Other: ________________________ |
3. Validate within context(s)
| Who does it? | When does it happen? | How is it done? | Current Conformance Level / Target Conformance Level | |
|---|---|---|---|---|
| Verification | Postdocs/investigators | Throughout the project | The convergence of the core algorithms are verified. | Extensive |
| Validation | Postdocs/investigators | As the unsupervised model was established | 1) The integrated data is validated by ground truth. 2) The imputed data by integration is evaluted by downstream analysis. | Extensive |
| Uncertainty quantification | ||||
| Sensitivity analysis | ||||
| Other:__________ | ||||
| Additional Comments |
4. Limitations
| Disclaimer statement (explain key limitations) | Who needs to know about this disclaimer? | How is this disclaimer shared with that audience? | Current Conformance Level / Target Conformance Level |
|---|---|---|---|
| When integrating time-course datasets, the temporal information is not used. | Scientific community who intends to apply this method to time-course scRNA-seq data with similar cell types across temporal points.. | In discussion of the paper. | Adequate |
5. Version control
| Current Conformance Level / Target Conformance Level |
|---|
| Extensive |
| Naming Conventions? | Repository? | Code Review? | |
|---|---|---|---|
| individual modeler | Yes | Yes | Peer |
| within the lab | Yes | Yes | Peer |
| collaborators |
6. Documentation
| Current Conformance Level / Target Conformance Level | |
|---|---|
| Code commented? | Adequate |
| Scope and intended use described? | Extensive |
| User’s guide? | Extensive |
| Developer’s guide? | Adequate |
7. Dissemination
| Current Conformance Level / Target Conformance Level |
|---|
| Extensive |
| Target Audience(s): | “Inner circle” | Scientific community | Public |
|---|---|---|---|
| Simulations | |||
| Models | |||
| Software | R package and tutorials: https://github.com/amsszlh/scMC | R package and tutorials: https://github.com/amsszlh/scMC | R package and tutorials: https://github.com/amsszlh/scMC |
| Results | Paper and Github repo: https://github.com/amsszlh/scMC | Paper and Github repo: https://github.com/amsszlh/scMC | Paper and Github repo: https://github.com/amsszlh/scMC |
| Implications of results |
8. Independent reviews
| Current Conformance Level / Target Conformance Level |
|---|
| Insufficient |
| Reviewer(s) name & affiliation: | |
|---|---|
| When was review performed? | |
| How was review performed and outcomes of the review? |
9. Test competing implementations
| Current Conformance Level / Target Conformance Level |
|---|
| Extensive |
| Yes or No (briefly summarize) | |
|---|---|
| Were competing implementations tested? | Yes. The method has been compared to several other commonly used methods on benchmark datasets. |
| Did this lead to model refinement or improvement? | No |
10. Conform to standards
| Current Conformance Level / Target Conformance Level |
|---|
| Extensive |
| Yes or No (briefly summarize) | |
|---|---|
| Are there operating procedures, guidelines, or standards for this type of multiscale modeling? | Yes. There are several standard procedures for preprocessing scRNA-seq data. |
| How do your modeling efforts conform? | Common data preprocessing procedures are followed. |