FastProject: a tool for low-dimensional analysis of single-cell RNA-Seq data
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Abstract
Background: A key challenge in the emerging field of single-cell RNA-Seq is to characterize phenotypic diversity between cells and visualize this information in an informative manner. A common technique when dealing with high-dimensional data is to project the data to 2 or 3 dimensions for visualization. However, there are a variety of methods to achieve this result and once projected, it can be difficult to ascribe biological significance to the observed features. Additionally, when analyzing single-cell data, the relationship between cells can be obscured by technical confounders such as variable gene capture rates. Results: To aid in the analysis and interpretation of single-cell RNA-Seq data, we have developed FastProject, a software tool which analyzes a gene expression matrix and produces a dynamic output report in which two-dimensional projections of the data can be explored. Annotated gene sets (referred to as gene ‘signatures’) are incorporated so that features in the projections can be understood in relation to the biological processes they might represent. FastProject provides a novel method of scoring each cell against a gene signature so as to minimize the effect of missed transcripts as well as a method to rank signature-projection pairings so that meaningful associations can be quickly identified. Additionally, FastProject is written with a modular architecture and designed to serve as a platform for incorporating and comparing new projection methods and gene selection algorithms. Conclusions: Here we present FastProject, a software package for two-dimensional visualization of single cell data, which utilizes a plethora of projection methods and provides a way to systematically investigate the biological relevance of these low dimensional representations by incorporating domain knowledge.
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Factors defining cell identity may be discrete categories, continuous spectra, or combinations of discrete and continuous categories.
“These factors may not only be the discrete categories that are often assumed when classifying cells into major types4,30,94 but may also represent a continuous spectrum1, or a combination of discrete and continuous categories36,95–97,227.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [94]
SPADE down-sampling makes rare cell types as prevalent as abundant types, increasing the chance that rare types form their own clusters rather than becoming outliers, at the cost of lower signal-to-noise.
“This increases the probability that rare cell types will form their own clusters rather than becoming outliers to clusters of more abundant types, albeit at the cost of a decreased biological signal-to-noise ratio.”
From Revealing the vectors of cellular identity with single-cell genomics (2016) · cites this paper as [94]
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MISC: missing imputation for single-cell RNA sequencing data
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Shallow Sparsely-Connected Autoencoders for Gene Set Projection
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- Authors
- DeTomaso, D., & Yosef, N.
- Affiliations
- University of California, Berkeley (US)
- Venue
- BMC Bioinformatics
- Published
- 2016-08-23
- Volume
- 17
- Issue
- 1
- Pages
- 315-315
- ISSN
- 1471-2105 (ISSN-L)
- PMC
- PMC4995760
- OpenAlex
- W2342016684
- Cited by
- 58
- References
- 40
- Citations by year
- 2026: 1 · 2025: 2 · 2024: 3 · 2022: 3 · 2021: 7 · 2020: 11 · 2019: 11 · 2018: 14 · 2017: 5 · 2016: 1
- FWCI
- 5.37
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- BMC Bioinformatics (publishedVersion, cc-by)
- bioRxiv (Cold Spring Harbor Laboratory) (acceptedVersion)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Digital Access to Scholarship at Harvard (DASH) (Harvard University) (submittedVersion, cc-by)
- eScholarship (California Digital Library) (submittedVersion)
- eScholarship (California Digital Library) (submittedVersion, cc-by)
- Funders
- National Institutes of Health (U01MH105979, U01HG007910, T32)
- BASF Corporation (CARA)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Single-cell and spatial transcriptomics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- single-cell RNA sequencing, dimensionality reduction, gene expression signatures, visualization tools, gene selection
- MeSH terms
- Algorithms, Glioblastoma, Humans, Software, Biomarkers, Tumor, Models, Statistical, Sequence Analysis, RNA, Computational Biology, Gene Expression Profiling, Single-Cell Analysis, High-Throughput Nucleotide Sequencing
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