The impact of dropouts in scRNAseq dense neighborhood analysis
Single cell RNA sequencing (scRNAseq) provides the possibility to investigate transcriptomic profiles on a single cell level. However, the data show unique challenges in comparison to bulk transcriptomic data, one being high dropout rates, which yields high sparsity data. Many classical analysis and...
Saved in:
| Main Authors: | Alisa Pavel, Manja Gersholm Grønberg, Line H. Clemmensen |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-01-01
|
| Series: | Computational and Structural Biotechnology Journal |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2001037025001023 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Spatiotemporal analysis of lung immune dynamics in lethal Coccidioides posadasii infection
by: Oscar A. Davalos, et al.
Published: (2025-05-01) -
Joint cell segmentation and cell type annotation for spatial transcriptomics
by: Russell Littman, et al.
Published: (2021-05-01) -
Antennal RNAseq reveals odorant receptors with sex-biased expression in the common eastern firefly, Photinus pyralis
by: Sarah E. Lower, et al.
Published: (2025-07-01) -
Exposure-inducible genes may contribute to missingness in RNAseq-based gene expression analyses
by: Olga Y. Gorlova, et al.
Published: (2025-08-01) -
Ensemblex: an accuracy-weighted ensemble genetic demultiplexing framework for population-scale scRNAseq sample pooling
by: Michael R. Fiorini, et al.
Published: (2025-07-01)