Fig. 1: Schematic overview of Isosceles design. | Nature Communications

Fig. 1: Schematic overview of Isosceles design.

From: Accurate long-read transcript discovery and quantification at single-cell, pseudo-bulk and bulk resolution with Isosceles

Fig. 1

a Splice-graph building and path representation of transcripts (colored lines). Augmentation with de novo nodes and edges (dashed). Ambiguous reads are assigned to Transcript Compatibility Counts (TCCs) to be quantified using the expectation-maximization (EM) algorithm (bottom; panel b). b The Isosceles approach to multi-resolution quantification using the EM algorithm. Transcripts quantified from single-cell TCCs using EM (gray cell, right) can be used for dimensionality reduction (DimRed) with UMAP or to derive a k-nearest neighbors graph (kNN). The original single-cell TCCs can be grouped based on user-defined pseudo-bulk definition and transcripts re-quantified, either for clusters/markers or for each cell based on its neighborhood from kNN. Figure 1/panel b, created with BioRender.com, released under a Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International license.

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