Fig. 2: Analysis of simulated scRNA-seq data with fiveĀ classes.
From: Leveraging data-driven self-consistency for high-fidelity gene expression recovery
The histograms of the reference data, observed data (1% sampling efficiency), and imputed data by MAGIC, mcImpute, and SERM are shown in the first row of (a). Visualization of reference, observed, and imputed data by t-SNE and UMAP are shown in the second and third rows, respectively. t-SNE and UMAP results from SERM imputed data are much better in separating the classes, whereas MAGIC degrades the data due toĀ imputation. The clustering accuracy and cluster quality indices for UMAP visualizations of imputed data from different methods are shown in (b). Data are presented as mean valuesā+/āāstandard deviation (SD). Error bars represent the standard deviation of the indices for nā=ā1000 different initializations of k-means clustering. Source data are provided as a Source Data file.
