Figure 1
From: A novel method for single-cell data imputation using subspace regression
Single-cell Imputation using Subspace Regression (scISR). (A) Input data visualized in cell/sample space. (B) Hypergeometric test to determine whether each zero value is induced by dropout. Based on the computed p-values for each entry, we separate the original data into two sets of data: training data and imputable data. (C) Training data in which none of the values is induced by dropout events. (D) Imputable data in which each gene has at least one entry that is likely to be induced by dropout events. (E) Gene subspaces determined by perturbation clustering. We perturb the training data to discover the natural structure of the genes. Based on the pair-wise similarity between genes, we separate genes into groups that share similar patterns. (F) Subspace regression. We assign each gene in the imputable data to the closest subspace and then perform a generalized linear regression on the subspace to estimate the zero-valued entries that are impacted by dropouts. (G) Output expression matrix obtained by concatenating the training data and imputed data.
