SDImpute: A statistical block imputation method based on cell-level and gene-level information for dropouts in single-cell RNA-seq data
Fig 2
SDImpute improves the distribution and maintains the heterogeneity of gene expression in the Camp dataset.
(A) Boxplots show the results of the difference between the CV of gene expressions after imputation and the CV of non-zero expressions (FPKM (fragment per kilobase million) is greater than 0) before imputation in DE cells. (B) The plot shows the results of the genes unexpressed across DE cells in the raw data. Here, the CV of unexpressed genes is defined as zero, and different colored bars show the number of these genes with the zero CV and non-zero CV in the imputed data, respectively. (C) Scatter plots show the results of the genes expressed in all DE cells before imputation. Here, the x-axis and y-axis represent the CV before imputation and the CV after imputation, respectively. (D) Density plots show the distribution of six genes across iPS cells in raw data vs imputed data by SDImpute.