A comparative performance evaluation of imputation methods in spatially resolved transcriptomics data

Abstract

Spatially resolved transcriptomics have a sparse structure and the selection of the imputation method should be done by a detailed evaluation.

Abstract

Spatially resolved transcriptomics technologies have drawn enormous attention by providing RNA expression patterns together with their spatial information. Even though improved techniques are being developed rapidly, the technologies which give spatially whole transcriptome level profiles suffer from dropout problems because of the low capture rate. Imputation of missing data is one strategy to eliminate this technical problem. We evaluated the imputation performance of five available methods (SpaGE, stPlus, gimVI, Tangram and stLearn) which were indicated as capable of making predictions for the dropouts in spatially resolved transcriptomics datasets. The evaluation was performed qualitatively via visualization of the predictions against the original values and quantitatively with Pearson's correlation coefficient, cosine similarity, root mean squared log-error, Silhouette Index and Calinski Harabasz Index. We found that stPlus and gimVI outperform the other three. However, the performance of all methods was lower than expected which indicates that there is still a gap for imputation tools dealing with dropout events in spatially resolved transcriptomics.