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With the rise of single cell RNA sequencing new bioinformatic tools became available to handle specific demands, such as quantifying unique molecular identifiers and correcting cell barcodes. Here, we analysed several datasets with the most common alignment tools for scRNA-seq data. We evaluated differences in the whitelisting, gene quantification, overall performance and potential variations in clustering or detection of differentially expressed genes.We compared the tools Cell Ranger 5, STARsolo, Kallisto and Alevin on three published datasets for human and mouse, sequenced with different versions of the 10X sequencing protocol.Striking differences have been observed in the overall runtime of the mappers. Besides that Kallisto and Alevin showed variances in the number of valid cells and detected genes per cell. Kallisto reported the highest number of cells, however, we observed an overrepresentation of cells with low gene content and unknown celtype. Conversely, Alevin rarely reported such low content cells.Further variations were detected in the set of expressed genes. While STARsolo, Cell Ranger 5 and Alevin released similar gene sets, Kallisto detected additional genes from the Vmn and Olfr gene family, which are likely mapping artifacts. We also observed differences in the mitochondrial content of the resulting cells when comparing a prefiltered annotation set to the full annotation set that includes pseudogenes and other biotypes.Overall, this study provides a detailed comparison of common scRNA-seq mappers and shows their specific properties on 10X Genomics data.Key messagesMapping and gene quantifications are the most resource and time intensive steps during the analysis of scRNA-Seq data.The usage of alternative alignment tools reduces the time for analysing scRNA-Seq data.Different mapping strategies influence key properties of scRNA-SEQ e.g. total cell counts or genes per cellA better understanding of advantages and disadvantages for each mapping algorithm might improve analysis results. |
