CellHeap: A scRNA-seq workflow for large-scalebioinformatics data analysis
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Link to bioRxiv paper: http://biorxiv.org/cgi/content/short/2023.04.19.537508v1?rss=1

Authors: Castro, M. C., Silva, V. S., Costa, M. O., Silva, H. S., Walter, M. E., Melo, A., Ocana, K., dos Santos, M. T., Nicolas, M. F., Carvalho, A. C., Pons, A. H., Silva, F. A. B.

Abstract: Several hundred terabytes of single-cell RNA-seq (scRNA-seq) data are available in public repositories. These data refer to various research projects, from microbial population cells to multiple tissues, involving patients with a myriad of diseases and comorbidities. An increase to several Petabytes of these scRNA-seq data is a realistic prediction for coming years. Therefore, thoughtful analysis of these data requires large scale computing infrastructures and software systems optimized for such platforms to generate correct and reliable biological knowledge.This paper presents CellHeap, a flexible, portable, and robust platform for analyzing large scRNA-seq datasets, with quality control throughout the execution steps, and deployable on platforms that support large-scale data, such as supercomputers or clouds. In addition, CellHeap includes an efficient solution to make use of parallel computational resources, improving total execution time. Furthermore, we show a case study of a this platform, with its deployment in the Brazilian Santos Dumont supercomputer, which was used to process dozens of Terabytes of COVID-19 scRNA-seq raw data. Our results show that most of the total execution time is spent in CellHeap initial phases and that there is great potential for a parallel solution to speed up scRNA-seq data analysis significantly. Additionally, we analysed the produced results, with a focus on particular modulations of Fc receptors considering mild and severe cases verified to be coherent from a biological perspective.

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