The rapid increase in the size of single-cell RNAseq datasets presents significant performance challenges when conducting evaluations and extracting information. We research an alternative input data format that utilizes binarization. Our main focus is an analysis of peak memory usage. An in-depth exploration of the solution’s design and implementation is provided, specifically emphasizing the strategies used to minimize memory usage. We analyzed and validated memory usage patterns and asymptotes using memory profiling tools. However, our findings suggest that gains in reducing memory usage on big modern datasets are attributable only to binarized data format rather than workflow interaction with the new format, which we found to be independent of the input format.

Analysing single-cell RNA sequencing data is becoming an increasingly tedious task as the size of data sets grows. As a proposed solution, recent discoveries suggest that these data sets can be binarized without losing much information. This in turn should allow for memory and time efficient methods of storage and computation. Numerous analyses techniques require cell clustering as a preliminary procedure, which suggests the need to evaluate binary representation performance under that context. In this work we present a comparison between binary clustering results and the state-of-the-art, with a focus on similarity metric choice and the impact on intermediate steps of the procedure (i.e. similarity matrices and kNN graphs). The method was evaluated on single-cell transcriptomic data sets, utilizing a combination of R and C++ as an evaluation framework. Through these means we found that some of the similarity metrics operating on continuous input can possibly be reproduced with similarity metrics operating on binary input.

Single-cell RNA sequencing data clustering is a valuable technique for demonstrating cell-to-cell heterogeneity and revealing cell dynamics within and amongst groups. Large up-scaling of scRNA-seq datasets in recent years pose computational challenges for existing state-of-the-art clustering techniques. A possible solution to tackle these challenges is to binarize the scRNA-seq data and perform clustering using optimized binary methods. Using a binary clustering pipeline we demonstrate that binary clustering solutions resemble conventional clustering solutions for large clusters, but show less resemblance for smaller clusters. We also show that the Leiden community detection algorithm can achieve higher cluster quality compared to the Louvain algorithm for the binarized data.

As single-cell RNA sequencing techniques improve and more cells are measured in individual experiments, cell clustering procedures become increasingly more computationally intensive. This paper studies the runtime performance impact of a specialized clustering algorithm for data converted to a binary format, in order to reduce computational burden. We experimentally show that our specialized algorithm runs faster than the Seurat library on small datasets, and that with proper dimensionality reduction and approximation techniques, the algorithm could be more scalable than current methods. Optimizations for cluster quality and memory efficiency are not considered in this paper.

Understanding the role of genes and genetic variants is a key challenge in unraveling the driving mechanisms of Alzheimer's disease (AD). Single-cell RNA sequencing is a technique that quantifies gene expression at the cell (type) level enabling investigation of the roles of different cell types in disease. We analyzed changes in gene (co-)expression associated with genetic variants using single-cell RNA sequencing data (>1.3 million cells) from the dorsolateral prefrontal cortex (DLPFC) of 379 individuals of the ROSMAP cohort. Our single cell expression quantitative trait loci (sc-eQTL) analysis determined 3,337,065 sc-eQTLs, linking 1,882,645 SNPs to changes in expression of 8,057 genes in 7 major cell types. Next, we investigated the association of genetic variants with changes in co-expression for gene pairs (co-eQTLs), focusing on a set of variants and genes relevant to AD. Our novel non-parametric method for co-eQTL analysis compares gene co-expression distributions between SNP genotypes. We found 6,878 cell type specific co-eQTLs (variant-gene-gene combinations) relating to 18 AD variants. Although a substantial proportion of the findings is driven by eQTL effects, our method identified co-eQTLs that would not have been discovered in a correlation-based analysis. Most notable, we found variant rs13237518 (located in the TMEM106B gene) to associate with expression changes in a subset of 25 genes in excitatory neurons which is possibly indicative of higher-level disruptions related to the variant. Overall, we show that exploring genetic variant-associated changes in gene (co-)expression is a promising approach in finding cell type specific mechanisms that may be altered in AD.