CellAtlasSearch: a scalable search engine for single cells

Abstract

Owing to the advent of high throughput single cell transcriptomics, past few years have seen exponential growth in production of gene expression data. Recently efforts have been made by various research groups to homogenize and store single cell expression from a large number of studies. The true value of this ever increasing data deluge can be unlocked by making it searchable. To this end, we propose CellAtlasSearch, a novel search architecture for high dimensional expression data, which is massively parallel as well as light-weight, thus infinitely scalable. In CellAtlasSearch, we use a Graphical Processing Unit (GPU) friendly version of Locality Sensitive Hashing (LSH) for unmatched speedup in data processing and query. Currently, CellAtlasSearch features over 300 000 reference expression profiles including both bulk and single-cell data. It enables the user query individual single cell transcriptomes and finds matching samples from the database along with necessary meta information. CellAtlasSearch aims to assist researchers and clinicians in characterizing unannotated single cells. It also facilitates noise free, low dimensional representation of single-cell expression profiles by projecting them on a wide variety of reference samples. The web-server is accessible at: http://www.cellatlassearch.com.

Cell BLAST: Searching large-scale scRNA-seq databases via unbiased cell embedding

“…Lastly, 4 random seeds were tested for each cutoff and each method to reflect stability. Several other cell querying tools (CellAtlasSearch 3 , scQuery 33 , scMCA 34 ) were not included in our benchmark because they do not support custom reference datasets.…”

“…Analogous to biological sequence analysis 1 , identifying expression similarity to well-curated references via a cell querying algorithm is becoming the first step of annotating newly sequenced cells. Tools have been developed to identify similar cells using approximate cosine distance 2 or LSH Hamming distance 3, 4 calculated from a subset of carefully selected genes. Such intuitive approach is efficient, especially for large-scale data, but may suffer from non-biological variation across datasets, i.e.…”

UNCURL-App: Interactive database-driven analysis of scRNA-Seq data

“…There also exist tools that perform single cell similarity search on reference datasets, such as CellAt-lasSearch and scMatch [22,42]. Rather than using marker genes, these methods compare the entire gene expression profile of every single cell to a reference database, using locality-sensitive hashing in the case of CellAtasSearch [22] or Pearson or Spearman correlation in the case of scMatch [42]. These tools do not include functionality for clustering or low-dimensional visualization.…”

CellFishing.jl: an ultrafast and scalable cell search method for single-cell RNA sequencing

“…However, the scalability of scmap-cell is limited and is not applicable to extremely large data sets. Srivastava et al [15] have also developed a web service named CellAt-lasSearch that searches existing scRNA-seq experiments using locality-sensitive hashing (LSH) and graphical processing units (GPUs) to accelerate the search. In LSH, expression profiles are hashed into bit vectors, and their similarities are estimated from the Hamming distance between bit vectors calculated by LSH [16].…”