Single-cell RNA sequence analysis (scRNA-seq analysis) is a method of transcriptome analysis that has opened new doors in answering many biological questions concerning comprehensive transcriptome study.
This innovative method of scRNA-seq analysis involves the use of a novel high-throughput shotgun to sequence cDNA molecules through reverse transcription of RNA. Using next-generation sequencing technologies, the RNA strand is then sequenced inside a biological sample to guide the primary sequence and to quantify the relative abundance of each RNA strand.
Increasing evidence suggests that this ground-breaking technology has enabled researchers, scientists, and technologists to quantify the expression levels of an entire gene pool (thousands of genes) concurrently while highlighting the underlying biological pathways and functional processes of a living organism. Furthermore, this novel high-throughput technology has transformed the way biologists analyze transcriptomes by providing specific insights regarding unannotated exons, alternative splicing, novel transcripts (gene or non-coding RNAs) and allele-specific expression.
This breakthrough advancement in the domain of NGS has allowed scientists across the globe to know more about the complicated biological processes and in understanding the comprehensive transcriptome landscape, including stem-cell differentiation, embryo development and tumor invasion at the accurate resolution of a single cell. Although the experimental methods of the scRNA-seq analysis technique offer countless benefits and are readily available in laboratories, they still remain in infancy due to amplified experimentation duration, robustness, and accuracy of results.
In this review, we will highlight the recent advancements of computational analysis and the development of the bioinformatics tools that have completely transformed the course of scRNA-seq analysis and have allowed biologists to obtain genomic insights within no time.
- Clustering
Clustering is an avant-garde innovation in computational bioinformatics, which aims to detect low-quality cells by highlighting clusters that have an abundance of mitochondrial genes. It is followed by the identification of marker genes, which are differentially expressed between unique clusters. In this regard, the PanoView algorithm embedded in any clustering program uses an iterative model to screen for cell clusters in a varying three-dimensional PCA space. The program significantly identifies the scRNA-seq analysis clusters with each iteration and repeats the clustering algorithm in the remaining cells in a novel PCA space. In a nutshell, clustering is a reliable and easy-to-use tool that is applicable to diverse types of single-cell RNA-sequencing datasets.
- Dimensionality Reduction Methods
As the world advanced from a pinhole camera to a Polaroid camera, the world of bioinformatics has also revolutionized with the advent of various mathematical algorithms in order to facilitate the massive data visualization of scRNA-seq analysis. These methods allow the researchers to retrieve the snapshots of complex gene-expression data sets not only at a higher resolution but also at a faster speed.
The pioneer dimensionality reduction technique in accelerating scRNA-seq analysis is Principal component analysis (PCA). PCA is an unsupervised linear dimensionality reduction method that involves projecting cells into two-dimensional space for visualizing sequential datasets having enhanced interpretability.
In addition, other non-linear dimensionality algorithms such as multidimensional scaling, t-distributed stochastic neighbor embedding (t-SNE), Isomap and locally linear embedding (LLE) can also be integrated with the sequential dataset of RNA to rapidly retrieve the results.t-SNE can be utilized in the famous Seurat or Cell Ranger pipeline (10× Genomics) found in the R programming software. The integration of Isomap and LLE has proved to exhibit superior performance in the case of microarray data and thus can be successfully evaluated for scRNA-seq datasets. However, the only drawback of integrating dimensional reduction algorithms with scRNA-seq analysis is the loss of significant biological information.
All these innovations in the field of computational biology have an immense potential to minimize the interpretation time of a million-point single-cell RNA-sequencing (scRNA-seq) data set from a number of hours to only a few minutes. The fast multipole method (FMM) is also a revolutionary numerical technique that speeds up the analysis of long-range forces in the n-body problem. It has been successfully integrated with t-SNE, which has earned it a new name FIt-SNE due to its fast analysis time. The adoption of all these approaches has not only enabled the researchers in the efficient analysis of single-cell RNA-sequencing data more rapidly but they also can be used for the characterization of rare cell subpopulations that cannot be identified alone through t-SNE. Additionally, it has also empowered the researchers to visualize the gene expression patterns of an entire gene pool at the cellular level, simultaneously.
- Computational Algorithms
Advancements in Bioinformatics have also resulted in the formulation of complex algorithms designed to address gene expression and sequence analysis data from comprehensive population analysis to yield GRNs. These innovative methods have significantly reduced the time for scRNA-seq analysis and are categorized as information theory-based, machine learning-based, model-based and co-expression-based models, respectively. Co-expression-based models are easier to integrate with the results of sequential data, however; these approaches are limited in accurately impersonating the dynamics of the cellular environment. Additionally, model-based inferences such as Bayesian networks involve the use of many parameters but are not recommended for large data sets of RNA sequences since they are time-consuming. Another Artificially intelligent technology significant in the analysis of RNA sequential data sets is the use of probabilistic graphical models, which searches for probable pathways of a number of genes and is an NP-hard problem.
- Neural Networks
A learning machine is an adaptive process that allows computers to learn and know more from experience, learn by example, and learn by analogy. The neural network is one of the machine learning algorithms that has been successfully applied to the solution of a wide variety of bioinformatics problems, specifically pertaining to scRNA-seq analysis. Two most popular gene search engines that gave place to Artificial Neural Networks area GRAIL and Gene Parser. GRAIL is the first gene search program, which was designed to identify genes, exons, and various characteristics in DNA sequences; it uses a neural network that combines a series of coding algorithms of prediction to recognize the coding potential infixed-length windows without looking for additional features.
Another gene search system is Gene Parser, which was designed to identify and determine the fine structure of protein genes in RNA/ DNAgenomic sequences. An artificial neural system for gene classification called GenCANS was developed to analyze and manage a large volume of sequencing data molecular Project of the Human Genome. The genetic algorithm has been successfully applied to solve many practical problems in many disciplines, in particular, in bioinformatics; these have been used to solve the alignment problems of multiple sequences of RNA. A well-known approach is a SAGA, which creates randomly an initial population of alignments and evolves through generations, by gradually improving the fitness of the population.