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Data Science & Machine Learning

Challenges and Solutions in Single Cell RNA-Seq Data Analysis

Prathamesh Dhamale, Deepthi Das
February 10, 2023

The sustenance of life depends on complex biological pathways, interactions, and cellular responses. Researchers use many tools and techniques to unravel the mysteries of life.

Single-Cell RNA sequencing is one of the emerging tools in our quest to understand biological complexity. It enables us to study genomes or transcriptomes of individual cells and understand cellular heterogeneity. High-resolution information on cell type variations, temporal expression patterns, and unique biochemical signatures can also be achieved using this technique.

Advancements in single-cell RNA seq data analysis have opened up new dimensions for studying cancer biology, cell proliferation, and embryonic differentiation. However, there are some limitations and challenges in using single-cell RNA sequencing data effectively. Here, we detail out few of the major challenges and their plausible solutions.

Challenges in Analyzing Single-Cell RNA-Seq Data:

Single-cell RNA sequencing enables high granularity and visualization of changes in the expression pattern between cell types and during different states. However, this generates data that has high variability, errors, and background noise. The problems and challenges arising in the analysis of such data require specialized computational tools and annotation processes. Lack of data standardization and arbitrary methodologies are other hindrances to making single-cell RNA seq a more robust and reliable tool for genomic or transcriptomic research.

Currently available tools for single-cell RNA seq analysis (Source)

Some of the Major Challenges and Their Possible Solutions Are Highlighted Below:

A] Normalization of Data:

The low starting material in single-cell RNA seq generates a lower yield of sequenced data per cell. However, there is a high number of cell types to track from this data. It thus becomes necessary to load the entire data matrix of thousands of cell types onto the computational framework. Such large-scale processing of data may introduce bias and result in a high level of noise.
Most analysis pipelines rely on clustering or cell type assignment, along with high parallelization. It is thus mandatory to normalize such variable datasets before processing. Quality control (QC) and annotation is also challenging due to high dimensionality and lack of reference data sets.

Solutions: Manual data curation on a case-to-case basis is currently the best practice as it helps reduce data dimensionality. Some ML techniques which use primary clustering based on closely related cellular transcription profiles are gaining popularity. Another technique is to use huge databases to improve matrices and normalize extremely variable data. Repurposing the QC and annotation tools developed for bulk RNA sequencing data analysis can also help. The key to obtaining reliable and repeatable results is using the right tools/ curation methods for accurate annotations and eliminating low-quality seq data.

B] Missing Reads or Errors:

Single-cell sequencing data generally contains significant proportions of absent or zero values (dropouts). Such data sparsity can impede downstream analysis and is difficult to model through computation. Dead or rare cell types can potentially contaminate the sequencing data. These issues may result in many missing data points or sequence-read mistakes.  

Solutions: Next Generation Sequencing data can be used to determine the presence of missing gene counts or replicability errors. For example, statistical comparison of mitochondrial to genomic sequence abundance can be used to estimate dead cell data.
In addition, focusing on better annotation and ontology data could help identify erroneous sequences.
Imputing can also be done to fill in the missing expression values from single-cell RNA seq data. Several imputation modalities such as kNN (k-nearest neighboring) are being developed to achieve read data adjustments. These steps can enable better representation of true expression values and reduce count errors.

C] Batch Effect:
Curating disparately produced single-cell RNA seq data sets can be difficult. Independently generated batches of data can have a systemic bias or huge variations in detected transcripts. This can cause spurious results and mask the actual biological relevance of samples.

Solutions: Inaccurate interpretation can be reduced by curating several batches of data using statistical methods. In most cases, closely spaced batches have lower data variability and thus can be statistically clubbed together. Also, similar cell types among different batches can be used to generate a standard reference. These two techniques can enable us to compute variability and eliminate bias induced by it. Several statistical tools or algorithms such as ComBat or BBKNN (batch-balanced k nearest neighbors) have been specifically designed to eliminate batch effect errors.

D] Mapping Cells:

Classification of cells into cell types or states is crucial for downstream analyses and gene networking. Reliable reference systems with resolutions down to cell states and cell cycles are needed to generate cell clusters. Temporal cell types that undergo lineage formation and cellular differentiation are very difficult to track by current methods. The lack of pertinent, readily available reference databases has also slowed down the mapping process. Manual cluster annotation is still one of the best methods to map cells based on their expression. However, this process also takes a long time and has issues with reproducibility.

Solutions: A combinatorial map of expression and cell types using wider classification methods to can be used to develop multi-omics databases and stable reference frameworks. Databases like DISCO and PanglaoDB have shown their effectiveness in large-scale comparisons to develop better cell cluster maps. Algorithms such as ScanPy and Seurat are helpful in cluster identification, determining cell lineages, and inferring cell trajectories. ML methods that use unsupervised cell clustering are developing and will expedite the cellular mapping process.  

E] Gene Regulation and Networking:

Molecular and genetic regulation of cellular transcription is a crucial biological feature. Single-cell RNA seq can be a potential tool for genetic regulation and networking research. However, due to technical noise and high variability, it is often a tedious task to develop a substantial gene networking model. Impediments in the upstream analysis also potentially disrupt the gene networking process substantially.

Solutions: Consolidating data from similar cell types or cell states is an essential requirement for gene networking. Most of the gene network modeling algorithms make use of approaches like the use of processed (normalized and clustered) data for gene regulatory network analysis or analysis of target genes with respect to up-regulated transcription factors, etc. A compute infrastructure that can utilize such multivariate information and generate reasonable insights will enable better gene network research through single-cell RNA seq data.

To conclude, it will be accurate to say that single-cell RNA sequencing analysis methods need to develop a lot more before they provide comprehensive representation and be considered extremely reliable. Despite advancements in the annotation process and availability of omics databases, there are several issues with the present techniques that can be rectified. Nevertheless, as new techniques and analysis pipelines are developed, we may soon witness improved outcomes and simplified analytical procedures.

Elucidata has partly solved the above-mentioned challenges by developing an AI-powered data-centric platform for deep curation of RNA seq data. We have the world’s largest collection of highly curated ML-ready single-cell and bulk RNA seq data. In our data warehouse, the metadata is harmonized, data is standardized and normalized through consistent pipelines, cell types are accurately expert-annotated and standard ontologies are followed to ensure reliable results and empower scientists in achieving their research goals.

Reach out to us to learn more!

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