What are some challenges and solutions in single cell RNA-Seq data analysis?

With RNA-Seq data analysis, scRNA-Seq usually requires low RNA input resulting in incomplete reverse amplification and transcription. As a consequence, this leads to insufficient coverage and technical noise. The solution to this issue is to optimize RNA through standardizing the cell lysis and RNA extraction steps to maximize RNA quality and yield. Pre-amplification techniques may be used to increase the amount of cDNA before sequencing. Dropout events occur when a transcript is unable to be captured or amplified in a single cell resulting in a false-negative signal. This is an issue that can be seen in rare cell populations and lowly expressed genes. This problem can be solved by using computational methods and impute missing gene expression data. These techniques use statistical models and algorithms to predict expression levels of missing genes based on the observed trends in the data. Amplification bias may arise due to stochastic variation in amplification efficiency. As a result, there may be a skewed characterization of specific genes and an overestimate of their expression levels. Correcting the amplification bias by using techniques such as spike-in controls and unique molecular identifiers will help solve this issue. Cell doublets occur when scRNA-seq captures multiple cells in a single droplet. Doublets affect downstream analysis and cause the misidentification of cell types. In order to combat this issue, cell hashing can be used to identify cell doublets. Computational techniques can help identify and exclude cell droplets from downstream analysis based on differences in gene expression levels. Data normalization, as scRNA-Seq data relies on normalization to take into consideration the differences in sequencing depth, and library size. The solution is to introduce machine learning (ML) techniques which utilize primary clustering based on related transcription profiles. These methods are time efficient and possess massive datasets required for accurate normalization. Quality control is another challenge, as poor quality-samples may result in low coverage, biased results, and technical noise. Quality control measures should be implemented for every step of the process. Analyzing the library complexity, cell viability, and sequencing depth is crucial for identifying low-quality samples, as well as improving the reproducibility and accuracy of scRNA-seq data.