What differences need consideration for processing single cell data compared to bulk RNA-seq data?

The main difference between bulk and single cell RNA-seq is that each sequencing library represents a single cell, instead of a population of cells. Therefore, significant attention has to be paid to comparison of the results from different cells (sequencing libraries).

What is an advantage of RNA-Seq over microarrays?

The advantage of RNA-Seq over microarrays is that it provides an unbiased insight into all transcripts (Zhao et al., 2014). Thus, RNA-Seq is generally reliable for accurately measuring gene expression level changes.

Why is a single cell important?

Single-cell analysis allows the study of cell-to-cell variation within a cell population (organ, tissue, and cell culture). This information is important for cancer research for detection of rare tumor cells, preimplantation, and genetic diagnosis [11-13].

What is an advantage of single cell RNA sequencing over bulk RNA sequencing?

Single-cell RNA sequencing helps in exploring the complex systems beyond the different cell types. It enables cell-by-cell molecular as well as cellular characterization of the cells. The scRNA-Seq makes it possible to explore complex systems such as the immune system without any limitation.

What is dropout in single cell?

One important characteristic of scRNA-seq data that feeds into all these challenges is a phenomenon called “dropout”, where a gene is observed at a low or moderate expression level in one cell but is not detected in another cell of the same cell type16.

What are the advantages of RNA-Seq over hybridization based approaches microarrays for studying gene expression?

RNA-seq provides several advantages over hybridisation-based approaches: RNA-seq has higher sensitivity for genes expressed either at low or very high level and higher dynamic range of expression levels over which transcripts can be detected (> 8000-fold range).