| | 53 | === Perform and visualize dimensional analysis === |
| | 54 | |
| | 55 | === Partition cells into clusters === |
| | 56 | |
| | 57 | === Identify genes that differentially expressed between samples or clusters === |
| | 58 | |
| | 59 | Years of research has led to effective algorithms to quantify differential expression between RNA-seq samples that have been assayed genome-wide. Single cell expression profiles, however, typically assay only a small fraction of all genes, and this single property greatly complicates differential expression analysis. Two general approaches exist for differential expression: |
| | 60 | |
| | 61 | * consider each cell as a sample |
| | 62 | * aggregate counts across all cells in a group/cluster, and treat them as one sample |
| | 63 | |
| | 64 | |
| | 65 | === Perform trajectory analysis === |
| | 66 | |
| | 67 | This step is relevant for projects that include cells at different stages of a developmental process or other change that is associated with a time course. Specific methods/algorithms for dimensional reduction are available to do this, but they often give very different results. Most of the methods have some concept of pseudotime, metric that one expects is correlated with actual time, but given that they aren't identical, interpretation needs to be performed with caution. |
| | 68 | |
| | 69 | === Combine multiple scRNA-seq datasets === |
| | 70 | |
| | 71 | Many experiments are especially informative when compared to other experiments, either performed by the same or different laboratories. This is challenging, however, especially when the different experiments profile different types of cells. In these cases, biological and technical differences are confounded, and one needs to make thoughtful assumptions about how to perform batch correction and achieve "success" during dataset integration. |
| | 72 | |
