Timeline for How to select a set of independent features predictive of all other features without a target variable
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| Apr 15, 2021 at 15:49 | comment | added | EdM | @perlusha In principle if you want to cluster genes rather than cells, you transpose the data matrix and use whichever clustering method best addresses your interest. The sparsity issues specific to scRNA-seq are essentially the same in either direction. The problem with clustering genes across a heterogeneous population of cells is that it will work best if groups of genes are alway co-regulated regardless of the cell type or cell state. That's unlikely biologically. Even in an apparently homogeneous culture of a cell line, cells will be heterogeneous in terms of cell-cycle state. | |
| Apr 15, 2021 at 15:43 | comment | added | EdM | @perlusha after t-SNE reaches dimensionality down to 2, it's quite possible to distinguish clusters. See Figure 2 of this scRNA-seq paper for example. So it's possible to consider t-SNE in practice as a clustering method. | |
| Apr 15, 2021 at 8:18 | comment | added | perlusha | Thank you for your answer! Most of the literature on scRNAseq deals with clustering cells in the space of genes, not the other way around. The difference is that if you're looking at sufficiently different cell types it's easy to end up with well-defined tight cell clusters. Generally this is not going to be the case for majority of genes. With the exception of conserved cell type markers they don't form tight clusters in the cell space. Also, t-SNE is dimensionality reduction method, not clustering method. | |
| Apr 14, 2021 at 17:53 | history | answered | EdM | CC BY-SA 4.0 |