Get to Know Your Cell

Just like people, cells are sometimes mysterious and do not readily reveal their true identity. If you want an example, just think of metastatic cancer cells, which sometimes lose all the hallmark features of their mother tissue, making detection of the primary tumor site difficult. Or another example, single cell RNA-Seq. Getting a nice t-distributed stochastic neighbor embedding (t-SNE) chart based on sequencing data is pretty much straightforward but figuring out their biological nature can be quite challenging.

One approach to classifying the cells into groups is to use marker genes. An extension of that concept is to use gene groups, such as pathways. Although these strategies are very useful, they are not applicable to every research situation. For instance, you may want to come up with a completely new set of marker genes, or you may want to work hypothesis-free.

To handle those situations, use Partek Flow to combine clustering results with t-SNE visualization. If you perform clustering with compute biomarkers and then invoke the t-SNE plot on the result, genes specific for every cluster can be used for classification (Figure 1). Moreover, a clustering algorithm does not have to be involved, you can simply classify cells into groups by selecting them directly on the chart and Partek Flow will produce biomarkers of your custom groups. The biomarkers table is based on a comparison of one cluster at a time versus all the other clusters combined. The gene list is then filtered by using p-value (0.05) and fold-change criteria (|1.5|), ranked by the p-value, and the top 10 genes are then listed in the table by default (the full list can be viewed in the Data Viewer or downloaded).

Figure 1. Detect biomarkers using Graph-based clustering or K-means clustering for the differentially expressed features between each cluster and the rest of the data; t-SNE colored by Graph-based clusters (left) with table of Graph-based biomarkers (right). Drag and drop the biomarkers from the table to the t-SNE plot to visualize genes

What if you do not want to compare a particular cluster with all the remaining cells, but would rather pick two clusters and contrast them directly? That is also easily performed in Partek Flow.