Fig. 2

Behavior of Signature Scores. Behavior of signature scores calculated on the human glioblastoma scRNA-seq data from Patel et al., 2014 [2]. a Distribution of Signature/Projection consistency scores across four different types of signatures, Signed (signed immunological signatures from MSigDB), Unsigned (various unsigned hallmark and pathway signatures from MSigDB), Random Signed (signed signatures with randomly selected genes), and Random Unsigned (unsigned signatures with randomly selected genes). Lower panel shows distributions from the same signatures, run on data in which gene expression levels have been shuffled within each cell. Comparing these, it can be seen that biological signatures tend to have higher consistency scores than random signatures and this distinction disappears using shuffled data. b Distribution of the Pearson’s correlation coefficient between signature scores and a confounding variable - the proportion of undetected genes in a sample. Upper plot shows correlations when signature are calculated by simply taking the unweighted average of log expression level for genes in the signature. Lower panel shows the effect of using the weighted method presented here