Find association between gene-mutation and cell count fold change

Question

[jason]

Hi, my statistic is bad. I got two datasets

cell count fold change depending on the knocked-out gene (quantile normalized)

Model   GeneA_KO    GeneB_KO    GeneC_KO
ModelM  0.075638312 -0.870157286    -2.009107843
ModelO  -0.162841966    -0.359008725    -0.826469109
ModelS  -0.1107031  -0.990406888    -1.118059869
ModelA  -0.457154616    -0.120656982    -0.23181682
ModelI  -0.505815194    -1.414276318    -0.875524634

presence (1) or absence (0) of a set of known cancer driver mutations

Mutation    ModelM  ModelO  ModelS  ModelA  ModelI
Gene1_mut   0   0   0   0   0
Gene2_mut   0   0   0   0   0
Gene3_mut   0   0   0   0   0
Gene4_mut   1   1   1   1   1

how do i figure out, for each gene-mutation pair, whether the presence of that variant is associated to a change in the outcome of the CRISPR knock-out experiment for that gene, across all cell lines?

Edit: or is there any tool in python that does this kind of analysis?

Answer 1

[Daniel]

Your CRISPR data is continuous, while your mutation data is binary. If I was you, I would google "How to predict continuous variable using binary data" (I don't know off the top of my head how other people do it).

But, I have done something similar where I looked at mutation presence in predicting categorical data (radiation toxicity), and have found this link helpful: https://medium.com/the-researchers-guide/generate-numerical-correlation-and-nominal-association-plots-using-python-c8548aa4a663

It introduces cramer's v and theil's U, which is like a correlation for categorical data.

[jason]

Thanks Daniel for your reply

I found Point-biserial correlation which is a way to get the correlation between categorical variables and continuous variable, similar to Pearson correlation.

The x could be mutation 0/1 I'm not quite sure about the y either I should average the cell count fold change?