So, if you have 2 variables of interest, one of which (sBMCA) is basically an ordered factor, Pearson correlation is not the best choice (for that, both variables should be numeric continuous variables). I would start with linear regression with sBMCA as a categorical predictor of BCMA and check whether regression assumptions are met via regression diagnostics.

If your data meets the regression assumptions, you could use a linear contrast of sBMCA (meaning that the model tests whether BCMA expression increases when sBCMA level increases), or consecutive contrasts (low vs medium and medium vs high), or run all pairwise comparisons.

Also, not related to statistics but it doesn't sound like this is an experiment as it sounds like you are not manipulating anything, just measuring.

So, if you have 2 variables of interest, one of which (sBMCA) is basically an ordered factor, Pearson correlation is not the best choice (for that, both variables should be numeric continuous variables). I would start with linear regression with sBMCA as a categorical predictor of BCMA and check whether regression assumptions are met via regression diagnostics.

If your data meets the regression assumptions, you could proceed with the regression and use a linear contrast of sBMCA (meaning that the model tests whether BCMA expression increases when sBCMA level increases), or consecutive contrasts (low vs medium and medium vs high), or run all pairwise comparisons.

Also, not related to statistics but it doesn't sound like this is an experiment as it sounds like you are not manipulating anything, just measuring.

So, if you have 2 variables of interest, one of which (sBMCA) is basically an ordered factor, Pearson correlation is not the best choice. I would start with linear regression with sBMCA as a categorical predictor of BCMA and check whether regression assumptions are met via regression diagnostics.

If your data meets the regression assumptions, you could use a linear contrast of sBMCA (meaning that the model tests whether BCMA expression increases when sBCMA level increases), or consecutive contrasts (low vs medium and medium vs high), or run all pairwise comparisons.

So, if you have 2 variables of interest, one of which (sBMCA) is basically an ordered factor, Pearson correlation is not the best choice (for that, both variables should be numeric continuous variables). I would start with linear regression with sBMCA as a categorical predictor of BCMA and check whether regression assumptions are met via regression diagnostics.

If your data meets the regression assumptions, you could use a linear contrast of sBMCA (meaning that the model tests whether BCMA expression increases when sBCMA level increases), or consecutive contrasts (low vs medium and medium vs high), or run all pairwise comparisons.

Also, not related to statistics but it doesn't sound like this is an experiment as it sounds like you are not manipulating anything, just measuring.