Question: How to merge zscore value of replicate samples in LINCS data
gravatar for liuyang
5.7 years ago by
liuyang20 wrote:

I'm now working with the LINCS level 4 data. It is the gene expression profiles of human genes when cells are exposed to a variety of perturbing agents. For a single perturbation, there exists many replicate samples. And the LINCS level 4 data provides modified zscore for all the replicates. So if I want to merge the zscores of the replicates samples, is it reasonable to simply average the zscores? But I doubt the distribution will change with this method?

lincs • 3.2k views
ADD COMMENTlink modified 5.7 years ago by Jean-Karim Heriche22k • written 5.7 years ago by liuyang20

Did you find the answer?

I have a same problem!

ADD REPLYlink written 4.7 years ago by hajramezanali0
gravatar for Jean-Karim Heriche
5.7 years ago by
EMBL Heidelberg, Germany
Jean-Karim Heriche22k wrote:

I don't know what LINCS data are but it seems your'e looking for Stouffer's method.

ADD COMMENTlink written 5.7 years ago by Jean-Karim Heriche22k

The way I interpret the question (and I also don't know LINCS data) is that gene expression levels are normalized to z-scores and the OP wants to summarize these gene expressions in each experiment by averaging the z-scores. Stouffer's method instead would combine p-values, which could be extracted from z-scores, to get an overall level of significance of the underlying tests.

E.g. if two genes have z = 2 and 4, the summarized expression would be 3. Instead Stouffer's method would give p = 0.999989 corresponding to z = 4.2.

If my interpretation is correct I don't see it wrong to average z-scores, or taking the median if one is worried about outliers.

ADD REPLYlink written 5.7 years ago by dariober11k

Fisher's method combines the p-values directly whereas Stouffer's method (or Z-transfom) gets at a combined p-value using the average of the z-scores (which can be weighted). The two methods are compared here and here.


ADD REPLYlink written 5.7 years ago by Jean-Karim Heriche22k

Yes, but the point I wanted to make is that Stouffer's (or Fisher's) method combine the significance from different independent tests, expressed as p-value or z-score, in order to obtain an overall probability for the null hypothesis. My understanding (which my be wrong) of the question is that  liuyang wants to combine expression values not probabilities or significance. So if two genes are expressed with z=2 and z=4 the combined expression is somewhere between 2 and 4 (arithmetic mean would say z=3). Combining these expression with Stouffer's method would give an overall expression >4.

ADD REPLYlink written 5.7 years ago by dariober11k

You're right. I guess I jumped to conclusions because I had recently been looking into combining p-values.

ADD REPLYlink written 5.7 years ago by Jean-Karim Heriche22k

Sure now I'm not concerned about the p-value, I just need the combined z-score for subsequent calculations. Finally I take the Stouffer's method. The weight is the average value of spearman coefficient.


ADD REPLYlink modified 5.7 years ago • written 5.7 years ago by liuyang20

Thanks for your suggestion, but the questions is what weight should I give to the different samples. I think arithmetic mean is ok, but how about arithmetic mean weighted by Spearman's rank correlation coefficient? 

ADD REPLYlink written 5.7 years ago by liuyang20

Quoting this paper:

How are these weights to be chosen? Ideally each study is weighted proportional to the inverse of its error variance, that is, by the reciprocal of its squared standard error. For studies that use t-tests, for example, this is done by weighting each study by its d.f.: *wi = νi. More generally, the weights should be the inverse of the squared standard error of the effect size estimate for each study.

So I guess using the correlation coefficient is OK.

ADD REPLYlink modified 9 months ago by RamRS27k • written 5.7 years ago by Jean-Karim Heriche22k
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