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I am trying to calculate the Z-score for several columns with Log fold change (LFC) data from RNA-seq expression values. But the mean for all columns, except one of them, is -inf and the standard deviation is NaN. I have 22269 gene LFC values in each column.

Somehow, I found the answer but not completely. Some of expression values in treated cells are equal to zero. So, When I want to calculate the LFC, the values would be 0 or -1 (based on the two formula you choose for calculation of LFC ( first- (b-a)/a or second- (b/a) when b is treated and a is control)). Accordingly, the log2(0) and/or log2(-1) is not meaningful. So, it cause a "#NAME?" in excel file. Now the question is: Shall I replace all #NAME? with zero to calculate the Z-score?

To figure out the total report I have used

> print (df.describe())

And the report was like this:

A549-trt_cp-1126  A549-trt_cp-1132  A549-trt_cp-1138  A549-trt_cp-1146  \
count      2.226800e+04      2.226800e+04      2.226800e+04      2.226800e+04   
mean               -inf              -inf              -inf              -inf   
std                 NaN               NaN               NaN               NaN   
min                -inf              -inf              -inf              -inf   
25%       -4.539195e-02     -1.021755e-01     -6.721568e-02     -2.657099e-02   
50%        4.465054e-03     -3.905336e-02     -6.798975e-03      1.116264e-01   
75%        5.161382e-02      2.408127e-02      6.178798e-02      2.433404e-01   
max        1.533103e+00      1.315935e+00      2.228510e+00      2.483255e+00   

       A549-trt_cp-1152  A549-trt_cp-1158  A549-trt_cp-1164  A549-trt_cp-1170  \
count      2.226800e+04      2.226800e+04      2.226800e+04      2.226800e+04   
mean               -inf              -inf              -inf              -inf   
std                 NaN               NaN               NaN               NaN   
min                -inf              -inf              -inf              -inf   
25%       -5.830911e-02     -5.150910e-02     -4.667450e-02     -7.890445e-02   
50%       -6.879490e-03      5.579830e-04     -8.169799e-03     -2.576761e-02   
75%        4.991595e-02      5.347726e-02      2.838781e-02      3.474615e-02   
max        5.237704e+00      2.875934e+00      1.008515e+00      1.737813e+00 

I will enclose a part of my data.

enter image description here

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  • $\begingroup$ You shouldn't be calculating fold-changes yourself, use a package like limma or DESeq2 instead. $\endgroup$ – Devon Ryan Dec 25 '19 at 9:21

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