How can I select the top up- and down-regulated genes from more than 8000 DEG in microarray set

Question

[AAhmed]

I am using microarray (GSE38642) database,that includes 54 non-diabetic and 9 diabetic islets samples, platform ( Affymetrix Human Gene 1.0 ST Array). I used Bioconductor to mining the data and I got 8500 DEGs. Now I want to select the top up- and down-regulated genes. I am not sure what I have to do for example I used: FC_thresh = 10 and pvalue_thresh= 1.2e-8 to selcet the top up-regulated genes according to the formula:

(DES>=FC_thresh && DES<pvalue_thresh).

what have I done with down-regulated?

I am new in this field. any help will be highly appreciated

[Prasad]

you could have the same cut off for down regulated genes as well with negative fold change. DES<= -10 && DES<pvalue_thresh

Answer 1

[Devon Ryan]

1. Ensure you're filtering on the adjusted p-value, rather than the raw p-value.
2. Take the absolute value (abs()) of the fold change. Then you have less typing and will always get the top genes in each direction.

[AAhmed]

Could you clarify a little bit how can I filter on the adjusted p-value? They are equal (p-value=adjusted p-value)

        logFC AveExpr      t    P.Value  adj.P.Val      B
8040338 13.171  13.175 408.57 2.194e-114 2.194e-114 243.97
8153903 12.281  12.300 395.29 1.955e-113 1.955e-113 242.32
7895588 12.150  12.132 390.52 4.371e-113 4.371e-113 241.71
7892902 13.140  13.143 378.48 3.473e-112 3.473e-112 240.11
7895610 11.531  11.527 373.39 8.514e-112 8.514e-112 239.42
8177732 12.529  12.519 372.59 9.820e-112 9.820e-112 239.30
7896050 12.867  12.855 370.93 1.318e-111 1.318e-111 239.07
7896349 12.121  12.133 368.41 2.072e-111 2.072e-111 238.72
7892509 12.027  12.038 367.11 2.619e-111 2.619e-111 238.53
7894666 11.718  11.729 366.33 3.013e-111 3.013e-111 238.42
8179041 12.541  12.533 365.72 3.366e-111 3.366e-111 238.34
7935320 11.418  11.417 363.72 4.840e-111 4.840e-111 238.05
8164060 12.564  12.566 363.21 5.307e-111 5.307e-111 237.98
7893345 12.606  12.601 361.74 6.945e-111 6.945e-111 237.76
7893316 12.573  12.578 361.41 7.371e-111 7.371e-111 237.72
7892829 11.970  11.975 359.39 1.068e-110 1.068e-110 237.42
8119993 12.127  12.135 357.49 1.517e-110 1.517e-110 237.14
8110022 11.298  11.294 357.33 1.564e-110 1.564e-110 237.12
7987449 10.551  10.549 356.89 1.696e-110 1.696e-110 237.06
7895547 11.805  11.798 356.33 1.884e-110 1.884e-110 236.97
7893530 11.930  11.936 355.86 2.056e-110 2.056e-110 236.90
7895320 11.603  11.589 354.20 2.801e-110 2.801e-110 236.66
7895475 12.724  12.721 353.71 3.067e-110 3.067e-110 236.58
8085026 11.892  11.896 353.59 3.136e-110 3.136e-110 236.56
7893015 12.123  12.131 353.17 3.394e-110 3.394e-110 236.50
7894236 12.432  12.429 353.00 3.507e-110 3.507e-110 236.48
7894991 12.363  12.344 352.06 4.181e-110 4.181e-110 236.33
7894098 12.835  12.829 351.81 4.385e-110 4.385e-110 236.30
8061136 12.390  12.428 350.77 5.333e-110 5.333e-110 236.14
7894596 11.619  11.633 349.11 7.304e-110 7.304e-110 235.89
8117777 11.286  11.279 349.03 7.416e-110 7.416e-110 235.87
7895484 11.300  11.304 348.58 8.066e-110 8.066e-110 235.81
7893264 12.364  12.370 348.53 8.147e-110 8.147e-110 235.80
8169740 11.351  11.349 348.38 8.379e-110 8.379e-110 235.78
7918825 12.042  12.030 347.97 9.056e-110 9.056e-110 235.71
7895317 12.159  12.176 347.88 9.219e-110 9.219e-110 235.70
7910124 10.923  10.922 346.46 1.209e-109 1.209e-109 235.48
7896438 11.940  11.939 346.01 1.318e-109 1.318e-109 235.41

@Devon

[Devon Ryan]

This is the first case I have ever seen where raw and adjusted p-values are the same. That's incredibly unusual and I would recommend double checking that (use the p.adjust() function) to make sure they're correct.