Hello there, I've tried to use NanoStringDiff package to find out differential expression analysis between 2 group (NC vs Treat) data.
And here is the data of mRNAs counts from each condition. Input data. head(data) # CodeClass Name Accession NC Treat #1 Positive POS_A ERCC_00117.1 43570 42535 #2 Positive POS_B ERCC_00112.1 14932 14485 #3 Positive POS_C ERCC_00002.1 4043 3922 #4 Positive POS_D ERCC_00092.1 915 854 #5 Positive POS_E ERCC_00035.1 184 163 #6 Positive POS_F ERCC_00034.1 84 94 #7 Endogenous A2M NM_000014.4 17 30 tail(data) # CodeClass Name Accession NC Treat #782 Housekeeping TLK2 NM_006852.3 210 146 156 #783 Housekeeping TMUB2 NM_177441.2 118 70 122 #784 Housekeeping UBB NM_018955.3 1340 1067 1129
And i followed vignette carefully and get the differential expression results below.
#'[Generalize linear model likelihood ratio test:] result = glm.LRT(NanoStringData,design.full,contrast=contrast) head(result$table) # # logFC lr pvalue qvalue #A2M 13.2536182 4.9222949 2.651228e-02 2.687055e-01 #ACVR1C -18.5250820 0.7255828 3.943187e-01 9.085508e-01 #ADAM12 0.1832845 0.3307115 5.652404e-01 9.085508e-01
- But the problem is: In the vignette there were no explanation about how "logFC" is generated. I mean is this log2 fold change between these two groups? or log10 fold change?
- Also is it possible to reproduce logFC value, for example from A2M gene by manually? (without package) (You can see count value for the A2M gene above)
Here is the link for the NanoStringDiff package (https://www.bioconductor.org/packages/release/bioc/html/NanoStringDiff.html)