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Dear all,
I am facing a personal dilemma, as I have no idea how to tackle this problem from a statistical standpoint.
In brief, I have done qPCR validation for my RNAseq results (mRNAseq on human cells, two conditions, 10 replicates each, and using the same RNA source for sequencing and qPCR).
Apart from the validated genes (confirming the significance found with sequencing) I have a group of three interesting genes that are simply almost not expressed in the control group. Basically, I have the expression data for 9 samples (treatment group) and only for 3-4 of the control.
t-test comparison is not giving me any significant difference -but is it the right method to use at all when you have missing data???
Thanks anyone for inputs!
Manu
I am facing a personal dilemma, as I have no idea how to tackle this problem from a statistical standpoint.
In brief, I have done qPCR validation for my RNAseq results (mRNAseq on human cells, two conditions, 10 replicates each, and using the same RNA source for sequencing and qPCR).
Apart from the validated genes (confirming the significance found with sequencing) I have a group of three interesting genes that are simply almost not expressed in the control group. Basically, I have the expression data for 9 samples (treatment group) and only for 3-4 of the control.
t-test comparison is not giving me any significant difference -but is it the right method to use at all when you have missing data???
Thanks anyone for inputs!
Manu