How to do a pooled t-test (or an appropriate test) on 3 samples, with 2 samples from one category, the other sample from the other category I have 3 experimental conditions, each generate a series of values for some genes, like the following:
    c1  c2  c3

g1     n11 n12 n13
g2     n21 n22 n23
.
.
.
c1, c2, c3 are conditions; g1, g2, ... are gene names; n11, ... are the expression levels. c1 & c2 belong to one group, and c3 is the other group by itself. 
The biological question is: which genes are differentially expressed? 
Thank you!
 A: This is a classical application of statistical biology where you have small sample size. Your example is even worse, you don't have any technical replicate!! Without any biological replicate, you can't model the volatility for each gene. This is a bit like doing t-test between two samples, each with a single respondent.
All modern statistical techniques rely on borrowing information from similar abundant genes. Although we don't have lots of samples, we have lots of genes.
Unfortunately, those packages also rely on replicates to estimate the negative binomial dispersion correct. Without replicates, you may:


*

*Calculate the fold-ratio for each gene and use it to compare with your chosen threshold. One disadvantage is that you don't get a p-value.

*Apply Fischer's exact test to each gene against all the remaining genes. This implicitly assumes most genes are not statistically differentiated.

The paper is here.


*

*Follow the limma package described by @Gordon_Smyth.


EDITED
I apology I thought your experiment had no replicate. I misread the question.
EDITED
Just to prove Fischer's exact test on 2x2 table is a possibility. Statistical Methods for Detecting Differentially Abundant Features in Clinical Metagenomic Samples has:

A: Two options:
 1. Pool together all of the data from c1 and c2, and perform an independent samples t-test. However, this only tells you that c3 is different from the joint c1 and c2.
 2. Use Dunnett's test, but, what you are calling the 'treatment' will be the 'control' in the terminology of this test. This will test whether c1 is different from c3 and whether c2 is different from c3.
