Here's a combination of general advice and my thoughts about your specific data:
If the samples are not matched, e.g. by time point, then it will be unpaired. You need a way to say sample 1 KO and sample 1 WT are uniquely matched to be paired, sample 2 KO and sample WT will be paired etc ... pairing is great for a time series. If you have replicates within the paired groups then ANOVA is the way to go, particularly if the sample sizes are uneven.
Note: in your case the sample sizes are uneven, you cannnot perform a paired T-test between two uneven sample sizes. HOWEVER you appear to a natural pairing, but I don't know whether this is biologically justified, i.e. liver and spleen. So you appear to have a biological pairing with uneven sample sizes. If this is correct it suggests a 1-way ANOVA is a better idea than a T-test.
Parametric test: check for normality in each data set, e.g.
- A Q-Q plot against the normal distribution (a cool way to do this)
- Homogeneity of variances is Hartley's Fmax test (good for T-test and ESSENTIAL for 1-way ANOVA)
- 68% of the data should be less than ±1 standard deviation around the mean, 95% less than ±2 standard deviation and 99% less than ±3
- Kolmogorov-Smirnov and/or Shapiro-Wilk test
- Skewness & kurtosis magnitudes < 1.96 respectively
Note if you combine liver and spleen data sets and find a nice normal distribution, and one or both is not normal by itself, then you would need to consider the biological justification for combining the data sets.
If the data passes all those tests, you are good to go. If not .... gets complicated,
- Normalisation, this is where you transform the data, in your case I'm not sure, but typically log transformation and then perform the checks for normality above. There are lots of ways to normalise data.
- Repeat the T-test/1-way ANOVA with normalised data
- Ignore it - yup its top level statistical advice, it is context specific and never a universally shared idea ... I suspect that is where the advice "just use a parametric test" came from. Please be aware if you do this, someone could turn around and sau "you've done it wrong". The statistical arguments about why this assumption can be valid get complicated.
- Use a non-parametric test, orthodox statistical advice, in this case it is the Mann-Whitney U test and Kruskal–Wallis test if you have paired replicates (i.e. non-parametric version of 1-way ANOVA).
Non-parametric tests often lack power and I suspect for this sample size that will be the case. Personally I would try and ignore all this advice :-) - okay I would definately check for normality and investigate avenues towards normality - then I would perform a general linear model (GLM). The reason is that it is a much more powerful method for identifying whether the two gropus are different. Performing a single GLM with two paired groups is not trivial, so I would have to give serious consideration to 1-way ANOVA, or KW-test, which would be a cleaner solution - but could miss a critical part of the data.