Dear Lon,

1. Please make sure you are working with normalized data across samples before making any comparison.

2. Try comparing the 1st quartile (low-expressing) vs the 3rd quartile (high-expressing) instead of using the median.

3. If you are going to use a parametric test to compare your groups, applying some transformation (e.g. logarithm) in order to make the distribution normal is also recommended.

4. Adding 10E-04 to all genes it is also helpful when dividing by 0 in order to capture increase or decrease in gene expression.

Regards,

Rolando

Dear Lon,

1. Please make sure you are working with normalized data across samples before making any comparison.

2. Try comparing the 1st quartile (low-expressing) vs the 3rd quartile (high-expressing) instead of using the median.

3. If you are going to use a parametric test to compare your groups, applying some transformation (e.g. logarithm) in order to make the distribution normal is also recommended.

4. Adding 10E-04 to all genes it is also helpful when dividing by 0 in order to capture increase or decrease in gene expression.

Regards,

Rolando

In my experience plotting log2(normalized_rsem + 1) and dividing the samples by the mean value (high = above mean; low = below mean) is sufficient to capture significant differences. Although it may be useful to try different cutoffs, the statistical power decreases with decreasing sample size. It may be instructive then to plot the confidence intervals to visualize the extent of overlap between lines.

Dear Lon,

you could also try to run ROC curve analysis using survival status as classification variable and gene expression data as numerical variable to obtain potential cut-off value of gene expression that you can further test in survival analysis. This is not always optimal approach but might help at finding best cut-off.

Additionally, if you did your molecular analyses on both diseased and control patients, you can run ROC curve analysis using disease status as classification variable to find "optimal" cut-off value of gene expression for discriminating diseased and control subjects, then test whether this cut-off value discriminates survival among diseased patients (some kind of normal vs high expression among diseased). I'm not sure whether your data have to follow normal distribution for ROC curve analyses to be reliable or not, however this is only auxiliary first step and your cut-offs need to be tested on survival data as a next step and you can try to play around given cut-off value to see whether it affects prognosis or not. Data separated at median or at quartiles do not need to follow normal distribution.

If you don't obtain significant result, but see that survival curves diverge apart, this might be due to too small number of events in your data-set. Your study is then probably underpowered to detect statistical significance for this particular analysis.

I hope this would help you, Kind regards, Marko

Hi Lon-

Normalization is a precondition for analysis of Gene expression data.Therefore. one must do normalization of data.with best wishes.

ajit

thanks for recommending my answer@

• Syed Ismyl Mahmood Rizvi
• Yehya A. Salih

Dear Lon

RNA-seq data does not follow a normal distribution and even after normalization it is very difficult that you will get it. Indeed, the distribution of the reads counts follows a negative binomial distribution, in fact, most of the programs which are used to perform statistical analysis of RNA-seq data do not apply models based on normality. So my suggestion would be to apply non-parametric tests to check for differences in survival.

Kind regards

Riccardo

Bioinformatics data analyst at www.sequentiabiotech.com