Dealing with NAs in genomic data statistics I have done a GC content (counting C and G in genomes) and GC variation within genomes, in a slide-window (1000 pb-base pairs). I have each genomic data separated in tsv files. When I work with pandas and merge all data by columns, I got a lot of lines with NAs because each species has a different genome size. Pandas works well with this data, because I have means and all other statistics working fine, I can even plot the data with no problem.
But my doubt is this, I can keep the data merged as it is or it is BETTER (statistically speaking) to drop all lines with NAs?
I ask that because I would like to use R, but I cant merge the data because R complains about the different column sizes.
Any tip or commentary about this subject would be very appreciate! Even a good reading about the subject.
Thank you all by your time.
Paulo

Generally speaking, outside of extremely specific reasons, listwise deletion of missing values is not encouraged. It would be better if you could employ some alternative like multiple imputation (MI) or full information maximum likelihood (FIML). I have linked a couple very readible papers here that discuss missingness, modern techniques, and misconceptions. There is also a phenomenal book on MI using R that I can recommend called Flexible Imputation of Missing Data by Stef van Buuren.
The main reason you don't want to remove missing values willy-nilly is because you will lose a ton of valuable information. Normally listwise deletion deletes an entire row of data rather than just that unit that is missing. Regardless, this leads also to a loss of power. Additionally, there may be missingness patterns that you need to examine which predicate decisions to remove or impute values. Is the missingness due to chance? Is it systematic? Is there clustering involved? (i.e. a specific hospital constantly has missing values for medical exam data). These are also things to consider when dealing with missingness.
References:

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*Little et al., 2014: Missingness, issues with listwise deletion, and modern techniques.

*Ginkle et al., 2020: Misconceptions about modern missingness techniques.

