Hello,

We have RNAseq data on a sample from a few years ago that looked odd (possibly mislabeled), so we re-sequenced a bunch of candidates that could possibly have been that sample.

Is there any way to find out which of the re-sequenced samples the original sample is closest to? I could run PCA but these re-sequenced candidates and the original would show some sort of batch effect and that might interfere with how the samples stratify on a PCA plot. Is there any way I could account for that?

I'd appreciate any pointers. Thank you!
You can try the plotPCA function from DESeq2 after normalizing data with `vst` which has a mode `blind` that can be set to TRUE and FALSE. In FALSE, it will respect the design formula so the batch effect would be respected. Pearson as suggested might make sense in combination with hierarchical clustering to visualize the results (see `corrplot` package). Another PCA-like metric is the Multidimensional Scaling (`plotMDS` function) from `edgeR`. In any case the individual approaches should at least in part confirm each other before you make a decision. 

Apart from those dimensionality-reduction techniques, it **might** be possible to perform variant calling (even though difficult from RNA-seq) and see if some characteristic SNPs or patterns of SNPs can give valuable information. You could take variants that are common in humans (e.g. find some with high MAF in the hg38 VCF from dbSNP latest release containing 1KG and TOPMED MAFs) and then see if for those variants the resequenced sample is similar to the odd ones. 