How to interpret this cuffcompare result? Sensitivity over 100?
I don't know if this is still needed but I went through the source code of cuffcompare to see if this phenomenon is indicative of the false positive rate or an issue with the tool (and to provide a more thorough answer). It seems like it may be a bug in the code for cuffcompare (which is a massive amount of code). If you look at line ~1518 in the source code at this URL:

https://github.com/cole-trapnell-lab/cufflinks/blob/master/src/cuffcompare.cpp


There are several statements that suggest Sn values > 100% would be possible and should be reassigned to 100%. These results may occur from keeping "redundant" transcripts if they start with the same 5' intron. How do your "fuzzy" Sn and Sp compare to this result? are they are similar?

The developers are no longer maintaining this software in favor their new faster version, Hisat/Stringtie. However I find the output to be more complete from Tophat and Cufflinks. To get a better understanding of the output, you could compare the number of potentially novel features in your cuffcmp.combined.gtf to the total number of features in your new cuffcmp.combined.gtf using overlapping and contained features as a proxy for false positive rate. Comparing these values with the total number of lines in your reference annotation may provide you with another Sn estimate without performing qPCR.

# to count the number of potentially novel
grep -c "\"j\"" cuffcmp.combined.gtf 

# to count the overlapping and contained features
grep -c "\"[c,o]\"" cuffcmp.combined.gtf 

# to count the number of exact matches
grep -c "\"=\"" cuffcmp.combined.gtf

# To count how many features are identified for each gene
cut -f4  -d ";" cuffcmp.combined.gtf |   uniq -c

# To count how many known genes gene were identified 
cut -f4  -d ";" cuffcmp.combined.gtf |   uniq -c | wc -l

# To count number of features in the reference gtf
wc -l reference_annotation.gtf



or if you prefer to use Bioconductor packages like rtracklayer in an R environment, you could read the cuffcmp.combined.gtf file and analyze the data further

library(rtracklayer) # if you have the package installed
rtracklayer::readGFF("~/full/path/to/cuffcmp.combined.gtf")