Regarding differential expression analysis and FPKM, please read these:

You should abandon RPKM / FPKM. They are not ideal where cross-sample differential expression analysis is your aim; indeed, they render samples incomparable via differential expression analysis:

Please read this: A comprehensive evaluation of normalization methods for Illumina high-throughput RNA sequencing data analysis

The Total Count and RPKM [FPKM] normalization methods, both of which are still widely in use, are ineffective and should be definitively abandoned in the context of differential analysis.

Also, by Harold Pimental: What the FPKM? A review of RNA-Seq expression units

The first thing one should remember is that without between sample normalization (a topic for a later post), NONE of these units are comparable across experiments. This is a result of RNA-Seq being a relative measurement, not an absolute one.

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You should aim to obtain the raw counts for your dataset of interest and then reprocess these using a normalisation strategy that is more amenable to differential expression (like those implemented in DESeq2, EdgeR, and Limma in R Programming Language).

You will not find much advice for conducting statistical comparisons on FPKM data in Excel on this forum, but I could be proved incorrect on that.

Kevin