Seurat for clustering bulk RNA-seq? Is it ever ok to use Seurat for clustering bulk samples?

I am looking at FPKM data from ~750 bulk RNA-seq samples generated using Cufflinks. As suggested for FPKM data, I manually input log transformed data to the @data slot [cd138_bm@data &lt;- log(cd138_bm@raw.data + 1)] and skip the NormalizeData() function. I then use functions FindVariableGenes, ScaleData, RunPCA, FindClusters, RunTSNE, FindAllMarkers in their usual ways to find clusters &amp; cluster markers. My clustering results are quite reasonable, and reflect published work clustering similar samples.

What are the potential pitfalls of using these Seurat functions on bulk data? In FindAllMarkers, would you recommend I use the "negbinom" test? (currently using wilcox) Any other arguments you would recommend changing from the default?
I'm not sure Seurat is the best tool for this as it was developed for single cell RNA seq data and there are a few intricacies of that type of data that are very different from bulk RNA seq. 
For bulk there are really good packages available and corresponding workflows, e.g. limma, edgeR and DESeq2.

The main problems with Seurat for bulk RNA-seq:


Seurat expects counts as input - FPKM are not counts nor are they log counts or log norm counts. It's a specific normalisation method that takes into account gene length and library size and breaks the link between gene counts and variance. 
Because of the distribution of reads from a single cell that you get from the sequencer Seurat scales all unique reads (i.e. UMIs) to 10,000 per cell and usually regresses out the nUMI count. Again, becuase you've got FPKMs this makes no sense.
The other functions you can use on bulk data, i.e. tSNE, finding clusters and markers (which is essentially DGE analysis). The one thing to note for the latter is that again because you've got FPKMs they don't follow the negative binomial distribution. Your best bet is limma trend but I'm not sure it's included in Seurat.