I've got results from a quantitative proteomic analysis that was run on stomach cells generated from stem cells. I'd like to confirm that my cells resemble normal human stomach cells, but I've got problems.
I planned to search this protein expression dataset for a small set of proteins that are highly tissue-specific. The problem is that some of these proteins aren't actually expressed very reliably, and the mass-spec run isn't as sensitive as RNA-seq. There are many genes which are expressed more reliably, but they aren't strictly unique markers for a single tissue.
As a result, I think that my previous plan to confirm the presence of a small handful of reliable markers is impossible. I think instead, I need a more holistic way of analyzing the various levels of several dozen markers or hundred markers. I have access to lists of proteins and their typical expression patterns using the Human Protein Atlas, but I don't have data sets from ideal samples against which I might compare my samples.
Can anyone refer me to notes on a paper which has done something like this? Can anyone make any general suggestions? Thanks.
I planned to search this protein expression dataset for a small set of proteins that are highly tissue-specific. The problem is that some of these proteins aren't actually expressed very reliably, and the mass-spec run isn't as sensitive as RNA-seq. There are many genes which are expressed more reliably, but they aren't strictly unique markers for a single tissue.
As a result, I think that my previous plan to confirm the presence of a small handful of reliable markers is impossible. I think instead, I need a more holistic way of analyzing the various levels of several dozen markers or hundred markers. I have access to lists of proteins and their typical expression patterns using the Human Protein Atlas, but I don't have data sets from ideal samples against which I might compare my samples.
Can anyone refer me to notes on a paper which has done something like this? Can anyone make any general suggestions? Thanks.