As my understanding the package pRoloc allows one to study the localisation of protein inside cells , using relative quantitation of known organelle residents, termed organelle markers.

In the the vignette it uses `tan2009r1` data of markers that have been obtained by mining the pRolocdata datasets and curation by various members of the Cambridge Centre for Proteomics. 

table(pRolocmarkers("dmel"))

## 
##  Cytoskeleton            ER         Golgi      Lysosome       Nucleus 
##             7            24             7             8            21 
##            PM    Peroxisome    Proteasome  Ribosome 40S  Ribosome 60S 
##            25             4            14            22            32 
## mitochondrion 
##            15

This shows only markers for a subset of organelles: Cytoskeleton, ER, Golgi, Lysosome, Nucleus, PM (Plasma Membrane I assume), Peroxisome, ?Proteasome? (not sure what this is?), Ribosome 40S, Ribsome 60S, and Mitochondrion.  It misses many other sub-cellular compartments like Cytosol, Actin Filaments, Vesicles, etc.

Are these markers for proteins that are only specific to the organelle of interest? Meaning, it is not a protein that can be found in other subcellular compartments (multi-localizing protein)

What I would like to do is use information from the Human Proteome Atlas with pRoloc's `addMarkers()` function. This way instead of 11 organelles I have data on more subcellular compartments. The subcellular location data from their website has many proteins which mult-localize. Can this data be used instead as marker data? Or can you only use pRolocmarkers (Homo sapiens only has 872 fir example) with their Uniprot Protein Identifiers

Markers are defined as proteins that localise to a sub-cellular niche with high confidence. They are subsequently used to infer localisation of non-markers proteins (of unknown localisation), hence the need for high confidence for all or most markers. They are often defined based on a set of organelles of interest; in the case of whole proteome maps, one would want to document the sub-cellular diversity as much as possible. But it isn't always possible to reliably identify enough markers for all (or the majority of) know organelles. As a result, it is important to set threshold after classification, to limit assignment to this limited (but trustworthy) number of classes to high confident results (this is described in the main pRoloc vignette).

Choosing markers can be done in multiple ways: expert curation, or using GO and other resources, such as HPA or the markers we provide via pRolocmarkers as a guide. The important point is to validate these marker proteins. Cellular compartment documented in a generic resource might not reflect the localisation of that protein in your data (i.e. cell type under specific conditions). Validation is generally done by visualising the data (on a PCA plot for example - see plot2D) with highlighted markers, as assure that they define credible cluster. If a set of markers are scattered around, it might be because they actually aren't good markers, that sub-cellular niche isn't resolved in the data, or the data are noisy (in that case, all organelle clusters will suffer from a the lack of resolution).

The papers A foundation for reliable spatial proteomics data analysis describe this in more details.

Regarding the use of multi-localised markers, there is no reason not to define any, as long as you can find enough that share the same multi-localisation pattern. Subsequently, any proteins assigned to that class would be a candidate localising to that set of organelles.

PS: From Wikipedia: The proteasome are protein complexes which degrade unneeded or damaged proteins by proteolysis, a chemical reaction that breaks peptide bonds. Enzymes that help such reactions are called proteases.