Proteomics data Vs Transcriptomics data?
This is what I found from doing some research. Comments are welcome at any point!


Capture percentage for data gathering:


Transcriptomics Data: There's an amplification step in Transcriptomics data gathering methods. Hence, it's possible to capture almost the totality of the Transcriptome using those methods(scRNA seq methods, Nanopore tech, Spatial transcriptomics, etc)
Proteomics Data: There's no amplification step in Proteomics data gathering methods. Those methods have fragmentation and a rejoining step which leads to a loss of the data.

Scalability of methods:


Transcriptomics Data: Scalable methods available
Proteomics Data: Less scalable methods for protein studies

Reference availability:


Transcriptomics Data: Fully annotated references available on consortiums such as ensembl biomart, etc
Proteomics Data: Universal and Comprehensive Human Proteome reference is still in question and because of the incomplete and inaccurate references, much of the newly generated data is being rendered useless

Uniformity


Transcriptomics Data: Lots of publications on pipelines, and their benchmarking available
Proteomics Data: Lack of uniformity across labs/research groups and lack of related literature lead to differences in protein fragmentation and solubilization and differences in algorithms to run analyses

Technical Bias


Transcriptomics Data: Many methods available to tackle cell-bias, noise in data, and batch effects, so as to capture maximum biological variability
Proteomics Data: Cell-wide Mass spectrometry has a bias towards the identification of peptides with higher concentration or contamination from other experiments