PMC:4996411 / 33515-35514
Annnotations
{"target":"https://pubannotation.org/docs/sourcedb/PMC/sourceid/4996411","sourcedb":"PMC","sourceid":"4996411","source_url":"https://www.ncbi.nlm.nih.gov/pmc/4996411","text":"3.9. Further Considerations\nAlthough more than 1000 samples may be printed on a single slide and up to a few hundred different proteins can be probed by RPPA [6,15], certain experimental settings might still involve larger numbers of samples to be analyzed in a high-throughput-fashion. Thus, several slides might be required to accommodate all samples of a study. In addition, in clinical settings it might be of interest to compare results from different studies or across different labs.\nIn this instance, issues such as inter-slide variability and normalization are pivotal but finding an appropriate normalization approach presents still a challenge [17,36]. Data of positive control spots, serial dilutions and of slides stained for total protein are required for several reasons. This data increases the accuracy of the estimated protein expression levels, adjusts for slide-to-slide variability and compensates for differences in sample loading. Additionally, effects caused by spatial artifacts can be compensated as these might be additive in comparisons of slides from different print runs or from independent signal detection runs. Thus, the experimental design should include a well-defined set of controls shared by RPPA printing runs over many years and by different groups.\nAgain, the advantage of standardized array design and analysis approaches comes forward and points towards the fact that uniquely designed experimental set-ups might be confounded by a lack of standardization. For this reason, existing methods need to be compared to define an appropriate work flow as standard for RPPA experimentation. Nevertheless, a certain flexibility regarding the experimental design of new projects and new biological questions will require different set-ups. Consequently, flexible methods are needed which in addition can be used in a standardized pipeline for data analysis. Non-flexible tools only developed for in-house solutions do not help to reach this goal in the near future.","divisions":[{"label":"Title","span":{"begin":0,"end":27}}],"tracks":[{"project":"2_test","denotations":[{"id":"27600238-23552733-69479246","span":{"begin":159,"end":160},"obj":"23552733"},{"id":"27600238-24777629-69479247","span":{"begin":161,"end":163},"obj":"24777629"},{"id":"27600238-19336447-69479248","span":{"begin":656,"end":658},"obj":"19336447"},{"id":"27600238-19055840-69479249","span":{"begin":659,"end":661},"obj":"19055840"}],"attributes":[{"subj":"27600238-23552733-69479246","pred":"source","obj":"2_test"},{"subj":"27600238-24777629-69479247","pred":"source","obj":"2_test"},{"subj":"27600238-19336447-69479248","pred":"source","obj":"2_test"},{"subj":"27600238-19055840-69479249","pred":"source","obj":"2_test"}]}],"config":{"attribute types":[{"pred":"source","value type":"selection","values":[{"id":"2_test","color":"#93e1ec","default":true}]}]}}