| Next revision | Previous revisionNext revisionBoth sides next revision |
| start [2012/09/03 13:16] – external edit 127.0.0.1 | start [2020/10/12 14:58] – [BioConnector Wiki] pk7z |
|---|
| The BioConnector Wiki is a standards compliant, simple to use wiki, mainly aimed at creating documentation. It's simple but powerful [[https://www.dokuwiki.org/syntax|syntax]] makes it easy to share ideas, collaborate on projects, and document workflows. The wiki keeps a record of every [[https://www.dokuwiki.org/attic|revision]] and a list of [[https://www.dokuwiki.org/recent_changes|recent changes]], enabling transparency, reproducibility, and provenance throughout the data analysis and research life cycle. The wiki also supports [[https://www.dokuwiki.org/syntax#syntax_highlighting|syntax highlighting]] of nearly any programming language (including Perl, Python, and R), uploading and embedding of [[https://www.dokuwiki.org/images|images]] and other media, and organization of pages and permissions via [[https://www.dokuwiki.org/namespaces|namespaces]]. | The BioConnector Wiki is a standards compliant, simple to use wiki, mainly aimed at creating documentation. It's simple but powerful [[https://www.dokuwiki.org/syntax|syntax]] makes it easy to share ideas, collaborate on projects, and document workflows. The wiki keeps a record of every [[https://www.dokuwiki.org/attic|revision]] and a list of [[https://www.dokuwiki.org/recent_changes|recent changes]], enabling transparency, reproducibility, and provenance throughout the data analysis and research life cycle. The wiki also supports [[https://www.dokuwiki.org/syntax#syntax_highlighting|syntax highlighting]] of nearly any programming language (including Perl, Python, and R), uploading and embedding of [[https://www.dokuwiki.org/images|images]] and other media, and organization of pages and permissions via [[https://www.dokuwiki.org/namespaces|namespaces]]. |
| |
| Interested in documenting your group's work using the wiki? [[http://www.google.com/recaptcha/mailhide/d?k=016RBE9TN7Hvz1Ju8fMFOpzA==&c=tKJn9IgI_y0sZjVgHqezgmc0aljyrZADEE2snwoA7FE=|Email Stephen Turner]], director of the [[http://bioinformatics.virginia.edu|Bioinformatics Core]], for an account. | Interested in documenting your group's work using the wiki? [[http://www.google.com/recaptcha/mailhide/d?k=016RBE9TN7Hvz1Ju8fMFOpzA==&c=tKJn9IgI_y0sZjVgHqezgmc0aljyrZADEE2snwoA7FE=|Email Pankaj Kumar]], director of the [[http://bioinformatics.virginia.edu|Bioinformatics Core]], for an account. |
| |
| ====== Example Entry ====== | ====== Example Entry ====== |
| |
| The output from Signaling Pathway Impact Analysis is a list of pathways, whether they're activated or inhibited, and three different p-values. p(NDE) is the p-value on the Number of Differentially Expressed genes. This is nearly identical to the GO-overrepresentation analysis - it's the significance of the over-representation of differentially expressed genes in the given pathway. The p(PERT) is the significance of the overall perturbation of the pathway, which takes into account topology. [[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732297/figure/F1/|Figure 1 in the SPIA paper]] explains this well. p(G) is the combined p-value from both p(NDE) and p(PERT). p(G)<sub>FDR</sub> is the FDR-corrected overall p-value. The SPIA plots show both the p(NDE) and p(PERT) on each axis, so the most significant things are up in the upper right corner. [[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732297/figure/F4/|Figure 4 in the SPIA paper]] explains this well. | The output from Signaling Pathway Impact Analysis is a list of pathways, whether they're activated or inhibited, and three different p-values. p(NDE) is the p-value on the Number of Differentially Expressed genes. This is nearly identical to the GO-overrepresentation analysis - it's the significance of the over-representation of differentially expressed genes in the given pathway. The p(PERT) is the significance of the overall perturbation of the pathway, which takes into account topology. [[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732297/figure/F1/|Figure 1 in the SPIA paper]] explains this well. p(G) is the combined p-value from both p(NDE) and p(PERT). p(G)<sub>FDR</sub> is the FDR-corrected overall p-value. The SPIA plots show both the p(NDE) and p(PERT) on each axis, so the most significant things are up in the upper right corner. [[http://www.ncbi.nlm.nih.gov/pmc/articles/PMC2732297/figure/F4/|Figure 4 in the SPIA paper]] explains this well. |
| |
| {{:spia-plot.png?nolink&|}} | |
| |
| Limitations: It's worth noting that the method employed above has limitations. We don't fully understand biology, and our understanding of molecular networks and signaling pathways is still very low-resolution. We also don't have information about how different isoforms have different effects - which is something we'll get from RNA-seq experiments. Annotations are often incorrect and inaccurate, and we don't have very much cell-type specific or dynamic information about these pathways. Finally, the analysis of large gene lists with the current enrichment tools is still more of an exploratory data-mining procedure rather than a pure statistical solution and analytical endpoint. | Limitations: It's worth noting that the method employed above has limitations. We don't fully understand biology, and our understanding of molecular networks and signaling pathways is still very low-resolution. We also don't have information about how different isoforms have different effects - which is something we'll get from RNA-seq experiments. Annotations are often incorrect and inaccurate, and we don't have very much cell-type specific or dynamic information about these pathways. Finally, the analysis of large gene lists with the current enrichment tools is still more of an exploratory data-mining procedure rather than a pure statistical solution and analytical endpoint. |
