From 7af27a2c036949f2e4c6e8e89ad04c9446098072 Mon Sep 17 00:00:00 2001 From: DnlRKorn Date: Mon, 8 Jul 2024 11:23:50 -0400 Subject: [PATCH] Update data-integration.md to fix Figure numbering discrepancies detailed in Issue 952. --- docs/reference/data-integration.md | 14 +++++++------- 1 file changed, 7 insertions(+), 7 deletions(-) diff --git a/docs/reference/data-integration.md b/docs/reference/data-integration.md index 7b2b968b..24e88f1e 100644 --- a/docs/reference/data-integration.md +++ b/docs/reference/data-integration.md @@ -182,9 +182,9 @@ Note though that such methods lack the transparency of basic methods, which mean ![uPheno integration](../images/phenotype-integration.png) -!!! note "Figure: data integration with uPheno" +!!! note "Figure 3: data integration with uPheno" - The Figure shows 4 different kinds of integration: + Figure 3 shows 4 different kinds of integration: - A: Measurement data. A measurement in conjunction with a normal range and a mapping to a trait term is transformed to a Phenotypic abnormality term in HPO. - B: Unstructured data. Free text, for example in a paper, is translated into pre-coordinated uPheno expressions @@ -210,11 +210,11 @@ In the following we discuss a few of the most common forms of knowledge. _Core ontological relationships_ such as "is-a" or "part-of" are the most boring of all kinds of knowledge, but they have a huge potential for data analysis. For example, in Figure 1 above we can see that "Hypolysinemia" (a human phenotype) is a subclass of "decreased level of lysine in the blood" (a species independent class). -This is already nice, but lets look at what we _really_ get when we employ uPheno in Figure 2: +This is already nice, but lets look at what we _really_ get when we employ uPheno in Figure 4: ![uPheno Class Hierarchy](../images/upheno_hierarchy.png) -!!! note "Figure 2: uPheno class hierarchy of Hypolysinemia." +!!! note "Figure 4: uPheno class hierarchy of Hypolysinemia." The class hierarchy of uPheno, rendered using OLS. The screenshot only displays a fraction of the actual hierarchy, which is heavily poly-hierarchical. @@ -230,7 +230,7 @@ Here we can see just how deeply a concept like "Hypolysinemia" can be integrated !!! warning - The exact naming conventions in uPheno are under review at the moment, so the reader may experience some discrepancies between Figure 2, the listing above, and the [ontology in Monarch's OLS](https://ols.monarchinitiative.org/ontologies/upheno2). + The exact naming conventions in uPheno are under review at the moment, so the reader may experience some discrepancies between Figure 4, the listing above, and the [ontology in Monarch's OLS](https://ols.monarchinitiative.org/ontologies/upheno2). Not everyone will agree that all of these groupings are particularly useful (`changed blood amino acid level` may not have that many realy world use cases), but the fact that we _can_ aggregate our data on so many levels is compelling. @@ -346,9 +346,9 @@ All of these phenotype assocations can be augmented with many others, such as ge ![uPheno Integration of Knowledge](../images/integration_links.png) -!!! note "Figure: Integrating Knowledge in the uPheno framework." +!!! note "Figure 5: Integrating Knowledge in the uPheno framework." - This picture looks complicated, but it shows only a fraction of the available relationships. + Figure 5 looks complicated, but it shows only a fraction of the available relationships. Most of the relationships are phenotypic or core ontological, only the Hypolysinemia link to `SLC7A7` is an KG associations. There are dozens of different kinds of assocations that could be added here!