diff --git a/package.json b/package.json index 5c94bca5..155b01bf 100644 --- a/package.json +++ b/package.json @@ -33,6 +33,7 @@ "react-tooltip": "^5.11.2", "redux": "^4.0.1", "redux-thunk": "^2.3.0", + "tocbot": "^4.25.0", "victory": "36.4.1", "vis-data": "^7.1.7", "vis-network": "^9.1.8", diff --git a/src/AnalysisPage/GraphicalClustering/Pass1bLandscapeTissues/ADRNL.jsx b/src/AnalysisPage/GraphicalClustering/Pass1bLandscapeTissues/ADRNL.jsx new file mode 100644 index 00000000..f58acabd --- /dev/null +++ b/src/AnalysisPage/GraphicalClustering/Pass1bLandscapeTissues/ADRNL.jsx @@ -0,0 +1,306 @@ +import React, { useEffect } from 'react'; +import * as tocbot from 'tocbot'; +import { + tocbotConfig, + pass1b06GraphicalClusteringLandscapeImageLocation, +} from '../sharedLib'; + +function GraphicalAnalysisAdrenal() { + // initialize table of contents + useEffect(() => { + tocbot.init(tocbotConfig); + }, []); + + // load plot images + const tissueImageFolder = 'adrenal'; + const imageURL = `${pass1b06GraphicalClusteringLandscapeImageLocation}/${tissueImageFolder}`; + + return ( +
+ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
+ ++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
+ ++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
+ ++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
+ ++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for BAT:8w_F-1_M1 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
+ ++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
+ ++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
+ ++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
+ ++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for CORTEX:8w_F-1_M0 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
+ ++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
+ ++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ + + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
+ ++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
+ ++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for HIPPOC:8w_F-1_M0 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ + + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + No significant enrichments for + KIDNEY:1w_F-1_M0->2w_F-1_M0->4w_F-1_M0->8w_F-1_M-1 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ + + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ + + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + No significant enrichments for PLASMA:8w_F-1_M-1 +
++ + + No significant enrichments for PLASMA:8w_F-1_M0 +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for PLASMA:8w_F1_M1 +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
+ ++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
+ ++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ + + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for SMLINT:8w_F1_M1 +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ + + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + No significant enrichments for + ADRNL:1w_F1_M-1->2w_F1_M0->4w_F1_M0->8w_F1_M0 +
++ + + No significant enrichments for + ADRNL:1w_F0_M1->2w_F-1_M0->4w_F-1_M0->8w_F-1_M0 +
++ + + No significant enrichments for + ADRNL:1w_F-1_M1->2w_F-1_M1->4w_F-1_M0->8w_F-1_M0 +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for ADRNL:8w_F-1_M-1 +
++ + + +
++ + + No significant enrichments for ADRNL:8w_F0_M-1 +
++ + + No significant enrichments for ADRNL:8w_F0_M1 +
++ + + No significant enrichments for ADRNL:8w_F1_M0 +
++ + + No significant enrichments for ADRNL:8w_F1_M1 +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + No significant enrichments for + BAT:1w_F0_M0->2w_F0_M0->4w_F0_M0->8w_F0_M-1 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for + COLON:1w_F0_M-1->2w_F0_M-1->4w_F0_M0->8w_F1_M0 +
++ + + No significant enrichments for + COLON:1w_F-1_M0->2w_F-1_M-1->4w_F-1_M0->8w_F0_M0 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ + +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for LIVER:8w_F-1_M1 +
++ + + +
++ + + +
++ + + No significant enrichments for LIVER:8w_F1_M0 +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for LUNG:8w_F1_M1 +
++ + + +
++ + + +
++ + + No significant enrichments for LUNG:1w_F0_M1—2w_F0_M1 +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for + SKM-GN:1w_F0_M0->2w_F0_M0->4w_F1_M0->8w_F1_M1 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for SKM-GN:8w_F0_M-1 +
++ + + No significant enrichments for SKM-GN:8w_F0_M1 +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for + SKM-VL:1w_F1_M0->2w_F1_M0->4w_F1_M0->8w_F1_M0 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + No significant enrichments for + SPLEEN:1w_F0_M0->2w_F0_M0->4w_F0_M0->8w_F1_M1 +
++ + + +
++ + + No significant enrichments for SPLEEN:1w_F1_M0 +
++ + + No significant enrichments for SPLEEN:8w_F0_M1 +
++ + + No significant enrichments for SPLEEN:8w_F1_M0 +
++ + + No significant enrichments for SPLEEN:1w_F1_M0—2w_F1_M0 +
++ + + No significant enrichments for SPLEEN:1w_F0_M1—2w_F0_M1 +
++ Graphical analysis has replaced clustering as the primary method + of exploring main patterns in the PASS1B data. This approach is + more flexible and gives us better resolution. +
+Tree of ALL differential analytes (all paths)
++ +
++ A “cluster” is a path, node, or edge. Here we show the size and + ome distributions for selected clusters in this tissue. +
++ +
++ Here we show trees of the 5 largest trajectories (also called + paths) in this tissue, either with all differential features or + features split by ome group. +
++ +
++ +
++ +
++ Here we show highlighted trees, top pathway enrichments, network + view of all pathway enrichments, and sample-level + trajectories for each selected cluster (node, edge, or path). +
++ Interactive networks of pathway enrichments +
++ These networks summarize all significant pathway enrichments for a + set of differential analytes. Results from all omes are combined. +
++ Each node is a pathway. Hover over a node to see the pathway name + (and parent pathway in parentheses), nominal enrichment p-value, + datasets in which this pathway was significantly enriched, and the + union of genes at the intersection of the input features and + pathway members. Larger nodes indicate that more datasets (e.g. + METAB;SKM-GN) were significantly enriched for this pathway.{' '} + + Pathways only enriched with metabolites are not shown because + edges are defined using genes, not KEGG IDs. + +
++ Edges are drawn between nodes if there is a substantial overlap in + the intersection of the input features and pathway members for + both pathways. Hover over an edge to see the similarity score and + list of genes in the intersection. +
++ Nodes are colored to visually separate groups of related pathway + enrichments. Each group has a label (rectangular node), which + corresponds to the most frequently occurring parent pathway in the + group. These labels are meant to help summarize groups of related + pathway enrichments. +
+Explore these interactive plots!
++ + + +
++ + + +
++ + + No significant enrichments for + WAT-SC:1w_F0_M-1->2w_F0_M-1->4w_F0_M-1->8w_F0_M-1 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + No significant enrichments for WAT-SC:8w_F-1_M-1 +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ + + +
++ Objectives of these reports: +
++ Background about graphical clustering analysis: +
+
+ A graphical approach with repfdr
has replaced multiomics
+ clustering as the primary method to characterize and explore main
+ patterns of training-differential analytes in the PASS1B data. To learn
+ more about this approach, see presentations by David Amar{' '}
+
+ Briefly, each differential molecule is assigned one of nine states
+ [(male up/1, male null/0, male down/-1) x (female up/1, female null/0,
+ female down/-1)] for each training time point (1, 2, 4, and 8 weeks).
+ These states are our nodes
in the graphs. Then, for each
+ pair of nodes (x,y) such that y is from a time point that is immediately
+ after x (e.g., x is a node from week 4 and y is a node from week 8), we
+ define their edge set as the intersection of their analytes. This
+ defines the edges
in the graphs. By visualizing these
+ graphs and characterizing different nodes, edges, and paths (i.e. a set
+ of edges that traverses all time points), we can extract meaningful
+ biology.
+
+ We refer to sets of molecules in specific edges, nodes, or paths as{' '}
+ graphical clusters. Throughout this report, you will
+ see labels for these clusters, e.g.{' '}
+
+ "SKM-GN:1w_F-1_M-1->2w_F-1_M-1->4w_F-1_M-1->8w_F-1_M-1"
+
+ . Here’s how to break it down:
+
SKM-GN:
+ 1w_F-1_M-1
is a node that characterizes molecules at the{' '}
+ 1w
time point that are down-regulated in females (
+ F-1
) and down-regulated in males (M-1
).
+ These three pieces of information (time point, female state, male
+ state) are separated by underscores (_
)---
and connect a pair of nodes
+ ->
and connect four nodes
+