DataBiosphere · NoopDog · Sep 29, 2021 · Sep 29, 2021 · Sep 29, 2021
diff --git a/content/about/platform/attributions.md b/content/about/platform/attributions.md
@@ -1,7 +1,7 @@
 ---
-path: "/about/platform/attributions"
+componentName: "attributions"
 date: "2018-05-03"
-title: "Attributions"
 description: "Credits, copyrights and attributions for data.humancellatlas.org."
-componentName: "attributions"
+path: "/about/platform/attributions"
+title: "Attributions"
 ---
diff --git a/content/about/platform/data-use-agreement.md b/content/about/platform/data-use-agreement.md
@@ -1,9 +1,9 @@
 ---
-path: "/about/platform/data-use-agreement"
 date: "2018-05-03"
-title: "Data Use Agreement"
 description: "Data Use Agreement for the HCA DCP."
+path: "/about/platform/data-use-agreement"
 subTitle: ""
+title: "Data Use Agreement"
 ---
 
 # Data Use Agreement
@@ -13,4 +13,3 @@ Each dataset in this release is licensed under a [Creative Commons Attribution 4
 
 ## Data Use Policy
 For information regarding data sharing and data use, please see our [Data Release Policy](https://www.humancellatlas.org/data-release-policy/)
-
diff --git a/content/about/platform/dcp-roadmap.md b/content/about/platform/dcp-roadmap.md
@@ -1,16 +1,15 @@
 ---
-path: "/about/platform/dcp-roadmap"
 date: "2019-12-03"
-title: "DCP Roadmap"
 description: "The Data Coordination Platform’s (DCP) purpose is to support the creation of the Human Cell Atlas by providing a cloud-based platform for researchers to share, organize, analyze, and interrogate single-cell data, as described in five Strategic Aims."
 draft: true
+path: "/about/platform/dcp-roadmap"
+title: "DCP Roadmap"
 ---
 
 # Our Mission
 
 The Data Coordination Platform’s (DCP) purpose is to support the creation of the Human Cell Atlas by providing a cloud-based platform for researchers to share, organize, analyze, and interrogate single-cell data, as described in five Strategic Aims. You can read more about these aims on the DCP Strategy page. 
 
-
 # DCP Strategic Aims
 
 Aim 1. Create a data resource that maximizes the value and use of Human Cell Atlas data across the scientific community.
@@ -23,7 +22,6 @@ Aim 4. Build alignment amongst the Human Cell Atlas community around a core set
 
 Aim 5. Create standards for the community to use to describe single-cell experimental designs, including assay types, data and metadata.
 
-
 # Current Role of the DCP in HCA Atlas Development
 
 To support initial efforts in atlas development, the DCP team is focusing on describing experimental design, collecting and harmonizing data, and assuring data integrity, serving both the biologists generating single-cell data and the computational biologists needing to access it. 
@@ -35,4 +33,3 @@ To support initial efforts in atlas development, the DCP team is focusing on des
 The DCP Quarterly Roadmap lists the in progress and upcoming activities for achieving the DCP Strategic Aims as they relate to our current role in atlas development.
 
 ![Practice_table](../_images/Practice_table.png)
-
diff --git a/content/about/platform/dcp.md b/content/about/platform/dcp.md
@@ -1,8 +1,8 @@
 ---
 date: "2018-05-03"
-title: "About the Data Coordination Platform"
 description: "The Human Cell Atlas Data Coordination Platform (HCA DCP) is an open source, cloud-based platform developed to organize, standardize, and make accessible the data that constitute the Human Cell Atlas."
 draft: false
+title: "About the Data Coordination Platform"
 ---
 
 # About the Data Coordination Platform
@@ -11,14 +11,12 @@ The Human Cell Atlas (HCA) community is profiling millions of human cells, a pro
 
 To help coordinate this data collection and processing, the HCA established the **Data Coordination Platform (DCP), a public, cloud-based platform where scientists can share, organise and interrogate single-cell data**.
 
+<img alt="Data Flow" src="../_images/Data_flow.png" width="750">
 
-<img src="../_images/Data_flow.png" width="750">
-
-\
 The platform was developed and is operated by a dedicated team of scientists, engineers and bioinformaticians from the European Bioinformatics Institute (EBI), the Broad Institute (Broad), the Chan Zuckerberg Initiative (CZI) and the University of California, Santa Cruz (UCSC). 
 
-
 ## Data Coordination Platform Strategic Aims
+
 The DCP Strategic Aims detail the DCP team’s long-term goals for atlas development: 
 
 Aim 1. Create a data resource that maximizes the value and use of Human Cell Atlas data across the scientific community.
@@ -32,8 +30,9 @@ Aim 4. Build alignment amongst the Human Cell Atlas community around a core set
 Aim 5. Create standards for the community to use to describe single-cell experimental designs, including assay types, data and metadata.
 
 ## Contribute
-  
+
 We encourage you to contribute your data or analysis portals and methods. Learn more on the [Contribute](/contribute) page.
 
 ## Have Questions or Feedback? Contact Us!
+
 The Data Coordination Platform is continuously developed and improved in response to researchers’ needs and feedback. Navigate to the [Contact](/contact) page to ask questions or provide feedback.
diff --git a/content/about/platform/hca.md b/content/about/platform/hca.md
@@ -1,15 +1,15 @@
 ---
 date: "2018-05-03"
-title: "About the Human Cell Atlas"
 description: "Our mission is to create comprehensive reference maps of all the cells in the human body as a basis for both understanding human health and diagnosing, monitoring, and treating disease."
+title: "About the Human Cell Atlas"
 ---
 
 # About the Human Cell Atlas
+
 The Human Cell Atlas (HCA) is a collaborative community of international scientists. Our mission is to create comprehensive reference maps of all the cells in the human body as a basis for both understanding human health and diagnosing, monitoring, and treating disease.
 
 The HCA registry has more than one thousand member scientists from hundreds of institutions around the world. The project is steered and governed by an Organizing Committee, co-chaired by Aviv Regev and Sarah Teichmann.
 
 To learn more about the HCA, visit <https://www.humancellatlas.org>.
 
 To join the HCA community, you can register on this page: <https://www.humancellatlas.org/join-hca>
-
diff --git a/content/analyze/methods/methods-packages.md b/content/analyze/methods/methods-packages.md
@@ -1,9 +1,7 @@
 ---
-path: "/analyze/methods/methods-packages"
+componentName: "analyze"
 date: "2018-05-03"
-title: "Methods Packages"
 description: "Methods packages for performing analyses involving computational biology approaches for analyzing single-cell data."
-componentName: "analyze"
 linked:
     - ./methods-packages/infer.md
     - ./methods-packages/magic.md
@@ -12,13 +10,14 @@ linked:
     - ./methods-packages/slingshot.md
     - ./methods-packages/spectre.md
     - ./methods-packages/stream.md
+path: "/analyze/methods/methods-packages"
+title: "Methods Packages"
 ---
 
-#  Methods Packages
+# Methods Packages
 
 Methods packages listed below are tools for performing analyses involving computational biology approaches for analyzing single-cell data. Method software is pre-installed in container images. Registry methods can be called programmatically for easy integration into portals. These methods provide domain-specific ways to analyze biological data produced by Human Cell Atlas.
 
 These solutions are built by third parties. This information is provided as a service to the community and does not constitute an endorsement by the HCA.
 
-
->Are you developing a package that can consume HCA data? Please [submit it](/contribute/analysis-tools-registry) for inclusion in the registry.
+> Are you developing a package that can consume HCA data? Please [submit it](/contribute/analysis-tools-registry) for inclusion in the registry.
diff --git a/content/analyze/methods/methods-packages/infer.md b/content/analyze/methods/methods-packages/infer.md
@@ -1,13 +1,13 @@
 ---
-path: "/analyze/methods/methods-packages/infer"
-date: "2019-03-26"
-title: "inferCNV"
+appUrl: "https://github.com/broadinstitute/infercnv"
 author: "Brian J. Haas, Christophe H. Georgescu, Maxwell P. Brown, Timothy L. Tickle, Livnat Jerby, Matan Hofree, Itay Tirosh, Aviv Regev"
+componentName: "analysisDetail"
+date: "2019-03-26"
 description: "InferCNV is used to explore tumor single cell RNA-Seq data to identify evidence for large-scale chromosomal copy number variations."
 githubUrl: "https://github.com/broadinstitute/infercnv"
-appUrl: "https://github.com/broadinstitute/infercnv"
+path: "/analyze/methods/methods-packages/infer"
+title: "inferCNV"
 upstreamRegistryUrl: "Submitted to bioconductor on 03/13/2019"
-componentName: "analysisDetail"
 ---
 
 [![Build Status](https://travis-ci.com/broadinstitute/infercnv.svg?branch=master )](https://travis-ci.com/broadinstitute/infercnv)
@@ -44,17 +44,18 @@ docker run -v ${PWD}:/data -w /data --rm -it singlecellportal/infercnv:0-99-5 /i
      --denoise
 ```
 
-
-
 ## Validate
+
 Run this command to confirm your container produces correct reference output: 
 
 ```
 docker run -v ${PWD}:/data -w /data --rm -it singlecellportal/infercnv:0-99-5  Rscript /inferCNV/scripts/infercnv_validate.R
 ```
 
 ## Integrate
+
 [Run inferCNV](https://github.com/broadinstitute/single_cell_portal/wiki/Running-inferCNV) in Single Cell Portal.
 
 ## Contact
+
 Christophe Georgescu ([[email protected]](mailto:[email protected]))
diff --git a/content/analyze/methods/methods-packages/magic.md b/content/analyze/methods/methods-packages/magic.md
@@ -1,13 +1,13 @@
 ---
-path: "/analyze/methods/methods-packages/magic"
-date: "2019-01-23"
-title: "Markov Affinity-based Graph Imputation of Cells (MAGIC)"
+appUrl: "https://pypi.org/project/magic-impute/"
 author: "David van Dijk, Kevin Moon, Scott Gigante, Daniel Dager, Guy Wolf, Smita Krishnaswamy"
+componentName: "analysisDetail"
+date: "2019-01-23"
 description: "Markov Affinity-based Graph Imputation of Cells (MAGIC) is an algorithm for denoising and imputation of single cells applied to single-cell RNA sequencing data"
 githubUrl: "https://github.com/KrishnaswamyLab/MAGIC/"
-appUrl: "https://pypi.org/project/magic-impute/"
+path: "/analyze/methods/methods-packages/magic"
+title: "Markov Affinity-based Graph Imputation of Cells (MAGIC)"
 upstreamRegistryUrl: "https://pypi.org/project/magic-impute/"
-componentName: "analysisDetail"
 ---
 
 [![Build Status](https://travis-ci.com/KrishnaswamyLab/MAGIC.svg?branch=master)](https://travis-ci.com/KrishnaswamyLab/MAGIC#)
@@ -22,13 +22,10 @@ docker pull scottgigante/magic:release-1.1
 
 Here we download a csv file containing raw scRNA-seq counts, preprocess it by filtering cells with less than 2000 counts, library size normalize and then apply a square root transform before running MAGIC, then save the smoothed data matrix to magic_output.csv in your current working directory.
 
-
-
 ```
 docker run -v ${PWD}:/data --rm scottgigante/magic:release-1.1 --filename  https://github.com/KrishnaswamyLab/MAGIC/raw/master/data/HMLE_TGFb_day_8_10.csv.gz  --min-library-size 2000 --normalize --transform sqrt --knn 5 --decay 15 --all-genes  --output /data/magic_output.csv
 ```
 
-
 ## Validate
 Run this command to confirm your container produces correct reference output:
 
@@ -37,4 +34,5 @@ docker run --rm scottgigante/magic:release-1.1 --validate
 ```
 
 ## Contact
+
 Scott Gigante ([[email protected]](mailto:[email protected]))
diff --git a/content/analyze/methods/methods-packages/phate.md b/content/analyze/methods/methods-packages/phate.md
@@ -1,13 +1,13 @@
 ---
-path: "/analyze/methods/methods-packages/phate"
-date: "2019-01-23"
-title: "Potential of Heat-diffusion for Affinity-based Transition Embedding (PHATE)"
+appUrl: "https://pypi.org/project/phate/"
 author: "Kevin Moon, David van Dijk, Scott Gigante, Smita Krishnaswamy"
+componentName: "analysisDetail"
+date: "2019-01-23"
 description: "PHATE is a tool for visualizing high dimensional single-cell data with natural progressions or trajectories."
 githubUrl: "https://github.com/KrishnaswamyLab/PHATE/"
-appUrl: "https://pypi.org/project/phate/"
+path: "/analyze/methods/methods-packages/phate"
+title: "Potential of Heat-diffusion for Affinity-based Transition Embedding (PHATE)"
 upstreamRegistryUrl: "https://pypi.org/project/phate/"
-componentName: "analysisDetail"
 ---
 
 [![Build Status](https://travis-ci.com/KrishnaswamyLab/PHATE.svg?branch=master)](https://travis-ci.com/KrishnaswamyLab/PHATE#)
@@ -26,13 +26,14 @@ Here we download a csv file containing raw scRNA-seq counts, preprocess it by fi
 docker run -v ${PWD}:/data --rm scottgigante/phate:release-1.1 --filename https://github.com/KrishnaswamyLab/MAGIC/raw/master/data/HMLE_TGFb_day_8_10.csv.gz --min-library-size 2000 --normalize --transform sqrt --knn 5 --decay 15 --output /data/phate_output.csv
 ```
 
-
 ## Validate
+
 Run this command to confirm your container produces correct reference output:
 
 ```
 docker run --rm scottgigante/phate:release-1.1 --validate
 ```
 
 ## Contact
+
 Scott Gigante ([[email protected]](mailto:[email protected]))
diff --git a/content/analyze/methods/methods-packages/sc3.md b/content/analyze/methods/methods-packages/sc3.md
@@ -1,13 +1,13 @@
 ---
-path: "/analyze/methods/methods-packages/sc3"
-date: "2019-02-28"
-title: "Single-cell consensus clustering (SC3)"
+appUrl: "http://bioconductor.org/packages/SC3"
 author: "Martin Hemberg (SC3), Gene Expression Team (sc3-scripts)"
+componentName: "analysisDetail"
+date: "2019-02-28"
 description: "SC3 is an unsupervised clustering method for scRNA-seq data."
 githubUrl: "https://github.com/hemberg-lab/SC3"
-appUrl: "http://bioconductor.org/packages/SC3"
+path: "/analyze/methods/methods-packages/sc3"
+title: "Single-cell consensus clustering (SC3)"
 upstreamRegistryUrl: "http://bioconductor.org/packages/SC3"
-componentName: "analysisDetail"
 ---
 
 [![Build Status](http://www.bioconductor.org/shields/build/release/bioc/SC3.svg)](https://git.bioconductor.org/packages/SC3)
@@ -31,6 +31,7 @@ docker run -v ${PWD}:/data -w /data --rm quay.io/biocontainers/bioconductor-sc3-
 ```
 
 ## Validate
+
 Run this command to confirm your container produces correct reference output:
 
 ```
@@ -40,5 +41,6 @@ docker run -v ${PWD}:/data -w /data --rm quay.io/biocontainers/bioconductor-sc3-
 ```
 
 ## Contact
+
 Martin Hemberg, SC3 ([[email protected]](mailto:[email protected]))\
 Gene Expression Team, sc3-scripts ([[email protected]](mailto:[email protected]))
diff --git a/content/analyze/methods/methods-packages/slingshot.md b/content/analyze/methods/methods-packages/slingshot.md
@@ -1,13 +1,13 @@
 ---
-path: "/analyze/methods/methods-packages/slingshot"
-date: "2018-09-10"
-title: "Slingshot"
+appUrl: "http://bioconductor.org/packages/slingshot"
 author: "Kelly Street, Davide Risso, Diya Das, Sandrine Dudoit, Koen Van den Berge, and Robrecht Cannoodt"
+componentName: "analysisDetail"
+date: "2018-09-10"
 description: "Slingshot provides functions for inferring continuous, branching lineage structures in low-dimensional data."
 githubUrl: "https://github.com/kstreet13/slingshot"
-appUrl: "http://bioconductor.org/packages/slingshot"
+path: "/analyze/methods/methods-packages/slingshot"
+title: "Slingshot"
 upstreamRegistryUrl: "http://bioconductor.org/packages/slingshot"
-componentName: "analysisDetail"
 ---
 
 [![Build Status](https://travis-ci.org/kstreet13/slingshot.svg?branch=master)](https://travis-ci.org/kstreet13/slingshot)
@@ -46,11 +46,13 @@ docker run -v ${PWD}:/data -w /data --rm -it quay.io/kstreet13/slingshot-docker:
 ```
 
 ## Validate
+
 Run this command to confirm your container produces correct reference output: 
 
 ```
 docker run -v ${PWD}:/data -w /data --rm -it quay.io/kstreet13/slingshot-docker:1.1.2 Rscript /software/scripts/run_slingshot.R  --validate
 ```
 
 ## Contact
+
 Kelly Street ([[email protected]](mailto:[email protected]))
diff --git a/content/analyze/methods/methods-packages/spectre.md b/content/analyze/methods/methods-packages/spectre.md
@@ -1,13 +1,13 @@
 ---
-path: "/analyze/methods/methods-packages/spectre"
-date: "2021-08-03"
-title: "Spectre"
+appUrl: ""
 author: "Thomas Ashhurst, Felix Marsh-Wakefield, Givanna Putri"
+componentName: "analysisDetail"
+date: "2021-08-03"
 description: "Spectre is an R package and computational toolkit that enables comprehensive end-to-end integration, exploration, and analysis of high-dimensional cytometry or imaging data."
 githubUrl: "https://github.com/ImmuneDynamics/Spectre"
-appUrl: ""
+path: "/analyze/methods/methods-packages/spectre"
+title: "Spectre"
 upstreamRegistryUrl: ""
-componentName: "analysisDetail"
 ---
 
 ![Build Status](https://camo.githubusercontent.com/e778875bb2f2b2aa0bcf9e842d1a5d4a97e6185a02c4b3118d286b39364e2dcf/68747470733a2f2f63692e6170707665796f722e636f6d2f6170692f70726f6a656374732f7374617475732f616b68766238777562366436786874643f7376673d74727565)
@@ -21,6 +21,7 @@ Spectre streamlines the analytical stages of raw data pre-processing, batch alig
 For users unfamiliar with using R, we also provide workflow instructions for replicating many of our analysis approaches in programs such as FlowJo.
 
 ## Docker URL
+
 [Method-ready Docker image URL](https://hub.docker.com/r/immunedynamics/spectre)
 
 ![Spectre](../../_images/methods/spectre.png)
@@ -29,7 +30,6 @@ For users unfamiliar with using R, we also provide workflow instructions for rep
 
 Extensive command line usage in R or RStudio provided at https://immunedynamics.github.io/spectre/.
 
-
 ## Contact
-Thomas Ashhurst (<mailto:[email protected]>)
 
+Thomas Ashhurst (<mailto:[email protected]>)