add book

01-metadata.Rmd

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+# Overview of Metadata Types
+
+For each outcome type, include information on:
+
+- Overview of experimental procedures performed prior to collecting the data (and relevant technical parameters) -> refer to timeline diagram
+- Glossary of every variable in the tool: what precisely does it mean, what are the units, etc
+- Who collected the data (institution)
+- Replicates - were there any, are they different groups of islets, different individual cells, etc
+- Explanation of any changes in protocols (ie. switch to different GSIS concentrations, maybe different platform used halfway through, etc)
+- Whether data was intentionally collected on specific subsets of donors
+- Versions of data available for download
+- Data processing prior to statistical analysis (merging replicates, transformations, removed outliers, definition of any computed/normalized values)
+- Any additional notes
+- Citations of papers that have published parts of the data
+
+
+## Donor Characteristics:
+
+Donor characteristics are provided to the Isletcore team through the organ procurement programs. These characteristics can come from the family and aren’t always accurate reflections of the medical history. Hba1c measurements are performed in the Isletcore when the donor blood sample is available. 
+
+## Organ Characteristics and Processing:
+
+Organ characteristic and processing data is recorded during the islet isolation by the lead technician. Cold Ischemia time will be calculated based on the cross clamp time during organ procurement until the start of the isolation in Edmonton. 
+
+## Isolation Outcomes:
+
+Islet purity, IEQ, % trapped, IPI are determined by the lead technician. Full details on determining IEQ, purity etc can be found [here](https://www.protocols.io/view/human-islet-quantification-and-purity-assessment-14egnxxwml5d/v3/) or in our [IsletCore](http://www.bcell.org/uploads/5/1/3/3/51338649/adi_isletcore_welcome_booklet.pdf) welcome booklet. Details on determining Insulin and DNA samples can always be found [here](https://www.protocols.io/view/sampling-of-human-islets-for-quality-control-purpo-j8nlk553dl5r/v2). Cell proportion data is determined through our proteomics data. Proteomics samples are shipped to Jim Johnson’s lab at UBC. Samples are then hand picked and snap frozen for processing. 
+
+## Cell Culture Outcomes:
+
+On the day of shipping, Islets are recounted to determine IEQ, % recovery, IPI after culture. Culture time is determined by the end of the isolation until approximately 9am on a shipping day. Post culture samples are collected for insulin and DNA as [previously described](https://www.protocols.io/view/sampling-of-human-islets-for-quality-control-purpo-j8nlk553dl5r/v2).
+
+## Static Insulin Secretion:
+
+In triplicates, 15  hand picked islets from each donor were sequentially treated with low glucose for 1 hour followed by high glucose 1 hour. Collected supernatant for low glucose, high glucose or insulin content are measured by ELISA and recorded in the database. For each donor, there are multiple glucose combinations that may have been used. Glucose pairs are 1mM to 10mM glucose, 1mM to 16.7mM glucose or 2.8mM glucose to 16.7mM glucose. Islets used in the 1mM to 10mM group are unique from the 1mM to 16.7mM group. The secretion data are commonly presented as a percentage of the total content (secreted insulin/total insulin *100), and as a stimulation index (high glucose secretion/low glucose secretion). Biological outliers were defined as “Seth is coming up with the definition now”, and identified outliers were removed from the dataset. The data for each replicate (after outlier removal) can be downloaded from the data export page. The total insulin content, secretion percentages, and stimulation indices, averaged across replicates, can be queried, analyzed, and visualized in the other parts of the tool. Complete experimental details can be found [here](https://www.protocols.io/view/static-glucose-stimulated-insulin-secretion-gsis-p-n2bvjkzxgk5w/v3). 
+
+
+## Islet Oxygen Consumption (Seahorse assay):
+
+
+## Dynamic Insulin Responses to Macronutrients:
+
+Human islets are shipped to Jim Johnson’s lab at UBC. Islets are hand picked and cultured in RPMI for 24-72hrs before the experiment to allow the islets to recover from shipping. 65 islets are loaded per chamber and pre-incubated for 1 hour. Islets are stimulated with 6 or 15mM glucose, 5mM Leucine or 0.75mM Oleic acid/0.75mM palmitic acid as indicated. Samples are stored at -20C until analysis with RIA. Full details are described [here](https://www.medrxiv.org/content/10.1101/2023.05.24.23290298v1.full.pdf). 
+
+## Single-cell Function (electrophysiology)
+
+Human islets are hand picked and dispersed into single cells and plated on 35mm cell culture dishes by the MacDonald lab. 24-72 hours after the dispersion, electrophysiological measurements are recorded. Cell identity is confirmed by immunohistochemistry. 
+
+
+
+
+

02-omics-data.Rmd

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+# Overview of Omics Data Types
+
+For each data type, include information on:
+
+- Platform(s)
+- Batches (and whether batch effect is significant)
+- Any logistical (shipping, storage)/lab procedures conducted prior to collecting data (and indicate relevant technical parameters - ie. culture times, etc)
+- Whether data was intentionally collected on specific subsets of donors
+- Raw data processing pipeline
+- Where you can find raw data files
+- Missing value strategy, filtering, and normalization prior to statistical analysis
+- Any additional notes
+- Citations of papers that have published parts of the data
+
+## Gene expression (bulk RNA-seq)
+RNA-seq profiles were collected starting in <span style="color: blue;">[YEAR]</span>in batches of islets from ~10-20 donors. <span style="color: blue;">[Include information on batches]</span>. Raw reads were aligned to the <span style="color: blue;">[BLANK GENOME]</span> using <span style="color: blue;">[BLANK SOFTWARE version BLANK]</span>. The counts matrix was filtered to remove any genes with fewer than <span style="color: blue;">[X]</span> mean counts. The counts were normalized for sequencing depth and adjusted to account for high variance/high abundant genes using the <span style="color: blue;">[BLANK]</span> method in the limma voom R package (version <span style="color: blue;">#</span>). Batch effect was corrected using the Combat-Seq R package (version <span style="color: blue;">#</span>). Since 
+
+## Gene expression (Nanostring)
+50 islets are hand picked in Edmonton. Cells are washed with PBS then collected in 100ul of RLT Beta-mercaptoethanol. Islets arestored at -80C before shipping to the Lynn Lab.
+
+## Protein expression (proteomics)
+Human islets are shipped to Jim Johnson’s lab at UBC. 300 Islets are hand picked, washed with pbs and pellet is snap frozen and stored at -80C. Details of lysis and analysis are found [here](https://www.medrxiv.org/content/10.1101/2023.05.24.23290298v1.full.pdf). 
+
+## Gene expression (single-cell RNA-seq)
+
+## Gene expression (pseudobulk RNA-seq)
+150 islets are hand picked at the University of Alberta and transferred into a tube with 1ml of Trizol reagent. Samples are stored at -80C until shipping to Anna Gloyn’s lab (originally Oxford now Stanford). 
+
+
+
+
+
+
+
+
+
+
+
+

03-statistical-analysis-.Rmd

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+# Overview of statistical analysis methods
+
+For each analysis, include details on:
+
+- Specific methods (and R packages) used for each task (aimed for statisticians/data scientists)
+- Explanation of how the methods work, what they are useful for, and their limitations (aimed for biologists, not statisticians/data scientists)
+- Which parameters are exposed in the interface, what they do, and rationale for which parameters were exposed
+- How to interpret the results
+- How to report the results (which details/citations to include in methods sections of papers)
+- Any additional notes
+
+## Multiple linear regression
+
+## Spearman ranked correlation
+
+## Overrepresentation analysis
+
+## Gene set enrichment analysis
+
+## Pipelines to generate significant feature associations for feature search page
+
+## Sources of annotation files, pathway libraries, & link to code repository
+
+
+
+
+
+
+
+
+
+
+

README.md

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+Welcome! 
+
+This is a minimal example of a book based on R Markdown and **bookdown** (https://github.com/rstudio/bookdown). 
+
+This template provides a skeleton file structure that you can edit to create your book. 
+
+The contents inside the .Rmd files provide some pointers to help you get started, but feel free to also delete the content in each file and start fresh.
+
+Additional resources:
+
+The **bookdown** book: https://bookdown.org/yihui/bookdown/
+
+The **bookdown** package reference site: https://pkgs.rstudio.com/bookdown

_bookdown.yml

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+delete_merged_file: true
+language:
+  ui:
+    chapter_name: "Chapter "

_output.yml

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+bookdown::gitbook:
+  css: style.css
+  config:
+    toc:
+      before: |
+        <li><a href="./">Human Islets</a></li>
+ 
+    edit: https://github.com/yao-lzy/HumanIslets/edit/main/example/%s
+    download: ["pdf", "epub"]
+  search: 
+    enabled: true
+  content_selector: '.markdown-body'
+bookdown::pdf_book:
+  includes:
+    in_header: preamble.tex
+  latex_engine: xelatex
+  citation_package: natbib
+  keep_tex: yes
+bookdown::epub_book: default

book.bib

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+@Book{xie2015,
+  title = {Dynamic Documents with {R} and knitr},
+  author = {Yihui Xie},
+  publisher = {Chapman and Hall/CRC},
+  address = {Boca Raton, Florida},
+  year = {2015},
+  edition = {2nd},
+  note = {ISBN 978-1498716963},
+  url = {http://yihui.org/knitr/},
+}

docs/404.html

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+<!DOCTYPE html>
+<html lang="" xml:lang="">
+<head>
+
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+  <meta http-equiv="X-UA-Compatible" content="IE=edge" />
+  <title>Page not found | Human Islets</title>
+  <meta name="description" content="This is an introduction of the Human Islet project." />
+  <meta name="generator" content="bookdown 0.37 and GitBook 2.6.7" />
+
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+
+<ul class="summary">
+<li><a href="./">Human Islets</a></li>
+
+<li class="divider"></li>
+<li class="chapter" data-level="1" data-path="index.html"><a href="index.html"><i class="fa fa-check"></i><b>1</b> General Introduction</a></li>
+<li class="chapter" data-level="2" data-path="overview-of-metadata-types.html"><a href="overview-of-metadata-types.html"><i class="fa fa-check"></i><b>2</b> Overview of Metadata Types</a>
+<ul>
+<li class="chapter" data-level="2.1" data-path="overview-of-metadata-types.html"><a href="overview-of-metadata-types.html#donor-characteristics"><i class="fa fa-check"></i><b>2.1</b> Donor Characteristics:</a></li>
+<li class="chapter" data-level="2.2" data-path="overview-of-metadata-types.html"><a href="overview-of-metadata-types.html#organ-characteristics-and-processing"><i class="fa fa-check"></i><b>2.2</b> Organ Characteristics and Processing:</a></li>
+<li class="chapter" data-level="2.3" data-path="overview-of-metadata-types.html"><a href="overview-of-metadata-types.html#isolation-outcomes"><i class="fa fa-check"></i><b>2.3</b> Isolation Outcomes:</a></li>
+<li class="chapter" data-level="2.4" data-path="overview-of-metadata-types.html"><a href="overview-of-metadata-types.html#cell-culture-outcomes"><i class="fa fa-check"></i><b>2.4</b> Cell Culture Outcomes:</a></li>
+<li class="chapter" data-level="2.5" data-path="overview-of-metadata-types.html"><a href="overview-of-metadata-types.html#static-insulin-secretion"><i class="fa fa-check"></i><b>2.5</b> Static Insulin Secretion:</a></li>
+<li class="chapter" data-level="2.6" data-path="overview-of-metadata-types.html"><a href="overview-of-metadata-types.html#islet-oxygen-consumption-seahorse-assay"><i class="fa fa-check"></i><b>2.6</b> Islet Oxygen Consumption (Seahorse assay):</a></li>
+<li class="chapter" data-level="2.7" data-path="overview-of-metadata-types.html"><a href="overview-of-metadata-types.html#dynamic-insulin-responses-to-macronutrients"><i class="fa fa-check"></i><b>2.7</b> Dynamic Insulin Responses to Macronutrients:</a></li>
+<li class="chapter" data-level="2.8" data-path="overview-of-metadata-types.html"><a href="overview-of-metadata-types.html#single-cell-function-electrophysiology"><i class="fa fa-check"></i><b>2.8</b> Single-cell Function (electrophysiology)</a></li>
+</ul></li>
+<li class="chapter" data-level="3" data-path="overview-of-omics-data-types.html"><a href="overview-of-omics-data-types.html"><i class="fa fa-check"></i><b>3</b> Overview of Omics Data Types</a>
+<ul>
+<li class="chapter" data-level="3.1" data-path="overview-of-omics-data-types.html"><a href="overview-of-omics-data-types.html#gene-expression-bulk-rna-seq"><i class="fa fa-check"></i><b>3.1</b> Gene expression (bulk RNA-seq)</a></li>
+<li class="chapter" data-level="3.2" data-path="overview-of-omics-data-types.html"><a href="overview-of-omics-data-types.html#gene-expression-nanostring"><i class="fa fa-check"></i><b>3.2</b> Gene expression (Nanostring)</a></li>
+<li class="chapter" data-level="3.3" data-path="overview-of-omics-data-types.html"><a href="overview-of-omics-data-types.html#protein-expression-proteomics"><i class="fa fa-check"></i><b>3.3</b> Protein expression (proteomics)</a></li>
+<li class="chapter" data-level="3.4" data-path="overview-of-omics-data-types.html"><a href="overview-of-omics-data-types.html#gene-expression-single-cell-rna-seq"><i class="fa fa-check"></i><b>3.4</b> Gene expression (single-cell RNA-seq)</a></li>
+<li class="chapter" data-level="3.5" data-path="overview-of-omics-data-types.html"><a href="overview-of-omics-data-types.html#gene-expression-pseudobulk-rna-seq"><i class="fa fa-check"></i><b>3.5</b> Gene expression (pseudobulk RNA-seq)</a></li>
+</ul></li>
+<li class="chapter" data-level="4" data-path="overview-of-statistical-analysis-methods.html"><a href="overview-of-statistical-analysis-methods.html"><i class="fa fa-check"></i><b>4</b> Overview of statistical analysis methods</a>
+<ul>
+<li class="chapter" data-level="4.1" data-path="overview-of-statistical-analysis-methods.html"><a href="overview-of-statistical-analysis-methods.html#multiple-linear-regression"><i class="fa fa-check"></i><b>4.1</b> Multiple linear regression</a></li>
+<li class="chapter" data-level="4.2" data-path="overview-of-statistical-analysis-methods.html"><a href="overview-of-statistical-analysis-methods.html#spearman-ranked-correlation"><i class="fa fa-check"></i><b>4.2</b> Spearman ranked correlation</a></li>
+<li class="chapter" data-level="4.3" data-path="overview-of-statistical-analysis-methods.html"><a href="overview-of-statistical-analysis-methods.html#overrepresentation-analysis"><i class="fa fa-check"></i><b>4.3</b> Overrepresentation analysis</a></li>
+<li class="chapter" data-level="4.4" data-path="overview-of-statistical-analysis-methods.html"><a href="overview-of-statistical-analysis-methods.html#gene-set-enrichment-analysis"><i class="fa fa-check"></i><b>4.4</b> Gene set enrichment analysis</a></li>
+<li class="chapter" data-level="4.5" data-path="overview-of-statistical-analysis-methods.html"><a href="overview-of-statistical-analysis-methods.html#pipelines-to-generate-significant-feature-associations-for-feature-search-page"><i class="fa fa-check"></i><b>4.5</b> Pipelines to generate significant feature associations for feature search page</a></li>
+<li class="chapter" data-level="4.6" data-path="overview-of-statistical-analysis-methods.html"><a href="overview-of-statistical-analysis-methods.html#sources-of-annotation-files-pathway-libraries-link-to-code-repository"><i class="fa fa-check"></i><b>4.6</b> Sources of annotation files, pathway libraries, &amp; link to code repository</a></li>
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