README.Rmd

---
title: Visium_SPG_AD
output:
  github_document:
    html_preview: true
  html_document:
    toc: true
    toc_flot: true
    includes:
      in_header: header.html
      after_body: footer.html
---

<!-- README.md is generated from README.Rmd. Please edit that file -->

```{r 'setup', include = FALSE}
knitr::opts_chunk$set(
    collapse = TRUE,
    comment = "#>",
    fig.path = "img/",
    out.width = "100%"
)
```

[![DOI](https://zenodo.org/badge/377886452.svg)](https://zenodo.org/badge/latestdoi/377886452)

## Overview

<img src="http://research.libd.org/Visium_SPG_AD/img/Br3880_D1_pathology.png" align="left" width="300px" />

Welcome to the `Visium_SPG_AD` project! This project involves several interactive websites, which are publicly accessible for you to browse and download.

We spatially resolved transcriptomics of the inferior temporal cortex (ITC) from postmortem human brain tissue to study local gene expression profiles of brain microenvironments associated with Alzheimer’s disease (AD) related neuropathology bearing abnormal protein aggregation of amyloid beta (Abeta; Ab) and phosphorylated Tau (pTau). Using a total of 10 tissue samples from 3 donors with late-stage Alzheimer's disease (AD) and 1 age-matched control (Br3874), we generated proteomic-based, spatially-resolved transcriptome-scale (SRT) maps of the human ITC in which the immunofluorescence staining of Ab and pTau was integrated into the complementary SRT data of the identical tissue sections. With Visium Spatial Proteogenomics ([Visium-SPG](https://www.10xgenomics.com/products/spatial-proteogenomics)), we utilized a within-subjects design to study gene expression changes associated with spatially localized brain neuropathology across the 3 donors with AD (Br3854, Br3873, and Br3880). Here, using _BayesSpace_ to identify the gray-matter associated spots in a data-driven manner, we discovered statistically significant differentially expressed genes in the core (Ab spots) and peripheral (next_Ab spots) compartments of Ab-associated microenvironment in the gray matter of ITC. For more detailed information about study design and experimental results, please refer to our [manuscript](https://doi.org/10.1101/2023.04.20.537710). This work was performed by the [Keri Martinowich](https://www.libd.org/team/keri-martinowich-phd/), [Kristen Maynard](https://www.libd.org/team/kristen-maynard-phd/), and [Leonardo Collado-Torres](http://lcolladotor.github.io/) teams at the [Lieber Institute for Brain Development](libd.org) as well as [Stephanie Hicks](https://www.stephaniehicks.com/)'s group from [JHBSPH's Biostatistics Department](https://publichealth.jhu.edu/departments/biostatistics). This project was done in collaboration with [10x Genomics](https://www.10xgenomics.com/).

This project involves the [LieberInstitute/Visium_SPG_AD](https://github.com/LieberInstitute/Visium_SPG_AD) GitHub repository.

If you tweet about this website, the data or the R package please use
the <code>\#Visium_SPG_AD</code> hashtag. You can find previous tweets
that way as shown
<a href="https://twitter.com/search?q=%23Visium_SPG_AD&src=typed_query">here</a>.

Thank you for your interest in our work!

## Study Design

<img src="http://research.libd.org/Visium_SPG_AD/img/study_overview.png" width="1000px" align="left" />

**Spatial transcriptomics combined with immunodetection of Ab and pTau in the human inferior temporal cortex (ITC)**. (**A**) Schematic of experimental design using Visium Spatial Proteogenomics (Visium-SPG) to investigate the impact of Ab and pTau aggregates on the local microenvironment transcriptome in the post-mortem human brain. Human ITC blocks were acquired from 3 donors with AD and 1 age-matched neurotypical control. Tissue blocks were cryosectioned at 10μm to obtain 2-3 replicates per donor and sections were collected onto individual capture arrays of a Visium spatial gene expression slide, yielding a total of 3 gene expression experiments. The entire slide (4 tissue sections) was stained and scanned using multispectral imaging methods to detect Ab and pTau immunofluorescence (IF) signals as well as autofluorescence. Following imaging, tissue sections were permeabilized and subjected to on-slide cDNA synthesis after which libraries were generated and sequenced. Transcriptomic data was aligned with the respective IF image data to generate gene expression maps of the local transcriptome with respect to Ab plaques and pTau elements, including neurofibrillary tangles. (**B**) High magnification images show Ab plaques (white triangles) and various neurofibrillary elements such as tangles (white arrowheads), neuropil threads (red arrowheads), and neuritic tau plaques (yellow arrowheads). Lipofuscin (cyan) was identified through spectral unmixing and pixels confounded with this autofluorescent signal were excluded from analysis, scale bar, 20μm. (**C**) ITC tissue block from Br3880 (left) and corresponding spotplots (right) from the Visium data show gene expression of _MOBP_ and _SNAP25_, which demarcates the border between gray matter (GM) and white matter (WM), scale bar, 1mm. Color scale indicates spot-level gene expression in logcounts. (**D**) Image processing and quantification of Ab and pTau per Visium spot. Ab and pTau signals were thresholded in their single IF channels for segmentation against autofluorescence background, including lipofuscin. Thresholded Ab and pTau signals were aligned to the gene expression map of the same tissue section from Br3880 and quantified as the proportion of number of pixels per Visium spot, which is visualized in a spotplot, scale bar, 1mm.


## Interactive Websites

All of these interactive websites are powered by open source software, namely:

* 🔭 [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w)
* 👀 [`iSEE`](https://doi.org/10.12688%2Ff1000research.14966.1)
* 🔍 [`samui`](http://dx.doi.org/10.1017/S2633903X2300017X)

We provide the following interactive websites, organized by software labeled with emojis:


* 🔭 `spatialLIBD`
  - [Visium_SPG_AD_wholegenome](https://libd.shinyapps.io/Visium_SPG_AD_wholegenome): [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) website showing the spatially-resolved Visium-SPG data (n = 10 tissue sections from 3 AD donors and 1 age-matched control) with statistical results comparing the 7 different spot categories bearing Ab and pTau pathology within donors with AD. This version shows the whole genome gene expression data.
  - [Visium_SPG_AD_TGE](https://libd.shinyapps.io/Visium_SPG_AD_TGE): [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) website that the same as the previous one, but with the targeted gene expression (TGE) panel data.
  - [Visium_SPG_AD_wholegenome_Abeta_microenv](https://libd.shinyapps.io/Visium_SPG_AD_wholegenome_Abeta_microenv): [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) website similar to `Visium_SPG_AD_wholegenome`, but with the `Ab` and `next_Ab` pathologies collapsed into a single one `Ab_env` for studying the Abeta micro environment.
  - [Visium_SPG_AD_wholegenome_Abeta_and_pTau_microenv](https://libd.shinyapps.io/Visium_SPG_AD_wholegenome_Abeta_and_pTau_microenv/): [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) website similar to `Visium_SPG_AD_wholegenome_Abeta_microenv`, but after also combining `pTau` and `next_pTau` into `pTau_env` as well as `both` and `next_both` into `both_env`, thus resulting in only 4 categories: `none`, `Ab_env`, `pTau_env`, and `both_env`.
* 👀 `iSEE`
  - [Visium_SPG_AD_pseudobulk_AD_pathology_wholegenome](https://libd.shinyapps.io/Visium_SPG_AD_pseudobulk_AD_pathology_wholegenome): [`iSEE`](https://doi.org/10.12688%2Ff1000research.14966.1) website showing the pseudo-bulked spatial data of the 7 different pathological categories.
* 🔍 `samui`
  - [Visium SPG AD on Samui](https://samuibrowser.com/from?url=visium-spg-ad.s3.amazonaws.com/&s=V10A27106_D1_Br3880_AD&s=V10A27004_A1_Br3874_control&s=V10A27004_D1_Br3880_AD&s=V10A27106_A1_Br3874_control&s=V10A27106_B1_Br3854_AD&s=V10A27106_C1_Br3873_AD&s=V10T31036_A1_Br3874_control&s=V10T31036_B1_Br3854_AD&s=V10T31036_C1_Br3873_AD&s=V10T31036_D1_Br3880_AD): [`samui`](https://github.com/chaichontat/samui) website that allows to zoom in the raw immunostaining image data at the Visium spot level.


### Local `spatialLIBD` apps

If you are interested in running the [`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) applications locally, you can do so thanks to the [`spatialLIBD::run_app()`](http://research.libd.org/spatialLIBD/reference/run_app.html), which you can also use with your own data as shown in our [vignette for publicly available datasets provided by 10x Genomics](http://bioconductor.org/packages/release/data/experiment/vignettes/spatialLIBD/inst/doc/TenX_data_download.html).

```{r run_app, eval = FALSE}
## Run this web application locally with:
spatialLIBD::run_app()
## You will have more control about the length of the session and memory usage.
## See http://research.libd.org/spatialLIBD/reference/run_app.html#examples.
## See also:
## * https://github.com/LieberInstitute/Visium_SPG_AD/tree/master/code/05_deploy_app_wholegenome
## * https://github.com/LieberInstitute/Visium_SPG_AD/tree/master/code/06_deploy_app_targeted
## * https://github.com/LieberInstitute/Visium_SPG_AD/tree/master/code/18_deploy_app_wholegenome_Abeta_microenv
## 
## You could also use spatialLIBD::run_app() to visualize your
## own data given some requirements described
## in detail in the package vignette documentation
## at http://research.libd.org/spatialLIBD/.
```

## Contact

We value public questions, as they allow other users to learn from the answers. If you have any questions, please ask them at [LieberInstitute/Visium_SPG_AD/issues](https://github.com/LieberInstitute/Visium_SPG_AD/issues) and refrain from emailing us. Thank you again for your interest in our work!


## Citing our work

Please cite this [manuscript](https://doi.org/10.1101/2023.04.20.537710) if you use data from this project.

> Influence of Alzheimer's disease related neuropathology on local microenvironment gene expression in the human inferior temporal cortex.
> Sang Ho Kwon, Sowmya Parthiban, Madhavi Tippani, Heena R Divecha, Nicholas J Eagles, Jashandeep S Lobana, Stephen R Williams, Michelle Mark, Rahul A Bharadwaj, Joel E Kleinman, Thomas M Hyde, Stephanie C Page, Stephanie C Hicks, Keri Martinowich, Kristen R Maynard, Leonardo Collado-Torres.
> GEN Biotechnology; doi: https://doi.org/10.1089/genbio.2023.0019

Below is the citation in [`BibTeX`](http://www.bibtex.org/) format.

```
@article{kwon_influence_2023,
	title = {Influence of {Alzheimer}'s {Disease} {Related} {Neuropathology} on {Local} {Microenvironment} {Gene} {Expression} in the {Human} {Inferior} {Temporal} {Cortex}},
	volume = {2},
	issn = {2768-1572},
	url = {https://liebertpub.com/doi/10.1089/genbio.2023.0019},
	doi = {10.1089/genbio.2023.0019},
	number = {5},
	urldate = {2024-04-19},
	journal = {GEN Biotechnology},
	author = {Kwon, Sang Ho and Parthiban, Sowmya and Tippani, Madhavi and Divecha, Heena R. and Eagles, Nicholas J. and Lobana, Jashandeep S. and Williams, Stephen R. and Mak, Michelle and Bharadwaj, Rahul A. and Kleinman, Joel E. and Hyde, Thomas M. and Page, Stephanie C. and Hicks, Stephanie C. and Martinowich, Keri and Maynard, Kristen R. and Collado-Torres, Leonardo},
	month = oct,
	year = {2023},
	note = {Publisher: Mary Ann Liebert, Inc., publishers},
	pages = {399--417}
}
```

### Cite `spatialLIBD`

Below is the citation output from using `citation('spatialLIBD')` in R. Please run this yourself to check for any updates on how to cite __spatialLIBD__.

```{r 'citation_spatialLIBD'}
print(citation("spatialLIBD")[1], bibtex = TRUE)
```

Please note that the `spatialLIBD` was only made possible thanks to many other R and bioinformatics software authors, which are cited either in the vignettes and/or the paper(s) describing the package.

### Cite `VistoSeg`

To cite [`VistoSeg`](http://research.libd.org/VistoSeg/) please use:

> VistoSeg: {Processing utilities for high-resolution images for spatially resolved transcriptomics data.
> Madhavi Tippani, Heena R. Divecha, Joseph L. Catallini II, Sang Ho Kwon, Lukas M. Weber, Abby Spangler, Andrew E. Jaffe, Thomas M. Hyde, Joel E. Kleinman, Stephanie C. Hicks, Keri Martinowich, Leonardo Collado-Torres, Stephanie C. Page, Kristen R. Maynard
> Biological Imaging ; doi: https://doi.org/10.1017/S2633903X23000235

Below is the citation in [`BibTeX`](http://www.bibtex.org/) format.

```
@article{tippani_vistoseg_2023,
	title = {{VistoSeg}: {Processing} utilities for high-resolution images for spatially resolved transcriptomics data},
	volume = {3},
	issn = {2633-903X},
	shorttitle = {{VistoSeg}},
	url = {https://www.cambridge.org/core/journals/biological-imaging/article/vistoseg-processing-utilities-for-highresolution-images-for-spatially-resolved-transcriptomics-data/990CBC4AC069F5EDC62316919398404B},
	doi = {10.1017/S2633903X23000235},
	language = {en},
	urldate = {2024-04-19},
	journal = {Biological Imaging},
	author = {Tippani, Madhavi and Divecha, Heena R. and Catallini, Joseph L. and Kwon, Sang H. and Weber, Lukas M. and Spangler, Abby and Jaffe, Andrew E. and Hyde, Thomas M. and Kleinman, Joel E. and Hicks, Stephanie C. and Martinowich, Keri and Collado-Torres, Leonardo and Page, Stephanie C. and Maynard, Kristen R.},
	month = jan,
	year = {2023},
	keywords = {hematoxylin and eosin, immunofluorescence, MATLAB, segmentation, spatially resolved transcriptomics, Visium, Visium-Spatial Proteogenomics},
	pages = {e23}
}
```


## Data Access

We highly value open data sharing and believe that doing so accelerates science, as was the case between our [`HumanPilot`](https://doi.org/10.1038/s41593-020-00787-0) and the external [`BayesSpace`](https://doi.org/10.1038/s41587-021-00935-2) projects, documented [on this slide](https://speakerdeck.com/lcolladotor/hca-la-2022?slide=18).

### Processed Data

[`spatialLIBD`](https://doi.org/10.1186/s12864-022-08601-w) also allows you to access the data from this project as ready to use R objects. That is, a:

* [`SpatialExperiment`](https://doi.org/10.1093/bioinformatics/btac299) object for the Visium-SPG samples (n = 10)

You can use the [`zellkonverter`](https://bioconductor.org/packages/zellkonverter/) Bioconductor package to convert any of them into Python [`AnnData`](https://anndata.readthedocs.io/en/latest/) objects. If you browse our code, you can find examples of such conversions.

If you are unfamiliar with these tools, you might want to check the [LIBD rstats club](http://research.libd.org/rstatsclub/#.Y4hWlOzMJUM) (check and search keywords on the [schedule](https://docs.google.com/spreadsheets/d/1is8dZSd0FZ9Qi1Zvq1uRhm-P1McnJRd_zxdAfCRoMfA/edit?usp=sharing)) videos and resources.

#### Installing spatialLIBD

Get the latest stable `R` release from [CRAN](http://cran.r-project.org/). Then install `spatialLIBD` from [Bioconductor](http://bioconductor.org/) with the following code:

```{r 'install', eval = FALSE}
## Install BiocManager in order to install Bioconductor packages properly
if (!requireNamespace("BiocManager", quietly = TRUE)) {
    install.packages("BiocManager")
}
## Check that you have a valid R/Bioconductor installation
BiocManager::valid()
## Now install spatialLIBD from Bioconductor
## (this version has been tested on macOS, winOS, linux)
BiocManager::install("spatialLIBD")
## If you need the development version from GitHub you can use the following:
# BiocManager::install("LieberInstitute/spatialLIBD")
## Note that this version might include changes that have not been tested
## properly on all operating systems.
```

### R objects

Using `spatialLIBD` you can access the spatialDLPFC
transcriptomics data from the 10x Genomics Visium platform. For example,
this is the code you can use to access the spatially-resolved data. For more
details, check the help file for `fetch_data()`.

As of August 23, 2023 the processed data includes spot-level deconvolution results generated using [`cell2location`](https://doi.org/10.1038/s41587-021-01139-4) with the six broad cell types from the [Mathys et al snRNA-seq dataset](https://doi.org/10.1038/s41586-019-1195-2) in which they found AD associated gene expression changes. To access these results check the `colData(spe)` columns starting with the `c2l_` prefix.

```{r 'access_data', message=FALSE, fig.height = 8, fig.width = 9, eval = FALSE}
## Check that you have a recent version of spatialLIBD installed
stopifnot(packageVersion("spatialLIBD") >= "1.11.12")
## Download the spot-level data
spe <- spatialLIBD::fetch_data(type = "Visium_SPG_AD_Visium_wholegenome_spe")

## This is a SpatialExperiment object
spe
#> class: SpatialExperiment 
#> dim: 27853 38115 
#> metadata(0):
#> assays(2): counts logcounts
#> rownames(27853): ENSG00000243485 ENSG00000238009 ... ENSG00000278817
#>   ENSG00000277196
#> rowData names(7): source type ... gene_type gene_search
#> colnames(38115): AAACAACGAATAGTTC-1 AAACAAGTATCTCCCA-1 ...
#>   TTGTTTGTATTACACG-1 TTGTTTGTGTAAATTC-1
#> colData names(113): key sample_id ... c2l_oli c2l_opc
#> reducedDimNames(15): 10x_pca 10x_tsne ... TSNE_perplexity50.HARMONY
#>   TSNE_perplexity80.HARMONY
#> mainExpName: NULL
#> altExpNames(0):
#> spatialCoords names(2) : pxl_col_in_fullres pxl_row_in_fullres
#> imgData names(4): sample_id image_id data scaleFactor
lobstr::obj_size(spe)
#> 2.29 GB

## Remake the logo image
p_pathology <- spatialLIBD::vis_clus(
    spe = spe,
    clustervar = "path_groups",
    sampleid = "V10A27106_D1_Br3880",
    colors = spe$path_groups_colors[!duplicated(spe$path_groups_colors)],
    spatial = FALSE,
    ... = " Visium SPG AD\nPathology groups -- made with spatialLIBD"
)
p_pathology
```

<a href="https://libd.shinyapps.io/spatialDLPFC_Visium_Sp_pathology"><img src="http://research.libd.org/Visium_SPG_AD/img/Br3880_D1_pathology.png" width="800px" align="center" /></a>

```{r "p_BayesSpace", fig.height = 8, fig.width = 9, eval = FALSE}
## Repeat but for BayesSpace at k = 28 (max 28 clusters, can be less)
k_observed <- unique(spe$BayesSpace_harmony_k28)
p_BayesSpace <- spatialLIBD::vis_clus(
    spe = spe,
    clustervar = "BayesSpace_harmony_k28",
    sampleid = "V10A27106_D1_Br3880",
    spatial = FALSE,
    colors = setNames(Polychrome::palette36.colors(length(k_observed)), k_observed),
    ... = " Visium SPG AD\nBayesSpace k28 -- made with spatialLIBD"
)
p_BayesSpace
```

```{r "save_plots", include = FALSE, eval = FALSE}
ggplot2::ggsave(here::here("img", "Br3880_D1_pathology.png"),
    p_pathology,
    width = 8,
    height = 7
)
ggplot2::ggsave(here::here("img", "Br3880_D1_BayesSpace.png"),
    p_BayesSpace,
    width = 8,
    height = 7
)
```

<a href="https://libd.shinyapps.io/spatialDLPFC_Visium_Sp_BayesSpace"><img src="http://research.libd.org/Visium_SPG_AD/img/Br3880_D1_BayesSpace.png" width="800px" align="center" /></a>


### Raw data

You can access all the raw data through [Globus](http://research.libd.org/globus/) ([jhpce#Visium_SPG_AD](http://research.libd.org/globus/jhpce_Visium_SPG_AD/index.html)). This includes all the input FASTQ files as well as the outputs from tools such as [`SpaceRanger`](https://support.10xgenomics.com/spatial-gene-expression/software/pipelines/latest/what-is-space-ranger). The files are organized following the [LieberInstitute/template_project](https://github.com/LieberInstitute/template_project) project structure.

## Internal

* JHPCE locations: `/dcs04/lieber/lcolladotor/with10x_LIBD001/Visium_SPG_AD`
* Slack channel: [`libd_visium_if_ad_itg`](https://jhu-genomics.slack.com/archives/C01GJ0F731Q).

### Files:

- `code`: R, python, and shell scripts for running various analyses.
- `plots`: plots generated by R analysis scripts in `.pdf` or `.png` format
- `processed-data`
  * `Images`: images used for running `SpaceRanger` and other images
  * `spaceranger`: `SpaceRanger` output files
- `raw-data`
  * `10x_files`: files 10x Genomics transferred to us.
  * `FASTQ`: FASTQ files.
  * `Images`: raw images from the scanner in `.tif` format for each Visium-SPG slide (around 8GB each). Each slide contains the image for four capture areas.

This GitHub repository is organized along the [_R/Bioconductor-powered Team Data Science_ group guidelines](https://lcolladotor.github.io/bioc_team_ds/organizing-your-work.html#.Yaf9fPHMIdk).  It follows the [LieberInstitute/template_project](https://github.com/LieberInstitute/template_project) structure.

### Other related files

- Reference transcriptome from 10x Genomics: `/dcs04/lieber/lcolladotor/annotationFiles_LIBD001/10x/refdata-gex-GRCh38-2020-A/`