00_datasets.Rmd

# Datasets & Packages

## Datasets to download

Here we provide links to the original datasets that were used for this workshop. 
Some of the datasets were modified (e.g. downsampled or subsetted) for the purpose of this workshop.
You can download them from their original source or download all of them - including 
intermediate files - from the following Zenodo repository:

### Zenodo repository

https://zenodo.org/communities/gw2024/


### 10X Genomics Visium Mouse Brain Section (Coronal) dataset

https://support.10xgenomics.com/spatial-gene-expression/datasets/1.1.0/V1_Adult_Mouse_Brain

### 10X Genomics Visium HD: FFPE Human Colon Cancer

https://www.10xgenomics.com/datasets/visium-hd-cytassist-gene-expression-libraries-of-human-crc

### 10X Genomics multi-modal dataset

https://www.10xgenomics.com/products/xenium-in-situ/preview-dataset-human-breast 

### 10X Genomics multi-omics Visium CytAssist Human Tonsil dataset

https://www.10xgenomics.com/resources/datasets/gene-protein-expression-library-of-human-tonsil-cytassist-ffpe-2-standard 

### 10X Genomics Human Prostate Cancer Adenocarcinoma with Invasive Carcinoma (FFPE)

https://www.10xgenomics.com/datasets/human-prostate-cancer-adenocarcinoma-with-invasive-carcinoma-ffpe-1-standard-1-3-0

### 10X Genomics Normal Human Prostate (FFPE)

https://www.10xgenomics.com/datasets/normal-human-prostate-ffpe-1-standard-1-3-0

### Xenium 

https://www.10xgenomics.com/datasets/preview-data-ffpe-human-lung-cancer-with-xenium-multimodal-cell-segmentation-1-standard

### MERFISH cortex dataset

https://doi.brainimagelibrary.org/doi/10.35077/g.21 

### Lunaphore IF dataset

https://zenodo.org/records/13175721

## Needed packages

To run all the tutorials from this Giotto Suite workshop you will need to install
additional R and Python packages. Here we provide detailed instructions and discuss 
some common difficulties with installing these packages. The easiest way would be
to copy each code snippet into your R/Rstudio Console using fresh a R session.

### CRAN dependencies:

```{r, eval=FALSE}
cran_dependencies <-
    c("BiocManager",
      "devtools",
      "pak")

install.packages(cran_dependencies, Ncpus = 4)
```

### *terra* installation

*terra* may have some additional steps when installing depending on which system you are on. Please see the *terra* [repo](https://github.com/rspatial/terra) for specifics. Installations of the CRAN release on Windows and Mac are expected to be simple, only requiring the code below.

For Linux, there are several prerequisite installs:

```         
GDAL (>= 2.2.3), GEOS (>= 3.4.0), PROJ (>= 4.9.3), sqlite3
```

On our AlmaLinux 8 HPC, the following versions have been working well:

-   gdal/3.6.4

-   geos/3.11.1

-   proj/9.2.0

-   sqlite3/3.37.2

```{r, eval=FALSE}
install.packages("terra")
```

### *Matrix* installation

!! FOR R VERSIONS LOWER THAN 4.4.0 !!

*Giotto* requires *Matrix* 1.6-2 or greater, but when installing *Giotto* with *pak* on an *R* version lower than 4.4.0, the installation can fail asking for R 4.5 which doesn't exist yet. We can solve this by installing the 1.6-5 version directly by un-commenting and running the line below.

```{r, eval=FALSE}
# devtools::install_version("Matrix", version = "1.6-5")
```

### *Rtools* installation
Before installing Giotto on a windows PC please make sure to install the relevant version of [Rtools](https://cran.r-project.org/bin/windows/Rtools/). If you have a Mac or linux PC, or have already installed Rtools, please ignore this step.

### *Giotto* installation 
```{r, eval=FALSE}
pak::pak("drieslab/Giotto")
pak::pak("drieslab/GiottoData")
```

### *irlba* install

Reinstall *irlba* from source. Avoids the common `function 'as_cholmod_sparse' not provided by package 'Matrix'` error. See this [issue](https://github.com/bwlewis/irlba/issues/70) for more info.

```{r, eval=FALSE}
install.packages("irlba", type = "source")
```

### *arrow* install

*arrow* is a suggested package that we use here to open `parquet` files. The parquet files that 10X provides use zstd compression which the default *arrow* installation may not provide.

```{r, eval=FALSE}
has_arrow <- requireNamespace("arrow", quietly = TRUE)
zstd <- TRUE
if (has_arrow) {
    zstd <- arrow::arrow_info()$capabilities[["zstd"]]
}
if (!has_arrow || !zstd) {
    Sys.setenv(ARROW_WITH_ZSTD = "ON") 
    install.packages("assertthat", "bit64")
    install.packages("arrow", repos = c("https://apache.r-universe.dev"))
}
```

### Bioconductor dependencies:

```{r, eval=FALSE}
bioc_dependencies <- c(
    "scran",
    "ComplexHeatmap",
    "SpatialExperiment",
    "ggspavis",
    "scater",
    "nnSVG"
)
```

### CRAN packages:

```{r, eval=FALSE}
needed_packages_cran <- c(
    "dplyr",
    "gstat",
    "hdf5r",
    "miniUI",
    "shiny",
    "xml2",
    "future",
    "future.apply",
    "exactextractr",
    "tidyr",
    "viridis",
    "quadprog",
    "Rfast",
    "pheatmap",
    "patchwork",
    "Seurat",
    "harmony",
    "scatterpie",
    "R.utils",
    "qs"
)

pak::pkg_install(c(bioc_dependencies,
                   needed_packages_cran))
```

### Packages from GitHub

```{r, eval=FALSE}
github_packages <- c(
    "satijalab/seurat-data"
)
pak::pkg_install(github_packages)
```

### Python environments

```{r, eval=FALSE}
# default giotto environment
Giotto::installGiottoEnvironment()

reticulate::py_install(
    pip = TRUE,
    envname = 'giotto_env',
    packages = c(
        "scanpy"
    )
)

# install another environment with py 3.8 for cellpose
reticulate::conda_create(envname = "giotto_cellpose",
                         python_version = 3.8)
#.re.restartR()
reticulate::use_condaenv('giotto_cellpose')
reticulate::py_install(
  pip = TRUE,
  envname = 'giotto_cellpose',
  packages = c(
    "pandas",
    "networkx",
    "python-igraph",
    "leidenalg",
    "scikit-learn",
    "cellpose",
    "smfishhmrf",
    'tifffile',
    'scikit-image'
  )
)
```