### Install Development Version

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Install the latest development version of the clustifyr package directly from GitHub.

```r
BiocManager::install("rnabioco/clustifyr")
```

--------------------------------

### Install Bioconductor Version

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Install the stable version of the clustifyr package from Bioconductor. Ensure BiocManager is installed first.

```r
if (!requireNamespace("BiocManager", quietly = TRUE))
    install.packages("BiocManager")

BiocManager::install("clustifyr")
```

--------------------------------

### Get Citation for clustifyr Package

Source: https://github.com/rnabioco/clustifyr/wiki/Frequently-asked-questions

Use the `citation()` function to retrieve the recommended citation for the clustifyr R package.

```r
citation("clustifyr")
```

--------------------------------

### Get Assignments as Vector

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Obtain cell type assignments directly as a vector, which is useful for integrating into existing metadata dataframes or other workflows. This option bypasses object output.

```r
# New output option, directly as a vector (in the order of the metadata), which can then be inserted into metadata dataframes and other workflows
clustify(
    input = s_small,
    cluster_col = "RNA_snn_res.0.5",
    ref_mat = cbmc_ref,
    vec_out = TRUE
)[1:10]
```

--------------------------------

### Build Reference from UCSC Cell Browser Link

Source: https://github.com/rnabioco/clustifyr/wiki/Frequently-asked-questions

Use this function to build a reference dataset directly from a UCSC Cell Browser link. Ensure the link points to a valid UCSC dataset.

```r
get_ucsc_reference()
```

--------------------------------

### Run Clustifyr Shiny App

Source: https://github.com/rnabioco/clustifyr/wiki/Frequently-asked-questions

Launches the Clustifyr Shiny application, which allows for direct preview and use of GEO files for reference building.

```r
run_clustifyr_app()
```

--------------------------------

### Use Marker Gene Lists with Clustifyr

Source: https://github.com/rnabioco/clustifyr/wiki/Frequently-asked-questions

Demonstrates how to use marker gene lists as input for Clustifyr, including handling positive and negative markers and uneven marker counts per cell type.

```r
# pbmc_markers as FindAllMarkers output gene list
pbmc_input <- split(pbmc_markers$gene, pbmc_markers$cluster)
# reference gene list that is uneven length
pbmc_ref_mm <- pos_neg_marker(
  list(B = c("CD79A", "CD79B", "MS4A1"), 
       NK = c("GZMB", "GNLY"))
)

```

--------------------------------

### Calculate and Print Assignments

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Calculate correlation coefficients between input data and a reference matrix, then print the resulting cell type assignments. Requires loading the library and having input data objects defined.

```r
library(clustifyr)

# calculate correlation
res <- clustify(
    input = pbmc_matrix_small,
    metadata = pbmc_meta$classified,
    ref_mat = cbmc_ref,
    query_genes = pbmc_vargenes
)

# print assignments
cor_to_call(res)
```

--------------------------------

### Create Reference Matrix from SingleCellExperiment

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Generate a reference matrix for `clustifyr` from a SingleCellExperiment object. Specify the column in the object's colData that contains the cell identities.

```r
# make reference from SingleCellExperiment objects
sce_small <- sce_pbmc()
object_ref(
    input = sce_small, # SCE object
    cluster_col = "cell_type" # name of column in colData containing cell identities
)
```

--------------------------------

### Create Reference from Seurat Object

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Generates a reference profile from a Seurat object using specified cluster annotations. This is useful for downstream cell type identification.

```r
s_small <- so_pbmc()
s_ref <- seurat_ref(
    seurat_object = s_small,
    cluster_col = "RNA_snn_res.0.5"
)

head(s_ref)
```

--------------------------------

### Plot Assignments on Projection

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Visualize the cell type assignments on a pre-calculated projection using the results from `clustify` and the original metadata. Requires the correlation matrix and metadata object.

```r
# plot assignments on a projection
plot_best_call(
    cor_mat = res,
    metadata = pbmc_meta,
    cluster_col = "classified"
)
```

--------------------------------

### Annotate Cells using Marker Gene Lists

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Assigns cell types to input data (matrix or SingleCellExperiment) based on provided marker gene lists and cluster metadata. Set `marker_inmatrix = FALSE` if markers are gene names, not in the input matrix.

```r
clustify_lists(
    input = pbmc_matrix_small,
    metadata = pbmc_meta,
    cluster_col = "classified",
    marker = pbmc_markers,
    marker_inmatrix = FALSE
)
```

--------------------------------

### Annotate Seurat Object using Marker Gene Lists

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Annotates a Seurat object directly using marker gene lists. The function returns a Seurat object with updated metadata and potentially new assays or dimensional reductions. `seurat_out = TRUE` ensures the output is a Seurat object.

```r
clustify_lists(
    input = s_small,
    marker = pbmc_markers,
    marker_inmatrix = FALSE,
    cluster_col = "RNA_snn_res.0.5",
    seurat_out = TRUE
)
```

--------------------------------

### Classify Seurat Object

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Use `clustify` to assign cell types to a Seurat object. The function can output the modified Seurat object with cell type assignments. Requires the Seurat package to be loaded.

```r
# for Seurat
library(Seurat)
s_small <- so_pbmc()
clustify(
    input = s_small,
    cluster_col = "RNA_snn_res.0.5",
    ref_mat = cbmc_ref,
    seurat_out = TRUE
)
```

--------------------------------

### Classify SingleCellExperiment Object

Source: https://github.com/rnabioco/clustifyr/blob/devel/README.md

Use `clustify` to assign cell types to a SingleCellExperiment object. The function can directly output the modified object with cell type assignments added.

```r
# for SingleCellExperiment
sce_small <- sce_pbmc()
clustify(
    input = sce_small, # anSCE object
    ref_mat = cbmc_ref, # matrix of RNA-seq expression data for each cell type
    cluster_col = "cell_type", # name of column in meta.data containing cell clusters
    obj_out = TRUE # output SCE object with cell type inserted as "type" column
)
```

--------------------------------

### Extract Overlapping Genes with clustify_lists

Source: https://github.com/rnabioco/clustifyr/wiki/Frequently-asked-questions

Use `details_out = TRUE` in `clustify_lists()` to obtain a matrix of overlapping genes between reference and query pairs, in addition to the standard score matrix.

```r
res <- clustify_lists(
  pbmc_ref_mm,
  pbmc_input,
  metric = "jaccard",
  input_markers = TRUE
)
```

```r
res <- clustify_lists(
  pbmc_matrix_small,
  metadata = pbmc_meta,
  cluster_col = "classified",
  marker = cbmc_m,
  details_out = TRUE
)

names(res)
```

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