### Install and Load psupertime

Source: https://github.com/wmacnair/psupertime/blob/master/README.md

Use devtools to install the development version of the package from GitHub and load it into the R environment.

```R
devtools::install_github('wmacnair/psupertime', build_vignettes=TRUE)
library('psupertime')
```

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

### Browse psupertime Vignettes

Source: https://github.com/wmacnair/psupertime/blob/master/README.md

Access the included vignettes to explore basic functionality and examples using the provided small dataset.

```R
browseVignettes(package = 'psupertime')
```

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

### Access psupertime package documentation

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

Command to open the full help documentation for the psupertime package within R.

```R
?psupertime
```

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

### project_onto_psupertime(psuper_obj, new_x = NULL, new_y = NULL, process = FALSE)

Source: https://github.com/wmacnair/psupertime/blob/master/man/project_onto_psupertime.Rd

Projects input data onto a psupertime object. If new data is provided, it uses that; otherwise, it uses the original data associated with the object.

```APIDOC
## project_onto_psupertime

### Description
Gives projection of data onto psupertime (either using original data, or new data).

### Parameters
- **psuper_obj** (Psupertime object) - Required - The output object from the psupertime function.
- **new_x** (matrix/data.frame) - Optional - New data to project.
- **new_y** (vector) - Optional - New labels corresponding to the new data.
- **process** (boolean) - Optional - Whether to process the data.

### Returns
- **data.table** - A data table containing the projection and associated labels.
```

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

### Adjust psupertime performance and regularization settings

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

Shows how to toggle between '1se' and 'best' penalization settings and change the performance scoring metric.

```R
# run psupertime with different settings
psuper_1se  = psupertime(acinar_hvg_sce, y, sel_genes='all', penalization='1se')
psuper_best = psupertime(acinar_hvg_sce, y, sel_genes='all', penalization='best')
psuper_acc  = psupertime(acinar_hvg_sce, y, sel_genes='all', score='class_error')

# display results
psuper_1se
psuper_best
psuper_acc
```

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

### Configure gene selection methods for psupertime

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

Demonstrates various ways to select genes for psupertime analysis, ranging from default highly variable genes to user-defined gene lists.

```R
# Option 1 (default): Select highly variable genes, using default settings for `scran`.
psuper_hvg      = psupertime(acinar_hvg_sce, y)
psuper_hvg      = psupertime(acinar_hvg_sce, y, sel_genes='hvg')

# Option 2: Select highly variable genes, using your own settings.
psuper_hvg_custom1  = psupertime(acinar_hvg_sce, y, sel_genes=list(hvg_cutoff=0.1, bio_cutoff=0.5))
psuper_hvg_custom2  = psupertime(acinar_hvg_sce, y, sel_genes=list(hvg_cutoff=0.1, bio_cutoff=0.5, span=0.1))

# Option 3: Use all genes
psuper_all      = psupertime(acinar_hvg_sce, y, sel_genes='all')

# Option 4: Use transcription factors
psuper_tf       = psupertime(acinar_hvg_sce, y, sel_genes='tf_human')

# Option 5: Use user-defined list of genes
psuper_sel      = psupertime(
  acinar_hvg_sce, y, 
  sel_genes='list', 
  gene_list=c('ITM2A', 'CLU', 'HSPH1', 'ADH1C', 'AMY2B')
  )
```

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

### double_psupertime(psuper_1, psuper_2, run_names = NULL, process = FALSE)

Source: https://github.com/wmacnair/psupertime/blob/master/man/double_psupertime.Rd

Projects two different psupertimes onto each other.

```APIDOC
## double_psupertime

### Description
Projects two different psupertimes onto each other.

### Usage
double_psupertime(psuper_1, psuper_2, run_names = NULL, process = FALSE)

### Arguments
- **psuper_1, psuper_2** (psupertime objects) - Two previously calculated psupertime objects.
- **run_names** (Character vector) - Optional, length two, labelling the psupertime inputs.
- **process** (logical) - Whether to process the projections.

### Value
Returns a data.table containing projections in both directions.
```

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

### Run psupertime with pre-selected genes

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

Load the psupertime package and a sample dataset, then execute the psupertime function using all genes.

```r
# load psupertime package
suppressPackageStartupMessages({
  library('psupertime')
  library('SingleCellExperiment')
  })

# load the data
data(acinar_hvg_sce)

# run psupertime
y           = acinar_hvg_sce$donor_age
psuper_obj  = psupertime(acinar_hvg_sce, y, sel_genes='all')
psuper_obj
```

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

### Plot labels over psupertime

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

Visualizes the one-dimensional projection learned by psupertime with densities of sequential labels over pseudotime.

```r
g       = plot_labels_over_psupertime(psuper_obj, label_name='Donor age')
(g)
```

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

### psupertime_plot_all(psuper_obj, output_dir = ".", tag = "", label_name = "Ordered labels", ext = "png")

Source: https://github.com/wmacnair/psupertime/blob/master/man/psupertime_plot_all.Rd

Generates and saves multiple plots based on the provided psupertime object.

```APIDOC
## psupertime_plot_all

### Description
Convenience function to generate and save multiple plots from a psupertime object.

### Arguments
- **psuper_obj** (Psupertime object) - Required - The output object from the psupertime function.
- **output_dir** (string) - Optional - The directory path where the plots will be saved. Defaults to ".".
- **tag** (string) - Optional - A label to be applied to all generated files.
- **label_name** (string) - Optional - Description for the ordered labels in the legend (e.g., 'Developmental stage (days)'). Defaults to 'Ordered labels'.
- **ext** (string) - Optional - The image format for outputs, compatible with ggsave (e.g., 'png', 'pdf', 'jpeg'). Defaults to 'png'.
```

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

### psupertime_go_analysis(psuper_obj, org_mapping, k = 5, sig_cutoff = 5)

Source: https://github.com/wmacnair/psupertime/blob/master/man/psupertime_go_analysis.Rd

Performs GO enrichment analysis for genes learned from different psupertimes.

```APIDOC
## psupertime_go_analysis

### Description
Performs GO enrichment analysis for genes learned from different psupertimes.

### Parameters
- **psuper_obj** (object) - Required - A previously calculated psupertime object.
- **org_mapping** (string) - Required - Organism to use for annotations (e.g. 'org.Mm.eg.db', 'org.Hs.eg.db').
- **k** (integer) - Optional - Number of clusters or groups to analyze (default: 5).
- **sig_cutoff** (integer) - Optional - Significance cutoff for enrichment results (default: 5).

### Returns
- **data.table** - A data.table containing results of the GO enrichment analysis.
```

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

### psupertime_go_analysis_old(psuper_obj, org_mapping)

Source: https://github.com/wmacnair/psupertime/blob/master/man/psupertime_go_analysis_old.Rd

Performs GO enrichment analysis for genes learned from different psupertimes.

```APIDOC
## psupertime_go_analysis_old

### Description
Performs GO enrichment analysis for genes learned from different psupertimes.

### Arguments
- **psuper_obj** (object) - A previously calculated psupertime object.
- **org_mapping** (string) - Organism to use for annotations (e.g. 'org.Mm.eg.db', 'org.Hs.eg.db').

### Returns
- **data.table** - A data.table containing results of GO enrichment analysis.
```

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

### plot_predictions_against_classes(psuper_obj, new_x = NULL, new_y = NULL, process = FALSE, plot_var = "prop_true", palette = "BuPu")

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_predictions_against_classes.Rd

Plots a confusion matrix of true labels against predicted labels based on a psupertime object.

```APIDOC
## plot_predictions_against_classes

### Description
Plots a confusion matrix of true labels against predicted labels.

### Arguments
- **psuper_obj** (Psupertime object) - Required - The output object from the psupertime function.
- **new_x, new_y** (data) - Optional - Data to predict with the psuper_obj.
- **process** (boolean) - Optional - Whether to process the data.
- **plot_var** (string) - Optional - The variable to plot. Options are 'prop_true' (proportion of true labels), 'prop_predict' (proportion of predicted labels), or 'N' (number of cells).
- **palette** (string) - Optional - The RColorBrewer palette to use for the plot.

### Returns
- **ggplot2 object** - A ggplot2 object representing the confusion matrix.
```

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

### plot_double_psupertime_confusion(double_obj = NULL, psuper_1 = NULL, psuper_2 = NULL, run_names = NULL, plot_var = "prop_true", palette = "BuPu")

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_double_psupertime_confusion.Rd

Plots the confusion matrices of two psupertime objects against each other. This function requires either a combined double_obj or two individual psupertime objects to generate the comparison plot.

```APIDOC
## plot_double_psupertime_confusion

### Description
Plots the confusion matrices of two psupertime objects against each other to visualize known versus predicted labels.

### Parameters
- **double_obj** (object) - Optional - Result of applying double_psupertime to two previously calculated psupertime objects.
- **psuper_1** (object) - Optional - A previously calculated psupertime object.
- **psuper_2** (object) - Optional - A previously calculated psupertime object.
- **run_names** (character vector) - Optional - A vector of length two, labelling the psupertime inputs.
- **plot_var** (string) - Optional - The variable to plot, defaults to "prop_true".
- **palette** (string) - Optional - RColorBrewer palette to use, defaults to "BuPu".

### Returns
- **cowplot plot_grid** - A plot object showing known and predicted labels for each dataset and each set of predictions.
```

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

### psupertime(x, y, ...)

Source: https://github.com/wmacnair/psupertime/blob/master/man/psupertime.Rd

Performs supervised pseudotime analysis on single-cell data.

```APIDOC
## psupertime(x, y, y_labels = NULL, assay_type = "logcounts", sel_genes = "hvg", gene_list = NULL, scale = TRUE, smooth = TRUE, min_expression = 0.01, penalization = "1se", method = "proportional", score = "xentropy", n_folds = 5, test_propn = 0.1, lambdas = NULL, max_iters = 1000, seed = 1234)

### Description
Performs supervised pseudotime analysis using ordinal logistic regression. It takes single-cell expression data and a vector of labels to model the progression of cells.

### Parameters
- **x** (SingleCellExperiment or matrix) - Required - Input data containing genes by cells.
- **y** (Vector) - Required - Vector of labels representing the intended order for training.
- **y_labels** (Vector) - Optional - Alternative ordering or subset of labels in y.
- **assay_type** (string) - Optional - Assay to use if x is a SingleCellExperiment object (default: "logcounts").
- **sel_genes** (string or list) - Optional - Method for gene selection (default: "hvg").
- **gene_list** (vector) - Optional - List of genes if sel_genes is set to 'list'.
- **scale** (boolean) - Optional - Whether to scale expression data (default: TRUE).
- **smooth** (boolean) - Optional - Whether to smooth data over neighbors (default: TRUE).
- **min_expression** (numeric) - Optional - Cutoff for excluding genes (default: 0.01).
- **penalization** (string) - Optional - Method for L1-penalization selection (default: "1se").
- **method** (string) - Optional - Statistical model for ordinal logistic regression (default: "proportional").
- **score** (string) - Optional - Cross-validated accuracy measure (default: "xentropy").
- **n_folds** (integer) - Optional - Number of cross-validation folds (default: 5).
- **test_propn** (numeric) - Optional - Proportion of data for testing (default: 0.1).
- **lambdas** (numeric vector) - Optional - User-specified sequence of lambda values.
- **max_iters** (integer) - Optional - Maximum iterations for glmnet (default: 1000).
- **seed** (integer) - Optional - Random seed (default: 1234).

### Returns
- **psupertime object** - The resulting psupertime model object.
```

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

### plot_double_psupertime(double_obj = NULL, psuper_1 = NULL, psuper_2 = NULL, run_names = NULL, process = FALSE)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_double_psupertime.Rd

Projects two different psupertimes onto each other and returns a ggplot object. Can be called by providing the two psupertime objects directly or by providing a pre-constructed double_psupertime object.

```APIDOC
## plot_double_psupertime

### Description
Projects two different psupertimes onto each other, using points, side by side. Returns a ggplot object.

### Parameters
- **double_obj** (object) - Optional - Result of applying double_psupertime to two previously calculated psupertime objects.
- **psuper_1** (object) - Optional - A previously calculated psupertime object.
- **psuper_2** (object) - Optional - A previously calculated psupertime object.
- **run_names** (character vector) - Optional - Character vector of length two, labelling the psupertime inputs.
- **process** (boolean) - Optional - Whether to process the data.

### Returns
- **ggplot** - A ggplot object plotting the two psupertimes against each other.
```

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

### plot_new_data_over_psupertime(psuper_obj, new_x, new_y, labels, palette, process)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_new_data_over_psupertime.Rd

Plots profiles of hand-selected genes against psupertime using an existing psupertime object and optional new data.

```APIDOC
## plot_new_data_over_psupertime

### Description
Plots profiles of hand-selected genes against psupertime.

### Arguments
- **psuper_obj** (Psupertime object) - Required - Output from psupertime.
- **new_x, new_y** (Optional) - Optional - Data to predict with psuper_obj.
- **labels** (vector) - Optional - Labels for the plot, default is c("Original", "New data").
- **palette** (string) - Optional - RColorBrewer palette to use, default is "BrBG".
- **process** (boolean) - Optional - Whether to process the data, default is FALSE.

### Returns
- **ggplot2 object** - The resulting plot object.
```

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

### plot_double_psupertime_contour(double_obj = NULL, psuper_1 = NULL, psuper_2 = NULL, run_names = NULL)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_double_psupertime_contour.Rd

Projects two different psupertimes on top of each other. Returns a ggplot object.

```APIDOC
## plot_double_psupertime_contour

### Description
Projects two different psupertimes on top of each other. See `plot_double_psupertime` for further detail.

### Parameters
- **double_obj** (object) - Optional - Result of applying double_psupertime to two previously calculated psupertime objects.
- **psuper_1** (object) - Optional - A previously calculated psupertime object.
- **psuper_2** (object) - Optional - A previously calculated psupertime object.
- **run_names** (character vector) - Optional - Character vector of length two, labelling the psupertime inputs.

### Returns
- **ggplot** - A ggplot object plotting the two psupertimes against each other.
```

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

### Plotting using a double_psupertime object

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_double_psupertime.Rd

Construct a double_psupertime object first, then pass it to the plotting function.

```R
double_obj = double_psupertime(psuper_1, psuper_2)
plot_double_psupertime(double_obj=double_obj)
```

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

### Plot psupertime training results

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

Generates a diagnostic plot to evaluate the fitting process and regularization selection for a psupertime object.

```r
g       = plot_train_results(psuper_obj)
(g)
```

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

### Standard psupertime function call

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

The standard way to call psupertime on a full-size dataset without pre-selection of genes.

```r
## not run
# psuper_obj  = psupertime(x, y)
```

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

### plot_identified_genes_over_psupertime(psuper_obj, label_name, n_to_plot, palette, plot_ratio)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_identified_genes_over_psupertime.Rd

Plots profiles of identified genes against psupertime.

```APIDOC
## plot_identified_genes_over_psupertime

### Description
Plots profiles of identified genes against psupertime.

### Parameters
- **psuper_obj** (Psupertime object) - Required - Output from psupertime.
- **label_name** (string) - Optional - Description for the ordered labels in the legend (default: 'Ordered labels').
- **n_to_plot** (integer) - Optional - Maximum number of genes to plot (default: 20).
- **palette** (string) - Optional - RColorBrewer palette to use (default: 'RdBu').
- **plot_ratio** (numeric) - Optional - Ratio of columns to rows (default: 1.25).

### Returns
- **ggplot2 object** - A ggplot2 object containing the plotted profiles.
```

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

### plot_specified_genes_over_psupertime(psuper_obj, extra_genes, label_name, palette, plot_ratio)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_specified_genes_over_psupertime.Rd

Plots profiles of hand-selected genes against psupertime.

```APIDOC
## plot_specified_genes_over_psupertime

### Description
Plots profiles of hand-selected genes against psupertime.

### Parameters
- **psuper_obj** (psupertime object) - Required - Output from psupertime.
- **extra_genes** (list) - Required - List of genes to be plotted (must be in the set of genes used for calculating psupertime).
- **label_name** (string) - Optional - Description for the ordered labels in the legend (default: 'Ordered labels').
- **palette** (string) - Optional - RColorBrewer palette to use (default: 'RdBu').
- **plot_ratio** (numeric) - Optional - Ratio of columns to rows (default: 1.25).

### Returns
- **ggplot2 object** - A ggplot2 visualization of the gene profiles.
```

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

### plot_labels_over_psupertime(psuper_obj, label_name = "Ordered labels", palette = "RdBu")

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_labels_over_psupertime.Rd

Plots labels over their projected values on psupertime.

```APIDOC
## plot_labels_over_psupertime

### Description
Plots labels over their projected values on psupertime.

### Parameters
- **psuper_obj** (Psupertime object) - Required - The output object from the psupertime function.
- **label_name** (string) - Optional - Description for the ordered labels in the legend (e.g., 'Developmental stage (days)'). Defaults to 'Ordered labels'.
- **palette** (string) - Optional - The RColorBrewer palette to use for the plot. Defaults to 'RdBu'.

### Returns
- **ggplot2 object** - A ggplot2 object representing the projected labels.
```

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

### Plotting two psupertime objects directly

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_double_psupertime.Rd

Directly project two psupertime objects onto each other using the psuper_1 and psuper_2 arguments.

```R
plot_double_psupertime(psuper_1=psuper_1, psuper_2=psuper_2)
```

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

### plot_go_results(go_list, sig_cutoff = 5, p_cutoff = 0.1)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_go_results.Rd

Generates a bar plot of significant GO terms for each cluster.

```APIDOC
## plot_go_results

### Description
Plots the significant GO terms for each cluster based on the output from a GO analysis.

### Usage
plot_go_results(go_list, sig_cutoff = 5, p_cutoff = 0.1)

### Arguments
- **go_list** (object) - Required - Output from GO analysis.
- **sig_cutoff** (numeric) - Optional - The minimum number of annotated genes to display a GO term (default: 5).
- **p_cutoff** (numeric) - Optional - The maximum p-value to display a GO term (default: 0.1).

### Returns
- **bar plot** (plot) - A bar plot visualization of the significant GO terms.
```

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

### plot_profiles_of_gene_clusters(go_list, label_name = "Ordered labels", palette = "RdBu")

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_profiles_of_gene_clusters.Rd

Generates a ggplot object representing a heatmap of gene clusters.

```APIDOC
## plot_profiles_of_gene_clusters

### Description
Plots a heatmap of gene clusters using the output from a GO analysis.

### Usage
plot_profiles_of_gene_clusters(go_list, label_name = "Ordered labels", palette = "RdBu")

### Parameters
- **go_list** (object) - Required - The output object from a GO analysis.
- **label_name** (string) - Optional - Description for the ordered labels in the legend (default: 'Ordered labels').
- **palette** (string) - Optional - The RColorBrewer palette to use for the heatmap (default: 'RdBu').

### Returns
- **ggplot object** - A ggplot object representing the gene cluster heatmap.
```

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

### plot_double_psupertime_genes(psuper_1, psuper_2, run_names = NULL)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_double_psupertime_genes.Rd

Compares coefficients for genes learned from different psupertimes and returns a ggplot object.

```APIDOC
## plot_double_psupertime_genes

### Description
Compares coefficients for genes learned from different psupertimes.

### Usage
plot_double_psupertime_genes(psuper_1, psuper_2, run_names = NULL)

### Arguments
- **psuper_1, psuper_2** (psupertime objects) - Required - Two previously calculated psupertime objects.
- **run_names** (character vector) - Optional - Character vector of length two, labelling the psupertime inputs.

### Returns
- **ggplot** - A ggplot object plotting the two sets of coefficients.
```

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

### plot_heatmap_of_gene_clusters(go_list)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_heatmap_of_gene_clusters.Rd

Generates a ggplot heatmap object from the provided GO analysis results.

```APIDOC
## plot_heatmap_of_gene_clusters

### Description
Plots a heatmap of gene clusters based on the provided GO analysis output.

### Usage
plot_heatmap_of_gene_clusters(go_list)

### Arguments
- **go_list** (object) - Required - The output object obtained from a GO analysis.

### Returns
- **ggplot** (object) - A ggplot object representing the heatmap of gene clusters.
```

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

### plot_train_results(psuper_obj)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_train_results.Rd

Generates a ggplot2 object visualizing the training and testing performance of the classifier.

```APIDOC
## plot_train_results

### Description
Plots the results of the training process for a Psupertime object.

### Arguments
- **psuper_obj** (Psupertime object) - Required - The output object from the psupertime function.

### Returns
- **ggplot2 object** - A plot showing the test and training performance of the classifier.
```

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

### Usage of plot_new_data_over_psupertime

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_new_data_over_psupertime.Rd

The function signature for plotting profiles of hand-selected genes against psupertime.

```R
plot_new_data_over_psupertime(
  psuper_obj,
  new_x,
  new_y,
  labels = c("Original", "New data"),
  palette = "BrBG",
  process = FALSE
)
```

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

### plot_identified_gene_coefficients(psuper_obj, n = 20, abs_cutoff = 0.05)

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_identified_gene_coefficients.Rd

Plots the top coefficients from a psupertime object.

```APIDOC
## plot_identified_gene_coefficients

### Description
Plots the top coefficients from a psupertime object as a ggplot2 object.

### Usage
plot_identified_gene_coefficients(psuper_obj, n = 20, abs_cutoff = 0.05)

### Parameters
- **psuper_obj** (Psupertime object) - Required - The output object from the psupertime function.
- **n** (integer) - Optional - The number of top coefficients to plot. Defaults to 20.
- **abs_cutoff** (numeric) - Optional - The absolute value cutoff for coefficients. Defaults to 0.05.

### Returns
- **ggplot2 object** - A plot visualizing the identified gene coefficients.
```

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

### Plot predictions against classes

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

Generates a plot comparing predicted cell labels against known classes using the psuper_obj object.

```r
g       = plot_predictions_against_classes(psuper_obj)
(g)
```

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

### plot_profiles_of_gene_clusters usage

Source: https://github.com/wmacnair/psupertime/blob/master/man/plot_profiles_of_gene_clusters.Rd

The function signature for plotting gene cluster profiles, requiring the output from a GO analysis.

```R
plot_profiles_of_gene_clusters(
  go_list,
  label_name = "Ordered labels",
  palette = "RdBu"
)
```

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

### Plot identified genes over psupertime

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

Plots z-scored log expression values of identified genes against the learned pseudotime values.

```r
g       = plot_identified_genes_over_psupertime(psuper_obj, label_name='Donor age')
(g)
```

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

### Plot identified gene coefficients

Source: https://github.com/wmacnair/psupertime/blob/master/vignettes/psuper_intro.Rmd

Displays the top 20 genes with the largest absolute coefficient values identified by psupertime.

```r
g       = plot_identified_gene_coefficients(psuper_obj)
(g)
```

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