---
title: "Visualization with built-in plots"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{visualization}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
options(rmarkdown.html_vignette.check_title = FALSE)
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>",
  fig.width = 5,
  fig.height = 4
)
```

```{r load}
library(MOSuite)
```

## Default plots from each step

Default plots can be printed to the screen and/or saved to the disk.

```{r options}
# set options to print & save the plots
options(moo_print_plots = TRUE)
options(moo_save_plots = TRUE)
# when moo_save_plots is TRUE, plots are saved to this directory:
options(moo_plots_dir = "./figures")
```

See `?MOSuite::options` for more information.

### clean

```{r nidap_data_clean}
moo <- create_multiOmicDataSet_from_dataframes(
  sample_metadata = as.data.frame(nidap_sample_metadata),
  counts_dat = as.data.frame(nidap_raw_counts)
) |>
  clean_raw_counts()
```

### filter

```{r nidap_filter}
moo <- moo |>
  filter_counts(group_colname = "Group")
```

### normalize

```{r nidap_norm}
moo <- moo |>
  normalize_counts(group_colname = "Group")
```

### batch correct

```{r nidap_batch}
moo <- moo |>
  batch_correct_counts(
    covariates_colname = "Group",
    batch_colname = "Batch",
    label_colname = "Label"
  )
```

### differential expression

```{r diff_counts}
moo <- moo |>
  diff_counts(
    count_type = "filt",
    covariates_colnames = c("Group", "Batch"),
    contrast_colname = c("Group"),
    contrasts = c("B-A", "C-A", "B-C"),
    input_in_log_counts = FALSE,
    return_mean_and_sd = FALSE,
    voom_normalization_method = "quantile",
  )
```

### filter differential features

```{r filter_diff}
moo <- moo |> filter_diff()
```

## Customize plots

TODO

- show how to use individual plotting functions
- how to customize & override default color palettes
- how to customize ggplot objects

### 3D PCA

```{r pca_3D}
plot_pca(
  moo@counts$batch,
  moo@sample_meta,
  principal_components = c(1, 2, 3),
  group_colname = "Group",
  label_colname = "Label",
  color_values = moo@analyses[["colors"]][["Group"]]
)
```

### Expression Heatmap

```{r expr_heatmap}
heatmap_plot <- plot_expr_heatmap(
  moo,
  count_type = "norm",
  sub_count_type = "voom"
)
print(heatmap_plot)
```

### Volcano

#### Summary

```{r volcano_summary}
dat_volcano_summary <- moo@analyses$diff |>
  join_dfs_wide() |>
  plot_volcano_summary()

head(dat_volcano_summary)
```

#### Enhanced

```{r volcano_enhanced}
dat_volcano_enhanced <- moo@analyses$diff |>
  join_dfs_wide() |>
  plot_volcano_enhanced()
```


### Venn Diagram

```{r venn_diagram}
venn_dat <- dat_volcano_summary |> plot_venn_diagram()
head(venn_dat)
```