---
title: "Introduction to `hermes`"
package: hermes
author:
  - Namrata Bhatia
  - Stefanie Bienert
  - Benoit Falquet
  - Haocheng Li
  - Jeff Luong
  - Lyndsee Midori Zhang
  - Simona Rossomanno
  - Tim Treis
  - Mark Yan
  - Naomi Chang
  - Chendi Liao
  - Carolyn Zhang
  - Joseph N. Paulson
  - name: Daniel Sabanés Bové
    email: daniel.sabanes_bove@roche.com
output: 
  BiocStyle::html_document:
    toc_float: true
vignette: |
  %\VignetteIndexEntry{Introduction to `hermes`}
  %\VignetteEncoding{UTF-8}
  %\VignetteEngine{knitr::rmarkdown}
editor_options:
  chunk_output_type: console
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

# Acknowledgments

`hermes` is a successor of the Roche internal `rnaseqTools` R package, and therefore many code ideas have been borrowed from it. Therefore we would like to thank the `rnaseqTools` authors for their work.

In particular, we would like to acknowledge Chendi Liao and Joe Paulson for their guidance and explanations during the development of `hermes`. We also discussed the class design with Valerie Obenchain, and discussed RNAseq data standards with Armen Karapetyan. We borrowed some ideas from the Roche internal `biokitr` R package and discussed them with its maintainer Daniel Marbach.

Finally, `hermes` originated as part of the NEST project. 
We are grateful for the entire team's support.

Thanks a lot to everyone involved!

# Installation

First let's see how we can install the `hermes` package.

## BioConductor

With the development version (3.15) of BioConductor, 
you can install the current package version with:

```{r bioc-installation, eval = FALSE}
# install.packages("BiocManager")
BiocManager::install("hermes")
```

## GitHub

You can install the unstable development version from GitHub with:

```{r gh-installation, eval = FALSE}
# install.packages("devtools")
devtools::install_github("insightsengineering/hermes")
```

# Introduction

The `hermes` R package provides classes, methods and functions to import, quality-check, filter, normalize, analyze RNAseq counts data. The core functionality is built on the BioConductor ecosystem, especially `SummarizedExperiment`.
New users should first begin by reading the "Introduction to `hermes`" vignette to become familiar with the `hermes` concepts.

```{r, eval=FALSE}
vignette(topic = "introduction", package = "hermes")
```

In this vignette you are going to learn how to:

* Import RNAseq count data into the `hermes` ready format.
* Annotate gene information automatically from a central database (e.g. BioMart).
* Add quality control (QC) flags to genes and samples.
* Filter the data set.
* Normalize the counts.
* Quickly produce descriptive plots.
* Perform principal components analysis.
* Produce a templated QC report.
* Perform differential expression analysis.

The packages used in this vignette are:
```{r, message=FALSE}
library(hermes)
library(SummarizedExperiment)
```

The datasets used in this vignette are:
```{r, message=FALSE, warning=FALSE}
?expression_set
?summarized_experiment
```

# Importing Data

The data for `hermes` needs to be imported into the `HermesData` or `RangedHermesData` format.

## Importing a `SummarizedExperiment`

The simplest way to import data is from a `SummarizedExperiment` (SE) object. This is because a `HermesData` object
is just a special SE, with few additional requirements and slots.

In a nutshell, the object needs to have a `counts` assay, have certain
gene and sample variables, and have unique row and column names. The row names, i.e. the gene names, must
start with a common prefix `GeneID` or `ENSG` to enable easy annotations.
See `?HermesData` for the detailed requirements.

When the SE follows the minimum conventions, we can just call the `HermesData` constructor on it:

```{r}
object <- HermesData(summarized_experiment)
```

And we have a `HermesData` object.

```{r}
object
```

Note that in this case deprecated names were used for the `rowData` and `colData` variables,
therefore they appear under "additional" gene and sample information. However we can
still call the default constructor because the new names will be filled with missing values, e.g.:

```{r}
head(annotation(object))
```

If we want to map old column names to new column names to avoid duplication with new missing value columns,
we can do this using the `rename()` method. For example here:

```{r}
object <- summarized_experiment %>%
  rename(
    row_data = c(
      symbol = "HGNC",
      desc = "HGNCGeneName",
      chromosome = "Chromosome",
      size = "WidthBP",
      low_expression_flag = "LowExpressionFlag"
    ),
    col_data = c(
      low_depth_flag = "LowDepthFlag",
      technical_failure_flag = "TechnicalFailureFlag"
    )
  ) %>%
  HermesData()
```

For example we can now see in the annotations that we successfully carried over the information
since we mapped the old annotations to the new required names above:

```{r}
head(annotation(object))
```

For a bit more details we can also call `summary()` on the object.

```{r}
summary(object)
```

For the below, let's use the already prepared `HermesData` object.

```{r}
object <- hermes_data
```

## Importing an `ExpressionSet`

If we start from an `ExpressionSet`, we can first convert it to a `RangedSummarizedExperiment` and then import it to `RangedHermesData`:

```{r}
se <- makeSummarizedExperimentFromExpressionSet(expression_set)
object2 <- HermesData(se)
object2
```

## Importing a Matrix

In general we can also import a matrix of counts. We just have to pass the required
gene and sample information as data frames to the constructor.

```{r}
counts_matrix <- assay(hermes_data)
object3 <- HermesDataFromMatrix(
  counts = counts_matrix,
  rowData = rowData(hermes_data),
  colData = colData(hermes_data)
)
object3
identical(object, object3)
```

Note that we can easily access the counts assay (matrix) in the final object with `counts()`:

```{r}
cnts <- counts(object)
cnts[1:3, 1:3]
```

# Annotations

`hermes` provides a modular approach for querying gene annotations, in order to
allow for future extensions in this or other downstream packages.

## Connection to Database

The first step is to connect to a database. In `hermes` the only option is currently databases that utilize the
BioMart software suite.
However due to the generic function design, it is simple to extend `hermes` with other data base
connections.

In order to save time during vignette build, we zoom in here on a subset of the original `object`
containing only the first 10 genes.

```{r}
small_object <- object[1:10, ]
```

The corresponding function takes the common gene ID prefix as argument to determine the
format of the gene IDs and the filter variable to use in the query later on.

```{r eval=FALSE}
httr::set_config(httr::config(ssl_verifypeer = 0L))
connection <- connect_biomart(prefix(small_object))
```

Here we are using the `prefix()` method to access the prefix saved in the `HermesData` object.

## Querying and Saving Annotations

Then the second step is to query the gene annotations and save them in the object.

```{r eval=FALSE}
annotation(small_object) <- query(genes(small_object), connection)
```

Here we are using the `genes()` method to access the gene IDs (row names) of the `HermesData` object.
Note that not all genes might be found in the data base and the corresponding rows would then be `NA` in the annotations.

# Quality Control Flags

`hermes` provides automatic gene and sample flagging, as well as manual sample flagging functionality.

## Automatic Gene and Sample Flagging

For genes, it is counted how many samples don't pass a minimum expression CPM (counts per million reads mapped) threshold.
If too many, then this gene is flagged as a "low expression" gene.

For samples, two flags are provided. The "technical failure" flag is based on the average Pearson correlation with other
samples. The "low depth" flag is based on the library size, i.e. the total sum of counts for a sample across all genes.

Thresholds for the above flags can be initialized with `control_quality()`, and the flags are added with `add_quality_flags()`.

```{r}
my_controls <- control_quality(min_cpm = 10, min_cpm_prop = 0.4, min_corr = 0.4, min_depth = 1e4)
object_flagged <- add_quality_flags(object, control = my_controls)
```

## Manual Sample Flagging

Sometimes it is necessary to manually flag certain samples as technical failures, e.g. after looking
at one of the analyses discussed below. This is possible, too.

```{r}
object_flagged <- set_tech_failure(object_flagged, sample_ids = "06520011B0023R")
```

## Accessing Flags

All flags have access functions.

```{r}
head(get_tech_failure(object_flagged))
head(get_low_depth(object_flagged))
head(get_low_expression(object_flagged))
```

# Filtering Data

We can either filter based on the default QC flags, or based on custom variables from the gene or sample information.

## Based on Default QC Flags

This is simple with the `filter()` function. It is also possible to selectively only filter the genes or the samples using the `what` argument.

```{r}
object_flagged_filtered <- filter(object_flagged)
object_flagged_genes_filtered <- filter(object_flagged, what = "genes")
```

## Based on Custom Variables

This can be done with the `subset()` function. Genes can be filtered with the `subset` argument via expressions using the gene information variables, and samples can be filtered with the `select` argument using the sample information variables. In order to see which ones are available these can be queries first.

```{r}
names(rowData(object_flagged))
names(colData(object_flagged))
head(rowData(object_flagged)$chromosome)
head(object_flagged$ARMCD)
object_flagged_subsetted <- subset(
  object_flagged,
  subset = chromosome == "5",
  select = ARMCD == "COH1"
)
```

# Normalizing Counts

Normalizing counts within samples (CPM), genes (RPKM) or across both (TPM) can be
achieved with the `normalize()` function. The `normalize()` function can also transform the counts by the variance stabilizing transformation (`vst`) and the regularized log transformation (`rlog`) as proposed in the `DESeq2` package.

```{r}
object_normalized <- normalize(object_flagged_filtered)
```

The corresponding assays are saved in the object and can be accessed with `assay()`.

```{r}
assay(object_normalized, "tpm")[1:3, 1:3]
```

The used control settings can be accessed afterwards from the metadata of the object:

```{r}
metadata(object_normalized)
```

Note that also the filtering settings are saved in here. For custom normalization options,
use `control_normalize()`. For example, to not use log scale but the original scale of the counts:

```{r}
object_normalized_original <- normalize(
  object_flagged_filtered,
  control = control_normalize(log = FALSE)
)
assay(object_normalized_original, "tpm")[1:3, 1:3]
```

# Descriptive Plots

## Simple Plots

A series of simple descriptive plots can be obtained by just calling `autoplot()` on an object.

```{r}
autoplot(object)
```

Note that individual plots from these can be produced with the series of `draw_*()` functions, see `?plot_all` for the
detailed list. Then, these can be customized further.
For example, we can change the number and color of the bins in the library size histogram:

```{r}
draw_libsize_hist(object, bins = 10L, fill = "blue")
```

## Top Genes

Top genes can be calculated and visualized in a barplot.

```{r}
most_expr_genes <- top_genes(object_normalized, assay_name = "tpm")
autoplot(most_expr_genes)
```

By passing another summary function, also the variability can be ranked for example.

```{r}
most_var_genes <- top_genes(object_normalized, summary_fun = rowSds)
autoplot(most_var_genes)
```

## Correlation between Samples

A sample correlation matrix between samples can be obtained with the `correlate()` function. This can be visualized in a heatmap using `autoplot()` again. See `?calc_cor` for detailed options.

```{r}
cor_mat <- correlate(object)
autoplot(cor_mat)
```

# Principal Components

Let's see how we can perform Principal Components Analysis (PCA).

## PCA of Samples

PCA can be performed with `calc_pca()`. The result can be summarized or plotted.

```{r}
pca_res <- calc_pca(object_normalized, assay_name = "tpm")
summary(pca_res)$importance
autoplot(pca_res)
```

Note that various options are available for the plot, for example we can look at different principal components, and color the samples by sample variables. See `?ggfortify::autoplot.prcomp` for details.

```{r}
autoplot(
  pca_res,
  x = 2, y = 3,
  data = as.data.frame(colData(object_normalized)), colour = "SEX"
)
```

## Correlation with Sample Variables

Subsequently it is easy to correlate the obtained principal components with the sample variables. We obtain a matrix of
R-squared (R2) values for all combinations, which can again be visualized as a heatmap.
See `?pca_cor_samplevar` for details.

```{r}
pca_cor <- correlate(pca_res, object_normalized)
autoplot(pca_cor)
```

# QC Report Template

In order to quickly obtain a quality control report for a new RNAseq data set, you can proceed as follows.

1. Save your input `SummarizedExperiment` using R's `save()` function in a binary data file (e.g. ending with `.rda` suffix).
1. Load the `hermes` package in RStudio and click on: `File` > `New File` > `R Markdown` > `From Template` and select the QC report template from `hermes`.
![Rmd_template_selection](rmd_template_selection.png){width=4in}
1. Fill in the few parameters in the yaml header, including the required file paths for the input file from above, and where the resulting `HermesData` object should be saved.
1. Knit the document.

The report contains the above mentioned descriptive plots and PCA analyses and can be a useful starting point for your analysis.

# Differential Expression

In addition to the above QC analyses, simple differential expression analysis is supported by `hermes`.
In addition to the filtered object (normalization of counts is not required) the variable name of the factor to contrast the samples needs to be provided to `diff_expression()`.

```{r}
colData(object) <- df_cols_to_factor(colData(object))
diff_res <- diff_expression(object, group = "SEX", method = "voom")
head(diff_res)
```

Note that we use here the utility function `df_cols_to_factor()` which converts by default all character and logical variables to factor variables. This is one possible way here to ensure that the utilized group variable is a factor.

Afterwards a standard volcano plot can be produced.

```{r, fig.small = TRUE}
autoplot(diff_res, log2_fc_thresh = 8)
```

# Summary

The `hermes` R package provides classes, methods and functions to import, quality-check, filter, normalize and analyze RNAseq counts data. In particular, the robust object-oriented framework allows for easy extensions in the future to address user feature requests. These and other feedback are very welcome - thank you very much in advance for your thoughts on `hermes`!

# Session Info {.unnumbered}

Here is the output of `sessionInfo()` on the system on which this document was
compiled running pandoc `r rmarkdown::pandoc_version()`:

```{r sessionInfo, echo=FALSE}
sessionInfo()
```
