---
title: "Training basic model classifying a cell type from scRNA-seq data"
author: "Vy Nguyen"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
%\VignetteIndexEntry{2. Training basic model}
%\VignetteEngine{knitr::rmarkdown}
\usepackage[utf8]{inputenc}
---
```{r setup, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>"
)
options(rmarkdown.html_vignette.check_title = FALSE)
```
## Introduction
One of key functions of the scClassifR package is to provide users
easy tools to train their own model classifying new cell types from labeled
scRNA-seq data.
This vignette shows how to train a basic
classification model for an independant cell type, which is not a child of
any other cell type.
## Preparing train object and test object
The workflow starts with either a [Seurat](https://satijalab.org/seurat/) or
[SingleCellExperiment](https://osca.bioconductor.org/) object where cells have already
been assigned to different cell types.
To do this, users may have annotated
scRNA-seq data (by a FACS-sorting process, for example), create a Seurat/
SingleCellExperiment (SCE) object based on the sequencing data and assign the
predetermined cell types as cell meta data. If the scRNA-seq data has not
been annotated yet, another possible approach is to follow the basic
workflow (Seurat, for example) until assigning cell type identity to clusters.
In this vignette, we use the human lung dataset from Zilionis et al., 2019,
which is available in the scRNAseq (2.4.0) library. The dataset is stored as a
SCE object.
To start the training workflow, we first install and load the necessary libraries.
```{r, eval = FALSE}
# use BiocManager to install from Bioconductor
if (!requireNamespace("BiocManager", quietly = TRUE))
install.packages("BiocManager")
# the scClassifR package
if (!require(scClassifR))
BiocManager::install("scClassifR")
# we use the scRNAseq package to load example data
if (!require(scRNAseq))
BiocManager::install("scRNAseq")
```
```{r}
library(scRNAseq)
library(scClassifR)
```
First, we load the dataset. To reduce the computational complexity of this vignette, we only use the first 5000 cells of the dataset.
```{r}
zilionis <- ZilionisLungData()
zilionis <- zilionis[, 1:5000]
```
We split this dataset into two parts, one for the training and the other for the testing.
```{r}
pivot = ncol(zilionis)%/%2
train_set <- zilionis[, 1:pivot]
test_set <- zilionis[, (1+pivot):ncol(zilionis)]
```
In this dataset, the cell type meta data is stored in the *Most likely LM22 cell type*
slot of the SingleCellExperiment object (in both the train object and test object).
If the cell type is stored not stored as the default identification (set through
`Idents` for Seurat object) the slot must be set as a parameter in the training
and testing function (see below).
```{r}
unique(train_set$`Most likely LM22 cell type`)
```
```{r}
unique(test_set$`Most likely LM22 cell type`)
```
We want to train a classifier for B cells and their phenotypes. Considering memory B cells,
naive B cells and plasma cells as B cell phenotypes, we convert all those cells to a uniform
cell label, ie. B cells. All non B cells are converted into 'others'.
```{r}
# change cell label
train_set$B_cell <- unlist(lapply(train_set$`Most likely LM22 cell type`,
function(x) if (is.na(x)) {'ambiguous'} else if (x %in% c('Plasma cells', 'B cells memory', 'B cells naive')) {'B cells'} else {'others'}))
test_set$B_cell <- unlist(lapply(test_set$`Most likely LM22 cell type`,
function(x) if (is.na(x)) {'ambiguous'} else if (x %in% c('Plasma cells', 'B cells memory', 'B cells naive')) {'B cells'} else {'others'}))
```
We observe that there are cells marked NAs. Those can be understood as 1/different from all indicated cell types or 2/any unknown cell types. Here we consider the second case, ie. we don't know whether they are positive or negative to B cells. To avoid any effect of these cells, we can assign them as 'ambiguous'. All cells tagged 'ambiguous' will be ignored by scClassifR from training and testing.
We may want to check the number of cells in each category:
```{r}
table(train_set$B_cell)
```
## Defining features
Next, we define a set of features, which will be used in training the
classification model. Supposing we are training a model for classifying
B cells, we define the set of features as follows:
```{r}
selected_features_B <- c("CD19", "MS4A1", "CD79A", "CD79B", 'CD27', 'IGHG1', 'IGHG2', 'IGHM',
"CR2", "MEF2C", 'VPREB3', 'CD86', 'LY86', "BLK", "DERL3")
```
## Train model
When the model is being trained, three pieces of information must be
provided:
* the Seurat/SCE object used for training
* the set of applied features
* the cell type defining the trained model
In case the dataset does not contain any cell classified as the target
cell type, the function will fail.
If the cell type annotation is not set in the default identification slot
(`Idents` for `Seurat` objects) the name
of the metadata field must be provided to the `sce_tag_slot parameter`.
When training on an imbalanced dataset (f.e. a datasets containing 90% B cells and
only very few other cell types), the trained model may bias toward the
majority group and ignore the presence of the minority group. To avoid this,
the number of positive cells and negative cells will be automatically balanced
before training. Therefore, a smaller number cells will be randomly picked
from the majority group. To use the same set of cells while training multiple
times for one model, users can use `set.seed`.
```{r}
set.seed(123)
clf_B <- train_classifier(train_obj = train_set, cell_type = "B cells", features = selected_features_B,
sce_assay = 'counts', sce_tag_slot = 'B_cell')
```
```{r}
clf_B
```
The classification model is a `scClassifR` object.
Details about the classification model are accessible via getter methods.
For example:
```{r}
clf(clf_B)
```
## Test model
The `test_classifier` model automatically tests a classifier's performance
against another dataset. Here, we used the `test_set` created before:
```{r}
clf_B_test <- test_classifier(test_obj = test_set, classifier = clf_B,
sce_assay = 'counts', sce_tag_slot = 'B_cell')
```
### Interpreting test model result
Apart from the output exported to console, test classifier function also returns an object, which is a list of:
* **test_tag**: actual cell label, this can be different from the label provided by users because of ambiguous characters or the incoherence in cell type and sub cell type label assignment.
* **pred**: cell type prediction using current classifier
* **acc**: prediction accuracy at the fixed probability threshold, the probability threshold value can also be queried using *p_thres(classifier)*
* **auc**: AUC score provided by current classifier
* **overall_roc**: True Positive Rate and False Positive Rate with a certain number of prediction probability thresholds
Every classifier internally consists of a trained SVM and a probability threshold. Only cells that are classified with a probability exceeding
this threshold are classified as the respective cell type. The *overall_roc* slot summarizes the True Positive Rate (sensitivity) and False Positive Rate (1 - specificity) obtained by the trained model according to different thresholds.
```{r}
clf_B_test$overall_roc
```
In this example of B cell classifier, the current threshold is at 0.5. The higher sensitivity can be reached if we set the p_thres at 0.4. However, we will then have lower specificity, which means that we will incorrectly classify some cells as B cells. At the sime time, we may not retrieve all actual B cells with higher p_thres (0.6, for example).
There is of course a certain trade-off between the sensitivity and the specificity of the model. Depending on the need of the project or the user-own preference, a probability threshold giving higher sensitivity or higher specificity can be chosen. In our perspective, p_thres at 0.5 is a good choice for the current B cell model.
### Plotting ROC curve
Apart from numbers, we also provide a method to plot the ROC curve.
```{r}
roc_curve <- plot_roc_curve(test_result = clf_B_test)
```
```{r}
plot(roc_curve)
```
### Which model to choose?
Changes in the training data, in the set of features and in the prediction probability
threshold will all lead to a change in model performance.
There are several ways to evaluate the trained model, including the overall
accuracy, the AUC score and the sensitivity/specificity of the model when
testing on an independent dataset. In this example, we choose the model
which has the best AUC score.
*Tip: Using more general markers of the whole population leads to higher
sensitivity. This sometimes produces lower specificity because of close
cell types (T cells and NK cells, for example). While training some models,
we observed that we can use the markers producing high sensitivity but at
the same time can improve the specificity by increasing the probability
threshold. Of course, this can only applied in some cases, because
some markers can even have a larger affect on the specificity than the
prediction probability threshold.*
## Save classification model for further use
New classification models can be stored using the `save_new_model` function:
```{r}
# no copy of pretrained models is performed
save_new_model(new_model = clf_B, path.to.models = getwd(),include.default = FALSE)
```
Parameters:
* **new_model**: The new model that should be added to the database in the
specified directory.
* **path.to.models**: The directory where the new models should be stored.
* **include.default**: If set, the default models shipped with the package
are added to the database.
Users can also choose whether copy all pretrained models of the packages to the
new model database. If not, in the future, user can only choose to use either
default pretrained models or new models by specifying only one path to models.
Models can be deleted from the model database using the `delete_model` function:
```{r}
# delete the "B cells" model from the new database
delete_model("B cells", path.to.models = getwd())
```
## Session Info
```{r}
sessionInfo()
```