---
title: "SeqGate: Filter lowly expressed features"
author:
    - name: Christelle Reynès
      affiliation: IGF, CNRS, INSERM, Univ Montpellier, Montpellier France
      email: christelle.reynes@igf.cnrs.fr
    - name: Stéphanie Rialle
      affiliation: BioCampus Montpellier, CNRS, INSERM, Univ Montpellier, Montpellier France
      email: stephanie.rialle@mgx.cnrs.fr
package: SeqGate
abstract: >
    Differential expression studies are very common experiments in RNA-Seq. 
    They imply the application of statistical tests to a very high number of 
    genes (or transcripts). Some lowly expressed genes are not likely to be 
    significant, thus it is a good practice to filter them in order to increase 
    the differential genes detection sensitivity. The application of a filtering
    method for these lowly expressed genes is very common but generally an 
    arbitrary threshold is chosen. Here we propose a novel filtering method, 
    SeqGate, based on the replicates of the experiment that allows to 
    rationalize the determination of the threshold by taking advandage of the 
    data themselves.
output:
    BiocStyle::html_document
vignette: >
    %\VignetteEngine{knitr::rmarkdown}
    %\VignetteIndexEntry{SeqGate: Filter lowly expressed features}
    %\VignetteEncoding{UTF-8}
---

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# Introduction: SeqGate method description

In order to find a threshold value to filter lowly expressed features, SeqGate 
analyzes the distribution of counts found in replicates along with zero counts. 
More specifically, features with a customizable minimal proportion of zeros in 
one condition are selected. The distribution of counts found in replicates of 
that same condition along with those zeros is computed. The chosen threshold is 
the count value corresponding to the customizable percentile of this 
distribution. Finally, features having a customizable proportion (90% by 
default) of replicates with counts below that value in all conditions are 
filtered. Default value for all customizable parameters have been set through 
extensive simulation batch testing and can be considered as adequate in most 
situations.

# Installation

To install SeqGate, start R and enter:

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

BiocManager::install("SeqGate")
```

# Filtering with SeqGate

First load SeqGate:
```{r load_SeqGate}
library(SeqGate)
```

## Input data

The main input data is a
[SummarizedExperiment] (https://bioconductor.org/packages/release/bioc/html/
SummarizedExperiment.html)
object which contains an assay with count data. Briefly, a SummarizedExperiment
container contains one or more assays, each represented by a matrix-like object
of numeric or other mode. The rows typically represent genomic ranges of
interest and the columns represent samples. For SeqGate, the
SummarizedExperiment object must contain at least one assay of numeric counts,
and a DataFrame describing the columns, in particular a column telling the
biological condition the sample belongs to.
To apply SeqGate, the SummarizedExperiment object, the assay name and the column
describing the condition of each sample in the colData dataframe, must be given.

### Toy dataset

Let's load some toy data set. This data set is an extract from a human
transcriptome dataset produced by Strub *et al.* (2011), in which human cells 
expressing the Microphtalmia Transcription Factor (MiTF) are compared to cells 
in which the MiTF is repressed. The extract counts 1,000 genes with expression 
measured in 3 samples for each biological condition (the full table of read 
counts is available in the Supplementary materials of Dillies, M.A. *et al.* 
(2012)).

```{r load_dataTest}
data(data_MiTF_1000genes)
head(data_MiTF_1000genes)
```

And now we define a vector indicating the biological condition corresponding to 
each column of data_MiTF_1000. Here the two biological conditions are 'A' and 
'B'.
```{r define_cond}
cond<-c("A","A","B","B","A","B")
```

### Getting the SummarizedExperiment input

The toy dataset that we have just loaded is not yet a SummarizedExperiment
object, such as required in Seqgate input. We thus need to create it, 
from the count matrix and the biological condition annotation.

```{r create_se}
rowData <- DataFrame(row.names=rownames(data_MiTF_1000genes))
colData <- DataFrame(Conditions=cond)
counts_strub <- SummarizedExperiment(
                  assays=list(counts=data_MiTF_1000genes),
                  rowData=rowData,
                  colData=colData)
```


## Filtering with default options

By default, SeqGate only needs the SummarizedExperiment object along with the
name of the assay we want to work with, and the name of the column which
contains the biological conditions annotation. Thus, we can apply the SeqGate
method filtering, by calling the following code:

```{r apply_basic}
counts_strub <- applySeqGate(counts_strub,"counts","Conditions")
```
As a result, the input SummarizedExperiment object now includes a new column in
the rowData DataFrame, named onFilter. This column is a logical vector that
indicates if the gene should be kept after filtering (TRUE) or not (FALSE). The
metadata of the object also include a new element, named "threshold", which
gives the value of the applied threshold.

Thus, to get the matrix of features intended to be kept for the downstream
analysis:
```{r get_kept_features}
keptGenes <- assay(counts_strub[rowData(counts_strub)$onFilter == TRUE,])
head(keptGenes)
dim(keptGenes)
```

To get the applied threshold:

```{r threshold}
metadata(counts_strub)$threshold
```

We can also get the matrix of filtered genes:

```{r get_filtered}
filteredOut <- assay(counts_strub[rowData(counts_strub)$onFilter == FALSE,])
head(filteredOut)
```

To conclude, we can see that, from the initial set of 1,000 genes,
`r nrow(keptGenes)` have been kept, after the application of a threshold of
`r metadata(counts_strub)$threshold`: all genes having less than
`r names(metadata(counts_strub)$threshold)`
replicates with less than `r metadata(counts_strub)$threshold` reads are
discarded.

## Setting custom filtering parameters

### Parameters detailed explanation

Besides the three mandatory parameters described above, the applySeqGate 
function also have three other parameters, that can be set to refine the 
filtering:

* prop0: this is minimal proportion of zeros among a condition to consider that
    the feature is not or lowly expressed.
* percentile: percentile used on the 'max' distribution to determine the
    filtering threshold value.
* propUpThresh: proportion of counts to be above the threshold in at least one
    condition to keep the feature.

By default, 'prop0' is set to the maximum number of replicates minus one,
divided by the maximum number of replicates. In the example above, as we have 3
replicates in both conditions, the maximum number of replicates is 3. Thus, the
parameter 'prop0' is set to 2/3. This means that we consider that the gene is
lowly expressed if it has 2 zeros among its 3 replicates.

The distribution of maximum counts from all the lowly expressed genes (selected
according to 'prop0') is then computed. The idea is to see how high a count can
be in a replicate alongside a zero in another replicate. In order to introduce
flexibility, we do not simply take the maximum count of the distribution but a
'percentile' of this distribution. By default, when the number of replicates in
at least one condition is below 5, 'percentile' is set to 0.9. In the above
example, the 90th percentile of the distribution of maximum counts seen 
alongside a zero is `r metadata(counts_strub)$threshold`, and this is the 
threshold that we will apply in order to actually filter the lowly expressed 
genes.

Finally, the filter is applied according to a last parameter: propUpThresh.
SeqGate does keep those genes whose counts are above the computed threshold in
at least 'propUpThresh' replicates, in at least one condition. Still in the
example used precedently, this means that all genes whose counts are above
`r metadata(counts_strub)$threshold` in 3 x 0.9 = 2.7 replicates, are kept. 
As it is not possible to consider 2.7 replicates, the value is rounded to the 
next integer, that is 3 in this case. Finally in this example, a gene is kept if
all its 3 replicates have a count above `r metadata(counts_strub)$threshold`, 
in at least one condition.

### Custom filtering parameters example

Default value for all customizable parameters have been set through extensive 
simulation batch testing and can be considered as adequate in most situations.
However, one may consider that the default parameters are not suited to its 
experiment.
In that case, custom values can be given:

```{r apply_custom}
counts_strub <- applySeqGate(counts_strub,"counts","Conditions",
             prop0=1/3,
             percentile=0.8,
             propUpThresh=0.5)
```

This time, from the initial set of 1,000 genes, 
`r nrow(assay(counts_strub[rowData(counts_strub)$onFilter == TRUE,]))` have been kept, 
after the application of a threshold of `r metadata(counts_strub)$threshold`.

# SessionInfo

```{r sessionInfo}
sessionInfo()
```
