---
title: "A guide to metadata for samples and differential expression analyses"
author:
- name: B. Ogan Mancarci
  affiliation: Michael Smith Laboratories, University of British Columbia, Vancouver, Canada
package: gemma.R
output:
  BiocStyle::html_document
vignette: |
  %\VignetteIndexEntry{A guide to metadata for samples and differential expression analyses}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, message = FALSE}
library(gemma.R)
library(dplyr)
library(pheatmap)
library(purrr)
```

```{r, include = FALSE}
options('gemma.memoised' = TRUE)
```


```{r,include=FALSE,eval=FALSE}
# finding a good example
all_valid <- 
    get_result_sets(filter = "analysis.subsetFactorValue.characteristics.size > 0") %>% 
    get_all_pages() 

# remove bugged ones, should be temporary until #50 is fixed
contrasts_with_subsets <- all_valid[!all_valid$experimental.factors %>% sapply(is.null),]


# find datasets where the experimental factor is marked by multiple statements
contrasts_with_subsets <- contrasts_with_subsets[contrasts_with_subsets$experimental.factors %>% sapply(nrow) %>% {.>1},]

# find datasets where experimental factor is marked by multiple statements belonging to the same factor

contrasts_with_subsets$experimental.factors %>% sapply(function(x){
    any(duplicated(x$ID))
    }) %>% {contrasts_with_subsets[.,]} -> contrasts_with_subsets

# interaction
contrasts_with_subsets = contrasts_with_subsets[grepl("_",contrasts_with_subsets$contrast.ID),]
```

# Introduction 

The data in Gemma are manually annotated by curators with terms, often using an ontology term on both dataset and sample level. In Gemma.R three primary functions allow access to these annotations for a given dataset. 

- `get_dataset_annotations`: This function returns annotations associated with a dataset. These try to serve as tags describing the dataset as a whole and they characteristics that samples within the datasets have while also including some additional terms.

- `get_dataset_samples`: This function returns samples and associated annotations related to their experimental groups for an experiment

- `get_dataset_differential_expression_analyses`: This function returns information about differential expression analyses automatically performed by Gemma for a given experiment. Each row of the output is a contrast where a specific property or an interaction of properties are described.

In the examples below we will be referring to [GSE48962](https://gemma.msl.ubc.ca/expressionExperiment/showExpressionExperiment.html?id=8972) experiment, where striatum and cerebral cortex samples from control mice and mice belonging
to a Huntington model (R6/2) were taken from 8 week and 12 week old mice.

# Dataset tags

Terms returned via `get_dataset_annotations` are tags used to describe a dataset in general terms.

```{r}
get_dataset_annotations('GSE48962') %>%
    gemma_kable
```

These tags come as a class/term pairs and inherit any terms that is assigned to any of the samples.
Therefore we can see all chemicals and cell types used in the experiment.


# Factor values

Samples and differential expression contrasts in Gemma are annotated with factor
values. These values contain statements that describe these samples and which
samples belong to which experimental in a differential expression analysis
respectively.

## Sample factor values

In gemma.R these values are stored in nested `data.table`s and can be
found by accessing the relevant columns of the outputs. Annotations for samples
can be accessed using `get_dataset_samples`. `sample.factorValues` column contains
the relevant information

```{r}
samples <- get_dataset_samples('GSE48962')
samples$sample.factorValues[[
    which(samples$sample.name == "TSM490")
    ]] %>% 
    gemma_kable()
```

The example above shows a single factor value object for one sample. The rows of this
`data.table` are statements that belong to a factor value. Below each column of this
nested table is described. If a given field is filled by an ontology term, the corresponding
URI column will contain the ontology URI for the field.

```{r,include = FALSE}

doubled_id <- samples$sample.factorValues[[
    which(samples$sample.name == "TSM490")
]] %>% filter(value == "HTT [human] huntingtin") %>% {.$ID} %>% unique

```
- `category`/`category.URI`: Category of the individual statement, such as treatment,
phenotype or strain
- `value`/`value.URI`: The subject of the statement.
- `predicate`/`predicate.URI`: When a subject alone is not enough to describe all
details, a statement can contain a predicate and an object. The predicate describes
the relationship between the subject of the statement and the object. In the example
above, these are used to denote properties of the human HTT in the mouse models 
- `object`/`object.URI`: The object of a statement is a property further describing
it's value. In this example these describe the properties of the HTT gene in the mouse
model, namely that it has CAG repeats and it is overexpressed. If the value was a 
drug this could be dosage or timepoint.
- `summary`: A plain text summary of the factorValue. Different statements will
have the same summary if they are part of the same factor value
- `ID`: An integer identifier for the specific factor value. In the example above,
the genotype of the mouse is defined as a single factor value made up of two statements
stating the HTT gene has CAG repeats and that it is overexpressed. This factor value has the
ID of `r doubled_id` which is shared by both rows containing the statements describing it. 
This ID will repeat for every other patient that has the same genotype
or differential expression results using that factor as a part of their contrast. For
instance we can see which samples that was subjected to this condition using this ID
instead of trying to match the other columns describing the statements
```{r}
id <- samples$sample.factorValues[[
    which(samples$sample.name == "TSM490")
]] %>% filter(value == "HTT [human] huntingtin") %>% {.$ID} %>% unique


# count how many patients has this phenotype
samples$sample.factorValues %>% sapply(\(x){
    id %in% x$ID
}) %>% sum

```
- `factor.ID`: An integer identifier for the factor. A factor holds specific factor
values. For the example above whether or not the mouse is a wild type mouse or
if it has a wild type genotype is stored under the id `r samples$sample.factorValues[[which(samples$sample.name == "TSM490")]] %>% filter(value == "HTT [human] huntingtin") %>% {.$factor.ID} %>% unique`


We can use this to fetch all distinct genotypes
```{r}
id <- samples$sample.factorValues[[
    which(samples$sample.name == "TSM490")
    ]] %>% 
    filter(value == "HTT [human] huntingtin") %>% {.$factor.ID} %>% unique

samples$sample.factorValues %>% lapply(\(x){
    x %>% filter(factor.ID == id) %>% {.$summary}
}) %>% unlist %>% unique
```
This shows us the dataset has control mice and Huntington Disease model mice.. This ID can be used to match the factor between samples and between samples
and differential expression experiments
- `factor.category`/`factor.category.URI`: The category of the whole factor. Usually
this is the same with the `category` of the statements making up the factor value.
However in cases like the example above, where the value describes a treatment while
the factor overall represents a phenotype, they can differ.

gemma.R includes a convenience function to create a simplified design matrix out of
these factor values for a given experiment. This will unpack the nested data.frames and
provide a more human readable output, giving each available factor it's own column.

```{r}
design <- make_design(samples)
design[,-1] %>% head %>%  # first column is just a copy of the original factor values
    gemma_kable()
```

Using this output, here we look at the sample sizes for different experimental groups.
```{r}
design %>%
    group_by(`organism part`,timepoint,genotype) %>% 
    summarize(n= n()) %>% 
    arrange(desc(n)) %>% 
    gemma_kable()

```

## Differential expression analysis factor values

For most experiments it contains, Gemma performs automated differential expression
analyses. The kinds of analyses that will be performed is informed by the factor values
belonging to the samples.

```{r}
# removing columns containing factor values and URIs for brevity
remove_columns <- c('baseline.factors','experimental.factors','subsetFactor','factor.category.URI')

dea <- get_dataset_differential_expression_analyses("GSE48962")

dea[,.SD,.SDcols = !remove_columns] %>% 
    gemma_kable()
```

The example above shows the differential expression analyses results. Each row of this data.table 
represents a differential expression contrast connected to a fold change and a p value in the output of 
`get_differential_expression_values` function.
If we look at the `contrast.ID` 
we will see the factor value identifiers returned in the `ID` column of our 
`sample.factorValues`. These represent which factor value is used as the 
experimental factor. Note that some rows will have two IDs appended together. These
represent the interaction effects of multiple factors. For simplicity, we will start
from a contrast without an interaction.

```{r}
contrast <- dea %>% 
    filter(
        factor.category == "genotype" & 
            subsetFactor %>% map_chr('value') %>% {.=='cerebral cortex'} # we will talk about subsets in a moment
        )
```

```{r}
# removing URIs for brevity
uri_columns = c('category.URI',
                'object.URI',
                'value.URI',
                'predicate.URI',
                'factor.category.URI')

contrast$baseline.factors[[1]][,.SD,.SDcols = !uri_columns] %>% 
     gemma_kable()

contrast$experimental.factors[[1]][,.SD,.SDcols = !uri_columns] %>% 
     gemma_kable()
```

Here, we can see the baseline is the wild type mouse, being compared to the Huntington Disease models 

If we examine a factor with interaction, both baseline and experimental factor value columns will contain
two factor values.


```{r}
contrast <- dea %>% 
    filter(
        factor.category == "genotype,timepoint" & 
            subsetFactor %>% map_chr('value') %>% {.=='cerebral cortex'} # we're almost there!
        )
```

```{r}
contrast$baseline.factors[[1]][,.SD,.SDcols = !uri_columns] %>% 
     gemma_kable()

contrast$experimental.factors[[1]][,.SD,.SDcols = !uri_columns] %>% 
     gemma_kable()
```

A third place that can contain factorValues is
the `subsetFactor`. Certain differential expression analyses exclude certain samples
based on a given factor. In this example we can see that this analysis were only performed
on samples from the cerebral cortex.

```{r}
contrast$subsetFactor[[1]][,.SD,.SDcols = !uri_columns] %>%
     gemma_kable()
```


The ids of the factor values included in `baseline.factors` and `experimental.factors` along
with `subsetFactor` can be used to determine which samples represent a given contrast. 
For convenience, `get_dataset_object` function which is used to compile metadata
and expression data of an experiment in a single object, includes `resultSets` and `contrasts`
argument which will return the data already composed of samples representing a particular contrast.

```{r}
obj <-  get_dataset_object("GSE48962",resultSets = contrast$result.ID,contrasts = contrast$contrast.ID,type = 'list')
obj[[1]]$design[,-1] %>% 
    head %>% gemma_kable()
```


We suggested that the `contrast.ID` of a contrast also corresponded to a column 
in the differential expression results, acquired by `get_differential_expression_values`.
We can use what we have learned to take a look at the expression of genes at the top of the
phenotype, treatment interaction. Each result.ID returns its separate table when accessing differential expression values.

```{r}
dif_vals <- get_differential_expression_values('GSE48962')
dif_vals[[as.character(contrast$result.ID)]] %>% head %>%  
     gemma_kable()
```
To get the top genes found associated with this interaction we access the columns with
the correct `contrast.ID`.

```{r}
# getting the top 10 genes
top_genes <- dif_vals[[as.character(contrast$result.ID)]] %>% 
    arrange(across(paste0('contrast_',contrast$contrast.ID,'_pvalue'))) %>% 
    filter(GeneSymbol!='' | grepl("|",GeneSymbol,fixed = TRUE)) %>% # remove blank genes or probes with multiple genes
    {.[1:10,]}
top_genes %>% select(Probe,NCBIid,GeneSymbol) %>% 
     gemma_kable()
```

We can then use the expression data returned by `get_dataset_object` to
examine the expression values for these genes.

```{r}
exp_subset<- obj[[1]]$exp %>% 
    filter(Probe %in% top_genes$Probe)
genes <- top_genes$GeneSymbol

# ordering design file
design <- obj[[1]]$design %>% arrange(genotype,timepoint)

# shorten the resistance label a bit
design$genotype[grepl('HTT',design$genotype)] = "Huntington Model"

exp_subset[,.SD,.SDcols = rownames(design)] %>% t  %>% scale %>% t %>%
    pheatmap(cluster_rows = FALSE,cluster_cols = FALSE,labels_row = genes,
             annotation_col =design %>% select(genotype,timepoint))

```

# Session info {.unnumbered}

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