# Make groups of genes using expression profile.

`degPatterns.Rd`

Note that this function doesn't calculate significant difference between groups, so the matrix used as input should be already filtered to contain only genes that are significantly different or the most interesting genes to study.

    degPatterns(ma, metadata, minc = 15, summarize = "merge",
      time = "time", col = NULL, consensusCluster = FALSE,
      reduce = FALSE, cutoff = 0.7, scale = TRUE, pattern = NULL,
      groupDifference = NULL, eachStep = FALSE, plot = TRUE,
      fixy = NULL)

## Arguments

ma | log2 normalized count matrix
metadata | data frame with sample information. Rownames should match `ma` column names row number should be the same length than p-values vector.
minc | integer minimum number of genes in a group that will be return
summarize | character column name in metadata that will be used to group replicates. If the column doesn't exist it'll merge the `time` and the `col` columns, if `col` doesn't exist it'll use `time` only. For instance, a merge between summarize and time parameters: control_point0 ... etc
time | character column name in metadata that will be used as variable that changes, normally a time variable.
col | character column name in metadata to separate samples. Normally control/mutant
consensusCluster | Indicates whether using ConsensusClusterPlus or `cluster::diana()`
reduce | boolean remove genes that are outliers of the cluster distribution. `boxplot` function is used to flag a gene in any group defined by `time` and `col` as outlier and it is removed from the cluster. Not used if `consensusCluster` is TRUE.
cutoff | This is deprecated.
scale | boolean scale the `ma` values by row
pattern | numeric vector to be used to find patterns like this from the count matrix. As well, it can be a character indicating the genes inside the count matrix to be used as reference.
groupDifference | Minimum abundance difference between the maximum value and minimum value for each feature. Please, provide the value in the same range than the `ma` value ( if `ma` is in log2, `groupDifference` should be inside that range).
eachStep | Whether apply `groupDifference` at each stem over `time` variable. This only work properly for one group with multiple time points.
plot | boolean plot the clusters found
fixy | vector integers used as ylim in plot

## Value

list wiht two items:

- `df` is a data.frame with two columns. The first one with genes, the second with the clusters they belong.

- `pass` is a vector of the clusters that pass the `minc` cutoff.

- `plot` ggplot figure.

- `hr` clustering of the genes in hclust format.

- `profile` normalized count data used in the plot.

- `raw` data.frame with gene values summarized by biological replicates and with metadata information attached.

- `summarise` data.frame with clusters values summarized by group and with the metadata information attached.

- `normalized` data.frame with the clusters values as used in the plot.

- `benchmarking` plot showing the different patterns at different values for clustering cuttree function.

- `benchmarking_curve` plot showing how the numbers of clusters and genes changed at different values for clustering cuttree function.

## Details

It can work with one or more groups with 2 or more several time points. Before calculating the genes similarity among samples, all samples inside the same time point (`time` parameter) and group (`col` parameter) are collapsed together, and the `mean` value is the representation of the group for the gene abundance. Then, all pair-wise gene expression is calculated using `cor.test` R function using kendall as the statistical method. A distance matrix is created from those values. After that, `cluster::diana()` is used for the clustering of gene-gene distance matrix and cut the tree using the divisive coefficient of the clustering, giving as well by diana. Alternatively, if `consensusCluster` is on, it would use ConsensusClusterPlus to cut the tree in stable clusters. Finally, for each group of genes, only the ones that have genes higher than `minc` parameter will be added to the figure. The y-axis in the figure is the results of applying `scale()` R function, what is similar to creating a `Z-score` where values are centered to the `mean` and scaled to the `standard desviation` by each gene.

The different patterns can be merged to get similar ones into only one pattern. The expression correlation of the patterns will be used to decide whether some need to be merged or not.

## Examples

    data(humanGender)
    library(SummarizedExperiment)
    library(ggplot2)
    ma <- assays(humanGender)[[1]][1:100,]
    des <- colData(humanGender)
    des[["other"]] <- sample(c("a", "b"), 85, replace = TRUE)
    res <- degPatterns(ma, des, time="group", col = "other")#> Working with 100 genes.#> Working with 85 genes after filtering: minc > 15#> Joining, by = "merge"#> Joining, by = "merge"# Use the data yourself for custom figures
     ggplot(res[["normalized"]],
            aes(group, value, color = other, fill = other)) +
      geom_boxplot() +
       geom_point(position = position_jitterdodge(dodge.width = 0.9)) +
       # change the method to make it smoother
       geom_smooth(aes(group=other), method = "lm")