create_input_microarray: Creates an input object for downstream analysis
In ddeweerd/MODifieRDev: MODifieRDev

Description Usage Arguments Details Value Author(s) See Also

The MODifieR input object can be used in downstream analysis for the disease module inference methods included in this package.

create_input_microarray(expression_matrix, annotation_table, group1_indici,
  group2_indici, group1_label, group2_label, expression = T,
  differential_expression = T, method = "MaxMean",
  filter_expression = T, use_adjusted = T)

`expression_matrix`	Normalized expression matrix where the samples are columns and probes are rows
`annotation_table`	A dataframe providing annotation for the probes. The dataframe should have 3 columns: PROBEID: The probe id as it is in the expression matrix IDENTIFIER: The entrez id (if available) associated with the probe
`group1_indici`	vector containing indici for samples belonging to group 1 (Column numbers)
`group2_indici`	vector containing indici for samples belonging to group 2 (Column numbers)
`group1_label`	Label for each group 1, for example "patient" or "control"
`group2_label`	Label for each group 2, for example "patient" or "control"
`expression`	boolean, calculate expression values?
`differential_expression`	boolean, calculate differentially expressed data?
`method`	character string for determining which method is used to choose a probe among exactly 2 corresponding rows or when connectivityBasedCollapsing=FALSE. These are the options: "MaxMean" (default) or "MinMean" = choose the row with the highest or lowest mean value, respectively. "maxRowVariance" = choose the row with the highest variance (across the columns of `datET`). "absMaxMean" or "absMinMean" = choose the row with the highest or lowest mean absolute value. "ME" = choose the eigenrow (first principal component of the rows in each group). Note that with this method option, connectivityBasedCollapsing is automatically set to FALSE. "Average" = for each column, take the average value of the rows in each group "function" = use this method for a user-input function (see the description of the argument "methodFunction"). Note: if method="ME", "Average" or "function", the output parameters "group2row" and "selectedRow" are not informative.
`filter_expression`	boolean, remove 50 percent of the genes with lowest variance?
`use_adjusted`	boolean, use adjusted p value for differential expression analysis?

The function creates an input object to be used in all disease module inference methods. Differentially expressed genes are calculated using linear models from the limma package. Probes are collapsed into genes using collapseRows from WGCNA

The function returns an object of class "MODifieR_input". The object is a named list containing the following components:

`diff_genes`	A 2 two column data.frame where the first column are genes and the second column unadjusted p-values obtained by differential expression analysis
`limma_probe_table`	A data.frame from `limma topTable` with added gene annotation
`annotated_exprs_matrix`	A matrix where the rows are genes and the columns samples. Probes have been collapsed into genes using `collapse_method`
`expression_matrix`	A matrix, the original input expression matrix
`annotation_table`	A data.frame, the original annotation table used to annotate the probes
`group_indici`	A named list containing 2 numeric vectors. The names are the group labels and the values are the group indici