An example miRNApath data object containing miRNA data, gene and pathway associations. The object represents the end result of the miRNApath workflow, and serves a convenient source for example data.
The format is an S4 class
slotNames as follows:
data.frame containing the miRNA results data, expected to
contain columns with miRNA name, gene name, and ideally
some column(s) for filtering hits versus background, e.g.
fold change, expression abundance, P-value. Once the data
is filtered (see state below) there will be a column with
a flag indicating which entries are hits and which are
considered background. This column is found in
mirnaobj@columns["filterflagcolumn"] and is typically
Named list of column headers used throughout the analysis.
The purpose of the names is partly to retain the original
headers in the mirnaTable data.frame, and partly to
coordinate the names with the miRNA-gene and gene-pathway
tables used later in the analysis. The recognized headers:
mirnacol, assayidcol, genecol, pvaluecol, foldchangecol,
pathwaycol, pathwayidcol, groupcol, mirnagene. See the
documentation for the mirnapath object type for more
details about usage.
Numerical value indicating how many sample groups are available in the data, provided for convenience.
Character value indicating the current analysis state,
"unfiltered" if results are loaded but not
"filtered" if results are loaded and hits
are defined with the filterflagcol column;
the data is loaded, filtered, and analyzed for enrichment.
One can load mirna-gene and gene-pathway data at any point
which necessitates using the
mirnaobj@mirnaPathways object elements to determine if
that data has been loaded.
data.frame containing associations between miRNA and
genes. The data should contain one miRNA-to-gene
relationship per row, and should contain only those two
columns. Additional columns are maintained but ignored.
Note that one can use any values in the genecol column,
provided they match exactly with values found in the
mirnaobj@mirnaPathways element (see below.) Therefore,
if desired one can use transcript or gene associations,
or other integration methods as desired.
data.frame containing gene-pathway associations. The data should contain only one gene-to-pathway association per row of data. The data can have pathway ID values, which may facilitate comparisons to pathway databases (and may allow substantial data volume reduction if necessary.) If there is no pathwayidcol column, then one will be created using a numerical assignments of the pathway names. Note that this conversion is not sensitive to pathway sources, so care should be taken to include pathway source in the pathway name if two sources share the same pathway name. The same is true for pathway ID values, should they be purely numerical and have shared values across pathway sources.
Numerical value indicating how many pathways are available in the data, provided for convenience.
List of filters applied to the data, which may include:
"P-value", "Fold change", and/or
Enrichment summary data in the form of a list of elements
for each sample group (the sample group is the name of
each element.) Each list element is itself a list with
enrichment result data for each sample group, as
"pvalues" - list of P-values
named by pathway ID;
"Measured pathway mirnaGenes" -
total number of miRNA-gene-pathway combinations measured,
which gives some idea of the overall coverage of pathways.
The general point is that miRNAs have the potential to
cover many genes and pathways;
"Total mirnaGenes" - number
of miRNA-gene combinations represented in the data;
"Enriched pathway mirnaGenes" - number of miRNA-gene values
enriched in the pathway tested;
"Enriched by miRNA" - list
of miRNAs involved in the pathway tested, with the list of
genes in parentheses per miRNA;
"Enriched by Gene" - same
as previous except switching gene and miRNA;
enriched mirnaGenes" - the total number of miRNA-gene
values involved in any pathway enrichment (significant or
not.) The total values are useful when comparing across
sample groups, looking particularly for groups with few
changes or those with a uniquely high number of changes.
Named list of pathways contained in the
mirnaobj@mirnaPathways object, named by the pathway ID
values found in the pathwayidcol column. This list
facilitates converting the data in the enrichment element
to pathway names, since those values are named by the
pathway ID to conserve memory.
Journal of Alzheimers Disease 14, 27-41.
John Cogswell (2008) Identification of miRNA changes in Alzheimer's disease brain and CSF yields putative biomarkers and insights into disease pathways, Journal of Alzheimer's Disease 14, 27-41.
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