Description Usage Arguments Details Value Methods Compatibility Author(s) See Also Examples
The function singlelinc perfroms co-expression analysis for a single query. An input LINCmatrix will be converted to a LINCsingle object. As a first step (I) a set of co-expressed protein-coding genes of a query is determined. Secondly, (II) biological terms related to the these genes are derived. The result will show the co-expression for the query.
1 2 3 4 5 6 7 8 9 10 11 12 |
input |
an object of the class |
query |
the name of the (ncRNA) gene to be evaluated; has to be present in |
onlycor |
if |
testFun |
a function to test the robustness of correlations. User-defined functions are allowed. The expected output is a p-value. |
alternative |
one of |
threshold |
a single number representing the threshold for selecting co-expressed genes |
underth |
if |
coExprCut |
a single |
handleGeneIds |
if |
annotateFrom |
a function which will derive significant biological terms based on the set of co-expressed genes from a gene annotation resource. |
verbose |
whether to give messages about the progression of the function |
... |
further arguments, mainly for |
In comparison to the function clusterlinc this function will provide more flexibility in terms of the selection of co-expressed genes. The option onlycor = TRUE in combination with a suitable threshold can be used to choose co-expressed protein-coding genes based on the correlation values inherited from the input LINCmatrix object. For this to work it is required to set underth = FALSE because then, values higher than the threshold will be picked. By default, co-expression depnds on the p-values from the correlation test (stats::cor.test) which demonstrate the robustness of a given correlation between two genes. A user-defined test function supplied in testFun requires the formal arguments x, y, method and use. Moreover, the p-values of the output should be accessible by $pvalue. The number of co-expressed genes can be restricted not only by threshold, but also by coExprCut. The value n for coExprCut = n will be ignored in case the number of genes which fulfill the threshold criterion is smaller than n.
handleGeneIds = FALSE should only be used for an input with Entrez gene ids. Options for annotateFrom are for example: ReactomePA::enrichPathway(), clusterProfiler::enrichGO or clusterProfiler::enrichKEGG. Further arguments (...) are inteded to be passed to the called annotateFrom function. annotateFrom = 'enrichGO', ont = "CC" will call the subontology "Cellular Component" from GO. For more details see the documentation ofclusterProfiler.
an object of the class 'LINCmatrix' (S4) with 6 Slots
results |
a |
assignment |
a |
correlation |
a |
expression |
the original expression matrix |
history |
a storage environment of important methods, objects and parameters used to create the object |
linCenvir |
a storage environment ensuring the compatibility to other objects of the |
signature(input = "LINCmatrix")(see details)
plotlinc(LINCsingle, ...)), ...
Manuel Goepferich
1 2 3 4 5 6 7 8 9 10 11 12 13 14 | data(BRAIN_EXPR)
# selection based on absolute correlation
meg3 <- singlelinc(crbl_matrix, query = "55384", onlycor = TRUE, underth = FALSE, threshold = 0.5)
plotlinc(meg3)
# get biological processes from 'Gene Ontology'
meg3 <- singlelinc(crbl_matrix, query = "55384", threshold = 0.00005, ont = 'BP')
plotlinc(meg3)
# get enriched pathways from 'ReatomePA'
meg3 <- singlelinc(crbl_matrix, query = "55384", onlycor = TRUE, underth = FALSE, threshold = 0.5,
annotateFrom = 'enrichPathway')
plotlinc(meg3)
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