(f-divergence Cutoff Index), is to find DEGs in the transcriptomic & proteomic data, and identify DEGs by computing the difference between the distribution of fold-changes for the control-control and remaining (non-differential) case-control gene expression ratio data. fCI provides several advantages compared to existing methods.
|Date of publication||None|
|Maintainer||Shaojun Tang <firstname.lastname@example.org>|
|License||GPL (>= 2)|
call.npci: the s4 class function
call.npci-methods: ~~ Methods for Function 'call.npci' ~~
compute: the generic function 'compute' for s4 class
compute-methods: ~~ Methods for Function 'compute' ~~
deg.pairwise.fold.change: find targets that have a consistent fold change in the same...
deg.up.down.info: find targets and their detailed expression changes
deseq.median.ratio.normalization: data matrix normalization method
divergence.multivariate.distributions: estimate fCI divergence for given samples of aritrary...
fCI.call.by.index: top level function call to find targets based on expression...
fCI-class: Class '"fCI"'
fci.data: data frame of gene expression
figures: generic function to draw figures of the current analysis
figures-methods: generate figures for empirical null and case-control...
find.fci.targets: identify differentially expressed genes
find.fci.targets-methods: ~~ Methods for Function 'find.fci.targets' ~~
find.mid.point: find the middle value of the density distribution
get.fold.large.step: generate fold change cutoff values for fCI divergence...
get.npci.data: return a fCI object given the gene expression data
get.npci.distance.matrix: generate the divergence estimation based of fold change...
get.outline.index: find the outline genes of a given distribution
get.protein.fold.step: generate fold-change cutoff on proteomics data (with large...
get.rank.combinations: fold change values
get.rna.fold.step: generate fCI fold-change cutoff values for typical RNA-Seq...
initialize-methods: ~~ Methods for Function 'initialize' ~~
intersect.of.lists: find the common values of all vectors of a list
multi.dimensional.fci.data: data frame of gene expression
normalization: generic function to normalize gene expression matrix
normalization-methods: ~~ Methods for Function 'normalization' ~~
NPCI-class: Class '"NPCI"'
npci.gene.by.pvalues: find most signficantly change fCI targets
npci.index.reconsidered: find targets that have little evidence to be differentially...
npci.index.to.be.removed: gene indexes that will be considered as targets
npci.venn.diagram: generate venn diagram for multiple fCI analysis
pairwise.change.occupancy: find the targets whose fold changes occur consistently...
populate: generic function to populate the fCI object based on provided...
populate-methods: ~~ Methods for Function 'populate' ~~
report.target.summary: generate the results (gene ids) in the data frame
setfCI: the generic function 'setfCI' for s4 class
setfCI-methods: ~~ Methods for Function 'setfCI' ~~
show.targets: display the gene ids that are identified to be differentially...
show.targets-methods: ~~ Methods for Function 'show.targets' ~~
summarize: result summerization
summarize-methods: result summerization
total.library.size.normalization: normalize the gene expression based on the library size...
trim.size.normalization: normalize gene expression by exluding genes on the top 5 and...
two.sample.log.ratio: compute the log ratios of two vectors
two.sample.permutation.test: perform permuation test on two vectors
venndiagram: generate a venn diagram to show the differentially expression...
venndiagram-methods: ~~ Methods for Function 'venndiagram' ~~