Package 'fCI'

Title: f-divergence Cutoff Index for Differential Expression Analysis in Transcriptomics and Proteomics
Description: (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.
Authors: Shaojun Tang
Maintainer: Shaojun Tang <[email protected]>
License: GPL (>= 2)
Version: 1.37.0
Built: 2024-11-05 05:59:17 UTC
Source: https://github.com/bioc/fCI

Help Index

the s4 class function

Description

the s4 class function

Usage

call.npci(.Object)

Arguments

.Object

the fCI object

Details

The S4 method will compute DEGs and save the results to the original s4 object .Object

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("See README")

~~ Methods for Function call.npci ~~

Description

~~ Methods for function call.npci ~~

Methods

signature(.Object = "NPCI")

the generic function 'compute' for s4 class

Description

the generic function 'compute' for s4 class

Usage

compute(.Object)

Arguments

.Object

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("See README")

~~ Methods for Function compute ~~

Description

~~ Methods for function compute ~~

Methods

signature(.Object = "NPCI")

find targets that have a consistent fold change in the same direction (either up- or down-regulation)

Description

find targets that have a consistent fold change in the same direction

Usage

deg.pairwise.fold.change(pairwise.wt.up.down.fold, pairwise.df.up.down.fold, 
  d = 1, min.fold = 1.2)

Arguments

pairwise.wt.up.down.fold

a list of numeric values representing the fold changes between control replicates for every gene

pairwise.df.up.down.fold

a list of numeric values representing the fold changes between case and control replicates for every gene

d

the dimensionality of the database, if the dataset is from proteogenomics, then d=2

min.fold

minimum fold change to declare a gene to be dysregulated, by default, min.fold=2

Details

TBD

Value

expression ratio

a dataframe of fCI gene expression ratios (folds) with none zero values defined by given control-control index (i.e. 1 & 2) and control-case index (i.e. 3&4)

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

wt.fold.changes=list(c(1.2,1.3,1.5,1.6))
df.fold.changes=list(c(1.1,1.3,1.4,1.6))
deg.pairwise.fold.change(wt.fold.changes,df.fold.changes)

find targets and their detailed expression changes

Description

given expression matrix, find targets and their detailed expression changes

Usage

deg.up.down.info(wt.index.in.list, df.index.in.list, npci, 
use.normalization = FALSE, target.ratio = 0.5)

Arguments

wt.index.in.list

a list of numeric values representing the column indexes for control samples

df.index.in.list

a list of numeric values representing the column indexes for experimental samples

npci

the object npci

use.normalization

a boolean value indicating if the normalization will be applied or not

target.ratio

a numeric value indicating the expected fold changes, i.e, 1.5

Details

TBD

Value

expression ratio

a dataframe of fCI gene expression ratios (folds) defined by control-control index and control-case index

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("TBC")

data matrix normalization method

Description

normalize expression matrix by first replicate's median gene expression values

Usage

deseq.median.ratio.normalization(npci.data)

Arguments

npci.data

a data frame containing non-zero numeric values (the data frame must contain more than one row and one column)

Details

TBD

Value

data.frame

a new dataframe with each column having the same median value

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

udata=data.frame(matrix(sample(3:100, 6*4), 6,4))
normalized.udata=deseq.median.ratio.normalization(udata)

estimate fCI divergence for given samples of aritrary dimensions

Description

estimate fCI divergence for given samples of aritrary dimensions

Usage

divergence.multivariate.distributions(null.data, diff.data, choice = 2)

Arguments

null.data

the empirical null dataset (a dataframe of none-zero ratio values)

diff.data

the case-control dataset (a dataframe of none-zero ratio values)

choice

choice=1 => cross entropy choice=2 => Helligan distance choice=3 => KL distance

Details

TBD

Value

divergences

The estimated divergence given control-control and case- control expression ratios

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

null.data=data.frame(matrix(sample(seq(from=0.1,to=10, by=0.01), 100), 100,1))
diff.data=data.frame(matrix(sample(seq(from=0.1,to=10, by=0.01), 100), 100,1))
divergence.multivariate.distributions(null.data, diff.data, choice = 2)

Class "fCI"

Description

The main Class that defines the slots values

Objects from the Class

Objects can be created by calls of the form new("fCI", ...).

Slots

sample.data.file:

Object of class "character" ~~

distance.matrix:

Object of class "matrix" ~~

sample.data.normalized:

Object of class "data.frame" ~~

attr.info:

Object of class "data.frame" ~~

null.data.start:

Object of class "matrix" ~~

diff.data.start:

Object of class "matrix" ~~

expr.by.fold:

Object of class "matrix" ~~

fold.cutoff.list:

Object of class "list" ~~

rank.index.to.be.removed:

Object of class "list" ~~

diff.gene.ids:

Object of class "list" ~~

wt.index:

Object of class "numeric" ~~

df.index:

Object of class "numeric" ~~

ctr.indexes:

Object of class "numeric" ~~

trt.indexes:

Object of class "numeric" ~~

method.option:

Object of class "numeric" ~~

use.ratio:

Object of class "logical" ~~

percent.genes.to.scan:

Object of class "numeric" ~~

num.genes.to.skip.each:

Object of class "numeric" ~~

use.fold.change:

Object of class "logical" ~~

wt.comb:

Object of class "list" ~~

df.comb:

Object of class "list" ~~

diff.ids:

Object of class "list" ~~

result:

Object of class "numeric" ~~

indexes.reconsidered:

Object of class "numeric" ~~

center.by.gaussian.kernel:

Object of class "logical" ~~

symmetric.fold:

Object of class "logical" ~~

pairwise.diff.gene.ids:

Object of class "list" ~~

Methods

No methods defined with class "fCI" in the signature.

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

showClass("fCI")

top level function call to find targets based on expression data and control & case indexes

Description

top level function call to find targets based on expression data and control & case indexes

Usage

fCI.call.by.index(wt.indexes, df.indexes, data.file, use.normalization = FALSE, 
  npci=NULL, short.report=TRUE)

Arguments

wt.indexes

The wild type sample column indexes in the matrix, i.e. 1,2
df.indexes

The diseases type sample column indexes in the matrix, i.e. 3,4
data.file

The expression matrix
use.normalization

boolean value whether you want the data to be normalized or not
npci

the fCI object
short.report

whether you want to have a report summary

Details

TBD

Value

rtable

A data frame of the detected targets

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

wt.indexes=1:2
df.indexes=3:4
data.file=data.frame(matrix(sample(3:100, 100*4, replace=TRUE), 100,4))
use.normalization=FALSE
npci=NULL
short.report=TRUE
fCI.call.by.index(wt.indexes, df.indexes, data.file, use.normalization, 
  npci, short.report)

data frame of gene expression

Description

This data set gives the gene expression values for multiple control and case samples.

Usage

fci.data

Format

a matrix containing 1043 genes and 4 samples.

Value

dataframe

A data frame of expression values

Source

software.steen.org

References

http://software.steenlab.org/fCI/

generic function to draw figures of the current analysis

Description

generic function to draw figures of the current analysis

Usage

figures(.Object)

Arguments

.Object

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("See README")

generate figures for empirical null and case-control distributions

Description

~~ Methods for function figures ~~

Methods

signature(.Object = "NPCI")

identify differentially expressed genes

Description

identify differentially expressed genes

Usage

find.fci.targets(.Object, wt.indexes, df.indexes, data.file, use.normalization)

Arguments

.Object

the fCI object

wt.indexes

The wild type sample column indexes in the matrix, i.e. 1,2
df.indexes

The diseases type sample column indexes in the matrix, i.e. 3,4
data.file

The expression matrix
use.normalization

boolean value whether you want the data to be normalized or not

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

fci=new("NPCI")
 fci.data=data.frame(matrix(sample(3:100, 1043*6, replace=TRUE), 1043,6))
 targets=find.fci.targets(fci, c(1,2,3), c(4,5,6), fci.data)
 head(show.targets(targets))

~~ Methods for Function find.fci.targets ~~

Description

~~ Methods for function find.fci ~~

Methods

signature(.Object = "NPCI")

the built-in method to compute fCI DEGs.

find the middle value of the density distribution

Description

find the middle value of the density distribution

Usage

find.mid.point(Y)

Arguments

Y

Details

TBD

Value

position

The value the separates density into two halves

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

Y=density(sample(1:100, 50), bw=0.5)
find.mid.point(Y)

generate fold change cutoff values for fCI divergence computation

Description

generate fold change cutoff with a large step of 0.5 fold

Usage

get.fold.large.step()

Details

TBD

Value

fold_values

A vector of predefined fold changes for fCI computation

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

get.fold.large.step()

return a fCI object given the gene expression data

Description

return a fCI object given the gene expression data

Usage

get.npci.data(sample.data.normalized, wt.index, df.index)

Arguments

sample.data.normalized
wt.index
df.index

Details

TBD

Value

expression ratio

a dataframe of fCI gene expression ratios (folds) defined by control-control index and control-case index

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

sample.data.normalized=data.frame(matrix(sample(3:100, 100*4, replace=TRUE),
    100,4))
  wt.index=c(1,2)
  df.index=c(1,3)
  get.npci.data(sample.data.normalized, wt.index, df.index)

generate the divergence estimation based of fold change cutoff values

Description

generate the divergence estimation based of fold change cutoff values

Usage

get.npci.distance.matrix(npci.data, null.data.start, diff.data.start, choice = 2, rank.index.to.be.removed, expr.by.fold, ctr.indexes, trt.indexes, use.intersect = FALSE, symmetric.fold = TRUE, fold.cutoff.list)

Arguments

npci.data
null.data.start
diff.data.start
choice
rank.index.to.be.removed
expr.by.fold
ctr.indexes
trt.indexes
use.intersect
symmetric.fold
fold.cutoff.list

Details

TBD

Value

divergence

A matrix of computed divergences

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

data.file=data.frame(matrix(sample(3:100, 100*4, replace=TRUE), 100,4))
wt.index=c(1,2)
df.index=c(1,3)
npci=new("NPCI")
npci@wt.index=wt.index
npci@df.index=df.index
npci@sample.data.normalized=data.file
npci=initialize(npci)
npci=normalization(npci)
npci=populate(npci)

null.data.start=npci@null.data.start
diff.data.start=npci@diff.data.start
choice=2
rank.index.to.be.removed=npci@rank.index.to.be.removed
expr.by.fold=npci@expr.by.fold
ctr.indexes=npci@wt.index
trt.indexes=npci@df.index
use.intersect=FALSE
symmetric.fold=TRUE
fold.cutoff.list=npci@fold.cutoff.list

get.npci.distance.matrix(npci.data, null.data.start, diff.data.start, 
  choice = 2, rank.index.to.be.removed, expr.by.fold, ctr.indexes, trt.indexes, 
  use.intersect, symmetric.fold, fold.cutoff.list)

find the outline genes of a given distribution

Description

find the outline genes of a given distribution

Usage

get.outline.index(values)

Arguments

values

Details

TBD

Value

indexes

remove the index of values that are outliners based on the t-test with alpha=0.05

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

values=rnorm(100)
  get.outline.index(values)

generate fold-change cutoff on proteomics data (with large steps of 0.2-0.5 fold)

Description

generate fold-change cutoff on proteomics data (with large steps of 0.2-0.5 fold)

Usage

get.protein.fold.step()

Details

TBD

Value

folds

returning a vector of recommended fold ratios for proteomic study

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

get.protein.fold.step()

fold change values

Description

identify the fold change value indexes beyond the fCI estimation

Usage

get.rank.combinations(rank.index.to.be.removed, symmetric.fold)

Arguments

rank.index.to.be.removed

a list of integers representing the genes to be removed because it exceeds the predefined fold change, i.e 1.2 fold

symmetric.fold

a boolean value indicating the upregulation and downregulation are treatedly equally

Details

TBD

Value

combinations

a data frame of gene indexes

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

rank.index.to.be.removed=list(sample(1:100, 20))
  symmetric.fold=TRUE
  get.rank.combinations(rank.index.to.be.removed, symmetric.fold)

generate fCI fold-change cutoff values for typical RNA-Seq data

Description

generate fCI fold-change cutoff values for typical RNA-Seq data

Usage

get.rna.fold.step()

Details

TBD

Value

folds

a vector of fold changes fCI used for divergence computation

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

get.rna.fold.step()

~~ Methods for Function initialize ~~

Description

~~ Methods for function initialize ~~

Methods

signature(.Object = "NPCI")

this s4 class generic method initialize the fCI object once it is made

find the common values of all vectors of a list

Description

find the common values of all vectors of a list

Usage

intersect.of.lists(vectorlist)

Arguments

vectorlist

a list of list values which we want to use to find common values

Details

TBD

Value

intersects

the common values of lists

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("this function will be disabled!")

package

Description

test if a package is installed in the R library

Usage

is.installed(mypkg)

Arguments

mypkg

a R library name, such as FNN

Details

TBD

Value

installation

boolean value indicating the installation

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

is.installed('fCI')

data frame of gene expression

Description

This data set gives the gene expression values for 14204 genes and the control and case samples were generated at two time points (bivariate data).

Usage

fci.data

Format

a matrix containing 14204 genes and 8 samples.

Value

dataframe

A data frame of expression values

Source

software.steen.org

References

http://software.steenlab.org/fCI/

generic function to normalize gene expression matrix

Description

generic function to normalize gene expression matrix

Usage

normalization(.Object)

Arguments

.Object

the predefined class object (i.e fCI=new("NPCI"))

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("See README")

~~ Methods for Function normalization ~~

Description

~~ Methods for function normalization ~~

Methods

signature(.Object = "NPCI")

the built-in method for fCI data normalization, by default, the data is normalized according to mean excluding the top 5 and bottom 5 percent.

Class "NPCI"

Description

The main Class that defines the slots values

Objects from the Class

Objects can be created by calls of the form new("NPCI", ...).

Slots

sample.data.file:

Object of class "character" ~~

distance.matrix:

Object of class "matrix" ~~

sample.data.normalized:

Object of class "data.frame" ~~

attr.info:

Object of class "data.frame" ~~

null.data.start:

Object of class "matrix" ~~

diff.data.start:

Object of class "matrix" ~~

expr.by.fold:

Object of class "matrix" ~~

fold.cutoff.list:

Object of class "list" ~~

rank.index.to.be.removed:

Object of class "list" ~~

diff.gene.ids:

Object of class "list" ~~

wt.index:

Object of class "numeric" ~~

df.index:

Object of class "numeric" ~~

ctr.indexes:

Object of class "numeric" ~~

trt.indexes:

Object of class "numeric" ~~

method.option:

Object of class "numeric" ~~

use.ratio:

Object of class "logical" ~~

percent.genes.to.scan:

Object of class "numeric" ~~

num.genes.to.skip.each:

Object of class "numeric" ~~

use.fold.change:

Object of class "logical" ~~

wt.comb:

Object of class "list" ~~

df.comb:

Object of class "list" ~~

diff.ids:

Object of class "list" ~~

result:

Object of class "numeric" ~~

indexes.reconsidered:

Object of class "numeric" ~~

center.by.gaussian.kernel:

Object of class "logical" ~~

symmetric.fold:

Object of class "logical" ~~

pairwise.diff.gene.ids:

Object of class "list" ~~

Methods

No methods defined with class "NPCI" in the signature.

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/NPCI/

See Also

TBD

Examples

showClass("NPCI")

find most signficantly change fCI targets

Description

identify the genes that change most significantly using inverse of log ratio the smaller the results, the more signficant the changes.

Usage

npci.gene.by.pvalues(npci.data, gene.indexes, ctr.indexes, trt.indexes)

Arguments

npci.data

a data frame containing non-zero numeric values (the data frame must contain more than one row and one column)

gene.indexes

the row ids of genes used for p-value calculation

ctr.indexes

The wild type sample column indexes in the matrix, i.e. 1,2

trt.indexes

The experimental sample column indexes in the matrix, i.e. 1,2

Details

TBD

Value

pvalues

a vector of pvalues

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

npci.data=data.frame(matrix(sample(3:100, 100*4, replace=TRUE), 100,4))
  gene.indexes=sample(1:97, 25)
  ctr.indexes=c(1,2)
  trt.indexes=c(3,4)
  npci.gene.by.pvalues(npci.data, gene.indexes, ctr.indexes, trt.indexes)

find targets that have little evidence to be differentially expressed

Description

the function will be depreciated

Usage

npci.index.reconsidered(npci.data, expr.by.fold, null.data.start, diff.data.start, gene.indexes, ctr.indexes, trt.indexes, left.fold, right.fold)

Arguments

npci.data

a data frame containing non-zero numeric values (the data frame must contain more than one row and one column)

expr.by.fold

a 1xN matrix of case-control fold changes for every gene of the total N genes

null.data.start

a Nx1 matrix of control-control fold changes

diff.data.start

a Nx1 matrix of case-control fold changes

gene.indexes

the genes used for differential expression analysis.

ctr.indexes

the control sample column indexes

trt.indexes

the case sample column indexes

left.fold

the minimum fold changes for downregulation

right.fold

the minimum fold changes for upregulation

Details

TBD

Value

values

genes wrongly considered as differentially expressed

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

data.file=data.frame(matrix(sample(3:100, 100*4, replace=TRUE), 100,4))
wt.index=c(1,2)
df.index=c(1,3)
npci=new("NPCI")
npci@wt.index=wt.index
npci@df.index=df.index
npci@sample.data.normalized=data.file
npci=initialize(npci)
npci=normalization(npci)
npci=populate(npci)
npci=compute(npci)
npci=summarize(npci)

npci.data=npci@sample.data.normalized
null.data.start=npci@null.data.start
diff.data.start=npci@diff.data.start
choice=2
rank.index.to.be.removed=npci@rank.index.to.be.removed
expr.by.fold=npci@expr.by.fold

ctr.indexes=1:2
trt.indexes=3:4
use.intersect=FALSE
symmetric.fold=TRUE
fold.cutoff.list=npci@fold.cutoff.list
gene.indexes=npci@diff.gene.ids
left.fold=2
right.fold=2

gene indexes that will be considered as targets

Description

This function will be depreciated.

Usage

npci.index.to.be.removed(expr.by.fold, d, symmetric.fold, max.rank, 
l.max.rank, r.max.rank)

Arguments

expr.by.fold

a 1xN matrix of fold change between case and control for every genes in N genes

d

the dimension of the data, if RNA-Seq or LC-MS/MS data, d=1

symmetric.fold

a booleam valuable indicating whether to use the same fold change cutoff for upregulation and downregulation

max.rank

the maximum fold change, i.e 3 fold

l.max.rank

the maximum fold change for downregulation, i.e 1.5 fold

r.max.rank

the maximum fold change for upregulation, i.e 1.5 fold

Details

TBD

Value

indexes

gene (indexes) considered as differentially expressed

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("Function to be discarded!")

generate venn diagram for multiple fCI analysis

Description

plot the overlap differentially expressed genes by pairwise fCI analysis

Usage

npci.venn.diagram(diff.gene.ids, i = 1, k = 1)

Arguments

diff.gene.ids

gene ids for genes that are differentially expressed

i

number of comparisons for fCI analysis, i,e 1 or 2

k

number of genes for fCI analysis

Details

TBD

Value

figure

the venn diagram plot

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

targets.run1=c(2:10)
  targets.run2=c(1:8)
  targets.run3=c(6:12)
  diff.gene.ids=list(targets.run1, targets.run2, targets.run3)
  npci.venn.diagram(diff.gene.ids)

find the targets whose fold changes occur consistently (upregulated or downregulated) in all fCI analysis

Description

find the targets whose fold changes occur consistently (upregulated or downregulated) in all fCI analysis

Usage

pairwise.change.occupancy(common.ids, pairwise.index,
  pairwise.up.down, target.ratio)

Arguments

common.ids

the gene ids that are differentially expressed

pairwise.index

a list of the genes ids that differentially expressed in each of the fCI analysis

pairwise.up.down

a list of up regulatio (+1) or downregulation (-1) for each gene in fCI analysis

target.ratio

the expected fold changes

Details

TBD

Value

consistent targets

Gene (indexes) that are consistently changed in fCI pairwise analysis
direction

Gene (indexes) that are consistently upregulated (if < 0) or upregulated (if > 0)

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

common.ids=6:13
  pairwise.index=list(c(4:13), c(6:15))
  pairwise.up.down=list(c(sample(c(-1,1), 10, replace=TRUE)),
                        c(sample(c(-1,1), 10, replace=TRUE)))
  target.ratio=0.5
  pairwise.change.occupancy(common.ids, pairwise.index,
    pairwise.up.down, target.ratio)

generic function to populate the fCI object based on provided data

Description

generic function to populate the fCI object based on provided data

Usage

populate(.Object)

Arguments

.Object

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("See README")

~~ Methods for Function populate ~~

Description

~~ Methods for function populate ~~

Methods

signature(.Object = "NPCI")

after fCI object is initialized, popular the slot values for the object

generate the results (gene ids) in the data frame

Description

generate the results (gene ids) in the data frame

Usage

report.target.summary(pairwise.diff.gene.ids)

Arguments

pairwise.diff.gene.ids

a list of the the differentially expression genes (its index) for each pairwise fCI analysis.

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("See README")

the generic function 'setfCI' for s4 class

Description

the generic function 'setfCI' for s4 class

Usage

setfCI(.Object, wt.index, df.index, fold.cutoff.list, 
  center.distribution)

Arguments

.Object

the fCI object
wt.index

the control sample column ids, such as c(1,2)
df.index

the case sample column ids, such as c(1,2)
fold.cutoff.list

the predefined fold change cut-off such as list(seq(from=1.1, to=3.0, by=0.1))
center.distribution

a boolean value showing that if the users want to center the distribution or not

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

fci=new("NPCI")
 fci=setfCI(fci, 7:8, 11:12, seq(from=1.1,to=3,by=0.1), TRUE)

~~ Methods for Function setfCI ~~

Description

~~ Methods for function setfCI ~~

Methods

signature(.Object = "NPCI")

display the gene ids that are identified to be differentially regulated

Description

display the gene ids that are identified to be differentially regulated

Usage

show.targets(.Object)

Arguments

.Object

the class object, for example, fCI=new("NPCI")

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("See README")

~~ Methods for Function show.targets ~~

Description

~~ Methods for function show.targets ~~

Methods

signature(.Object = "NPCI")

the built-in method to show the fCI final DEGs.

result summerization

Description

summerize the result after fCI computation is done

Usage

summarize(.Object)

Arguments

.Object

the class object, for exaple, fci = new("NPCI")

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

data.file=data.frame(matrix(sample(3:100, 100*4, replace=TRUE), 100,4))
wt.index=c(1,2)
df.index=c(1,3)
npci=new("NPCI")
npci@wt.index=wt.index
npci@df.index=df.index
npci@sample.data.normalized=data.file
npci=initialize(npci)
npci=normalization(npci)
npci=populate(npci)
npci=summarize(npci)

result summerization

Description

summerize the result after fCI computation is done

Methods

signature(.Object = "NPCI")

normalize the gene expression based on the library size (summation) of the first sample replicate

Description

normalize the gene expression based on the library size (summation) of the first sample replicate

Usage

total.library.size.normalization(sample.data)

Arguments

sample.data

a data frame of gene expression (noen-zero) with columns being the sample and rows being genes

Details

TBD

Value

dataframe

a data frame where column values were normalized by total library size

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

sample.data=data.frame(matrix(sample(3:100, 100*4, replace=TRUE), 100,4))
  total.library.size.normalization(sample.data)

normalize gene expression by exluding genes on the top 5 and bottom 5 percentage

Description

normalize gene expression by exluding genes on the top 5 and bottom 5 percentage

Usage

trim.size.normalization(sample.data)

Arguments

sample.data

a data frame of gene expression (noen-zero) with columns being the sample and rows being genes

Details

TBD

Value

dataframe

a data frame where column values were normalized by all genes except the top 5 percent and bottom 5 percent genes

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

sample.data=data.frame(matrix(sample(3:100, 100*4, replace=TRUE), 100,4))
  trim.size.normalization(sample.data)

compute the log ratios of two vectors

Description

compute the log ratios of two vectors

Usage

two.sample.log.ratio(a, b)

Arguments

a

a vector of numeric values (value must be greater than 0)

b

a vector of numeric values (value must be greater than 0)

Details

TBD

Value

ratios

the log ratios of two vectors

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

a=10
  b=2
  two.sample.log.ratio(a, b)

perform permuation test on two vectors

Description

perform permuation test on two vectors

Usage

two.sample.permutation.test(a, b)

Arguments

a

a vector of numeric values (value must be greater than 0)

b

a vector of numeric values (value must be greater than 0)

Details

TBD

Value

pvalue

the pvalue of permutation test

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

two.sample.permutation.test(sample(1:100, 20), sample(5:104, 20))

generate a venn diagram to show the differentially expression summaries accross pairwise fCI analysis

Description

generate a venn diagram to show the differentially expression summaries accross pairwise fCI analysis

Usage

venndiagram(.Object)

Arguments

.Object

the class object, i.e, fci=new("NPCI")

Details

TBD

Value

NA

No values will be returned

Note

TBD

Author(s)

Shaojun Tang

References

http://software.steenlab.org/fCI/

See Also

TBD

Examples

print("See README")

~~ Methods for Function venndiagram ~~

Description

~~ Methods for function venndiagram ~~

Methods

signature(.Object = "NPCI")

generate the venn diagram to show the targets that shared among different fCI analysis