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Description

The normalized expression level of the ten housekeeping genes in fibroblast cells

Details

The normalized expression level of the ten housekeeping genes in fibroblast cells

References

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) <doi: 10.1186/gb-2002-3-7-research0034>.

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Description

The CT values of the ten housekeeping genes in fibroblast cells

Details

The CT values of the ten housekeeping genes in fibroblast cells

References

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) <doi: 10.1186/gb-2002-3-7-research0034>.

Determines stability of genes

Description

This function combines the results of cpSta(), pearsonCor() and bki().

Usage

bestKeeper(expression, ctVal = TRUE)

Arguments

Value

A list containing CP.statistics, pair.Wise.cor and HKG.vs.BestKeeper, which are returned by cpSta(), pearsonCor() and bki(), respectively.

References

Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Biotechnol Lett (2004) <doi: 10.1023/B:BILE.0000019559.84305.47>

Examples

FIBct
bestKeeper(FIBct)

Analyzes genes versus BestKeeper index

Description

All genes are combined into an index. Then, correlation between each genes and the index is calculated, describing the relation between the index and the contributing gene by the Pearson correlation coefficient (r), coefficient of determination (r2) and the p-value.

Usage

bki(expression, ctVal = TRUE)

Arguments

Value

A matrix of the Pearson correlation coefficient (r), coefficient of determination (r2) and the p-value.

References

Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Biotechnol Lett (2004) <doi: 10.1023/B:BILE.0000019559.84305.47>

Examples

FIBct
bki(FIBct)

Calculates descriptive statistics

Description

This function calculates descriptive statistics of genes.

Usage

cpSta(expression, ctVal = TRUE)

Arguments

Value

A matrix of descriptive statistics:

N: number of samples;

GM[CP]: the geometric mean of CP;

AM[CP]: the arithmetic mean of CP;

Min[CP] and Max [CP]: the extreme values of CP;

SD[+/- CP]: the standard deviation of the CP;

CV[CP]: the coefficient of variance expressed as a percentage on the CP level;

Min[x-fold] and Max [x-fold]: the extreme values of expression levels expressed as an absolute x-fold over- or under-regulation coefficient;

SD[+/- x-fold]: standard deviation of the absolute regulation coefficients.

References

Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Biotechnol Lett (2004) <doi: 10.1023/B:BILE.0000019559.84305.47>

Examples

FIBct
cpSta(FIBct)

Ranks genes

Description

Uses the geNorm algorithm to determine the most stably expressed genes.

Usage

geNorm(expression, genes = data.frame(Genes = character(0), Avg.M =
  numeric(0)), ctVal = TRUE)

Arguments

Value

A sorted dataframe with two columns, 'Genes' and 'Avg.M'. The last two genes are the two most stable control genes.

Avg.M is average expression stability values (M) of remaining control genes during stepwise exclusion of the least stable control gene.

References

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) <doi: 10.1186/gb-2002-3-7-research0034>.

Examples

FIB
geNorm(FIB,ctVal=FALSE)
FIBct
geNorm(FIBct)

Ranks genes

Description

Uses the geNorm algorithm to determine the most stably expressed genes.

Usage

geNorm2(expression, genes = data.frame(Genes = character(0), Avg.M =
  numeric(0)), ctVal = TRUE)

Arguments

Value

A sorted dataframe with two columns, 'Genes' and 'Avg.M'. The last two genes are the two most stable control genes.

Avg.M is average expression stability values (M) of remaining control genes during stepwise exclusion of the least stable control gene.

References

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) <doi: 10.1186/gb-2002-3-7-research0034>.

Examples

FIB
geNorm2(FIB,ctVal=FALSE)
FIBct
geNorm2(FIBct)

Calculates measure M

Description

This function calculates measure M according to algorithm of geNorm

Usage

measureM(expression, ctVal = TRUE)

Arguments

Value

A sorted dataframe with two columns, 'Genes' and 'M' (the relative stability; lower means more stable).

References

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) <doi: 10.1186/gb-2002-3-7-research0034>.

Examples

FIB
measureM(FIB,ctVal=FALSE)
FIBct
measureM(FIBct)

Calculates V(n+1/n) values

Description

Useful for establishing the quality of your normalization regime. See Vandesompele 2002 for advice on interpretation.

Usage

pairwiseV(expression, ctVal = TRUE)

Arguments

Value

A Series of values [V2/3, V3/V4, V4/V5, ...].

References

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) <doi: 10.1186/gb-2002-3-7-research0034>.

Examples

FIB
pairwiseV(FIB,ctVal=FALSE)
FIBct
pairwiseV(FIBct)

Analyzes pair-wise correlation

Description

This function performs numerous pair-wise correlation analyses of genes. Within each such correlation the Pearson correlation coefficient (r) and the probability p value are calculated.

Usage

pearsonCor(expression, ctVal = TRUE)

Arguments

Value

A matrix of the Pearson correlation coefficient (r) and the probability p value.

References

Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Biotechnol Lett (2004) <doi: 10.1023/B:BILE.0000019559.84305.47>

Examples

FIBct
pearsonCor(FIBct)

Plots average M of remaining genes

Description

This function plots the average expression stability values of remaining control genes.

Usage

plotM(Mrem)

Arguments

References

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) <doi: 10.1186/gb-2002-3-7-research0034>.

Examples

FIB
x=geNorm(FIB,ctVal=FALSE)
plotM(x)

FIBct
y=geNorm(FIBct)
plotM(y)

Plots V(n+1/n) values

Description

This function plots the average expression stability values of remaining control genes.

Usage

plotV(Vs)

Arguments

References

Vandesompele J, De Preter K, Pattyn F, Poppe B, Van Roy N, De Paepe A, Speleman F (2002) <doi: 10.1186/gb-2002-3-7-research0034>.

Examples

FIB
Vs1=pairwiseV(FIB,ctVal=F)
plotV(Vs1)

FIBct
Vs2=pairwiseV(FIBct)
plotV(Vs2)