| Title: | Assess the Stability of Candidate Housekeeping Genes |
|---|---|
| Description: | A simple way to assess the stability of candidate housekeeping genes is implemented in this package. |
| Authors: | Shanliang Zhong [aut, cre] |
| Maintainer: | Shanliang Zhong <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.1 |
| Built: | 2024-11-05 06:16:05 UTC |
| Source: | CRAN |
This function combines the results of cpSta(), pearsonCor() and bki().
bestKeeper(expression, ctVal = TRUE)bestKeeper(expression, ctVal = TRUE)
expression |
a matrix of expression levels. Each row corresponds to a sample and each column to a gene. |
ctVal |
a logical value indicating data type. If ct-values are input, ctVal=TRUE, otherwise, ctVal=FALSE. |
A list containing CP.statistics, pair.Wise.cor and HKG.vs.BestKeeper, which are returned by cpSta(), pearsonCor() and bki(), respectively.
Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Biotechnol Lett (2004) <doi: 10.1023/B:BILE.0000019559.84305.47>
FIBct bestKeeper(FIBct)FIBct bestKeeper(FIBct)
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.
bki(expression, ctVal = TRUE)bki(expression, ctVal = TRUE)
expression |
a matrix of expression levels. Each row corresponds to a sample and each column to a gene. |
ctVal |
a logical value indicating data type. If ct-values are input, ctVal=TRUE, otherwise, ctVal=FALSE. |
A matrix of the Pearson correlation coefficient (r), coefficient of determination (r2) and the p-value.
Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Biotechnol Lett (2004) <doi: 10.1023/B:BILE.0000019559.84305.47>
FIBct bki(FIBct)FIBct bki(FIBct)
This function calculates descriptive statistics of genes.
cpSta(expression, ctVal = TRUE)cpSta(expression, ctVal = TRUE)
expression |
a matrix of expression levels. Each row corresponds to a sample and each column to a gene. |
ctVal |
a logical value indicating data type. If ct-values are input, ctVal=TRUE, otherwise, ctVal=FALSE. |
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.
Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Biotechnol Lett (2004) <doi: 10.1023/B:BILE.0000019559.84305.47>
FIBct cpSta(FIBct)FIBct cpSta(FIBct)
The normalized expression level of the ten housekeeping genes in fibroblast cells
The normalized expression level of the ten housekeeping genes in fibroblast cells
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>.
The CT values of the ten housekeeping genes in fibroblast cells
The CT values of the ten housekeeping genes in fibroblast cells
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>.
Uses the geNorm algorithm to determine the most stably expressed genes.
geNorm(expression, genes = data.frame(Genes = character(0), Avg.M = numeric(0)), ctVal = TRUE)geNorm(expression, genes = data.frame(Genes = character(0), Avg.M = numeric(0)), ctVal = TRUE)
expression |
a matrix of expression levels. Each row corresponds to a sample and each column to a gene. |
genes |
a data frame to output the result of the function |
ctVal |
a logical value indicating data type. If ct-values are input, ctVal=TRUE, otherwise, ctVal=FALSE. |
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.
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>.
FIB geNorm(FIB,ctVal=FALSE) FIBct geNorm(FIBct)FIB geNorm(FIB,ctVal=FALSE) FIBct geNorm(FIBct)
Uses the geNorm algorithm to determine the most stably expressed genes.
geNorm2(expression, genes = data.frame(Genes = character(0), Avg.M = numeric(0)), ctVal = TRUE)geNorm2(expression, genes = data.frame(Genes = character(0), Avg.M = numeric(0)), ctVal = TRUE)
expression |
a matrix of expression levels. Each row corresponds to a sample and each column to a gene. |
genes |
a data frame to output the result of the function |
ctVal |
a logical value indicating data type. If ct-values are input, ctVal=TRUE, otherwise, ctVal=FALSE. |
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.
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>.
FIB geNorm2(FIB,ctVal=FALSE) FIBct geNorm2(FIBct)FIB geNorm2(FIB,ctVal=FALSE) FIBct geNorm2(FIBct)
This function calculates measure M according to algorithm of geNorm
measureM(expression, ctVal = TRUE)measureM(expression, ctVal = TRUE)
expression |
a matrix of expression levels. Each row corresponds to a sample and each column to a gene. |
ctVal |
a logical value indicating data type. If ct-values are input, ctVal=TRUE, otherwise, ctVal=FALSE. |
A sorted dataframe with two columns, 'Genes' and 'M' (the relative stability; lower means more stable).
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>.
FIB measureM(FIB,ctVal=FALSE) FIBct measureM(FIBct)FIB measureM(FIB,ctVal=FALSE) FIBct measureM(FIBct)
Useful for establishing the quality of your normalization regime. See Vandesompele 2002 for advice on interpretation.
pairwiseV(expression, ctVal = TRUE)pairwiseV(expression, ctVal = TRUE)
expression |
a matrix of expression levels. Each row corresponds to a sample and each column to a gene. |
ctVal |
a logical value indicating data type. If ct-values are input, ctVal=TRUE, otherwise, ctVal=FALSE. |
A Series of values [V2/3, V3/V4, V4/V5, ...].
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>.
FIB pairwiseV(FIB,ctVal=FALSE) FIBct pairwiseV(FIBct)FIB pairwiseV(FIB,ctVal=FALSE) FIBct pairwiseV(FIBct)
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.
pearsonCor(expression, ctVal = TRUE)pearsonCor(expression, ctVal = TRUE)
expression |
a matrix of expression levels. Each row corresponds to a sample and each column to a gene. |
ctVal |
a logical value indicating data type. If ct-values are input, ctVal=TRUE, otherwise, ctVal=FALSE. |
A matrix of the Pearson correlation coefficient (r) and the probability p value.
Pfaffl MW, Tichopad A, Prgomet C, Neuvians TP. Biotechnol Lett (2004) <doi: 10.1023/B:BILE.0000019559.84305.47>
FIBct pearsonCor(FIBct)FIBct pearsonCor(FIBct)
This function plots the average expression stability values of remaining control genes.
plotM(Mrem)plotM(Mrem)
Mrem |
the result returned by function of geNorm() |
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>.
FIB x=geNorm(FIB,ctVal=FALSE) plotM(x) FIBct y=geNorm(FIBct) plotM(y)FIB x=geNorm(FIB,ctVal=FALSE) plotM(x) FIBct y=geNorm(FIBct) plotM(y)
This function plots the average expression stability values of remaining control genes.
plotV(Vs)plotV(Vs)
Vs |
the result returned by function of pairwiseV() |
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>.
FIB Vs1=pairwiseV(FIB,ctVal=F) plotV(Vs1) FIBct Vs2=pairwiseV(FIBct) plotV(Vs2)FIB Vs1=pairwiseV(FIB,ctVal=F) plotV(Vs1) FIBct Vs2=pairwiseV(FIBct) plotV(Vs2)