| Type: | Package |
| Title: | Structural Model for Variances |
| Version: | 1.3.4 |
| Date: | 2022-04-12 |
| Author: | Guillemette Marot [aut, cre] |
| Maintainer: | Samuel Blanck <samuel.blanck@univ-lille.fr> |
| Depends: | R (≥ 2.6.0) |
| Description: | Implementation of the structural model for variances in order to detect differentially expressed genes from gene expression data. |
| License: | GPL-2 | GPL-3 [expanded from: GPL] |
| NeedsCompilation: | no |
| Packaged: | 2022-04-12 08:03:40 UTC; sblanck |
| Repository: | CRAN |
| Date/Publication: | 2022-04-12 16:12:36 UTC |
ApoAIdata
Description
Example dataset for unpaired data
Usage
data(ApoAIdata)Format
ApoAIdata is a list with 3 elements
- ApoAIGeneId
vector of fictive gene names)
- ApoAICond1
matrix with 6226 rows and 8 columns with normalized normal mice measurements
- ApoAICond2
matrix with 6226 rows and 8 columns with normalized KO mice measurements
Source
Similar to the example dataset used in the package Varmixt
References
M.J. Callow, S. Dudoit, E.L. Gong, T.P. Speed, and E.M. Rubin. Microarray expression profiling identifies genes with altered expression in hdl-deficien mice. Genome Res., 10(12) : 2022-9, 2000
Examples
data(ApoAIdata)
attach(ApoAIdata)
Structural Model for Variances
Description
Package containing moderated t-tests to detect differentially expressed genes for paired and unpaired data
Details
| Package: | SMVar |
| Type: | Package |
| Version: | 1.3.3 |
| Date: | 2011-08-03 |
| License: | GPL |
SMVar.unpaired and SMVar.paired are the most important functions.
Author(s)
Guillemette Marot <guillemette.marot@inria.fr>
References
F. Jaffrezic, Marot, G., Degrelle, S., Hue, I. and Foulley, J. L. (2007) A structural mixed model for variances in differential gene expression studies. Genetical Research (89) 19:25
Examples
library(SMVar)
data(ApoAIdata)
attach(ApoAIdata)
SMVar.unpaired(ApoAIGeneId,list(ApoAICond1,ApoAICond2))
Structural model for variances with paired data
Description
Function to detect differentially expressed genes when data are paired
Usage
SMVar.paired(geneNumbers, logratio, fileexport = NULL,
minrep = 2, method = "BH", threshold = 0.05)
Arguments
geneNumbers |
Vector with gene names or dataframe which contains all information about spots on the chip |
logratio |
matrix with one row by gene and one column by replicate giving the logratio |
fileexport |
file to export the list of differentially expressed genes |
minrep |
minimum number of replicates to take a gene into account, |
method |
method of multiple tests adjustment for p.values |
threshold |
threshold of False Discovery Rate |
Details
This function implements the structural model for variances described in (Jaffrezic et al., 2007).
Data must be normalized before calling the function. Matrix geneNumbers must have one of
the following formats: "matrix","data.frame","vector","character","numeric","integer".
Value
Only the number of differentially expressed genes is printed. If asked, the file giving the list of differentially expressed genes is created
If the user creates an object when calling the function (for example "Stat=SMVar.paired(...)") then Stat contains the information for all genes, is sorted by ascending p-values and
Stat$TestStat |
gives the test statistics as described in the paper |
Stat$StudentPValue |
gives the raw p-values |
Stat$DegOfFreedom |
gives the number of degrees of freedom for the Student distribution for the test statistics |
Stat$LogRatio |
gives the logratios |
Stat$AdjPValue |
gives the adjusted p-values |
Note
If the first column of the file geneNumbers contains identical names for two different spots, these two spots are only counted once if they are both differentially expressed. By default, the correction for multiple testing is Benjamini Hochberg with a threshold of False Discovery Rate (FDR) of 5%. The FDR threshold can be changed, and it is also possible to choose the multiple test correction method ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"). To see the references for these methods, use the R-help ?p.adjust.
Author(s)
Guillemette Marot with contributions from Anne de la Foye
References
F. Jaffrezic, Marot, G., Degrelle, S., Hue, I. and Foulley, J. L. (2007) A structural mixed model for variances in differential gene expression studies. Genetical Research (89) 19:25
Examples
library(SMVar)
data(Spleendata)
attach(Spleendata)
SMVar.paired(SpleenGeneId,SpleenLogRatio)
Structural model for variances with unpaired data
Description
Function to detect differentially expressed genes when data are unpaired
Usage
SMVar.unpaired(geneNumbers, listcond, fileexport = NULL,
minrep = 2, method = "BH", threshold = 0.05)
Arguments
geneNumbers |
Vector with gene names or dataframe which contains all information about spots on the chip |
listcond |
list of the different conditions to be compared |
fileexport |
file to export the list of differentially expressed genes |
minrep |
minimum number of replicates to take a gene into account, |
method |
method of multiple tests adjustment for p.values |
threshold |
threshold of False Discovery Rate |
Details
This function implements the structural model for variances described in (Jaffrezic et al., 2007).
Data must be normalized before calling the function. Matrix geneNumbers must have one of
the following formats: "matrix","data.frame","vector","character","numeric","integer".
Value
Only the number of differentially expressed genes is printed. If asked, the file giving the list of differentially expressed genes is created.
If the user creates an object when calling the function (for example "Stat=SMVar.paired(...)") then Stat contains the information for all genes, is sorted by ascending p-values and
Stat$TestStat |
gives the test statistics as described in the paper |
Stat$StudentPValue |
gives the raw p-values |
Stat$DegOfFreedom |
gives the number of degrees of freedom for the Student distribution for the test statistics |
Stat$Cond1 |
gives the first condition considered in the log-ratio |
Stat$Cond2 |
gives the second condition considered in the log-ratio |
Stat$LogRatio |
gives the logratios (listcond[[Cond2]]-listcond[[Cond1]]) |
Stat$AdjPValue |
gives the adjusted p-values |
Note
If the first column of the file geneNumbers contains identical names for two different spots, these two spots are only counted once if they are both differentially expressed. By default, the correction for multiple testing is Benjamini Hochberg with a threshold of False Discovery Rate (FDR) of 5%. The FDR threshold can be changed, and it is also possible to choose the multiple test correction method ("holm", "hochberg", "hommel", "bonferroni", "BH", "BY", "fdr", "none"). To see the references for these methods, use the R-help ?p.adjust.
Author(s)
Guillemette Marot with contributions from Anne de la Foye
References
F. Jaffrezic, Marot, G., Degrelle, S., Hue, I. and Foulley, J. L. (2007) A structural mixed model for variances in differential gene expression studies. Genetical Research (89) 19:25
Examples
library(SMVar)
data(ApoAIdata)
attach(ApoAIdata)
SMVar.unpaired(ApoAIGeneId,list(ApoAICond1,ApoAICond2))
Spleendata
Description
Example dataset for paired data
Usage
data(Spleendata)Format
Spleendata is a list with 2 elements
- SpleenGeneId
Gene names)
- SpleenLogRatio
Matrix with 4360 rows and 6 columns with normalized log-ratio
Source
Similar to the example dataset used in the package Varmixt
References
P. Delmar, Robin, S., Tronik-Le Roux S. and Daudin J.-J. (2005) Mixture model on the variance for the differential analysis of gene expression data, JRSS series C, 54(1), 31:50
Examples
data(Spleendata)
attach(Spleendata)