| TTCA {TTCA} | R Documentation |
TTCA: Transcript Time Course Analysis
Description
Background: The analysis of microarray time series promises a deeper insight into the dynamics of the cellular response following stimulation. A common observation in this type of data is that some genes respond with quick, transient dynamics, while other genes change their expression slowly over time. The existing methods for detecting significant expression dynamics often fail when the expression dynamics show a large heterogeneity. Moreover, these methods often cannot cope with irregular and sparse measurements. Results: The method proposed here is specifically designed for the analysis of perturbation responses. It combines different scores to capture fast and transient dynamics as well as slow expression changes, and performs well in the presence of low replicate numbers and irregular sampling times. The results are given in the form of tables including links to figures showing the expression dynamics of the respective transcript. These allow to quickly recognise the relevance of detection, to identify possible false positives and to discriminate early and late changes in gene expression. An extension of the method allows the analysis of the expression dynamics of functional groups of genes, providing a quick overview of the cellular response. The performance of this package was tested on microarray data derived from lung cancer cells stimulated with epidermal growth factor (EGF). Paper: Albrecht, Marco, et al. (2017)<DOI:10.1186/s12859-016-1440-8>.
Usage
TTCA(grp1, grp1.time, grp2, grp2.time, lambda = 0.6, annot = NA,
annotation = "annotation", timeInt = NULL, pVal = 0.05,
codetest = FALSE, file = getwd(), MaxPics = 10000, Stimulus1 = "",
Stimulus2 = "", S = "gene", mapGO = "", PeakMode = "norm")
Arguments
grp1 |
Data set with longitudinal sampled data (data.frame) |
grp1.time |
Time points for data set 1 (vector like: c(0,0,0.5,1,2,4,6,8,12,12) |
grp2 |
Data set with longitudinal sampled data for comparison (data.frame) |
grp2.time |
Time points for data set 2 (vector like: c(0,0,0.5,3,2,4,6,8,12,12,24) |
lambda |
Smoothing parameter in penalty term of quantil regression (default: lambda=0.6 ). Adjust, if fit is too strict or too flexible. |
annot |
Annotation for pictures and result (Data.frame with 2 columns with ID and GeneName). (Default: annot=NA) |
annotation |
Merges the TTCA by rowname with a table of your wish. Example: annotation<-annotation[,c("probeset_id", "gene_name","transkript_id","GO_BP","GO_CC","GO_mf")] (default: annotation="annotation") |
timeInt |
Defines early, middle and late time period. Defines the middle time period between 4 h and 12 h with timeInt<-c(4,12). (default: timeInt=NULL) |
pVal |
P-value for the local hypothesis test (default: 0.05). |
codetest |
Reduces the data set to 200 features for a quick run of the program. (default: codetest=FALSE) |
file |
Result folder will be saved at this location (default: file=getwd() ). |
MaxPics |
Limits the number of plots (default: MaxPics=10000) |
Stimulus1 |
Searches this term together with the gene name in PubMed. Stimulus1="Insulin+like+growth+factor" ( default: Stimulus1="") |
Stimulus2 |
Searches this term together with the gene name in PubMed. Stimulus2="epidermal+growth+factor" ( default: Stimulus2="") |
S |
Defines mode. S =="GO" changes programm to gene ontology mode (default: S="gene") |
mapGO |
Link genes to Gene Ontology terms (default: mapGO="") |
PeakMode |
Peakmode "norm" uses variance between replicates. If changed to another character value, a normal hypothesis test will be conducted (default: PeakMode="norm") |
Details
The package has not be applied to Hi-Seq data yet. The problem is the huge variety in the read counts. An additional transformation of normalized Hi-Seq data might be an option to scale the values between two values like 0 and 1 (Simple idea: Datalog<-log(data, base = max(data))). Not tested. IF you are interested to adjust my package to sequence data, feel free to contact me.
Value
The R-package delivers a table with different significance values, rankings, p-values. Moreover, it will plot the most important time courses and quality control images.
Examples
## Not run:
##########################################
#### Gene-ANALYSE
##########################################
require(quantreg);require(VennDiagram);require(tcltk2); require(tcltk);
require(RISmed);require(Matrix)
data(EGF,Control,annot,annotation)
S="gene"
Control.time <- c(0,0,0.5,1,4,6,24,24,48,48,48)
EGF.time <- c(0,0,0.5,0.5,1,2,4,6,8,12,18,24,24,48,48,48)
file = paste0(getwd(),"/TTCA_Gene")
dir.create(file)
######
TTCAresult<-TTCA(grp1=EGF, grp1.time=EGF.time, grp2=Control, grp2.time=Control.time,S="gene",
lambda=0.6, annot=annot, annotation=annotation,pVal=0.05,codetest=FALSE,
file=file, Stimulus1="epidermal+growth+factor", timeInt=c(4,12), MaxPics =10000)
## End(Not run)
## Not run:
##########################################
#### GO-ANALYSE
##########################################
require(quantreg);require(VennDiagram);require(tcltk2); require(tcltk);
require(RISmed);require(Matrix)
#source("https://bioconductor.org/biocLite.R")
#biocLite("biomaRt")
library(biomaRt)
data(EGF,Control,annot,annotation)
require(biomaRt)
ensembl <- useMart("ENSEMBL_MART_ENSEMBL",dataset="hsapiens_gene_ensembl")
mapGO <- getBM(attributes=c("go_id","name_1006",'affy_hugene_2_0_st_v1'),
filters = 'affy_hugene_2_0_st_v1', values=rownames(annot), mart =ensembl)
colnames(mapGO)<-c("go_id","GO_name","probeset_id")
S="GO"
Control.time <- c(0,0,0.5,1,4,6,24,24,48,48,48)
EGF.time <- c(0,0,0.5,0.5,1,2,4,6,8,12,18,24,24,48,48,48)
file = paste0(getwd(),"/TTCA_GO")
dir.create(file)
TTCAresult<-TTCA(grp1=EGF, grp1.time=EGF.time, grp2=Control, grp2.time=Control.time,
S="GO", pVal=0.05,lambda=0.6,codetest=FALSE, file=file,
Stimulus1="epidermal+growth+factor", timeInt=c(4,12),
MaxPics=10000, mapGO=mapGO)
## End(Not run)