Description Usage Arguments Value Author(s) References See Also Examples
DeMixT is a software that performs deconvolution on transcriptome data from a mixture of two or three components.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19  DeMixT(
data.Y,
data.N1,
data.N2 = NULL,
niter = 10,
nbin = 50,
if.filter = TRUE,
filter.sd = 0.5,
ngene.selected.for.pi = NA,
mean.diff.in.CM = 0.25,
nspikein = NULL,
gene.selection.method = "GS",
ngene.Profile.selected = NA,
tol = 10^(5),
output.more.info = FALSE,
pi01 = NULL,
pi02 = NULL,
nthread = parallel::detectCores()  1
)

data.Y 
A SummarizedExperiment object of expression data from mixed tumor samples. It is a G by My matrix where G is the number of genes and My is the number of mixed samples. Samples with the same tissue type should be placed together in columns. 
data.N1 
A SummarizedExperiment object of expression data from reference component 1 (e.g., normal). It is a G by M1 matrix where G is the number of genes and M1 is the number of samples for component 1. 
data.N2 
A SummarizedExperiment object of expression data from additional reference samples. It is a G by M2 matrix where G is the number of genes and M2 is the number of samples for component 2. Component 2 is needed only for running a threecomponent model. 
niter 
The maximum number of iterations used in the algorithm of iterated conditional modes. A larger value better guarantees the convergence in estimation but increases the running time. The default is 10. 
nbin 
The number of bins used in numerical integration for computing complete likelihood. A larger value increases accuracy in estimation but increases the running time, especially in a threecomponent deconvolution problem. The default is 50. 
if.filter 
The logical flag indicating whether a predetermined filter rule is used to select genes for proportion estimation. The default is TRUE. 
filter.sd 
The cutoff for the standard deviation of lognormal distribution. Genes whose log transferred standard deviation smaller than the cutoff will be selected into the model. The default is 0.5. 
ngene.selected.for.pi 
The percentage or the number of genes used for proportion estimation. The difference between the expression levels from mixed tumor samples and the known component(s) are evaluated, and the most differential expressed genes are selected, which is called DE. It is enabled when if.filter = TRUE. The default is min(1500, 0.3*My), where My is the number of mixed sample. Users can also try using more genes, ranging from 0.3*My to 0.5*My, and evaluate the outcome. 
mean.diff.in.CM 
Threshold of expression difference for selecting genes in the component merging strategy. We merge threecomponent to twocomponent by selecting genes with similar expressions for the two known components. Genes with the mean differences less than the threshold will be selected for component merging. It is used in the threecomponent setting, and is enabled when if.filter = TRUE. The default is 0.25. 
nspikein 
The number of spikes in normal reference used for proportion estimation. The default value is min(200, 0.3*My), where My the number of mixed samples. If it is set to 0, proportion estimation is performed without any spike in normal reference. 
gene.selection.method 
The method of gene selection used for proportion estimation. The default method is 'GS', which applies a profile likelihood based method for gene selection. If it is set to 'DE', the most differential expressed genes are selected. 
ngene.Profile.selected 
The number of genes used for proportion estimation ranked by profile likelihood. The default is min(1500,0.1*My), where My is the number of mixed samples. This is enabled only when gene.selection.method is set to 'GS'. 
tol 
The convergence criterion. The default is 10^(5). 
output.more.info 
The logical flag indicating whether to show the estimated proportions in each iteration in the output. 
pi01 
Initialized proportion for first kown component. The default is Null and pi01 will be generated randomly from uniform distribution. 
pi02 
Initialized proportion for second kown component. pi02 is needed only for running a threecomponent model. The default is Null and pi02 will be generated randomly from uniform distribution. 
nthread 
The number of threads used for deconvolution when OpenMP is available in the system. The default is the number of whole threads minus one. In our noOpenMP version, it is set to 1. 
pi 
A matrix of estimated proportion. First row and second row corresponds to the proportion estimate for the known components and unkown component respectively for two or three component settings, and each column corresponds to one sample. 
pi.iter 
Estimated proportions in each iteration. It is a niter* My*p array, where p is the number of components. This is enabled only when output.more.info = TRUE. 
ExprT 
A matrix of deconvolved expression profiles corresponding to Tcomponent in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample. 
ExprN1 
A matrix of deconvolved expression profiles corresponding to N1component in mixed samples for a given subset of genes. Each row corresponds to one gene and each column corresponds to one sample. 
ExprN2 
A matrix of deconvolved expression profiles corresponding to N2component in mixed samples for a given subset of genes in a threecomponent setting. Each row corresponds to one gene and each column corresponds to one sample. 
Mu 
A matrix of estimated Mu of log2normal distribution for both known (MuN1, MuN2) and unknown component (MuT). Each row corresponds to one gene. 
Sigma 
Estimated Sigma of log2normal distribution for both known (SigmaN1, SigmaN2) and unknown component (SigmaT). Each row corresponds to one gene. 
gene.name 
The names of genes used in estimating the proportions. If no gene names are provided in the original data set, the genes will be automatically indexed. 
Zeya Wang, Wenyi Wang
Wang Z, Cao S, Morris J S, et al. Transcriptome Deconvolution of Heterogeneous Tumor Samples with Immune Infiltration. iScience, 2018, 9: 451460.
http://bioinformatics.mdanderson.org/main/DeMixT
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37  # Example 1: simulated twocomponent data by using GS(gene selection method)
data(test.data.2comp)
# res < DeMixT(data.Y = test.data.2comp$data.Y,
# data.N1 = test.data.2comp$data.N1,
# data.N2 = NULL, nspikein = 50,
# gene.selection.method = 'GS',
# niter = 10, nbin = 50, if.filter = TRUE,
# ngene.selected.for.pi = 150,
# mean.diff.in.CM = 0.25, tol = 10^(5))
# res$pi
# head(res$ExprT, 3)
# head(res$ExprN1, 3)
# head(res$Mu, 3)
# head(res$Sigma, 3)
#
# Example 2: simulated twocomponent data by using DE(gene selection method)
# data(test.data.2comp)
# res < DeMixT(data.Y = test.data.2comp$data.Y,
# data.N1 = test.data.2comp$data.N1,
# data.N2 = NULL, nspikein = 50, g
# ene.selection.method = 'DE',
# niter = 10, nbin = 50, if.filter = TRUE,
# ngene.selected.for.pi = 150,
# mean.diff.in.CM = 0.25, tol = 10^(5))
#
# Example 3: threecomponent mixed cell line data applying
# component merging strategy
# data(test.data.3comp)
# res < DeMixT(data.Y = test.data.3comp$data.Y,
# data.N1 = test.data.3comp$data.N1,
# data.N2 = test.data.3comp$data.N2,
# if.filter = TRUE)
#
# Example: convert a matrix into the SummarizedExperiment format
# library(SummarizedExperiment)
# example < matrix(c(1, 2, 3, 4, 5, 6), nrow = 2, ncol = 3, byrow = TRUE)
# example.se < SummarizedExperiment(assays = list(counts = example))

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