epidish: Epigenetic Dissection of Intra-Sample-Heterogeneity

In sjczheng/EpiDISH: Epigenetic Dissection of Intra-Sample-Heterogeneity

Epigenetic Dissection of Intra-Sample-Heterogeneity

Description

A reference-based function to infer the fractions of a priori known cell subtypes present in a sample representing a mixture of such cell-types. Inference proceeds via one of 3 methods (Robust Partial Correlations-RPC, Cibersort-CBS, Constrained Projection-CP), as determined by the user.

Usage

epidish(
  beta.m,
  ref.m,
  method = c("RPC", "CBS", "CP"),
  maxit = 50,
  nu.v = c(0.25, 0.5, 0.75),
  constraint = c("inequality", "equality")
)

Arguments

beta.m	A data matrix with rows labeling the molecular features (should use same ID as in ref.m) and columns labeling samples (e.g. primary tumour specimens). Missing value is not allowed and all values should be positive or zero. In the case of DNA methylation, these are beta-values.
ref.m	A matrix of reference 'centroids', i.e. representative molecular profiles, for a number of cell subtypes. rows label molecular features (e.g. CpGs,...) and columns label the cell-type. IDs need to be provided as rownames and colnames, respectively. Missing value is not allowed, and all values in this matrix should be positive or zero. For DNAm data, values should be beta-values.
method	Chioce of a reference-based method ('RPC','CBS','CP')
maxit	Only used in RPC mode, the limit of the number of IWLS iterations
nu.v	Only used in CBS mode. It is a vector of several candidate nu values. nu is parameter needed for nu-classification, nu-regression, and one-classification in svm. The best estimation results among all candidate nu will be automatically returned.
constraint	Only used in CP mode, you can choose either of 'inequality' or 'equality' normalization constraint. The default is 'inequality' (i.e sum of weights adds to a number less or equal than 1), which was implemented in Houseman et al (2012).

Value

CP-mode A list with the following entries: estF: a matrix of the estimated fractions; ref: the reference centroid matrix used; dataREF: the subset of the input data matrix with only the probes defined in the reference matrix.

CBS-mode A list with the following entries: estF: a matrix of the estimated fractions; nu: a vector of 'best' nu-parameter for each sample; ref: the reference centroid matrix used; dataREF: the subset of the input data matrix with only the probes defined in the reference matrix.

RPC-mode A list with the following entries: estF: a matrix of the estimated fractions; ref: the reference centroid matrix used; dataREF: the subset of the input data matrix with only the probes defined in the reference matrix.

References

Teschendorff AE, Breeze CE, Zheng SC, Beck S. A comparison of reference-based algorithms for correcting cell-type heterogeneity in Epigenome-Wide Association Studies. BMC Bioinformatics (2017) 18: 105. doi: 10.1186/s12859-017-1511-5.

Houseman EA, Accomando WP, Koestler DC, Christensen BC, Marsit CJ, Nelson HH, Wiencke JK, Kelsey KT. DNA methylation arrays as surrogate measures of cell mixture distribution. BMC Bioinformatics (2012) 13: 86. doi:10.1186/1471-2105-13-86.

Newman AM, Liu CL, Green MR, Gentles AJ, Feng W, Xu Y, Hoang CD, Diehn M, Alizadeh AA. Robust enumeration of cell subsets from tissue expression profiles. Nat Methods (2015) 12: 453-457. doi:10.1038/nmeth.3337.

Examples

data(centDHSbloodDMC.m)
data(DummyBeta.m)
out.l <- epidish(DummyBeta.m, centDHSbloodDMC.m[,1:6], method = 'RPC')
frac.m <- out.l$estF

sjczheng/EpiDISH documentation built on Nov. 16, 2024, 11:54 a.m.