| Title: | Heterogeneous error model for identification of differentially expressed genes under multiple conditions |
|---|---|
| Description: | This package fits heterogeneous error models for analysis of microarray data |
| Authors: | HyungJun Cho <[email protected]> and Jae K. Lee <[email protected]> |
| Maintainer: | HyungJun Cho <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.79.0 |
| Built: | 2024-10-30 07:25:04 UTC |
| Source: | https://github.com/bioc/HEM |
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error using bootstrap samples for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error using bootstrap samples for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error using bootstrap samples for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Makes predictions using smoothing spine
HyungJun Cho and Jae K. Lee
Fits an error model with heterogeneous experimental and biological variances.
hem(dat, probe.ID=NULL, n.layer, design, burn.ins=1000, n.samples=3000, method.var.e="gam", method.var.b="gam", method.var.t="gam", var.e=NULL, var.b=NULL, var.t=NULL, var.g=1, var.c=1, var.r=1, alpha.e=3, beta.e=.1, alpha.b=3, beta.b=.1, alpha.t=3, beta.t=.2, n.digits=10, print.message.on.screen=TRUE)hem(dat, probe.ID=NULL, n.layer, design, burn.ins=1000, n.samples=3000, method.var.e="gam", method.var.b="gam", method.var.t="gam", var.e=NULL, var.b=NULL, var.t=NULL, var.g=1, var.c=1, var.r=1, alpha.e=3, beta.e=.1, alpha.b=3, beta.b=.1, alpha.t=3, beta.t=.2, n.digits=10, print.message.on.screen=TRUE)
dat |
data |
probe.ID |
a vector of probe set IDs |
n.layer |
number of layers; 1=one-layer EM, 2=two-layer EM |
design |
design matrix |
burn.ins |
number of burn-ins for MCMC |
n.samples |
number of samples for MCMC |
method.var.e |
prior specification method for experimental variance; "gam"=Gamma(alpha,beta), "peb"=parametric EB prior specification, "neb"=nonparametric EB prior specification |
method.var.b |
prior specification method for biological variance; "gam"=Gamma(alpha,beta), "peb"=parametric EB prior specification |
method.var.t |
prior specification method for total variance; "gam"=Gamma(alpha,beta), "peb"=parametric EB prior specification, "neb"=nonparametric EB prior specification |
var.e |
prior estimate matrix for experimental variance |
var.b |
prior estimate matrix for biological variance |
var.t |
prior estimate matrix for total variance |
var.g |
N(0, var.g); prior parameter for gene effect |
var.c |
N(0, var.c); prior parameter for condition effect |
var.r |
N(0, var.r); prior parameter for interaction effect of gene and condition |
alpha.e, beta.e
|
Gamma(alpha.e,alpha.e); prior parameters for inverse of experimental variance |
alpha.b, beta.b
|
Gamma(alpha.b,alpha.b); prior parameters for inverse of biological variance |
alpha.t, beta.t
|
Gamma(alpha.b,alpha.b); prior parameters for inverse of total variance |
n.digits |
number of digits |
print.message.on.screen |
if TRUE, process status is shown on screen. |
n.gene |
numer of genes |
n.chip |
number of chips |
n.cond |
number of conditions |
design |
design matrix |
burn.ins |
number of burn-ins for MCMC |
n.samples |
number of samples for MCMC |
priors |
prior parameters |
m.mu |
estimated mean expression intensity for each gene under each condition |
m.x |
estimated unobserved expression intensity for each combination of genes, conditions, and individuals (n.layer=2) |
m.var.b |
estimated biological variances (n.layer=2) |
m.var.e |
estimated experiemental variances (n.layer=2) |
m.var.t |
estimated total variances (n.layer=1) |
H |
H-scores |
HyungJun Cho and Jae K. Lee
Cho, H. and Lee, J.K. (2004) Bayesian Hierarchical Error Model for Analysis of Gene Expression Data, Bioinformatics, 20: 2016-2025.
#Example 1: Two-layer HEM data(pbrain) ##construct a design matrix cond <- c(1,1,1,1,1,1,2,2,2,2,2,2) #condition ind <- c(1,1,2,2,3,3,1,1,2,2,3,3) #biological replicate rep <- c(1,2,1,2,1,2,1,2,1,2,1,2) #experimental replicate design <- data.frame(cond,ind,rep) ##normalization pbrain.nor <- hem.preproc(pbrain[,2:13]) ##fit HEM with two layers of error ##using the small numbers of burn-ins and MCMC samples for a testing purpose; ##but increase the numbers for a practical purpose #pbrain.hem <- hem(pbrain.nor, n.layer=2, design=design, # burn.ins=10, n.samples=30) ##print H-scores #pbrain.hem$H #Example 2: One-layer HEM data(mubcp) ##construct a design matrix cond <- c(rep(1,6),rep(2,5),rep(3,5),rep(4,5),rep(5,5)) ind <- c(1:6,rep((1:5),4)) design <- data.frame(cond,ind) ##construct a design matrix mubcp.nor <- hem.preproc(mubcp) #fit HEM with one layers of error #using the small numbers of burn-ins and MCMC samples for a testing purpose; #but increase the numbers for a practical purpose #mubcp.hem <- hem(mubcp.nor, n.layer=1,design=design, burn.ins=10, n.samples=30) ##print H-scores #mubcp.hem$H ###NOTE: Use 'hem.fdr' for FDR evaluation ###NOTE: Use 'hem.eb.prior' for Empirical Bayes (EB) prior sepecification ###NOTE: Use EB-HEM ('hem' after 'hem.eb.prior') for small data sets#Example 1: Two-layer HEM data(pbrain) ##construct a design matrix cond <- c(1,1,1,1,1,1,2,2,2,2,2,2) #condition ind <- c(1,1,2,2,3,3,1,1,2,2,3,3) #biological replicate rep <- c(1,2,1,2,1,2,1,2,1,2,1,2) #experimental replicate design <- data.frame(cond,ind,rep) ##normalization pbrain.nor <- hem.preproc(pbrain[,2:13]) ##fit HEM with two layers of error ##using the small numbers of burn-ins and MCMC samples for a testing purpose; ##but increase the numbers for a practical purpose #pbrain.hem <- hem(pbrain.nor, n.layer=2, design=design, # burn.ins=10, n.samples=30) ##print H-scores #pbrain.hem$H #Example 2: One-layer HEM data(mubcp) ##construct a design matrix cond <- c(rep(1,6),rep(2,5),rep(3,5),rep(4,5),rep(5,5)) ind <- c(1:6,rep((1:5),4)) design <- data.frame(cond,ind) ##construct a design matrix mubcp.nor <- hem.preproc(mubcp) #fit HEM with one layers of error #using the small numbers of burn-ins and MCMC samples for a testing purpose; #but increase the numbers for a practical purpose #mubcp.hem <- hem(mubcp.nor, n.layer=1,design=design, burn.ins=10, n.samples=30) ##print H-scores #mubcp.hem$H ###NOTE: Use 'hem.fdr' for FDR evaluation ###NOTE: Use 'hem.eb.prior' for Empirical Bayes (EB) prior sepecification ###NOTE: Use EB-HEM ('hem' after 'hem.eb.prior') for small data sets
Estimates experimental and biological variances by LPE and resampling
hem.eb.prior(dat, n.layer, design, method.var.e="neb", method.var.b="peb", method.var.t="neb", rep=TRUE, baseline.var="LPE", p.remove=0, max.chip=4, q=0.01, B=25, n.digits=10, print.message.on.screen=TRUE)hem.eb.prior(dat, n.layer, design, method.var.e="neb", method.var.b="peb", method.var.t="neb", rep=TRUE, baseline.var="LPE", p.remove=0, max.chip=4, q=0.01, B=25, n.digits=10, print.message.on.screen=TRUE)
dat |
data |
n.layer |
number of layers |
design |
design matrix |
method.var.e |
prior specification method for experimental variance; "peb"=parametric EB prior specification, "neb"=nonparametric EB prior specification |
method.var.b |
prior specification method for biological variance; "peb"=parametric EB prior specification |
method.var.t |
prior specification method for total variance; "peb"=parametric EB prior specification, "neb"=nonparametric EB prior specification |
rep |
no replication if FALSE |
baseline.var |
baseline variance estimation method: LPE for replicated data and BLPE, PSE, or ASE for unreplicated data |
p.remove |
percent of removed rank-variance genes for BLPE |
max.chip |
maximum number of chips to estimate errors |
q |
quantile for paritioning genes based on expression levels |
B |
number of iterations for resampling |
n.digits |
number of digits |
print.message.on.screen |
if TRUE, process status is shown on screen. |
var.b |
prior estimate matrix for biological variances (n.layer=2) |
var.e |
prior estimate matrix for experiemtnal variances (n.layer=2) |
var.t |
prior estimate matrix for total variances (n.layer=1) |
HyungJun Cho and Jae K. Lee
#Example 1: Two-layer HEM with EB prior specification data(pbrain) ##construct a design matrix cond <- c(1,1,1,1,1,1,2,2,2,2,2,2) ind <- c(1,1,2,2,3,3,1,1,2,2,3,3) rep <- c(1,2,1,2,1,2,1,2,1,2,1,2) design <- data.frame(cond,ind,rep) ##normalization pbrain.nor <- hem.preproc(pbrain[,2:13]) ##take a subset for a testing purpose; ##use all genes for a practical purpose pbrain.nor <- pbrain.nor[1:1000,] ##estimate hyperparameters of variances by LPE #pbrain.eb <- hem.eb.prior(pbrain.nor, n.layer=2, design=design, # method.var.e="neb", method.var.b="peb") #fit HEM with two layers of error #using the small numbers of burn-ins and MCMC samples for a testing purpose; #but increase the numbers for a practical purpose #pbrain.hem <- hem(pbrain.nor, n.layer=2, design=design,burn.ins=10, n.samples=30, # method.var.e="neb", method.var.b="peb", # var.e=pbrain.eb$var.e, var.b=pbrain.eb$var.b) #Example 2: One-layer HEM with EB prior specification data(mubcp) ##construct a design matrix cond <- c(rep(1,6),rep(2,5),rep(3,5),rep(4,5),rep(5,5)) ind <- c(1:6,rep((1:5),4)) design <- data.frame(cond,ind) ##normalization mubcp.nor <- hem.preproc(mubcp) ##take a subset for a testing purpose; ##use all genes for a practical purpose mubcp.nor <- mubcp.nor[1:1000,] ##estimate hyperparameters of variances by LPE #mubcp.eb <- hem.eb.prior(mubcp.nor, n.layer=1, design=design, # method.var.t="neb") #fit HEM with two layers of error #using the small numbers of burn-ins and MCMC samples for a testing purpose; #but increase the numbers for a practical purpose #mubcp.hem <- hem(mubcp.nor, n.layer=1, design=design, burn.ins=10, n.samples=30, # method.var.t="neb", var.t=mubcp.eb$var.t)#Example 1: Two-layer HEM with EB prior specification data(pbrain) ##construct a design matrix cond <- c(1,1,1,1,1,1,2,2,2,2,2,2) ind <- c(1,1,2,2,3,3,1,1,2,2,3,3) rep <- c(1,2,1,2,1,2,1,2,1,2,1,2) design <- data.frame(cond,ind,rep) ##normalization pbrain.nor <- hem.preproc(pbrain[,2:13]) ##take a subset for a testing purpose; ##use all genes for a practical purpose pbrain.nor <- pbrain.nor[1:1000,] ##estimate hyperparameters of variances by LPE #pbrain.eb <- hem.eb.prior(pbrain.nor, n.layer=2, design=design, # method.var.e="neb", method.var.b="peb") #fit HEM with two layers of error #using the small numbers of burn-ins and MCMC samples for a testing purpose; #but increase the numbers for a practical purpose #pbrain.hem <- hem(pbrain.nor, n.layer=2, design=design,burn.ins=10, n.samples=30, # method.var.e="neb", method.var.b="peb", # var.e=pbrain.eb$var.e, var.b=pbrain.eb$var.b) #Example 2: One-layer HEM with EB prior specification data(mubcp) ##construct a design matrix cond <- c(rep(1,6),rep(2,5),rep(3,5),rep(4,5),rep(5,5)) ind <- c(1:6,rep((1:5),4)) design <- data.frame(cond,ind) ##normalization mubcp.nor <- hem.preproc(mubcp) ##take a subset for a testing purpose; ##use all genes for a practical purpose mubcp.nor <- mubcp.nor[1:1000,] ##estimate hyperparameters of variances by LPE #mubcp.eb <- hem.eb.prior(mubcp.nor, n.layer=1, design=design, # method.var.t="neb") #fit HEM with two layers of error #using the small numbers of burn-ins and MCMC samples for a testing purpose; #but increase the numbers for a practical purpose #mubcp.hem <- hem(mubcp.nor, n.layer=1, design=design, burn.ins=10, n.samples=30, # method.var.t="neb", var.t=mubcp.eb$var.t)
Computes resampling-based False Discovery Rate (FDR)
hem.fdr(dat, n.layer, design, rep=TRUE, hem.out, eb.out=NULL, n.iter=5, q.trim=0.9, target.fdr=c(0.001, 0.005, 0.01, 0.05, 0.1, 0.15, 0.20, 0.30, 0.40, 0.50), n.digits=10, print.message.on.screen=TRUE)hem.fdr(dat, n.layer, design, rep=TRUE, hem.out, eb.out=NULL, n.iter=5, q.trim=0.9, target.fdr=c(0.001, 0.005, 0.01, 0.05, 0.1, 0.15, 0.20, 0.30, 0.40, 0.50), n.digits=10, print.message.on.screen=TRUE)
dat |
data |
n.layer |
number of layers: 1=one-layer EM; 2=two-layer EM |
design |
design matrix |
rep |
no replication if FALSE |
hem.out |
output from hem function |
eb.out |
output from hem.eb.prior function |
n.iter |
number of iterations |
q.trim |
quantile used for estimtaing the proportion of true negatives (pi0) |
target.fdr |
Target FDRs |
n.digits |
number of digits |
print.message.on.screen |
if TRUE, process status is shown on screen. |
fdr |
H-values and corresponding FDRs |
pi0 |
estimated proportion of true negatives |
H.null |
H-scores from null data |
targets |
given target FDRs, corrsponding critical values and numbers of significant genes are provided |
HyungJun Cho and Jae K. Lee
data(pbrain) ##construct a design matrix cond <- c(1,1,1,1,1,1,2,2,2,2,2,2) ind <- c(1,1,2,2,3,3,1,1,2,2,3,3) rep <- c(1,2,1,2,1,2,1,2,1,2,1,2) design <- data.frame(cond,ind,rep) ##normalization pbrain.nor <- hem.preproc(pbrain[,2:13]) ##take a subset for a testing purpose; ##use all genes for a practical purpose pbrain.nor <- pbrain.nor[1:1000,] ##estimate hyperparameters of variances by LPE #pbrain.eb <- hem.eb.prior(pbrain.nor, n.layer=2, design=design, # method.var.e="neb", method.var.b="peb") ##fit HEM with two layers of error ##using the small numbers of burn-ins and MCMC samples for a testing purpose; ##but increase the numbers for a practical purpose #pbrain.hem <- hem(pbrain.nor, n.layer=2, design=design,burn.ins=10, n.samples=30, # method.var.e="neb", method.var.b="peb", # var.e=pbrain.eb$var.e, var.b=pbrain.eb$var.b) ##Estimate FDR based on resampling #pbrain.fdr <- hem.fdr(pbrain.nor, n.layer=2, design=design, # hem.out=pbrain.hem, eb.out=pbrain.eb)data(pbrain) ##construct a design matrix cond <- c(1,1,1,1,1,1,2,2,2,2,2,2) ind <- c(1,1,2,2,3,3,1,1,2,2,3,3) rep <- c(1,2,1,2,1,2,1,2,1,2,1,2) design <- data.frame(cond,ind,rep) ##normalization pbrain.nor <- hem.preproc(pbrain[,2:13]) ##take a subset for a testing purpose; ##use all genes for a practical purpose pbrain.nor <- pbrain.nor[1:1000,] ##estimate hyperparameters of variances by LPE #pbrain.eb <- hem.eb.prior(pbrain.nor, n.layer=2, design=design, # method.var.e="neb", method.var.b="peb") ##fit HEM with two layers of error ##using the small numbers of burn-ins and MCMC samples for a testing purpose; ##but increase the numbers for a practical purpose #pbrain.hem <- hem(pbrain.nor, n.layer=2, design=design,burn.ins=10, n.samples=30, # method.var.e="neb", method.var.b="peb", # var.e=pbrain.eb$var.e, var.b=pbrain.eb$var.b) ##Estimate FDR based on resampling #pbrain.fdr <- hem.fdr(pbrain.nor, n.layer=2, design=design, # hem.out=pbrain.hem, eb.out=pbrain.eb)
Generates null data by resampling
HyungJun Cho and Jae K. Lee
Generates null data by resampling
HyungJun Cho and Jae K. Lee
Generates null data by resampling
HyungJun Cho and Jae K. Lee
Performs IQR normalization, thesholding, and log2-transformation
hem.preproc(x, data.type = "MAS5")hem.preproc(x, data.type = "MAS5")
x |
data |
data.type |
data type: MAS5 or MAS4 |
HyungJun Cho and Jae K. Lee
data(pbrain) pbrain.nor <- hem.preproc(pbrain[,2:13])data(pbrain) pbrain.nor <- hem.preproc(pbrain[,2:13])
This data set consists of gene expression of the five consecutive stages (pre-B1, large pre-B2, small pre-B2, immature B, and mature B cells) of mouse B cell development. The data were obtained with high-density oligonucleotide arrays, Affymetrix Mu11k GeneChips, from flow-cytometrically purified cells.
data(mubcp)data(mubcp)
A matrix containing 13,207 probe sets and 26 chips; first 6 chips for pre-B1 cell and next 20 chips for other stages (5 chips for each)
Hoffmann, R., Seidl, T., Neeb, M., Rolink, A. and Melchers, F. (2002). Changes in gene expression profiles in developing B cells of murine bone marrow, Genome Research 12:98-111.
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
Estimates baseline error for oligonucleotide arrays
HyungJun Cho and Jae K. Lee
This data set consists of gene expression of primate brains (Affymetrix U95A GeneChip). The frozen brains of three humans (H1, H2, H3) and three chimpanzees (C1, C2, C3) were used to take the postmortem tissue samples, and two independent tissue samples for each individual were taken.
data(pbrain)data(pbrain)
A matrix containing 12,600 probe sets and 12 chips (H1,H1,H2,H2,H3,H3,C1,C1,C2,C2,C3,C3); the first column is probe set ID
Enard, W., Khaitovich, P., Klose, J., Zollner, S., Heissig, F., Giavalisco, P., Nieselt-Struwe, K., Muchmore, E., Varki, A., Ravid, R., Doxiadis, G.M., Bontrop, R.R., and Paabo, S. (2002) Intra- and interspecific variation in primate gene expression patterns, Science 296:340-343
Performs quantile normalization
HyungJun Cho and Jae K. Lee
Performs quantile normalization
HyungJun Cho and Jae K. Lee