BayClone2 (version 1.1)

fn_posterior_point: fn_posterior_point function

Description

This functions returns point estimates of the parameters such as a subclonal copy number matrix, a matrix of the number of copies with variant sequence in subclones, a matrix of composition weights of sampels in subclones and the expected read count with two copies (i.e posterior point estimate for L, Z, W, PHI, PI, P0 for a chosen value of C).

Usage

fn_posterior_point(CC, SS, TT, sam)

Arguments

CC

the number of subclones chosen by users. It should be less than or equal to 10(including background subclone). This limitation is due to the permutaion.

SS

the number of loci

TT

the number of tissue samples

sam

a list of MCMC samples returned from the function, BayClone2

Value

C: the value of C passed to the functionL: a posteror point esitmate of L in a S*C matrixZ: a posteror point esitmate of Z in a S*C matrixw: a posteror point esitmate of w in a T*C matrixp0: a posteror point esitmate of p0 as a scalorphi: a posteror point esitmate of phi in a vector of TM: a posterior point estimate of M in a S*T matrixp: a posterior point estimate of p in a S*T matrix

Details

The argument passed to this function, sam is a list returned from BayClone2; sam should be a list of posterior samples of random parameters (returned from the funtion, BayClone2); C, L, Z, w, th, phi, pi, p0_z, M and p

References

J. Lee, P. Mueller, S. Sengupta, K. Gulukota, Y. Ji, Bayesian Inference for Tumor Subclones Accounting for Sequencing and Structural Variants (http://arxiv.org/abs/1409.7158)

See Also

export_N_n, BayClone2, fn_post_C

Examples

##ILLUSTRATE BayClone2 WITH A SMALL SIMULATION.
###REPRODUCE SIMULATION 1 OF LEE ET AL.
library("BayClone2")

##READ IN DATA
data(BayClone2_Simulation1_mut)
data(BayClone2_Simulation1_tot)
##TOTAL NUMBER OF READS AT LOCUS s IN SAMPLE t
N <- as.matrix(BayClone2_Simulation1_tot)  
##NUMBER OF READS WITH VARIANT SEQUENCE AT LOCUS s IN SAMPLE t
n <- as.matrix(BayClone2_Simulation1_mut) 

S <- nrow(N)  # THE NUMBER OF LOCI (I.E. NUMBER OF ROWS OF N (AND n))
T <- ncol(N) #THE NUMBER OF TISSUE SAMPLES  (I.E. NUMBER OF COLUMNS OF N (AND n))

###################################
#HYPER-PARAMETER  ----SPECIFYING HYPERPARAMETER VALUES
######################################
#HYPER-PARAMETER
hyper <- NULL

#NUMBER OF SUBCLONES (GEOMETRIC DIST)
### C ~ GEOMETRIC(r) WHERE E(C)=1/r
hyper$r <- 0.2

#PRIOR FOR L
hyper$Q <- 3  #NUMBER OF COPIES -- q = 0, 1, 2, 3

##BETA-DIRICHLET
###PI_C | C ~ BETA-DIRICHLET (ALPHA/C, BETA, GAMMA)
hyper$alpha <- 2
hyper$beta <- 1
hyper$gam <- c(0.5, 0.5, 0.5)

#PRIOR FOR PHI--TOTAL NUMBER OF READS IN SAMPLE T
###PHI_T ~ GAMMA(A, B)
hyper$b <- 3
hyper$a <- median(N)*hyper$b

#PRIOR FOR P_O
###P0 ~ BETA(a, b)
hyper$a_z0 <- 0.3
hyper$b_z0 <- 5

#PRIOR FOR W
##W_T | L ~ DIRICHLET(D0, D, ..., D) WHERE W_T=(w_t0, w_t1, ..., w_tC)
hyper$d0 <- 0.5
hyper$d <- 1

#WE USE THE MCMC SIMULATION STRATEGY PROPOSED IN LEE AT EL (2014)
n.sam <- 10000;  ##NUMBER OF SAMPLES THAT WILL BE USED FOR INFERENCE
##NUMBER OF SAMPLES FOR BURN-IN 
#(USE THIS FOR A TRAINING DATA---FOR DETIALS, SEE THE REFERENCE)
burn.in <- 6000  

##############################################
###WE CONSIDER C BETWEEN 1 AND 15 IN ADDITION TO BACKGROUND SUBCLONE
####Max_C AND Min_C SPECIFIES VALUES OF C FOR POSTERIOR EXPLORATION
Min_C <- 2  ##INCLUDING THE BACKGROUND SUBCLONE
Max_C <- 16  ##INCLUDING THE BACKGROUND SUBCLONE


#################################################################
##DO MCMC SAMPLING FROM BAYCLONE2!
#################################################################
##THE LAST ARGUMENT (0.025) IS THE MEAN PROPORTION FOR THE TRAINING DATASET (SPECIFIED BY USERS)
##IT WILL BE USED TO SPLIT INTO TRAINING AND TEST DATASETS
##FOR DETAILS, SEE THE REFERENCE LEE AT EL (2014)
##TO RUN, COMMENT IN THE LINE BELOW (WARNING! THIS MAY TAKE APPROXIMATELY 30 MINUTES)
#set.seed(11615)
#MCMC.sam <- BayClone2(Min_C, Max_C, S, T, burn.in, n.sam, N, n, hyper, 0.025)


#################################################################
#COMPUTE THE POSTERIOR MARGINAL DIST OF C (THE NUMBER OF SUBCLONES)
#################################################################
##TO RUN, COMMENT IN THE LINE BELOW
#post_dist_C <- fn_post_C(MCMC.sam$C, Min_C, Max_C)

######################################################################################
####WE FIND POSTERIOR POINT ESTIMATES OF L, Z, W, PHI, PI, P0 FOR A CHOSEN VALUE OF C
######################################################################################
##THE FIRST ARGUMENT (3) IS A VALUE OF C CHOSEN BY USERS
#C IS THE NUMBER OF SUBCLONES INCLUDING THE BACKGROUPD SUBCLONE
##THE CHOSE VALUE OF C SHOULD BE LESS THAN OR EQUAL TO 10 (INCLUDING THE BACKGROUND SUBCLONE)
#DUE TO THE PERMUTATION (FOR DETAILS, SEE SEE THE REFERENCE LEE AT EL (2014))
##TO RUN, COMMENT IN THE LINE BELOW (WARNING! THIS MAY TAKE ARPPOXIMATELY 15 MINUTES)
#point.est <- fn_posterior_point(3, S, T, MCMC.sam)