| Title: | Tumor Clones Percentage Estimations |
|---|---|
| Description: | Includes R functions for the estimation of tumor clones percentages for both snp data and (whole) genome sequencing data. See Cheng, Y., Dai, J. Y., Paulson, T. G., Wang, X., Li, X., Reid, B. J., & Kooperberg, C. (2017). Quantification of multiple tumor clones using gene array and sequencing data. The Annals of Applied Statistics, 11(2), 967-991, <doi:10.1214/17-AOAS1026> for more details. |
| Authors: | Xuan You <[email protected]>, Yichen Cheng <[email protected]> |
| Maintainer: | Xuan You <[email protected]> |
| License: | GPL (>= 2) |
| Version: | 1.0.1 |
| Built: | 2024-12-11 07:09:27 UTC |
| Source: | CRAN |
It is a function that takes the LRR obtained from SNP array data and returns the estimated tumor and normal proportions. Currently, the function can performs the proportion estimations by assuming the number of tumor clones to be 1 or 2 or 3. The normalization step is not required and the normalization constant will be returned by this function. The function will output two sets of solutions corresponding to the top 2 optimal solutions based on the posterior distribution. You can choose according to your expertise the one that is more reasonable.
est_mixture(BAF, LRR, chr, x, GT, seg_raw = "NA", num_tumor = 1)est_mixture(BAF, LRR, chr, x, GT, seg_raw = "NA", num_tumor = 1)
BAF |
a numeric vector containing the B Allele Frequency for the sample, corresponding to the location (chr, x). |
LRR |
a numveric vector containing the Log R ratio for the sample, corresponding to the location (chr, x). In practice, the LRR values you include should be the raw LRR output devided by 0.55. |
chr |
a factor vector containing the chromosome. |
x |
a numeric vector containing the location on the chromosome, measured by base pair. |
GT |
a factor vector containing the genotype. Possible values are "AA", "AB", "BB" and NA. |
seg_raw |
Optional. A dataframe containing the segmentaiton results. If not supplied, function |
num_tumor |
1 or 2 or 3, indicating the number of tumor clones. 1 indicates a mixture for a normal and one tumor clone. 2 indicates a mixture for a normal and 2 tumors and so on. Default value is set to be 1. |
sol1_pct |
the estimated percentages for all tumor clones for optimal solution 1. Each value is between 0 and 100. |
sol1_scale |
a scaler that provide the normalization constant for LRR for optimal solution 1. That is 2*2^LRR/scale will be on the same scale as the copy number. |
sol1_cn1 |
a vector of length S, where S is the number of segments. It is the estimated copy number for tumor 1 for the optimal solution. |
sol1_cn2 |
a vector of length S, where S is the number of segments. It is the estimated copy number for tumor 2 for the optimal solution. |
sol1_pscn1 |
a vector of length S, where S is the number of segments. It is the estimated parent specifit copy number for tumor 1 for the optimal solution. |
sol1_pscn2 |
a vector of length S, where S is the number of segments. It is the estimated parent specifit copy number for tumor 2 for the optimal solution. |
sol2_pct |
the estimated percentages for all tumor clones for optimal solution 2. Each value is between 0 and 100. |
sol2_scale |
a scaler that provide the normalization constant for LRR for optimal solution 2. That is 2*2^LRR/scale will be on the same scale as the copy number. |
sol2_cn1 |
a vector of length S, where S is the number of segments. It is the estimated copy number for tumor 1 for the second optimal solution. |
sol2_cn2 |
a vector of length S, where S is the number of segments. It is the estimated copy number for tumor 2 for the second optimal solution. |
sol2_pscn1 |
a vector of length S, where S is the number of segments. It is the estimated parent specifit copy number for tumor 1 for the second optimal solution. |
sol2_pscn2 |
a vector of length S, where S is the number of segments. It is the estimated parent specifit copy number for tumor 2 for the second optimal solution. |
########################################################## ## ## short example ## ######################################################### ## first load the data BAF <- example_data$BAF LRR <- example_data$LRR ## In practice, the orignal LRR should be devided by 0.55 chr <- example_data$chr loc <- example_data$x GT <- example_data$GT gt = (GT=='BB')*2+(GT=='AB')*1.5+(GT=='AA')-1;gt[gt==(-1)]=NA ## then perform segmentation gaps = PSCBS::findLargeGaps(x=loc,minLength=5e6,chromosome=chr) if(!is.null(gaps)) knownSegments = PSCBS::gapsToSegments(gaps) p <- 0.0001 fit <- PSCBS::segmentByPairedPSCBS(CT=2*2^LRR,betaT=BAF,muN=gt,chrom=chr, knownSegments=knownSegments,tbn=FALSE,x=loc,seed=1, alphaTCN=p*.9,alphaDH=p*.1) seg_eg = fit$output ## then perform tumor mixture estimation by assuming 1 tumor clones out = est_mixture(BAF, LRR, chr, loc, GT, num_tumor = 1, seg_raw = seg_eg) out$sol1_pct out$sol1_scale ## References: Quantification of multiple tumor clones using gene array and sequencing data. ## Y Cheng, JY Dai, TG Paulson, X Wang, X Li, BJ Reid, C Kooperberg. ## Annals of Applied Statistics 11 (2), 967-991 ## Segmentation-based detection of allelic imbalance and loss-of-heterozygosity ## in cancer cells using whole genome SNP arrays. ## J Staaf, D Lindgren, J Vallon-Christersson, A Isaksson, H Goransson, G Juliusson, ## R Rosenquist, M H, A Borg, and M Ringner########################################################## ## ## short example ## ######################################################### ## first load the data BAF <- example_data$BAF LRR <- example_data$LRR ## In practice, the orignal LRR should be devided by 0.55 chr <- example_data$chr loc <- example_data$x GT <- example_data$GT gt = (GT=='BB')*2+(GT=='AB')*1.5+(GT=='AA')-1;gt[gt==(-1)]=NA ## then perform segmentation gaps = PSCBS::findLargeGaps(x=loc,minLength=5e6,chromosome=chr) if(!is.null(gaps)) knownSegments = PSCBS::gapsToSegments(gaps) p <- 0.0001 fit <- PSCBS::segmentByPairedPSCBS(CT=2*2^LRR,betaT=BAF,muN=gt,chrom=chr, knownSegments=knownSegments,tbn=FALSE,x=loc,seed=1, alphaTCN=p*.9,alphaDH=p*.1) seg_eg = fit$output ## then perform tumor mixture estimation by assuming 1 tumor clones out = est_mixture(BAF, LRR, chr, loc, GT, num_tumor = 1, seg_raw = seg_eg) out$sol1_pct out$sol1_scale ## References: Quantification of multiple tumor clones using gene array and sequencing data. ## Y Cheng, JY Dai, TG Paulson, X Wang, X Li, BJ Reid, C Kooperberg. ## Annals of Applied Statistics 11 (2), 967-991 ## Segmentation-based detection of allelic imbalance and loss-of-heterozygosity ## in cancer cells using whole genome SNP arrays. ## J Staaf, D Lindgren, J Vallon-Christersson, A Isaksson, H Goransson, G Juliusson, ## R Rosenquist, M H, A Borg, and M Ringner
It is a function that takes the count data obtained from whole genome sequencing (WGS) data and returns the estimated tumor and normal proportions. Currently, the function can performs the proportion estimations by assuming the number of tumor clones to be 1 or 2. The normalization step is not required and the normalization constant will be returned by this function. The function will output two sets of solutions corresponding to the top 2 optimal solutions based on the posterior distribution. You can choose according to your expertise the one that is more reasonable.
est_mixture_wgs(exp_data, normal_snp, tumor_snp, f_path, num_tumor = 1)est_mixture_wgs(exp_data, normal_snp, tumor_snp, f_path, num_tumor = 1)
exp_data |
a string. It provides the file name of interval. exp_data.intervals should be the name of the interval file. For the format of this file, please see the example section. The file should contain 6 and only 6 columns with each column corresponds to "ID","chrm","start","end","tumorCount" and "normalCount". It is very important to keep the order of the columns the same as listed. |
normal_snp |
a string. It provides the file name of WGS count data for a normal sample or a control sample. |
tumor_snp |
a string. It provides the file name of WGS count data for the tumor sample. |
f_path |
a string. It provides the absolute path of the folder that contains the files above. |
num_tumor |
1 or 2, indicating the number of tumor clones. 1 indicates a mixture for a normal and one tumor clone. 2 indicates a mixture for a normal and 2 tumors and so on. Default value is set to be 1. |
sol1_pct |
the estimated percentages for all tumor clones for optimal solution 1. Each value is between 0 and 100. |
sol1_scale |
sol1_scale: a scaler that provide the normalization constant for LRR for optimal solution 1. That is 2*tumor_count/normal_count will be on the same scale as the copy number. |
sol1_cn1 |
a vector of length S, where S is the number of segments. It is the estimated copy number for tumor 1 for the optimal solution. |
sol1_cn2 |
a vector of length S, where S is the number of segments. It is the estimated copy number for tumor 2 for the optimal solution. |
sol1_pscn1 |
a vector of length S, where S is the number of segments. It is the estimated parent specifit copy number for tumor 1 for the optimal solution. |
sol1_pscn2 |
a vector of length S, where S is the number of segments. It is the estimated parent specifit copy number for tumor 2 for the optimal solution. |
sol2_pct |
the estimated percentages for all tumor clones for optimal solution 2. Each value is between 0 and 100. |
sol2_scale |
sol1_scale: a scaler that provide the normalization constant for LRR for optimal solution 2. That is 2*tumor_count/normal_count will be on the same scale as the copy number. |
sol2_cn1 |
a vector of length S, where S is the number of segments. It is the estimated copy number for tumor 1 for the second optimal solution. |
sol2_cn2 |
a vector of length S, where S is the number of segments. It is the estimated copy number for tumor 2 for the second optimal solution. |
sol2_pscn1 |
a vector of length S, where S is the number of segments. It is the estimated parent specifit copy number for tumor 1 for the second optimal solution. |
sol2_pscn2 |
a vector of length S, where S is the number of segments. It is the estimated parent specifit copy number for tumor 2 for the second optimal solution. |
exp_data = "data_exp_eg" ## exp_data.intervals should be the file name of the segments. ## For the format of the input files, you can use the example code below. normal_snp = "snp_norm_eg" ## snp_norm_eg.txt should be the count file name for the normal sample. tumor_snp = "snp_tum_eg" ## snp_tum_eg.txt should be the count file name for the tumor sample. f_path = system.file("extdata",package="EstMix") ## f_path should be the absolute path of folder that contains the txt and interval files. out_wgs = est_mixture_wgs(exp_data, normal_snp, tumor_snp,f_path,num_tumor = 1) out_wgs$sol1_pct out_wgs$sol1_scale ## for the format of the input files, please see the following code data_exp_path = file.path(f_path, paste("/", exp_data, ".intervals", sep="")) snp_norm_path = file.path(f_path, paste("/",normal_snp, ".txt", sep="")) snp_tumor_path = file.path(f_path, paste("/",tumor_snp, ".txt", sep="")) data_exp = read.table(data_exp_path); colnames(data_exp) = c("ID","chrm","start","end","tumorCount","normalCount") snp_norm = read.table(snp_norm_path) snp_tum = read.table(snp_tumor_path) ## References: Quantification of multiple tumor clones using gene array and sequencing data. ## Y Cheng, JY Dai, TG Paulson, X Wang, X Li, BJ Reid, C Kooperberg. ## Annals of Applied Statistics 11 (2), 967-991exp_data = "data_exp_eg" ## exp_data.intervals should be the file name of the segments. ## For the format of the input files, you can use the example code below. normal_snp = "snp_norm_eg" ## snp_norm_eg.txt should be the count file name for the normal sample. tumor_snp = "snp_tum_eg" ## snp_tum_eg.txt should be the count file name for the tumor sample. f_path = system.file("extdata",package="EstMix") ## f_path should be the absolute path of folder that contains the txt and interval files. out_wgs = est_mixture_wgs(exp_data, normal_snp, tumor_snp,f_path,num_tumor = 1) out_wgs$sol1_pct out_wgs$sol1_scale ## for the format of the input files, please see the following code data_exp_path = file.path(f_path, paste("/", exp_data, ".intervals", sep="")) snp_norm_path = file.path(f_path, paste("/",normal_snp, ".txt", sep="")) snp_tumor_path = file.path(f_path, paste("/",tumor_snp, ".txt", sep="")) data_exp = read.table(data_exp_path); colnames(data_exp) = c("ID","chrm","start","end","tumorCount","normalCount") snp_norm = read.table(snp_norm_path) snp_tum = read.table(snp_tumor_path) ## References: Quantification of multiple tumor clones using gene array and sequencing data. ## Y Cheng, JY Dai, TG Paulson, X Wang, X Li, BJ Reid, C Kooperberg. ## Annals of Applied Statistics 11 (2), 967-991