| Type: | Package |
| Title: | Truncated Rank Correlation |
| Version: | 0.2 |
| Date: | 2025-04-15 |
| Maintainer: | Donghyeon Yu <dyu@inha.ac.kr> |
| Description: | A new measure of similarity between a pair of mass spectrometry (MS) experiments, called truncated rank correlation (TRC). To provide a robust metric of similarity in noisy high-dimensional data, TRC uses truncated top ranks (or top m-ranks) for calculating correlation. Truncated rank correlation as a robust measure of test-retest reliability in mass spectrometry data. For more details see Lim et al. (2019) <doi:10.1515/sagmb-2018-0056>. |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://sites.google.com/site/dhyeonyu/software |
| Packaged: | 2025-04-22 21:27:10 UTC; dyu |
| NeedsCompilation: | yes |
| Repository: | CRAN |
| Date/Publication: | 2025-04-24 17:20:02 UTC |
| Author: | Johan Lim [aut], Donghyeon Yu [aut, cre], Hsun-chih Kuo [aut], Hyungwon Choi [aut], Scott Walmsley [aut] |
Kendall's tau for two vector observations
Description
This function calculates the Kendall's tau for two vector observations for the purpose of checking inner calculation.
Usage
k_tau(X,Y)
Arguments
X |
An observed data vector from the first condition. |
Y |
An observed data vector from the second condition. |
Details
Kendall's tau for two vector observations.
Value
tau |
A calculated Kendall's tau value. |
References
Lim, J., Yu, D., Kuo, H., Choi, H., and Walmsely, S. (2019). Truncated Rank Correlation as a robust measure of test-retest reliability in mass spectrometry data. Statistical Applications in Genetics and Molecular Biology, 18(4).
Examples
p = 100
sig_z = 1.15
sig_e = 1
mu_z = 2
mu_e = 8
m0 = 30
S1 = rnorm(p,mean=mu_e,sd=sig_e)
S2 = rnorm(p,mean=mu_e,sd=sig_e)
if(m0!=0)
{
X = mu_z + rnorm(m0,mean=0,sd=sig_z)
indx = 1:p
s_indx = sort(sample(indx,m0))
S1[s_indx] = S1[s_indx] + X
S2[s_indx] = S2[s_indx] + X
}
S1 = exp(S1)
S2 = exp(S2)
# Kendall's tau
ktau <- k_tau(S1,S2)
ktau
Procedure for estimating the null distribution of the TRC tau with the m value chosen by the proposed rule.
Description
Procedure for estimating the null distribution of the TRC tau with the m value chosen by the proposed rule.
Usage
null_perm(X,Y,nperm=1000,start=3,range_m=0.5,span=0.5,seed=21,all_m=FALSE)
Arguments
X |
An observed data vector from the first condition. |
Y |
An observed data vector from the second condition. |
nperm |
the number of permutations to estimate the null distribution (default: 1000). |
start |
A lower bound of a search region for the threshold rank m (default: 3). |
range_m |
A proportion of length of X for specifying the end of the search region for m (default: 0.8). |
span |
A parameter alpha which controls the degree of smoothing in loess function. |
seed |
An initial seed for the permutation. |
all_m |
a logical flag for returning permuted TRC tau values for all m values (default: FALSE). |
Details
Null distributions of the TRC tau with a given m value, the Kendall's tau, and Pearson's correlation are estimated by the permuted samples.
Value
perm_trc |
A vector of TRC tau values from the permuted samples with the m value chosen by the proposed rule. |
hist_m |
A vector of the chosen m values for permutations. |
perm_ktau |
A vector of Kendall's tau values from the permuted samples. |
perm_rho |
A vector of Pearson's correlation values from the permuted samples. |
perm_trc_all_m |
A matrix of permuted TRC tau values for all m values, in which each column stores the permuted TRC tau values for corresponding m value. |
References
Lim, J., Yu, D., Kuo, H., Choi, H., and Walmsely, S. (2019). Truncated Rank Correlation as a robust measure of test-retest reliability in mass spectrometry data. Statistical Applications in Genetics and Molecular Biology, 18(4).
Examples
p = 100
sig_z = 1.15
sig_e = 1
mu_z = 2
mu_e = 8
m0 = 30
S1 = rnorm(p,mean=mu_e,sd=sig_e)
S2 = rnorm(p,mean=mu_e,sd=sig_e)
if(m0!=0)
{
X = mu_z + rnorm(m0,mean=0,sd=sig_z)
indx = 1:p
s_indx = sort(sample(indx,m0))
S1[s_indx] = S1[s_indx] + X
S2[s_indx] = S2[s_indx] + X
}
S1 = exp(S1)
S2 = exp(S2)
null_res = null_perm(S1,S2,nperm=1000,start=3,range_m=0.5,span=0.2,seed=21,all_m=FALSE)
Procedure for estimating the null distribution of the TRC tau with a given m value
Description
Procedure for estimating the null distribution of the TRC tau with a given m value.
Usage
null_perm_m0(X,Y,nperm=1000,m=5,seed=21)
Arguments
X |
An observed data vector from the first condition. |
Y |
An observed data vector from the second condition. |
nperm |
the number of permutations to estimate the null distribution (default: 1000). |
m |
A rank threshold for the calculation of TRC tau (default: 5). |
seed |
An initial seed for the permutation. |
Details
Null distribution of the TRC tau with a given m value is estimated by the permuted samples.
Value
perm_tau |
A vector of calculated TRC tau values from the permuted samples |
References
Lim, J., Yu, D., Kuo, H., Choi, H., and Walmsely, S. (2019). Truncated Rank Correlation as a robust measure of test-retest reliability in mass spectrometry data. Statistical Applications in Genetics and Molecular Biology, 18(4).
Examples
p = 100
sig_z = 1.15
sig_e = 1
mu_z = 2
mu_e = 8
m0 = 30
S1 = rnorm(p,mean=mu_e,sd=sig_e)
S2 = rnorm(p,mean=mu_e,sd=sig_e)
if(m0!=0)
{
X = mu_z + rnorm(m0,mean=0,sd=sig_z)
indx = 1:p
s_indx = sort(sample(indx,m0))
S1[s_indx] = S1[s_indx] + X
S2[s_indx] = S2[s_indx] + X
}
S1 = exp(S1)
S2 = exp(S2)
null_res = null_perm_m0(S1,S2,nperm=1000,m=5,seed=21)
Pearson's correlation for two vector observations
Description
This function calculates the Pearson's correlation for two vector observations for the purpose of checking inner calculation.
Usage
rho(X,Y)
Arguments
X |
An observed data vector from the first condition. |
Y |
An observed data vector from the second condition. |
Details
Pearson's correlation for two vector observations.
Value
rho |
A calculated Pearson's correlation value. |
References
Lim, J., Yu, D., Kuo, H., Choi, H., and Walmsely, S. (2019). Truncated Rank Correlation as a robust measure of test-retest reliability in mass spectrometry data. Statistical Applications in Genetics and Molecular Biology, 18(4).
Examples
p = 100
sig_z = 1.15
sig_e = 1
mu_z = 2
mu_e = 8
m0 = 30
S1 = rnorm(p,mean=mu_e,sd=sig_e)
S2 = rnorm(p,mean=mu_e,sd=sig_e)
if(m0!=0)
{
X = mu_z + rnorm(m0,mean=0,sd=sig_z)
indx = 1:p
s_indx = sort(sample(indx,m0))
S1[s_indx] = S1[s_indx] + X
S2[s_indx] = S2[s_indx] + X
}
S1 = exp(S1)
S2 = exp(S2)
# Pearson's correlation
pcor = rho(S1,S2)
pcor
Procedure for calculating p-values
Description
Procedure for calculating p-values of Pearson's rho, Kendall's tau, TRC tau for two-sided test for the null hypothesis correaltion is equal to 0 based on the estimated null distribution by permutation.
Usage
trc_cor_test(X,Y, nperm=10000,start=3,range_m=0.8, span=0.5, seed=21, m0=NULL)
Arguments
X |
An observed data vector from the first condition. |
Y |
An observed data vector from the second condition. |
nperm |
the number of permutations to estimate the null distribution (default: 10000). |
start |
A lower bound of a search region for the threshold rank m (default: 3). |
range_m |
A proportion of length of X for specifying the end of the search region for m (default: 0.8). |
span |
A parameter alpha which controls the degree of smoothing in loess function (default: 0.5). |
seed |
An initial seed for the permutation (default: 21). |
m0 |
a specific m value for p-value of the TRC tau with m (defalut: NULL (not reported)). |
Details
The p-values are caculated based on the estimated null distributions of the TRC tau with a given m value, the Kendall's tau, and Pearson's correlation with the permuted samples, respectively.
Value
measure |
a vector of calculated Pearson's rho, Kendall's tau, and TRC tau with m chosen by the proposed rule if m0 = NULL; a vector of calculated Pearson's rho, Kendall's tau, TRC tau with m0, TRC tau with m chosen by the proposed rule if m0 is specified. |
p_val |
a vector of p-values for Pearson's rho, Kendall's tau, and TRC tau with m chosen by the proposed rule if m0 = NULL; a vector of p-values for Pearson's rho, Kendall's tau, TRC tau with m0, TRC tau with m chosen by the proposed rule if m0 is specified. |
chs_m |
the chosen m value by the proposed procedure. |
mean_perm_trc |
a mean value of the estimated null distribution of TRC tau by permutation. |
References
Lim, J., Yu, D., Kuo, H., Choi, H., and Walmsely, S. (2019). Truncated Rank Correlation as a robust measure of test-retest reliability in mass spectrometry data. Statistical Applications in Genetics and Molecular Biology, 18(4).
Examples
p = 100
sig_z = 1.15
sig_e = 1
mu_z = 2
mu_e = 8
m0 = 30
S1 = rnorm(p,mean=mu_e,sd=sig_e)
S2 = rnorm(p,mean=mu_e,sd=sig_e)
if(m0!=0)
{
X = mu_z + rnorm(m0,mean=0,sd=sig_z)
indx = 1:p
s_indx = sort(sample(indx,m0))
S1[s_indx] = S1[s_indx] + X
S2[s_indx] = S2[s_indx] + X
}
S1 = exp(S1)
S2 = exp(S2)
trc_cor_test(S1,S2, nperm=1000,start=3,range_m=0.8, span=0.2, seed=21, m0=NULL)
Procedure for the choice of m for the TRC tau
Description
Procedure for the choice of m for the TRC tau.
Usage
trc_m_search(X,Y,start=3,range_m=0.8,span=0.3)
Arguments
X |
An observed data vector from the first condition. |
Y |
An observed data vector from the second condition. |
start |
A lower bound of a search region for the threshold rank m (default: 3). |
range_m |
A proportion of length of X for specifying the end of the search region for m (default: 0.8). |
span |
A parameter alpha which controls the degree of smoothing in loess function. |
Details
The thresholding rank m is chosen by the proposed procedure in Lim et al. (2019).
Value
tau |
A calculated TRC tau value with the chosen m value (chs_m). |
chs_m |
the chosen m value. |
km_tau_vec |
A vector of calculated k_m * TRC tau values for the given values of m [start, floor(range_m*n)] |
km_tau_loess |
A fitted values by the local regression with loess function for km_tau_vec . |
References
Lim, J., Yu, D., Kuo, H., Choi, H., and Walmsely, S. (2019). Truncated Rank Correlation as a robust measure of test-retest reliability in mass spectrometry data. Statistical Applications in Genetics and Molecular Biology, 18(4).
Examples
p = 100
sig_z = 1.15
sig_e = 1
mu_z = 2
mu_e = 8
m0 = 30
S1 = rnorm(p,mean=mu_e,sd=sig_e)
S2 = rnorm(p,mean=mu_e,sd=sig_e)
if(m0!=0)
{
X = mu_z + rnorm(m0,mean=0,sd=sig_z)
indx = 1:p
s_indx = sort(sample(indx,m0))
S1[s_indx] = S1[s_indx] + X
S2[s_indx] = S2[s_indx] + X
}
S1 = exp(S1)
S2 = exp(S2)
# tau_m
trc_res = trc_m_search(S1,S2,start=3,range_m=0.8,span=0.2)
trc_res$tau
trc_res$chs_m
Trucated Rank Correlation
Description
TRC tau is a robust corrleation measure based on the truncated rank values.
Usage
trc_tau(X,Y,m=5)
Arguments
X |
An observed data vector from the first condition. |
Y |
An observed data vector from the second condition. |
m |
A rank threshold for the calculation of TRC tau. |
Details
Given a rank threshold m, trc_tau calculates the TRC tau value.
Value
tau |
A calculated TRC tau value. |
References
Lim, J., Yu, D., Kuo, H., Choi, H., and Walmsely, S. (2019). Truncated Rank Correlation as a robust measure of test-retest reliability in mass spectrometry data. Statistical Applications in Genetics and Molecular Biology, 18(4).
Examples
p = 100
sig_z = 1.15
sig_e = 1
mu_z = 2
mu_e = 8
m0 = 30
S1 = rnorm(p,mean=mu_e,sd=sig_e)
S2 = rnorm(p,mean=mu_e,sd=sig_e)
if(m0!=0)
{
X = mu_z + rnorm(m0,mean=0,sd=sig_z)
indx = 1:p
s_indx = sort(sample(indx,m0))
S1[s_indx] = S1[s_indx] + X
S2[s_indx] = S2[s_indx] + X
}
S1 = exp(S1)
S2 = exp(S2)
tau0 = trc_tau(S1,S2,m=m0)
tau0