| Title: | Implementation of Marker-Assisted Mini-Pooling with Algorithm |
|---|---|
| Description: | To determine the number of quantitative assays needed for a sample of data using pooled testing methods, which include mini-pooling (MP), MP with algorithm (MPA), and marker-assisted MPA (mMPA). To estimate the number of assays needed, the package also provides a tool to conduct Monte Carlo (MC) to simulate different orders in which the sample would be collected to form pools. Using MC avoids the dependence of the estimated number of assays on any specific ordering of the samples to form pools. |
| Authors: | "Tao Liu, PhD <[email protected]> [aut, cre]" "Yizhen Xu, ScM <[email protected]> [aut]" |
| Maintainer: | "Tao Liu, PhD" <[email protected]> |
| License: | MIT + file LICENSE |
| Version: | 1.2.0 |
| Built: | 2024-10-31 20:39:25 UTC |
| Source: | CRAN |
Function minipool(...) calculates the number of assays required, when
using mini-pooling, for
pools that are formed following the order that individual samples appear in the data.
minipool(v, K = 5, vf_cut = 1000, lod = 0, msg = T)minipool(v, K = 5, vf_cut = 1000, lod = 0, msg = T)
v |
A vector of non-negative numerical assay results. |
K |
Pool size; default is |
vf_cut |
Cutoff value for defining positive cases;
default is |
lod |
A vector of lower limits of detection or a scalar if the limits are the
same; default is |
msg |
Message generated during calculation; default is |
Suppose that N samples are collected for pooled testing. The first
K samples are combined to
form a pool, the next K samples are combined to form the second
pool, and so on. If the number of samples for the last pool is less than
K, these remaining samples are not used to form a pool (i.e.
not included in the calculation). Therefore, a total of
N%/%K pools are formed. The function calculates the number of
assays needed for each of these pools. For mini-pooling, if a pool is
negative, no further tests are needed and all samples in the pool
are concluded as being negative; so the total number of
assays required is one. Otherwise if the pool is tested positive, all
individual samples in the pool are tested and the total number of assays
required is (K + 1).
A vectorof length N%/%K for the numbers of assays needed for all pools
that are formed.
Dorfman R. The detection of defective members of large populations. The Annals of Mathematical Statistics. 1943;14(4):436-440.
Liu T, Hogan JW, Daniels, MJ, Coetzer M, Xu Y, Bove G, et al. Improved HIV-1 Viral Load Monitoring Capacity Using Pooled Testing with Marker-Assisted Deconvolution. Journal of AIDS. 2017;75(5): 580-587.
K=5; n = 50; n.pool = n/K; n.pool # [1] 10 set.seed(100) pvl = rgamma(n, shape = 2.8, scale = 150) minipool(pvl) # A total of 10 pools are formed. # The numbers of assays required by these pools are: # [1] 6 6 6 6 6 6 6 6 6 6K=5; n = 50; n.pool = n/K; n.pool # [1] 10 set.seed(100) pvl = rgamma(n, shape = 2.8, scale = 150) minipool(pvl) # A total of 10 pools are formed. # The numbers of assays required by these pools are: # [1] 6 6 6 6 6 6 6 6 6 6
Function mmpa(...) calculates the number of assays required, when
using mMPA, for
pools that are formed following the order of individual samples in the data.
mmpa(v, s, K = 5, vf_cut = 1000, lod = 0, msg = T)mmpa(v, s, K = 5, vf_cut = 1000, lod = 0, msg = T)
v |
A vector of non-negative numerical assay results. |
s |
A vector of risk scores; |
K |
Pool size; default is |
vf_cut |
Cutoff value for defining positive cases;
default is |
lod |
A vector of lower limits of detection or a scalar if the limits are the
same; default is |
msg |
Message generated during calculation; default is |
For a given sample (v_i, s_i), i = 1, ..., N, the first K samples are combined to
form a pool, the next K samples are combined to form the second
pool, and so on. If the number of samples for the last pool is less than
K, these remaining samples are not used to form a pool (i.e.
not included
in the calculation) . Therefore, a total of
N%/%K pools are formed. The function calculates the number of
assays needed for each of these pools.
A vectorof length N%/%K for the numbers of assays needed for all pools
that are formed.
Liu T, Hogan JW, Daniels, MJ, Coetzer M, Xu Y, Bove G, et al. Improved HIV-1 Viral Load Monitoring Capacity Using Pooled Testing with Marker-Assisted Deconvolution. Journal of AIDS. 2017;75(5): 580-587.
Bilder CR, Tebbs JM, Chen P. Informative retesting. Journal of the American Statistical Association. 2010;105(491):942-955.
May S, Gamst A, Haubrich R, Benson C, Smith DM. Pooled nucleic acid testing to identify antiretroviral treatment failure during HIV infection. Journal of Acquired Immune Deficiency Syndromes. 2010;53(2):194-201.
K=5; n = 50; n.pool = n/K; n.pool # [1] 10 set.seed(100) pvl = rgamma(n, shape = 2.8, scale = 150) riskscore = (rank(pvl)/n) * 0.5 + runif(n) * 0.5 mmpa(v = pvl, s = riskscore) # A total of 10 pools are formed. # The numbers of assays required by these pools are: # [1] 3 3 4 4 2 3 3 4 3 3K=5; n = 50; n.pool = n/K; n.pool # [1] 10 set.seed(100) pvl = rgamma(n, shape = 2.8, scale = 150) riskscore = (rank(pvl)/n) * 0.5 + runif(n) * 0.5 mmpa(v = pvl, s = riskscore) # A total of 10 pools are formed. # The numbers of assays required by these pools are: # [1] 3 3 4 4 2 3 3 4 3 3
Function mpa(...) calculates the number of assays required, when using MPA, for
pools that are formed following the order of individual samples in the data.
mpa(v, K = 5, vf_cut = 1000, lod = 0, msg = T)mpa(v, K = 5, vf_cut = 1000, lod = 0, msg = T)
v |
A vector of non-negative numerical assay results. |
K |
Pool size; default is |
vf_cut |
Cutoff value for defining positive cases;
default is |
lod |
A vector of lower limits of detection or a scalar if the limits are the
same; default is |
msg |
Message generated during calculation; default is |
For a given sample v_i, i = 1, ..., N, the first K samples v_1,
..., v_5 are combined to form a pool, the next K samples v_6, ...,
v_10 are combined to form the second
pool, and so on. If the number of samples for the last pool is less than
K, these remaining samples are not used to form a pool (i.e.
not included in the calculation) . Therefore, a total of
N%/%K pools are formed. The function calculates the number of
assays needed for each of these pools. See May et al (2010).
A vectorof length N%/%K for the numbers of assays needed for all pools
that are formed.
May, S., Gamst, A., Haubrich, R., Benson, C., & Smith, D. M. (2010). Pooled nucleic acid testing to identify antiretroviral treatment failure during HIV infection. Journal of acquired immune deficiency syndromes (1999), 53(2), 194.
Liu T, Hogan JW, Daniels, MJ, Coetzer M, Xu Y, Bove G, et al. Improved HIV-1 Viral Load Monitoring Capacity Using Pooled Testing with Marker-Assisted Deconvolution. Journal of AIDS. 2017;75(5): 580-587.
K=5; n = 50; n.pool = n/K; n.pool # [1] 10 set.seed(100) pvl = rgamma(n, shape = 2.8, scale = 150) mpa(v = pvl) # A total of 10 pools are formed. # The numbers of assays required by these pools are: # [1] 3 3 4 4 2 5 4 4 4 4K=5; n = 50; n.pool = n/K; n.pool # [1] 10 set.seed(100) pvl = rgamma(n, shape = 2.8, scale = 150) mpa(v = pvl) # A total of 10 pools are formed. # The numbers of assays required by these pools are: # [1] 3 3 4 4 2 5 4 4 4 4
This function uses Monte Carlo (MC) to simulate different orders in
which the samples would be collected to form pools. Unlike the
function minipool, mpa, and mmpa that calculate
the number of assays
needed for pools that are formed following the exact order
of the samples that are listed in the data, this function
pooling_mc permutes the data many (perm_num) times
so as to estimate the average number of
assays required (ATR) per individual. Using MC avoids the dependence
on any
specific ordering of forming pools.
pooling_mc(v, s = NULL, K = 5, vf_cut = 1000, lod = 0, method = "mmpa", perm_num = 100, msg = F)pooling_mc(v, s = NULL, K = 5, vf_cut = 1000, lod = 0, method = "mmpa", perm_num = 100, msg = F)
v |
A vector of non-negative numerical assay results. |
s |
A vector of risk scores; |
K |
Pool size; default is |
vf_cut |
Cutoff value for defining positive cases;
default is |
lod |
A vector of lower limits of detection or a scalar if the limits are the
same; default is |
method |
Method that is used for pooled testing; must be one of |
perm_num |
The number of permutation to be used for the calculation;
default is |
msg |
Message generated during calculation; default is |
The outcome is a matrix of dimension num_pool by perm_num.
The row number is the number of pools (num_pool) from each permutation
of the data, which is
determined by the sample size N and pool size K; num_pool
= N%/%K. The column number is the number of
permutations (num_pool).
Liu T, Hogan JW, Daniels, MJ, Coetzer M, Xu Y, Bove G, et al. Improved HIV-1 Viral Load Monitoring Capacity Using Pooled Testing with Marker-Assisted Deconvolution. Journal of AIDS. 2017;75(5): 580-587.
Bilder CR, Tebbs JM, Chen P. Informative retesting. Journal of the American Statistical Association. 2010;105(491):942-955.
May S, Gamst A, Haubrich R, Benson C, Smith DM. Pooled nucleic acid testing to identify antiretroviral treatment failure during HIV infection. Journal of Acquired Immune Deficiency Syndromes. 2010;53(2):194-201.
Dorfman R. The detection of defective members of large populations. The Annals of Mathematical Statistics. 1943;14(4):436-440.
### sample size = 300 n = 300; set.seed(100) pvl = rgamma(n, shape = 2.8, scale = 150) summary(pvl) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 53 225 392 424 564 1373 riskscore = (rank(pvl)/n) * 0.5 + runif(n) * 0.5 cor(pvl, riskscore, method = "spearman") # [1] 0.69 ### Pool size K is set to 5 K=5; ### so, the number of pools = 60 n.pool = n/K; n.pool # [1] 60 foo = pooling_mc(pvl, riskscore, perm_num = 100) ### Average number of assays needed per pool for each of the 100 ### permutations of the data apply(foo, 2, mean) # [1] 3.43 3.33 3.35 3.47 3.37 3.33 3.37 3.27 3.43 3.28 3.32 3.35 3.35 3.37 # [15] 3.38 3.37 3.30 3.43 3.28 3.38 3.42 3.35 3.35 3.48 3.30 3.47 3.40 3.35 # [29] 3.25 3.30 3.38 3.43 3.25 3.45 3.35 3.33 3.42 3.38 3.40 3.33 3.32 3.38 # [43] 3.33 3.37 3.37 3.33 3.35 3.38 3.38 3.30 3.30 3.33 3.37 3.32 3.30 3.40 # [57] 3.37 3.42 3.30 3.37 3.38 3.32 3.45 3.38 3.37 3.50 3.33 3.40 3.28 3.37 # [71] 3.23 3.33 3.23 3.42 3.32 3.32 3.45 3.35 3.32 3.32 3.33 3.33 3.30 3.38 # [85] 3.37 3.33 3.33 3.20 3.37 3.33 3.30 3.40 3.40 3.32 3.33 3.37 3.40 3.38 # [99] 3.30 3.33 ### Estimated average number of assays needed per pool mean(foo) # 3.35 ### Estimated average number of assays needed per individual mean(foo)/K # [1] 0.67### sample size = 300 n = 300; set.seed(100) pvl = rgamma(n, shape = 2.8, scale = 150) summary(pvl) # Min. 1st Qu. Median Mean 3rd Qu. Max. # 53 225 392 424 564 1373 riskscore = (rank(pvl)/n) * 0.5 + runif(n) * 0.5 cor(pvl, riskscore, method = "spearman") # [1] 0.69 ### Pool size K is set to 5 K=5; ### so, the number of pools = 60 n.pool = n/K; n.pool # [1] 60 foo = pooling_mc(pvl, riskscore, perm_num = 100) ### Average number of assays needed per pool for each of the 100 ### permutations of the data apply(foo, 2, mean) # [1] 3.43 3.33 3.35 3.47 3.37 3.33 3.37 3.27 3.43 3.28 3.32 3.35 3.35 3.37 # [15] 3.38 3.37 3.30 3.43 3.28 3.38 3.42 3.35 3.35 3.48 3.30 3.47 3.40 3.35 # [29] 3.25 3.30 3.38 3.43 3.25 3.45 3.35 3.33 3.42 3.38 3.40 3.33 3.32 3.38 # [43] 3.33 3.37 3.37 3.33 3.35 3.38 3.38 3.30 3.30 3.33 3.37 3.32 3.30 3.40 # [57] 3.37 3.42 3.30 3.37 3.38 3.32 3.45 3.38 3.37 3.50 3.33 3.40 3.28 3.37 # [71] 3.23 3.33 3.23 3.42 3.32 3.32 3.45 3.35 3.32 3.32 3.33 3.33 3.30 3.38 # [85] 3.37 3.33 3.33 3.20 3.37 3.33 3.30 3.40 3.40 3.32 3.33 3.37 3.40 3.38 # [99] 3.30 3.33 ### Estimated average number of assays needed per pool mean(foo) # 3.35 ### Estimated average number of assays needed per individual mean(foo)/K # [1] 0.67