| Type: | Package |
| Title: | A Network Tool to Dissect Spatial Community Ecology |
| Version: | 0.1.2 |
| Author: | Ang Dong |
| Maintainer: | Ang Dong <fantasys05227@gmail.com> |
| Description: | Most existing approaches for network reconstruction can only infer an overall network and, also, fail to capture a complete set of network properties. To address these issues, a new model has been developed, which converts static data into their 'dynamic' form. 'idopNetwork' is an 'R' interface to this model, it can inferring informative, dynamic, omnidirectional and personalized networks. For more information on functional clustering part, see Kim et al. (2008) <doi:10.1534/genetics.108.093690>, Wang et al. (2011) <doi:10.1093/bib/bbr032>. For more information on our model, see Chen et al. (2019) <doi:10.1038/s41540-019-0116-1>, and Cao et al. (2022) <doi:10.1080/19490976.2022.2106103>. |
| License: | GPL (≥ 3) |
| Imports: | grDevices, stats, mvtnorm, orthopolynom, parallel, deSolve, ggplot2, reshape2, glmnet, igraph, scales, patchwork |
| Encoding: | UTF-8 |
| LazyData: | true |
| Depends: | R (≥ 3.50) |
| RoxygenNote: | 7.2.3 |
| Suggests: | covr, knitr, rmarkdown |
| VignetteBuilder: | knitr |
| URL: | https://github.com/cxzdsa2332/idopNetwork |
| BugReports: | https://github.com/cxzdsa2332/idopNetwork/issues |
| NeedsCompilation: | no |
| Packaged: | 2023-04-18 06:29:49 UTC; ddaa |
| Repository: | CRAN |
| Date/Publication: | 2023-04-18 06:50:02 UTC |
Q-function to replace log-likelihood function
Description
Q-function to replace log-likelihood function
Usage
Q_function(par, prob_log, omega_log, X, k, times)
Arguments
par |
numeric vector for parameters need to be estimated |
prob_log |
mixture component weights(log) |
omega_log |
latent variables(log) |
X |
matrix for cluster |
k |
vector for the cluster number |
times |
vector for the x values or time points |
Value
the Loglikelihood value
Q-function to replace log-likelihood function
Description
Q-function to replace log-likelihood function
Usage
biQ_function(par, prob_log, omega_log, X, k, n1, n2, times1, times2)
Arguments
par |
numeric vector for parameters need to be estimated |
prob_log |
mixture component weights(log) |
omega_log |
latent variables(log) |
X |
matrix for cluster |
k |
vector for the cluster number |
n1 |
scalar for number of column contain first trait/location etc |
n2 |
scalar for number of column contain second trait/location etc |
times1 |
vector for the x values or time points |
times2 |
vector for the x values or time points |
Value
the Loglikelihood value
main function for bifunctional clustering
Description
main function for bifunctional clustering
Usage
bifun_clu(
data1,
data2,
k,
Time1 = NULL,
Time2 = NULL,
trans = log10,
inv.cov = NULL,
initial.pars = NULL,
iter.max = 100,
parscale = 0.001
)
Arguments
data1 |
matrix or data for cluster |
data2 |
matrix or data for cluster |
k |
vector for the cluster number |
Time1 |
vector for the time point |
Time2 |
vector for the time point |
trans |
indicate log/log2/log10 transform dataset |
inv.cov |
matrix for directly solve cov matrix, default not given(currently not available) |
initial.pars |
vector for manual give initial parameters, default not given |
iter.max |
scales control iteration for EM algorithm |
parscale |
scales control parameters scales for cov pars |
Value
the initial parameters for functional clustering
convert result of bifunctional clustering result
Description
convert result of bifunctional clustering result
Usage
bifun_clu_convert(result, best.k)
Arguments
result |
list directly from bifun_clu_parallel function |
best.k |
scale of BIC-determined cluster number |
Value
list contain module data and fitted data
parallel version for functional clustering
Description
parallel version for functional clustering
Usage
bifun_clu_parallel(
data1,
data2,
Time1 = NULL,
Time2 = NULL,
trans = log10,
start,
end,
iter.max = 100,
thread = 2
)
Arguments
data1 |
data for cluster |
data2 |
data for cluster |
Time1 |
vector for the time point |
Time2 |
vector for the time point |
trans |
indicate log/log2/log10 transform dataset |
start |
vector for the minimum cluster number |
end |
vector for the maximum cluster number |
iter.max |
scales control iteration for EM algorithm |
thread |
scales for how many thread used |
Value
the initial parameters for functional clustering
bifunctional clustering plot
Description
bifunctional clustering plot
Usage
bifun_clu_plot(
result,
best.k,
label = 10,
degree = 1/4,
show.legend = FALSE,
color1 = "#38E54D",
color2 = "#FF8787"
)
Arguments
result |
list directly from bifun_clu_parallel function |
best.k |
scale of BIC-determined cluster number |
label |
relabel x and y label due to log-transform, set 10 as default |
degree |
scalar control transparency degree |
show.legend |
show legend or not |
color1 |
Hex Color Codes for first data |
color2 |
Hex Color Codes for second data |
Value
functional clustering plot
acquire initial parameters for functional clustering
Description
acquire initial parameters for functional clustering
Usage
biget_par_int(X, k, times1, times2, n1, n2)
Arguments
X |
matrix for cluster |
k |
vector for the cluster number |
times1 |
vector for the x values or time points |
times2 |
vector for the x values or time points |
n1 |
scalar for number of column contain first trait/location etc |
n2 |
scalar for number of column contain second trait/location etc |
Value
the initial parameters for functional clustering
plot power equation fitting results for bi-variate model
Description
plot power equation fitting results for bi-variate model
Usage
bipower_equation_plot(
result,
label = 10,
n = 9,
show.legend = FALSE,
color1 = "#38E54D",
color2 = "#FF8787"
)
Arguments
result |
list object returned from data_match |
label |
relabel x and y label due to log-transform, set 10 as default |
n |
scales for how many subplots needed |
show.legend |
show legend or not |
color1 |
Hex Color Codes for first data |
color2 |
Hex Color Codes for second data |
Value
plot show power curve fitting result
plot all decompose plot for two data
Description
plot all decompose plot for two data
Usage
biqdODE_plot_all(
result1,
result2,
label = 10,
show.legend = FALSE,
remove.label = TRUE,
nrow = NULL,
ncol = NULL
)
Arguments
result1 |
list of qdODE all for first data |
result2 |
list of qdODE all for second data |
label |
relabel x and y label due to log-transform, set 10 as default |
show.legend |
to show legend |
remove.label |
to remove x and y label |
nrow |
scalar for subplot row number |
ncol |
scalar for subplot column number |
plot single decompose plot for two data
Description
plot single decompose plot for two data
Usage
biqdODE_plot_base(
result1,
result2,
label = 10,
show.legend = FALSE,
remove.label = FALSE
)
Arguments
result1 |
list of qdODE all for first data |
result2 |
list of qdODE all for second data |
label |
relabel x and y label due to log-transform, set 10 as default |
show.legend |
to show legend |
remove.label |
to remove x and y label |
make color more dark
Description
make color more dark
Usage
darken(color, factor = 1.2)
Arguments
color |
hex color code |
factor |
scalar for darken level |
Value
darkened hex color code
Examples
darken("#FF0000")
remove observation with too many 0 values
Description
remove observation with too many 0 values
Usage
data_cleaning(data, x = round(ncol(data) * 0.3))
Arguments
data |
dataframe of imported dataset, must have first column as ID |
x |
scales indicate how many 0 to remove |
Value
a dataframe without too many 0 observations
Examples
data_cleaning(matrix(c(c(0,1,1,0,0,1,1), c(2,1,0,3,5,2,2), c(1,1,3,2,4,5,1)), 3, 7), 2)
match power_equation fit result for bi-variate model
Description
match power_equation fit result for bi-variate model
Usage
data_match(result1, result2)
Arguments
result1 |
list object from power_equation fit |
result2 |
list object from power_equation fit |
Value
a id match list for input dataset
main function for functional clustering
Description
main function for functional clustering
Usage
fun_clu(
data,
k,
Time = NULL,
trans = log10,
inv.cov = NULL,
initial.pars = NULL,
iter.max = 100,
parscale = 0.1
)
Arguments
data |
matrix or data for cluster |
k |
vector for the cluster number |
Time |
vector for the time point |
trans |
indicate log/log2/log10 transform dataset |
inv.cov |
matrix for directly solve cov matrix, default not given(currently not available) |
initial.pars |
vector for manual give initial parameters, default not given |
iter.max |
scales control iteration for EM algorithm |
parscale |
scales control parameters scales for cov pars |
Value
the initial parameters for functional clustering
plot BIC results for functional clustering
Description
plot BIC results for functional clustering
Usage
fun_clu_BIC(result, crit = "BIC", title = NULL)
Arguments
result |
list directly from fun_clu_parallel function |
crit |
either BIC or AIC for module selection |
title |
title for the plot |
Value
the BIC plot
convert result of functional clustering result
Description
convert result of functional clustering result
Usage
fun_clu_convert(result, best.k)
Arguments
result |
list directly from fun_clu_parallel function |
best.k |
scale of BIC-determined cluster number |
Value
list contain module data and fitted data
parallel version for functional clustering
Description
parallel version for functional clustering
Usage
fun_clu_parallel(
data,
Time = NULL,
trans = log10,
start,
end,
iter.max = 100,
thread = 2
)
Arguments
data |
data for cluster |
Time |
vector for the time point |
trans |
indicate log/log2/log10 transform dataset |
start |
vector for the minimum cluster number |
end |
vector for the maximum cluster number |
iter.max |
scales control iteration for EM algorithm |
thread |
scales for how many threads used |
Value
the initial parameters for functional clustering
functional clustering plot
Description
functional clustering plot
Usage
fun_clu_plot(result, best.k, label = 10, degree = 1)
Arguments
result |
list directly from fun_clu_parallel function |
best.k |
scalar of BIC-determined cluster number |
label |
relabel x and y label due to log-transform, set 10 as default |
degree |
scalar control transparency degree |
Value
functional clustering plot
select result of functional clustering result
Description
select result of functional clustering result
Usage
fun_clu_select(result_fit, result_funclu, i)
Arguments
result_fit |
list directly from power_equation_fit |
result_funclu |
list from fun_clu_convert |
i |
scale of which cluster selected |
Value
list contain microbe data and fitted data
generate standard SAD1 covariance matrix
Description
generate standard SAD1 covariance matrix
Usage
get_SAD1_covmatrix(par, n)
Arguments
par |
vector with two number for SAD1 covariance matrix |
n |
scalar indicate length of time d |
Value
SAD1 covariance matrix
Examples
get_SAD1_covmatrix(par = c(2,0.5), n = 14)
generate biSAD1 covariance matrix
Description
generate biSAD1 covariance matrix
Usage
get_biSAD1(par, n1, n2)
Arguments
par |
vector with four number, first two for ck and the rest for stress |
n1 |
scalar indicate length of time1 |
n2 |
scalar indicate length of time2 |
Value
biSAD1 covariance matrix
Examples
get_biSAD1(par=c(2,0.5,2,0.1),n1=4, n2 = 5)
Lasso-based variable selection
Description
Lasso-based variable selection
Usage
get_interaction(data, col, reduction = FALSE)
Arguments
data |
data of clustered results, do not contain cluster column |
col |
scalar of which row number selected |
reduction |
use n/log(n) dimension reduction |
Value
list contain relationship of each row
generate legendre matrix
Description
generate legendre matrix
Usage
get_legendre_matrix(x, legendre_order)
Arguments
x |
vector equal to the x value for legendre polynomials(in this case times) |
legendre_order |
the order of legendre polynomials |
Value
the polynomials value of each order
Examples
get_legendre_matrix(1:14,4)
use legendre polynomials to fit a given data
Description
use legendre polynomials to fit a given data
Usage
get_legendre_par(y, legendre_order, x)
Arguments
y |
vector equal to the y observed data(in this case generic effect) |
legendre_order |
scalar of legendre polynomials |
x |
vector equal to the x value for legendre polynomials(in this case times) |
Value
the polynomials coefficients
Examples
get_legendre_par(14:1,4,1:14)
curve fit with modified logistic function
Description
curve fit with modified logistic function
Usage
get_mu(mu_par, times)
Arguments
mu_par |
vector with five number |
times |
vector of time point |
Value
numeric vector with the same length to times
Examples
get_mu(mu_par = 1:5, times = 1:14)
generate mean vectors with ck and stress condition
Description
generate mean vectors with ck and stress condition
Usage
get_mu2(par, times)
Arguments
par |
vector with ten number, first five for ck and the rest for stress |
times |
vector of time point |
Value
numeric vector with the double length to times
Examples
get_mu2(par = 1:10, times = 1:14)
acquire initial parameters for functional clustering
Description
acquire initial parameters for functional clustering
Usage
get_par_int(X, k, times)
Arguments
X |
matrix for cluster |
k |
vector for the cluster number |
times |
vector for the x values or time points |
Value
the initial parameters for functional clustering
gut microbe OTU data (species level)
Description
The original nucleotide sequences of this study were deposited to the NCBI Sequence Read Archive under accession number SRP128619.
Usage
data(gut_microbe)
Format
A data frame with 65 rows and 21 column, contain first column as microbe ID:
generate curve based on legendre polynomials
Description
generate curve based on legendre polynomials
Usage
legendre_fit(par, x)
Arguments
par |
vector of legendre polynomials coefficients |
x |
vector equal to the x value for legendre polynomials(in this case times) |
Value
the polynomials value
Examples
legendre_fit(rep(1,5),1:14)
calculate log-sum-exp values
Description
calculate log-sum-exp values
Usage
logsumexp(v)
Arguments
v |
numeric vector |
Value
log-sum-exp values
Examples
logsumexp(c(100, 1000, 10000))
mustard microbe OTU data
Description
Wagner, M. R. et al. Host genotype and age shape the leaf and root microbiomes of a wild perennial plant. Nat. Commun. 7:12151 doi: 10.1038/ncomms12151 (2016) This dataset is a subset of otuTable97, we select location = JAM, keep samples with both root and leaf data, and then run data_cleaning first (set x = 50) to reduce size of this data. Moreover, sample 8_1382 is removed for the outlier reason.
Usage
data(mustard_microbe)
Format
A data frame with 1557 rows and 176 column, contain first column as OTU ID:
convert ODE results(ODE_solving2) to basic network plot table
Description
convert ODE results(ODE_solving2) to basic network plot table
Usage
network_conversion(result)
Arguments
result |
list result from qsODE_parallel |
Value
a list with basic information to plot network
convert ODE results(ODE_solving2) to basic network plot table
Description
convert ODE results(ODE_solving2) to basic network plot table
Usage
network_maxeffect(result)
Arguments
result |
list result from qsODE_parallel |
Value
a list with basic information to plot network
generate network plot
Description
generate network plot
Usage
network_plot(result, title = NULL, maxeffect = NULL, type = NULL)
Arguments
result |
list result from network_conversion |
title |
text for plot title |
maxeffect |
control edge size when compare networks |
type |
select module effect or microbe effect |
Value
network plot
min-max normalization
Description
min-max normalization
Usage
normalization(x, z = 0.2)
Arguments
x |
numeric vector |
z |
scalar add minimum value to avoid 0 |
Value
normalized vector
Examples
normalization(runif(100,min = -100, max = 100))
use power equation parameters to generate y values
Description
use power equation parameters to generate y values
Usage
power_equation(x, power_par)
Arguments
x |
vector for x values |
power_par |
matrix contain parameters for power equation |
Value
y values for given power equation parameters
Examples
power_equation(c(1,2,3,5,7), matrix(c(2,1,1,2),2,2))
use power equation to fit observed values
Description
use power equation to fit observed values
Usage
power_equation_all(x, y, maxit = 100)
Arguments
x |
vector for x values |
y |
vector for y values |
maxit |
numeric value for maximum initial pars try |
Value
nls model
Examples
power_equation_all(c(1,2,3,5,7), c(5,10,15,17,20))
use power equation to fit observed values
Description
use power equation to fit observed values
Usage
power_equation_base(x, y)
Arguments
x |
vector for x values |
y |
vector for y valyes |
Value
nls model
Examples
power_equation_base(c(1,2,3,5,7), c(5,10,15,17,20))
use power equation to fit given dataset
Description
use power equation to fit given dataset
Usage
power_equation_fit(data, n = 30, trans = log10, thread = 2)
Arguments
data |
cleaned dataframe |
n |
scales for how many interpolation needed |
trans |
indicate log/log2/log10 transform dataset |
thread |
scales for how many thread used |
Value
list contain power equation parameters and fitted data
plot power equation fitting results
Description
plot power equation fitting results
Usage
power_equation_plot(result, label = 10, n = 9)
Arguments
result |
list object returned from power_equation_fit |
label |
relabel x and y label due to log-transform, set 10 as default |
n |
scales for how many subplots needed |
Value
plot show power curve fitting result
wrapper for qdODE model
Description
wrapper for qdODE model
Usage
qdODE_all(
result,
relationship,
i,
init_pars = 1,
LOP_order = 6,
method = "ls",
new_time = NULL,
n_expand = 100,
maxit = 1000
)
Arguments
result |
result from power_equation_fit |
relationship |
list contain variable selection results |
i |
scalar for which id used for qdODE solving, must <= nrow |
init_pars |
scalar for initial parameters |
LOP_order |
scalar of LOP order |
method |
scalar of qdODE solving methodm, cuurent only support least square |
new_time |
vector produce new defined time point |
n_expand |
scalar for how many interpolation needed |
maxit |
scalar of Optim iteration setting |
Value
list contain variable selection results and LOP parameters for every row
legendre polynomials fit to qdODE model
Description
legendre polynomials fit to qdODE model
Usage
qdODE_fit(
pars,
data,
Time,
power_par,
LOP_order = 6,
new_time = NULL,
n_expand = 100
)
Arguments
pars |
vector of qdODE parameters |
data |
dataframe of observed data |
Time |
vector of time point |
power_par |
matrix of power equation parameters for dependent effect |
LOP_order |
scalar of LOP order |
new_time |
vector produce new defined time point |
n_expand |
scalar for how many interpolation needed |
Value
list contain legendre polynomials parameters, qdODE values and LOP fitted values
least-square fit for qdODE model
Description
least-square fit for qdODE model
Usage
qdODE_ls(pars, data, Time, power_par)
Arguments
pars |
vector for unknown ODE parameters |
data |
data contain independent effect as first row and dependent effect |
Time |
vector of time point |
power_par |
matrix of power equation parameters for dependent effect |
Value
mean-square error
wrapper for qdODE_all in parallel version
Description
wrapper for qdODE_all in parallel version
Usage
qdODE_parallel(result, reduction = FALSE, thread = 2, maxit = 1000)
Arguments
result |
result from power_equation_fit |
reduction |
use n/log(n) dimension reduction |
thread |
scales for how many threads used |
maxit |
scalar of Optim iteration setting |
Value
list contain variable selection results and LOP parameters for every row
plot all decompose plot
Description
plot all decompose plot
Usage
qdODE_plot_all(
result,
label = 10,
show.legend = TRUE,
nrow = NULL,
ncol = NULL
)
Arguments
result |
list of qdODE parallel |
label |
relabel x and y label due to log-transform, set 10 as default |
show.legend |
to show legend |
nrow |
scalar for subplot row number |
ncol |
scalar for subplot column number |
Value
all effect curve decompose plot
plot single decompose plot
Description
plot single decompose plot
Usage
qdODE_plot_base(result, label = 10, show.legend = TRUE)
Arguments
result |
list of qdODE all |
label |
relabel x and y label due to log-transform, set 10 as default |
show.legend |
to show legend |
quasi-dynamic lotka volterra model
Description
quasi-dynamic lotka volterra model
Usage
qdODEmod(Time, State, Pars, power_par)
Arguments
Time |
vector of time point |
State |
vector of ODE initial state |
Pars |
vector for unknown ODE parameters |
power_par |
matrix of power equation parameters for dependent effect |
Value
list used in ode function
convert qdODE results to plot data
Description
convert qdODE results to plot data
Usage
qdODEplot_convert(result)
Arguments
result |
list of qdODE all |