| Title: | Rtreemix: Mutagenetic trees mixture models. |
|---|---|
| Description: | Rtreemix is a package that offers an environment for estimating the mutagenetic trees mixture models from cross-sectional data and using them for various predictions. It includes functions for fitting the trees mixture models, likelihood computations, model comparisons, waiting time estimations, stability analysis, etc. |
| Authors: | Jasmina Bogojeska |
| Maintainer: | Jasmina Bogojeska <[email protected]> |
| License: | LGPL |
| Version: | 1.69.0 |
| Built: | 2024-12-03 02:55:32 UTC |
| Source: | https://github.com/bioc/Rtreemix |
This method fits an RtreemixModel to a given dataset and then
analyzes its variance with the bootstrap method. The data and
the number of trees K have to be specified.
bootstrap(data, K, ...)bootstrap(data, K, ...)
data |
An |
K |
An |
... |
|
The function returns an object from the class
RtreemixModel. This is the mixture model learned on the given
data. Besides the edge weights it also contains their confidence
intervals resulting from the bootstrap analysis. Confidence intervals
for the mixture parameters are also comupted and available.
The bootstrap examples are time consuming. They are commented out because of the time restrictions of the check of the package. For trying out the code please copy it and uncomment it.
Jasmina Bogojeska
Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.
RtreemixData-class,
RtreemixModel-class, fit-methods
## Create an RtreemixData object from a randomly generated RtreemixModel object. #rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8)) #data <- sim(model = rand.mod, no.draws = 300) ## Create a RtreemixModel and analyze its variance with the bootstrap method. #mod.boot <- bootstrap(data = data, K = 2, equal.edgeweights = TRUE, B = 10) ## time consuming computation ## See the confidence intervals for the mixture parameters (the weights). #WeightsCI(mod.boot) ## See the confidence intervals of the conditional probabilities assigned to the edges. #edgeData(getTree(mod.boot, 2), attr = "ci")## Create an RtreemixData object from a randomly generated RtreemixModel object. #rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8)) #data <- sim(model = rand.mod, no.draws = 300) ## Create a RtreemixModel and analyze its variance with the bootstrap method. #mod.boot <- bootstrap(data = data, K = 2, equal.edgeweights = TRUE, B = 10) ## time consuming computation ## See the confidence intervals for the mixture parameters (the weights). #WeightsCI(mod.boot) ## See the confidence intervals of the conditional probabilities assigned to the edges. #edgeData(getTree(mod.boot, 2), attr = "ci")
These functions implement a similarity measure for comparing the topologies
of the nontrivial tree components of a specified mixture
model, and thereby quantifying their diversity.
All possible pairs of nontrivial components are considered when
computing the similarity. comp.trees uses the sum of the number of
different edges of all pairs for caracterizing
the difference of the trees in the model. comp.trees.levels uses
the sum of the number of different edges of all pairs and the
corresponding L1 distances of their level vectors. The model must have at
least two nontrivial components.
comp.trees(model) comp.trees.levels(model)comp.trees(model) comp.trees.levels(model)
model |
An |
The functions return a numeric value that quantifies the similarity (or diversity) of the nontrivial tree topologies of a given mixture models.
Jasmina Bogojeska
RtreemixModel-class, comp.models,
fit-methods, stability.sim
## Generate two random RtreemixModel objects each with 3 components. mix1 <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) mix2 <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) ## Inspect the diversity of the nontrivial tree components in a given model ## using the number of distinct edges and the levels of the events in ## the treesas dissimilarity measure. comp.trees.levels(model = mix1) comp.trees.levels(model = mix2)## Generate two random RtreemixModel objects each with 3 components. mix1 <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) mix2 <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) ## Inspect the diversity of the nontrivial tree components in a given model ## using the number of distinct edges and the levels of the events in ## the treesas dissimilarity measure. comp.trees.levels(model = mix1) comp.trees.levels(model = mix2)
The method first calculates the genetic progression score (GPS) for the
patterns in a given dataset data based on a fitted mutagenetic trees
mixture model with K components. The data and K
have to be specified. Then, it derives a 95% confidence intervals for
the GPS values with bootstrap analysis.
confIntGPS(data, K, ...)confIntGPS(data, K, ...)
data |
An |
K |
An |
... |
|
The function returns an object from the RtreemixGPS class that
containes the calculated GPS values, their 95% confidence intervals,
the model used for the computation, the data, and so on (see
RtreemixGPS-class). The GPS values are represented as a
numeric vector with length equal to the number of samples in
data. Their corresponding confidence intervals are given in a
matrix with two columns.
The data for which the GPS values and their corresponding confidence intervals are to be calculated should not have more than 20 genetic events. The reason for this is that the number of all possible patterns for which the GPS values are calculated during a computationally intensive simulations is in this case $2^20$. This demands too much memory. The GPS examples are time consuming. They are commented out because of the time restrictions of the check of the package. For trying out the code please copy it and uncomment it.
Jasmina Bogojeska
RtreemixGPS-class, gps-methods,
RtreemixData-class, RtreemixModel-class,
fit-methods
## Create an RtreemixData object from a randomly generated RtreemixModel object. #rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8)) #data <- sim(model = rand.mod, no.draws = 400) ## Create an RtreemixGPS object by calculating GPS values for a given dataset ## and their 95% confidence intervals using the bootstrap method. #modGPS2 <- confIntGPS(data = data, K = 2, B = 100) ## time consuming computation #show(modGPS2) ## See the GPS values for the object modGPS2 and their confidence intervals. #GPS(modGPS2) #gpsCI(modGPS2) ## See data. #getData(modGPS2)## Create an RtreemixData object from a randomly generated RtreemixModel object. #rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8)) #data <- sim(model = rand.mod, no.draws = 400) ## Create an RtreemixGPS object by calculating GPS values for a given dataset ## and their 95% confidence intervals using the bootstrap method. #modGPS2 <- confIntGPS(data = data, K = 2, B = 100) ## time consuming computation #show(modGPS2) ## See the GPS values for the object modGPS2 and their confidence intervals. #GPS(modGPS2) #gpsCI(modGPS2) ## See data. #getData(modGPS2)
These functions are used for calculating different distances between
two given vectors. Thus, L1.dist calculates the L1 distance,
cosin.dist calculates the cosine distance, euclidian.dist
computes the Euclidian distance, and rank.cor.dist computes
the rank correlation distance. The vectors have to have same length.
When using rank.cor.dist the vectors have to have length larger
than 4.
L1.dist(p, q) cosin.dist(p, q) euclidian.dist(x, y) rank.cor.dist(x, y)L1.dist(p, q) cosin.dist(p, q) euclidian.dist(x, y) rank.cor.dist(x, y)
p |
A |
q |
A |
x |
Same as |
y |
Same as |
The functions return the distance between the two given vectors.
Jasmina Bogojeska
kullback.leibler, L2.norm, stability.sim
## Define two numeric vectors with equal lengths (> 4). x <- c(1, 2, 3, 4, 5) y <- c(5, 6, 7, 8, 9) ## Calculate the L1 distance between the vectors x and y L1.dist(x, y) ## Calculate the Euclidian distance between the vectors x and y euclidian.dist(x, y) ## Calculate the cosine distance between the vectors x and y cosin.dist(x, y) ## Calculate the rank-correlation distance between the vectors x and y rank.cor.dist(x, y)## Define two numeric vectors with equal lengths (> 4). x <- c(1, 2, 3, 4, 5) y <- c(5, 6, 7, 8, 9) ## Calculate the L1 distance between the vectors x and y L1.dist(x, y) ## Calculate the Euclidian distance between the vectors x and y euclidian.dist(x, y) ## Calculate the cosine distance between the vectors x and y cosin.dist(x, y) ## Calculate the rank-correlation distance between the vectors x and y rank.cor.dist(x, y)
These functions generate the probability distribution induced with a
given mutagenetic trees mixture model model on the space of all possible
patterns of genetic events. The model has to be specified. The
sampling mode and the parameters for the sampling times of the observed
input and output probabilities are optional. The number of genetic
events in the model cannot exceed 30.
distribution(model, sampling.mode, sampling.param, output.param)distribution(model, sampling.mode, sampling.param, output.param)
model |
An |
sampling.mode |
A |
sampling.param |
A |
output.param |
A |
The function returns a dataframe of all possible patterns with their
corresponding probabilities derived from the specified trees mixture
model. When the sampling mode and the sampling parameters (input and
output) are specified their values are printed out.
Jasmina Bogojeska
Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.
RtreemixModel-class, fit-methods
## Generate a random RtreemixModel object with 3 components. mod <- generate(K = 3, no.events = 8, noise.tree = TRUE, prob = c(0.2, 0.8)) show(mod) ## See the probability distribution encoded by the model on the set of all possible patterns. distr <- distribution(model = mod) distr ## Get the probabilities. distr$probability ## See the probability distribution encoded by the model on the set of all possible patterns ## calculated for given sampling mode, and corresponding input and output parameters. distr1 <- distribution(model = mod, sampling.mode = "exponential", sampling.param = 1, output.param = 1) distr1## Generate a random RtreemixModel object with 3 components. mod <- generate(K = 3, no.events = 8, noise.tree = TRUE, prob = c(0.2, 0.8)) show(mod) ## See the probability distribution encoded by the model on the set of all possible patterns. distr <- distribution(model = mod) distr ## Get the probabilities. distr$probability ## See the probability distribution encoded by the model on the set of all possible patterns ## calculated for given sampling mode, and corresponding input and output parameters. distr1 <- distribution(model = mod, sampling.mode = "exponential", sampling.param = 1, output.param = 1) distr1
Function for fitting a mutagenetic trees mixture model to a given dataset
data. The dataset and the number of trees K have to be specified.
The function estimates K-oncogenetic trees mixture model from the
specified data by using an EM-like learning algorithm. The first tree
component of the model has a star topology and is referred to as the
noise component.
fit(data, K, ...)fit(data, K, ...)
data |
An |
K |
An |
... |
|
When K = 1 and noise = FALSE a single mutagenetic tree is fit to the data. When K = 1 and noise = TRUE a star mutagenetic tree is fit to the data. If K > 1 the first mutagenetic tree is always the star, i.e. the case K > 1 and noise = FALSE is not possible.
The method returns an RtreemixModel object that represents the
K-trees mixture model learned from the given dataset.
When you have too few data samples always use the default value TRUE
for the equal.edgeweights. Like this you make sure that all possible
patterns (sets of events) have non-zero probabilities. If they don't the
fitting procedure will not be completed and you will get an error!
Jasmina Bogojeska
Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.
RtreemixData-class, RtreemixModel-class,
generate-methods, bootstrap-methods,
confIntGPS-methods
## Create an RtreemixData object from a randomly generated RtreemixModel object. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) data <- sim(model = rand.mod, no.draws = 300) show(data) ## Create an RtreemixModel object by fitting model to the given data. mod <- fit(data = data, K = 3, equal.edgeweights = TRUE, noise = TRUE) show(mod) ## See the number of tree components in the mixture model. numTrees(mod) ## See the weights of the branchings from the fitted mixture model. Weights(mod) ## See a specific tree component k. getTree(object = mod, k = 2)## Create an RtreemixData object from a randomly generated RtreemixModel object. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) data <- sim(model = rand.mod, no.draws = 300) show(data) ## Create an RtreemixModel object by fitting model to the given data. mod <- fit(data = data, K = 3, equal.edgeweights = TRUE, noise = TRUE) show(mod) ## See the number of tree components in the mixture model. numTrees(mod) ## See the weights of the branchings from the fitted mixture model. Weights(mod) ## See a specific tree component k. getTree(object = mod, k = 2)
Function for generating a random mutagenetic mixture model. Each tree component from the model is drawn uniformly at random from the tree topology space by using the Pr\"ufer encoding of trees. The number of tree components and the number of genetic events have to be specified.
generate(K, no.events, ...)generate(K, no.events, ...)
K |
An |
no.events |
An |
... |
|
The method returns an RtreemixModel object that represents the
randomly generated K-trees mixture model.
Jasmina Bogojeska
Beweis eines Satzes \"uber Permutationen, H. Pr\"ufer; Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.; Model Selection for Mixtures of Mutagenetic Trees, Yin et al.
## Generate a random RtreemixModel object with 3 components and 9 genetic events. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) show(rand.mod)## Generate a random RtreemixModel object with 3 components and 9 genetic events. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) show(rand.mod)
Function that assignes to each node the level at which that node is
in a specific tree (tree.num) of the mutagenetic trees mixture model mixture.
The start.val is the number assigned to the events pruned from
the tree. This usually is the maximum depth of the tree with which the
tree specified with tree.num will be compared.
get.tree.levels(mixture, tree.num, start.val)get.tree.levels(mixture, tree.num, start.val)
mixture |
An object of the class |
tree.num |
A |
start.val |
A |
The function returns a named numeric vector. Its length equals the
number of genetic events in mixture minus one (for the initial
null event which is always on level 0). The vector names correspond to
the names of the genetic events and each vector component gives the
level at which the respective event is in the num.tree tree of mixture.
Jasmina Bogojeska
comp.models, comp.trees, stability.sim,
RtreemixModel-class, fit-methods
## Generate two random RtreemixModel objects each with 3 components. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) ## Get the tree levels of the 2nd component of the model rand.mod. get.tree.levels(mixture = rand.mod, tree.num = 2, start.val = 10)## Generate two random RtreemixModel objects each with 3 components. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) ## Get the tree levels of the 2nd component of the model rand.mod. get.tree.levels(mixture = rand.mod, tree.num = 2, start.val = 10)
These functions compute the genetic progression score (GPS) of each
sample in the given data by performing a waiting time
simulation along the branchings of the mixture model model. The
model has to be specified. If a dataset is missing a GPS for all
possible patterns is calculated. The number of events of the samples
in data equals the number of genetic events in the model.
gps(model, data, ...)gps(model, data, ...)
model |
An object of the class |
data |
An |
... |
|
The function returns an object from the RtreemixGPS class that
containes the calculated GPS values, the model used for the
computation, the data, and so on (see
RtreemixGPS-class). The GPS values are represented as a
numeric vector with length equal to the number of samples in data.
A method for calculating
the GPS values of the data given as RtreemixData object.
A method for calculating
the GPS values of the data given as 0-1 matrix.
A method for calculating the GPS values of the set of all possible patterns.
The mixture model used for deriving the GPS values should not have more than 20 genetic events. The reason for this is that the number of all possible patterns for which the GPS values are calculated during a computationally intensive simulations is in this case $2^20$. This demands too much memory. The GPS examples are time consuming. They are commented out because of the time restrictions of the check of the package. For trying out the code please copy it and uncomment it.
Jasmina Bogojeska
Estimating cancer survival and clinical outcome based on genetic tumor progression scores, J. Rahnenf\"urer et al.
RtreemixGPS-class, RtreemixData-class,
RtreemixModel-class,
fit-methods, confIntGPS-methods
## Create an RtreemixData object from a randomly generated RtreemixModel object. #rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8)) #data <- sim(model = rand.mod, no.draws = 400) ## Create an RtreemixModel object by fitting model to the given data. #mod <- fit(data = data, K = 2, equal.edgeweights = TRUE, noise = TRUE) #show(mod) ## Create an RtreemixGPS object by calculating the GPS for all possible patterns. #modGPS.all <- gps(model = mod, no.sim = 1000) ## time consuming copmutations #show(modGPS.all) ## See the GPS values for all possible data. #GPS(modGPS.all) ## time consuming copmutations ## Create an RtreemixGPS object by calculating the GPS for the data based on the model mod. #modGPS <- gps(model = mod, data = data, no.sim = 1000) #show(modGPS) ## time consuming copmutations ## See the GPS values for data. #GPS(modGPS) ## time consuming copmutations## Create an RtreemixData object from a randomly generated RtreemixModel object. #rand.mod <- generate(K = 2, no.events = 7, noise.tree = TRUE, prob = c(0.2, 0.8)) #data <- sim(model = rand.mod, no.draws = 400) ## Create an RtreemixModel object by fitting model to the given data. #mod <- fit(data = data, K = 2, equal.edgeweights = TRUE, noise = TRUE) #show(mod) ## Create an RtreemixGPS object by calculating the GPS for all possible patterns. #modGPS.all <- gps(model = mod, no.sim = 1000) ## time consuming copmutations #show(modGPS.all) ## See the GPS values for all possible data. #GPS(modGPS.all) ## time consuming copmutations ## Create an RtreemixGPS object by calculating the GPS for the data based on the model mod. #modGPS <- gps(model = mod, data = data, no.sim = 1000) #show(modGPS) ## time consuming copmutations ## See the GPS values for data. #GPS(modGPS) ## time consuming copmutations
This data object was created by using the Stanford HIV Drug Resistance Database that comprises genetic measurements of 364 HIV patients treated only with the drug zidovudine. The data contains the six classical major zidovudine resistance mutations: M41L, D67N, K70R, L210W, T215F/Y, and K219E/Q.
data(hiv.data)data(hiv.data)
Human immunodeficiency virus reverse transcriptase and protease sequence database, S. Rhee et al.
data(hiv.data) ## print the object hiv.datadata(hiv.data) ## print the object hiv.data
A function for calculating the Kullback-Leibler divergence between two discrete probability distributions. The vectors specifying the probability distributions must have the same length.
kullback.leibler(p, q)kullback.leibler(p, q)
p |
A |
q |
A |
The function returns the Kullback-Leibler divergence between the two specified descrete probability distributions.
The function does not check whether the values in the vectors specifying the discrete probability distributions sum up to one.
Jasmina Bogojeska
L1.dist, L2.norm, stability.sim
## Define two discrete probability distributions with equal lengths. p <- c(0.1, 0.2, 0.3, 0.4) q <- c(0.2, 0.5, 0.1, 0.2) ## Calculate the Kullback-Leibler divergence ## between the probability distributions p and q kullback.leibler(p, q)## Define two discrete probability distributions with equal lengths. p <- c(0.1, 0.2, 0.3, 0.4) q <- c(0.2, 0.5, 0.1, 0.2) ## Calculate the Kullback-Leibler divergence ## between the probability distributions p and q kullback.leibler(p, q)
A function for calculating the L2 norm of a given numeric vector.
L2.norm(x)L2.norm(x)
x |
A |
The function returns the L2 norm of the given vector x.
Jasmina Bogojeska
## Define a numeric vector y <- c(1, 2, 3, 4) ## Calculate the L2 norm of the vector y L2.norm(y)## Define a numeric vector y <- c(1, 2, 3, 4) ## Calculate the L2 norm of the vector y L2.norm(y)
This function predicts the (log, weighted) likelihoods of the samples in a given dataset according to a given mutagenetic trees mixture model. The dataset and the model have to be specified.
## S4 method for signature 'RtreemixModel,RtreemixData' likelihoods(model, data)## S4 method for signature 'RtreemixModel,RtreemixData' likelihoods(model, data)
model |
An |
data |
An |
This method returns an RtreemixStats object that containes the
weghted- and log-likelihoods of the samples in the given dataset with
respect to the given mutagenetic trees mixture model.
Jasmina Bogojeska
Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.
RtreemixData-class, RtreemixModel-class,
fit-methods, distribution-methods
## Create an RtreemixData object from a randomly generated RtreemixModel object. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) data <- sim(model = rand.mod, no.draws = 300) show(data) ## Compute the likelihoods of the samples in data with respect to the model rand.mod mod.stat <- likelihoods(model = rand.mod, data = data) show(mod.stat)## Create an RtreemixData object from a randomly generated RtreemixModel object. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) data <- sim(model = rand.mod, no.draws = 300) show(data) ## Compute the likelihoods of the samples in data with respect to the model rand.mod mod.stat <- likelihoods(model = rand.mod, data = data) show(mod.stat)
These functions implement a similarity measure
for comparing the topologies of the trees of two mixture models
mixture1 and mixture2. comp.models chaaracterizes
the similarity of the models based on sum of the number of different
edges of matched tree components (similarity
pairs). comp.models.levels quantifies the similarity of two
mixture models by adding to the edge ddifference of each similarity
pair in the previously described sum the L1 distance of the level vectors of the
trees comprising the pair. A level vector can be associated to each
tree component and denotes the depth of each of the genetic
events in the tree.
It is necessary that the two models have the same number of tree
components build on the same number of genetic events. It is assumed
that the mixtures have at least two tree components.
comp.models(mixture1, mixture2) comp.models.levels(mixture1, mixture2)comp.models(mixture1, mixture2) comp.models.levels(mixture1, mixture2)
mixture1 |
An |
mixture2 |
An |
The value returned by the function comp.models is between 0 (no
similarity) and 1 (identical models).
The functions return a numeric value that quantifies the similarity of the tree topologies of two mixture models.
Jasmina Bogojeska
RtreemixModel-class, comp.trees,
fit-methods, stability.sim
## Generate two random RtreemixModel objects each with 3 components. rand.mod1 <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) rand.mod2 <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) ## Compare the topologies of the tree components of the two randomly ## generated models comp.models(rand.mod1, rand.mod2) comp.models.levels(rand.mod1, rand.mod2)## Generate two random RtreemixModel objects each with 3 components. rand.mod1 <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) rand.mod2 <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) ## Compare the topologies of the tree components of the two randomly ## generated models comp.models(rand.mod1, rand.mod2) comp.models.levels(rand.mod1, rand.mod2)
This function calculates the p-value of a given similarity value, i.e. the probability for obtaining the same or a smaller value than the given one in a vector of random similarity values. The p-value is used to determine whether the given similarity value is significant.
Pval.dist(dist.val, random.vals)Pval.dist(dist.val, random.vals)
dist.val |
A |
random.vals |
A |
It returns a numeric value between 0 and 1 that specifies the
p-value of the given dist.val.
Jasmina Bogojeska
L1.dist, kullback.leibler,
comp.models, stability.sim
## The function is currently defined as function(dist.val, random.vals) { return((sum(random.vals <= dist.val) + 1) /(length(random.vals) + 1)) } ## Define the similarity value and a vector of random similarities sim.val <- 0.2 rand.vals <- c(0.1, 0.24, 0.28, 0.35, 0.15, 0.5, 0.14, 0.6, 0.8, 0.3) ## Calculate the p-value of sim.val using the vector of random ## similarities Pval.dist(dist.val = sim.val, random.vals = rand.vals)## The function is currently defined as function(dist.val, random.vals) { return((sum(random.vals <= dist.val) + 1) /(length(random.vals) + 1)) } ## Define the similarity value and a vector of random similarities sim.val <- 0.2 rand.vals <- c(0.1, 0.24, 0.28, 0.35, 0.15, 0.5, 0.14, 0.6, 0.8, 0.3) ## Calculate the p-value of sim.val using the vector of random ## similarities Pval.dist(dist.val = sim.val, random.vals = rand.vals)
This class is used to represent the results of genetic measurements of the occurence of subsets of a given set of genetic events in a group of patients. Each observation is a binary vector that indicates which events occured in a specific patient. The length of the vector equals the size of the set of genetic events that is taken into consideration.
Objects can be created by calls of the form new("RtreemixData",
Sample, Patients, Events, Description, File).
The RtreemixData class represents patterns of occurences of subsets of
a given set of genetic events in a specific group of patients. The
patterns are given as binary vectors with length equal to the size of
the set of genetic events. In other words, it provides a
representation of the dataset used for learning an mutagenetic trees
mixture model.
The Sample is a binary matrix where each row corresponds to the pattern of
genetic events observed in one of the given patients. Hence, the
number of rows gives the number of patients, i.e. the size of the
dataset. Each column corresponds to one of the genetic events.
Missing measurement for the presence or absence of a certain genetic
event in a given pattern is marked with -1. The initial null event
(that initially occurs in all patients) is
not present in the sample, i.e. the first component in each
observation (which is always equal to 1) is left out. This is done for
saving space and avoiding the process of checking for correctly
specified samples.
The Patients is a character vector that contains the IDs
of the patients. The length of this vector must be equal to the number
of rows in the Sample.
The Events is a character vector that contains the
labels of the genetic events taken into consideration. Its length
equals one plus the number of columns in the Sample. This is
because of the label of the null event. When the object of class
RtreemixData is a parent of a randomly generated
RtreemixModel object, the events specify the labels of the
genetic events present in the random model, although the Sample slot
is an empty matrix. This is because the random mixture models
are not estimated from a given dataset, but generated randomly for
some set of genetic events.
The Description is a character giving a short description for
the created object.
The File specifies the path to a text file with a specific
format which contains the infromation needed to create an
RtreemixData object (the patient IDs, the names of the events,
the matrix with the observations).
Sample:Object of class "matrix".
Patients:Object of class "character". The
Patients must be of same length as the number of rows in Sample.
Events:Object of class "character". The length
of Events must be identical to the number of columns in
Sample plus one (for specifying the label of the null event).
Description:Object of class "character".
signature(object = "RtreemixData"): A
method for obtaining the description of the "RtreemixData" object.
signature(object = "RtreemixData"): A
method for specifying the Description of the data object.
signature(object = "RtreemixData"): A
method for obtaining the labels of the genetic events.
signature(object = "RtreemixData"): A method
for replacing the names of the genetic events in the data object. It checks
to be sure the values have the right length. As a parent data
of a random RtreemixModel object the suitable labels of
events present in the model components can be specified although the
Sample slot is an empty matrix.
signature(object = "RtreemixData"): A
method for obtaining the IDs of the patients.
signature(object = "RtreemixData"): A method
for replacing the IDs of the patients in the data object. It checks
to be sure the values have the right length.
signature(object = "RtreemixData"): A
method for obtaining the matrix of observations.
signature(object = "RtreemixData"): A
method for obtaining the number of genetic events.
signature(object = "RtreemixData"): A
method for obtaining the size of the sample (the number of
patients).
Jasmina Bogojeska
RtreemixGPS-class, RtreemixStats-class,
RtreemixModel-class,
fit-methods, bootstrap-methods
## Create an RtreemixData object from a file given in the examples directory of the package. data1 <- new("RtreemixData", File = paste(system.file(package = "Rtreemix"), "/examples/treemix.pat", sep = "")) show(data1) ## show the RtreemixData object ## Create an RtreemixData object from a randomly generated RtreemixModel object. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) data2 <- sim(model = rand.mod, no.draws = 300) show(data2) ## Create an RtreemixData object from a given binary matrix. bin.mat <- cbind(c(1, 0, 0, 1, 1), c(0, 1, 0, 0, 1), c(1, 1, 0, 1, 0)) data3 <- new("RtreemixData", Sample = bin.mat, Events = c("0", "1", "2", "3")) show(data3)## Create an RtreemixData object from a file given in the examples directory of the package. data1 <- new("RtreemixData", File = paste(system.file(package = "Rtreemix"), "/examples/treemix.pat", sep = "")) show(data1) ## show the RtreemixData object ## Create an RtreemixData object from a randomly generated RtreemixModel object. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) data2 <- sim(model = rand.mod, no.draws = 300) show(data2) ## Create an RtreemixData object from a given binary matrix. bin.mat <- cbind(c(1, 0, 0, 1, 1), c(0, 1, 0, 0, 1), c(1, 1, 0, 1, 0)) data3 <- new("RtreemixData", Sample = bin.mat, Events = c("0", "1", "2", "3")) show(data3)
A class for describing the genetic progression scores (GPS) of a given set of patterns resulting from a waiting time simulation along the edges of the tree components of a given mutagenetic trees mixture model. It also contains GPS confidence intervals derived with the bootstrap method.
Objects can be created by calls of the form new("RtreemixGPS",
Data, Model, SamplingMode, SamplingParam, GPS, gpsCI).
The RtreemixGPS class contains the GPS values each assigned to
the corresponding pattern from the dataset given by Data (the
parent class). The GPS values are derived in a waiting time simulation
for a specified sampling mode and its corresponding sampling
parameter. Moreover, this class specifies the confidence intervals for
the GPS values derived with the bootstrap method.
The Data is an RtreemixData object that specifies the
patterns for which the GPS values are calculated.
The Model is an RtreemixModel object that specifies the
mutagenetic trees mixture model used for deriving the GPS values.
The SamplingMode is a character that specifies the
sampling mode ("constant" or "exponential") used in the waiting time
simulations.
The SamplingParam is a numeric that specifies the
sampling parameter corresponding to the sampling mode given by
SamplingMode.
The GPS is a numeric vector that specifies the
GPS value of each pattern in the given dataset Data.
Its length equals the number of patterns in Data.
The gpsCI is a numeric matrix that specifies the
confidence intervals for each GPS value in the vector GPS. The
number of rows equals the number of patients in Data and the
number of columns equals 2. The first column gives the lower bound and
the second column gives the upper bound of each confidence interval.
Model:Object of class "RtreemixModel".
SamplingMode:Object of class "character". It
can have one of the two possible values: "constant" or "exponential".
SamplingParam:Object of class "numeric".
GPS:Object of class "numeric". The
length of GPS must be equal to the number
of patterns in the parent RtreemixData object.
gpsCI:Object of class "matrix". It number of
columns has to be 2 and the number of rows has to be equal to the
length of GPS.
Class "RtreemixData", directly.
signature(object = "RtreemixGPS"): A method for
obtaining the GPS values corresponding to the patterns in the
parent RtreemixData object.
signature(object = "RtreemixGPS"): A method for
obtaining the model used for deriving the GPS values.
signature(object = "RtreemixGPS"): A
method for obtaining the sampling mode ("constant" or
"exponential") used for the waiting time simulations.
signature(object = "RtreemixGPS"): A
method for obtaining the sampling parameter corresponding to the
specified SamplingMode.
signature(object = "RtreemixGPS"): A method
for obtaining the set of patterns for which the GPS values are calculated.
signature(object = "RtreemixGPS"): A method for
obtaining the GPS confidence intervals.
The GPS examples are time consuming. They are commented out because of the time restrictions of the check of the package. For trying out the code please copy it and uncomment it.
Jasmina Bogojeska
Estimating cancer survival and clinical outcome based on genetic tumor progression scores, J. Rahnenf\"urer et al.
RtreemixData-class,
RtreemixModel-class,
gps-methods,
fit-methods, confIntGPS-methods
## Generate a random RtreemixModel object with 3 components and 9 genetic events. #mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) #show(mod) ## Generate an artificial dataset from the model mod. #data <- sim(model = mod, no.draws = 300) #show(data) ## Create an RtreemixGPS object by calculating the GPS for all possible patterns. #modGPS.all <- gps(model = mod, no.sim = 1000) #show(modGPS.all) ## Create an RtreemixGPS object by calculating the GPS for the data based on the model mod. #modGPS <- gps(model = mod, data = data, no.sim = 1000) #show(modGPS) ## See the slots from the RtreemixGPS object. #Model(modGPS) #SamplingMode(modGPS) #SamplingParam(modGPS) #GPS(modGPS) ## See data. #getData(modGPS) ## Create an RtreemixGPS object by calculating GPS values for a given dataset ## and their 95% confidence intervals using the bootstrap method. #modGPS2 <- confIntGPS(data = data, K = 2, B = 10) #show(modGPS2) ## See the GPS values for the object modGPS2 and their confidence intervals. #GPS(modGPS2) #gpsCI(modGPS2)## Generate a random RtreemixModel object with 3 components and 9 genetic events. #mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) #show(mod) ## Generate an artificial dataset from the model mod. #data <- sim(model = mod, no.draws = 300) #show(data) ## Create an RtreemixGPS object by calculating the GPS for all possible patterns. #modGPS.all <- gps(model = mod, no.sim = 1000) #show(modGPS.all) ## Create an RtreemixGPS object by calculating the GPS for the data based on the model mod. #modGPS <- gps(model = mod, data = data, no.sim = 1000) #show(modGPS) ## See the slots from the RtreemixGPS object. #Model(modGPS) #SamplingMode(modGPS) #SamplingParam(modGPS) #GPS(modGPS) ## See data. #getData(modGPS) ## Create an RtreemixGPS object by calculating GPS values for a given dataset ## and their 95% confidence intervals using the bootstrap method. #modGPS2 <- confIntGPS(data = data, K = 2, B = 10) #show(modGPS2) ## See the GPS values for the object modGPS2 and their confidence intervals. #GPS(modGPS2) #gpsCI(modGPS2)
This class contains all the data needed for characterizing the mutagenetic
trees mixture model (mixture parameters, mixture components, ...).
The tree components of the model are given as a list of directed graphNEL objects.
Objects can be created by calls of the form new("RtreemixModel",
ParentData, Weights, WeightsCI, Resp, CompleteMat, Star, Trees).
The RtreemixModel class extends the RtreemixData class
and specifies the mutagenetic trees mixture model. If the model is not
randomly generated the parent class gives the RtreemixData
used for learning the mixture model. The directed trees that build up
the model are represented as a list of directed graphNEL
objects, and their weights (the mixture parameters) are given as a
numeric vector. This class can also contain other useful information
connected with the mixture model like confidence intervals for the
mixture parameters and the edge weights (resulting from a bootstrap
analysis), an indicator for the presence of the star component, etc.
They are all listed in the text below with brief descriptions.
The ParentData is an RtreemixData object that specifies the
data used for estimating the mutagenetic trees mixture model. It is
not specified for random mixture models, since they are not estimated
from a given dataset but generated randomly.
The Weights is a numeric vector that contains the mixture
parameters of the model. Its length equals the length of the
list of tree components Trees.
The WeightsCI is a named list with length equal to the
length of the Weights. Each list element is a numeric
vector of length two specifying the lower and upper bound of
the confidence interval for the corresponding mixture parametar. The
confidence intervals are derived using the bootstrap method.
The Resp is a numeric matrix that specifies the responsibility
of each tree component to generate each of the patterns in the
ParentData. The number of rows in Resp is equal
to the number of trees in the mixture (the length of the list
Trees) and the number of columns equals the number of patients
in ParentData. For random mixture models it is an empty matrix,
since they are not estimated from a given dataset.
The CompleteMat is a binary matrix that specifies the complete
data in case some measurements for some patients are missing in
the data used for learning the model (the ParentData). It has
the same size as the matrix specifying the data in ParentData.
The missing data are estimated in the EM-algorithm used for fitting
the mixture model. When there are no missing data in
ParentData, or the model is randomly generated the CompleteMat is an
empty matrix.
The Star is an indicator of the presence of a noise (star) component
and is mostly relevant for models with a single tree component, since it is assumed that
mixture models with at least two components always have the noise
as a first component. It is of type logical.
The Trees is a list of directed graphNEL
objects, each for every tree component in the mixture model. The
genetic events are represented as nodes in the graphs. The
edgeData of each tree can have two attributes: "weight"
and "ci". Please see the help page on graph-class and
graphNEL-class in the package graph. The "weight" attribute is for edge weight,
i.e. the conditional probability that the child event of the edge occured given
that the parent event already occured. The "ci" attribute is
for the bootstrap confidence intervals for the edge weight (a numeric vector
with length two).
Weights:Object of class "numeric". The length
of the Weights must be equal to the length of Trees.
WeightsCI:Object of class "list". The length
of the WeightsCI must be equal to the length of Weights.
Resp:Object of class "matrix". The number of
rows of Resp must be identical to the length of
Trees, and its number of columns to the number of patients
in the dataset used for estimating the mixture model (ParentData).
CompleteMat:Object of class "matrix". When
specified (when there are missing data) the size of the
CompleteMat must be equal to the size of the matrix used to
estimate the model.
Star:Object of class "logical".
Trees:Object of class "list". The length of
Trees equals the length of Weights.
Class "RtreemixData", directly.
signature(object = "RtreemixModel"): A
method used for obtaining the complete dataset, in case there were
any missing measurements for some patients in the dataset used to
learn the mixture model.
signature(object = "RtreemixModel"): A method for
obtaining the matrix of responsibilities for the trees to generate
each of the samples in the dataset used for learning the model (ParentData).
signature(object = "RtreemixModel"): A method for
checking the presence of a noise component in the mixture model
(informative only for models with one tree component).
signature(object = "RtreemixModel"): A method
for obtaining the tree components of the mixture model as a list
of directed graphNEL objects.
signature(object = "RtreemixModel"): A method
for obtaining the mixture parameters (the weights of the trees in
the model).
signature(object = "RtreemixModel"): A
method for replacing the value of the slot Weights with a
specified numeric vector. The components of this vector
have to sum up to one.
signature(object = "RtreemixModel"): A method
for obtaining the weights of the mixture parameters.
signature(object = "RtreemixModel"): A method
for obtaining the ParentData of the mixture model, i.e. the
data used for learning the model.
signature(object = "RtreemixModel", k =
"numeric"): A method for obtaining the k-th tree component of the
mixture model as a directed graphNEL object.
signature(object = "RtreemixModel"): A method
for obtaining the number of tree components building up the mixture model.
signature(x = "RtreemixModel", y = "missing"): A method
for visualizing the tree components comprising a mutagenetic trees mixture
model. The user can also specify the fontSize (the default value is 8)
used for the text labels of the nodes and the edges of the plotted trees.
Additionally, one can use the parameter k to plot a certain tree
component from the mixture model.
Jasmina Bogojeska
Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.
RtreemixGPS-class, RtreemixStats-class,
RtreemixData-class, RtreemixSim-class,
fit-methods, bootstrap-methods,
generate-methods, comp.models, comp.trees
## Generate a random RtreemixModel object with 2 components. rand.mod <- generate(K = 2, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) show(rand.mod) plot(rand.mod) ## plot the tree components of the model plot(rand.mod, k = 2) ## plot the second component of the model ## Draw data from a specified mixture model. draws <- sim(model = rand.mod, no.draws = 200) show(draws) ## Create an RtreemixModel object by fitting model to the drawn data. mod <- fit(data = draws, K = 2, equal.edgeweights = TRUE, noise = TRUE) show(mod) ## See the values of the slots of the RtreemixModel object. Weights(mod) Resp(mod) CompleteMat(mod) Star(mod) Trees(mod) ## See data used for learning the model. getData(mod) ## See the number of tree components in the mixture model. numTrees(mod) ## See a specific tree component k. getTree(object = mod, k = 2) ## See the conditional probabilities assigned to edges of the second tree component. edgeData(getTree(object = mod, k = 2), attr = "weight") ## See the probability distribution encoded by the model on the set of all possible patterns. distr <- distribution(model = mod) distr ## Get the probabilities. distr$probability ## See the probability distribution encoded by the model on the set of all possible patterns ## calculated for given sampling mode, and input and output parameters. distr1 <- distribution(model = mod, sampling.mode = "exponential", sampling.param = 1, output.param = 1) distr1 ## Create a RtreemixModel and analyze its variance with the bootstrap method. mod.boot <- bootstrap(data = draws, K = 2, equal.edgeweights = TRUE, B = 100) ## See the confidence intervals for the mixture parameters (the weights). WeightsCI(mod.boot) ## See the confidence intervals of the conditional probabilities assigned to the edges. edgeData(getTree(mod.boot, 2), attr = "ci")## Generate a random RtreemixModel object with 2 components. rand.mod <- generate(K = 2, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) show(rand.mod) plot(rand.mod) ## plot the tree components of the model plot(rand.mod, k = 2) ## plot the second component of the model ## Draw data from a specified mixture model. draws <- sim(model = rand.mod, no.draws = 200) show(draws) ## Create an RtreemixModel object by fitting model to the drawn data. mod <- fit(data = draws, K = 2, equal.edgeweights = TRUE, noise = TRUE) show(mod) ## See the values of the slots of the RtreemixModel object. Weights(mod) Resp(mod) CompleteMat(mod) Star(mod) Trees(mod) ## See data used for learning the model. getData(mod) ## See the number of tree components in the mixture model. numTrees(mod) ## See a specific tree component k. getTree(object = mod, k = 2) ## See the conditional probabilities assigned to edges of the second tree component. edgeData(getTree(object = mod, k = 2), attr = "weight") ## See the probability distribution encoded by the model on the set of all possible patterns. distr <- distribution(model = mod) distr ## Get the probabilities. distr$probability ## See the probability distribution encoded by the model on the set of all possible patterns ## calculated for given sampling mode, and input and output parameters. distr1 <- distribution(model = mod, sampling.mode = "exponential", sampling.param = 1, output.param = 1) distr1 ## Create a RtreemixModel and analyze its variance with the bootstrap method. mod.boot <- bootstrap(data = draws, K = 2, equal.edgeweights = TRUE, B = 100) ## See the confidence intervals for the mixture parameters (the weights). WeightsCI(mod.boot) ## See the confidence intervals of the conditional probabilities assigned to the edges. edgeData(getTree(mod.boot, 2), attr = "ci")
This class contains data simulated from the RtreemixModel it
extends together with their sampling and waiting times. It also
includes the sampling mode and the sampling parameter used for the time simulation.
Objects can be created by calls of the form new("RtreemixSim",
Model, SimPatterns, SamplingMode, SamplingParam, WaitingTimes,
SamplingTimes).
The RtreemixSim class specifies patterns (RtreemixData)
simulated from the parent RtreemixModel together with their
waiting and sampling times resulting from the waiting time simulation
along the branchings in the parent model.
The Model is an RtreemixModel object used in the data
and time simulation process. In other words, this model is used for
simulating patterns with their sampling and waiting times.
The SimPatterns is an RtreemixData object that contains
the patterns simulated from the given Model.
The SamplingMode is a character that specifies the
sampling mode ("constant" or "exponential") used in the time simulations.
The SamplingParam is a numeric that specifies the
sampling parameter corresponding to the sampling mode given by
SamplingMode.
The WaitingTimes is a numeric vector that specifies the
waiting times for the simulated patterns. Its length equals the number
of patterns in SimPatterns.
The SamplingTimes is a numeric vector that specifies the
sampling times for the simulated patterns. Its length equals the number
of patterns in SimPatterns.
SimPatterns:Object of class "RtreemixData".
SamplingMode:Object of class "character". It
can have one of the two possible values: "constant" or "exponential".
SamplingParam:Object of class "numeric".
WaitingTimes:Object of class "numeric". The
length of WaitingTimes must be equal to the number
of patterns in SimPatterns.
SamplingTimes:Object of class "numeric". The
length of SamplingTimes must be equal to the number
of patterns in SimPatterns.
Class "RtreemixModel", directly.
Class "RtreemixData", by class "RtreemixModel", distance 2.
signature(object = "RtreemixSim"): A
method for obtaining the sampling mode ("constant" or
"exponential") used for the time simulations.
signature(object = "RtreemixSim"): A
method for obtaining the sampling parameter corresponding to the
specified SamplingMode.
signature(object = "RtreemixSim"): A
method used for obtaining the sampling times of the patterns
in SimPatterns.
signature(object = "RtreemixSim"): A
method used for obtaining the patterns simulated from the parent model.
signature(object = "RtreemixSim"): A
method used for obtaining the waiting times of the patterns
in SimPatterns.
signature(object = "RtreemixSim"): A method
for obtaining the mixture model used in the simulations.
signature(object = "RtreemixSim"): A method
for obtaining the number of simulated patterns, i.e. the size of SimPatterns.
Jasmina Bogojeska
Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.
RtreemixGPS-class, RtreemixData-class,
RtreemixModel-class,
fit-methods, sim-methods
## Generate a random RtreemixModel object with 3 components and 9 genetic events. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) show(rand.mod) ## Create an RtreemixSim object by simulating patterns with their sampling and waiting times from a given mixture model. sim.data <- sim(model = rand.mod, sampling.mode = "exponential", sampling.param = 1, no.sim = 200) show(sim.data) ## See the slots from the RtreemixSim object. SimPatterns(sim.data) SamplingMode(sim.data) SamplingParam(sim.data) WaitingTimes(sim.data) SamplingTimes(sim.data) ## See model. getModel(sim.data) ## See number of simulated patterns. noDraws(sim.data)## Generate a random RtreemixModel object with 3 components and 9 genetic events. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) show(rand.mod) ## Create an RtreemixSim object by simulating patterns with their sampling and waiting times from a given mixture model. sim.data <- sim(model = rand.mod, sampling.mode = "exponential", sampling.param = 1, no.sim = 200) show(sim.data) ## See the slots from the RtreemixSim object. SimPatterns(sim.data) SamplingMode(sim.data) SamplingParam(sim.data) WaitingTimes(sim.data) SamplingTimes(sim.data) ## See model. getModel(sim.data) ## See number of simulated patterns. noDraws(sim.data)
The RtreemixStats class contains the (weighted, log)
likelihoods for a given dataset (specified by the parent class)
derived from the probability distribution induced by an underlying
mutagenetic trees mixture model.
Objects can be created by calls of the form new("RtreemixStats",
Data, Model, LogLikelihoods, WLikelihoods).
The class RtreemixStats extends the RtreemixData class
and specifies (log, weighted) likelihoods for these data derived from
a given RtreemixModel. The number of the genetic events in the
patterns from the given dataset (Data) has to be equal to the number
of genetic events in the branchings from the mixture model
given by the slot Model. When having the weighted likelihoods,
one can easily derive the responsibilities of the model components in
Model for generating the patterns in the specified dataset
(Data).
The Data is an RtreemixData object that specifies the
patterns for which the likelihoods are calculated.
The Model is an RtreemixModel object that specifies the
mutagenetic trees mixture model used for deriving the likelihoods of
the given data.
The LogLikelihoods is a numeric vector that contains the
log-likelihoods of the patterns in Data. Its length equals the
sample size, i.e. the number of patients in Data.
The WLikelihoods is a numeric matrix that specifies the
weighted likelihoods of each pattern in the given dataset
Data. The number of columns in WLikelihoods equals the
number of tree components in Model and the number of rows
equals the number of patients in Data.
Model:Object of class "RtreemixModel".
LogLikelihoods:Object of class "numeric". The
length of LogLikelihoods must be equal to the number of
patients of the dataset specified with the parent
"RtreemixData" class.
WLikelihoods:Object of class "matrix". The
number of rows must be equal to the sample size of the dataset
specified with the parent "RtreemixData" class. The number
of columns must be identical with the number of tree components in
the mixture model Model.
Class "RtreemixData", directly.
signature(object = "RtreemixStats"): A
method for obtaining the log-likelihoods of the patterns in
the dataset specified with the parent "RtreemixData" class.
signature(object = "RtreemixStats"): A method
for obtaining the mutagenetic trees mixture model used for
deriving the likelihoods.
signature(object = "RtreemixStats"): A
method for obtaining the weighted likelihoods of the patterns in
the dataset specified with the parent "RtreemixData" class.
signature(object = "RtreemixStats"): A method
for obtaining the dataset specified with the
parent "RtreemixData" class.
signature(object = "RtreemixStats"): A method for
computing the matrix of responsibilities for the trees to generate
each of the samples in the parent dataset from their weighted
likelihoods WLikelihoods.
Jasmina Bogojeska
Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.
RtreemixData-class,
RtreemixModel-class,
fit-methods, likelihoods-methods
## Generate a random RtreemixModel object with 3 components and 9 genetic events. mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) show(mod) ## Draw a data sample from the model mod. data <- sim(model = mod, no.draws = 400) ## Create an RtreemixStats object. mod.stat <- likelihoods(model = mod, data = data) show(mod.stat) ## See the slots from the RtreemixStats object. Model(mod.stat) LogLikelihoods(mod.stat) WLikelihoods(mod.stat) ## See data. getData(mod.stat) ## Calculate the responsibilities from the weighted likelihoods. getResp(mod.stat)## Generate a random RtreemixModel object with 3 components and 9 genetic events. mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) show(mod) ## Draw a data sample from the model mod. data <- sim(model = mod, no.draws = 400) ## Create an RtreemixStats object. mod.stat <- likelihoods(model = mod, data = data) show(mod.stat) ## See the slots from the RtreemixStats object. Model(mod.stat) LogLikelihoods(mod.stat) WLikelihoods(mod.stat) ## See data. getData(mod.stat) ## Calculate the responsibilities from the weighted likelihoods. getResp(mod.stat)
This function draws a certain number of patterns from a specified mutagenetic trees mixture model. Thus, the mixture model has to be specified. When besides the mixture model also the sampling mode and its respective sampling parameter are specified, this function simulates patterns together with their waiting and sampling times from the respective model.
sim(model, sampling.mode, sampling.param, ...)sim(model, sampling.mode, sampling.param, ...)
model |
An object of the class |
sampling.mode |
A |
sampling.param |
A |
... |
|
The function returns an RtreemixData object in the case when
one wants to draw a certain number of patterns from a given mixture
model, i.e. when only the mutagenetic trees mixture model and the
number of patterns to be drawn are specified. When besides the model
also the sampling mode and the sampling parameter are given, the
function returns an object from the RtreemixSim class where the
simulated patterns together with their sampling and waiting times are stored.
Jasmina Bogojeska
Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.
RtreemixSim-class, RtreemixModel-class,
RtreemixData-class
## Create a random RtreemixModel object with 3 branchings and 9 genetic events. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) ## Draw 300 samples from the randomly generated model rand.mod data <- sim(model = rand.mod, no.draws = 300) show(data) ## Create an RtreemixSim object by simulating patterns with their sampling and waiting times from a given mixture model. sim.data <- sim(model = rand.mod, sampling.mode = "exponential", sampling.param = 1, no.sim = 100) show(sim.data) WaitingTimes(sim.data) SamplingTimes(sim.data)## Create a random RtreemixModel object with 3 branchings and 9 genetic events. rand.mod <- generate(K = 3, no.events = 9, noise.tree = TRUE, prob = c(0.2, 0.8)) ## Draw 300 samples from the randomly generated model rand.mod data <- sim(model = rand.mod, no.draws = 300) show(data) ## Create an RtreemixSim object by simulating patterns with their sampling and waiting times from a given mixture model. sim.data <- sim(model = rand.mod, sampling.mode = "exponential", sampling.param = 1, no.sim = 100) show(sim.data) WaitingTimes(sim.data) SamplingTimes(sim.data)
The function includes stability analysis on different levels of the mutagenetic trees mixture model: GPS values, encoded probability distribution, tree topologies. Each analysis contains the values of different similarity measures with their corresponding p-values.
stability.sim(no.trees = 3, no.events = 9, prob = c(0.2, 0.8), no.draws = 300, no.rands = 100, no.sim = 1)stability.sim(no.trees = 3, no.events = 9, prob = c(0.2, 0.8), no.draws = 300, no.rands = 100, no.sim = 1)
no.trees |
An |
no.events |
An |
prob |
A |
no.draws |
An |
no.rands |
An |
no.sim |
An |
The stability analysis is performed by first drawing a true mixture model uniformly at random from the model space, and drawing a data sample from it. Afterwards, a mutagenetic trees model is fitted to the drawn sample. The quality of the features derived from the model is then assessed by comparing its quality with the quality of the corresponding features of a sufficient number of random mixture models sampled uniformly from the model space. A p-value is obtained as a percentage of cases in which the true model is closer to a random model tnah to the fitted model.
comp1 |
Results from the stability analysis of the GPS values
derived from a fitted mixture model.
A |
comp2 |
Results from the stability analysis of the probability
distributions induced by a fitted mixture model. A |
comp3 |
Results from the stability analysis of the topologies
of the tree components of a fitted mixture model. A |
comp4 |
Similar to |
comp5 |
A |
comp6 |
Same as |
comp7 |
A |
comp8 |
Same as |
The stability simulation examples are time consuming. They are commented out because of the time restrictions of the check of the package. For trying out the code please copy it and uncomment it.
Jasmina Bogojeska
Learning multiple evolutionary pathways from cross-sectional data, N. Beerenwinkel et al.; Estimating cancer survival and clinical outcome based on genetic tumor progression scores, J. Rahnenf\"urer et al.
RtreemixData-class, RtreemixModel-class, RtreemixGPS-class,
RtreemixStats-class, fit-methods,
gps-methods, distribution-methods,
generate-methods, sim-methods,
L1.dist, Pval.dist,
comp.models, comp.trees,
get.tree.levels, kullback.leibler
## Stability analysis - a toy example #stability.sim(no.trees = 3, no.rands = 5, no.sim = 4, no.draws = 300)## Stability analysis - a toy example #stability.sim(no.trees = 3, no.rands = 5, no.sim = 4, no.draws = 300)