Package 'CAEN'

would be a SummarizedExperiment Bioconductor object, then it would be transformed into a p times n matrix - i.e. features on the rows and observations on the columns in the function, or dataTable would be a p times n matrix.

the category for each sample

the number of class

the number of differentially expressed genes you want to select

would be a SummarizedExperiment Bioconductor object, then it would be transformed into a n times p matrix - i.e. observations on the rows and features on the columns in the function, or x would be a n times p matrix.

A numeric vector of class labels of length n: 1, 2, ...., K if there are K classes.Each element of y corresponds to a row of x; i.e. these are the class labels for the observations in x.

would be a SummarizedExperiment Bioconductor object, then would be transformed into a m-by-p data matrix: m test observations and p features, or xte would be a m-by-p data matrix. The classifier fit on the training data set x will be tested on this data set. If NULL, then testing will be performed on the training set.

A standardized parameter

the method of normality

vector of length equal to the number of classes, representing prior probabilities for each class.If NULL then uniform priors are used (i.e. each class is equally likely)

Number of observations desired.

Number of features desired. Note that drate of the features will differ between classes, though some of those differences may be small.

Number of classes desired. Note that the function requires that n be at least equal to 4K.i.e. there must be at least 4 observations per class on average.

The dispersion parameter for the negative binomial distribution. The negative binomial distribution is parameterized using "mu" and "size" in the R function "rnbinom". That is, Y ~ NB(mu, param) means that E(Y)=mu and Var(Y) = mu+mu^2/param.So when param is very large this is essentially a Poisson distribution, and when param is smaller then there is a lot of overdispersion relative to the Poisson distribution.

The extent to which the classes are different. If this equals zero then there are no class differences and if this is large then the classes are very different.

The proportion of differentially expressed genes

would be a SummarizedExperiment Bioconductor object, then it would be transformed into a n times p matrix - i.e. observations on the rows and features on the columns in the function, or x would be a n times p matrix.

A numeric vector of class labels of length n: 1, 2, ...., K if there are K classes.Each element of y corresponds to a row of x; i.e. these are the class labels for the observations in x.

would be a SummarizedExperiment Bioconductor object, then it would be transformed into a m-by-p data matrix: m test observations and p features, or xte would be a m-by-p data matrix. The classifier fit on the training data set x will be tested on this data set. If NULL, then testing will be performed on the training set.

Tuning parameter controlling the amount of soft thresholding performed, i.e. the level of sparsity, i.e. number of nonzero features in classifier. Rho=0 means that there is no soft-thresolding, i.e. all features used in classifier. Larger rho means that fewer features will be used.

A smoothing term. A Gamma(beta,beta) prior is used to fit the Zero-inflated Poisson model.Recommendation is to just leave it at 1, the default value.

A vector of tuning parameters that control the amount of soft thresholding performed. If "rhos" is provided then a number of models will be fit (one for each element of "rhos"), and a number of predicted class labels will be output (one for each element of "rhos").

The probability that the read is 0

How should the observations be normalized within the Zero-inflated Poisson model, i.e. how should the size factors be estimated? Options are "quantile" or "deseq" (more robust) or "mle" (less robust). In greater detail: "quantile" is quantile normalization approach of Bullard et al 2010 BMC Bioinformatics,"deseq" is median of the ratio of an observation to a pseudoreference obtained by taking the geometric mean, described in Anders and Huber 2010 Genome Biology and implemented in Bioconductor package "DESeq", and "mle" is the sum of counts for each sample; this is the maximum likelihood estimate under a simple Zero-inflated Poisson model.

vector of length equal to the number of classes, representing prior probabilities for each class.If NULL then uniform priors are used (i.e. each class is equally likely)

should data matrices x and xte first be power transformed so that it more closely fits the Zero-inflated Poisson model? TRUE or FALSE. Power transformation is especially useful if the data are overdispersed relative to the Zero-inflated Poisson model.

if transform=TRUE, this determines the power to which the data matrices x and xte are transformed.If alpha=NULL then the transformation that makes the Zero-inflated Poisson model best fit the data matrix x is computed.(Note that alpha is computed based on x, not based on xte). Or a value of alpha, 0<alpha<=1, can be entered by the user.

would be a SummarizedExperiment Bioconductor object, then it would be transformed into a n times p matrix - i.e. observations on the rows and features on the columns in the function, or x would be a n times p matrix.

A numeric vector of class labels of length n: 1, 2, ...., K if there are K classes.Each element of y corresponds to a row of x; i.e. these are the class labels for the observationsin x.

A vector of tuning parameters to try out in cross-validation. Rho controls the level of shrinkage performed, i.e. the number of features that are not involved in the classifier. When rho=0 then all features are involved in the classifier, and when rho is very large no features are involved. If rhos=NULL then a vector of rho values will be chosen automatically.

A smoothing term. A Gamma(beta,beta) prior is used to fit the zero-inflated Poisson model.Recommendation is to leave it at 1, the default value.

The number of folds in the cross-validation; default is 5-fold cross-validation.

The probability that the read is 0

How should the observations be normalized within the zero-inflated Poisson model, i.e. how should the size factors be estimated? Options are "quantile" or "deseq" (more robust) or "mle" (less robust). In greater detail: "quantile" is quantile normalization approach of Bullard et al 2010 BMC Bioinformatics, "deseq" is median of the ratio of an observation to a pseudoreference obtained by taking the geometric mean, described in Anders and Huber 2010 Genome Biology and implemented in Bioconductor package "DESeq", and "mle" is the sum of counts for each sample; this is the maximum likelihood estimate under a simple Poisson model.

Instead of specifying the number of folds in cross-validation, one can explicitly specify the folds. To do this, input a list of length r(to perform r-fold cross-validation). The rth element of the list should be vector containing the indices of the test observations in the rth fold.

Should data matrices x and xte first be power transformed so that it more closely fits the zero-inflated Poisson model? TRUE or FALSE. Power transformation is especially useful if the data are overdispersed relative to the zero-inflated Poisson model.

If transform=TRUE, this determines the power to which the data matrices x and xte are transformed. If alpha=NULL then the transformation that makes the zero-inflated Poisson model best fit the data matrix x is computed. (Note that alpha is computed based on x, not based on xte). Or a value of alpha, 0<alpha<=1, can be entered by the user.

Vector of length equal to the number of classes, representing prior probabilities for each class. If NULL then uniform priors are used (i.e.each class is equally likely).