Description Usage Arguments Details Value Author(s) Examples

This function considers a two-part semi-parametric model for metabolomics and proteomics data. A kernel-smoothed method is applied to estimate the regression coefficients. And likelihood ratio test is constructed for differential abundance analysis.

1 |

`sumExp` |
An object of 'SummarizedExperiment' class. |

`VOI` |
Variable of interest. Default is NULL, when there is only one covariate, otherwise it must be one of the column names in colData. |

`...` |
Additional arguments passed to |

The differential abundance analysis is to compare metabolomic or proteomic
profiles between different experimental groups, which utilizes a two-part
model: a logistic regression model to characterize the zero proportion and a
semi-parametric model to characterize non-zero values. Let
*Y_ig* be the random variable representing the abundance of
feature *g* in subject *i*. This two-part model has the following
form:

* log(pi_ig/(1-pi_ig))=gamma_0g +
gamma_g*X_i*

* log(Y_ig)=beta_g*X_i+ epsilon_ig *

where *pi_ig=Pr(Y_ig=0)* be the probability of
point mass, *X_i=(X_i1,
X_i2,..., X_iQ)^T* is a Q-vector covariates that specifies the treatment
conditions applied to subject *i*. The corresponding Q-vector of model
parameters *gamma_g=(gamma_1g, gamma_2g,...,gamma_Qg)^T* quantify the
covariates effects on the fraction of zero values for feature *g* and
*gamma_0g* is the intercept. *beta_g=(beta_1g, beta_2g,...,
beta_Qg)
^T* is a Q-vector of model parameters quantifying the covariates effects on
the non-zero values for the feature. And *epsilon_ig*
are independent error terms with a common but completely unspecified density
function *f_g*.

Hypothesis testing on the effect of the *q*th covariate on the *g*th
feature is performed by assessing *gamma_qg* and *beta_qg*. Consider the null hypothesis *H_0*: *gamma_qg* and *beta_qg* against alternative
hypothesis *H_1*: at least one of the two parameters is non-zero.
The p-value is calculated based on a chi-square distribution with 2 degrees
of freedom. To adjust for multiple comparisons across features, the false
discovery discovery rate (FDR) q-value is calculated based on the
qvalue function in R/Bioconductor.

A list containing the following components:

`gamma ` |
a vector of point estimators for |

`beta ` |
a vector of point estimators for |

`pv_gamma ` |
a vector of one-part p-values for |

`pv_beta ` |
a vector of one-part p-values for |

`qv_gamma ` |
a vector of one-part q-values for |

`qv_beta ` |
a vector of one-part q-values for |

`pv_2part ` |
a vector of two-part p-values for overall test |

`qv_2part ` |
a vector of two-part q-values for overall test |

`feat.names ` |
a vector of feature names |

Yuntong Li <yuntong.li@uky.edu>, Chi Wang <chi.wang@uky.edu>, Li Chen <lichenuky@uky.edu>

1 2 3 4 5 6 7 | ```
##--------- load data ------------
data(exampleSumExp)
results = SDA(exampleSumExp)
##------ two part q-values -------
results$qv_2part
``` |

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