Description Usage Arguments Value Author(s) References See Also Examples

Fit a quasi-likelihood model to RNA-seq expression count data using the methods detailed in Lund, Nettleton, McCarthy, and Smyth (2012).

1 2 3 |

`counts` |
RNA-seq data matrix of integer expression counts. Each row contains observations from a single gene. Each column contains observations from a single experimental unit. |

`design.list` |
List of design matrices for the full model and reduced model(s). The first element of |

`test.mat` |
T by 2 matrix dictating which designs are to be compared, where T is the total number of desired hypothesis tests for each gene. Each row contains two integers, which provide the indices within |

`log.offset` |
A vector of log-scale, additive factors used to adjust estimated log-scale means for differences in library sizes across samples. Commonly used offsets include |

`Model` |
Must be one of "Poisson" or "NegBin", specifying use of a quasi-Poisson or quasi-negative binomial model, respectively. |

`print.progress` |
logical. If |

`NBdisp` |
Used only when |

`bias.fold.tolerance` |
A numerical value no smaller than 1. If the bias reduction of maximum likelihood estimates of (log) fold change is likely to result in a ratio of fold changes greater than this value, then bias reduction will be performed on such genes. Setting |

`...` |
arguments to be passed to the function |

list containing:

`"LRT"` |
matrix providing unadjusted likelihood ratio test statistics. Each column contains statistics from a single hypothesis test, applied separately to each gene. |

`"LRT.Bart"` |
The same as |

`"phi.hat.dev"` |
vector providing unshrunken, deviance-based estimates of quasi-dispersion (phi) for each gene. |

`"phi.hat.pearson"` |
vector providing unshrunken, Pearson estimates of quasi-dispersion (phi) for each gene. |

`"mn.cnt"` |
vector providing average count for each gene. |

`"den.df"` |
denominator degrees of freedom. Equal to the number of samples minus the number of fitted parameters in the full model, which is specified by the first element of |

`"num.df"` |
vector of numerator degrees of freedom for each test, computed as the difference in the number of fitted parameters between the full and reduced models for each test. |

`"Model"` |
Either "Poisson" or "NegBin", specifying which model (quasi-Poisson or quasi-negative binomial, respectively) was used. |

`"nb.disp"` |
Only appears when |

`fitted.values` |
matrix of fitted mean values |

`coefficients` |
matrix of estimated coefficients for each gene. Note that these are given on the log scale. (i.e. intercept coefficient reports log(average count) and non-intercept coefficients report estimated (and bias reduced, when appropriate) log fold-changes.) |

Originally authored by Steve Lund [email protected]; later modified by Long Qu [email protected]

Kosmidis and Firth (2009) "Bias reduction in exponential family nonlinear models" *Biometrika*, **96**, 793–804.

Lund, Nettleton, McCarthy and Smyth (2012) "Detecting differential expression in RNA-sequence data using quasi-likelihood with shrunken dispersion estimates" *SAGMB*, **11**(5).

McCarthy, Chen and Smyth (2012) "Differential expression analysis of multifactor RNA-Seq experiments with respect to biological variation" *Nucleic Acids Res.* **40**(10), 4288–97.

Cordeiro (1983) "Improved Likelihood Ratio Statistics for Generalized Linear Models" *Journal of the Royal Statistical Society. Series B (Methodological)*, **45**, 404–413.

`QL.results`

, `NBDev`

, `PoisDev`

, `mockRNASeqData`

1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 | ```
data(mockRNASeqData)
### Create list of designs describing model under null and alternative hypotheses
design.list=list(
mockRNASeqData$design.matrix,
matrix(1, nrow=rep(mockRNASeqData$nsamples))
)
## Not run:
### Analyze using QL, QLShrink and QLSpline methods applied to quasi-Poisson model
fit =
with(mockRNASeqData,
QL.fit(counts, design.list,
log.offset=log(estimated.normalization),
Model="Poisson", bias.fold.tolerance=Inf)
)
results = QL.results(fit)
### How many significant genes at FDR=.05 from QLSpline method?
apply(results$Q.values[[3]]<.05,2,sum)
### Indexes for Top 10 most significant genes from QLSpline method
head(order(results$P.values[[3]]), 10)
## End(Not run)
## Not run:
### Analyze using QL, QLShrink and QLSpline methods
### applied to quasi-negative binomial model
fit2 =
with(mockRNASeqData,
QL.fit(counts, design.list,
log.offset=log(estimated.normalization),
Model="NegBin")
)
##########################################################
## Note: 'NBdisp' typically will not be specified when ##
## calling QL.fit while analyzing real data. Providing ##
## numeric values for 'NBdisp' prevents neg binomial ##
## dispersions from being estimated from the data. ##
##########################################################
results2<-QL.results(fit2)
### How many significant genes at FDR=.05 for QLSpline method?
apply(results2$Q.values[[3]]<.05,2,sum)
### Indexes for Top 10 most significant genes from QLShrink method
head(order(results2$P.values[[2]]), 10)
## End(Not run)
``` |

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