fitPoisthNorm_sp | R Documentation |

Poisson threshold model based normalization-log2 transformation for multiple slides

```
fitPoisthNorm_sp(object, ...)
## S4 method for signature 'matrix'
fitPoisthNorm_sp(
object,
probenum,
features_high,
features_all = colnames(object),
sizefact_start,
sizefact_BG,
threshold_mean,
preci2 = 10000,
id,
iterations = 2,
prior_type = c("contrast", "equal"),
sizefactrec = TRUE,
size_scale = c("sum", "first"),
sizescalebythreshold = FALSE,
covrob = FALSE,
preci1con = 1/25,
cutoff = 15,
confac = 1
)
```

`object` |
count matrix with features in rows and samples in columns |

`...` |
additional argument list that might be used |

`probenum` |
a vector of numbers of probes in each gene |

`features_high` |
subset of features which are well above the background |

`features_all` |
full feature vector to apply the normalization on |

`sizefact_start` |
initial value for size factors |

`sizefact_BG` |
size factor for background |

`threshold_mean` |
average threshold level |

`preci2` |
precision for threshold, default=10000 |

`id` |
character vector of slide name of each sample |

`iterations` |
iteration number, default=2, the first iteration using the features_high to construct the prior for parameters then refit the model on all features. precision matrix for threshold: preci2 |

`prior_type` |
prior type for preci1, "equal" or "contrast", default="contrast" |

`sizefactrec` |
XXXX, default = TRUE |

`size_scale` |
method to scale the sizefact, sum(sizefact)=1 when size_scale="sum", sizefact[1]=1 when size_scale="first" |

`sizescalebythreshold` |
XXXX, default = FALSE |

`covrob` |
whether to use robust covariance in calculating the prior precision matrix 1, default = FALSE |

`preci1con` |
The user input constant term in specifying precision matrix 1, default=1/25 |

`cutoff` |
term in calculating precision matrix 1, default=15 |

`confac` |
The user input factor for contrast in precision matrix 1, default=1 |

a list of following items

threshold0, matrix of estimated threshold for iter=1, features in columns and threshold for different slides in rows.

threshold, matrix of estimated threshold for iter=2, features in columns and threshold for different slides in rows.

normmat0, matrix of log2 expression for iter=1, features in columns and log2 expression in rows.

normmat, matrix of log2 expression for iter=2, features in columns and log2 expression in rows.

sizefact, estimated sizefact

sizefact0, estimated sizefact in iter=1

preci1, precision matrix 1

Im0, Information matrix in iter=1

Im, Information matrix in iter=2

conv0, vector of convergence for iter=1, 0 converged, 1 not converged

conv, vector of convergence for iter=2, 0 converged, 1 not converged

features_high, same as the input features_high

features_all, same as the input features_all

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