Estimates treatment effects from a two-colour microarray design (D.B. Baird).

### Options

`PRINT` = string tokens |
What to print (`design` , `summary` , `monitoring` ); default `desi` , `summ` , `moni` |
---|---|

`DYEBIASMETHOD` = string token |
Whether to estimate dye bias effects (`estimate` , `omit` ); default `esti` |

`SPREADSHEET` = string tokens |
What results to put in spreadsheets (`estimates` , `df` , `rsd` , `dyebias` , `seestimates` , `tvalues` , `probabilities` , `contrasts` , `secontrasts` , `tcontrasts` , `prcontrasts` ); default `esti` , `df` , `rsd` , `dyeb` , `sees` , `tval` , `prob` , `cont` , `seco` , `tcon` , `prco` |

### Parameters

`LOGRATIOS` = variates or pointers |
Log-ratios |
---|---|

`PROBES` = factors or texts |
Probes for the log-ratios |

`SLIDES` = factors or texts |
Slides for the log-ratios |

`REDTREATMENTS` = factors |
Targets on red dye for slides |

`GREENTREATMENTS` = factors |
Targets on green dye for slides |

`CHECK` = texts or variates |
Slide ID’s of the red and green treatments for a check matching the slide order with the labels or levels of `SLIDE` |

`XCONTRASTS` = matrices |
Contrasts to estimate |

`IDPROBES` = texts |
Saves the probe names for each output row |

`DF` = variates |
Saves degrees of freedom for t-values |

`RSD` = variates |
Saves the residual standard deviation |

`DYEBIAS` = variates |
Saves estimated dye swap bias effects |

`ESTIMATES` = pointers |
Saves the estimates |

`SEESTIMATES` = pointers |
Saves the standard errors of the estimates |

`TVALUES` = pointers |
Saves t-values of the estimates |

`PROBABILITIES` = pointers |
Saves probabilities for the t-values |

`CONTRASTS` = pointers |
Saves estimates of the contrasts |

`SECONTRASTS` = pointers |
Saves the standard errors of the contrasts |

`TCONTRASTS` = pointers |
Saves t-values for the contrasts |

`PRCONTRASTS` = pointers |
Saves probabilities for the contrasts |

### Description

`MAESTIMATE`

estimate effects from the within-slide differences between targets (or treatments). This information is contained in the log-ratios. Usually, these log-ratios will have normalized using the `MNORMALIZE`

procedure. `MAESTIMATE`

uses analysis of variance with a pooled error across the targets (i.e. treatments) for each probe (or gene). The normalization of each slide effectively removes the block effects, so the log-ratios now reflect the differences between treatments on each slide, and the constant represents the dye-swap effect for the probe. The `DYEBIASMETHOD`

option controls whether or not the dye biases are estimated, and the `XCONTRASTS`

parameter allows you to specify a matrix defining contrasts to estimate between treatments.

The log-ratios are supplied by the `DATA`

parameter. If these are in a single variate, the `SLIDE`

parameter should supply a factor to index the slides, and the `PROBES`

parameter should index the probes or genes. Alternatively, you can supply a pointer containing a variate for each slide. The `SLIDES`

parameter can then be omitted, or it can supply a text with an entry for each slide. The `PROBES`

parameter can supply either a factor or a text, defining the probes on a single slide, and all slides must have a common layout.

The `REDTREATMENTS`

parameter should supply a factor to indicate the target assigned to the red dye. This is assumed to be the channel on the top of the log-ratios. This factor must have the same number of values as the number of levels of the Slides factor. Similarly, the `GREENTREATMENTS`

parameter should supply a factor to indicate the target assigned to the green dye. The `CHECK`

parameter can supply a text or variate identifying the slide in each unit of the `REDTREATMENTS`

and `GREENTREATMENTS`

factors. This can then be used to check that these units match the slides according to the labels or levels of the `SLIDE`

factor. If the labels of the slides and check factor match, but are in a different order, the treatment factors will be sorted into the correct order, and a warning is given.

The other parameters allow you to save results from the analysis, and the `SPREADSHEET`

option allows these to be put into Genstat spreadsheets.

Options: `PRINT`

, `DYEBIASMETHOD`

, `SPREADSHEET`

.

Parameters: `LOGRATIOS`

, `PROBES`

, `SLIDES`

, `REDTREATMENTS`

, `GREENTREATMENTS`

, `CHECK`

, `XCONTRASTS`

, `IDPROBES`

, `DF`

, `RSD`

, `DYEBIAS`

, `ESTIMATES`

, `SEESTIMATES`

, `TVALUES`

, `PROBABILITIES`

, `CONTRASTS`

, `SECONTRASTS`

, `TCONTRASTS`

, `PRCONTRASTS`

.

### See also

Procedures: `DMADENSITY`

, `FDRBONFERRONI`

, `FDRMIXTURE`

, `MACALCULATE`

, `MAHISTOGRAM`

, `MAPCLUSTER`

, `MAPLOT`

, `MASCLUSTER`

, `MASHADE`

, `MAVOLCANO`

, `MA2CLUSTER`

, `MNORMALIZE`

.

Commands for: Microarray data.

### Example

CAPTION 'MAESTIMATE example'; STYLE=meta ENQUIRE CHANNEL=3(-1); EXIST=check[1...3]; NAME=\ '%GENDIR%/Data/Microarrays/ApoAIKnockOutStacked.GSH',\ '%GENDIR%/Data/Microarrays/ApoAIKnockOutSlides.gsh',\ '%GENDIR%/Data/Microarrays/ApoAIKnockOutContrast.GSH' IF VSUM(check).EQ.3 SPLOAD '%GENDIR%/Data/Microarrays/ApoAIKnockOutStacked.GSH' SPLOAD '%GENDIR%/Data/Microarrays/ApoAIKnockOutSlides.gsh' SPLOAD '%GENDIR%/Data/Microarrays/ApoAIKnockOutContrast.GSH' " Estimate Effects from Microarray Data." MAESTIMATE [PRINT=design,summary; DYEBIAS=omit;\ SPREADSHEET=* "df,rsd,estimates,seestimates,tvalues,\ probabilities,contrasts,secontrasts,tcontrasts,prcontrasts"]\ LOGRATIO=cLogRatio;SLIDES=Slide; PROBES=NAME;\ REDTREATMENTS=Red_Treat; GREENTREATMENTS=Green_Treat;\ CHECK=SlideName; XCONTRASTS=KOvsN; IDPROBES=Probes; DF=DF;\ RSD=Res_SD; ESTIMATES=Est; SEESTIMATES=SE; TVALUES=TEst;\ PROBABILITIES=PrEst; CONTRASTS=Cont; SECONTRASTS=SECont;\ PRCONTRASTS=PrCon; TCONTRASTS=TCon ELSE CAPTION 'Microarray example datasets have not been installed.' ENDIF