View source: R/higherOrderNormMethods.R

getRTNormalizedMatrix | R Documentation |

The function orders the retention times and steps through them using the supplied step size (in minutes). If smaller than a fixed lower boundary the window is expanded to ensure a minimum amount of data in each normalization step. An offset can be specified which can be used to perform multiple RT-segmentations with partial overlapping windows.

```
getRTNormalizedMatrix(
rawMatrix,
retentionTimes,
normMethod,
stepSizeMinutes = 1,
windowMinCount = 100,
offset = 0,
noLogTransform = FALSE
)
```

`rawMatrix` |
Target matrix to be normalized |

`retentionTimes` |
Vector of retention times corresponding to rawMatrix |

`normMethod` |
The normalization method to apply to the time windows |

`stepSizeMinutes` |
Size of windows to be normalized |

`windowMinCount` |
Minimum number of values for window to not be expanded. |

`offset` |
Whether time window should shifted half step size |

`noLogTransform` |
Don't log-transform the data |

Normalized matrix

```
data(example_data_small)
data(example_design_small)
data(example_data_only_values)
dataMat <- example_data_only_values
retentionTimes <- as.numeric(example_data[, "Average.RT"])
performCyclicLoessNormalization <- function(rawMatrix) {
log2Matrix <- log2(rawMatrix)
normMatrix <- limma::normalizeCyclicLoess(log2Matrix, method="fast")
colnames(normMatrix) <- colnames(rawMatrix)
normMatrix
}
rtNormMat <- getRTNormalizedMatrix(dataMat, retentionTimes,
performCyclicLoessNormalization, stepSizeMinutes=1, windowMinCount=100)
```

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