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Normalize set of signals with peaks

* Yout* = msnorm(

`X`

`Intensities`

[

`Yout`

`NormParameters`

msnorm(

`X`

`NewY`

`NormParameters`

msnorm(..., 'Quantile',

`QuantileValue`

msnorm(..., 'Limits',

`LimitsValue`

msnorm(..., 'Consensus',

`ConsensusValue`

msnorm(..., 'Method',

`MethodValue`

msnorm(..., 'Max',

`MaxValue`

`X` | Vector of separation-unit values for
a set of signals with peaks. The number of elements in the vector
equals the number of rows in the matrix .
The separation unit can quantify wavelength, frequency, distance,
time, or m/z depending on the instrument that generates the signal
data.`Intensities` |

`Intensities` | Matrix of intensity values for a set
of peaks that share the same separation-unit range. Each row corresponds
to a separation-unit value, and each column corresponds to either
a set of signals with peaks or a retention time. The number of rows
equals the number of elements in vector .`X` |

Use the following syntaxes with data from any separation technique that produces signal data, such as spectroscopy, NMR, electrophoresis, chromatography, or mass spectrometry.

normalizes
a group of signals with peaks by standardizing the area under the
curve (AUC) to the group median.* Yout* = msnorm(

`X`

`Intensities`

`[`

returns a structure containing the parameters
to normalize another group of signals.* Yout*,

`NormParameters`

`msnorm(`

uses
the parameter information from a previous normalization specified
by * X*,

`NewY`

`NormParameters`

`NormParameters`

`NewY`

`NormParameters`

`msnorm`

. If a consensus proportion, `ConsensusValue`

`msnorm(..., '`

calls * PropertyName*',

`PropertyValue`

`msnorm`

with optional properties
that use property name/property value pairs. You can specify one or
more properties in any order. Each `PropertyName`

```
msnorm(..., 'Quantile',
```

specifies a * QuantileValue*,
...)

`1`

-by-`2`

vector
with the quantile limits for reducing the set of separation-unit values
in `X`

`QuantileValue`

is ```
[0.9
1
```

], only the largest `10`

`%`

of
intensities in each signal are used to compute the AUC. When `QuantileValue`

is
a scalar, the scalar value represents the lower quantile limit and
the upper quantile limit is set to `1`

. The default
value is `[0 1]`

(use the whole area under the curve,
AUC).`msnorm(..., 'Limits', `

specifies a * LimitsValue*,
...)

`1`

-by-`2`

vector
with a separation-unit range for picking normalization points. This
parameter is useful to eliminate low-mass noise from the AUC calculation,
for example the matrix noise that appears in the low-mass region of
SELDI mass spectrometers. Default is `[0, max(``X`

)]

.`msnorm(..., 'Consensus', `

sets a consensus rule. To be included in the AUC,
a separation-unit position must have an intensity within the quantile
limits of at least part (specified by * ConsensusValue*,
...)

`ConsensusValue`

`Intensities`

`0`

to `1`

.Use the `'Consensus'`

property to eliminate
low-intensity peaks and noise from the normalization.

`msnorm(..., 'Method', `

selects a method for normalizing the AUC of every
signal. Enter either * MethodValue*,
...)

`'Median'`

(default) or `'Mean'`

.`msnorm(..., 'Max', `

, after individually normalizing each signal, scales
each signal to an overall maximum intensity specified by * MaxValue*,
...)

`MaxValue`

`MaxValue`

`QuantileValue`

```
[1
1]
```

, then a single point (peak height of the tallest peak)
is normalized to `MaxValue`

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