View source: R/function_Spectra_metrics.R
precursorIntensityQuartiles | R Documentation |
MS:4000116
"From the distribution of MS2 precursor intensities, the quantiles. E.g. a
value triplet represents the quartiles Q1, Q2, Q3. The intensity
distribution of the precursors informs about the dynamic range of the
acquisition." [PSI:MS]
MS:40000161
From the distribution of identified MS2 precursor intensities, the quantiles.
E.g. a value triplet represents the quartiles Q1, Q2, Q3. The intensity
distribution of the precursors informs about
the dynamic range of the acquisition in relation to identifiability.
The used type of identification should be noted in the metadata or analysis
methods section of the recording file for the respective run. In case of
multiple acceptance criteria (FDR) available in proteomics, PSM-level FDR
should be used for better comparability." [PSI:MS]"
id: MS:4000162
"From the distribution of unidentified MS2 precursor intensities, the
quantiles. E.g. a value triplet represents the quartiles Q1, Q2, Q3.
The intensity distribution of the precursors informs about the
dynamic range of the acquisition in relation to identifiability. The used
type of identification should be noted in the metadata or analysis methods
section of the recording file for the respective run. In case of multiple
acceptance criteria (FDR) available in proteomics, PSM-level FDR should be
used for better comparability." [PSI:MS]"
The metric is calculated as follows:
(1) the Spectra
object is filtered according to the MS level,
(2) the intensity of the precursor ions within the Spectra
object
are obtained,
(3) the 25%, 50%, and 75% quantile of the precursor intensity values are
obtained (NA
values are removed) and returned.
precursorIntensityQuartiles(
spectra,
msLevel = 1L,
identificationLevel = c("all", "identified", "unidentified"),
...
)
spectra |
|
msLevel |
|
identificationLevel |
|
... |
not used here |
id: MS:4000116
is_a: MS:4000004 ! n-tuple
relationship: has_metric_category MS:4000009 ! ID free metric
relationship: has_metric_category MS:4000022 ! MS2 metric
relationship: has_value_concept STATO:0000291 ! quantile
relationship: has_value_type xsd:float ! The allowed value-type for this CV
term
relationship: has_units MS:1000043 ! intensity unit
MS:4000161
is_a: MS:4000004 ! n-tuple
is_a: MS:4000008 ! ID based
relationship: has_metric_category MS:4000022 ! MS2 metric
relationship: has_value_concept STATO:0000291 ! quantile
relationship: has_value_type xsd:float ! The allowed value-type for this CV
term
relationship: has_units MS:1000043 ! intensity unit
id: MS:4000162
is_a: MS:4000004 ! n-tuple
is_a: MS:4000008 ! ID based
relationship: has_metric_category MS:4000022 ! MS2 metric
relationship: has_value_concept STATO:0000291 ! quantile
relationship: has_value_type xsd:float ! The allowed value-type for this CV
term
relationship: has_units MS:1000043 ! intensity unit
numeric(3)
The Spectra
object might contain features that were (not) identified. If
the calculation needs to be done according to *MS:4000161*/*MS:4000162*, the
Spectra
object should be prepared accordingly.
Thomas Naake
library(S4Vectors)
library(Spectra)
spd <- DataFrame(
msLevel = c(2L, 2L, 2L),
polarity = c(1L, 1L, 1L),
id = c("HMDB0000001", "HMDB0000001", "HMDB0001847"),
name = c("1-Methylhistidine", "1-Methylhistidine", "Caffeine"))
## Assign m/z and intensity values
spd$mz <- list(
c(109.2, 124.2, 124.5, 170.16, 170.52),
c(83.1, 96.12, 97.14, 109.14, 124.08, 125.1, 170.16),
c(56.0494, 69.0447, 83.0603, 109.0395, 110.0712,
111.0551, 123.0429, 138.0662, 195.0876))
spd$intensity <- list(
c(3.407, 47.494, 3.094, 100.0, 13.240),
c(6.685, 4.381, 3.022, 16.708, 100.0, 4.565, 40.643),
c(0.459, 2.585, 2.446, 0.508, 8.968, 0.524, 0.974, 100.0, 40.994))
spd$precursorIntensity <- c(100.0, 100.0, 100.0)
sps <- Spectra(spd)
precursorIntensityQuartiles(spectra = sps, msLevel = 2L)
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