Title: | PepSAVI-MS Data Analysis |
---|---|
Description: | An implementation of the data processing and data analysis portion of the PepSAVI-MS pipeline developed by principal investigator Christine Kirkpatrick and the Hicks Laboratory at the University of North Carolina, as presented in the paper \emph{The "PepSAVI-MS" Pipeline for Natural Product Bioactive Peptide Discovery} (DOI:10.1021/acs.analchem.6b03625). The statistical analysis package presented herein provides a collection of software tools used to facilitate the prioritization of putative bioactive peptides from a complex biological matrix. Tools are provided to deconvolute mass spectrometry features into a single representation for each peptide charge state, filter compounds to include only those possibly contributing to the observed bioactivity, and prioritize these remaining compounds for those most likely contributing to each bioactivity data set. |
Authors: | Pritchard David [aut, cre], Kirkpatrick Christine [aut] |
Maintainer: | Pritchard David <[email protected]> |
License: | CC BY-NC-SA 4.0 |
Version: | 0.9.2 |
Built: | 2024-10-22 04:15:15 UTC |
Source: | https://github.com/dapritchard/pepsavims |
Combines mass spectrometry observations that are believed to belong to the
same underlying compound into a single observation. In concept, the data
produced by the mass spectrometer may produce multiple reads for a single
compound; thus, binMS
attempts to recover these underlying compounds
through a binning procedure, described in more detail in Details
.
binMS(mass_spec, mtoz, charge, mass = NULL, time_peak_reten, ms_inten = NULL, time_range, mass_range, charge_range, mtoz_diff, time_diff)
binMS(mass_spec, mtoz, charge, mass = NULL, time_peak_reten, ms_inten = NULL, time_range, mass_range, charge_range, mtoz_diff, time_diff)
mass_spec |
Either a For example, suppose that a collection of mass spectrometry intensity
observations has provided data for 50 fractions across 20,000
mass-to-charge values. Then the input for |
mtoz |
A vector of either length 1 or length equal to the number of mass-to-charge values for which mass spectrometry data was collected, and which helps identify the mass-to-charge values for this data in one of several ways. One way to provide the information is to provide a numeric vector where
each entry provides the mass-to-charge value for a corresponding row of
mass spectrometry data. Then the A second way is to provide a single number which specifies the column
index in the A third way is provide a single character string which provides the
column name in the |
charge |
The information for the |
mass |
The information for the mass need not be provided, as it can be
derived using the mass-to-charge and charge information; in this case the
parameter should be given its default, i.e. |
time_peak_reten |
The information for the |
ms_inten |
Either |
time_range |
A length-2 numeric vector specifying the lower bound and upper bound (inclusive) of allowed peak retention time occurance for an observation to be included in the consolidation process. |
mass_range |
A length-2 numeric vector specifying the lower bound and upper bound (inclusive) of allowed mass for an observation to be included in the consolidation process. |
charge_range |
A length-2 numeric vector specifying the lower bound and upper bound (inclusive) of allowed electrical charge state for an observation to be included in the consolidation process. |
mtoz_diff |
A single numerical value such that any two observations with a larger absolute difference between their mass-to-charge values are considered to have originated from different underlying compounds. Two observations with a smaller absolute difference between their mass-to-charge values could potentially be considered to originate from the same underlying compound, contingent on other criteria also being met. Nonnegative values are allowed; such a value has the effect of not consolidating any groups, and consequently reduces the function to a filtering routine only. |
time_diff |
A single numerical value such that any two observations with a larger absolute difference between their peak elution times are considered to have originated from different underlying compounds. Two observations with a smaller absolute difference between their peak elution times could potentially be considered to originate from the same underlying compound, contingent on other criteria also being met. Nonnegative values are allowed; such a value has the effect of not consolidating any groups, and consequently reduces the function to a filtering routine only. |
The algorithm described in what follows attempts to combines mass spectrometry observations that are believed to belong to the same underlying compound into a single observation for each compound. There are two conceptually separate steps.
The first step is as follows. All observations must satisfy each of the following criteria for inclusion in the binning process.
Each observation must have its peak elution time occur during the
interval specified by time_range
Each observation must have a mass that falls within the interval
specified by mass_range
Each observation must have an electrical charge state that falls
within the interval specified by charge_range
Once that a set of observations satisfying the above criteria is obtained, then a second step attempts to combine observations believed to belong to the same underlying compound. The algorithm considers two observations that satisfy each of the following criteria to belong to the same compound.
The absolute difference in Daltons of the mass-to-charge value
between the two observations is less the the value specified by
mtoz_diff
The absolute difference of the peak elution time between the two
observations is less than the value specified by time_pr_diff
The electrical charge state must be the same for the two observations
Then the binning algorithm is defined as follows. Consider an observation that satisfies the inclusion criteria; this observation is compaired pairwise with every other observation that satisfies the inclusion criteria. If a pair of observations satisfies the criteria determining them to belong to the same underlying compound then the two observations are merged into a single observation. The two previous compounds are removed from the working set, and the process starts over with the newly created observation. The process repeats until no other observation in the working set meets the criteria determining it to belong to the same underlying compound as that of the current observation; at this point it is considered that all observations belonging to the compound have been found, and the process starts over with a new observation.
The merging process has not yet been defined; it is performed by averaging the mass-to-charge values and peak elution times, and summing the mass spectrometry intensities at each fraction. Although observations are merged pairwise, when multiple observations are combined in a sequence of pairings, the averages are given equal weight for all of the observations. In other words, if a pair of observations are merged, and then a third observation is merged with the new observation created by combining the original two, then the mass-to-charge value and peak elution time values of the new observation are obtained by summing the values for each of the three original observations and dividing by three. The merging process for more than three observations is conducted similarly.
Having described the binning algorithm, it is apparent that there are scenarios in which the order in which observations are merged affects the outcome of the algorithm. Since it seems that a minumum requirement of any binning algorithm is that the algorithm is invariant to the ordering of the observations in the data, this algorithm abides by the following rules. The observations in the data are sorted in increasing order by mass-to-charge value, peak elution time, and electical charge state, respectively. Then when choosing an observation to compare to the rest of the set, we start with the observation at the top of the sort ordering, and compare it one-at-a-time to the other elements in the set according to the same ordering. When a consolidated observation is complete in that no other observation left in the working set satisfies the merging criteria, then this consolidated observation can be removed from consideration for all future merges.
Returns an object of class binMS
which inherits from
msDat
. This object is a list
with elements described
below. The class is equipped with a print
, summary
, and
extractMS
function.
msDatObj
An object of class msDat
that
encapsulates the mass spectrometry data for the consolidated data.
summ_info
A list containing information pertaining to the consolidation process; for use by the summary function.
# Load mass spectrometry data data(mass_spec) # Perform consolidation via binMS bin_out <- binMS(mass_spec = mass_spec, mtoz = "m/z", charge = "Charge", mass = "Mass", time_peak_reten = "Reten", ms_inten = NULL, time_range = c(14, 45), mass_range = c(2000, 15000), charge_range = c(2, 10), mtoz_diff = 0.05, time_diff = 60) # print, summary function bin_out summary(bin_out) # Extract consolidated mass spectrometry data as a matrix or msDat object bin_matr <- extractMS(msObj = bin_out, type = "matrix") bin_msDat <- extractMS(msObj = bin_out, type = "matrix")
# Load mass spectrometry data data(mass_spec) # Perform consolidation via binMS bin_out <- binMS(mass_spec = mass_spec, mtoz = "m/z", charge = "Charge", mass = "Mass", time_peak_reten = "Reten", ms_inten = NULL, time_range = c(14, 45), mass_range = c(2000, 15000), charge_range = c(2, 10), mtoz_diff = 0.05, time_diff = 60) # print, summary function bin_out summary(bin_out) # Extract consolidated mass spectrometry data as a matrix or msDat object bin_matr <- extractMS(msObj = bin_out, type = "matrix") bin_msDat <- extractMS(msObj = bin_out, type = "matrix")
The relative relative growth inhibition of bioactivity levels for the bacteria and virus strains studies in Kirkpatrick et al. (2016).
data(bioact)
data(bioact)
A list
containing relative growth inhibition of bioactivity
levels for the bacteria and virus strains listed below. Each of the
following elements in the list
is a data.frame
with 3 rows
and 44 columns (with the exception of F. graminearum which has 2
rows). The rows in each data.frame
correspond to replications of
the data collection process, while the columns correspond to relative
growth inhibition bioactivity levels when subject to peptide libraries
across fractions 1-43 and fraction 47.
E. coli
S. aureus
K. pneumoniae
A. baumannii
A. baumannii
P. aeruginosa
F. graminearum
Extract an ordered list of candidate compounds from a rankEN
object.
The list is presented in the form of a data.frame
, such that each row
provides the identifying information for a particular candidate compound, and
with the rows arranged in the order that the compounds entered the elastic
net model (i.e. row 1 is the earliest, row 2 the 2nd earliest, etc.). The
columns of the data.frame
provide the mass-to-charge information,
charge information, and possibly the correlation between the compound and the
within-fraction average of the bioactivity replicates in the region of
interest.
extract_ranked(rankEN_obj, include_cor = TRUE)
extract_ranked(rankEN_obj, include_cor = TRUE)
rankEN_obj |
An object of class |
include_cor |
Either |
Extract mass spectrometry data from an object with class binMS
, class
filterMS
, or class msDat
.
extractMS(msObj, type = "matrix")
extractMS(msObj, type = "matrix")
msObj |
An an object with class |
type |
A character string with value either "matrix", or "msDat". If
"matrix" is provided as the argument, then the mass-to-charge values,
charge values, and mass spectrometry data are combined into a single
matrix and returned. If "msDat" is provided as the argument, then an
|
A convenience function for extracting and inspecting the mass
spectrometry data in a binMS
, filterMS
, or msDat
object. binMS
and filterMS
objects are lists that contain
an msDat object, and specifying "msDat"
for type
merely
returns the msDat
element from the list for these classes of
object. specifying "msDat"
for an object with class "msDat"
merely returns the argument, i.e. is the identity function. When
"matrix"
is specified, then the elements in the embedded
msDat
object are combined into a single matrix using cbind
and returned.
Returns either a matrix containing the mass spectrometry data if
"matrix"
is specified as the argument to type
, or an object
with class msDat
if "msDat"
is specified as the argument to
type
. See Details
for more detail regarding the return
objects.
Filters mass spectrometry data using a set of criteria, described in
Details
. Returns an object of classes msDat
and
filterMS
.
filterMS(msObj, region, border = "all", bord_ratio = 0.05, min_inten = 1000, max_chg = 7L)
filterMS(msObj, region, border = "all", bord_ratio = 0.05, min_inten = 1000, max_chg = 7L)
msObj |
An object class |
region |
A vector either of mode character or mode numeric. If numeric
then the entries should provide the indices for the region of interest in
the mass spectrometry data provided as the argument for |
border |
Either a character string |
bord_ratio |
A single nonnegative numeric value. A value of 0 will not admit any compounds, while a value greater than 1 will admit all compounds (see criterion 2). |
min_inten |
A single numeric value. A value less than the minimum mass spectrometry value in the data will admit all compounds (see criterion 4). |
max_chg |
A single numeric value specifying the maximum charge which a compound may exhibit (see criterion 5) |
Attempts to filter out candidate compounds via subject-matter knowledge, with the goal of removing spurious noise from downstream models. The criteria for the downstream inclusion of a candidate compound is listed below.
The m/z intensity maximum must fall inside the range of the bioactivity region of interest
The ratio of the m/z intensity of a species in the areas bordering
the region of interest and the species maximum intensity must be less
than bord_ratio
. When there is no bordering area then it is
taken to mean that all observations satisfy this criterion.
The immediately right adjacent fraction to its maximum intensity fraction for a species must have a non-zero abundance. In the case of ties for the maximum, it is the fraction immediately to the right of the rightmost maximum fraction which cannot have zero abundance. When the fraction with maximum intensity is the rightmost fraction in the data for an observation, then it is taken to mean that the observation satisfies this criterion.
At least 1 fraction in the region of interest must have intensity
greater than min_inten
Compound charge state must be less than or equal to max_chg
Returns an object of class filterMS
which inherits from
msDat
. This object is a list
with elements described
below. The class is equipped with a print
, summary
, and
extractMS
function.
msDatObj
An object of class msDat
such that
the encapsulated mass spectrometry data corresponds to each of the
candidate compounds that satisfed each of the criteria. If no
criteria are satisfied then NULL
is returned.
cmp_by_crit
A list containing data.frame
s, one for
each criterion. Each row (if any) in one of the
sub-data.frame
s contains the mass-to-charge and charge
information for a candidate compound that satisfies the criterion
represented by the data.frame
; all of the compounds that
satisfied the criterion are included in the data. The
data.frame
s are named c1
, ..., c5
, etc
corresponding to criterion 1, ..., criterion 5.
summ_info
A list containing information pertaining to the filtering process; for use by the summary function.
# Load mass spectrometry data data(mass_spec) # Convert mass_spec from a data.frame to an msDat object ms <- msDat(mass_spec = mass_spec, mtoz = "m/z", charge = "Charge", ms_inten = c(paste0("_", 11:43), "_47")) # Filter out potential candidate compounds filter_out <- filterMS(msObj = ms, region = paste0("VO_", 17:25), border = "all", bord_ratio = 0.01, min_inten = 1000, max_chg = 7) # print, summary function filter_out summary(filter_out) # Extract filtered mass spectrometry data as a matrix or msDat object filter_matr <- extractMS(msObj = filter_out, type = "matrix") filter_msDat <- extractMS(msObj = filter_out, type = "matrix")
# Load mass spectrometry data data(mass_spec) # Convert mass_spec from a data.frame to an msDat object ms <- msDat(mass_spec = mass_spec, mtoz = "m/z", charge = "Charge", ms_inten = c(paste0("_", 11:43), "_47")) # Filter out potential candidate compounds filter_out <- filterMS(msObj = ms, region = paste0("VO_", 17:25), border = "all", bord_ratio = 0.01, min_inten = 1000, max_chg = 7) # print, summary function filter_out summary(filter_out) # Extract filtered mass spectrometry data as a matrix or msDat object filter_matr <- extractMS(msObj = filter_out, type = "matrix") filter_msDat <- extractMS(msObj = filter_out, type = "matrix")
The mass spectrometry data collected for and described in Kirkpatrick et al. (2016). See paper for a full description of the data collection process, or the package vignette for an abridged description.
data(mass_spec)
data(mass_spec)
A data.frame
with 30,799 mass spectrometry levels and 38
variables:
mass-to-charge ratio
The time in minutes at which the peak retention time was achieved
mass in daltons
charge state
intensity of each MS feature in fraction 11
intensity of each MS feature in fraction 12
intensity of each MS feature in fraction 13
intensity of each MS feature in fraction 14
intensity of each MS feature in fraction 15
intensity of each MS feature in fraction 16
intensity of each MS feature in fraction 17
intensity of each MS feature in fraction 18
intensity of each MS feature in fraction 19
intensity of each MS feature in fraction 20
intensity of each MS feature in fraction 21
intensity of each MS feature in fraction 22
intensity of each MS feature in fraction 23
intensity of each MS feature in fraction 24
intensity of each MS feature in fraction 25
intensity of each MS feature in fraction 26
intensity of each MS feature in fraction 27
intensity of each MS feature in fraction 28
intensity of each MS feature in fraction 29
intensity of each MS feature in fraction 30
intensity of each MS feature in fraction 31
intensity of each MS feature in fraction 32
intensity of each MS feature in fraction 33
intensity of each MS feature in fraction 34
intensity of each MS feature in fraction 35
intensity of each MS feature in fraction 36
intensity of each MS feature in fraction 37
intensity of each MS feature in fraction 38
intensity of each MS feature in fraction 39
intensity of each MS feature in fraction 40
intensity of each MS feature in fraction 41
intensity of each MS feature in fraction 42
intensity of each MS feature in fraction 43
intensity of each MS feature in fraction 47
msDat
Creates a data structure encapsulating the mass spectrometry intensity readings as well as identifying information
msDat(mass_spec, mtoz, charge, ms_inten = NULL)
msDat(mass_spec, mtoz, charge, ms_inten = NULL)
mass_spec |
Either a For example, suppose that a collection of mass spectrometry intensity
observations has provided data for 50 fractions across 20,000
mass-to-charge values. Then the input for |
mtoz |
A vector of either length 1 or length equal to the number of mass-to-charge values for which mass spectrometry data was collected, and which helps identify the mass-to-charge values for this data in one of several ways. One way to provide the information is to provide a numeric vector where
each entry provides the mass-to-charge value for a corresponding row of
mass spectrometry data. Then the A second way is to provide a single number which specifies the column
index in the A third way is provide a single character string which provides the
column name in the |
charge |
The information for the |
ms_inten |
Either |
Since the mass spectrometry data could conceivably be available to
the researcher in a variety forms, this function attempts to provide a
uniform data structure for encapsulating this information. It is the
fundamental data structure containing the mass spectrometry data used
internally by the filterMS
and rankEN
routines. The
external interface for msDat
is provided to the user so that
specifying the mass spectrometry information can be made in a distinct
step from performing statistical analyses, which it is hoped makes
interfaces for the downstream analysis routines simpler and more
intuitive to use.
Returns an object of class msDat
. This class is a list
with elements described below. The class is equipped with a print
and extractMS
function.
ms
A matrix
containing mass spectrometry intensity
readings. Each column provides the mass spectrometry values for a
given fraction, and each row provides the mass spectrometry values
for a given mass-to-charge ratio value across the fractions.
mtoz
A vector with length equal to the number of
mass-to-charge values provided in the mass spectrometry data, such
that the k
-th entry in the vector provides the mass-to-charge
value for the k
-th row of mass spectrometry data
chg
A vector with length equal to the number of
mass-to-charge values provided in the mass spectrometry data, such
that the k
-th entry in the vector provides the charge
information for the k
-th row of mass spectrometry data
# Load mass spectrometry data data(mass_spec) # Convert mass_spec from a data.frame to an msDat object ms <- msDat(mass_spec = mass_spec, mtoz = "m/z", charge = "Charge", ms_inten = c(paste0("_", 11:43), "_47")) # Dimension of the data dim(ms) # Print the first few rows and columns ms[1:5, 1:2] # Let's change the fraction names to something more concise colnames(ms) <- c(paste0("frac", 11:43), "frac47") # Print the first few rows and columns with the new fraction names ms[1:5, 1:8] # Suppose there are some m/z levels that we wish to remove ms <- ms[-c(2, 4), ] # Print the first few rows and columns after removing rows 2 and 4 ms[1:5, 1:8] # Suppose that there was an instrumentation error and that we need to change # some values ms[1, paste0("frac", 12:17)] <- c(55, 57, 62, 66, 71, 79) # Print the first few rows and columns after changing some of the values in # the first row ms[1:5, 1:10]
# Load mass spectrometry data data(mass_spec) # Convert mass_spec from a data.frame to an msDat object ms <- msDat(mass_spec = mass_spec, mtoz = "m/z", charge = "Charge", ms_inten = c(paste0("_", 11:43), "_47")) # Dimension of the data dim(ms) # Print the first few rows and columns ms[1:5, 1:2] # Let's change the fraction names to something more concise colnames(ms) <- c(paste0("frac", 11:43), "frac47") # Print the first few rows and columns with the new fraction names ms[1:5, 1:8] # Suppose there are some m/z levels that we wish to remove ms <- ms[-c(2, 4), ] # Print the first few rows and columns after removing rows 2 and 4 ms[1:5, 1:8] # Suppose that there was an instrumentation error and that we need to change # some values ms[1, paste0("frac", 12:17)] <- c(55, 57, 62, 66, 71, 79) # Print the first few rows and columns after changing some of the values in # the first row ms[1:5, 1:10]
filterMS
Displays the number of candidate compounds left in the data after filtering
## S3 method for class 'filterMS' print(x, ...)
## S3 method for class 'filterMS' print(x, ...)
x |
An object of class |
... |
Arguments passed to dot-dot-dot are ignored |
msDat
Prints the mass spectrometry data encapsulated by the msDat
object
## S3 method for class 'msDat' print(x, ...)
## S3 method for class 'msDat' print(x, ...)
x |
An object of class |
... |
Arguments passed to dot-dot-dot are ignored |
rankEN
Displays the data dimensions used to fit the elastic net model
## S3 method for class 'rankEN' print(x, ...)
## S3 method for class 'rankEN' print(x, ...)
x |
An object of class |
... |
Arguments passed to dot-dot-dot are ignored |
Returns identifying information for the compounds in the order in which the corresponding regression coefficient for a given compound first becomes nonzero as part of the Elastic Net path
rankEN(msObj, bioact, region_ms = NULL, region_bio = NULL, lambda, pos_only = TRUE, ncomp = NULL)
rankEN(msObj, bioact, region_ms = NULL, region_bio = NULL, lambda, pos_only = TRUE, ncomp = NULL)
msObj |
An object of class |
bioact |
Either a numeric vector or matrix, or a data frame providing bioactivity data. If a numeric vector, then it is assumed that each entry corresponds to a particular fraction. If the data is 2-dimensional, then it is assumed that each column corresponds to a particular fraction, and that each row corresponds to a particular bioactivity replicate. |
region_ms |
Either |
region_bio |
Either |
lambda |
A single nonnegative numeric value providing the quadratic penalty mixture parameter argument for the elastic net model. The elastic net fits the least squares model with penalty function
where |
pos_only |
Either |
ncomp |
Either |
rankEN
prepares the data by extracting the region of interest
from the mass spectrometry abundance data and from the bioactivity data.
If bioactivity replicates are present, then the within-fraction
replicates are averaged. Once the data has been converted into the
appropriate form, then an elastic net model is fitted by invoking the
enet
function from the elasticnet
package, and an ordered
list of candidate compounds is constructed such that compounds are ranked
by the order in which they first enter the model. The list may be
filtered and / or pruned before being returned to the user, as determined
by the arguments to pos_only
and ncomp
.
Returns an object of class rankEN
. This object is a
list
with elements described below. The class is equipped with a
print
, summary
, and extract_ranked
function.
mtoz
A vector providing the mass-to-charge values of the
candidate compounds, such that the k
-th element of the vector
provides the mass-to-charge value of the k
-th compound to
enter the elastic net model, possibly after removing compounds
nonpositively correlated with bioactivity levels.
charge
A vector providing the charge state of the
candidate compounds, such that the k
-th element of the vector
provides the charge state of the k
-th compound to enter the
elastic net model, possibly after removing compounds nonpositively
correlated with bioactivity levels.
comp_cor
A vector providing the correlation between each
of the candidate compounds and the bioactivity levels, such that the
k
-th element of the vector provides the correlation between
the k
-th compound to enter the elastic net model and the
bioactivity levels, possibly after removing compounds nonpositively
correlated with bioactivity levels.
enet_fit
The fitted model object produced by
rankEN
's internal invokation of the enet
function from
the elasticnet
package.
summ_info
A list containing information related to the data used to fit the elastic net model; used by the summary function.
# Load mass spectrometry data data(mass_spec) # Convert mass_spec from a data.frame to an msDat object ms <- msDat(mass_spec = mass_spec, mtoz = "m/z", charge = "Charge", ms_inten = c(paste0("_", 11:43), "_47")) # Load growth inhibition bioactivity data. Each element in bioact is a # stand-alone dataset for a species of virus or bacteria. data(bioact) # Perform the candidate ranking procedure with fractions 21-24 as the region # of interest. Note that it is not advisable to calculate the elastic net # estimates with 30,799 candidate compounds on 4 data points! ## Not run: rank_out <- rankEN(msObj = ms, bioact = bioact$ec, region_ms = paste0("_", 21:24), region_bio = paste0("_", 21:24), lambda = 0.001, pos_only = TRUE, ncomp = NULL) # print, summary function rank_out summary(rank_out) # Extract ranked compounds as a data.frame ranked_candidates <- extract_ranked(rank_out) ## End(Not run)
# Load mass spectrometry data data(mass_spec) # Convert mass_spec from a data.frame to an msDat object ms <- msDat(mass_spec = mass_spec, mtoz = "m/z", charge = "Charge", ms_inten = c(paste0("_", 11:43), "_47")) # Load growth inhibition bioactivity data. Each element in bioact is a # stand-alone dataset for a species of virus or bacteria. data(bioact) # Perform the candidate ranking procedure with fractions 21-24 as the region # of interest. Note that it is not advisable to calculate the elastic net # estimates with 30,799 candidate compounds on 4 data points! ## Not run: rank_out <- rankEN(msObj = ms, bioact = bioact$ec, region_ms = paste0("_", 21:24), region_bio = paste0("_", 21:24), lambda = 0.001, pos_only = TRUE, ncomp = NULL) # print, summary function rank_out summary(rank_out) # Extract ranked compounds as a data.frame ranked_candidates <- extract_ranked(rank_out) ## End(Not run)
Prints a text description of the binning process. Displays arguments passed
to the binMS
routine, how many m/z levels were chosen for each
criterion, how many candidate compounds were chosen overall, and how many
candidate compounds were obtained after consolidation.
## S3 method for class 'binMS' summary(object, ...)
## S3 method for class 'binMS' summary(object, ...)
object |
An object of class |
... |
Arguments passed to dot-dot-dot are ignored |
Prints a description of the filtering process. Displays arguments chosen for
the filterMS
constructor, how many candidate compounds were chosen for
each criterion, and how many candidate compounds were chosen overall.
## S3 method for class 'filterMS' summary(object, ...)
## S3 method for class 'filterMS' summary(object, ...)
object |
An object of class |
... |
Arguments passed to dot-dot-dot are ignored |
Prints a description of the elastic net variable selection process. Includes the dimensions used to fit the elastic net model, the fraction names for the mass spectrometry and the bioactivity data in the region of interest, the parameter specifications for the model, and a table with the identifying information of the candidate compounds produced by the model fit.
## S3 method for class 'rankEN' summary(object, max_comp_print = 20L, ...)
## S3 method for class 'rankEN' summary(object, max_comp_print = 20L, ...)
object |
An object of class |
max_comp_print |
A numeric value >= 1 specifying the maximum number of compounds to print |
... |
Arguments passed to dot-dot-dot are ignored |