createBaseline creates and initialize a Baseline object.
createBaseline(
description = "",
db = data.frame(),
regionDefinition = createRegionDefinition(),
testStatistic = "",
regions = NULL,
numbOfSeqs = matrix(),
binomK = matrix(),
binomN = matrix(),
binomP = matrix(),
pdfs = list(),
stats = data.frame()
)A Baseline object.
character providing general information regarding the
sequences, selection analysis and/or object.
data.frame containing annotation information about
the sequences and selection results.
RegionDefinition object defining the regions and boundaries of the Ig sequences.
character indicating the statistical framework
used to test for selection. For example, "local" or
"focused" or "imbalanced".
character vector defining the regions the BASELINe
analysis was carried out on. For "cdr" and "fwr"
or "cdr1", "cdr2", "cdr3", etc. If NULL
then regions will be determined automatically from regionDefinition.
matrix of dimensions r x c containing the number of
sequences or PDFs in each region, where:
r = number of rows = number of groups or sequences.
c = number of columns = number of regions.
matrix of dimensions r x c containing the number of
successes in the binomial trials in each region, where:
r = number of rows = number of groups or sequences.
c = number of columns = number of regions.
matrix of dimensions r x c containing the total
number of trials in the binomial in each region, where:
r = number of rows = number of groups or sequences.
c = number of columns = number of regions.
matrix of dimensions r x c containing the probability
of success in one binomial trial in each region, where:
r = number of rows = number of groups or sequences.
c = number of columns = number of regions.
list of matrices containing PDFs with one item for each
defined region (e.g. cdr and fwr). Matrices have dimensions
r x c dementions, where:
r = number of rows = number of sequences or groups.
c = number of columns = length of the PDF (default 4001).
data.frame of BASELINe statistics,
including: mean selection strength (mean Sigma), 95% confidence
intervals, and p-values with positive signs for the presence of
positive selection and/or p-values with negative signs for the
presence of negative selection.
Create and initialize a Baseline object.
The testStatistic indicates the statistical framework used to test for selection.
For example,
local = CDR_R / (CDR_R + CDR_S).
focused = CDR_R / (CDR_R + CDR_S + FWR_S).
immbalance = CDR_R + CDR_s / (CDR_R + CDR_S + FWR_S + FWR_R)
For focused the regionDefinition must only contain two regions. If more
than two regions are defined, then the local test statistic will be used.
For further information on the frame of these tests see Uduman et al. (2011).
Hershberg U, et al. Improved methods for detecting selection by mutation analysis of Ig V region sequences. Int Immunol. 2008 20(5):683-94.
Uduman M, et al. Detecting selection in immunoglobulin sequences. Nucleic Acids Res. 2011 39(Web Server issue):W499-504.
Yaari G, et al. Models of somatic hypermutation targeting and substitution based on synonymous mutations from high-throughput immunoglobulin sequencing data. Front Immunol. 2013 4(November):358.
See Baseline for the return object.
# Creates an empty Baseline object
createBaseline()
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