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generateAdjacencyMatrix() has new parameter
method used to specify the algorithm. Accepts value
"pattern" to call the new routine for the pattern-based
algorithm.output_type = "individual"
now also saves the entire network list as an RData file
(.rda).ggplot2 (thanks to Teun van den Brand and the
ggplot2 development team for contributing the updates)igraph package. The test no longer passes with
igraph version 1.6.0. Rather than update the test to pass,
it has been removed to avoid future occurrences of this issue.levDistBounded() that causes undefined
behavior when either string is empty after removing the common prefix
and suffix. This bug does not appear to affect the returned value.levDistBounded.cpp and hamDistBounded.cpp
now use the string.h header instead of
strings.hgetClusterStats() now requires the cluster ID column to
be specified and present in the provided node metadata; it will no
longer compute cluster membership since it does not return the node
metadata (so any membership values computed are lost).addClusterMembership() now accepts and returns the list
of network objects instead of accepting and returning the node metadata
with the igraph as an additional input. The first parameter
data has been deprecated and moved in position, with the
second parameter net becoming the first parameter and
accepting the list of network objects instead of just the igraph. The
function still also supports the old usage (for now), as long as
net and data are specified by name (or the
updated argument positions are used). See section “Unified Primary
Argument Across Functions” for context."individual" as a default value for
output_type have been changed to "rds".
"rds" is the preferred default since it reduces file
size/clutter and the list of network objects can be restored intact (the
list is the primary input/output of core NAIR functions)
under any name desired. "rda" should be used if the file
will be transferred across machines (the list will be restored under the
name net), and "individual" should be used
when the output is to be accessed from outside of R.output_type = "individual" now writes the row names of
the node metadata to the first column of the csv file. These contain the
original row IDs from the input data.output_type in
findAssociatedClones() and input_type in
buildAssociatedClusterNetwork() changed from
"csv" to "rds", since these files are
intermediate outputs and typically there should be no need to access
them from outside of R or from another machine.buildPublicClusterNetworkByRepresentative() default
value of output_type changed from "rda" to
"rds".This section covers general new features. Other new features are grouped by subject in the following few sections.
buildRepSeqNetwork() now has the convenient alias
buildNet().buildRepSeqNetwork() now contains
an element details with network metadata such as the
argument values used in the function call.Several changes and additions have been made in favor of using the
list of network objects returned by buildRepSeqNetwork() as
a unified primary input and output across the core NAIR
functions. Adopting this convention offers several benefits: It greatly
simplifies usage, since users no longer need to know which components of
the list to input to which function (or what each function returns); it
eliminates the task of manually updating the list of network objects; it
results in the core functions working with the pipe operator; and most
importantly, it improves functionality within and between functions,
since functions can read and modify anything in the network list. For
instance, addPlots() can use the coordinate layout of any
existing plots to ensure a consistent layout across plots (which is no
longer guaranteed otherwise), while addClusterStats() can
add cluster membership values to the node metadata and record in
details that the cluster properties correspond to these
membership values (and not the values from a different instance of
clustering using a different algorithm).
The following changes encompass the move toward using the network list as a primary input/output:
addClusterMembership() parameters and return value have
changed. See the Breaking Changes section for details.addPlots() added as the preferred alternative to
generateNetworkGraphPlots() and
plotNetworkGraph()addClusterStats() added as the preferred alternative to
getClusterStats()addNodeStats() added as the preferred alternative to
addNodeNetworkStats()labelClusters() added as the preferred alternative to
addClusterLabels()labelNodes() added as the preferred alternative to
addGraphLabels()See the new “Supplementary Functions” vignette for examples.
The following changes and additions have been made to facilitate multiple instances of clustering on the same network using different clustering algorithms. See the new “Cluster Analysis” vignette for examples.
cluster_id_name that can be used to specify a custom name
for the cluster membership variable added to the node metadata.details recording the
clustering algorithm used and the name of the corresponding cluster
membership variable.addClusterStats(), information is added to
details recording the cluster membership variable
corresponding to the cluster properties.labelClusters() and addClusterLabels() now
check details to confirm that the cluster properties match
the specified cluster membership variable before using the node counts
in the cluster properties.labelClusters() and addClusterLabels() can
now be used without cluster properties; node count is computed from the
cluster membership values.labelClusters() can be used to label multiple plots at
once.addClusterMembership(), addClusterStats()
and addNodeStats() now allow custom argument values for
optional parameters of the clustering algorithm through the ellipses
(...) argument.It may also be of interest in the future to add functionality allowing the network list to contain multiple sets of cluster properties corresponding to different instances of clustering.
Plotting functions no longer fix the random seed when generating the coordinate layout for a plot. In order to facilitate a consistent layout across multiple plots of the same network graph, the following changes have been made.
buildRepSeqNetwork(), addPlots() and
generateNetworkGraphPlots() will all use a common
layout.buildRepSeqNetwork(),
addPlots() and generateNetworkGraphPlots() now
include a matrix graph_layout containing the layout used in
the plots.addPlots() will automatically use the
graph_layout mentioned above to ensure that new plots use
the same layout as existing plots.graph_layout is absent, addPlots() will
extract the layout from the first plot and use it for the new
plots.generateNetworkGraphPlots() has a new parameter
layout that can be used to specify the layout. Can be used
to generate new plots with the same layout as existing plots (though
addPlots() is easier). Can also be used to generate plots
with custom layout types other than the default layout created using
igraph::layout_components().saveNetworkPlots() has a new parameter
outfile_layout that can be used to save the graph
layout.saveNetwork() automatically saves the graph layout when
output_type = "individual".Essentially, generating new plots with addPlots() will
ensure a consistent layout with the initial plots. Fixing a random seed
before calling buildRepSeqNetwork() (or before the first
call to addPlots(), if buildRepSeqNetwork() is
called with plots = FALSE) allows the same layout to be
reproduced across multiple executions of the same code in which the
initial plots are generated.
file_list argument now accept a
list containing connections and file paths instead of only a character
vector of file paths. This allows a greater variety of data sources to
be used.input_type parameter that accept text formats have a
new parameter read.args that accepts a named list of
optional arguments to read.table() and its variants
read.csv(), etc. Dedicated arguments for
header and sep still exist apart from
read.args for backwards compatibility, but their defaults
now match input_type (e.g., sep defaults to
"," for input_type = "csv" and to
"" for input_type = "table").input_type = "tsv" now reads files using
read.delim() instead of read.table().input_type argument now also
support the value "csv2" for reading files using
read.csv2().<major>.<minor>.<patch>, and
in-development versions will follow the format
<major>.<minor>.<patch>.<dev>.plotNetworkGraph() deprecated in favor of
addPlots().filterInputData() argument count_col
deprecated. Rows with NA counts are no longer dropped.getClusterFun() argument cluster_fun
deprecated (see Breaking Changes)addNodeNetworkStats() deprecated in favor of
addNodeStats() (see section “Unified Primary Argument
Across Functions”)addClusterMembership() argument data
deprecated (see section “Unified Primary Argument Across
Functions”)addClusterMembership() argument fun
deprecated in favor of cluster_fun for consistency with
other functions.sparseAdjacencyMatFromSeqs() argument
max_dist deprecated in favor of dist_cutoff
for consistency with other functions.saveNetwork() argument output_filename
deprecated in favor of output_name for consistency with
other functions.sparseAdjacencyMatFromSeqs() deprecated in favor of its
better-named twin generateAdjacencyMatrix().generateNetworkFromAdjacencyMat() deprecated in favor
of its better-named twin generateNetworkGraph().output_type = "individual" now also saves the list of
plots (if present) to an RDS file. This prevents the ggraph
objects containing the plots from being lost, in case the user wishes to
modify these plots in the future.output_name parameter now
automatically replace potentially unsafe characters with underscores and
removes any leading or trailing non-alphanumeric characters. Safe
characters include alphanumeric characters, underscores and
hyphens.verbose argument which can be
set to TRUE to enable printing of console messages. For
logging purposes, these messages are now generated using
message() rather than cat(), and so send their
output to std.err() rather than
std.out().buildRepSeqNetwork(), addPlots() and
generateNetworkGraphPlots() now have
print_plots set to FALSE by default (plots are
no longer printed to the R plotting window unless manually
specified).buildAssociatedClusterNetwork() now removes duplicate
observations after loading the data from all neighborhoods. When
multiple associated sequences are similar, the same clone from a given
sample can belong to multiple neighborhoods. Previously, this occurrence
resulted in the same clone appearing multiple times in the global
network.simulateToyData() argument seed_value
removed. Users can set a seed prior to calling the function if
desired.generateNetworkGraphPlots() now handles the case where
color_nodes_by contains duplicate values by removing the
duplicate values with a warning. If color_scheme is a
vector, the corresponding entries of color_scheme are also
removed. Previously, this case resulted in a list of plots containing
two elements with the same name.generateNetworkGraphPlots() is called with a
non-numeric variable specified for size_nodes_by, the
function now defaults to fixed node sizes with a warning.addClusterStats() and
buildRepSeqNetwork(cluster_stats = TRUE) now call
sum() and max() with na.rm = TRUE
when computing abundance-based properties. This change reflects the fact
that buildRepSeqNetwork() no longer drops input data rows
with NA and NaN values in the count
column.combineSamples() and
loadDataFromFileList() now preserve the original row IDs of
each input file, which are prepended in the combined data by sample IDs
(if available) or the file number based on the order in
file_list.installPythonModules()kmeansAtchley()adjacencyMatAtchleyFromSeqs()encodeTCRSeqsByAtchleyFactor()dist_type argument of various package functions no
longer accepts the value "euclidean_on_atchley".hamDistBounded(),
levDistBounded(),
sparseAdjacencyMatFromSeqs(), and low-level argument
checks.dist_type
argument now accepts abbreviations of both "hamming" and
"levenshtein", such as "ham",
"lev", "h" and "l".fun argument of addClusterMembership()
is now passed to match.fun() before being called. This
change affects the cluster_fun argument of higher-level
functions, allowing users to specify clustering algorithms using the
syntax, e.g., cluster_fun = "cluster_walktrap" in addition
to the previously-accepted
cluster_fun = cluster_walktrap.findAssociatedClones() now cleans up after itself,
removing temporary files and directories it creates within the temporary
directory while performing its tasks.lifecycle package functions.Searching for Associated TCR/BCR Clusters,
Searching for Public TCR/BCR Clusters and
Network Visualization have been removed from the package
and now exist as articles on the package’s website. This was done to
reduce the size of the installed package.NAIR-package).Depends
field of DESCRIPTION, since version 3.0.2 or greater is needed to
require specific minimum versions of RcppArmadillo and Rcpp in the
LinkingTo field (requiring 3.1.0 since CRAN advises against
requiring R versions that don’t have 0 as the third value).lifecycle package to Imports,
imported the deprecated() function and copied lifecycle
badge images into the package files. Functions and their arguments can
now be assigned lifecycle stages and badges can be used in package
documentation files.reticulate package from
Imports and removed the associated scaffolding throughout
the package that was set up for integration with python scripts.packageStartupMessage() added to
.onAttach(): When loaded, the package will provide a
welcome message with instructions for getting started.findAssociatedClones
SampleID created in the output data is now
forced to be of type character. Previously, values from the argument
sample_ids were sometimes unintentionally converted from
character to numeric, such as the default values in
sample_ids, which were "1", "2",
etc. This was causing these variables to be treated as continuous
variables when used to color nodes in the network graph plot, which
resulted in their color scales being depicted in the wrong format in the
plot legend.sample_ids argument is now coerced to a character
vector. This prevents an error when saving the output that occurred when
sample_ids used numeric values.sample_ids now has entries
"Sample1", "Sample2", etc., instead of
"1", "2", etc.findPublicClusters
SampleID, SubjectID and
GroupID created in the output data are now forced to be of
type character. Previously, values from the arguments
sample_ids, subject_ids and
group_ids were sometimes unintentionally converted from
character to numeric, such as the default values in
sample_ids, which were "1", "2",
etc. This was causing these variables to be treated as continuous
variables when used to color nodes in the network graph plot, which
resulted in their color scales being depicted in the wrong format in the
plot legend.sample_ids argument is now coerced to a character
vector. This prevents an error when saving the output that occurred when
sample_ids used numeric values (which was the previous
default!).sample_ids now has entries
"Sample1", "Sample2", etc., instead of
1, 2, etc.buildPublicClusterNetwork
plot_title added with default value
"Global Network of Public Clusters". Previously this
argument was passed to buildRepSeqNetwork through the
ellipses ... argument, and thus used a default value of
"auto", which resulted in the default plot title being the
value of the output_name argument, which is
"PublicClusterNetwork" by default.plotNetworkGraph
pdf_width and pdf_height
for adjusting the dimensions of the pdf when saving this function’s
output directly using the outfile argument. Other package
functions use saveNetworkPlots for saving plots created
using plotNetworkGraph, so the absence of these arguments
in the plotNetworkGraph function had gone unnoticed
previously. But since the function has an option to save the output
directly to pdf using the outfile argument, it is only
appropriate to also provide control over the pdf dimensions.kmeansAtchley
amino_col and
sample_col arguments removed as the previous defaults are
no longer useful. They were originally designed based on a previous
version of the associated clusters workflow."atchley_kmeans_TCR_fraction_per_cluster.pdf" and
"atchley_kmeans_correlation_heatmap.pdf". The previous
values
"atchley_kmeans_cluster_relative_size_profiles_by_sample.pdf"
and
"atchley_kmeans_corr_in_cluster_size_profile_between_samples.pdf"
were longer and potentially more confusing in their meaning.Searching for Associated TCR/BCR Clusters
Searching for Public Clusters
buildRepSeqNetwork
Network Visualization
buildAssociatedClusterNetwork
cluster_id network property. This is because performing
clustering and obtaining the cluster membership is a primary purpose of
this function. It is still desirable for the user to be able to prevent
other node-level properties as well as cluster-level properties from
being computed if desired, but now doing so will not interfere with the
function accomplishing its purpose.findPublicClusters
SampleLevelCloseness to be left named as
closeness in the data frames for the filtered node-level
data. This bug was in turn causing this property to be overwritten by
the global network node property PublicCloseness when
calling buildPublicClusterNetwork.Searching for Associated TCR/BCR Clusters
Searching for Public Clusters
buildRepSeqNetwork
Network Visualization
buildAssociatedClusterNetwork
data_symbols argument changed from
NULL to "data" in order to match the output
format of findAssociatedClones when
findAssociatedClones is called with
output_type = "rda". Note this change only affects the case
when buildAssociatedClusterNetwork is called with
input_type = "rda"cluster_id network property. This is because performing
clustering and obtaining the cluster membership is a primary purpose of
this function. It is still desirable for the user to be able to prevent
other node-level properties as well as cluster-level properties from
being computed if desired, but now doing so will not interfere with the
function accomplishing its purpose.buildRepSeqNetwork
buildRepSeqNetwork and
saveNetwork, the vignette now specifies the R environment
variable name for the output list when it is saved to an Rdata file
using output_type = "rda".Searching for Associated TCR/BCR Clusters
buildRepSeqNetwork
cluster_id network property are now more clearly
explainedSearching for Associated TCR/BCR Clusters
filterInputData that raised an error
when the count_col and subset_cols arguments
were both non-nullplotNetworkGraph directly with a vector
provided to color_nodes_by and with
color_title = "auto" (the default), the function will
attempt to use the name of the vector for the color legend title. A
similar change applies with respect to the arguments
size_nodes_by and size_title.buildPublicClusterNetwork arguments
node_stats, stats_to_include and
cluster_stats are now deprecated and do nothing. All
node-level and cluster-level network properties are now automatically
computed. The arguments remain in order to maintain backwards
compatibility with user code, but raise a warning notifying the user of
their deprecated state when a non-null value is provided.igraph package (such as cluster_fast_greedy)
are now exported in the package NAMESPACE file so that they are
available to users. These functions can now be used as inputs to the
cluster_fun argument of various NAIR package
functions without the need to use the igraph:: prefix.Utility Functions vignette (formerly titled
Downstream Analysis) removed. Its content has been absorbed
into the buildRepSeqNetwork and
Network Visualization vignettesbuildRepSeqNetworkNetwork VisualizationSearching for Public ClustersplotNetworkGraph now recommends that
users prefer the higher-level function
generateNetworkGraphPlots over
plotNetworkGraph, since the former has arguments that
behave identically to those of buildRepSeqNetwork and
supports generation of multiple plots. plotNetworkGraph is
called by generateNetworkGraphPlots, so users should have
no need to call plotNetworkGraph directly. However,
plotNetworkGraph remains as an exported function available
to the user in order to maintain backwards compatibility with user
code.findAssociatedSeqs:
groups argument still exists but is now deprecated and
no longer used. Group labels are now automatically determined from the
unique values of group_idssample_ids argument still exists but is now deprecated
and no longer used. Custom sample IDs play no role in
findAssociatedSeqs; the argument was inherited from a
previous function that included the functionality of both
findAssociatedSeqs and
findAssociatedClonesfindPublicClusters now ignores
plots = TRUE when print_plots = FALSE and
output_dir_unfiltered = NULL. This prevents unused plots
from being generatedbuildAssociatedClusterNetwork now uses group ID as the
default variable for node colorsbuildPublicClusterNetwork and
buildPublicClusterNetworkByRepresentative now use sample ID
as the default variable for node colorsbuildPublicClusterNetworkByRepresentative default plot
title and subtitle updated for better claritybuildRepSeqNetwork, generateNetworkObjects
and generateNetworkGraphPlots now use
count_col as the default variable for node colors if
available, followed in priority by cluster ID, then network degree.addClusterLabels:
cluster_id_col added to permit use with node data where
the cluster ID variable has a custom name (e.g., with the output of
buildPublicClusterNetwork)greatest_values added, which can be set to
FALSE to prioritize the clusters to label based on the
least values of the criterion variable rather than the
greatest valuesexclusiveNodeStats has been added. This
function behaves in the same manner as chooseNodeStats, but
all arguments are set to FALSE by default. Useful when the
user only wishes to specify a small number of node-level properties to
compute, with all other properties excluded.NAIR: Network Analysis of Immune RepertoireSearching for Public TCR/BCR ClustersSearching for Associated TCR/BCR ClustersbuildRepSeqNetworkNetwork Visualization (incomplete, in progress)Downstream Analysis vignette title renamed to
Utility Functions. A revision to this vignette is planned
prior to version 1.0.buildRepSeqNetwork no longer returns an error with
dist_cutoff = 0 (fixed a bug involving the argument checks
added in version 0.0.9035).buildRepSeqNetworkbuildRepSeqNetwork now automatically attempts to
perform the following conversions:
filterInputData, which affect top-level functions such
as buildRepSeqNetwork that call it:
NA values in the
sequence column, with a warning producedcount_col arg; if provided, the count
column will be coerced to numeric and rows with NA/NaN
values in the count column will be dropped with a warningnode_stat_settings function now has a duplicate
with the less-confusing name chooseNodeStats; the newer
name is now used in place of node_stat_settings for
defaults and in the tutorialsstats_to_include argument of
addNodeNetworkStats, buildRepSetNetwork, etc.,
now also accepts a named logical vector with the same named elements as
the list previously required. A list will still work, for backwards
compatibility.chooseNodeStats / node_stat_settings now
generate a named logical vector rather than a list.dist_type argument is now more flexible in the
values it will accept; for example "lev" or simply
"l" is now equivalent to "levenshtein"simulateToyData to generate
datasimulateToyDatasimulateToyData to generate
data\code{} environmentaddGraphLabelsaddClusterLabelsDual-Chain Network Analysis for dual-chain
network analysis on single-cell databuildRepSeqNetwork vignette:
cluster_fun argument (clustering algorithm)Network Visualization vignette:
addClusterLabels functionDownstream Analysis vignette:
addClusterLabels functionFinding Associated Clones vignette:
addClusterLabels function used to label the
clustersaddGraphLabels for adding text labels to
the nodes of a graph plotaddClusterLabels for adding labels to
certain clusters in a graph plotaddClusterMembership() can now be controlled via a new
argument fun.cluster_fun
that is passed to the fun argument of
addClusterMembership():
addNodeNetworkStats()getClusterStats()buildRepSeqNetwork()buildAssociatedClusterNetwork()findPublicClusters()buildPublicClusterNetwork()buildPublicClusterNetworkByRepresentative()buildRepSeqNetwork() and
generateNetworkObjects() now return NULL with
a warning when the constructed network contains no edges.getClusterStats() now computes sequence-based
statistics (e.g., sequence with max count) for dual-chain networks,
including a separate set of such statistics for each chain.getClusterStats() have been changed to reflect broader
applicability to single-cell data:
max_clone_count changed to max_countagg_clone_count changed to agg_countverbose to
findAssociatedClones() that can be optionally set to
TRUE in order to print additional console output reporting
the number of clones in each neighborhood, both by sample and in
total.findAssociatedSeqs() was not
correctly computing the counts used for Fisher’s exact testfindPublicClones() involving
identification of the top n clusters by node count in each sample: when
more than one cluster possessed the nth highest node count, all of these
clusters were included in the top n clusters, resulting in more than n
clusters identified by this criterion. This has been reverted to the
behavior that existed prior to version 0.0.9018, whereby the first n
clusters are selected after sorting data rows by descending node count
using the order function.filterInputData() that was preventing
filtering by minimum sequence lengthBiocManager from Suggests field of
DESCRIPTION, since it is no longer used to access demonstration data
when building vignettes.simulateToyData()simulateToyData for
generating example (toy) data, primarily for use in vignettes, examples
and tests.simulateToyDatagenerateNetworkObjects(),
generateNetworkGraphPlots(),
filterInputData(), getNeighborhood(),
loadDataFromFileList(), combineSamples(),
saveNetwork(), and saveNetworkPlots().levAdjacencyMatSparse,
hamAdjacencyMatSparse,
generateNetworkFromSeqs, getSimilarClones and
filterClonesBySequenceLength.utils.R that
caused errors or warnings in rare cases.saveNetwork changed to user-facing
function saveNetwork, for use in saving output during
downstream analysisbuildRepSeqNetwork() (many of these changes
carry over to other functions):
output_type argument can be used to save the output list to
a rds or rda file, rather than the default behavior of saving each item
in an individual, uncompressed file. Rather than specifying the filename
of each item individually, the output_name argument accepts
a character string to be used as a common prefix for any files saved.
All items are now saved, and the save_all argument has been
removed.other_cols to
subset_cols to more accurately reflect its current role
(for keeping only certain input columns rather than all)drop_chars to
drop_matches to better imply that it takes regular
expressions and character stringsplot_width and
plot_height to pdf_width and
pdf_height to more clearly indicate that they affect the
dimensions of the saved pdf file, but not those of the plot at the
R object level (ggplot) or as it appears in
the R plotting window.plots which can be used to
prevent plots from being generated.generateNetworkGraphPlots via elipses
(...)return_all argument has been removed.cluster_stats = TRUE), the corresponding data frame in the
output list is now named cluster_data (previously was
cluster_stats)NULL with a warning when fewer
than 2 sequences exist after filtering; previously it returned an
error.buildRepSeqNetwork now supports a dual-chain approach
to analyzing single-cell RepSeq data: two cells (nodes) are considered
adjacent if and only if they possess similar receptor sequences in
both of two chains (e.g., alpha chain and beta chain). This is
done by supplying a vector with two column references to
seq_col instead of a single column reference, where the two
columns each contain the receptor sequence from a different chain (e.g.,
CDR3 sequences from alpha and beta chains) and each row corresponds to a
unique cell. This functionality can more generally be used to perform
network analysis where similarity is based on any two types of sequences
instead of one.findPublicClusters, it is now split across multiple
functions in a manner that reduces memory usage and increases the
flexibility of the workflow.
findPublicClusters now performs network analysis on
each sample individually to search for public clustersbuildPublicClusterNetwork combines the public clusters
across samples and performs network analysisbuildPublicClusterNetworkByRepresentative can be used
to perform network analysis on the combined public clusters using only a
single representative clone from each clusterkmeansAtchley functionfindAssociatedSeqs searches across samples for
associated clone sequences based on sample membership and Fisher’s exact
test P-valuefindAssociatedClones searches across samples for clones
within a neighborhood of each associated clone sequencebuildAssociatedClusterNetwork combines the
neighborhoods and performs network analysis and clusteringbuildRepSeqNetwork function
on the desired subset of the output from
buildAssociatedClusterNetworkkmeansAtchley functiongenerateNetworkGraphPlots() has been
added, which is capable of generating multiple plots with argument usage
similar to that used in buildRepSeqNetwork (e.g., multiple
color-code variables can be supplied, in which case color scheme and
color legend title arguments will meaningfully accept either a scalar or
vector valued argument)plotNetworkGraph():
buildRepSeqNetwork()show_color_legend = "auto",
which will show the color legend if color_nodes_by is a
continuous variable or a discrete variable with at most 20 distinct
values.getClusterStats() can now be used with
seq_col = NULL, as the sequence variable is only used for a
small number of statistics; similar to when
count_col = NULL, the dependent statistics will be
NA in the returned data frame, but other cluster properties
will still be computed.buildRepSeqNetwork() and other high-level functions
that generate a network from sequences now coerce the list of sequences
to a character vector if it is not already in this format (e.g.,
factors).buildRepSeqNetwork() and other top-level functions now
skip automatic plot generation when more than 1 million nodes are
present in the network. This is done to avoid a potential error when
calling ggplot that occurs when the combined nodes and
edges exceed its limitations. After the network is generated and
returned, the user can still attempt to manually generate the plot using
plotNetworkGraph(); in this manner, the potential error
will not interfere with completion of building the network.buildDualChainNetwork() function addedbuildRepSeqNetwork()
functionfindPublicClusters() now supports .rds and
.rda file types; the csv_files argument has
been replaced with an argument named file_type.filterClonesBySequenceLength() that occurs when the input
data only has a single column; this was affecting higher-level functions
including buildRepSeqNetwork()getAssociatedClusters() that occurred when
neighborhood_plots = FALSE and
return_all = TRUE (the function tried to include output
related to the neighborhood plots when none existed).findAssociatedClones() now returns an informative error
when no sequences pass the filter for minimum sample membership.clone_col to seq_colclones to seqs, (except for
embedClonesByAtchleyFactor(), for which it was changed to
cdr3_AA)edge_dist to dist_cutoffgenerateNetworkFromClones to
generateNetworkFromSeqssparseAdjacencyMatFromClones to
sparseAdjacencyMatFromSeqsadjacencyMatAtchleyFromClones to
adjacencyMatAtchleyFromSeqsembedClonesByAtchleyFactor to
embedTCRSeqsByAtchleyFactorgetSimilarClones(): changed default value to of
drop_chars argument to NULLbuildRepSeqNetwork() had its usage revised, primarily
regarding the arguments related to the input data:
nucleo_col, amino_col and
clone_seq_type arguments have been replaced by a single
seq_col argument; the function no longer requires both
nucleotide and amino acid sequences in the data, and no longer
distinguishes between the twocount_col is now optional
freq_col,
vgene_col, cdr3_length, etc.) have been
removed; these columns were not used for anything specific in the
pipeline, so it is not necessary for them to each have their own
dedicated argument.other_cols argument.aggregateIdenticalClones()
function.print_plots has been added to allow the
option not to print the plot(s) in R. The default is
TRUE, which corresponds to the previous behavior (all plots
are printed).findAssociatedClones(),
getAssociatedClusters() and
findPublicClusters() have had their arguments revised
according to the changes to buildRepSeqNetwork().plotNetworkGraphsparseAdjacencyMatFromSeqsadjacencyMatAtchleyFromSeqsembedTCRSeqsByAtchleyFactoraggregateIdenticalClonesfilterClonesBySequenceLengthgetSimilarClonesgenerateNetworkFromClonesgenerateNetworkFromAdjacencyMataddNodeNetworkStatsnode_stat_settingsaddClusterMembershipgetClusterStatstestthatfindPublicClusters()buildClustersAroundSelectedClones() renamed to
getAssociatedClusters()getPotentialAssociatedClones() renamed to
findAssociatedClones()generateAtchleyCorrHeatmap() renamed to
kmeansAtchley()levAdjacencyMatSparse() and
hamAdjacencyMatSparse() have a new argument
drop_isolated_nodes that can be set to FALSE
to keep isolated nodes. This argument has been added to higher-level
functions that dispatch calls to these routines.embedClonesByAtchleyFactor() created
to perform embedding of TCR CDR3 amino acid sequences in Euclidean
30-space based on Atchley factor representation; this was previously
done within the function adjacencyMatAtchleyFromClones(),
but has now been placed in its own function for more general useanalyzeDiseaseAssociatedClusters()
created, which is used to perform a combined network analysis on the
disease-associated clusters generated by
generateDiseaseAssociatedClusters()generateAtchleyCorrHeatmap() createdgraphics, reshape2, gplots,
viridisLite and RColorBrewer added as package
dependencies via the Imports directive of the
DESCRIPTION filecomputeMetaForCandidateSeqs() (helper for
findDiseaseMotifsFromMergedSamples()) redesigned and
renamed to findDiseaseAssociatedClones(); this function now
takes only the merged sample data as its input data, and filters
sequences by a set of criteria (number of samples shared by and minimum
seq length) to obtain the list of candidates before conducting the
Fisher’s exact tests; previously the list of candidates was obtained as
input data to the function.findDiseaseMotifsFromMergedSamples() redesigned to use
candidate sequence metadata as input (previously it computed this
metadata from the merged sample data) and renamed to
generateDiseaseAssociatedClusters()generateNetworkWithStats() now automatically prints the
ggraph in R when called (previously the user needed to access the
variable graph_plot contained in the returned list)dplyr as a dependency via the
Imports directive of the DESCRIPTION filebuildNetwork() renamed to
generateNetworkWithStats()adjacencyMatrix() renamed to
sparseAdjacencyMatFromClones()genNetworkGraph() renamed to
generateNetworkFromAdjacencyMat()aggregateCountsByAminoAcidSeqfilterDataBySequenceLengthgenerateNetworkFromClonescomputeNodeNetworkStatsaddClusterMembershipcomputeClusterNetworkStatsplotNetworkGraphadjacencyMatAtchleyFromClonesfindDiseaseMotifsFromMergedSamples and
helper functions:
computeMetaForCandidateSeqssubsetDataNearTargetMotifgenerateNetworkWithStats()hamDistBounded for computing bounded
Hamming distance in C++
hamAdjacencyMatSparse for computing
Hamming adjacency matrix in C++adjacencyMatrix:
dist_type = "hamming" to use Hamming
distance for determining network adjacencydimnames())col_ids.txt created by the C++ function that
computes the adjacency matrix is now deleted by
adjacencyMatrix() after it has finished its other tasks.
The information in the file is now stored in the row names of the output
matrix and so the file is no longer needed..genNetworkGraph() internal helper for
buildNetwork() renamed into a public version
genNetworkGraph() for use by other package functions and by
users; moved to a new file utils.R that will be used to
house shared helper functions used by multiple package functions.inst/python/Atchley_factors.csv, which stores the
Atchley factor amino acid embedding used by
BriseisEncoder.pytensorflow added to
installPythonModules() and Config/reticulate field of the
DESCRIPTION file. tensorflow is required by the Python
module keras.zzz.R created with .onUnload() directive to unload
package dll via call to library.dynam.unload() when the
package is unloadedThese binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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