High-Performance Open-Source Archive
Substantially updated and optimized the exir
function for improved scalability, flexibility, and usability across
bulk and single-cell omics datasets.
Updated exir to accept experimental data in multiple
formats, including data frames, tibbles, matrices, sparse matrices, and
Seurat objects.
Updated the expected non-Seurat experimental data format for
exir to support the common omics layout, with
features/genes in rows and samples/cells in columns. Internally,
exir automatically converts the input to the required
analysis format.
Replaced the previous Condition_colname workflow
with the more flexible condition argument. The
condition argument can now be either a condition row/column
name or a character/factor vector with the same order as the
samples/cells in the input data. For Seurat objects,
condition can be the name of a metadata column.
Added Seurat object support to exir via the new
assay and layer arguments.
Added the Exptl_data_type argument to specify
whether the experimental data are "bulk" or
"sc", enabling data-type-aware preprocessing,
normalization, pseudo-sampling, and warnings.
Restored and redesigned the normalize argument. For
bulk count-like data, normalize = TRUE now applies TMM
normalization followed by logCPM transformation using
edgeR. For single-cell data, normalization is applied after
pseudo-bulk aggregation when pseudo-sampling is enabled.
Added pseudo-sampling/pseudo-bulking support to exir
through the new pseudo_sample and
pseudo_samples_per_group arguments. This is particularly
useful for large datasets and single-cell RNA-seq data.
Implemented condition-stratified, non-overlapping
pseudo-sampling. For bulk data, pseudo-samples are generated by
averaging normalized expression values within condition-specific groups.
For single-cell data, pseudo-bulk samples are generated by summing raw
counts within condition-specific groups followed by TMM/logCPM
normalization using edgeR.
Added the Exptl_data_size_check argument to
optionally prompt users to consider pseudo-sampling when the number of
samples/cells is large.
Added conservative feature filtering to exir through
the new feature_filter,
min_feature_prevalence, min_feature_total,
min_feature_variance, and
always_keep_diff_features arguments. This filter removes
uninformative features with insufficient prevalence, total signal, or
variance without performing highly variable gene selection. Features in
Diff_data and Desired_list can be forced to
remain in the analysis.
Optimized exir data preparation to delay dense
conversion of sparse input data until after optional pseudo-sampling and
feature filtering, reducing memory pressure for large omics
datasets.
Optimized PCA in exir by replacing full PCA with
truncated PCA using irlba::prcomp_irlba for the first
principal component.
Optimized the correlation table handling in exir to
avoid unnecessary full-table duplication while preserving the original
association analysis logic and output.
Optimized graph reconstruction in exir by removing
unintended self-loops while preserving multiple edges.
Optimized neighbourhood score calculation in exir by
replacing row-by-row igraph::neighbors() calls with sparse
adjacency matrix multiplication.
Vectorized row-wise scoring and classification operations in
exir, including primitive driver score calculation and
driver/biomarker type assignment.
Optimized the extraction of first- and second-order associated
drivers for mediator tables by replacing repeated regex-based
grep() searches with batched neighbourhood retrieval and
set-based matching.
Optimized several IVI-related routines while preserving output consistency with the original implementation.
Optimized clusterRank by avoiding repeated degree
calculations and reducing redundant graph traversal.
Optimized lh_index and h_index by
precomputing repeated neighbourhood-size and H-index components where
possible.
Optimized neighborhood.connectivity by precomputing
first-order neighbourhood sizes and reducing repeated calls to
igraph::neighborhood.size.
Optimized collective.influence while preserving
identical output.
Replaced older verbose output in exir with cleaner
cli-based progress and stage reporting.
Updated error and warning messages in the exir
workflow using cli for clearer user-facing
feedback.
Updated the ExIR vignette to document the new input formats, data orientation, condition handling, normalization options, pseudo-sampling workflow, Seurat support, and conservative feature filtering.
Updated exir documentation to clarify recommended
input requirements for bulk and single-cell data.
Updated exir documentation to clarify that
TMM/logCPM normalization is appropriate for many bulk RNA-seq count
datasets but may not be appropriate for all omics data
modalities.
Updated examples for the new condition,
Exptl_data_type, Exptl_data_orientation,
normalize, pseudo_sample, and
feature_filter workflows.
Added edgeR for TMM/logCPM normalization of
count-like bulk data and pseudo-bulked single-cell data.
Added use of cli for improved messages and progress
reporting.
Added use of Matrix utilities for sparse matrix
handling and efficient graph/neighbourhood calculations.
Added optional Seurat object support through
SeuratObject.
Improved handling of missing values in experimental data during preprocessing.
Improved detection of count-like, sparse, bulk-like, and single-cell-like input data characteristics.
Improved validation of Seurat assays, layers, and metadata-derived condition labels.
Improved validation of pseudo-sampling settings, including condition-specific sample/cell counts.
Improved memory cleanup after full correlation table reduction in
exir.
Improved consistency of output table preparation after vectorized score/type calculations.
Added first-order and second-order associated drivers of mediators to the final mediator result tables.
Implemented matrix-based linear algebra formulation and C++ code
in the fcor function and consequently the association
analysis module of the function exir, resulting in a highly
optimized and significantly faster association analysis.
Removed the data.table dependency, as it is no longer required.
Enabled parallel multi-core processing across multiple components
of the function exir, including the supervised machine
learning module.
The function exir is optimized. It now automatically
handles NA values of the input experimental data and convert them to
zero.
Debug the function cent_network.vis.
Add the reexports function.
Update the documentation of the modified functions.
The syntax of the functions reexported from the igraph package are corrected.
The package version is updated.
The function comp_manipulate is optimized.
The function exir is optimized. Also, the local and
online version of the ExIR shiny apps are updated according to this
optimization (calculation of zscores from the raw data rather than range
normalized data).
The argument ‘scaled’ of the functions ivi,
spreading.score, and hubness_score is changed
to ‘scale’ and its functionality is optimized. Also, the performance and
documentations of these functions are updated accordingly. Additionally,
the local and online version of the IVI shiny apps are updated according
to these changes.
SIRIR function, sirir, is optimized by providing
access to several cores. Also, its documentation is updated.
The function collective.influence is updated to
match the current updates of the igraph package.
The package readr is added to the ‘Suggests’ section
of the Description file.
The function runShinyApp is updated to require the
readr package.
The IVI local shiny app is updated to use the readr
package for loading the datasets.
The IVI local shiny app is debugged.
The README file is updated.
DESCRIPTION of the package is updated.
New packages including foreach, and doParallel are added to the Imports section of the DESCRIPTION.
ExIR function, exir, is optimized by providing
access to several cores. Also, its documentation is updated.
IVI function, IVI, is optimized by providing access
to several cores. Also, its documentation is updated.
LH-index function, clusterRank, is optimized by
providing access to several cores. This also significantly speeds up the
ivi function. Also, its documentation is updated.
LH-index function, lh_index, is optimized by
providing access to several cores. This also significantly speeds up the
ivi function. Also, its documentation is updated.
A verbose argument is added all centrality measure
functions and their corresponding documentations are updated as
well.
The fcor function, fcor, as well as its
documentation are optimized and updated.
The documentation of the SIRIR function, sirir, is
updated.
Hubness score function, hubness.score, is debugged
and optimized.
Spreading score function and its documentation,
spreading.score, is debugged and optimized.
IVI function, ivi, is debugged and
optimized.
IVI from indices function, ivi.from.indices, is
debugged and optimized.
Collective Influence function, collective.influence,
is optimized.
The documentation of the Collective Influence function,
collective.influence, is updated.
ExIR Shiny app is updated according to the ExIR function.
ExIR visualization function, exir.vis, is
optimized.
The package vignettes are updated and the fcor
function described and exemplified in the vignettes.
The ExIR function is optimized (corrected for proper Spearman correlation analysis and added Mutual rank as a measure for filtering top correlations) and speeded up (using the data.table package).
A function named fcor is added to the package for
super-fast correlation analysis of large datasets and simultaneous
P-value and Mutual Rank calculations.
The package vignettes are updated.
The betweenness function is corrected.
Documentation of some functions corrected.
Links to my personal website are corrected.
The package vignettes are updated.
The implementations of shiny apps are optimized.
Shiny apps are debugged.
A new argument, label.position, is added to the
exir.vis function.
The exir.vis function is debugged and
optimized.
The upload file size limit is removed from the local shiny apps.
The package website built with pkgdown is added to github.
The default correlation coefficient is changed from 0.3 to 0.5 in the ExIR function and its shiny app.
The ExIR shiny app is debugged and updated.
The ExIR function is debugged and updated.
The DESCRIPTION file is updated.
The function ExIR as well as the ExIR shiny app are
debugged.
Documentations of some functions are updated.
The DESCRIPTION file is updated.
The name of the function for running shiny apps is changed to
runShinyApp.
Shiny apps are updated in the package for local use.
Documentations of several functions are updated.
The Vignettes are updated.
The Read Me file is updated.
Update package Vignettes.
Documentation on how to access ExIR shiny app is added to the Read Me file, vignettes, and the corresponding functions’ documentations.
The ExIR shiny app is added to the package.
A Shiny app is developed for the running the ExIR model and visualization of its output.
Add the comp_manipulate function for the simulation
of gene knockout and up-regulation.
Debug the function IVI.
Debug the function ExIR.
Add dependence to R package janitor for the correction of illegal characters in feature names.
Documentation on how to access IVI shiny app is added to the Read Me file, vignettes, and the corresponding functions’ documentations.
The IVI shiny app is added to the package.
A Shiny app is developed for the calculation of IVI as well as IVI-based network visualization.
The dependence of the function ExIR on the package
reshape2 is removed.
The function ExIR is debugged.
ExIR is debugged.The ReadMe file is updated.
The citation details of the package is updated.
Vignettes of the package are updated and extended.
A new function named exir.vis is added for the
visualization of the results of the function exir.
A new function named cent_network.vis is added for
the visualization of a network based on a centrality measure.
A new function named graph_from_incidence_matrix is
added (imported from the igraph package) for the network
reconstruction from an incidence matrix.
A new column named Type is added to the
Biomarker table of the function ExIR.
The Z-score and statistical significance are added to the results
of the function ExIR.
The function ExIR is updated so that the
experimental data are ranked prior to correlation analysis. This will
result in the assessment of the association based on the Spearman
method, a more robust algorithm in variable conditions and/or
non-parametric distributions.
The version of Roxygen2 is updated in the DESCRIPTION file.
The documentation of the ExIR function is
updated.
The ExIR function is updated to prevent outputting
NULL results.
The package logo is updated in accordance with the logo design of well-known R packages.
Some documentations in the Read Me file are corrected.
The NC function is improved.
The documentation of the exir function is
updated.
The DESCRIPTION file is updated according to all of the modifications.
The citation details of the package is updated.
The function diff.data.assembly is added for
assembling the differential/regression data required as an input for the
exir function.
The function exir is added for the
experimental-data-based classification and ranking of top candidate
features.
The function clusterrank is upgraded to
clusterRank in order to calculate the ClusterRank
centrality internally.
The function neighborhood.connectivity is undergone
minor modifications to enhance the performance speed.
The function sif2igraph is added for automatic
importing and conversion of a SIF file from the users’ local hard drive,
cloud space, or internet into a graph with an igraph class.
The thesis advisors are added to the DESCRIPTION file.
Some minor corrections are done in the documentation of functions and DESCRIPTION file.
The DESCRIPTION file is updated.
The Vignettes file is updated.
The function sirir is added for the unsupervised
influence ranking of network nodes.
The function hubness.score is added for the
calculation of the Hubness score.
The function spreading.score is added for the
calculation of the Spreading score.
The function ivi is added for the influential node
identification from a graph.
The function ivi.from.indices is added for the
influential node identification from indices.
The function ihs is removed as a refined formula is
added for influential node identification.
Updating the normality assessment results of association functions for vectors of length < 4
The function clusterrank is added for the
calculation of ClusterRank.
The function collective.influence is added for the
calculation of Collective Influence.
The function lh_index is added for the calculation
of local H-index.
The function h_index is added for the calculation of
H-index.
The function neighborhood.connectivity is updated so
that it will not return any NA or NaN values and the output will be a
numeric vector.
The formatting of return values in the documentation of each function is corrected.
The citation details of the package are updated
according to its associated published paper.
The error regarding the use of NNS since the second
use of association assessment functions including
cond.prob.analysis and
double.cent.assess.noRegression is now corrected.
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