input_processing() so that an alias that is not already
present is selectedscale_vals)
in color_kegg_pathway(), the default is now
scale_vals=TRUEterm_gene_heatmap() function so that legend
title is shown and can be customizedterm_gene_heatmap() function so that
coloring is proper when no change values are provided in
genes_dfsort_terms_by_p argument to the
term_gene_heatmap() function to enable sorting of terms by
‘lowest_p’vertex.label.cex and
vertex.size.scaling arguments to
cluster_graph_vis()show_legend argument to
visualize_term_interactions() to toggle the legendcolor_kegg_pathway()color_kegg_pathway() the default value for
normalize_vals is now FALSEget_kegg_gsets() where empty result
was returned for some organisms due to an error in parsing (#72)repel = TRUE in term_gene_graph()
and combined_results_graph() for better visualization of
labelsenrichment_chart() (#75)visualize_term_interactions()get_biogrid_pin() where the download
method was set to wget (now set to auto, per
#83)get_biogrid_pin() (if
tab3 is available for the chosen release, otherwise tab2 format is
used)get_biogrid_pin() to ‘4.4.200’get_kegg_gsets(), improved parsing of KEGG term
descriptions so that no description is duplicated (#87)score_terms(), if using descriptions, the ID is now
appended for (any) duplicated term descriptions (#87)obtain_colored_url(), swapped bg_color
with fg_color due to an issue with
KEGGRESTterm_gene_heatmap() (#95)get_biogrid_pin(), the “download.file.method” from
global options is usedcombined_results_graph() raises an error if there are
no common terms in the combined data framerun_pathfindR(), the default iterations
was set back to 10 (the default for all other v1.x)run_pathfindR(), as “GR” (the default active
subnetwork search method) provides nearly identical results in each
iteration, the default iterations is set to 1get_biogrid_pin() as
BioGRID updated the URL for downloadvisualize_term_interactions() where
the file name was too long, it was causing an error on Windows. Limited
to 100 characters (#58)check_java_version() where java version
14 could not be parsed (#49)combined_results_graph() where gene
nodes were not colored correctly (#55)pathfindR.data for storing
pathfindR datavisualize_active_subnetworks() for
visualizing graphs of active subnetworkscombine_pathfindR_results() and
combined_results_graph() for comparison of 2 pathfindR
results and term-gene graph of the combined results, respectivelyget_pin_file() for obtaining
organism-specific PIN data (only from BioGRID for now)get_gene_sets_list() for obtaining
organism-specific gene sets list from KEGG, Reactome and MSigDBterm_gene_heatmap() to create
heatmap visualizations of enriched terms and the involved input genes.
Rows are enriched terms and columns are involved input genes. If
genes_df is provided, colors of the tiles indicate the
change valuesUpSet_plot() to create UpSet plots
of enriched termscell_markers_gsets and
cell_markers_descriptionsparallel::makeCluster() in
run_pathfindR() (#45)download_kegg_png() (#37,
@rix133)RA_comparison_output of pathfindR
results on another RA-related dataset (GSE84074)visualize_hsa_KEGG(), fixed the issue where >1
entrez ids were returned for a gene symbol (the first one is kept)visualize_hsa_KEGG(), implemented a tryCatch to
avoid any issues when KEGGREST::color.pathway.by.objects()
might fail (#28)visualize_hsa_KEGG(), now limiting the number of
genes passes onto KEGGREST::color.pathway.by.objects() to
< 60 (because the KEGG API now limits the number?)term_gene_heatmap()
(i.e. when genes_df is not provided) to binary colored
heatmap (by default, “green” and “red”, controlled by low
and high) by up-/down- regulation statusget_pin_file() and get_gene_sets_list() and
fixed a minor issue in the vignette (#46)create_kappa_matrix() when
chance is 1, the metric is turned into 0class(.) == * in
cluster_graph_vis()max_to_plot to
visualize_hsa_KEGG() and to run_pathfindR().
This argument controls the number of pathways to be visualized (default
is NULL, i.e. no filter). This was implemented not to slow down the
runtime of run_pathfindR() as downloading the png files is
slow.enriched_ters.RmdDESCRIPTION was updatedannotate_pathway_DEGs(),
calculate_pw_scores(), cluster_pathways(),
fuzzy_pw_clustering(),
hierarchical_pw_clustering(),
visualize_pw_interactions() and
visualize_pws() were renamed to
annotate_term_DEGs(), score_terms(),
cluster_enriched_terms(),
fuzzy_term_clustering(),
hierarchical_term_clustering(),
visualize_term_interactions() and
visualize_terms() respectivelyenriched_pathways.Rmd was renamed to
enriched_terms.Rmdterm_gene_graph(),
which creates a graph of enriched terms - involved genesenrichment() and
enrichment_analyses() to get enrichment results fasterfetch_gene_set() for obtaining gene
set data more easilymin_gset_size,
max_gset_size in fetch_gene_set() and
run_pathfindR())gaCrossover during active subnetwork
search which controls the probability of a crossover in GA (default = 1,
i.e. always perform crossover)testthatcreate_kappa_matrix())mmu_kegg_genes &
mmu_kegg_descriptions: mmu KEGG gene sets datamyeloma_input & myeloma_output:
example mmu input and output datasig_gene_thr in subnetwork filtering via
filterActiveSnws() now serves the threshold proportion of
significant genes in the active subnetwork. e.g., if there are 100
significant genes and sig_gene_thr = 0.03, subnetwork that
contain at least 3 (100 x 0.03) significant genes will be accepted for
further analysispathview dependency by implementing colored
pathway diagram visualization function using KEGGREST and
KEGGgraphhierarchical_term_clustering(), redefined the
distance measure as 1 - kappa statisticcluster_graph_vis() (during the
calculations for additional node colors)cluster_graph_vis()active_snw_search(), unnecessary warnings during
active subnetwork search were removedenrichment_chart(), supplying
fuzzy clustered results no longer raises an errorinput_testing() and
input_processing() to ensure that both the initial input
data frame and the processed input data frame for active subnetwork
search contain at least 2 genes (to fix the corner case encountered in
issue #17)enrichment_chart(), ensuring that
bubble sizes displayed in the legend (proportional to # of DEGs) are
integersenrichment_chart(), added the arguments
num_bubbles (default is 4) to control number of bubbles
displayed in the legend and even_breaks (default is
TRUE) to indicate if even increments of breaks are
requiredterm_gene_graph() (create the igraph
object as an undirected graph for better auto layout)visualize_term_interactions(). The legend
no longer displays “Non-input Active Snw. Genes” if they were not
providedhuman_genes in
run_pathfindR() and input_processing() was
renamed as convert2aliastop_terms to
enrichment_chart(), controlling the number top enriched
terms to plot (default is 10)run_pathfindR into
individual functions: active_snw_search,
enrichment_analyses,
summarize_enrichment_results,
annotate_pathway_DEGs, visualize_pws.pathmap as
visualize_hsa_KEGG, updated the function to produce
different visualizations for inputs with binary change values (ordered)
and no change values (the input_processing function,
assigns a change value of 100 to all).visualize_pw_interactions, which creates PNG files
visualizing the interactions (in the selected PIN) of genes involved in
the given pathways.create_kappa_matrix,
hierarchical_pw_clustering,
fuzzy_pw_clustering and cluster_pathways.cluster_graph_vis for
visualizing graph diagrams of clustering results.score_quan_thr and
sig_gene_thr for run_pathfindR were not being
utilized.run_pathfindR, added message at the end of run,
reporting the number enriched pathways.run_pathfindR now creates a variable
org_dir that is the “path/to/original/working/directory”.
org_dir is used in multiple functions to return to the
original working directory if anything fails. This changes the previous
behavior where if a function stopped with an error the directory was
changed to “..”, i.e. the parent directory. This change was adapted so
that the user is returned to the original working directory if they
supply a recursive output folder (output_dir,
e.g. “./ALL_RESULTS/RESULT_A”).input_processing, added the argument
human_genes to only perform alias symbol conversion when
human gene symbols are provided. - Updated the Rmd files used to create
the report HTML filesGO-All, all annotations in the GO
database (BP+MF+CC)pathfindR - An R Package for Pathway Enrichment Analysis Utilizing Active Subnetworks
to reflect the new functionalities.plot_scores, added the argument
label_cases to indicate whether or not to label the cases
in the pathway scoring heatmap plot. Also added the argument
case_control_titles which allows the user to change the
default “Case” and “Control” headers. Also added the arguments
low and high used to change the low and high
end colors of the scoring color gradient.plot_scores, reversed the color
gradient to match the coloring scheme used by pathview (i.e. red for
positive values, green for negative values)parseActiveSnwSearch, replaced
score_thr by score_quan_thr. This was done so
that the scoring filter for active subnetworks could be performed based
on the distribution of the current active subnetworks and not using a
constant empirical score value threshold.parseActiveSnwSearch, increased
sig_gene_thr from 2 to 10 as we observed in most of the
cases, this resulted in faster runs with comparable results.choose_clusters, added the argument
p_val_threshold to be used as p value threshold for
filtering the enriched pathways prior to clustering.pathview. ## Minor
changes and bug fixeschoose_clusters, added option to use
pathway names instead of pathway ids when visualizing the clustering
dendrogram and heatmap.run_pathfindR. For this, the gene_sets
argument should be set to “Custom” and custom_genes and
custom_pathways should be provided.calculate_pw_scores where if there
was one DEG, subsetting the experiment matrix failedcalculate_pw_scores. If there is none, the pathway is
skipped.calculate_pw_scores, if cases are
provided, the pathways are reordered before plotting the heat map and
returning the matrix according to their activity in cases.
This way, “up” pathways are grouped together, same for “down”
pathways.calculate_pwd, if a pathway has perfect overlap with
other pathways, change the correlation value with 1 instead of NA.choose_clusters, if result_df has less
than 3 pathways, do not perform clustering.run_pathfindR checks whether the output directory
(output_dir) already exists and if it exists, now appends
“(1)” to output_dir and displays a warning message. This
was implemented to prevent writing over existing results.run_pathfindR, recursive creation for the output
directory (output_dir) is now supported.run_pathfindR, if no pathways are found, the
function returns an empty data frame instead of raising an error.Implemented the (per subject) pathway scoring function
calculate_pw_scores and the function to plot the heatmap of
pathway scores per subject plot_scores.
Added the auto parameter to
choose_clusters. When auto == TRUE (default),
the function chooses the optimal number of clusters k
automatically, as the value which maximizes the average silhouette
width. It then returns a data frame with the cluster assignments and the
representative/member statuses of each pathway.
Added the Fold_Enrichment column to the resulting
data frame of enrichment, and as a corollary to the
resulting data frame of run_pathfindR.
Added the option bubble to plot a bubble chart
displaying the enrichment results in run_pathfindR using
the helper function enrichment_chart. To plot the bubble
chart set bubble = TRUE in run_pathfindR or
use enrichment_chart(your_result_df).
Add the parameter silent_option to
run_pathfindR. When silent_option == TRUE
(default), the console outputs during active subnetwork search are
printed to a file named “console_out.txt”. If
silent_option == FALSE, the output is printed on the
screen. Default was set to TRUE because multiple console
outputs are simultaneously printed when running in parallel.
Added the list_active_snw_genes parameter to
run_pathfindR. When
list_active_snw_genes == TRUE, the function adds the column
non_DEG_Active_Snw_Genes, which reports the non-DEG active
subnetwork genes for the active subnetwork which was enriched for the
given pathway with the lowest p value.
Added the data RA_clustered, which is the example
output of the clustering workflow.
In the function, run_pathfindR added the option to
specify the argument output_dir which specifies the
directory to be created under the current working directory for storing
the result HTML files. output_dir is “pathfindR_Results” by
default.
run_pathfindR now checks whether the output
directory (output_dir) already exists and if it exists,
stops and displays an error message. This was implemented to prevent
writing over existing results.
genes_table.html now contains a second table
displaying the input gene symbols for which there were no interactions
in the PIN.
gene_sets option in
run_pathfindR to chose between different gene sets.
Available gene sets are KEGG, Reactome,
BioCarta and Gene Ontology gene sets (GO-BP,
GO-CC and GO-MF)cluster_pathways automatically recognizes the ID type
and chooses the gene sets accordinglyinput_processinginput_processing, genes for which no interactions
are found in the PIN are now removed before active subnetwork
searchinput_processingrun_pathfindR returns to the user’s
working directory.