NEWS.md
colorRamp2D() changes below.circlize::colorRamp2() was still producing grey, despite using the “LAB” (or “LUV”, “XYZ”) color models. This is usually a symptom of using RGB color space, blending “blue” with “red/green” (yellow) produces “red/green/blue” (grey), and not usually seen when using “LUV” which is a 360-degree hue radial color wheel. It is probably still converted to RGB before blending, then back to LUV.use_model="sRGB" which produces a somewhat green color when blending blue and gold. The red-gold blending is improved as well, producing a more saturated orange color. End result: The intermediate directional colors are more recognizable as partly up (orange) or partly down (green).* finally displays the x-axis label and numerical units,
by default `"z-score"`.reorder_igraph_nodes(),reorderIgraphNodes()
list form were entirely NA, one section did not use unlist() properly. The bug was fixed.mem_plot_folio() and mem_enrichment_heatmap() threw errors
pathway_column_split when there was only one column (one enrichment), this error is corrected.cluster_rows as a function caused the resulting Heatmap object not to store the obj with the dendrogram/hclust, instead it stored the function. This error was also corrected.The theme of this update is “Customizing mark.groups labeling”. Useful with jam_igraph() when supplying a list of node groups or communities via mark.groups, with labels defined in names(mark.groups). The labels are automatically placed outside the mark polygon, and can be adjusted in size with mark.cex, and position with mark.x.nudge, mark.y.nudge.
label.cex, label.x.nudge, label.y.nudge to customize the label font size, and label placement, when label is provided.jam_igraph() new arguments:
mark.cex passed to make_point_hull() as label.cex
mark.x.nudge passed to make_point_hull() as label.x.nudge
mark.y.nudge passed to make_point_hull() as label.y.nudge
mem_gene_path_heatmap(), now inherits this clustering defined using the gene-pathway incidence matrix and only when called by mem_plot_folio(). This change is the primary motivation for the update, so that the enrichment heatmap clustering is informed and driven by gene content, and no longer reflects only the enrichment P-values.point_size_min=1 changed to point_size_min=2 so the smallest points are still clearly visible, including the fill color.p_cutoff=1e-6 changed to p_cutoff=1e-10
p_cutoff=1e-6 changed to p_cutoff=1e-10
geneHitList could be supplied as a list of signed values, a directional hit list as used in venndir::venndir(). It did not get recognized, and was converted using the numeric values rather than using the names of the values.geneHitIM for signed data, however it now works with a signed hit list as well.clusterProfiler object enrichResult which causes these genes to break that compatibility. The new arguments represent a workaround to handle IPA data, so that downstream functions do not require changes.convert_ipa_slash=TRUE enables the workaround, which converts forward-slash “/” to another delimiter.ipa_slash_sep=":" defines the alternate delimiter to use, the default ":" was chosed because it does not interfere with other common delimiters used in gene symbols, and does not cause problems with regular expressions, which would have been a risk with using "|".removeIgraphBlanks() was throwing an error when size2 was not already defined in igraph vertex attributes. When there is no size2 it now calls default_igraph_values()$vertex$size2 to use the appropriate default value.
list vertex attributes, now it properly ignores pie attributes which were previously empty, and only updates entries with non-zero results.enrichDF2enrichResult() to a separate R file, for future maintenance.col were defined with more saturated default colors: c("blue", "grey80", "firebrick3")
col_l_max=80 to accommodate higher "grey80" middle color.pt.lwd=2 to control line width of open circles, used when do_direction=TRUE. The previous alternative was to define par(lwd=2) prior to calling mem_legend(), then setting par(lwd=1) afterwards.directional_colors use the same default colors used by apply_cnet_direction(); and now includes "no change" as a specific legend entry.Fixed errors rendering igraph nodes with shape="circle" only when called by jam_igraph(), and only with newer versions of igraph R package that expect the new attribute vertex.frame.width (not vertex.frame.lwd as I had hoped).
default_igraph_values() now defines vertex.frame.width and vertex.frame.lwd.vertex.frame.lwd is very likely to be removed from this package altogether, for compliance with igraph. However, I will do proper testing before making the change.Bug in rank_mem_clusters() which created a data.frame, and did not specify stringsAsFactos=FALSE, is fixed. Possibly similar bugs in other functions.
Bug in mem_plot_folio() and mem_gene_path_heatmap() when enrichment only involves one gene, causing the row_split and row_title values to be incorrect. It is not a useful workflow, but the functions should handle the error edge case.
apply_cnet_direction() now checks input argument cnet and hitim to make sure they are non-empty before processing. In theory it should not happen, but apparently it does when enrichment results are sparse or possibly empty.
jam_plot_igraph() internal function uses proper igraph::shapes(shape) instead of previously internal list object igraph:::.igraph.shapes[[shape]].
mem object by sets (pathways) or genes. It will subset all internal incidence matrix objects for consistency.subset_mem() then mem2cnet().mem_gene_path_heatmap() currently subsets mem data internally, but for consistency may call subset_mem() instead. The arguments are designed to be very similar.igraph object. Because there is some logic to the process, it makes sense to put into its own function.jam_igraph(), jam_plot_igraph()
recognizes new igraph attributes: vertex.label.fontsize, edge.label.fontsize.
igraph attributes and will not be honored by default igraph::plot.igraph() functions.vertex.label.fontsize is specified as a font size in points, this font size is used with no modification by vertex.label.cex.vertex.label.fontsize value is NA, the default behavior is used to calculate font size, which uses vertex.label.cex. Therefore the vertex.label.fontsize can be defined for a single node, as long as all other node values are NA, and only the one node font size will be adjusted to this specific fontsize.igraph labels are drawn using text(), and the final exact font size is calculated for nodes: par("ps") * par("cex") * vertex.label.cex and for edges: par("ps") * par("cex") * edge.label.cex
new argument label_fontsize_l used to apply specific vertex.label.fontsize based upon node attribute values. For example label_fontsize_l=list(nodeType=c(Gene=10, Set=14)) will define Gene nodes with fontsize 10, Set nodes with fontsize 14.
frame_blank=NULL is passed as an argument, it is interpreted as frame_blank=NA which uses no frame.color for blank nodes.reorder_igraph_nodes(), reorderIgraphNodes()
nodeSortBy, to allow specific order based upon the data, or the natural order per the locale.orderByAspect and aspectThreshold control when to sort left-right or top-bottom.mem2cnet() threw an error for certain custom input that did not meet expected constraints. The function was updated to prevent these errors and to be more robust to this type of issue.
jam_mypie() is called when rendering shape="jampie" nodes.
frame.lwd=0 and frame.color="black" a small black border was drawn around the node. The new behavior when frame.lwd=0 is to replace the color with NA so no outer border is drawn.reorder_igraph_nodes(), reorderIgraphNodes()
nodesets argument was not always matched due to truncating the nodeset label to 25 characters. Now the nodeset label is not truncated.igraph is created using pathway column clusters. The pathway cluster nodes are colorized based upon the proportion of each enrichment in that cluster, however it uses mem$enrichIMcolors by default. This function now applies statistical thresholds p_cutoff and min_set_ct_each prior to this step so the resulting colors will reflect those thresholds."subscript out of bounds" when referencing vertex.pie.lty[[i]]."pie", which are now filled in with uniform values of 1 based upon lengths(vertex.pie.color). This scenario usually occurs when trying to create pie nodes outside the “typical” scenarios, for example manually assigning attributes and not populating all the necessary values.communities2nodegroups(), nodegroups2communities()
igraph communities objects, and nodegroups which is a list of node names.enrichMapJam()
mem output to an igraph object with multienrichment features, such as pie nodes with appropriate color fill, pie.border colored by direction when enrichIMdirection is defined.mem_gene_path_heatmap(), mem_enrichment_heatmap()
raster_device to ComplexHeatmap::Heatmap() to work around temporary error when the "magick" package is not available, use_raster=TRUE, which causes an error during rasterization. The error is resolved when changing from default raster device to raster_device="agg_png", although this change requires the "ragg" R package is installed. So the change tests if "ragg" is available, and if so it passes raster_device="agg_png". The change should not affect any other scenarios.bundle_self=FALSE changes previous default behavior by not bundling nodes that connect from and to the same nodegroups. Previously, nodes connecting within the same nodegroup would bundle through the center point of the cluster, which does minimize the busy edge lines, but makes it difficult to follow any paths.label, label_preset, label_adj_preset are ussed to define optional label to appear outside the resulting hull. The label is intended to be used for network communities, to allow a label associated with each community when relevant.apply_cnet_direction() default frame_lwd=0.2 changed from frame_lwd=1.
jam_igraph(), jam_plot_igraph()
bundle_self=FALSE passed to jam_plot_igraph() and ultimately to edge_bundle_nodegroups(). When TRUE any edges that connect from and to the same nodegroup will be bundled through the nodegroup center. The default FALSE does not bundle within nodegroup, so edges are only bundled when connecting two different nodegroups.shape.jampie.plot() now handles various combinations of missing pie.lwd and frame.lwd more gracefully.
mem2cnet() uses smaller node size by default.
It turns out that graphics::polygon() only properly closes the polygon when it has a color fill with non-zero alpha transparency. In this case, the final “closed” corner is correctly extended to complete the sharp corner edge using line join par("ljoin"). With no color fill, or with completely transparent color fill, the final corner is not completed, the lines are “ended” using par("lend"), and therefore there is no sharp corner. The workaround is to apply a color with alpha transparency 1 (on scale of 0 to 255), which causes the border to be drawn completely. However, some rare graphical output devices do not support alpha transparency, so there is the chance of rendering unintended opaque color fill. The situation should only affect node shapes "pie" and "jampie", which are designed to draw the outer border then inner border, so the impact should be minimal. Shape "coloredrectangle" actually calls graphics::symbols() which avoids this issue. However, cases where outer border is expected to be drawn after the inner border, it has small risk of rendering the fill color fully opaque, covering the inner border. One day I probably need to replace graphics::polygon() with a custom function polygon_with_borders() that can handle inner and outer border properly.
jam_mypie() which is used to render igraph nodes shape="jampie" was modified to use col="#FFFFFF01" for color fill of polygon outer borders.adjust_polygon_border() examples were modified to show the effect of using col=NA to col="#FFFFFF01" on polygon border rendering.jam_mypie() and shape.coloredrectangle.plot() were updated to force stringsAsFactors=FALSE when creating data.frame objects, fixing weird color glitch in the examples for shape.jampie.plot().igraph nodes with shape="pie" and shape="jampie"
New attributes vertex.frame.lwd and vertex.pie.lwd to customize the respective line widths of node borders. These attributes require using jam_igraph() for shape="pie", or require using vertex.shape="jampie" with igraph::plot().
jam_igraph() is now recommended as a more complete replacement for igraph::plot.igraph().
shape="jampie" no longer uses par("lwd"), which was global change that affected all nodes, edges, and plot features.
Pie wedges use “inner borders” for each node, so adjacent wedge borders will not overlap. Note that inner border slightly overlaps the interior node fill color, to maintain consistent node sizes.
frame.color uses “outer borders” for each node, so these borders will not overlap inner pie wedge borders. Nodes are slightly adjusted smaller to maintain consistent node sizes, by default.
apply_cnet_direction() has slightly different logic for pie and frame colors, and now assigns pie.lwd and frame.lwd.
removeIgraphBlanks() no longer changes single-color shape="pie" nodes to shape="circle" by default.
Changes suggested:
jam_igraph() over plot() or igraph::plot.igraph().par("lwd") should be removed, and replaced with vertex.pie.lwd and frame.lwd.shape="pie" or shape="jampie" nodes were changed to shape="circle" by removeIgraphBlanks(), however they now stay shape="pie" in order to maintain control of line widths. The igraph nodes shape="circle" do not respect line width lwd.shape="circle" should be changed to vertex.shape="pie" or vertex.shape="jampie", this change affects nodeType="Gene" moreso than nodeType="Set".Edges are now clipped to the outer border of nodes, which helps when using transparent node fill, or when using edge arrows.
mem2cnet() and memIM2cnet() changed some default sizes. The new defaults should work well without adjustment.
Node sizes are 4x larger than before, because when calling downstream functions we found ourselves always scaling nodes 4x larger.
categorySize=20, geneSize=10 are exactly 4x larger.node_factor=4 to node_factor=1 for reproducibility; or when calling memIM2cnet() use arguments memIM2cnet(..., categorySize=5, geneSize=2.5).node label sizes also have new defaults, similarly because we most often applied label_factor=1.3 by default.
categoryCex=1.2, geneCex=0.9 are adjusted from previous categoryCex=0.9, geneCex=0.7.label_factor or label_factor_l to 1, or change the call memIM2cnet(..., categoryCex=0.9, geneCex=0.7).igraph defaults were changed when using jam_igraph() for plotting:
vertex.label.family changed from "serif" to "sans".vertex.pie.border changed to "grey30", the igraph default values were identical to vertex.pie.color and therefore not visible.vertex.frame.lwd set to 1, although this value is ignored by igraph::plot.igraph() since it only uses par("lwd") for all lines.arules was removed as a dependency, as two functions that used its class "transactions" were rewritten to remove the requirement. The previous version of those functions are internal and renamed from: im2list() to im2list_dep(); and imSigned2list() to imSigned2list_dep(). The replacement functions should return identical data.bezier was added as dependency to generate edge bundling curves, in head-to-head tests, it out-performed graphics::xspline(), and produced identical results to internal function ggforce::bezierPath(). Since bezier has no dependencies, this addition should feel small.jamba was bumped to version 0.0.88.900 for an important fix to mixedSort().colorjam was bumped to version 0.0.23.900 for consistency.pie_to_circle=FALSE so single-item nodes shape="pie" are no longer converted to shape="circle", since "jampie" nodes are rendered much better. Haha.igraph node shape=“ellipse”, but they could be used in future.shape.jampie.plot(), shape.coloredrectangle.plot()
These functions draw custom igraph node shapes.
shape="jampie" draws a pie node shape with more features than vanilla igraph pie shape. Each pie wedge is color filled with pie.color, with new optional inner border pie.border, pie.lwd. The pie node overall can have an outer border defined by frame.color and frame.lwd. Also, pie nodes with only one color are rendered as a circle, with no tiny internal line.shape="coloredrectangle" draws a series of square boxes with color fill coloredrect.color, and optional inner border coloredrect.border. The node overall can have an outer border defined by frame.color, and frame.lwd.Both functions now use inner and outer borders via adjust_polygon_border(), so the various borders no longer overlap. Previously, pie nodes drew each wedge with default graphics::polygon(), which allows adjacent borders to overlap 100%.
Note that nodes are resized internally so the rendered node size is equal across all nodes even when the line widths (lwd) vary.
There are some idiosyncracies from calling graphics::polygon() to render pie wedges, since it does not by default allow vectorized plotting of multiple polygons with different line widths. Therefore shape="jampie" renders line widths in subsets with identical line widths for vectorized plotting, which is 10-100x faster than plotting each pie node individually as done by igraph::plot.igraph(). The main potential issue would be seen with partially overlapping nodes, with the potential to display inconsistent overlap order.
It seems hopeless to evaluate ggraph/tidygraph to render visualizations, in part due to visualization and rendering details. Also, ggraph/tidygraph does not store nor recognize many igraph visualization details in the igraph object, instead they must be encoded as ggplot2 visualization options and settings. Using that ecosystem would also require creating new ggplot2 node geom types, and edge bundling functions.
This update represents a substantial refactor of logic.
Edge bundles can be “invalid”, which causes edges to be drawn as linear edges between nodes. The criteria were based upon a series of test cases which were all so common that they warranted being fixed.
Edge bundling “midpoint” represents the position along the line between two nodegroups.
Edges are properly clipped using the relevant igraph shape clip function. See igraph::shapes(), and igraph::shapes("circle")$clip for specific examples.
Edge labels are rendered along edges as follows: Linear edges are encoded with three coordinates: start, middle, end. Spline edges are encoded using default graphics::xspline() which returns 100 points by default. Edge labels are placed using the coordinate most distant from the start and end node. For linear edges, the middle coordinate is used, for splines a point very near the middle of the edge is used.
This function is intended as an enhanced drop-in replacement for igraph::plot.igraph(). It was updated to fulfill previously un-implemented features, so fulfill the promise of being a replacement.
New arguments, formally passed to internal jam_plot_igraph():
mark.groups, mark.shape, mark.col, mark.border, mark.expand, mark.lwd, mark.lty, mark.smooth - arguments to enable and customize the rendering of nodes within clusters or groups.mark.lwd, mark.lty for each group border; mark.smooth to control whether the group polygon is smoothed; mark.alpha to control alpha transparency of fill colors when not already defined.mark.expand is now expected to be provided as a fraction of plot layout range, which is very close to default behavior in igraph since the default rescale=TRUE forced all layout ranges between c(-1, 1).edge_bundling new option "default" will try to detect the most appropriate bunding method, based upon whether nodegroups, mark.groups are defined, otherwise it chooses "connections".
Edge labels are now rendered for straight edges and edge bundled edges.
Edge labels can now accept multiple "edge.family" values, in the unlikely event of multiple fonts on the same plot. This scenario will cause an error with igraph::plot.igraph().
Edges are properly clipped based upon the igraph node shape.
Internal adjustments to node_factor, edge_factor, node_factor_l, edge_factor_l, label_factor, label_factor_l, label_dist_factor, and label_dist_factor_l were adjusted to be applied more consistently.
Undefined layout is now properly calculated dynamically and passed to the internal rendering function jam_plot_igraph(), so the values for xlim,ylim are now properly calculated.
make_cnet_test() to create Cnet plot igraph data for testing.
adjust_polygon_border() defines inner and outer borders for polygons.
shape.jampie.clip(), shape.coloredrectangle.clip()
"jampie" and "coloredrectangle", respectively.igraph function, which cannot be called by CRAN-approved R packages.igraph package data in a function call, for CRAN compliance.igraph:::igraph.Arrows() for CRAN compliance.igraph:::i.get.arrow.mode() for CRAN compliance.added packages to Suggests to support new functions for node layout, and creation of more “correct” alpha hull polygons around points.
alphahull - best implementation of alpha hull. However, it also requires sp package, a heavy install which not advised because it is being retired in 2023 in favor of sf. Slight risk that the alphahull package is removed from CRAN if it is not updated.Added to Depends
sf - lightweight replacement of sp that provides useful geometric functions. It is added primarily because it improves rendering pie node borders, which are resized by the exact line width determined at the time of plotting pie nodes.alphahull::ashape(), then expands using sf::st_buffer().alphahull is not available, it uses grDevices::chull() which does not produce the “ideal” shape but has no additional R depedencies. It is also identical to output from igraph and sf packages for point hulls, so it has strong precedent.H,C,L values.reorder_igraph_nodes() when colorV is not explicitly provided, by detecting the probable order of colors based upon order of colors in multi-color nodes.colorV or when defining colorV ad hoc."color" the order will be defined by colorV whenever colors are aligned by colorV, otherwise colors are generally sorted by hue "H" in HCL space.jam_mypie() rendering of pie nodes.Visual examples are in reorderIgraphNodes(), since the jam_mypie() function is internal to the igraph plotting scheme.
This function draws pie nodes in vectorized fashion, properly drawing each set of pie polygons per node, then the appropriate borders defined by pie.border and frame.color.
pie.border has the option to be drawn inside the border of the polygon, so that adjacent Venn wedges will have the entire outer border color visible without overlapping the adjacent Venn wedge. To enable, set: options("inner_pie_border"=TRUE)
frame.color also has the option to be drawn in a manner that does not overlap pie.border, it will be drawn just outside the pie.border.
In cases where frame.color is not drawn, the pie.border radius is adjusted to exactly the line width of the frame.color border, so nodes will always be exactly the same sizes with or without the frame.border.
In most cases there should either be pie.border or frame.color, however it is possible at some point that frame.color and pie.border will both need to be applied, and this function can handle it.
Note this process now draws three layers of polygons:
pie.color wedge fill color and no borderpie.border wedge border color with no fillframe.color border color with no fillchanged to remove calls to matrixStats::rowMaxs() and matrixStats::rowMins() to use base functions instead. This change was due to several R crashes that appear to be bugs somewhere in the upstream packages, that also occurred on Mac OSX, and on linux, but in both scenarios involved R-3.6.1 which is not likely to gain support traction with other package authors. Understandable.
mem_enrichment_heatmap() fixed potential bug:
row_split is passed via ... to the underlying ComplexHeatmap::Heatmap(), it was not aligned to the order of rownames(matrix) to be displayed in the heatmap, therefore the rows were split in the wrong visual order.row_split is now a formal argument, and when supplied as a vector, the names(row_split) are used to align rownames(matrix) appropriately.nodesets to define a subset of nodes for which the reordering will be applied, which may be helpful when nodes in a nodeset are horizontal or vertical. In future, this option may be applied based upon the aspect ratio of nodes in a nodeset, or so that the nodeSortBy can be defined as a list named by nodesets. It gets complicated.verbose=TRUE
rownames(layout) to V(g)$name, just in case the order is not identical.... to child function reorder_igraph_nodes(), therefore the colorV color order was not properly used when called in this manner.rotate_igraph_nodes()
center="origin" to center="median". Rare change to argument default, justified by the change being fairly benign. Also the new default is more consistent with expectations, that nodes would be “rotated in place”. Practical outcome is the same, nodes are rotated exactly as before, but the coordinate range is more likely to remain consistent, when input layout is not already centered at coordinates c(0, 0).verbose=FALSE
rownames(layout) to V(g)$name, just in case the order is not identical.center="origin" to center="median". Rare change to argument default, justified by the change being fairly benign. Also the new default is more consistent with expectations, that coordinates would be “rotated in place”. Practical outcome is the same, points are rotated exactly as before, but the coordinate range is more likely to remain consistent, especially when input layout is not already centered at coordinates c(0, 0).Numerous changes were made to functions in order to improve the overall Cnet plot layout experience. New functions were developed offline that focus on layout specific to Cnet plots, useful for bipartite graphs in general.
jam_igraph() and jam_plot_igraph()
plot_grid=FALSE which optionally plots a grey grid in the background, with units equal to “percentage” across the layout coordinate range. This option is intended to help when manually adjusting node and node_set positions with nudge_igraph_node() and adjust_cnet_nodeset(), both of which take units x,y in the form of fraction of the overall layout dimensions.nodes_xy is intended to help enter adjustments when many nodes need to be nudged. The list is named by node (e.g. by gene), and contains x,y coordinate adjustments. It is completely equivalent to entering node, x, y as three independent vectors, but may be easier to use.use_grep=TRUE to use_grep=FALSE, because nudging a node "A" should not also nudge every node that contains an "a" or "A". This rare change in default argument value seems more helpful to avoid erroneous moves by default.constrain which takes a character vector of node names, then ensures those node coordinate positions are properly configured in constraints so they do not move during iterative layout.init using the current graph layout stored in igraph::graph_attr(g, "layout"), instead of using a random circular initial layout.jam_mypie() internal function inner_pie_border
inner_pie_border is intended to draw the pie wedge border on the inside of the pie wedge shape, so it does not directly overlap the border of an adjacent pie wedge shape. I could not find a polygon function in R that can draw borders on the inside edge of the polygon, which is surprising considering the considerable effort to draw GIS world maps in R. Any adjacent borders are directly overwritten, with no option for borders to be displayed side-by-side along the polygon edge. Seems like an opportunity for someone.do_directional, directional_column, and directional_colors are intended to add new directional circles indicating up- and down-regulation.plot_layout_scale() plots a grey grid background to an igraph plot indicating percentage units across the range of layout coordinates.reorderIgraphNodes() argument sortAttributes now includes "frame.color" in order to include "pie.border" for pie shape nodes, and "frame.color" for nodes overall.
emptyValue to control how empty incidence entries should appear, either as zero 0, or as NA. This update fixes rare issue where missing enrichment P-values were reported as zero 0 instead of 1 by default.venndir package, however we do not want to make this package dependent upon venndir just yet, so they remain here for now. In venndir these functions are named: venndir::list2im_opt() and venndir::list2im_value().spread_igraph_labels() now uses sortAttributes=NULL default, when it is NULL it uses defaults from reorderIgraphNodes(). Previously there was inconsistent defaults between the two functions.
geneIMdirection, enrichIMdirection are used to call new function apply_cnet_direction() to colorize node borders by default when data is available.mem_plot_folio() now calls memIM2cnet() and no longer edits colors itself, those steps are performed by memIM2cnet(); no longer calls removeIgraphBlanks().
colorV colors to geneIMcolors without calling colorjam::matrix2heatColors() since that process returned slightly darker colors by default, and for little benefit.geneHitIM intended to allow directional hit matrix to be supplied, thus enabling other features as described above, specifically colorized border on Cnet igraph plots.geneIMdirection and enrichIMdirection when available.reorder_igraph_nodes() is step one in migrating function names away from camelCase, toward snake_case. Not really a new function, but a new function name.
mem2cnet() is a similar rename of memIM2cnet() which mostly takes mem as input instead of memIM anyway.
"caption" is formatted more cleanly."draw_caption" which is a function that draws the caption in the bottom-right corner of the heatmap, mainly because this location is least likely to overlap other heatmap labels. The location and style can be customized as needed.sortAttributes was updated to include "pie.border" in the default sort order, for future when Cnet nodes also include border color with the direction of change.bumped dependency on jamba to 0.0.87.900 to pick up all the recent updates.
cluster_rows to control row clustering, specifically to allow no row clustering.do_plot=TRUE to honor do_plot=FALSE from mem_plot_folio()
attr(hm, "caption").do_plot to mem_enrichment_heatmap()
caption generated by mem_gene_path_heatmap() and returns both the gene-pathway heatmap gp_hm and the caption gp_hm_caption. The caption is relevant because the parameters define the clusters represented in subsequent Cnet plots.remove_blank_colnames=TRUE, same as previous behavior, with new option to disable this behavior and keep columns with all values in c(NA, ""). We found zScore is sometimes reported as entirely NA but may be useful to keep, for consistency with other enrichment results.edge_bundle_nodegroups() no longer includes ... when calling lines(), which should silence a fair number of harmless but annoying warning messages. We don’t need a warning message for everything, tysm.
reorderIgraphNodes() somehow broke in a recent igraph update, apparently the return type is no longer coerced to character vector, so needs to be converted directly.
mem_plot_folio() and subsetCnetIgraph() were impacted as well.mem_plot_folio() was modified to call subsetCnetIgraph(), then failing that call, will return the full cnet_collapsed graph without subset. Messages are printed for review.edge_bundle_nodegroups() initial work on allowing custom midpoints within node groups, which would allow defining a custom midpoint in x,y coordinates, which may or may not be between the two sets of nodes. Implementation is not in place yet, but in progress.
reorderIgraphNodes() was updated to improve the consistency of sorting by different node properties, specifically to allow sorting by node fill, and node border color(s).
shape.jampie.plot() is the function used to render node shape="pie" by jam_igraph(), is an optimized, vectorized method to render nodes in one shot, rather than drawing each in a for() loop by default.
Drawing even 20 or more pie nodes is substantially faster using shape.jampie.plot() compared to default igraph function.
Speed was improved, the method of converting list of polygon coordinates to numeric vectors spaced by NA was much improved.
It can render pie.border for each pie wedge of each node with shape="pie" or shape="jampie". Note that the outer line may be covered by subsequent frame.color. Attribute pie.border is expected to be a list where lengths(pie.border) are equal to lengths(pie).
It can render frame.color around the full circle of each node with shape="pie" or shape="jampie". This line may cover the pie.border if also drawn. Attribute frame.color is expected to have length equal to igraph::vcount(g) which is the total number of nodes, one frame.color value per node.
It is recommended to use one style or the other for each node:
pie.border=NA, and frame.color="red"
pie.border=c("red", "gold"), and frame.color=NA
Internal function jam_mypie() is called by shape.jampie.plot(), and was updated to handle frame.color.
mem_plot_folio() default arguments changed:
min_set_ct=1, previously was 2min_gene_ct=1, previously was 2multiEnrichMap() was not verifying names(geneHitList) also matched names(enrichList), therefore a mismatch could cause downstream errors in mem_plot_folio() related functions. This issue now only uses geneHitList with names that match.amap package as dependency, it provides amap::hcluster().mem_plot_folio() verbosity was reduced when verbose=FALSE.mem_gene_pathway_heatmap() now honors p_floor when defining the incidence matrix values to be used in clustering the weighted and combined enrichment and heatmap gene-pathway incidence matrix data.
mem_gene_pathway_heatmap() was throwing an error when supplying column_split and the default cluster_columns=TRUE.
cluster_columns then supplying ComplexHeatmap::Heatmap() with a character vector column_split, and a dendrogram/hclust for cluster_columns. Instead it allows passing a function to cluster_columns, which also requires using custom data, since the data used for clustering is a weighted combination of the enrichment P-values across the top of the heatmap, and the data inside the heatmap.row_split or column_split are character will be to define cluster_rows or cluster_columns, respectively, to a function that calls amap::hcluster() on the combined and weighted heatmap and respective annotation data matrices.mem_plot_folio() was not properly defining gene row cluster names for mem_gene_pathway_heatmap(), leaving them empty by default instead of assigning from letters.
jamba (>= 0.0.84.900) to retire call_fn_ellipsis()
topEnrichListBySource() and topEnrichBySource() were updated:
nameColname is handled properly, without relying upon "Name" colname, and without relying upon rownames() of the enrichment data.frame.nameColname.data.frame with values in nameColname are subset using subset(), which means in some rare cases multiple rows might be returned, if the input enrichment data has the same name in nameColname for multiple rows. This change is intentional, in order to retain all rows with matching names, not just the first that occurs.geneDelim has a default value, consistent with multiEnrichMap()
enrichList2IM() will return data with zero rows without error, although it usually occurs because of an error somewhere else (for example input data).call_fn_ellipsis() was moved to the jamba package version 0.0.84.900.directionColname. Related, the current approach ignored the directionColname when all values were NA. Both situations have been corrected, to allow flexible mish-mash of NA and non-NA values, and presence/absence of directionColname in each enrichment input. Also related, mem_plot_folio() was by default no enabling the directionality via mem_enrichment_heatmap(mem, apply_direction=TRUE), therefore there is a new argument to mem_plot_folio() default apply_direction=NULL which will auto-detect whether there is directional non-zero and non-NA values that can be used in the heatmap. Also, apply_direction can be defined on its own to force the issue.Issue #7 reported an error, traced back to the vignette. The error was caused by passing arguments to mem_plot_folio() by overloading ..., when not all arguments were valid in ComplexHeatmap::Heatmap(). The fix:
call_fn_ellipsis() which passes arguments including ... to another function, and when that function arguments do not allow ... then it limits the arguments in ... to those arguments accepted by the other function.x <- some_function(a=1, b=2, ...)
x <- call_fn_ellipsis(some_function, a=1, b=2, ...)
mem_enrichment_heatmap() and mem_gene_pathway_heatmap() were updated to use call_fn_ellipsis().Another error was noted during the vignette workflow, that the directionColname was being populated even when no enrichment data contained non-NA values, which was inconsistent with find_colname() in function enrich2IM(). This situation was corrected by requiring only one enrichment result to contain a non-NA value in this column.
enrichList2df(), multiEnrichMap(), enrichList2IM() were updated to call find_colname() with the list of enrichResult objects.
find_colname() can accept a list object, which is expected to contain a list of data.frame and/or enrichResult objects. An enrichResult is converted to data.frame by enrichResult@result, all other objects must contain colnames(x). When require_non_na=TRUE it will test each object, and return max unique entries that match pattern. The entries should all contain the same matching colnames for most purposes in multienrichjam.multiEnrichMap() was incorrectly populating empty gene counts with default 1 instead of 0. The effect is mainly during filtering by gene count, where the minimum is usually never below 1, however it can cause issues in point sizing particularly in mem_enrichment_heatmap(). The previous default 1 is a remnant of using this function to generate a matrix of enrichment P-values.enrichList2IM() argument default was changed to emptyValue=NA so in the absence of data to populate into the incidence matrix, the default cell value will be NA to indicate there is no available data.multiEnrichMap() was updated to supply a specific emptyValue for all calls to enrichList2IM().cell_fun_bivariate() new argument type is intended to allow re-using this same function for univariate color functions, so heatmaps features can be made consistently, specifically for dotplot or normal heatmap output, and optionally labeling cells with statistical values.mem_enrichment_heatmap() is updated to share common heatmap code for bivariate and univariate color gradients. One by-product is that output cannot be raster format, which is typically not an issue for pathway enrichment, since pathways should not represent more than 1,000 or so pathways. In that event, output should probably be rasterized (PNG, JPG) instead of vector graphics (PDF, SVG).make_legend_bivariate() new argument digits used to prevent displaying weird labels like 5.9999999998 and instead will display 6.jam_igraph() default argument was changed to edge_bundling="connections" which will enable edge bundling by default. It can be disabled with edge_bundling="none", although it is only active when there are edge connections that can be bundled.mem_enrichment_heatmap() was updated to customize directional information in heatmaps. Cell labels are optionally displayed, defined by show, to display one or more of z-score, -log10pvalue, and gene count. Argument sets was updated to handle presence of enrichIM and enrichIMgeneCount and enrichIMdirection - as well as future measurements with prefix enrichIM. New argument min_count applies the gene count filter to the dot plot output heatmap.This update mainly focuses on implementing directionality when available in the pathway enrichment data during multiEnrichMap().
colorRamp2D() - implements bivariate color scale, in this case for enrichment -log10pvalue for color intensity, and z-score for color hue directionality: blue “inhibited”, gold “neutral”, and red “activated”.display_colorRamp2D() - display the color ramp defined by colorRamp2D()
cell_fun_bivariate() - define a heatmap cell function that uses the color defined by colorRamp2D(), optionally plots circle points, and optional text labelsmake_legend_bivariate() - creates a 2-D color ramp legend suitable for ComplexHeatmap::draw(..., annotation_legend_list=x)
mem_enrichment_heatmap() new argument apply_direction=TRUE will enable bivariate colors, where color intensity is defined by the enrichment -log10 P-value, and color hue is defined by z-score direction:
Points are sized by number of genes.
Moved some functions into their own .R file for better organization:
topEnrichListBySource() and topEnrichBySource() new arguments directionColname to define an optional column name that contains numeric values indicating direction of pathway enrichment. These values are often in the form of an "Activation z-score", as is the case with IPA “Upstream Regulators”. Argument direction_cutoff refers to the absolute value required for direction be given a “sign” up or down.
multiEnrichMap() new argument directionColname used to determine directionality of pathway enrichment, useful for things like "Activation z-score", as returned by IPA “Upstream Regulators”. Output included mem$enrichIMdirection which contains the numeric values, where NA values are substituted with 0 zero. These values will be used in near future, likely in mem_enrichment_heatmap() to indicate predicted direction of impact on particular pathways. Argument direction_cutoff is passed to topEnrichListBySource() for optional filtering to require at least one pathway to contain an absolute direction score at or above this threshold. The IPA z-score recommends a threshold z=score >= 2 for “activation” or “inhibition”. Note that many pathways have no z-score, so applying this threshold will remove those pathways from downstream analysis.
mem_enrichment_heatmap() new argument apply_direction and direction_cutoff determine whether to indicate direction if it exists, and optionally applies a threshold. Still in testing currently. Note that applying this cutoff will hide pathways whose numeric direction is below the threshold. New argument gene_count_max to apply a max gene count threshold for the point size for the dot plot format. New argument legend_height to control the heatmap legend color bar height.
mem_gene_path_heatmap() will now hide all but the main enrichment colors in color legends when there are more than 8 combinations, to prevent the legend from taking the entire plot device space and not displaying the heatmap. The threshold is configurable with show_heatmap_legend=8. New arguments show_gene_legend, show_pathway_legend are logical and are intended to allow hiding the other color legends. A minimalist style is to show only the main enrichment colors, which are used for all other colors anyway.mem_enrichment_heatmap() was updated so the default dotplot format has properly controlled point sizes, and legend point sizes. Previously the two sizes were independent and required manual adjustment. The current approach ensures the legend point size exactly match the heatmap dot plot point size. An optional parameter cexCellnote will display labels unless cexCellnote=0 in which case gene count labels are hidden.mem_legend() new argument inset passed to legend(). Also this function uses tryCatch() to try to pass ... arguments, and if they fail it tries again without .... Fun.jam_igraph() is a drop-in replacement for igraph::plot.igraph(), and its options were described in much more detail. In brief:
it plots nodes vectorized which is substantially faster
it changes the default to rescale=FALSE and maintains aspect ratio 1:1, so layout coordinates are rendered without distorting the x- and y-axis ranges
it optionally bundles edge connections, which helps particularly with large bipartite graphs (especially gene-to-pathway graphs.)
it allows bulk adjustment to node size, label size, and label distance from node center.
mem_plot_folio() new argument edge_bundling="connections" that by default will bundle edges appropriate for Cnet plots. Disable with edge_bundling="none".mem_enrichment_heatmap() new argument style="dotplot" will create a dotplot styled heatmap, whose points are sized proportional to the number of genes involved in enrichment. This style is under development and may require additional customization options such as setting a max gene count for point sizes.mem_gene_path_heatmap() fixed bug where enrichment names were mangled by data.frame(...), avoided by using data.frame(check.names=FALSE, ...).Several functions were updated to include proper package prefix for function calls:
matrixStats::colMins()igraph::V()igraph::E()igraph::degree()igraph::components()igraph::vcount()igraph::neighbors()igraph::vertex_attr_names()igraph::list.graph.attributes()igraph::ego()igraph::set_graph_attr()igraph::graph_attr()affecting functions:
mem_gene_path_heatmap() was updated to handle edge cases:
gene_im_weight 0 or 1; enrich_im_weight 0 or 1cluster_rows or cluster_columns with colorize_by_gene
colorize_by_gene logic was changed to convert to an integer matrix that refers to colors and labels by factor levels, which helps for user-defined clustering methods, also helps labeling the color legend.colramp for user-defined color gradient.row_title is no longer assigned when user-defined argument is NULL. This workaround helps the edge case with gene_im_weight=1 where the row_split is forced to have a limited number of values regardless what row_split integer value is sent, thus causing row_title mismatch in length.if statement, only called when user supplies a custom cluster function.topEnrichBySource() was not correctly handling multiple sourceColnames values, instead was only using the first value. This bug has been corrected.topEnrichBySource() and topEnrichListBySource() now return enrichResult when supplied with enrichResult, instead of coercing to data.frame which would then need to be converted back to enrichResult.topEnrichBySource() and topEnrichListBySource() argument default sourceColnames was changed to reflect usage with R package msigdbr for MSigDB gene set data, specifically sourceColnames=c("gs_cat", "gs_subcat"). Similarly, default values were removed from curateFrom and curateTo, since these defaults imposed a specific outcome.multiEnrichMap() default values for topEnrichCurate* arguments were changed to NULL; default argument values changed to topEnrichSources=c("gs_cat", "gs_subat").mem_gene_path_heatmap() new argument rotate_heatmap=TRUE will rotate the heatmap layout so pathway names are displayed as rows, and genes are displayed as columns.I still need example data to use for function document examples, to show utility of rotating the gene-pathway heatmap.
color_edges_by_nodes() was implemented twice (smh) so the older function was renamed color_edges_by_nodes_deprecated(). The older function blended colors using a simpler approach with avg_colors_by_list() that took a very fast hue average; while the new function uses colorjam::blend_colors() that uses red-yellow-blue additive color blending model. Honestly the function color_edges_by_nodes() is not used much yet, but is likely to be used more with community detection, and Cnet/community edge bundling. The new color_edges_by_nodes() also applies alpha to the intermediate blended colors, to retain the relative weight of colors during the blending step.The edge bundling update!
The new functions are under rapid development, and represent potentially quite useful functions for visualizing complex igraph networks, specifically Cnet plots with Gene and Set node types.
The general technique of bundling edges between two groups of nodes is relatively stable. The details and extent that edges are bundled, including visual display of bundled edges, is under active evaluation and development. So far it doesn’t seem to take much to improve the output figure, compared to using straight edges.
In the near future, Cnet plots may by default enable some form of edge bundling, as Cnet plots were in fact the motivating example.
See pkgdown docs for edge_bundle_nodegroups() for visual examples using the Karate network.
edge_bundle_nodegroups() is the core function, and it does not yet implement:
igraph:::.igraph.shapes[["circle"]]$clip().get_bipartite_nodeset() - is a general use function to return nodes that are grouped by having identical node neighbors. This situation usually happens rarely, except with bipartite graphs where there are often clusters of nodes with the same neighbors, especially common for Cnet plot data.edge_bundle_bipartite() is the first edge bundling function, it is actually a light wrapper around edge_bundle_nodegroups(). Bipartite bundling connects a nodeset (defined as having the same neighbor nodes) to each neighbor node.edge_bundle_nodegroups() is a general edge bundling technique that bundles edges between node groups. These node groups can be defined by any relevant technique, typically a community detection algorithm such as igraph::cluster_walktrap() or any other of several igraph::cluster_* functions.color_edges_by_nodes() is a simple function that blends the colors of the two nodes involved in each edge, using colorjam::blend_colors() since it uses RYB color blending. It is optimized so it only blends unique combinations of colors.deconcat_df2() is a necessary utility function that “expands” a data.frame that has one or more columns with multiple delimited values. It simply expands the rows to represent one individual value per row for those columns. This function will very likely be moved into the "jamba" package for much broader use.handle_igraph_param_list() is a helper function for jam_igraph() which is intended to update node and edge attributes in bulk based upon another attribute. For example “make all the Gene nodes small, and all the Set nodes large.” Same with labels, colors, etc.jam_igraph() has new arguments:
edge_bundling - for "nodegroups", "connections" and "none" This argument will enable edge bundling by calling edge_bundle_nodegroups() and get_bipartite_nodeset() when needed. See examples.
render_nodes,render_edges,render_nodelabels,render_nodegroups are options to skip various aspects of igraph plot features, either to save time, or to allow more detailed visual layering.
vectorized_node_shapes toggle the vectorized node shape plotting when there is more than one node shape in the igraph object. Note this feature is substantially faster, but changes the order that nodes are rendered, by design. As a result, nodes are drawn in order of shape, so each render is a bulk operation. For nodes that overlap, or partially overlap, this feature will visibly change the ordering of nodes. In general, speed is still ideal, and reducing node layout overlaps should be a separate step.
adjust_cnet_nodeset() logic was updated for expand which was not properly applying negative values to compress the spacing between nodes in a node set. This use is relatively rare but feasible.adjust_cnet_nodeset() argument set_nodes now optionally allows referring to a nodeset using the name of one node contained in the nodeset, as a convenience.apply_nodeset_spacing() attempts to automate the process of applying adjust_cnet_nodeset() for each subcluster of nodes in a Cnet plot to enforce a minimum spacing between nodes. The Fruchterman-Reingold layout algorithm might offer a minimum distance threshold, but I could not find it. Incidentally, this new function can also compress node spacing, which might be useful when gene labels are not shown.mem_plot_folio() has much more detailed help text, describing more details about clustering parameters, and describing the returned objects in detail.mem_plot_folio() new argument do_plot=TRUE determines whether each plot is rendered, or just returned as a plot object to be reviewed separately.mem_plot_folio() the returned data now includes the gene-pathway heatmap caption, which descibes the gene/set filtering criteria, and the row/column distance methods used for clustering. Since these arguments have substantial effect on the pathway clusters, it is helpful to keep this information readily available. Similarly, the cnet plots as igraph objects store the title as a graph attribute, accessible using graph_attr(cnet, "title").mem_plot_folio() argument colorize_by_gene=TRUE now uses colorjam::blend_colors().mem_gene_pathway_heatmap() is tolerant of '...' entries not valid with ComplexHeatmap::Heatmap(), in order to allow overloading '...' for other function arguments.nudge_igraph_node() is intended to help move individual nodes in an igraph layout, useful for adjusting nodes to reduce label overlaps.get_cnet_nodeset() is a helper function to get the nodes in a nodeset, defined as the "Gene" nodes that all connect to the same "Set" nodes. It is helpful when looking at a Cnet plot, and wanting easy access to the "Gene" nodes in a particular cluster of nodes in the plot.adjust_cnet_nodeset() is intended to manipulate the layout coordinates for nodes in a nodeset, useful to expand, shift, or rotate nodes to help with visibility.adjust_cnet_set_relayout_gene() is a very useful function with complicated name. It is intended to help move Set nodes in a Cnet igraph layout, then re-positions all the Gene nodes while keeping the Set nodes in fixed positions.rotate_igraph_layout() which allows rotating an igraph layout. It can also reflect layout coordinates across one or more axes. It also calls other helper functions as needed, spread_igraph_labels() and reorderIgraphNodes().rotate_coordinates() is the underlying function to rotate coordinates, it operates on a numeric matrix and is called by rotate_igraph_layout().The next version should include plot_cnet_heatmaps() which creates nice Cnet cluster plots where each cluster has a corresponding expression heatmap displayed at the edge of the figure.
mem_plot_folio() returns NULL when there is an error during the Cnet collapse step, and does not report the error. Now it at least reports the error. The underlying cause in this case was ComplexHeatmap not accepting horizontal color legend orientation in R-3.6.1 (grid package version 3.6.1) because of some new unit arithmetic only available in grid 4.0.0+. Hiding the color legend, or using vertical color legend fixed the issue.collapse_mem_clusters() - fixed error when using apply() on a matrix that had one row or one column.subsetCnetIgraph() fixed issue when the Cnet igraph object has no internal layout, it runs relayout_with_qfr() by default.mem_plot_folio() now returns clusters_mem in the list, which includes the pathway set names represented in each cluster shown in the gene-pathway incidence matrix heatmap.cnetplot_internalJam() was updated to include "nodeType" as a node attribute, which helps distinguish "Gene" and "Set" nodes in downstream operations. This change helps address #5 to hide the gene labels on Cnet plots.enrichDF2enrichResult() with new argument descriptionColname which will force the resulting colname to be "Description" to fit expectations of enrichplot:::fortify.internal() which requires df$Description. When descriptionColname is not supplied, or not found in the input enrichDF a warning is issued that describes the problem.heatmap_row_order() and heatmap_column_order() now also work with HeatmapList objects. By default they use the first heatmap in the list, which should be consistent with all other heatmaps.multiEnrichMap() argument subsetSets was never implemented; existing arguments descriptionGrep and nameGrep were used for similar but insufficient purpose. The subsetSets argument defines specific pathway names to retain for analysis, and is implemented through topEnrichBySource() and by proxy topEnrichListBySource(). These functions might better be called subsetEnrichResult() and subsetEnrichList(), but will not rename these functions.mem_plot_folio() includes the number of rows (genes) and columns (pathways) displayed in the gene-pathway incidence matrix heatmap.enrichDF2enrichResult() was updated to fix #3, thanks to @john-lee-johnson for reporting. Issue arose because enrichplot::cnetplot() expected enrichResult rownames to be equal to values in the "ID" column of the enrichment data.frame.multiEnrichMap() new argument min_count requires a pathway to contain at least min_count genes in order to be considered a “hit”. This filter is mostly important when used with topEnrichN to use the top pathways – it will only sort pathways then take the top topEnrichN number of pathways that also contain at least min_count genes.topEnrichBySource() and topEnrichListBySource() new arguments min_count, p_cutoff require pathways to contain at least min_count genes, and have no higher than p_cutoff enrichment P-Value. Previously these functions only took the top pathways, regardless of these filters (which were applied later). This change allows the filters to be applied before taking the top topEnrichN pathways, which mostly helps when min_count is greater than 1 – sometimes pathways with only one gene involved in enrichment have statistically significant P-value, but are not biologically relevant for interpretation or follow-up experiments. For topEnrichListBySource() the filter is applied to each enrichment in the list, and any pathways meeting the criteria are taken for all enrichment lists. So a pathway must be present in the top topEnrichN entries which meet both the p_cutoff and min_count criteria to be retained by these functions.mem_gene_path_heatmap() was updated to fix a small issue with min_set_ct_each which requires at least one enrichment to have min_set_ct_each genes. However, this filter was not applied alongside the p_cutoff – therefore some pathways with enough genes which were not significantly enriched were fulfilling these criteria, as along as another enrichment was significant. The new behavior (as expected) is to requires an pathway to meet both the min_set_ct_each and p_cutoff thresholds in the same enrichment in order to be retained in the gene-pathway incidence matrix.mem_gene_path_heatmap() fixed edge case where genes present in multiple enrichments counted more toward min_set_ct_each, but should only count once per gene.mem_gene_path_heatmap() new arguments column_title, and row_title allow custom cluster names, which are also carried to Cnet cluster names as needed.mem_gene_path_heatmap() was updated to handle row_split and column_split arguments more intuitively, and to allow row_split values FALSE,0,1 to inactivate the split completely. Previously it was not possible to turn off split.mem_plot_folio() argument was renamed from pathway_row_split to gene_row_split to reflect the intent of this argument more accurately. This change is early in the function lifecycle and not in broader use yet – better to change it now. Otherwise, future argument name changes will not occur without some type of backward compatibility.mem_gene_path_heatmap(), mem_enrichment_heatmap() and mem_plot_folio() new arguments row_cex and column_cex used to adjust row and column heatmap labels, which is helpful when used with auto-sized axis labels to make minor adjustments.mem_enrichment_heatmap() was updated to make the color ramp more consistent with mem_gene_path_heatmap(), so the color scale atop the gene-pathway heatmap more accurately reflects the color scale used in the enrichment heatmap itself.mem_gene_path_heatmap() new argument colorize_by_gene=TRUE will color the heatmap body using blended colors from the geneIMcolors which represents the enrichments in which the gene is involved. The default colorize_by_gene=FALSE instead colors the heatmap body by the number of enrichments, which can be confusing if one of the enrichment colors is also red. The goal is to make it visually apparent when a gene is involved in one enrichment, by using the color from that enrichment. This feature is still in development and testing.mem_gene_path_heatmap() changed argument name from enrich_gene_weight to enrich_im_weight, before this argument is in wider use. Added new argument gene_im_weight. These arguments more accurately reflect the relative weight between enrichment and incidence matrix for enrich_im_weight.collapse_mem_clusters() was updated with new argument cluster_color_min_fraction to help filter the enrichment colors to include in each resulting cnet cluster. The intent is not to represent colors where the number of significant pathways is below this threshold. For example, a cluster of 10 pathways may have only one significant pathway for a given enrichment set – therefore this enrichment color would not be included in the cnet cluster colors.mem_gene_pathway_heatmap() cleaned up the heatmap overall:
c(0,1); for the top it uses -log10 integer steps."-log10P" to the label, to indicate that the color values are based upon log10() transform of the enrichment P-value.mem_gene_pathway_heatmap() new argument enrich_gene_weight used to adjust the relative influence of the enrichment -log10 P-value and the gene incidence matrix on the column clustering. The effect is to adjust how much the enrichment P-values, or the gene content, affects the clusters. In principle both should have similar effects, but sometimes it helps to favor gene incidence or pathway enrichment.mem_gene_pathway_heatmap() was altered to allow filtering pathways for a minimum number of genes represented by at least one enrichment result. For example, a pathway may be required to have at least 4 genes, despite having a statistically significant enrichment P-value. The new argument min_set_ct_each tests each enrichment to see if any one has enough genes per pathway. There are two main effects of this filter: The gene-pathway heatmap will display fewer pathways; and any resulting Cnet plots will have the same pathways removed.mem_plot_folio() new argument byCols which is passed along to rank_mem_clusters(), and is used to sort the pathway names within each Cnet cluster. The default uses "composite_rank", which sorts by the floor(-log10(pvalue)) then by the highest number of genes per pathway. The floor() function effectively sorts by the order of magnitude of the enrichment P-value, dropping the details. The obvious alternative is "minp_rank" which uses the -log10(pvalue) directly, which therefore does not effectively sort by gene count since enrichment P-values rarely tie. In our experience, between two pathways with reasonably similar enrichment P-value (within one order of magnitude such as 2.4e-5 and 3.5e-5) the pathway with more genes was usually the more interesting/relevant biological pathway.im2list() and imSigned2list() are the reciprocal functions to list2im() and list2imSigned(). The new functions convert incidence matrix to list, or signed incidence matrix to list, respectively. They’re notable because they’re blazing fast in our testing, thanks to efficient methods from the arules R package for interconverting list to compressed logical matrix, and vice versa.
mem_enrichment_heatmap() was modified to use p_floor as the ceiling for the row hierarchical clustering, previously it used ceiling=3 which caused the dendrogram to have height=0 when all enrichment results were lower than 0.001.mem_enrichment_heatmap() was modified to calculate its own matrix colors when color_by_column=TRUE, until the colorjam::matrix2heatColors() can be updated to the improved method.mem_plot_folio() now correctly honors p_floor.Added a new TODO.md file to track some new feature ideas.
colors_from_list() infers the proper order of colors from a list of color vectors. It is called by reorderIgraphNodes() where attributes "pie.color" and "coloredrect.color" contain subsets of colors, but in the proper order. This function returns all colors in their proper order.colors_from_list() takes a list of colors, and returns the unique colors, ordered by the overall order inferred from the list. It is internally called by reorderIgraphNodes() when argument colorV is not suppleid, since the attribute "pie.color" can be used to infer the correct order of colors.collapse_mem_clusters() now uses "; " as delimiter for the "set_names" attribute, to make it easier to distinguish individual pathway names.reorderIgraphNodes() was refactored to handle specific color order defined either by argument colorV or by inferring the correct order of colors from attributes such as "pie.color". This change should help order node colors more consistent to the original input colors to multiEnrichMap(), specifically the argument colorV.reorderIgraphNodes() now has an example that shows the effects of ordering pie nodes by color.collapse_mem_clusters() now includes the cluster name by default with the set_labels which are used to display the top n pathway names for each collapsed Cnet node. For example "Cluster A: Aryl Hydrocarbons; Granzyme Signaling".collapse_mem_clusters() arguments max_labels and max_char_labels accept multiple values, which are recycled and applied to filter each cluster. For example max_labels=c(5,2) will filter the first cluster to display up to 2 labels, and the second cluster to display up to 5 labels.simang5c the function calls to renameColumn() were corrected to jamba::renameColumn(), to resolve issue #1. Also fixed several other references to jamba functions: nameVector(), nameVectorN(), makeNames(), printDebug(), mixedSortDF(), cPaste(), igrepHas(), provigrep(), vigrep(), unvigrep(), rbindList(), noiseFloor(), rmNA(), rmNULL(), normScale(), getColorRamp(), deg2rad(). Omg! There were so many more cases than I thought! (Package-building options to import functions from packages seem to go too far, the imported functions appear to be contained in the new package which seems misleading… Ah well.)multiEnrichMap() was updated to enable analysis with only one enrichment result. Testing whether the downstream capabilities are useful in the context of one enrichment result.jamba::printDebug() output when verbose=TRUE.jam_plot_igraph() and jam_igraph() new argument use_shadowText=TRUE will enable jamba::shadowText() to replace graphics::text() for igraph text, which affects node and edge labels. The goal is to make labels more widely legible when they are placed on top of light and dark colors, common when using dark colored nodes on a white background.jam_igraph() now tries harder to respect a pre-defined xlim and ylim, before using the range of layout coordinatesmem_gene_path_heatmap() slightly adjusted the number of pathway clusters to use based upon the number of columns in the gene-pathway matrix, slightly increasing the number for low number of columns.mem_plot_folio() new argument pathway_column_split allows setting a specific number of pathway clusters.mem_plot_folio() argument do_which allows creating only the requested plots from the full sequence of plots. This function is likely to become the core part of the analysis workflow:gene-pathway heatmap -> pathway clusters -> Cnet using pathway clusters
shape.jampie.plot() provides a new igraph vertex shape "jampie". It is a complete clone to the default igraph shape "pie" except that it offers vectorized plotting, which can be substantially faster for large igraph objects that use "pie" nodes.jam_plot_igraph() is a similar clone to igraph::plot.igraph() except that it uses vectorized plotting specifically when there are multiple shapes in the same igraph object. Formerly, each node is drawn individually which is substantially slower, especially for igraph objects with more than 100 nodes. It also by default converts shape "pie" to "jampie" in order to use vectorized plotting via shape.jampie.plot() above.jam_igraph() new argument plot_function allows use of custom plot function, specifically to use jam_plot_igraph().grid_with_title() draws a grid object with title and optional subtitle, allowing for grid objects such as ComplexHeatmap::Heatmap objects, or even multiple heatmaps. That said, any grid "gTree" object should work.mem_plot_folio() is a new function that creates the current recommended folio of multienrichment plots:multiEnrichMap() now includes p_cutoff in the output, which represents the enrichment P-value threshold used for the analysis. This cutoff is useful in making other color gradients respect the same threshold required for significant enrichment results, so P-values that do not meet this threshold can be colored white (or the background color.)mem_plot_folio() new argument (minor release) do_which to help produce selected plot pages from a folio of plots.collapse_mem_clusters() is an intriguing new method of simplifying the numerous pathways, by using each pathway cluster from mem_gene_pathway_heatmap(). Each cluster is condensed to one result, combining all genes in each cluster. The results are surprisingly insightful, especially when numerous pathways are present per cluster. This function also calls rank_mem_clusters(), so it’s possible to pick a handful of the top pathways per cluster, as relevant.rank_mem_clusters() is a convenience function which ranks pathways/sets within clusters, useful with a list of clusters following mem_gene_pathway_heatmap(). It makes it easier to rank pathways within a cluster, potentially choosing one exemplar pathway to represent each cluster. This function is part of more effort to streamline the overall analysis workflow.mem_gene_path_heatmap() which was properly applying min_gene_ct and min_path_ct however it did not check the resulting data to remove empty columns and rows. This change has been made.color_edges_by_nodes() takes an igraph, determines a color for each node based upon the shape (using avg_colors_from_list() for shape "pie" and "coloredrectangle"), then creates an average color between two nodes, and uses that as the edge color.jam_igraph() has more configurable arguments, in the form of named lists. The driving example is label_factor_l=list(nodeType=c(Gene=0.01, Set=1)), that will apply label.cex*0.01 to “Gene” nodes, and label.cex*1.5 to “Set” nodes. Similar arguments: node_factor_l to apply to node size, and label_dist_factor_l to apply to label distance from node center.shape.coloredrectangle.plot() was updated to fix a small bug in order of rendering colored rectangles, it was somehow drawing the frame after the fill colors, which caused the frames to overlap each other and appear transparent.The vignette was updated to use the new functions, for a much cleaner overall workflow.
reorderIgraphNodes() uses new function avg_colors_by_list() to create one color to represent each node, then sort_colors() to sort nodes by color hue, instead of previous behavior which sorted by the hex color string. This change affects attributes "pie.color", "coloredrect.color" and "color". Recall that this function reorderIgraphNodes() is intended to help visually organize groups of nodes by their colors, to make it easier to tell how many nodes are each color.avg_colors_by_list() now uses weighted.mean() based upon the "c" channel (chroma, color saturation) in order to down-weight the effect of grey colors, which really should have no hue. The default weight for grey is 0.1, while the maximum value for fully saturated colors is 100. Blending "green" with "grey30" yields slightly less saturated green, as expected.subsetCnetIgraph() now includes convenience calls to removeIgraphBlanks(), spread_igraph_labels(), reorderIgraphNodes(), and relayout_with_qfr(). In almost all cases, creating a subset of a Cnet necessitates a new layout, which therefore requires new node ordering (sorting groups of equivalent nodes by color), and then label orientation (the angle used to offset the node label from the center of each node).sort_colors() and order_colors() uses the farver::encode_colour() function to convert colors to HCL, then sorts by hue, and returns the original sorted vector or corresponding order, respectively. For sorting colors inside a data.frame, convert colors to a factor whose levels are sort_colors(unique(colors)), in order to maintain ties during multi-column sorting.apply_color_cap() imposes a numeric range onto a color channel for a vector or list of colors, using jamba::noiseFloor(). Values outside the allowed range are forced to the range.subgraph_jam() is a custom version of igraph::induced_subgraph(), that correctly handles subsetting the graph layout when the graph itself is subsetted.jam_igraph() is a custom igraph::plot() with proper sizing when rescale=FALSE, allowing better aspect ratios for certain network layout coordinates.with_qfr() is a wrapper to layout_with_qfr() that returns a layout specification object, for use with igraph::add_layout_() in a more confusing workflow pattern than I expected.multiEnrichMap() several updates to improve robustness in the overall workflow.heatmap_row_order() and heatmap_column_order() provide enhanced output from ComplexHeatmap::row_order(), mainly that they return the actual rownames and colnames of data in the heatmap. Very useful when the data used for the heatmap has been filtered internal to the function.list2im() removed argument makeUnique because the underlying conversion to matrix no longer requires that step, and it was a performance hit for extremely large lists. The argument keepCounts is the only requirement to maintain the count of each entry per list.multiEnrichMap() now subsets the geneIM gene incidence matrix to match the genes after topEnrichN filtering is applied.layout_with_qfr() now by default will use edge attribute "weight" as the weights argument when calling qgraph::qgraph.layout.fruchtermanreingold().importIPAenrichment() was enhanced for more robust import conditions, specifically for different variations of missing IPA enrichment results. Also, empty colnames are removed, to help recognize the proper identifier in each scenario.enrichList2geneHitList() takes a list of enrichResult and returns the list of genes represented in each enrichResult. Intended mainly for internal use by multiEnrichMap().mem_multienrichplot() allows customized enrichMap-style plotting of igraph objects. Notably, you can filter by Jaccard overlap, or by overlap count – the number of genes involved in the overlap. There are otherwise a lot of overlaps that involve only one gene, which is not the best way to build this type of network.relayout_with_qfr() is a light extension of layout_with_qfr(), but adds default calls to removeIgraphBlanks(), and spread_igraph_labels() to help things look pretty.mem_legend() draws a color legend in the corner of a figure, using the colors defined in the multiEnrichMap() output.subset_igraph_components() subsets an igraph based upon connected components – i.e. distinct subclusters.memIM2cnet() takes the pathway-gene incidence matrix and produces a Cnet plot igraph object. If given the output from multiEnrichMap() it will also color nodes using the gene and enrichment incidence matrix colors.avg_colors_by_list() and avg_angles() are intended for very rapid color blending.mem_gene_pathway_heatmap() is a wrapper to ComplexHeatmap::Heatmap() which takes output from multiEnrichMap() and produces a heatmap styled similar to a pathway-gene incidence matrix. It includes row and column annotations to help interpret the results.mem_enrichment_heatmap() produces a heatmap of the pathways and enrichment P-values for each comparison.spread_igraph_labels() is a general use function for igraph objects, it takes an igraph network and associated layout, and arranges igraph nodes at an angel opposite the majority of edges from each node. The result arranges labels around the outside of the network, and typically away from other nodes. It isn’t perfect, but is visually a big step in the right direction.removeIgraphSinglets() is a simple function that removes nodes that have no edges. I found myself doing it manually enough times I wanted something quick and easy.multiEnrichMap() now includes two new edge attributes on MultiEnrichMap igraphs: overlap_count which contains the number of shared genes between two sets, and overlap_max_pct which contains the max percent overlap between two sets – based upon the # overlapped divided by the smaller of the two sets. The overlap_count is useful as an optional edge label, to show how many genes are involved.enrichDF2enrichResult() now forces "setSize" to contain integer values, useful when pathway size is inferred from something like gene ratio.multiEnrichMap() was not properly assigning colnames(memIM), although they were inferred from the rownames(enrichIM). The bug was because as.character() removes names from a character vector, and the conversion was done to enforce proper handling by strsplit() which gives unexpected results when the list may contain a factor.importIPAenrichment() no longer calls commandline grep to remove blank rows, for now it uses readLines() and jamba::vigrep() to select rows with at least one character.strsplit() that failed when sent factors, which only happens when R options("stringsAsFactors"=TRUE) which is the default in base R. Now all calls to strsplit() enforce as.character() unless character type has already been enforced.topEnrichListBySource() and topEnrichBySource() were updated to handle more default colnames for P-values, now including the default colnames for enrichResult objects c("pvalue","padjust"). When no sort columns are found, a warning message is printed.multiEnrichMap() and other functions.importIPAenrichment() imports Ingenuity IPA enrichment results, by default splitting each enrichment table into its own data.frame. It curates colnames to be consistent with downstream analyses.curateIPAcolnames() will curate the colnames of IPA data, and ensures the values in the gene column are consistently delimited.gsubs() is similar to base::gsub() except that it applies a vector of pattern-replacement operations in order. This function may be moved to the "jamba" package.find_colname() is a helper function to find a colname given a vector of expected values, which is matched directly, then case-insensitively, then as a vector of patterns to match the start, end, then any part of the colnames. By default the first matching value from the first successful method is returned. Good for matching c(“P-Value”, “pvalue”, “Pval”)topEnrichBySource() and topEnrichListBySource() subset the input pathway enrichment results by taking the top n result, then making sure the overall selected pathways are retained for all enrichment tables.list2imSigned() is no longer dependent upon the input list x having names, nor having unique names.isColorBlank() now handles list input, which is helpful when applied to igraph objects where "pie" colors are accessed as a list.removeIgraphBlanks() was updated to use vectorized logic for "pie" vertex attributes.fixSetLabels().fixSetLabels() to handle words the should be kept uppercase, with some defaults pre-configured, e.g. “mRNA”.mergeAllXY() and unnestList() and moved them to the jamba package. Added corresponding version requirement on jamba.fixSetLabels() was updated to include a wider set of canonical pathway prefixes used in MsigDB v6.1.cnet2im() and cnet2df() are helper functions used to convert a Cnet igraph to either a incidence matrix, or a table summary useful for extracting subsets of pathways using various network descriptors.drawEllipse() and shape.ellipse.plot() add an igraph vertex shape “ellipse” whose shape is controlled by vertex.ellipse.ratio, where 1 creates a circular node.multiEnrichMap() the workhorse core function, implementing the full workflow to pre-process the results for later inspection.enrichDF2enrichResult() converts a data.frame into an enrichResult object as used in clusterProfiler and DOSE.cnetplotJam() custom function to create a cnet plot igraph object.mergeAllXY() which merges a list of data.frames while keeping all rows.unnestList() which un-nests a list of lists, resulting in a flattened list. It directly supports mergeAllXY() in order to provide a simple list of data.frame objects from potentially nested list of lists of data.frame objects.enrichList2IM() converts a list of enrichResult objects (or data.frames) into an indidence matrix of gene rows and pathway columns.enrichList2df() combines a list of enrichResult objects (or data.frames) into a single data.frame, using the union of genes, and the best P-value.fixSetLabels() to update pathway/gene set labels using some small set of logic.isColorBlank() checks if a color is a blank color, either by comparison to known colors, or transparency, or saturation/brightness.igraph2pieGraph() converts an igraph into one with pie node shapes, optionally coloredrectangle node shapes.layout_with_qfrf() is an extension to layout_with_qfr() that returns a function, therefore convenient to use in igraph::plot() with custom arguments.removeIgraphBlanks() removes blank colors in either pie or coloredrectangle node shapes, thereby making non-blank colors easier to see in plots.subsetCnetIgraph() takes an igraph with Set and Gene nodeType, and subsets based upon a fixed list of Set or Gene nodes, removing singlet disconnected nodes from the output.