NEWS.md
In jmw86069/jamses: Jam SummarizedExperiment Stats

jamses 0.0.63.900

se_contrast_stats() was not maintaining correct order of data matrix for a specific scenario, when handle_na was not "none", and when the order of groups in the design matrix was non-standard, that is with the first group not in the first column and first row. It obviously should not matter the order of groups, but this scenario limits when the bug occurred, which is somewhat comforting.
The cause was handle_na_values() which converted the design matrix to list in order to associated colnames to groups. The list was converted to vector, sometimes changing the order of columns returned.
The bug is fixed in handle_na_values() by returning columns in the same order they were provided at input.

jamses 0.0.62.900

save_sestats()
New argument data_content=c("data", "hits") changes default behavior to include a "hit sheet" in the output worksheets. This hit sheet includes all the "hit" columns from each contrast in one large table. For previous behavior use data_content="data".
New argument value type="list" will not save to file, instead returns a list of data.frames named by worksheet name. Default is the same as before type="xlsx" will export to file in Excel xlsx format.
New arguments hitRule, hitFormat, freezePaneColumn merely provide some previously internal defaults, for optional customization.

jamses 0.0.61.900

heatmap_se()
row_split now accepts data.frame input
row_split is more forgiving with vector input, now accepts a named numeric vector.

jamses 0.0.60.900

se_contrast_stats()
When normgroup and block were both defined, but one normgroup did not have multiple values for block, it threw an error. In this case, when there is only one unique value for block within a normgroup it is set to NULL for that normgroup so it is not use in limma::lmFit().
handle_na="full1" was not performing as intended, caused when migrating away from matrixStats::rowMins(x) to apply(x, 1, min), the logic was no longer being applied as described.

jamses 0.0.59.900

se_detected_rows() was not properly applying detect_minpct for the minimum percentage of replicates per group, impacting cases where groups each differed in size. Bug has been resolved.

se_contrast_stats()
Arguments block and normgroup both allow three types of input, which were modified slightly to reduce the risk of values being out of sync.
1. Unnamed character vector, must be equal to length(isamples), or when isamples is not provided, ncol(se). In this case, isamples or colnames(se) are assigned as names. This option prints an alter message suggesting that values be provided with names. However, values are named by isamples before any other subsetting is performed relative to isamples.
2. Named character vector, whose names match colnames(se) and isamples.
3. character vector matching colnames(colData(se)) to use one or more annotation columns, which also automatically uses isamples as appropriate, to maintain synchrony.
Note that when using normgroup and/or block the intermediate processing can be reviewed by including verbose=TRUE. The output will also print each correlation value for each normgroup, when block is also provided.

jamses 0.0.58.900

se_contrast_stats()
Caused an error when using normgroup and block together, caused by the block not being subset to match isamples. Fortunately the bug would always cause failure, since it did not return an incorrect statistical result.
block is expected to equal length(isamples), and will be named as such so it can also be subset within each normgroup.
when names(block) are defined, they are validated to match isamples, and will be subset and re-ordered to match isamples as needed.
when names(block) is not defined, its length must equal length(isamples), and then names(block) will be defined using isamples.
in each normgroup the block is only applied when there is more than one unique value for the relevant block[isamples].

se_contrast_stats()
Arguments normgroup and block both accept colnames(colData(se)), or a vector of values; however when supplying a vector of values, they must now be named using isamples - it no longer assumes they are the same order as isamples. The names(block) are used to confirm absolute synchrony.

jamses 0.0.56.900

Added edgeR to Suggests, in order to enable new normalization methods "TMM", "TMMwsp", and "RLE".

se_detected_rows() - fixed missing package prefix with jamba::tcount().
Added more package prefixes, most of which are used only with verbose=TRUE.

se_normalize(), matrix_normalize() gained new normalization methods:
edgeR::calcNormFactors() for methods: "TMM", "TMMwsp", "RLE"
- the methods perform very similarly to jamma::jammanorm(), log-ratio normalization, with some subtle differences in how they filter the features to be used during normalization. TMM,TMMwsp perform nearly identical to each other. RLE also performs more closely to both TMM than to jammanorm.
- Ultimately this effort was not time well spent, the new methods appear to work well when data is high quality, but appear to produce questionable output when data is not consistent across samples. Meanwhile, "jammanorm" appears to work consistently, at least producing output that can be reasonably explained.
- Data should be reviewed with jamma::jammaplot() MA-plots to visualize whether assumptions are met and are valid. For example, when the distribution of signal is inconsistent across samples, it appears as non-horizontal signal on MA-plots, and ultimately either the signal should be filtered prior to downstream statistics (so that the horizontal range of signal is analyzed) or another normalization method should be used in order to adjust the signal to horizontal, for example "quantile" normalization is intended for such purposes. In future, "LOESS" normalization may also fit this purpose.
se_normalize()
New argument output_method_prefix to customize the method name used to formulate the output assay_name.
New argument output_assay_names to customize the full assay_name used for each output. When supplied, it overrides New argument.
mcols(assays(se)) is now populated with a S4Vectors::DataFrame containing the normalization_method, source_assay_name, and associated params converted to column values. Currently experimental, but enabled by default to test the utility.
Converted stop() to cli::cli_abort() for improved message handling.
heatmap_column_group_labels()
New argument group_line_requires_label=TRUE will hide the group line when the associated label contains only whitespace or is NA. This defines new behavior, although for a rare scenario where there exists an empty (whitespace) group label.

jamses 0.0.55.900

se_contrast_stats()
floor_min is now appropriately applied before handle_na specifically for the case where values may be filtered to NA then handled by handle_na using further logic.
heatmap_column_group_labels()
new argument use_gridtext=TRUE which by default enables markdown and limited HTML output, such as superscripts and subscripts. Implemented to render 37 degrees C using superscript o.
heatmap_se()
subtle change to limit rowData_colnames to those values present in colnames(rowData(se)).
new argument hm_name which defaults to the same legend title, derived from data_type. However, the heatmap name appears in grid graphical components ComplexHeatmap::list_components() and sometimes needs to be specified in order for multiple heatmaps to have unique grid names.
new argument hm_title to provide a completely custom heatmap title. This argument is useful especially when using apply_hm_column_title=TRUE.

jamses 0.0.54.900

plot_sedesign()
when group_buffer is adjusted, it also adjusts placement of replicate labels "n=3" proportionally inside the group box.
new argument arrow_ex where NULL is adjusted based upon the number of contrast bumps per axis, so more parallel contrasts would cause proportionally smaller arrow sizes.
New argument contrast_depths to filter contrasts based upon depth: 1=oneway, 2=twoway. More convenient than filtering contrasts upfront.
contrasts_to_venn_setlists()
Names are now user-friendly to indicate the contents of each Venn set list.

jamses 0.0.53.900

heatmap_se()
new argument: column_title mainly used for convenience alongside the next new argument:
new argument: apply_hm_column_title=FALSE will optionally hide any existing column_title and instead applies the heatmap title hm_title to the column_title. This option is convenient when adding two heatmaps together, since it ensures each heatmap has its own individual title. The downside is that the usual column_title defined by column_split will not be shown.
new argument: hm_title_buffer adds an optional number of whitespace lines to the end of the heatmap title hm_title. This whitespace is useful when calling heatmap_column_group_labels() which draws additional text above the heatmap.
heatmap_column_group_labels()
arguments hm_title_base,hm_body_base are now auto-detected using detect_heatmap_components() by default.
when there is no global_column_title in the drawn heatmap, it means the heatmap itself did not have column_title, and therefore the output of this function is shifted down so it appears on the bottom of the column_title elements. This behavior is intended when using the new heatmap_se() argument apply_hm_column_title=TRUE to draw the heatmap title in the column_title region. It is recommended to use hm_title_buffer=4 to add sufficient whitespace below the column title to allow room for the column group labels. In principle, the column group labels should behave the same as before when there is a hm_title included above the heatmap. And if both global_column_title and column_title elements are present, by default the labels from this function are placed above the column_title and below the global_column_title. whew

jamses 0.0.52.900

contrasts_to_venn_setlists()
fixed missing contrasts in certain designs
added method to split "equivalent" subset of contrasts using max_venn_size which simply subdivides remaining contrasts into subsets when needed.
Removed splitting contrasts solely by depth, because it produced too much nonsense.
Added example showing how to plot the resulting contrasts with plot_sedesign(), which might become a useful "you are here" type of schematic to accompany a Venn diagram. Ideally, the contrast_colors should match the set_colors in venndir() for consistency.

sub_split_vector() is an internal function, used to split venn sets by max_venn_size, but in its own function because it may get some tweaks.

jamses 0.0.51.900

added cli to package dependencies, to use messaging.

contrasts_to_factors()
simple function to convert contrasts to a data.frame where each column is an experimental factor, and in the case of contrasts, at least one column per row should represent a comparison of two factor levels.
contrasts_to_venn_setlists()
Convenience function to subset an overall set of contrasts to something meaningful to use in Venn diagrams. Mainly intended for RMarkdown use where the set of contrasts is not always known upfront.
Fixed early issue where certain sets of Venn results were not created.

contrast_names<-
now catches errors, printing an informative message, and returns the original data without subsetting.

jamses 0.0.50.900

plot_sedesign()
Fixed error when plotting a twoway contrast and one of the oneway contrast components in the opposite orientation of the twoway contrast components. Only observed in unique cases with a subset of contrasts.
Fixed errors ultimately caused by having duplicated contrast_names in the SEDesign object. Updated contrast_names<- method to throw an error with clear message.

filter_contrast_names()
Experimental function intended to help reduce "all-vs-all" style contrasts to those with one specific control factor level, per design factor.
Driven by slicejam design with 5 treatments, 3 genotypes, which produced 75 total oneway and twoway contrasts. Yike. Instead this function produces 30 total contrasts, only 22 oneway contrasts versus the baseline level per factor.

jamses 0.0.49.900

se_rbind()
Wrapper function to automate steps required for SummarizedExperiment::rbind()
Confirms that no rownames() are shared across se_list.
Optionally edits colnames() for each object, so they can be compared.
Determines shared colnames(), discarding other columns.
Optionally subsets colnames() by colnames_keep.
Adjusts colData() in one of two ways:
1. colData_action="identical": keeps only colData columns which have identical values across se_list, discarding other columns. This option is default, since it retains common annotations.
2. colData_action="all": keeps all colData columns, using a delimiter between multiple values whenever the values differ across se_list. This option is useful for detailed record-keeping, but the annotations are not likely to be useful otherwise.
finally it calls SummarizedExperiment::rbind() returning one valid SummarizedExperiment object.

jamses 0.0.48.900

design, design<- contrasts, contrasts<-
Previously when loading jamses, a message indicate that new generic methods were defined, which was caused either by creating methods for an existing function or S4 method that did not have the same signature.
design now imports and matches the arguments of BiocGenerics::design
contrasts now sets generic functions matching stats::contrasts() then adds S4 generics for object SEDesign.
Now, no more warning message when loading jamses!

jamses 0.0.47.900

se_contrast_stats()
New argument max_correlation_rows with a maximum number of rows to use when calculating limma::duplicateCorrelation().
- This process is only relevant when block is defined, and correlation=NULL. In this case, limma::lmFit() by default already callslimma::duplicateCorrelation()to calculatecorrelation. However, when there are more than 10k rows of data, the process is non-linearly slow (they said quadratic) although increasing number of rows does not substantially change the calculatedcorrelation`.
- Therefore max_correlation_rows=10000 limits the calculation to 10,000 rows by default, substantially improving speed from 10 minutes in 500k rows, down to 10 seconds.
New argument voom_block_twostep=TRUE which enables a change to previous behavior.
- Only relevant when use_voom=TRUE and block is defined, but correlation is not defined.
- Previous behavior was to perform "one-step voom" which calculated weights without the blocking factor, then calculated correlation by limma::duplicateCorrelation() with Voom weights, and blocking factor.
- Recommendation by limma authors is to run these steps twice, so the second time running voom() will use the block and correlation arguments to empower a better model fit, and more accurate weights used subsequently by lmFit(). This change should improve output, but will change previous results, only for count data with a blocking factor, which has been fairly rare.
- Previous behavior can be re-enabled with voom_block_twostep=FALSE.
- Hid some verbose output.

jamses 0.0.46.900

plot_sedesign()
new argument sestats_style to customize how stat hits are labaled. It allows including directionality, only the number of hits, or only the number by itself.
fixed error when any argument of axis1,axis2,axis3,axis4 referred to a non-existent column. The error was not a bug, but was avoidable. Now the plot is drawn as if the corresponding argument were NULL.
draw_twoway_contrast()
new argument contingency to customize how two-way connectors are drawn, based upon how close the two contrasts are to each other. The "loop" option draws the connector to loop around the far side of contrast 2, which is effective at avoiding overlaps.
contrast_colors_by_group()
sample_color_list now allows more flexibility in defining colors, specifically allowing a character vector, or single color to be used for all contrasts.
new arguments C_min and L_max to complete the chroma (C) and luminance (L) numerical limits with min/max.

jamses 0.0.45.900

plot_sedesign()
So far, early testing is working quite well, minor aesthetic issues, such as unexpected ordering of axis labels when changing the axis order, since it followed the input order and did not re-sort based upon axis label values. Overlapping labels are already an issue. They can be manually adjusted, but it would be nice to have that part automated. Sadly, I don't think ggrepel accepts rotated text.
change: contrasts are now sorted by axis to organize factor levels
change: axis labels are now sorted by combined factor order along x- and y-axes
change: contrasts are sorted by axis length, so shorter contrasts have less "bump"
fixed bug with missing contrast labels when subsetting the overall contrasts with argument which_contrasts
fixed handling of colorset to match one-way contrast_names then fill missing color assignments as needed.
arguments group_color,group_border accept named vectors to define colors for each square drawn per experimental group.
draw_oneway_contrast(), draw_twoway_contrast()
default label_font=2 for bold contrast labels
plot_sedesign(), draw_oneway_contrast(), draw_twoway_contrast()
changed argument name from label_position to oneway_position and from twoway_label_position to twoway_position, before these functions are used

contrast_colors_by_group()
Takes sedesign and sample_color_list and blends group colors into contrasts based upon the groups in each contrast.
sedesign_to_factors()
Convenience function to convert SEDesign object into the associated data.frame with one column per experimental design factor.
The data.frame rownames match the samples(sedesign).
The colnames are matched with optional se object in order to re-use colnames(colData(se)) and, if present, factor levels. Maintaining factor level order is useful for downstream applications.
This function will be called by plot_sedesign() and should be useful for platjam::design2colors() as well.

jamses 0.0.44.900

This release adds plot_sedesign() and several helper functions. This function is still in development, released for more convenient testing for lots of edge cases. This function may be ported to use grid R graphics, but currently uses base R graphics with fixed aspect ratio asp=1 to maintain proper visual proportions for block arrows, and group boxes.

plot_sedesign()
It takes an sedesign object and plots the one-way and two-way contrasts in block arrow format.
Block arrows are "bumped" to reduce directly overlapping arrows.
The design factors can be repositioned along x- and y-axes, at the bottom or top, with combinations of factors, or absence.
It indicates the n=3 number of replicates observed per group, and draws shaded squares for groups present, and lack of square for groups which are absent in the design.
When sestats is also supplied, it adds labels with the number of statistical hits per contrast.
Labels can be positioned by one-way or two-way contrast type, or by specific contrast name.
Two-way contrasts can optionally be "flipped" to show the equivalent two-way contrast with different group ordering.
draw_oneway_contrast(), draw_twoway_contrast() are both called by plot_sedesign(), and internally call point_handedness() and make_block_arrow_polygon(). These functions help make the process more component-oriented.
make_block_arrow_polygon() is a wrapper function to define block arrow polygons with consistent dimensions, where the arrow stem width also defines the relative size of the arrow head. When the stem has shorter length than the arrow head, the full arrow head is shown without the stem, avoiding the visual artifact of showing an inverted (negative length) arrow stem.
point_handedness() is a helper function which takes two points and a vector angle, then determines "which side" the first point is located along that vector angle relative to the second point. It is used for two-way contrasts to decide how to define intermediate control points between two parallel contrast arrows. The goal is to create an "S" shape, where control points are angled "toward each other".
point_slope_intercept() is a simple geometry helper function.
Given a point and slope, it returns the intercept.
However, when the slope is infinite (as with a vertical line) instead of returning an infinite intercept, which is not helpful for describing the line, it returns the x-axis intercept.
The intercept is used by point_handedness() to determine the "handedness" of two parallel lines.

The method design() now imports the Bioconductor BiocGenerics in order to extend (and thus be indexed alongside) the corresponding method design().

jamses 0.0.43.900

Added multienrichjam to dependencies, since it is used by handle_na_values().
Added statmod to "Suggests" since this package is required when using a blocking factor with limma.

heatmap_se()
The default heatmap title now also includes the number of columns, whose label is controlled by column_type="samples" by default.
When row_type or column_type are NULL or "" (empty) their respective label is hidden from the heatmap title. When both are hidden, the first line will be hidden completely.

contrast2comp()
Weird edge case when calling heatmap_se(), sestats contains only one contrast, and the hit matrix annotation contrast is in the form "Group1-Group2". The output is correct inside RStudio, the hit matrix name displays "Group1-Group2" as intended. When the output is knitted inside an RMarkdown, the hit matrix name is displayed "1".
Issue was avoided with argument rename_contrasts=FALSE when calling heatmap_se().
User is using R-3.6.2, so the best guess is a weird edge case where options(stringsAsFactors=TRUE).
The contrast2comp() was updated to impose stringsAsFactors=FALSE and check.names=FALSE in two steps that converted matrix to data.frame.
The process_sestats_to_hitim() was updated to check output of contrast2comp() before assigning to colnames().
handle_na_values()
Error was thrown due to data returned in list form instead of matrix form. Instead, optional weights are returned as an attribute, so the data is always consistently returned in numeric matrix form. Frankly, it is unclear how this bug was tolerated except calling se_contrast_stats() with handle_na="none".
The argument na_value was not being passed to handle_na_values() which is important for certain types of data that have high background noise floor values, for example proteomics SpectrumMill has background around 20, also this is in log2 units.

se_contrast_stats()
now properly passes na_value to handle_na_values()
handle_na_values()
Nnow returns only numeric matrix, and optionally includes a weight matrix as an attribute named "weights".

jamses 0.0.42.900

se_collapse_by_column()
changed argument default noise_floor_value=NA to noise_floor_value=0 so that values are not lost during this process by default. Rare change to default argument, intended to be more consistent with "default" expectations of this function.
new argument useMedian=FALSE ensures the default behavior is to calculate row group mean and not median.
the groupFunc() is now called using jamba::call_fn_ellipse() which ensures that arguments are only passed when permitted by that function, either by named argument, or by .... This change makes it easier to use a custom function which may not have to accept additional arguments.
se_collapse_by_row()
new argument option group_func_name="mean" which enables basic mean() across group values.
help docs were reworded to clarify the group_func_name and data_transform arguments.

jamses 0.0.41.900

heatmap_column_group_labels()
This function adds alternative labels at the top of a rendered Heatmap, sometimes a cleaner alternative than using colored annotation boxes.
The specific scenarios to use this function are described in the help docs, and this function may be updated to be more efficient in future.
Although this function can only be applied after drawing a Heatmap, and requires some blank whitespace inside that heatmap. Nonetheless, this function does make remaining steps substantially more convenient than otherwise possible, and the output looks quite nice for a publication-quality final figure.
detect_heatmap_components()
a helper function intended to be used with heatmap_column_group_labels() to identify the grid layout elements defined by the drawn Heatmap in the current display device.

heatmap_se()
The heatmap title was altered so the centering label uses the pattern "centered within *centerby_colnames*, versus *control_label*"
help docs were expanded and re-organized to improve clarity.

sestats_to_df()
fixed edge case where columns could be returned as list type, instead of character vector. It resulted in the data.frame appearing to have abbreviated counts, instead of including the full hits, with up- and down-regulated numbers in parentheses.

jamses 0.0.40.900

se_contrast_stats()
MAJOR: default argument handle_na="partial" changed to handle_na="full1". This change is done to "recover" all comparisons where one group has entirely NA values, by substituting one numeric value for one replicate in the group in order to avoid mis-representing the variability of the group. Meanwhile, groups with numeric and NA values together will use only numeric values in the group, without substituting a numeric replacement.
New argument rowData_colnames to retain gene annotation columns for se input where the rownames are not sufficient description of each row. This argument removes the need to merge additional annotation afterward.
Arguments are now fully documented.
handles empty assay_name by returning NULL for each empty assay. Recovers when multiple assay_name values are supplied but some are missing. It does not recover when no assays are found.
Added minor verbose output indicating each assay_name.
run_limma_replicate(), ebayes2dfs()
New argument rowData_df to support new argument rowData_colnames in se_contrast_stats(). This argument merges annotations into the resulting top tables so that gene annotations can be retained per row.

jamses 0.0.39.900

heatmap_se()
visual glitch apparent in version R-4.2.3, the border color for color legend boxes was not black, so new argument legend_border_color was added, and is applied to all legends as relevant.
se_normalize() - Fixed minor issue with if (class(x)) logic to avoid error when comparing multiple values in an if() statement.

jamses 0.0.38.900

contrast_names_to_sedesign()
convenience function to convert contrast names to corresponding sedesign object.
Its core utility is that it provides a way to test whether two-way or higher contrasts are mathematically equivalent.
It may also be useful for test cases to produce a quick sedesign with only a specific set of contrasts.
In future plot_sedesign() will display the design with layout similar to vcd::mosaic(), then will use block arrows to indicate contrasts across groups.

contrast_names()
enables quickly retrieving the vector of contrasts in a sedesign object
also enables re-defining contrasts given a specific vector of contrast names.

contrast2comp() and comp2contrast() are first.
will provide unit tests for all core functions.

jamses 0.0.37.900

heatmap_se()
new argument color_floor passed to colorjam::col_div_xf() which optionally prevents coloring numeric values below the floor, therefore starts the color gradient at this value up to color_max.

jamses 0.0.36.900

heatmap_se()
row_split passed as integer value when cluster_rows=FALSE caused an error: "Error: Length or nrow of row_split should be same as nrow of matrix." The error was caused because rows cannot be split into subgroups in the absence of a row clustering.
The same error for column_split was also present, and was fixed.
Resolution is that row_split or column_split is converted to NULL when there is no respective row or column dendrogram or clustering function.

save_sestats()
Default format for mgm and mean is "num" instead of "int", which assumes log2-scaled values that are generally at or less than 20.
heatmap_se()
the default gap between row annotations is defined by the ComplexHeatmap option "ROW_ANNO_PADDING", so the gap between heatmap and annotation is the same as the gap between two annotations. In future the gap width might become a separate option but not just yet.

jamses 0.0.35.900

Dependency on jamma bumped to version 0.0.28.900 for new arguments to naControlAction, see below.

heatmap_se()
New arguments for data centering: controlFloor, naControlAction, naControlFloor
controlFloor is a numeric value for optional noise floor used for control groups during centering. If the control group mean/median value is below controlFloor, it is set to controlFloor to define a minimum baseline for centering. It is mostly useful when the effective noise floor for a platform is above zero.
naControlAction and naControlFloor handle the specific case where all controlSamples have NA values for a particular row. By default, all centered values would become NA thereby making platform measurements disappear from the heatmap. Sometimes it is preferable to define another reference so the non-NA values can be indicated in the heatmap.
See jamma::centerGeneData() for more information.
The most common alternatives:
- naControlAction="row": uses the remaining non-NA values from that row
- naControlAction="floor": assigns the numeric value naControlFloor, which is useful when the reference value should be a known baseline limit of detection.
shrink_df()
Argument numShrinkFunc now includes na.rm=TRUE by default.

jamses 0.0.34.900

heatmap_se()
new argument use_raster=TRUE which allows turning it off. When R package magick is not installed, on some systems it causes an error when trying to render ComplexHeatmap with use_raster=TRUE. Using use_raster=FALSE avoids the error.
groups_to_sedesign()
small update to enforce mixedSortDF(..., honorFactor=TRUE) to ensure that data.frame columns that contain factor values will use the levels to define the proper sort order.

jamses 0.0.33.900

ComplexHeatmap now requires version 2.13.4 or higher, and points to the Github repository instead of Bioconductor. This change is required for R-3.6.1 whose matching Bioconductor version 3.10 only offers ComplexHeatmap version 2.2.0, and throws an error with heatmap_se() when rendering row annotations.
jamba dependency now requires version 0.0.88.900, to pick up the subtle but important change to mixedSort()
colorjam was bumped to version 0.0.23.900 for consistency.

groups_to_sedesign()

jamses 0.0.32.900

se_collapse_by_column() which is the equivalent of se_collapse_by_row() except that the default math is much simpler, to perform mean calculation with optional outlier removal prior to calculating the mean.
new experimental function shrink_df() as potential drop-in replacement for shrinkDataFrame(). This new function is intended to have simpler processing, with less complicated history than shrinkDataFrame().

minor updates to help text for se_collapse_by_row(), added @family to this and other functions.
se_contrast_stats() was changed from using matrixStats::rowMaxs() instead to use apply(x, 1, max, na.rm=TRUE) - due to persistent Segfaults caused by matrixStats::rowMaxs() on multiple architectures (OSX, linux) and with recent package versions, at least for R-3.6.1.

jamses 0.0.31.900

names_contrast2comp() and names_comp2contrast() are analogous to contrast2comp() and comp2contrast() except they operate on the object names, making it convenient to wrapper around list objects where the list names are the contrasts or comps.

se_contrast_stats()
argument normgroup now accepts colnames(colData(se)) to define normgroups by column annotations.

fixed apparent regression in groups_to_sedesign() for interaction contrasts
version 0.0.30.900 apparently introduced regression bug in interaction contrasts that returns only the interactions in list form instead of all contrasts in sedesign form. Short-lived, fortunately.
Some debug potential was added with verbose=TRUE, that prints iterative contrast name generation, with indent to help show when the function is iteratively calling itself.
heatmap_se()
fixed bug with user-supplied row_names_gp or column_names_gp that does not also define fontsize.
new arguments cluster_column_slices=FALSE, cluster_row_slices=FALSE to allow this behavior to be overridden when relevant

heatmap_se()
moved substantial logic out, and into process_sestats_to_hitim(), which handled the hit incidence matrix, and deciding which rows are included in the heatmap with default rows=NULL.
quality of life change: when there is only one assayNames(se), then assay_name defaults to the only possible value, instead of acting like it couldn't possibly know what I mean. Haha.
legend_at is smarter when centerby_colnames=FALSE and there is no data centering, the legend only includes negative values when data contains negative values.
legend_labels are smarter when color_max <= 1, it uses the numerical value without transformation.
vastly expanded the examples and the param help text.

process_sestats_to_hitim()
previously internal logic to heatmap_se(), it surpassed the amount of logic hidden inside a function, especially when it's used twice.

jamses 0.0.30.900

se_contrast_stats()
removed duplicated merge() called across stats data.frame results.
implemented normgroups for independent model fit among subsets of samples.

jamses 0.0.29.900

heatmap_se() did not properly check cluster_columns for function input, so it did not allow using a custom clustering function.

heatmap_se()
new arguments: row_names_gp and column_names_gp allow custom settings for row and column labels, respectively.

jamses 0.0.28.900

se_contrast_stats() with posthoc_test="DEqMS" and only one contrast threw an error "Error in fit$sca.t[results.table$gene, coef_col] : subscript out of bounds". The error actually occurs in ebayes2dfs() the function that converts statistical model fit into a list of data.frame objects.
The DEqMS package produces numeric matrix results that it also summarizes into a data.frame via DEqMS::outputResult(). When there is only one contrast, many of the numeric matrix objects also have one column, and R apparently dropped the colnames() for the sca.t matrix, causing DEqMS::outputResult() to fail when argument coef_col is given the character name of the contrast instead of the integer column number.
The contrast name is preserved in the coefficients matrix, so the contrast is converted to column number, then passed as coef_col, and resolves the error.
Calls to DEqMS::outputResult() use integer values for argument coef_col.
Some internal step in DEqMS does not properly handle data with only one contrast, probably forgot to include drop=FALSE in matrix subsetting.

groups_to_sedesign() updates
input SummarizedExperiment is now recognized, using group_colnames to define the experimental design data.frame. Data can also be subset using isamples for example.
new argument contrast_names to provide specific contrasts upfront, therefore skipping all internal logic of defining contrasts.
internal values max_depth and factor_order are validated using the design data.frame such that it will not pursue max_depth nor factor_order higher than the number of design columns available.
When there are no valid contrasts for the given design groups, the function returns empty contrasts instead of throwing an error. One could argue that an error is actually preferred, since it forces the input data to be valid, and forces the user to review the data.
contrast2comp() and comp2contrast() were reworked to handle alternate delimiters, which mainly involved wrapping the delimiter in proper regular expression patterns. During the process, obscure testing showed certain delimiters cause problems with regular expressions, so those problems were pre-emptively fixed. The help docs were expanded somewhat.
sestats_to_df() was refactored to be cleaner, correcting the issue of colname mislabling when dimname_order was different than expected. The function now works smoothly with any dimname_order values, and accepts character or integer input to specify the dimname ordering.

format_hits() is an internal function, but it might be a useful convenience function so it is exported. It is called by sestats_to_df() to provide a summary data.frame of statistical results. It converts a hit vector into either:
a summary string with hits/up/down,
one integer count of hits, or
a vector of integers for c("hits", "up", "down")`.

jamses 0.0.27.900

groups_to_sedesign() threw an error in some circumstances when the input ifactors only contained one column, but the column contained levels with delimiters recognized by factor_sep, and some levels without the delimiter. The error was: "Error in combn(iMatch, 2) : n < m"
The workaround was to supply levels with consistent delimiters, or a design that used more than one column initially.
However, the R code was adjusted to handle these situations properly without workarounds.

jamses 0.0.26.900

heatmap_se() updates:
still in progress: several new arguments intended to customize exact font size and grid size for color legends, annotations, around the heatmap.
new arguments: show_heatmap_legend, show_top_legend, show_left_legend intended to allow hiding various color legends, mostly to save plot space.
all_sample_colors sometimes does not match left or top annotation names, and now will create categorical colors in more scenarios.
new argument mark_rows to enable optional ComplexHeatmap::anno_mark() row labels for a subset of heatmap rows. mark_labels_gp to customize the displayed font.
new argument right_annotation for custom annotation
new arguments show_top_annotation_name, show_left_annotation_name, left_annotation_name_rot to customize these label positions.
new arguments mark_rows and mark_labels_gp used to define anno_mark() call-out labels for a subset of rows in the heatmap. This step is difficult to define upfront when the data in se is subset by some other aspect of heatmap_se(), since anno_mark() requires numeric index positions for each label, and the order of rows may not be known upfront.

hit_array_to_list() was returning NA values when the sestats$hit_array also included NA values. The function now removes any NA values before returning each vector.
heatmap_se() argument alt_sestats was throwing an error when supplied with a numeric matrix instead of sestats object, this input type was been corrected, and the code slightly refactored for clarity.
heatmap_se() was passing multiple values to the left_annotation argument show_legend, which only accepts one logical value. This bug was corrected.

contrasts2comp_dev() is an experiment in converting the code to be vectorized, though the exceptions to handle unbalanced contrasts may still prevent this function from being as fast as intended.

jamses 0.0.25.900

heatmap_se() updates:
new argument data_type used in color legend title, and heatmap title.
new arguments legend_at, legend_labels to customize heatmap color gradient legend positions and labels.
data centering can be turned off with centerby_colnames=FALSE.
sestats and alt_sestats can be supplied as incidence matrices directly, without requiring them to be in sestats or hit_array formats.
top_colnames can be hidden with top_colnames=FALSE; also when colData(se) is empty, top_annotation is hidden.
assay_name may contain multiple values to define gene hits in sestats; however only the first matching assay_name in names(assays(se)) is used for the heatmap data.

jamses 0.0.24.900

heatmap_se() new argument row_subcluster:
intended to help drill-down into a subset of heatmap rows by using elements of row_split as returned by jamba::heatmap_row_order().
The rows are split by row_split using the full heatmap data, and can be split by...
- cluster number using row_split=5 integer values,
- annotation columns in rowData(se),
- character vector, or
- data.frame
In any case, heatmap_row_order() returns a list of rownames(se), which is used to create the heatmap. In fact, rows are displayed in the same order, but without any associated dendrogram even if there is a dendrogram associated with the subset of rows.
row_subcluster can be an integer vector referring to the row list element in order, or character vector of one or more names() associated with the row list elements.
heatmap_se() arguments cutoff_name and alt_cutoff_name default values changed to NULL, so hits in sestats and alt_sestats will use all cutoffs by default, unless specified otherwise.

jamses 0.0.23.900

heatmap_se() was updated
isamples is applied to subset the columns displayed in the heatmap, however it no longer subsets data during the data centering step. This change allows centering by patient with time 0 as the control point, which produces a stripe of 0 values for each patient at time zero, all other times become difference from time zero. The heatmap can then use isamples with only samples not at time zero.
new argument subset_legend_colors=TRUE will now filter colors in the top_annotation and left_annotation color legends to remove categorical colors which are not represented in the data being displayed.
- This update prevents showing a huge list of categorical colors that are not relevant to the heatmap. Note that even when two heatmaps are added together (with ComplexHeatmap + or %v%) the amazing coding in ComplexHeatmap will merge together color legends by annotation color name, showing only the unique set of colors.
- It seems that ComplexHeatmap retains all colors in the heatmap object, but hides colors that are unused. The heatmap_se() code was actually encoding the at color labels for all colors regardless of which colors were present. So it is unclear the benefit of this option, but the function itself was improved.
color legends are displayed either in order of factor levels, or sorted using jamba::mixedSort()
new argument row_anno_fontsize to control the specific fontsize for row annotation labels. These labels appear for row annotations, and appears beside the column labels on the heatmap.

heatmap_se() when sestats is supplied but se does not contain all rows present in sestats hit array, it produced an error.
heatmap_se() when annotations with factor columns were subset to remove a color from the legend, the color legend was still encoding at labels for the full set of colors, causing ComplexHeatmap::HeatmapAnnotation() to print a warning message.

choose_annotation_colnames() is a wrapper function to hold the simple logic for choosing annotation columns to include for heatmap_se(). By default it chooses columns with at least one repeated value, at least two unique values, and only includes the first column with matching cardinality. The last criterion ensures it won't display six columns which show the same pattern of information for all samples.

call_fn_ellipsis() was removed since it was migrated to jamba. (Previous change.)

jamses 0.0.22.900

hit_array_to_list() was returning matrix for single contrasts, due to apply() tendency to simplify output to vector. The method was changed to use a different approach guaranteed to return list.
heatmap_se() had a typo in an edge condition when using alt_sestats, which has been corrected.

jamses 0.0.21.900

heatmap_se() was not properly auto-detecting show_row_names, which only worked when rows was supplied specifically. Now it works when rows is not supplied and/or only sestats is also supplied.

jamses 0.0.20.900

sestats_to_df() was updated:
now reports "" whenever the hit_array value is NA, because the NA in this context means the cutoff was not tested for statistical hits. This situation happens when using int_adjp_cutoff,int_fold_cutoff,int_mgm_cutoff that differ from the main contrast cutoffs. Therefore, reporting 0 implies the threshold was tested and no hits were found, which is incorrect.
also whenever there are zero hits it reports "0 hits" and not "0 hits (0 up, 0 down)". This change saves unnecessary text, also makes it easier to see which entries have hits.

jamses 0.0.19.900

heatmap_se() was updated:
The logic of obtaining a hit list for a contrast must account for multiple cutoffs, particularly when interaction contrast cutoffs differ from pairwise cutoffs. This logic was pushed into new function hit_array_to_list().
The default color legend for left_annotation and top_annotation now includes color_bar="discrete" for numeric annotation columns. The legend will display discrete color breaks instead of a continuous color bar. The breaks are defined in the color function, in most cases the recommended method is calling platjam::design2colors(), which typically defines reasonable color breaks.

hit_array_to_list()
Input is hit_array from sestats, as output from se_contrast_stats().
It optionally takes a subset of the following, which match the dimnames() in hit_array:
- cutoff_names
- contrast_names
- assay_names
Then it iterates each contrast_names:
- It returns one named vector of hits for that contrast, named by the measurement, with values -1 or 1 indicating the direction.
- When multiple assay_names or cutoff_names are involved, it combines results into one set representing the union of hits.
The intent is to retrieve a list of hits across contrast_names, regardless if there is one or more cutoff_names or assay_names.

jamses 0.0.18.900

sestats_to_df() was updated:
when se_contrast_stats() uses different interaction statistical thresholds from the pairwise thresholds, the output hit_array contains NA values.
- previously any non-empty vector simply counted the length
- new behavior is to count non-NA entries
- future "fix" will be for the cell to have length=0, with no NA. This situation only happens when *_cutoff and int_*_cutoff define different thresholds.

jamses 0.0.17.900

call_fn_ellipse() was removed and placed in the jamba package, package dependency was bumped up to jamba(>= 0.0.81.900)

heatmap_se() error when not supplied rows nor sestats, now defaults to show all rownames(se).
heatmap_se() error when colorjam package not previously loaded, added missing package prefix.

heatmap_se() argument normgroup_colname=NULL and centerby_colnames=NULL, instead of previous defaults which should not be used unless relevant to the design.

save_sestats() was updated to prioritize sorting row_type column headers first in the output Excel table, previously the method looked for known patterns, which obviously failed when row_type differed from those known patterns. Now the row_type value itself is used in addition to the expected patterns.

jamses 0.0.16.900

call_fn_ellipse()

heatmap_se() new argument correlation
when correlation=TRUE it calculates a sample correlation matrix, and will display a correlation heatmap.
The option was added to this function in order to share much of the remaining logic involving data centering, and heatmap column annotations.
Note that it is possible to create a correlation heatmap using a subset of statistical hits, which although typically creates a nice-looking correlation structure, may not be that informative since it only includes statistical hits.

heatmap_se() error when row_split was an integer number of clusters, and cluster_rows is a function, which is not permitted by ComplexHeatmap::Heatmap(). Instead, when cluster_rows or cluster_columns is a function, the function is evaluated upfront, so the resulting dendrogram or hclust is passed to Heatmap(), which does permit integer row and column split.
Note that when row_split or column_split are not single numeric values, the function must not be evaluated, otherwise that process fails. Haha. Makes sense. Now both scenarios are handled, hopefully this covers all scenarios.

jamses 0.0.15.900

heatmap_se()
Argument row_split now also accepts named vector where names correspond to rownames in the heatmap.
Added help text for all function arguments.
save_sestats()`
new argument use_assay_suffix=TRUE will try to add abbreviated assay_names to the end of each Excel sheet name, when there is more than one unique assay_names value.
The Excel sheet names are required to be unique, and have no more than max_nchar_sheetname characters in each string. The sheet names are progressively shortened as necessary, then are made unique when required by calling jamba::makeNames().

jamses 0.0.14.900

This update attempts to extend se_contrast_stats() with methods from the Bioconductor package DEqMS. This package models error as a function of PSM_count, which is the number of peptide score matrix entries that were combined to formulate an abundance estimate for each protein. The approach is analogous to using voom as an extension of limma. In fact, DEqMS is another extension of the limma error model.

se_contrast_stats()
argument posthoc_test=c("none", "DEqMS") to allow post-hoc tests such as DEqMS to be applied after the core limma steps. This argument is pushed into run_limma_replicates().
argument posthoc_args is a list named by the posthoc_test value, containing a list of named elements used as arguments for the relevant post-hoc test functions.
run_limma_replicate()
argument posthoc_test=c("none", "DEqMS") to allow post-hoc tests such as DEqMS to be applied after the core limma steps. This argument is also passed to ebayes2dfs().
argument posthoc_args is a list named by the posthoc_test value, containing a list of named elements used as arguments for the relevant post-hoc test functions.
argument trim_colnames added "sca.t" as additional column to omit by default.
ebayes2dfs()
argument lmFit4 is optional and intended to contain additional output from post-hoc methods such as DEqMS::spectraCounteBayes().
argument trim_colnames added "sca.t" as additional column to omit by default.
The order of colnames in the data.frame returned was updated to be more consistent for different formats. Specifically, when posthoc_test="DEqMS" the P-value columns sort with the "sca.P.Value" and "sca.adj.pval" columns before the usual limma columns.
when calling mark_stat_hits() it updates the expected colnames when posthoc_test="DEqMS", using adjp_colname="sca.adj.pval" and p_colname="sca.P.Value". In this way, hits are filtered by the DEqMS adjusted P-value and P-value, respectively. All other statistics are carried over from lmFit3 and work as before.
heatmap_se()
The incidence matrix data for alt_sestats was updated to handle proper hit matrix, in haste to make the left_annotation extensible the sestats hit matrix was used twice. This is why the package is private, for now.

jamses 0.0.13.900

heatmap_se() was modified to handle updated sample_color_list that contains function output from circlize::colorRamp2().

jamses 0.0.12.900

heatmap_se() was modified to accomodate other object types, such as "MethyLumiSet" which does not have rowData(),colData(),assays(), accessor functions, instead uses a common alternative for Bioconductor objects: featureData(), phenoData() and assayData(). The heatmap_se() changes will try to use those accessor functions for any object whose class does not contain "SummarizedExperiment".

jamses 0.0.11.900

se_detected_rows() is a new function to assist the process of defining "detected" rows which are suitable for downstream analysis. The rules are essentially simple heuristics based upon the total number of replicates, group replicates, fraction of group replicates, and number of sample groups where a row has "valid" measurements. In this case "valid" is defined as meeting a minimum abundance threshold, and is not NA.

jamses 0.0.10.900

contrast2comp(), comp2contrast() are two reciprocal functions intended to convert long contrast names to a short comparison form called a "comp". The "comp" can be converted back to the full contrast name. In cases where a two-way contrast can be written in two equivalent forms, argument factor_order can be used to define the desired order. The statistical results are the same, but sometimes it helps to have specific ordering. See help docs and examples for details, and worked examples. Main driving motivation:
ComplexHeatmap::Heatmap() labels are too long to fit onscreen without making other non-ideal adjustments to plot dimensions.
venndir::venndir() labels are also too long.
Excel worksheets are limited to 31 characters, a typical two-way contrast with 3-characters for each factor level uses 35 characters, the "comp" for uses only 15.
- contrast: "(Aaa_Bbb-Ccc_Bbb)-(Aaa_Ddd-Ccc_Ddd)" (35 characters)
- comp: "Aaa-Cca:Bba-Dda" (15 characters)

argument rename_contrasts was added to:
heatmap_se()
sestats_to_df()
save_sestats()

jamses 0.0.9.900

save_sestats() was updated to use jamba-version-0.0.79-900 new writeOpenxlsx(), which uses a substantially faster method of saving worksheets, and allows building a multi-sheet Workbook prior to saving to a file. This change avoids the time-consuming, repeated steps: loading, saving, updating, saving, loading, updating, saving, etc. Previously, each successive worksheet added to an Excel file took longer to load and save. For a 15,000 row, 8 column data.frame, saving once took 3 seconds, but saving 12 worksheets took 15 minutes! The new approach took about 25 seconds.

heatmap_se() is a work in progress, but attempts to automate display of expression data as a heatmap using ComplexHeatmap::Heatmap(). By default it uses statistical hits defined in sestats output from se_contrast_stats().
call_fn_ellipsis() is copied from multienrichjam::call_fn_ellipsis(). This function allows passing ellipsis argument ... to another function, where that function does not allow ... and therefore all arguments must be strictly defined. Any arguments in ... which are not defined in the other function are removed before calling that function.

jamses 0.0.8.900

groups_to_sedesign() new behavior
two-way interaction contrasts now retain the order of first-order contrasts, preferring to alter the reverse rev(factor_order) in order to build interaction contrasts on top of existing contrasts.
some verbose output now requires (verbose >= 2)
added verbose output for two-way contrasts removed because they are equivalent, but written with different order of comparisons.

jamses 0.0.7.900

groups_to_sedesign() new argument default_sort=c("asis", "sort_samples", "mixedSort"):
Note in all cases, a factor column is sorted by its factor levels.
"asis": will convert any non-factor column to factor, with levels defined in the order they appear in the input data. Any existing factor column will remain as-is.
"sort_samples": will call sort_samples() in order to recognize control terms, and preferentially place them first, for example "Control", or "Wildtype". All other terms are sorted by jamba::mixedSort() for alphanumeric sorting.
"mixedSort": will use jamba::mixedSortDF() to sort group columns:
- factor columns are sorted according to factor levels
- character columns are alphanumeric sorted using jamba::mixedSort()
To influence the sort order, data.frame columns should have factor values with defined levels in order. Otherwise by default, the first entries in the data.frame provided will become the first factor levels.
validate_sedesign() default argument verbose=FALSE, also silencing other internal messages which were previously always turned on.
matrix_normalize() new argument normgroup normalizes subsets of matrix columns independently, then re-assembles the original matrix.
Purpose is convenience of maintaining one data matrix, while also applying normalization independently on each sub-matrix. The attributes returned from the normalization method are also combined, for example the normalization factors "nf" reported by jamma::jammanorm() are combined in proper order.
This feature is distinct from batch adjustment, in that the data in each sub-matrix are never normalized relative to each other.
- For example, one could normalize total RNA-seq and nascent 4sU-seq data independently, without expectation that the two would ever have a common frame of reference to normalize one relative to another.
- Similarly, one could normalize each tissue type independently, which may be appropriate when analyzing data that contains very different mammalian tissue organ samples, such as muscle and brain. It would generally not be appropriate to use quantile normalization across muscle and brain samples, since the overall pattern and distribution of expression values is not expected to be similar. Quantile normalize assumes (and imposes) a common distribution, by adjusting mean expression signal at each quantile to a common mean expression across all samples.
- For a rough approximation of cross-tissue normalization, one could apply "quantile" normalization within each normgroup defined by tissue type, then apply "jammanorm" median normalization to apply a linear adjustment of signal across tissue types. The median normalization does not affect distribution, thus will not affect intra-tissue contrasts, except by adjusting its overall signal which may change downstream assumptions regarding signal thresholds. It is still not advise to compare directly across tissue types, except in some cases a two-way contrast may be appropriate (comparing intra-tissue fold change to intra-tissue fold change.)
se_normalize() also gained new argument normgroup, although this argument is merely passed through to matrix_normalize().

sestats_to_df() - simple method to convert sestats output from se_contrast_stats() to a data.frame summary with number of hits for each comparison. It should be suitable for kable() output, with row groups by cutoff.

jamses 0.0.6.900

se_contrast_stats()
new argument sedesign allows providing SEDesign output from groups_to_design(). When isamples is also provided, it will be used to subset the sedesign as appropriate, using validate_design().
validate_sedesign() was updated to prevent the annoying warning when comparing vectors of two different sizes.

save_sestats() is a wrapper function intended to help export stat summary tables into Excel using jamba::writeOpenxlsx(). This function defines useful column style functions for statistical data types, such as P-values, fold changes, log fold changes, etc. It also saves all statistical contrasts, with one contrast in each worksheet. The worksheet names are limited to 31 characters, the limitation of MS Excel.

jamses 0.0.5.900

Bumped version, to sync the transition of slicejam functions into jamses.

jamses 0.0.4.900

se_collapse_by_row() is a new function intended to collapse rows, for example peptides into protein abundance.

jamses 0.0.3.900

updates to existing functions

se_contrast_stats() new arguments block, and correlation intended to allow blocking factors in experiment design.
run_limma_replicate() new arguments block, and correlation recognized from se_contrast_stats().

jamses 0.0.2.900

updates to existing functions

ebayes2dfs() was updated to handle data that does not have a column with specific probe or gene annotations. In this situation rownames() will be used from output of limma::topTable().

jamses 0.0.1.900

Methods are being ported from another internal R package.

matrix_normalize() - wrapper function to normalize numeric matrix data
se_normalize() - function to normalize SummarizedExperiment assay data
groups_to_sedesign() - function to convert experiment group into SEDesign with proper design matrix, and contrast matrix. This function was ported from splicejam::groups2contrasts.
se_contrast_stats() - wrapper function to perform numerous statistical contrasts, in this case calling limma methods with or without limma-voom.
voom_jam() - lightly extended variation of limma::voom() solely to enable presence of NA values without distortion of the model fit.
handle_na_values() - function to manage presence of NA values using one of several appropriate strategies.
run_limma_replicate() - wrapper function to perform limma moderated t-test steps, and to produce consistent, fully-annotated summary data.frame.
ebayes2dfs() - internal wrapper function to convert limma::eBayes() output to a list of data.frame tables.
mark_stat_hits() - wrapper function used to apply one or more statistical thresholds to mark statistical hits among rows of statistical contrast results.
update_function_params() - internal function used to apply user-specific parameters to a pre-defined set of parameters stored in function formal arguments.
update_list_elements() - internal function called by update_function_params() as a generic way to update individual elements in a list with user-defined values.
log2fold_to_fold() and fold_to_log2fold()

SEDesign: SummarizedExperiment design object

design: numeric matrix indicating the experiment design
contrasts: numeric matrix indicating statistical contrasts
samples: character vector of sample replicates, also equivalent to rownames(design)

Some constraints:

rownames(design) contains sample replicate identifiers, intended to correspond to colnames(SummarizedExperiment)
colnames(design) contains the name of each sample group
rownames(contrasts) contains the name of each sample group
Enforced constraint: colnames(design) == rownames(contrasts)

Related accessor and assignment methods:

[ subset operations use [samples, groups] to enable re-ordering, or subsetting data by sample and/or groups. For example, one may want to remove a technical outlier sample from an analysis, or include only a subset of groups in downstream statistical contrasts.
validate_sedesign() contains the full logic for constraints and subset operations:
the order of samples must match rownames(design)
the order of colnames(design) must match rownames(contrasts)
when a subset of samples is defined, design is updated to remove groups which are no longer represented by min_reps replicates. By default, a group with zero replicates is no longer a valid group
When a subset of groups is defined, perhaps by using a subset of samples as described above, the contrasts entries are evaluated to see which entries require the groups to be removed. For each contrast that requires a group being removed, the contrast is also removed. This step prevents retaining an incomplete contrast.
samples() will return the object@samples
samples()<- to assign new values to object@samples, which will also update rownames(design) accordingly. Note these values are expected to match colnames(SummarizedExperiment).
groups() will return colnames(object@design), representing all sample groups defined.
groups()<- will assign values to colnames(object@design), which will also update rownames(object@contrasts) if there are statistical contrasts already defined in the object.
contrastnames() will return colnames(object@contrasts) representing the name of each contrast.
contrastnames()<- will assign values to colnames(object@contrasts), which may be useful to define a sensible label.
design() will return the design matrix object@design.
design()<- will assign a new design matrix to object@design, then will call validate_sedesign() to enforce the relevant constraints. For example colnames(object@design) must match rownames(object@contrasts) if contrasts are defined in the object.
contrasts() will return the contrast matrix object@contrasts.
contrasts()<- will assign a new contrast matrix to object@contrasts, then will call validate_sedesign() to enforce the relevant constraints. For example colnames(object@design) must match rownames(object@contrasts).

jmw86069/jamses documentation built on May 31, 2024, 1:36 p.m.

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jmw86069/jamses Jam SummarizedExperiment Stats

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jmw86069/jamses
Jam SummarizedExperiment Stats

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