lute algorithm class definitions and uses
29 October, 2024
Package
lute 1.3.0
1 About
This guide describes lute’s generics, methods, and classes for algorithms,
including deconvolution and marker selection algorithms. This software and the
method to rescale on cell type-specific sizes is detailed in the manuscript
Maden et al. (2024). This may be useful to algorithm developers and
researchers interested in conducting systematic algorithm benchmarks.
1.1 Background
The class structure used by lute is based on the bluster R/Bioconductor
package. It expands on that class structure by defining a hierarchy.
1.2 Motivation
Many algorithms are maintained and versioned in GitHub or Zenodo rather than a routinely versioned repository such as Bioconductor or CRAN. This can prove an obstacle when tracing package development and attempting comprehensive benchmarks, as software that is not actively maintained can become deprecated over time, and not all software will use compatible dependency versions (Maden et al. (2023)).
lute classes can help to (1.) encourage use of common Bioconductor
object classes (e.g. SummarizedExperiment, SingleCellExperiment,
DelayedArray, etc.) and (2.) to use more standard inputs and outputs to
encourage code reuse, discourage duplicated efforts, and enable more rapid and
exhaustive benchmarks.
2 Classes
In a general sense, the class hierarchy is a wrapper allowing access to many algorithms using a single function and shared methods. However, it is possible to share data reformatting and preprocessing tasks, making the hierarchy more effectively similar to a workflow.
2.1 typemarkerParam
Topmost parameter class for cell type gene markers. This is used to manage the marker IDs.
2.2 deconvolutionParam
This is the parent class for all deconvolution algorithm param objects. The
deconvolutionParam class is minimal, and simply defines slots for
bulkExpression, or a matrix of bulk expression data, and returnInfo,
a logical value indicating whether the default algorithm output will be stored
and returned with standard output from running the deconvolution() method on
a valid algorithm param object.
2.2.1 referencebasedParam
As shown in the class hierarchy diagram (above), referencebasedParam is a
parent subclass inheriting attributes from deconvolutionParam. It is meant to
contain and manage all tasks shared by reference-based deconvolution algorithms,
or algorithms that utilize a cell type summary dataset. This is to be
distinguished from reference-free algorithms.
This param class adds slots for referenceExpression, the cell type reference
data, and cellScaleFactors, an optional vector of cell type size factors used
to transform the reference.
2.2.2 independentbulkParam
This class is a subset of referencebasedParam algorithms specifying explicit
samples used separately, such as for discrete training and test stages.
This param class adds a slot called bulkExpressionIndependent, which is for a
dataset of bulk samples independent from samples specified in the
bulkExpression slot.
2.3 Helper functions
lute provides a number of helper functions used to make the algorithm classes
work. These include the parent classes and subclasses, and several functions to
convert between object classes. These helper functions may be useful to
developers. The following table indicates the functions and a short summary of
what they do.
| function_name | description |
|---|---|
referenceFromSingleCellExperiment() |
Makes the Z cell atlas reference from a SingleCellExperiment. |
eset_to_sce() |
Convert ExpressionSet to SingleCellExperiment. |
sce_to_eset() |
Convert SingleCellExperiment to ExpressionSet |
se_to_eset() |
Convert SummarizedExperiment to ExpressionSet. |
get_eset_from_matrix() |
Makes an ExpressionSet from a matrix. |
parseDeconvolutionPredictionsResults() |
Gets formatted predicted cell type proportions table from deconvolution results list. |
show() |
Method to inspect and summarize param object contents. |
deconvolution() |
Method to perform deconvolution with a param object. |
typemarkers() |
Method to get cell type markers with a param object. |
deconvolutionParam() |
Defines the principal parent class for all deconvolution method parameters. |
referencebasedParam() |
Class and methods for managing reference-based deconvolution methods. |
independentbulkParam() |
Class and methods for managing methods requiring independent bulk samples. |
typemarkersParam() |
Main constructor for class to manage mappings to the typemarkers() generic. |
3 Algorithms
3.1 findMarkers
The param class findmarkersParam is defined for the function findMarkers()
from scran (see ?findmarkersParam). This is a function to identify cell type
marker genes from a single-cell or single-nucleus expression dataset.
The findmarkersParam class is organized under its parent classes as
typemarkersParam->findMarkersParam. It includes the typemarkers() method for
the identification of marker genes, and show() for inspecting the param
contents.
The following images annotate the constructor function and the typemarkers()
generic defined for the findmarkersParam class.
3.2 NNLS
The param class nnlsParam is defined for the function nnls from the nnls
R/CRAN package (see ?nnlsParam). Non-negative least squares (NNLS) is commonly
used for deconvolution.
The nnlsParam class is organized under its parent classes as
deconvolutionParam->referencebasedParam->nnlsParam. It includes the
deconvolution() generic for cell type deconvolution, and the show() method
for inspecting the param contents.
The following images annotate the constructor function and the deconvolution()
generic defined for the nnlsParam class.
3.3 Bisque
The param class bisqueParam is defined for the function
ReferenceBasedDeconvolution from the BisqueRNA R/Bioconductor package (see
?bisqueParam). The Bisque algorithm adjusts on assay-specific biases arising
between the bulk and single-cell or single-nucleus platforms used to generate
expression datasets for deconvolution.
The bisqueParam class is organized under its parent classes as
deconvolutionParam->referencebasedParam->independentbulkParam->bisqueParam.
It includes the deconvolution() generic for cell type deconvolution, and the
show() method for inspecting the param contents.
The following images annotate the constructor function and the deconvolution()
generic defined for the bisqueParam class.
4 Extensions
We demonstrated the extensibility and flexibility of lute’s generic, method,
and class system by extending support for additional algorithms beyond the 3
described above.
These algorithms can be used by sourcing the provided R/GitHub packages which pair the classes and functions with YML files for easier dependency management.
4.1 meanRatios
The param class meanratiosParam is defined for the function
get_mean_ratios2() from the DeconvoBuddies R/GitHub package at LieberInstitute/DeconvoBuddies.
This function uses the mean of cell type summary ratios to rank and select for
top marker genes.
The meanratiosParam class is organized under its parent classes as
typemarkersParam->meanratiosParam. It includes the typemarkers() generic for
the identification of marker genes, and the show() method for inspecting the
param contents.
meanratiosParam is available from GitHub at metamaden/meanratiosParam.
4.2 DeconRNASeq
deconvolutionParam->referencebasedParam->deconrnaseqParam.
The param class deconrnaseqParam is defined for the function DeconRNASeq
(see ?deconrnaseqParam) from the DeconRNASeq R/Bioconductor package
(link).
The DeconRNASeq algorithm uses weighted averaged expression between types to
predicted cell type amounts more accurately for heterogeneous tissues
(Gong and Szustakowski (2013)).
The deconrnaseqParam class is organized under its parent classes as
deconvolutionParam->referencebasedParam->deconrnaseqParam. It includes the
deconvolution() generic for cell type deconvolution, and the show() method
for inspecting the param contents.
The deconrnaseqParam class is available from GitHub at
metamaden/deconrnaseqParam
4.3 EPIC
The param class epicParam is defined for the function EPIC from the EPIC
R/GitHub package (see ?epicParam). The EPIC algorithm was developed in blood
samples and incorporates cell size mRNA abundance (i.e. cell size) and variance
normalizations (Racle and Gfeller (2020)).
The epicParam class is organized under its parent classes as
deconvolutionParam->referencebasedParam->epicParam. It includes the
deconvolution() generic for cell type deconvolution, and the show() method for inspecting the param contents.
The epicParam class is available from GitHub at
metamaden/epicParam
4.4 MuSiC
The param class musicParam is defined for the function
ReferenceBasedDeconvolution from the MuSiC R/GitHub package (see
?musicParam). The MuSiC algorithm adjusts on between-source variances for
reference data from multiple sources (Wang et al. (2019)).
The musicParam class is organized under its parent classes as
deconvolutionParam->referencebasedParam->musicParam. It includes the
deconvolution() generic for cell type deconvolution, and the show() method
for inspecting the param contents.
The musicParam class is available from GitHub at metamaden/musicParam
4.5 MuSiC2
The param class music2Param is defined for 2 implementations of the MuSiC2
algorithm from the MuSiC and MuSiC2 R/GitHub packages, respectively (see
?music2Param). The MuSiC2 algorithm pairs the features of the MuSiC algorithm
with an additional filter for marker genes differentially expressed between
cases and controls in the bulk and expression datasets (Fan et al. (2022)).
The music2Param class is organized under its parent classes as
deconvolutionParam->referencebasedParam->independentbulkParam->music2Param.
It includes the deconvolution() generic for cell type deconvolution, and the
show() method for inspecting param contents.
The music2Param class is available from GitHub at metamaden/music2Param
5 Conclusions
This vignette showed how lute’s classes and methods are extensible and
modular, and can encourage further development with standard algorithm I/O and
object class management. First, we described lute’s algorithm class hierarchy,
including how its parent classes and subclasses manage common tasks shared among
algorithms and a table of functions developers may find useful. Further, we
showed the annotated class and generic functions for algorithms supported by
lute out of the box. Finally, we detail additional algorithms supported by
R/GitHub packages that may be individually installed.
6 Session info
## R Under development (unstable) (2024-10-21 r87258)
## Platform: x86_64-pc-linux-gnu
## Running under: Ubuntu 24.04.1 LTS
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## Matrix products: default
## BLAS: /home/biocbuild/bbs-3.21-bioc/R/lib/libRblas.so
## LAPACK: /usr/lib/x86_64-linux-gnu/lapack/liblapack.so.3.12.0
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## time zone: America/New_York
## tzcode source: system (glibc)
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## attached base packages:
## [1] stats4 stats graphics grDevices utils datasets methods
## [8] base
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## other attached packages:
## [1] lute_1.3.0 SingleCellExperiment_1.29.0
## [3] SummarizedExperiment_1.37.0 Biobase_2.67.0
## [5] GenomicRanges_1.59.0 GenomeInfoDb_1.43.0
## [7] IRanges_2.41.0 S4Vectors_0.45.0
## [9] BiocGenerics_0.53.0 MatrixGenerics_1.19.0
## [11] matrixStats_1.4.1 BiocStyle_2.35.0
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## loaded via a namespace (and not attached):
## [1] xfun_0.48 bslib_0.8.0 lattice_0.22-6
## [4] vctrs_0.6.5 tools_4.5.0 generics_0.1.3
## [7] parallel_4.5.0 tibble_3.2.1 fansi_1.0.6
## [10] cluster_2.1.6 pkgconfig_2.0.3 BiocNeighbors_2.1.0
## [13] Matrix_1.7-1 dqrng_0.4.1 lifecycle_1.0.4
## [16] GenomeInfoDbData_1.2.13 compiler_4.5.0 statmod_1.5.0
## [19] bluster_1.17.0 codetools_0.2-20 htmltools_0.5.8.1
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## [43] SparseArray_1.7.0 magrittr_2.0.3 S4Arrays_1.7.0
## [46] utf8_1.2.4 edgeR_4.5.0 UCSC.utils_1.3.0
## [49] rmarkdown_2.28 XVector_0.47.0 httr_1.4.7
## [52] igraph_2.1.1 scran_1.35.0 ScaledMatrix_1.15.0
## [55] beachmat_2.23.0 evaluate_1.0.1 knitr_1.48
## [58] irlba_2.3.5.1 rlang_1.1.4 Rcpp_1.0.13
## [61] scuttle_1.17.0 glue_1.8.0 BiocManager_1.30.25
## [64] jsonlite_1.8.9 R6_2.5.1 zlibbioc_1.53.0