# An introduction to contrast decomposition and querying using orthos

Panagiotis Papasaikas1,2*, Charlotte Soneson1,2** and Michael Stadler1,2***

1Friedrich Miescher Institute for Biomedical Research, Basel, Switzerland
2SIB Swiss Institute of Bioinformatics

*panagiotis.papasaikas@fmi.ch
**charlotte.soneson@fmi.ch
***michael.stadler@fmi.ch

#### 15 April 2025

#### Package

orthos 1.6.0

# 1 Introduction

RNAseq-based differential expression analysis upon cellular perturbations, such as gene knockouts, RNA knockdowns or compound treatment experiments, is the most commonly used tool for probing molecular mechanisms of action due to its simplicity and low cost.

However, interpretation of such gene expression contrasts is confounded by the complex and nuanced impacts of experimental treatments on cellular processes.

For example, knockout or over-expression of a transcription factor will not only alter the transcription of its direct target genes, but also cause many secondary expression changes. In addition, treatments or treatment delivery agents typically elicit a variety of unintended, systemic responses (such as immune, toxic, metabolic) that cannot be well-controlled for by the design of the study.

The final experimentally measured gene expression changes are a hard to assess convolution of specific and non-specific secondary and lateral treatment effects.

`orthos` is a generative modelling-based approach that disentangles the experiment-specific from the non-specific effects of perturbations on gene expression. It is trained on a large corpus of gene expression contrasts (per organism >60K annotated, >0.5M augmented), compiled from the ARCHS4 database of uniformly processed RNAseq experiments (Lachmann et al. (2018)). It accurately captures and isolates non-specific effects (effects that are observed across multiple treatments) while accounting for context (tissue or cell-line experimental background).

The residual specific component obtained from this decomposition offers a more unequivocal experimental signature and is more closely related to the direct molecular effects of the perturbation when compared to the raw signal.

In addition to providing a clearer understanding of the effects of experimental treatments on gene expression, `orthos` also enables researchers to query the contrast database with arbitrary contrasts and identify experiments with similar specific effects, ultimately helping to map treatments to mechanisms of action.

# 2 Installation and overview

`orthos` can be installed from from Bioconductor using `BiocManager::install()`:

    if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")
    BiocManager::install("orthos")
    # or also...
    BiocManager::install("orthos", dependencies = TRUE)

After installation, the package can be loaded with:

    library(orthos)

A typical analysis involves two steps:

1. Decomposing one or several contrasts into their corresponding specific and non-specific components using the `decomposeVar()` function and

2. Performing queries with the original and decomposed specific and non-specific contrasts against the contrast database using the `queryWithContrasts()` function.

# 3 Demonstration data

To demonstrate the functionality of `orthos` we use a dataset from the the GEO series GSE215150.

This series was not part of the `orthos` training or the `orthosData` contrast database; it was only publicly released on January 1st 2023 after the freeze of the training data to the ARCHS4 v2.1.2 database.

The performed experiment involves over-expression (OE) of the MKL/megakaryoblastic leukemia 1 gene (also termed MRTFA/myocardin related transcription factor A) and a constitutively active mutant MKL1 (caMKL1, described in Hu et al. (2019)). Both OE experiments were performed in mouse LM2 and human 4T1 tumor-derived breast cancer cell lines. In addition to the MKL1/caMKL1 OE samples, the series also contains no-treatment controls for each of the two cell lines.

For simplicity the three biological replicates of each available condition have been collapsed in the data provided in the package.

In the provided form each of the two datasets (Mouse, Human) contains raw counts for over 55,000 genes identified by gene symbols in three conditions: Control (Ctrl), MKL1 OE (MKL1) and constitutively-active MKL1 OE (caMKL1).

Load the human dataset:

    MKL1_human <- readRDS(system.file("extdata", "GSE215150_MKL1_Human.rds",
                                      package = "orthos"))
    head(MKL1_human)
    #>             Ctrl MKL1 caMKL1
    #> DDX11L1        6    3      4
    #> WASH7P        47   57     41
    #> MIR6859-1     10   10      6
    #> MIR1302-2HG    0    6      1
    #> MIR1302-2      0    0      0
    #> FAM138A        1    1      5
    dim(MKL1_human)
    #> [1] 59453     3

Load the mouse dataset:

    MKL1_mouse <- readRDS(system.file("extdata", "GSE215150_MKL1_Mouse.rds",
                                      package = "orthos"))
    head(MKL1_mouse)
    #>               Ctrl MKL1 caMKL1
    #> 4933401J01Rik    0    0      0
    #> Gm26206          0    0      0
    #> Xkr4             0    0      0
    #> Gm18956          0    0      0
    #> Gm37180          0    0      0
    #> Gm37363          0    0      0
    dim(MKL1_mouse)
    #> [1] 55367     3

# 4 Decomposition of differential gene expression variance into specific and non-specific components using `decomposeVar()`

## 4.1 Prelude: A short overview of the `orthos` models

The workhorse behind `orthos` are organism-specific conditional variational autoencoder (cVAE) models that break down the variance of a given differential expression experiment into a non-specific and an experiment-specific component.

The non-specific component corresponds to gene variance that has been observed across multiple instances during training, while the experiment-specific variance is fairly unique to the experiment.

The inputs to the models are gene counts in the form of log2-transformed counts per million (LCPMs) that are used to encode the context of the performed experiment as well as the actual gene expression contrasts in the form of log2 fold-changes (LFCs), i.e log2-transformed CPM ratios.

As we will see, calculation of those inputs is by default performed internally given only raw gene counts and a specification of the contrasted conditions.

Given these inputs, the model maps the contrast to a concise latent representation (zD) which retains its recurring -and therefore compressible- traits. The compressed latent representation is then used to reconstruct a decoded version of the contrast*.

The decoded contrast corresponds directly to the non-specific component of the observed effects; it subsumes regularities i.e gene variance that the model can account for because it has been repeatedly encountered, in some form, during training.

The residual obtained after removing the decoded contrast from the original one is the specific component; this encompasses the gene variance that the model cannot account for (experiment-specific biological effects + random noise).

From now on the terms decoded and non-specific will be used in conjunction or interchangeably. Ditto for the terms residual and specific.

* Notice that both the latent encoding and the decoded output are conditioned on context (i.e they are context-specific). This means that decomposing a contrast on a different context will produce a different output. An interesting ancillary application of this conditioning is that one can “morph” decoded contrasts to in-silico evaluate non-specific effects in new contexts. In essence, we can infer what the non-specific effects would look like had the experiment been performed in e.g different cell-lines, tissues, batches, patients or using different library-preparation protocols. The inferred decodings will reflect the new context in multiple ways. For example, genes that were non-detected/non-expressed in the original contrast (and therefore had neutral LFCs ) will produce new (possibly non-neutral) decoded LFCs if present in the new context and vice versa. This “out-of-context” type of inference is limited to the decoded contrast, as by definition, the residual is not part of the model’s generative capacity. Mechanically, out-of-context inference of non-specific effects is a simple as evaluating the same contrast using multiple contexts when calling `decomposeVar()` (see section below).

## 4.2 Contrast decomposition with `decomposeVar()`

The function that performs this contrast decomposition into the non-specific and specific components is `decomposeVar()`. There are two available modes in which the required inputs can be fed into the function:

- In the first mode the user passes the matrix `M` of raw counts (genes in rows, conditions in columns) and two vectors `treatm` and `cntr` of equal length specifying the column indices in `M` corresponding to the “treatments” and their respective “controls”. The same column indices can be repeated multiple times in these vectors, for example in the case where multiple treatments are paired to the same control.

- In the second mode the user passes the matrix `M` of raw counts and a second matrix `MD` that contains pre-calculated log2 fold-changes for the contrasts to be analyzed. In this mode, `M` specifies the contexts for the corresponding columns of `MD`, and thus the two matrices need to have the same dimensionality and identical row- and column-names. This would be the mode of choice if e.g one wishes to produce the LFCs independently or if one wants to evaluate the decoding of the same contrast(s) in multiple contexts (e.g for “out-of-context” inference of non-specific effects described above). In the latter case, copies of the same contrast in columns of `MD` will be paired with columns of `M` specifying the different contexts.

In both modes the rownames of `M` (and `MD` if specified) need to correspond to valid gene identifiers (currently `orthos` supports Entrez gene identifiers, ENSEMBL gene identifiers, gene symbols or ARCHS4 gene identifiers). By default the type of gene identifier is detected automatically.

The first time that `decomposeVar` is executed for a particular organism, the models required for inference will be automatically downloaded from `ExperimentHub` and cached in the user ExperimentHub directory (see `ExperimentHub::getExperimentHubOption("CACHE")`) using the `orthosData` companion package.

For the MKL1 data, which are stored as raw counts, it is more natural to call `decomposeVar()` using the first mode.

Decomposing the human contrasts:

    #Decompose MKL1-vs-Cntrl and caMKL1-vs-Cntrl contrasts for human:
    dec_MKL1_human <- decomposeVar(M = MKL1_human, treatm = c(2, 3), cntr = c(1, 1),
                                   organism = "Human", verbose = FALSE)
    #> 1/1 - 0s - 172ms/epoch - 172ms/step
    #> 1/1 - 0s - 94ms/epoch - 94ms/step
    #> 1/1 - 0s - 78ms/epoch - 78ms/step
    dec_MKL1_human
    #> class: SummarizedExperiment
    #> dim: 20411 2
    #> metadata(0):
    #> assays(4): INPUT_CONTRASTS DECODED_CONTRASTS RESIDUAL_CONTRASTS CONTEXT
    #> rownames(20411): A1BG A1BG-AS1 ... WFDC5 XAGE2
    #> rowData names(1): User_provided_IDs
    #> colnames(2): MKL1 caMKL1
    #> colData names(3): ACCOUNTED_VARIANCE.DECODED
    #>   ACCOUNTED_VARIANCE.RESIDUAL ACCOUNTED_VARIANCE.COMMON

Decomposing the mouse contrasts:

    #Decompose MKL1-vs-Cntrl and caMKL1-vs-Cntrl contrasts for mouse:
    dec_MKL1_mouse <- decomposeVar(M = MKL1_mouse, treatm = c(2, 3), cntr = c(1, 1),
                                   organism = "Mouse", verbose = FALSE)
    #> 1/1 - 0s - 78ms/epoch - 78ms/step
    #> 1/1 - 0s - 78ms/epoch - 78ms/step
    #> 1/1 - 0s - 78ms/epoch - 78ms/step
    dec_MKL1_mouse
    #> class: SummarizedExperiment
    #> dim: 20339 2
    #> metadata(0):
    #> assays(4): INPUT_CONTRASTS DECODED_CONTRASTS RESIDUAL_CONTRASTS CONTEXT
    #> rownames(20339): 0610005C13Rik 0610009B22Rik ... n-R5s146 n-R5s149
    #> rowData names(1): User_provided_IDs
    #> colnames(2): MKL1 caMKL1
    #> colData names(3): ACCOUNTED_VARIANCE.DECODED
    #>   ACCOUNTED_VARIANCE.RESIDUAL ACCOUNTED_VARIANCE.COMMON

The output of `decomposeVar()` is a `SummarizedExperiment` object with dimensions `N` x `M`, where `N` is the number of `orthos` genes* for that organism and `M` is the number of contrasts specified during input.

The `SummarizedExperiment` output also has 4 assay slots corresponding to the input contrasts, decoded (non-specific), and residual (specific) components, as well as the gene context. Contrasts are represented as log2 fold-changes (LFCs) and context is represented as log2-transformed counts per million (log2 CPM).

We can use the returned object to produce an MA plot for the original contrast or to check how the input and decomposed contrasts are related to each other.

For example for the mouse caMKL1 contrast:

    suppressPackageStartupMessages({
        library(ggplot2)
        library(SummarizedExperiment)
    })
    assays(dec_MKL1_mouse)
    #> List of length 4
    #> names(4): INPUT_CONTRASTS DECODED_CONTRASTS RESIDUAL_CONTRASTS CONTEXT

    #MA plot of for the input contrasts:
    DF <- data.frame(L2CPM= assay(dec_MKL1_mouse,"CONTEXT")[,2],
                     L2FC_INPUT=assay(dec_MKL1_mouse,"INPUT_CONTRASTS")[,2],
                     L2FC_DECODED=assay(dec_MKL1_mouse,"DECODED_CONTRASTS")[,2],
                     L2FC_RESIDUAL=assay(dec_MKL1_mouse,"RESIDUAL_CONTRASTS")[,2]
                     )

    #MA plot of for the input contrast
    P1 <- ggplot(data=DF, aes(x=L2CPM, y=L2FC_INPUT)) +
      geom_point(alpha=0.4, size=1.8) +
      geom_hline(aes(yintercept = 0), colour = "darkgray", linewidth = 0.5) +
      xlab("Expression (Log2 CPMs)") +
      ylab("Log2 Fold Change")

    #Delta-delta plots for the input and decomposed contrast fractions
    P2 <- ggplot(data=DF, aes(x=L2FC_INPUT, y=L2FC_DECODED)) +
      geom_point(alpha=0.4, size=1.8) +
      geom_hline(aes(yintercept = 0), colour = "darkgray", linewidth = 0.5) +
      xlab("Log2 Fold Change INPUT")  +
      ylab("Log2 Fold Change DECODED")

    P3 <- ggplot(data=DF, aes(x=L2FC_INPUT, y=L2FC_RESIDUAL)) +
      geom_point(alpha=0.4, size=1.8) +
      geom_hline(aes(yintercept = 0), colour = "darkgray", linewidth = 0.5) +
      xlab("Log2 Fold Change INPUT")  +
      ylab("Log2 Fold Change RESIDUAL")

    P4 <- ggplot(data=DF, aes(x=L2FC_DECODED, y=L2FC_RESIDUAL)) +
      geom_point(alpha=0.4, size=1.8) +
      geom_hline(aes(yintercept = 0), colour = "darkgray", linewidth = 0.5) +
      xlab("Log2 Fold Change DECODED")  +
      ylab("Log2 Fold Change RESIDUAL")

    cowplot::plot_grid(P1,P2,P3,P4)

As expected, both the decoded and residual components are correlated to the input contrast. However, the residual and decoded components are largely uncorrelated.

The `colData` of the object summarizes the proportion of variance accounted for in each decomposed component:

    colData(dec_MKL1_human)
    #> DataFrame with 2 rows and 3 columns
    #>        ACCOUNTED_VARIANCE.DECODED ACCOUNTED_VARIANCE.RESIDUAL
    #>                         <numeric>                   <numeric>
    #> MKL1                     0.326622                    0.834041
    #> caMKL1                   0.323083                    0.776842
    #>        ACCOUNTED_VARIANCE.COMMON
    #>                        <numeric>
    #> MKL1                   0.1606626
    #> caMKL1                 0.0999251

    colData(dec_MKL1_mouse)
    #> DataFrame with 2 rows and 3 columns
    #>        ACCOUNTED_VARIANCE.DECODED ACCOUNTED_VARIANCE.RESIDUAL
    #>                         <numeric>                   <numeric>
    #> MKL1                     0.340694                    0.873229
    #> caMKL1                   0.294274                    0.791121
    #>        ACCOUNTED_VARIANCE.COMMON
    #>                        <numeric>
    #> MKL1                   0.2139228
    #> caMKL1                 0.0853951

* Notice that, of the total gene features present in the input (over 55,000), only ~20,000 genes are part of the `orthos` model () and the `decomposeVar()` output.

These ~20,000 `orthos` genes are “sanctioned” according to several criteria (located on canonical chromosomes, no pseudogenes, no ribosomal protein genes, detected in at least a small fraction of the ARCHS4 database).

The model is highly robust to small fractions of `orthos` genes not being part of the user input, even if those genes are expressed in the context under consideration. That being noted, it is safer to feed-in inputs that are as comprehensive as possible, i.e not filtered in any way, in terms of gene features.

# 5 Querying the database of gene contrasts using `queryWithContrasts()`

Typically, the next step of the analysis involves querying the contrasts database (`orthosData`) to identify public experiments similar to the one(s) under investigation, either in terms of the original or decomposed decoded (non-specific) and residual (specific) contrasts. As we will see in the following examples the results’ of these queries can guide the interpretation of of the different contrast fractions.

`orthosData` contains over 100,000 differential gene expression experiments compiled from the ARCHS4 database of publicly available expression data (Lachmann et al. (2018)). Each entry in `orthosData` corresponds to a pair of RNAseq samples contrasting a treatment vs a control condition. A combination of metadata, semantic and quantitative analyses was used to determine the proper assignment of samples to such pairs in `orthosData`.

The function that performs the queries against `orthosData` is `queryWithContrasts()`. The input to this function is the `SummarizedExperiment` object obtained in the previous step from `decomposeVar()`, either the complete object or one that has been column-subsetted, allowing to query the contrast database with only a subset of the decomposed contrasts.

As was the case for the `orthos` models, a database will be automatically downloaded from `ExperimentHub` and cached in the user ExperimentHub directory (see `ExperimentHub::getExperimentHubOption("CACHE")`) using the `orthosData` companion package, the first time `queryWithContrasts()` is called for that database or the first time the user attempts to access the database directly with `loadContrastDatabase()` (see Accessing the contrast database) .

The `queryWithContrasts()` function returns a list with three elements per query contrast:

- “pearson.rhos” is itself a list with each element containing the Pearson correlation values against all the `orthosData` entries for a specific component (input, decoded/non-specific, residual/specific).
- “zscores” is also a list with each element containing the z-score transformed version of the “pearson.rhos” values.
- “TopHits” is also a list with detailed `orthosData` metadata for each of the top `detailTopn` hits per component (default 10).

In the following examples, please note that the queries are run using `mode = "DEMO"` in order to keep computations short. For actual analyses, the default `mode = "ANALYSIS"` should be used.

Examples queries using the decomposed human MKL1 data:

    # parallelization parameters:
    params <- BiocParallel::MulticoreParam(workers = 2)

    # for demonstration purposes (for actual analyses, use 'mode = "ANALYSIS"'):
    query.res.human <- queryWithContrasts(dec_MKL1_human, organism = "Human",
                                          BPPARAM = params, verbose = FALSE,
                                          mode = "DEMO")

    names(query.res.human)
    #> [1] "pearson.rhos" "zscores"      "TopHits"

    names(query.res.human$zscores)
    #> [1] "INPUT_CONTRASTS"    "DECODED_CONTRASTS"  "RESIDUAL_CONTRASTS"

    # query contrasts in rows, `orthosData` entries in columns:
    dim(query.res.human$zscores$RESIDUAL_CONTRASTS)
    #> [1]    2 1000
    summary(t(query.res.human$zscores$RESIDUAL_CONTRASTS))
    #>       MKL1              caMKL1
    #>  Min.   :-3.17999   Min.   :-2.47869
    #>  1st Qu.:-0.53837   1st Qu.:-0.44873
    #>  Median :-0.09769   Median :-0.09039
    #>  Mean   : 0.00000   Mean   : 0.00000
    #>  3rd Qu.: 0.36729   3rd Qu.: 0.30576
    #>  Max.   : 7.38595   Max.   :13.90709

    #Information on the top hits of the query using the residual human MKL1/caMKL1 contrasts:
    query.res.human$TopHits$RESIDUAL_CONTRASTS
    #> $MKL1
    #> DataFrame with 10 rows and 8 columns
    #>               Zscore TREATM_geo_accession                  title
    #>            <numeric>          <character>            <character>
    #> GSM2044531   7.38595           GSM2044531                MYOCD-1
    #> GSM2044532   7.31258           GSM2044532                MYOCD-2
    #> GSM3066023   6.63946           GSM3066023 HEL-iMKL1 Dox treate..
    #> GSM3544074   6.21459           GSM3544074        MEL270.SF3B1_WT
    #> GSM4869874   5.46485           GSM4869874 U2OS cells - DMSO - ..
    #> GSM3544069   5.23304           GSM3544069     MEL270.SF3B1_K700E
    #> GSM5013969   4.91199           GSM5013969 chase_4sU_merged.EtO..
    #> GSM4718649   4.79887           GSM4718649 F508delCFBE41o, siCT..
    #> GSM5013974   4.79434           GSM5013974 nochase_spiked_4sU_4..
    #> GSM5709388   4.73799           GSM5709388   EFM192A-LAP-DTP1_S11
    #>               characteristics_ch1           series_id        source_name_ch1
    #>                       <character>         <character>            <character>
    #> GSM2044531 transduction: ad-MYOCD            GSE77120 vascular smooth musc..
    #> GSM2044532 transduction: ad-MYOCD            GSE77120 vascular smooth musc..
    #> GSM3066023 treatment: Treated w.. GSE112277,GSE112279        HEL-iMKL1 cells
    #> GSM3544074 cell line: MEL270,le..           GSE124720                 MEL270
    #> GSM4869874 cell type: U2OS,trea.. GSE160253,GSE160254             U2OS cells
    #> GSM3544069 cell line: MEL270,le..           GSE124720                 MEL270
    #> GSM5013969 treatment (etoh or 4.. GSE164555,GSE164569 SH-EP-MYCNER shEXOSC10
    #> GSM4718649 cell type: Immortali..           GSE155987 Parental CFBE41o- Ce..
    #> GSM5013974 treatment (etoh or 4.. GSE164555,GSE164569 SH-EP-MYCNER shEXOSC10
    #> GSM5709388 treatment: 14 day la..           GSE156246                EFM192A
    #>            CNT_geo_accession corr_TREATM_CNT
    #>                  <character>       <numeric>
    #> GSM2044531        GSM2044530        0.963492
    #> GSM2044532        GSM2044530        0.963938
    #> GSM3066023        GSM3066021        0.995455
    #> GSM3544074        GSM3544065        0.995561
    #> GSM4869874        GSM4869870        0.993011
    #> GSM3544069        GSM3544065        0.994787
    #> GSM5013969        GSM5013971        0.994540
    #> GSM4718649        GSM4718652        0.993717
    #> GSM5013974        GSM5013976        0.992348
    #> GSM5709388        GSM5709390        0.959956
    #>
    #> $caMKL1
    #> DataFrame with 10 rows and 8 columns
    #>               Zscore TREATM_geo_accession                  title
    #>            <numeric>          <character>            <character>
    #> GSM2044531  13.90709           GSM2044531                MYOCD-1
    #> GSM2044532  13.85102           GSM2044532                MYOCD-2
    #> GSM4189923   6.84975           GSM4189923      HT11_iSM_old_rep3
    #> GSM4189922   6.83691           GSM4189922      HT15_iSM_old_rep2
    #> GSM3976594   4.62126           GSM3976594         FSK_IL-1beta_2
    #> GSM3066023   4.62013           GSM3066023 HEL-iMKL1 Dox treate..
    #> GSM3976593   4.52470           GSM3976593         FSK_IL-1beta_1
    #> GSM3976595   4.38356           GSM3976595         FSK_IL-1beta_3
    #> GSM4189918   3.97301           GSM4189918    RS17_iSM_young_rep1
    #> GSM4189920   3.89561           GSM4189920    RS14_iSM_young_rep3
    #>               characteristics_ch1           series_id        source_name_ch1
    #>                       <character>         <character>            <character>
    #> GSM2044531 transduction: ad-MYOCD            GSE77120 vascular smooth musc..
    #> GSM2044532 transduction: ad-MYOCD            GSE77120 vascular smooth musc..
    #> GSM4189923 treatment: induced c..           GSE140898 human induced smooth..
    #> GSM4189922 treatment: induced c..           GSE140898 human induced smooth..
    #> GSM3976594 cell type: hTERT-HM^..           GSE134896             myometrium
    #> GSM3066023 treatment: Treated w.. GSE112277,GSE112279        HEL-iMKL1 cells
    #> GSM3976593 cell type: hTERT-HM^..           GSE134896             myometrium
    #> GSM3976595 cell type: hTERT-HM^..           GSE134896             myometrium
    #> GSM4189918 treatment: induced c..           GSE140898 human induced smooth..
    #> GSM4189920 treatment: induced c..           GSE140898 human induced smooth..
    #>            CNT_geo_accession corr_TREATM_CNT
    #>                  <character>       <numeric>
    #> GSM2044531        GSM2044530        0.963492
    #> GSM2044532        GSM2044530        0.963938
    #> GSM4189923        GSM4189934        0.975019
    #> GSM4189922        GSM4189934        0.978935
    #> GSM3976594        GSM3976579        0.985832
    #> GSM3066023        GSM3066021        0.995455
    #> GSM3976593        GSM3976579        0.987186
    #> GSM3976595        GSM3976579        0.987181
    #> GSM4189918        GSM4189933        0.983446
    #> GSM4189920        GSM4189934        0.979353

Example queries using the decomposed mouse MKL1 data:

    # query the database using only the "caMKL1" mouse contrast, suppress plotting:
    # for demonstration purposes (for actual analyses, use 'mode = "ANALYSIS"'):
    query.res.mouse <- queryWithContrasts(dec_MKL1_mouse[, "caMKL1"], organism = "Mouse",
                                          BPPARAM = params, verbose = FALSE,
                                          plotType = "none", mode = "DEMO")

    # plot results for individual contrasts using violin plots:
    ViolinPlots_mouse <- plotQueryResultsViolin(query.res.mouse, doPlot = FALSE)
    ViolinPlots_mouse[["caMKL1"]]

    # plot results for individual contrasts using composite Manhattan/Density plots:
    ManhDensPlots_mouse <- plotQueryResultsManh(query.res.mouse, doPlot = FALSE)
    ManhDensPlots_mouse[["caMKL1"]]

    #Information on the top hits of the query using the residual mouse caMKL1 contrasts:
    query.res.mouse$TopHits$RESIDUAL_CONTRASTS
    #> $caMKL1
    #> DataFrame with 10 rows and 8 columns
    #>               Zscore TREATM_geo_accession                  title
    #>            <numeric>          <character>            <character>
    #> GSM5021181  14.91050           GSM5021181 E0771_pRetrox_MRTFA_..
    #> GSM5021173   9.44599           GSM5021173 B16F10_pRetrox_MRTFA..
    #> GSM5021171   7.65648           GSM5021171 B16F10_pRetrox_MRTFA..
    #> GSM5021172   5.90238           GSM5021172 B16F10_pRetrox_MRTFA..
    #> GSM5396603   5.09239           GSM5396603                    NA4
    #> GSM5396607   5.05168           GSM5396607                    NA8
    #> GSM5396602   5.01185           GSM5396602                    NA3
    #> GSM5396606   4.93570           GSM5396606                    NA7
    #> GSM4928843   4.68661           GSM4928843 Î²-actin knockout Mo..
    #> GSM3176949   4.61862           GSM3176949    11D_mmu-miR-1199-5p
    #>               characteristics_ch1           series_id        source_name_ch1
    #>                       <character>         <character>            <character>
    #> GSM5021181 strain: C57BL6,cell ..           GSE164860            E0771 cells
    #> GSM5021173 strain: C57BL6,cell ..           GSE164860           B16F10 cells
    #> GSM5021171 strain: C57BL6,cell ..           GSE164860           B16F10 cells
    #> GSM5021172 strain: C57BL6,cell ..           GSE164860           B16F10 cells
    #> GSM5396603 strain: C57BL/6,tiss..           GSE178725 Mouse embryonic fibr..
    #> GSM5396607 strain: C57BL/6,tiss..           GSE178725 Mouse embryonic fibr..
    #> GSM5396602 strain: C57BL/6,tiss..           GSE178725 Mouse embryonic fibr..
    #> GSM5396606 strain: C57BL/6,tiss..           GSE178725 Mouse embryonic fibr..
    #> GSM4928843 cell type: fibroblas..           GSE161964 Mouse Embronic Fibro..
    #> GSM3176949 cell line: NMUMG,sub.. GSE115375,GSE115376  NMUMG cells, clone E9
    #>            CNT_geo_accession corr_TREATM_CNT
    #>                  <character>       <numeric>
    #> GSM5021181        GSM5021179        0.974858
    #> GSM5021173        GSM5021170        0.995724
    #> GSM5021171        GSM5021168        0.994685
    #> GSM5021172        GSM5021169        0.994397
    #> GSM5396603        GSM5396601        0.993751
    #> GSM5396607        GSM5396601        0.992054
    #> GSM5396602        GSM5396600        0.993440
    #> GSM5396606        GSM5396600        0.991412
    #> GSM4928843        GSM4928841        0.975952
    #> GSM3176949        GSM3176958        0.977919

The top hits obtained for the residual (specific) fractions of either MKL1 or caMKL1 contrasts both in human and mouse are more clearly separated from the background compared to those obtained from the input or decoded (non-specific) fractions.

More importantly closer inspection of those top hits for the residual contrasts in both experiments (e.g hits from series GSE77120, GSE112277 or GSE140898 in human or GSE164860 in mouse) reveal that they correspond to treatments involving either MKL/MRTFA overexpression or overexpression of the MKL related transcription factor MYOCD. Of note, these treatments were performed in various cell contexts, different from the ones of the MKL study under consideration (LM2 and 4T1 cell lines for mouse and human respectively).

In general, as in this example, the residual specific fraction of a DGE profile will be a better query proxy for molecularly and mechanistically related treatments as it is largely stripped of nuisance variance present in the original contrast.

On the other hand the decoded non-specific fraction and its corresponding query hits can also be of interest in some applications as they provide information on the extent and type of downstream/secondary or lateral treatment effects.

# 6 Accessing the contrast database

The `orthos` package provides functionality for direct access to contrast databases of `orthosData` with the `loadContrastDatabase()` function.

This can be used to retrieve contrast values for all or subsets of genes or metadata for specific datasets, e.g for hits identified with `queryWithContrasts()`.

The organism-specific databases are compiled as HDF5SummarizedExperiment objects. As was the case for the `orthos` models, a database will be automatically downloaded from `ExperimentHub` and cached in the user ExperimentHub directory (see `ExperimentHub::getExperimentHubOption("CACHE")`) using the `orthosData` companion package, the first time `loadContrastDatabase()` is called for that database either directly or via `queryWithContrasts()`.

The HDF5SummarizedExperiment object contains pre-calculated INPUT, RESIDUAL and DECODED log2 fold change contrasts as well as the corresponding expression CONTEXT in log2 CPM representation for all the datasets in `orthosData`.

Extensive gene and contrast annotation is available in the object’s `rowData` and `colData` respectively.

    organism <- "Mouse"
    orthosDB <- loadContrastDatabase(organism = "Mouse", mode = "DEMO")

    orthosDB
    #> class: SummarizedExperiment
    #> dim: 20339 988
    #> metadata(0):
    #> assays(4): INPUT_CONTRASTS DECODED_CONTRASTS RESIDUAL_CONTRASTS CONTEXT
    #> rownames(20339): 0610005C13Rik 0610009B22Rik ... n-R5s146 n-R5s149
    #> rowData names(15): seqnames start ... ENTREZ_GENE_ID ARCHS4_ID
    #> colnames(988): GSM1053282 GSM1061179 ... GSM970471 GSM984548
    #> colData names(33): aligned_reads channel_count ... HasAssignedCNT
    #>   CNTname

    #Available contrast annotations:
    colnames(colData(orthosDB))
    #>  [1] "aligned_reads"         "channel_count"         "characteristics_ch1"
    #>  [4] "contact_address"       "contact_city"          "contact_country"
    #>  [7] "contact_institute"     "contact_name"          "contact_zip"
    #> [10] "data_processing"       "extract_protocol_ch1"  "geo_accession"
    #> [13] "instrument_model"      "last_update_date"      "library_selection"
    #> [16] "library_source"        "library_strategy"      "molecule_ch1"
    #> [19] "organism_ch1"          "platform_id"           "relation"
    #> [22] "series_id"             "singlecellprobability" "source_name_ch1"
    #> [25] "sra_id"                "status"                "submission_date"
    #> [28] "taxid_ch1"             "title"                 "type"
    #> [31] "Cor2CNT"               "HasAssignedCNT"        "CNTname"

    #Available gene annotations:
    colnames(rowData(orthosDB))
    #>  [1] "seqnames"                     "start"
    #>  [3] "end"                          "width"
    #>  [5] "strand"                       "ENSEMBL_GENE_ID"
    #>  [7] "gene_name"                    "gene_biotype"
    #>  [9] "seq_coord_system"             "description"
    #> [11] "ENSEMBL_GENE_ID_VERSION"      "ENSEMBL_CANONICAL_TRANSCRIPT"
    #> [13] "GENE_SYMBOL"                  "ENTREZ_GENE_ID"
    #> [15] "ARCHS4_ID"

    #Retrieve partial annotation for a specific contrast
    #returned as a top-hit in the mouse caMKL1 query above:
    colData(orthosDB)["GSM5021181", c("title", "series_id", "CNTname")]
    #> DataFrame with 1 row and 3 columns
    #>                             title   series_id     CNTname
    #>                       <character> <character> <character>
    #> GSM5021181 E0771_pRetrox_MRTFA_..   GSE164860  GSM5021179

    # Compare context and individual contrast fractions between
    # the mouse caMKL1 contrast under consideration and the "GSM5021181"
    # query hit:
    par(mfrow = c(2, 2))
    queryID <- "GSM5021181"
    for (contrast in names(assays(dec_MKL1_mouse))[c(4, 1, 2, 3)]) {
        unit <- "L2FC"
        if (contrast == "CONTEXT") {unit <- "L2CPM"}
        plot(assays(dec_MKL1_mouse)[[contrast]][, "caMKL1"],
             assays(orthosDB)[[contrast]][, queryID],
             pch = 16, cex = 0.5, col = "darkslategrey", main = contrast,
             xlab = paste0(unit, " caMKL1"), ylab = paste0(unit, " ", queryID))
        abline(0, 1, col = "darkred", lwd = 0.8, lty = 2)
    }

# 7 Advanced use cases: Directly accessing the orthos models

As (1) typical `orthos` use cases do not require direct access to the models and (2) use of the models requires loading of a conda environment via `basilisk` this functionality is by default not exposed to the user and is carried out transparently by the non-exported functions `.predictEncoder()` and `.predictEncoderD()`.

However, as we envision cases where directly accessing the models might be of interest we provide here a brief overview and examples for direct calls to these functions.

The `orthos` models are implemented in `Keras`. For each organism there are two types of models:

- A context encoder that produces a latent embedding for a specific input context (represented as L2CPMs) and
- A contrast conditional variational autoencoder that first produces latent embeddings of input contrasts (represented as LFCs) conditioned on context embeddings and then generates decoded versions of those contrasts, again conditioned on the context embeddings (see also figure in Prelude: A short overview of the `orthos` models).

As noted previously the first time these models are requested either by `decomposeVar` or directly by `.predictEncoder()` and `.predictEncoderD()` they are downloaded and cached in the user ExperimentHub directory (see `ExperimentHub::getExperimentHubOption("CACHE")`) using the `orthosData` companion package.

When calling the `.predictEncoder()` and `.predictEncoderD()` methods directly be attentive to the following:

- Before the call the predefined conda environment `orthos:::orthosenv` needs to be activated using `basilisk::basiliskStart()`
- The inputs passed to the models need to have appropriate size and value representation (see examples below).
- After the calls it’s good practice to deactivate the environment with `basilisk::basiliskStop()`.

We now demonstrate calls to the context encoder for generating a latent embedding of a specific context and to the contrast conditional variational autoencoder for producing a contrast latent embedding and decoding.

    # mouse MKL1 context and contrast with the appropriate shape and representation.
    #
    # Shape of models input is M x N,
    # where M is the number of conditions,
    # N the number of features -i.e orthos Genes
    #
    # Representation is L2CPMs for contexts and L2FCs for contrasts.
    #
    CONTEXT  <- t(assay(dec_MKL1_mouse,"CONTEXT")[,1])
    CONTRAST <- t(assay(dec_MKL1_mouse,"INPUT_CONTRASTS")[,1])

    # Activate the `basilisk` environment:
    library(basilisk)
    cl <- basiliskStart(orthos:::orthosenv,
                        testload = "tensorflow")

    # Produce a latent embedding for the context with .predictEncoder:
    LATC <- basilisk::basiliskRun(proc = cl,
                                  fun = orthos:::.predictEncoder,
                                  organism = "Mouse",
                                  gene_input = CONTEXT)
    #> 1/1 - 0s - 109ms/epoch - 109ms/step

    # Produce a latent embedding and decoding for the contrast with .predictEncoderD:
    res <- basilisk::basiliskRun(proc = cl,
                                 fun = orthos:::.predictEncoderD,
                                 organism = "Mouse",
                                 delta_input = CONTRAST, context = LATC)
    #> 1/1 - 0s - 94ms/epoch - 94ms/step
    #> 1/1 - 0s - 94ms/epoch - 94ms/step

    # Deactivate the `basilisk` environment:
    basilisk::basiliskStop(cl)

    # Access the contrast latent embedding and decoding from the .predictEncoderD returned result:
    LATD <- res$LATD
    DEC <- res$DEC

Calls similar to the ones above are carried out under the hood when `decomposeVar()` is called.

# Session information

    sessionInfo()
    #> R version 4.5.0 RC (2025-04-04 r88126 ucrt)
    #> Platform: x86_64-w64-mingw32/x64
    #> Running under: Windows Server 2022 x64 (build 20348)
    #>
    #> Matrix products: default
    #>   LAPACK version 3.12.1
    #>
    #> locale:
    #> [1] LC_COLLATE=C
    #> [2] LC_CTYPE=English_United States.utf8
    #> [3] LC_MONETARY=English_United States.utf8
    #> [4] LC_NUMERIC=C
    #> [5] LC_TIME=English_United States.utf8
    #>
    #> time zone: America/New_York
    #> tzcode source: internal
    #>
    #> attached base packages:
    #> [1] stats4    stats     graphics  grDevices utils     datasets  methods
    #> [8] base
    #>
    #> other attached packages:
    #>  [1] basilisk_1.20.0             reticulate_1.42.0
    #>  [3] ggplot2_3.5.2               keras_2.15.0
    #>  [5] orthosData_1.5.0            orthos_1.6.0
    #>  [7] SummarizedExperiment_1.38.0 Biobase_2.68.0
    #>  [9] GenomicRanges_1.60.0        GenomeInfoDb_1.44.0
    #> [11] IRanges_2.42.0              S4Vectors_0.46.0
    #> [13] BiocGenerics_0.54.0         generics_0.1.3
    #> [15] MatrixGenerics_1.20.0       matrixStats_1.5.0
    #> [17] knitr_1.50                  BiocStyle_2.36.0
    #>
    #> loaded via a namespace (and not attached):
    #>   [1] DBI_1.2.3               rlang_1.1.6             magrittr_2.0.3
    #>   [4] compiler_4.5.0          RSQLite_2.3.9           dir.expiry_1.16.0
    #>   [7] png_0.1-8               vctrs_0.6.5             stringr_1.5.1
    #>  [10] pkgconfig_2.0.3         crayon_1.5.3            fastmap_1.2.0
    #>  [13] backports_1.5.0         dbplyr_2.5.0            XVector_0.48.0
    #>  [16] labeling_0.4.3          fontawesome_0.5.3       rmarkdown_2.29
    #>  [19] UCSC.utils_1.4.0        purrr_1.0.4             bit_4.6.0
    #>  [22] xfun_0.52               cachem_1.1.0            jsonlite_2.0.0
    #>  [25] blob_1.2.4              rhdf5filters_1.20.0     DelayedArray_0.34.0
    #>  [28] Rhdf5lib_1.30.0         BiocParallel_1.42.0     tensorflow_2.16.0
    #>  [31] broom_1.0.8             parallel_4.5.0          R6_2.6.1
    #>  [34] stringi_1.8.7           bslib_0.9.0             car_3.1-3
    #>  [37] jquerylib_0.1.4         Rcpp_1.0.14             bookdown_0.43
    #>  [40] base64enc_0.1-3         Matrix_1.7-3            tidyselect_1.2.1
    #>  [43] abind_1.4-8             yaml_2.3.10             codetools_0.2-20
    #>  [46] curl_6.2.2              plyr_1.8.9              lattice_0.22-7
    #>  [49] tibble_3.2.1            withr_3.0.2             basilisk.utils_1.20.0
    #>  [52] KEGGREST_1.48.0         evaluate_1.0.3          BiocFileCache_2.16.0
    #>  [55] ExperimentHub_2.16.0    Biostrings_2.76.0       pillar_1.10.2
    #>  [58] BiocManager_1.30.25     ggpubr_0.6.0            filelock_1.0.3
    #>  [61] carData_3.0-5           whisker_0.4.1           BiocVersion_3.21.1
    #>  [64] munsell_0.5.1           scales_1.3.0            glue_1.8.0
    #>  [67] tools_4.5.0             AnnotationHub_3.16.0    ggsignif_0.6.4
    #>  [70] cowplot_1.1.3           rhdf5_2.52.0            grid_4.5.0
    #>  [73] tidyr_1.3.1             AnnotationDbi_1.70.0    colorspace_2.1-1
    #>  [76] GenomeInfoDbData_1.2.14 HDF5Array_1.36.0        Formula_1.2-5
    #>  [79] cli_3.6.4               rappdirs_0.3.3          tfruns_1.5.3
    #>  [82] viridisLite_0.4.2       S4Arrays_1.8.0          dplyr_1.1.4
    #>  [85] gtable_0.3.6            ggsci_3.2.0             rstatix_0.7.2
    #>  [88] zeallot_0.1.0           sass_0.4.10             digest_0.6.37
    #>  [91] SparseArray_1.8.0       ggrepel_0.9.6           farver_2.1.2
    #>  [94] memoise_2.0.1           htmltools_0.5.8.1       lifecycle_1.0.4
    #>  [97] h5mread_1.0.0           httr_1.4.7              mime_0.13
    #> [100] bit64_4.6.0-1

# References

Hu, Xiao, Zongzhi Z Liu, Xinyue Chen, Vincent P Schulz, Abhishek Kumar, Amaleah A Hartman, Jason Weinstein, et al. 2019. “MKL1-Actin Pathway Restricts Chromatin Accessibility and Prevents Mature Pluripotency Activation.” Nature Communications 10 (1): 1695.

Lachmann, Alexander, Denis Torre, Alexandra B Keenan, Kathleen M Jagodnik, Hoyjin J Lee, Lily Wang, Moshe C Silverstein, and Avi Ma’ayan. 2018. “Massive Mining of Publicly Available Rna-Seq Data from Human and Mouse.” Nature Communications 9 (1): 1366.