# deconvR : Simulation and Deconvolution of Omic Profiles

Irem B. Gunduz, Veronika Ebenal, Altuna Akalin

#### 2024-10-29

#### Package

deconvR 1.12.0

# 1 Introduction

Recent studies associated the differences of cell-type proportions may be correlated to certain phenotypes, such as cancer. Therefore, the demand for the development of computational methods to predict cell type proportions increased. Hereby, we developed `deconvR`, a collection of functions designed for analyzing deconvolution of the bulk sample(s) using an atlas of reference omic signature profiles and a user-selected model. We wanted to give users an option to extend their reference atlas. Users can create new reference atlases using `findSignatures` or extend their atlas by adding more cell types. Additionally, we included `BSMeth2Probe` to make mapping whole-genome bisulfite sequencing data to their probe IDs easier. So users can map WGBS methylation data (as in methylKit or GRanges object format) to probe IDs, and the results of this mapping can be used as the bulk samples in the deconvolution. We also included a comprehensive DNA methylation atlas of 25 different cell types to use in the main function `deconvolute`. `deconvolute` allows the user to specify their desired deconvolution model (non-negative least squares regression, support vector regression, quadratic programming, or robust linear regression), and returns a dataframe which contains predicted cell-type proportions of bulk methylation profiles, as well as partial R-squared values for each sample.

As an another option, users can generate a simulated table of a desired number of samples, with either user-specified or random origin proportions using `simulateCellMix`. `simulateCellMix` returns a second data frame called `proportions`, which contains the actual cell-type proportions of the simulated sample. It can be used for testing the accuracy of the deconvolution by comparing these actual proportions to the proportions predicted by `deconvolute`.

`deconvolute` returns partial R-squares, to check if deconvolution brings advantages on top of the basic bimodal profiles. The reference matrix usually follows a bimodal distribution in the case of methylation, and taking the average of the rows of methylation matrix might give a pretty similar profile to the bulk methylation profile you are trying to deconvolute. If the deconvolution is advantageous, partial R-squared is expect to be high.

# 2 Installation

The deconvR package can be installed from Bioconductor with:

    if (!requireNamespace("BiocManager", quietly = TRUE))
        install.packages("BiocManager")

    BiocManager::install("deconvR")

# 3 Data

## 3.1 Comprehensive Human Methylome Reference Atlas

The comprehensive human methylome reference atlas created by Moss et al. 1 1 1 Moss, J. et al. (2018). Comprehensive human cell-type methylation atlas reveals origins of circulating cell-free DNA in health and disease. Nature communications, 9(1), 1-12. https://doi.org/10.1038/s41467-018-07466-6 can be used as the reference atlas parameter for several functions in this package. This atlas was modified to remove duplicate CpG loci before being included in the package as the dataframe. The dataframe is composed of 25 human cell types and roughly 6000 CpG loci, identified by their Illumina Probe ID. For each cell type and CpG locus, a methylation value between 0 and 1 is provided. This value represents the fraction of methylated bases of the CpG locus. The atlas therefore provides a unique methylation pattern for each cell type and can be directly used as `reference` in `deconvolute` and `simulateCellMix`, and `atlas` in `findSignatures`. Below is an example dataframe to illustrate the `atlas` format.

    library(deconvR)

    data("HumanCellTypeMethAtlas")
    head(HumanCellTypeMethAtlas[,1:5])

    ##          IDs Monocytes_EPIC B.cells_EPIC CD4T.cells_EPIC NK.cells_EPIC
    ## 1 cg08169020         0.8866       0.2615          0.0149        0.0777
    ## 2 cg25913761         0.8363       0.2210          0.2816        0.4705
    ## 3 cg26955540         0.7658       0.0222          0.1492        0.4005
    ## 4 cg25170017         0.8861       0.5116          0.1021        0.4363
    ## 5 cg12827637         0.5212       0.3614          0.0227        0.2120
    ## 6 cg19442545         0.2013       0.1137          0.0608        0.0410

## 3.2 Illumina Infinium MethylationEPIC v1.0 B5 Manifest Probes (hg38)

The GRanges object `IlluminaMethEpicB5ProbeIDs` contains the Illumina probe IDs of 400000 genomic loci (identified using the “seqnames”, “ranges”, and “strand” values). This object is based off of the Infinium MethylationEPIC v1.0 B5 Manifest data. Unnecessary columns were removed and rows were truncated to reduce file size before converting the file to a GRanges object. It can be used directly as `probe_id_locations` in `BSmeth2Probe`.

    data("IlluminaMethEpicB5ProbeIDs")
    head(IlluminaMethEpicB5ProbeIDs)

    ## GRanges object with 6 ranges and 1 metadata column:
    ##       seqnames              ranges strand |          ID
    ##          <Rle>           <IRanges>  <Rle> | <character>
    ##   [1]    chr19     5236005-5236006      + |  cg07881041
    ##   [2]    chr20   63216298-63216299      + |  cg18478105
    ##   [3]     chr1     6781065-6781066      + |  cg23229610
    ##   [4]     chr2 197438742-197438743      - |  cg03513874
    ##   [5]     chrX   24054523-24054524      + |  cg09835024
    ##   [6]    chr14   93114794-93114795      - |  cg05451842
    ##   -------
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

# 4 Example Workflow For Whole Genome Bisulfate Sequencing Data

## 4.1 Expanding Reference Atlas

As mentioned in the introduction section, users can extend their reference atlas to incorporate new data. Or may combine different reference atlases to construct a more comprehensive one. This can be done using the `findSignatures` function. In this example, since we don’t have any additional reference atlas, we will add simulated data as a new cell type to reference atlas for example purposes. First, ensure that `atlas` (the signature matrix to be extended) and `samples` (the new data to be added to the signature matrix) are compliant with the function requirements. Below illustrates the `samples` format.

    samples <- simulateCellMix(3,reference = HumanCellTypeMethAtlas)$simulated
    head(samples)

    ##          IDs  Sample 1   Sample 2   Sample 3
    ## 1 cg08169020 0.3489525 0.23031384 0.14514387
    ## 2 cg25913761 0.5248291 0.33848593 0.28362919
    ## 3 cg26955540 0.4422956 0.23173118 0.17393843
    ## 4 cg25170017 0.4210387 0.28381768 0.19064289
    ## 5 cg12827637 0.2637960 0.16827808 0.12086729
    ## 6 cg19442545 0.1142888 0.06832329 0.05328442

`sampleMeta` should include all sample names in `samples`, and specify the origins they should be mapped to when added to `atlas`.

    sampleMeta <- data.table("Experiment_accession" = colnames(samples)[-1],
                             "Biosample_term_name" = "new cell type")
    head(sampleMeta)

    ##    Experiment_accession Biosample_term_name
    ##                  <char>              <char>
    ## 1:             Sample 1       new cell type
    ## 2:             Sample 2       new cell type
    ## 3:             Sample 3       new cell type

Use `findSignatures` to extend the matrix.

    extended_matrix <- findSignatures(samples = samples,
                                     sampleMeta = sampleMeta,
                                     atlas = HumanCellTypeMethAtlas,
                                     IDs = "IDs")

    ## CELL TYPES IN EXTENDED ATLAS:
    ## new cell type
    ## Monocytes_EPIC
    ## B.cells_EPIC
    ## CD4T.cells_EPIC
    ## NK.cells_EPIC
    ## CD8T.cells_EPIC
    ## Neutrophils_EPIC
    ## Erythrocyte_progenitors
    ## Adipocytes
    ## Cortical_neurons
    ## Hepatocytes
    ## Lung_cells
    ## Pancreatic_beta_cells
    ## Pancreatic_acinar_cells
    ## Pancreatic_duct_cells
    ## Vascular_endothelial_cells
    ## Colon_epithelial_cells
    ## Left_atrium
    ## Bladder
    ## Breast
    ## Head_and_neck_larynx
    ## Kidney
    ## Prostate
    ## Thyroid
    ## Upper_GI
    ## Uterus_cervix

    head(extended_matrix)

    ##          IDs new_cell_type Monocytes_EPIC B.cells_EPIC CD4T.cells_EPIC
    ## 1 cg08169020    0.24147006         0.8866       0.2615          0.0149
    ## 2 cg25913761    0.38231473         0.8363       0.2210          0.2816
    ## 3 cg26955540    0.28265506         0.7658       0.0222          0.1492
    ## 4 cg25170017    0.29849976         0.8861       0.5116          0.1021
    ## 5 cg12827637    0.18431380         0.5212       0.3614          0.0227
    ## 6 cg19442545    0.07863217         0.2013       0.1137          0.0608
    ##   NK.cells_EPIC CD8T.cells_EPIC Neutrophils_EPIC Erythrocyte_progenitors
    ## 1        0.0777          0.0164           0.8680                  0.9509
    ## 2        0.4705          0.3961           0.8293                  0.2385
    ## 3        0.4005          0.3474           0.7915                  0.1374
    ## 4        0.4363          0.0875           0.7042                  0.9447
    ## 5        0.2120          0.0225           0.5368                  0.4667
    ## 6        0.0410          0.0668           0.1952                  0.1601
    ##   Adipocytes Cortical_neurons Hepatocytes Lung_cells Pancreatic_beta_cells
    ## 1     0.0336           0.0168      0.0340     0.0416              0.038875
    ## 2     0.3578           0.3104      0.2389     0.2250              0.132000
    ## 3     0.1965           0.0978      0.0338     0.0768              0.041725
    ## 4     0.0842           0.2832      0.2259     0.0544              0.111750
    ## 5     0.0287           0.1368      0.0307     0.1607              0.065975
    ## 6     0.0364           0.0222      0.1574     0.0122              0.003825
    ##   Pancreatic_acinar_cells Pancreatic_duct_cells Vascular_endothelial_cells
    ## 1                  0.0209                0.0130                     0.0323
    ## 2                  0.2249                0.1996                     0.3654
    ## 3                  0.0314                0.0139                     0.2382
    ## 4                  0.0309                0.0217                     0.0972
    ## 5                  0.0370                0.0230                     0.0798
    ## 6                  0.0378                0.0347                     0.0470
    ##   Colon_epithelial_cells Left_atrium Bladder Breast Head_and_neck_larynx Kidney
    ## 1                 0.0163      0.0386  0.0462 0.0264               0.0470 0.0269
    ## 2                 0.2037      0.2446  0.2054 0.1922               0.2045 0.1596
    ## 3                 0.0193      0.1134  0.1269 0.1651               0.1523 0.1034
    ## 4                 0.0187      0.0674  0.0769 0.0691               0.0704 0.0604
    ## 5                 0.0193      0.0432  0.0459 0.0228               0.0687 0.0234
    ## 6                 0.0193      0.0287  0.0246 0.0081               0.0098 0.0309
    ##   Prostate Thyroid Upper_GI Uterus_cervix
    ## 1   0.0353  0.0553   0.0701        0.0344
    ## 2   0.1557  0.1848   0.1680        0.2026
    ## 3   0.0686  0.0943   0.1298        0.1075
    ## 4   0.0369  0.0412   0.0924        0.0697
    ## 5   0.0508  0.0726   0.0759        0.0196
    ## 6   0.0055  0.0188   0.0090        0.0166

WGBS methylation data first needs to be mapped to probes using `BSmeth2Probe` before being deconvoluted. The methylation data `WGBS_data` in `BSmeth2Probe` may be either a GRanges object or a methylKit object.

Format of `WGBS_data` as GRanges object:

    load(system.file("extdata", "WGBS_GRanges.rda",
                                         package = "deconvR"))
    head(WGBS_GRanges)

    ## GRanges object with 6 ranges and 1 metadata column:
    ##       seqnames    ranges strand |   sample1
    ##          <Rle> <IRanges>  <Rle> | <numeric>
    ##   [1]     chr1     47176      + |  0.833333
    ##   [2]     chr1     47177      - |  0.818182
    ##   [3]     chr1     47205      + |  0.681818
    ##   [4]     chr1     47206      - |  0.583333
    ##   [5]     chr1     47362      + |  0.416667
    ##   [6]     chr1     49271      + |  0.733333
    ##   -------
    ##   seqinfo: 1 sequence from an unspecified genome; no seqlengths

or as methylKit object:

    head(methylKit::methRead(system.file("extdata", "test1.myCpG.txt",
                                         package = "methylKit"),
                             sample.id="test", assembly="hg18",
                             treatment=1, context="CpG", mincov = 0))

    ##     chr   start     end strand coverage numCs numTs
    ## 1 chr21 9764513 9764513      -       12     0    12
    ## 2 chr21 9764539 9764539      -       12     3     9
    ## 3 chr21 9820622 9820622      +       13     0    13
    ## 4 chr21 9837545 9837545      +       11     0    11
    ## 5 chr21 9849022 9849022      +      124    90    34
    ## 6 chr21 9853296 9853296      +       17    10     7

`probe_id_locations` contains information needed to map cellular loci to probe IDs

    data("IlluminaMethEpicB5ProbeIDs")
    head(IlluminaMethEpicB5ProbeIDs)

    ## GRanges object with 6 ranges and 1 metadata column:
    ##       seqnames              ranges strand |          ID
    ##          <Rle>           <IRanges>  <Rle> | <character>
    ##   [1]    chr19     5236005-5236006      + |  cg07881041
    ##   [2]    chr20   63216298-63216299      + |  cg18478105
    ##   [3]     chr1     6781065-6781066      + |  cg23229610
    ##   [4]     chr2 197438742-197438743      - |  cg03513874
    ##   [5]     chrX   24054523-24054524      + |  cg09835024
    ##   [6]    chr14   93114794-93114795      - |  cg05451842
    ##   -------
    ##   seqinfo: 25 sequences from an unspecified genome; no seqlengths

Use `BSmeth2Probe` to map WGBS data to probe IDs.

    mapped_WGBS_data <- BSmeth2Probe(probe_id_locations = IlluminaMethEpicB5ProbeIDs,
                                     WGBS_data = WGBS_GRanges,
                                     multipleMapping = TRUE,
                                     cutoff = 10)
    head(mapped_WGBS_data)

    ##          IDs   sample1
    ## 1 cg00305774 1.0000000
    ## 2 cg00546078 0.8181818
    ## 3 cg00546307 0.5454545
    ## 4 cg00546971 0.5000000
    ## 5 cg00774867 0.8461538
    ## 6 cg00830435 0.9166667

This mapped data can now be used in `deconvolute`. Here we will deconvolute it using the previously extended signature matrix as the reference atlas.

    deconvolution <- deconvolute(reference = HumanCellTypeMethAtlas,
                                 bulk = mapped_WGBS_data)

    ##    Min. 1st Qu.  Median    Mean 3rd Qu.    Max.
    ##  0.9963  0.9963  0.9963  0.9963  0.9963  0.9963

    deconvolution$proportions

    ##         Monocytes_EPIC B.cells_EPIC CD4T.cells_EPIC NK.cells_EPIC
    ## sample1              0            0               0             0
    ##         CD8T.cells_EPIC Neutrophils_EPIC Erythrocyte_progenitors Adipocytes
    ## sample1               0                0                       0  0.5011789
    ##         Cortical_neurons Hepatocytes Lung_cells Pancreatic_beta_cells
    ## sample1        0.2917357           0          0                     0
    ##         Pancreatic_acinar_cells Pancreatic_duct_cells
    ## sample1                       0                     0
    ##         Vascular_endothelial_cells Colon_epithelial_cells Left_atrium Bladder
    ## sample1                          0            0.001012524           0       0
    ##         Breast Head_and_neck_larynx Kidney Prostate Thyroid Upper_GI
    ## sample1      0            0.2060729      0        0       0        0
    ##         Uterus_cervix
    ## sample1             0

## 4.2 Constructing tissue specific CpG signature matrix

Alternatively, users can set tissueSpecCpGs as TRUE to construct tissue based methylation signature matrix by using the reference atlas. Here, we used simulated samples to construct tissue specific signature matrix since we don’t have tissue specific samples.

    data("HumanCellTypeMethAtlas")
    exampleSamples <- simulateCellMix(1,reference = HumanCellTypeMethAtlas)$simulated
    exampleMeta <- data.table("Experiment_accession" = "example_sample",
                              "Biosample_term_name" = "example_cell_type")
    colnames(exampleSamples) <- c("CpGs", "example_sample")
    colnames(HumanCellTypeMethAtlas)[1] <- c("CpGs")

    signatures <- findSignatures(
      samples = exampleSamples,
      sampleMeta = exampleMeta,
      atlas = HumanCellTypeMethAtlas,
      IDs = "CpGs", K = 100, tissueSpecCpGs = TRUE)

    ## CELL TYPES IN EXTENDED ATLAS:
    ## example_cell_type
    ## Monocytes_EPIC
    ## B.cells_EPIC
    ## CD4T.cells_EPIC
    ## NK.cells_EPIC
    ## CD8T.cells_EPIC
    ## Neutrophils_EPIC
    ## Erythrocyte_progenitors
    ## Adipocytes
    ## Cortical_neurons
    ## Hepatocytes
    ## Lung_cells
    ## Pancreatic_beta_cells
    ## Pancreatic_acinar_cells
    ## Pancreatic_duct_cells
    ## Vascular_endothelial_cells
    ## Colon_epithelial_cells
    ## Left_atrium
    ## Bladder
    ## Breast
    ## Head_and_neck_larynx
    ## Kidney
    ## Prostate
    ## Thyroid
    ## Upper_GI
    ## Uterus_cervix

    print(head(signatures[[2]]))

    ## $hyper
    ## cg22017733 cg26460530 cg27189395 cg26033520 cg25913761 cg08857351 cg08474651
    ## 0.12600524 0.12597629 0.11230619 0.11204660 0.10981268 0.10946078 0.10931785
    ## cg23247274 cg10509607 cg22897141 cg22879098 cg03254916 cg08425810 cg26757820
    ## 0.10832996 0.10824763 0.10793901 0.10744847 0.10732494 0.10674439 0.10622242
    ## cg10361922 cg04913246 cg16416715 cg26047334 cg14057303 cg25158622 cg14845962
    ## 0.10609156 0.10591701 0.10587564 0.10537328 0.10535902 0.10475027 0.10346419
    ## cg08367326 cg16127573 cg16278496 cg05043794 cg15014975 cg09577804 cg04055490
    ## 0.10345153 0.10303663 0.10289927 0.10275084 0.10244074 0.10206257 0.10174044
    ## cg22138735 cg17847861 cg07865091 cg03063309 cg16402452 cg02297709 cg10661769
    ## 0.10168795 0.10087569 0.10076822 0.10071199 0.10043591 0.10035487 0.10005648
    ## cg00769843 cg06398251 cg06889535 cg01377358 cg16306706 cg15575375 cg16988611
    ## 0.09996319 0.09986923 0.09940101 0.09909563 0.09846355 0.09841387 0.09837649
    ## cg16017089 cg16829306 cg13852284 cg20809067 cg06375761 cg12946660 cg27016579
    ## 0.09827877 0.09823456 0.09785311 0.09712533 0.09589327 0.09567411 0.09545885
    ## cg26401541 cg22109530 cg10576280 cg26860970 cg04962480 cg08255475 cg18071071
    ## 0.09542222 0.09484646 0.09465896 0.09388169 0.09355430 0.09291882 0.09254453
    ## cg11402700 cg11798182 cg03803541 cg05560697 cg25843651 cg10906729 cg18569141
    ## 0.09170549 0.08993249 0.08982443 0.08947637 0.08878418 0.08865345 0.08823930
    ## cg26083458 cg00270878 cg05337711 cg07748583 cg17179862 cg03484180 cg04173586
    ## 0.08775441 0.08772798 0.08748882 0.08719099 0.08705684 0.08674674 0.08613523
    ## cg24019564 cg26264318 cg10672681 cg03419014 cg10807894 cg01615880 cg09942210
    ## 0.08594566 0.08493883 0.08486961 0.08408695 0.08392304 0.08309134 0.08255605
    ## cg09367425 cg18842353 cg01005506 cg14419102 cg23231163 cg02486646 cg02670123
    ## 0.08246639 0.08205506 0.08198595 0.08170611 0.08110233 0.08064820 0.08057160
    ## cg08979515 cg25607383 cg06653052 cg17936564 cg14515791 cg23977888 cg01409343
    ## 0.07991404 0.07940679 0.07906756 0.07896053 0.07826585 0.07796375 0.07732512
    ## cg05103231 cg20510285 cg13512830 cg25124966 cg18684142 cg13909895 cg22706007
    ## 0.07727902 0.07697936 0.07697643 0.07628307 0.07625775 0.07538525 0.07509645
    ## cg15935721 cg23051299
    ## 0.07410904 0.07404465
    ##
    ## $hypo
    ## cg03963853 cg24788483 cg05612654 cg15310871 cg13500029 cg10480329 cg11231069
    ##  0.2688812  0.2628838  0.2570439  0.2525055  0.2508795  0.2441790  0.2432044
    ## cg12655112 cg05923857 cg12458039 cg20610950 cg06988336 cg16636767 cg06517984
    ##  0.2334416  0.2323603  0.2317271  0.2272520  0.2161032  0.2105759  0.2079406
    ## cg23952578 cg06297318 cg14976569 cg04851465 cg15633603 cg11597902 cg22259797
    ##  0.2072207  0.2071382  0.2061714  0.2060525  0.2041609  0.2034775  0.2018671
    ## cg13403369 cg18990407 cg08425796 cg09322573 cg06125903 cg26538782 cg25517015
    ##  0.2017741  0.2010552  0.1987334  0.1970315  0.1960754  0.1957759  0.1950211
    ## cg22185977 cg27366072 cg01879591 cg08538581 cg19502671 cg13093111 cg10967114
    ##  0.1946142  0.1940471  0.1926210  0.1901806  0.1898164  0.1896026  0.1892458
    ## cg19380303 cg01024962 cg20966357 cg06978145 cg23403750 cg23755933 cg06721411
    ##  0.1890766  0.1879946  0.1879876  0.1877759  0.1877335  0.1874318  0.1874047
    ## cg05445326 cg06585734 cg04664897 cg12555086 cg26305504 cg07918933 cg11802666
    ##  0.1871227  0.1863038  0.1862822  0.1857564  0.1852024  0.1851816  0.1851672
    ## cg26923863 cg18835472 cg02244028 cg04217515 cg24250902 cg25596405 cg08428868
    ##  0.1850910  0.1844767  0.1844127  0.1838327  0.1831107  0.1830896  0.1821447
    ## cg17117981 cg00009088 cg26889953 cg17086773 cg15288326 cg13980609 cg16429499
    ##  0.1813471  0.1813368  0.1809110  0.1803767  0.1801492  0.1798623  0.1791699
    ## cg01622606 cg22666015 cg18066690 cg19849557 cg17976229 cg23514375 cg13414750
    ##  0.1781721  0.1772547  0.1736529  0.1669945  0.1669880  0.1657610  0.1620257
    ## cg12019801 cg09856274 cg22160073 cg24037651 cg25283432 cg03339817 cg07023317
    ##  0.1618213  0.1600346  0.1569647  0.1536555  0.1521649  0.1504428  0.1469249
    ## cg10624395 cg05921138 cg06623899 cg16732654 cg25605731 cg00754552 cg04917472
    ##  0.1454426  0.1443393  0.1426328  0.1392698  0.1369559  0.1360810  0.1357190
    ## cg21492378 cg24011073 cg11348106 cg14642045 cg19590591 cg19835973 cg06934654
    ##  0.1345858  0.1323484  0.1297006  0.1282956  0.1273287  0.1246727  0.1244257
    ## cg25045219 cg00058449 cg14345154 cg12422199 cg26514623 cg10239163 cg04422741
    ##  0.1238985  0.1226432  0.1218505  0.1218338  0.1204899  0.1196127  0.1188309
    ## cg18095675 cg12754495
    ##  0.1187921  0.1181912

## 4.3 Constructing tissue specific DMPs

Alternatively, users can set tissueSpecDMPs as TRUE to obtain tissue based DMPs by using the reference atlas. Here, we used simulated samples since we don’t have tissue specific samples. Note that both tissueSpecCpGs and tissueSpecDMPs can’t be TRUE at the same time.

    data("HumanCellTypeMethAtlas")
    exampleSamples <- simulateCellMix(1,reference = HumanCellTypeMethAtlas)$simulated
    exampleMeta <- data.table("Experiment_accession" = "example_sample",
                              "Biosample_term_name" = "example_cell_type")
    colnames(exampleSamples) <- c("CpGs", "example_sample")
    colnames(HumanCellTypeMethAtlas)[1] <- c("CpGs")

    signatures <- findSignatures(
      samples = exampleSamples,
      sampleMeta = exampleMeta,
      atlas = HumanCellTypeMethAtlas,
      IDs = "CpGs", tissueSpecDMPs = TRUE)

    ## CELL TYPES IN EXTENDED ATLAS:
    ## example_cell_type
    ## Monocytes_EPIC
    ## B.cells_EPIC
    ## CD4T.cells_EPIC
    ## NK.cells_EPIC
    ## CD8T.cells_EPIC
    ## Neutrophils_EPIC
    ## Erythrocyte_progenitors
    ## Adipocytes
    ## Cortical_neurons
    ## Hepatocytes
    ## Lung_cells
    ## Pancreatic_beta_cells
    ## Pancreatic_acinar_cells
    ## Pancreatic_duct_cells
    ## Vascular_endothelial_cells
    ## Colon_epithelial_cells
    ## Left_atrium
    ## Bladder
    ## Breast
    ## Head_and_neck_larynx
    ## Kidney
    ## Prostate
    ## Thyroid
    ## Upper_GI
    ## Uterus_cervix

    print(head(signatures[[2]]))

    ##            intercept        f         pval         qval
    ## cg10480329 -3.287511 193.4823 5.547550e-13 3.344063e-09
    ## cg06297318 -3.496336 167.2140 2.620393e-12 7.897864e-09
    ## cg18835472 -3.350244 147.9235 9.457995e-12 1.900426e-08
    ## cg05923857 -3.515739 138.3759 1.885664e-11 2.837094e-08
    ## cg27366072 -2.858145 135.4254 2.353263e-11 2.837094e-08
    ## cg15633603 -1.496293 129.0200 3.862380e-11 3.743900e-08

# 5 Example Workflow For RNA Sequencing Data

It is possible to use RNA-seq data for deconvolution via deconvR package. Beware that you have to set `IDs` column that contains `Gene names` to run deconvR functions. Therefore you can simulate bulk RNA-seq data via `simulateCellMix` and, extend RNA-seq reference atlas via `findSignatures`.

# 6 sessionInfo

    sessionInfo()

    ## R version 4.4.1 (2024-06-14 ucrt)
    ## Platform: x86_64-w64-mingw32/x64
    ## Running under: Windows Server 2022 x64 (build 20348)
    ##
    ## Matrix products: default
    ##
    ##
    ## 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] parallel  stats     graphics  grDevices utils     datasets  methods
    ## [8] base
    ##
    ## other attached packages:
    ## [1] dplyr_1.1.4       doParallel_1.0.17 iterators_1.0.14  foreach_1.5.2
    ## [5] deconvR_1.12.0    data.table_1.16.2 knitr_1.48        BiocStyle_2.34.0
    ##
    ## loaded via a namespace (and not attached):
    ##   [1] splines_4.4.1               BiocIO_1.16.0
    ##   [3] bitops_1.0-9                tibble_3.2.1
    ##   [5] R.oo_1.26.0                 preprocessCore_1.68.0
    ##   [7] XML_3.99-0.17               lifecycle_1.0.4
    ##   [9] lattice_0.22-6              MASS_7.3-61
    ##  [11] base64_2.0.2                scrime_1.3.5
    ##  [13] magrittr_2.0.3              minfi_1.52.0
    ##  [15] limma_3.62.0                sass_0.4.9
    ##  [17] rmarkdown_2.28              jquerylib_0.1.4
    ##  [19] yaml_2.3.10                 robslopes_1.1.3
    ##  [21] doRNG_1.8.6                 askpass_1.2.1
    ##  [23] minqa_1.2.8                 DBI_1.2.3
    ##  [25] RColorBrewer_1.1-3          abind_1.4-8
    ##  [27] zlibbioc_1.52.0             quadprog_1.5-8
    ##  [29] GenomicRanges_1.58.0        purrr_1.0.2
    ##  [31] R.utils_2.12.3              BiocGenerics_0.52.0
    ##  [33] RCurl_1.98-1.16             GenomeInfoDbData_1.2.13
    ##  [35] IRanges_2.40.0              S4Vectors_0.44.0
    ##  [37] rentrez_1.2.3               genefilter_1.88.0
    ##  [39] annotate_1.84.0             DelayedMatrixStats_1.28.0
    ##  [41] codetools_0.2-20            DelayedArray_0.32.0
    ##  [43] xml2_1.3.6                  tidyselect_1.2.1
    ##  [45] UCSC.utils_1.2.0            lme4_1.1-35.5
    ##  [47] beanplot_1.3.1              matrixStats_1.4.1
    ##  [49] stats4_4.4.1                illuminaio_0.48.0
    ##  [51] GenomicAlignments_1.42.0    jsonlite_1.8.9
    ##  [53] multtest_2.62.0             e1071_1.7-16
    ##  [55] survival_3.7-0              bbmle_1.0.25.1
    ##  [57] tools_4.4.1                 rsq_2.7
    ##  [59] Rcpp_1.0.13                 glue_1.8.0
    ##  [61] SparseArray_1.6.0           xfun_0.48
    ##  [63] qvalue_2.38.0               MatrixGenerics_1.18.0
    ##  [65] GenomeInfoDb_1.42.0         HDF5Array_1.34.0
    ##  [67] numDeriv_2016.8-1.1         BiocManager_1.30.25
    ##  [69] fastmap_1.2.0               boot_1.3-31
    ##  [71] rhdf5filters_1.18.0         fansi_1.0.6
    ##  [73] openssl_2.2.2               digest_0.6.37
    ##  [75] R6_2.5.1                    colorspace_2.1-1
    ##  [77] gtools_3.9.5                RSQLite_2.3.7
    ##  [79] R.methodsS3_1.8.2           utf8_1.2.4
    ##  [81] tidyr_1.3.1                 generics_0.1.3
    ##  [83] rtracklayer_1.66.0          class_7.3-22
    ##  [85] httr_1.4.7                  S4Arrays_1.6.0
    ##  [87] pkgconfig_2.0.3             gtable_0.3.6
    ##  [89] blob_1.2.4                  siggenes_1.80.0
    ##  [91] XVector_0.46.0              htmltools_0.5.8.1
    ##  [93] bookdown_0.41               scales_1.3.0
    ##  [95] Biobase_2.66.0              png_0.1-8
    ##  [97] deming_1.4-1                tzdb_0.4.0
    ##  [99] reshape2_1.4.4              rjson_0.2.23
    ## [101] nloptr_2.1.1                coda_0.19-4.1
    ## [103] nlme_3.1-166                curl_5.2.3
    ## [105] bdsmatrix_1.3-7             bumphunter_1.48.0
    ## [107] proxy_0.4-27                cachem_1.1.0
    ## [109] rhdf5_2.50.0                stringr_1.5.1
    ## [111] AnnotationDbi_1.68.0        restfulr_0.0.15
    ## [113] GEOquery_2.74.0             pillar_1.9.0
    ## [115] grid_4.4.1                  reshape_0.8.9
    ## [117] vctrs_0.6.5                 Deriv_4.1.6
    ## [119] xtable_1.8-4                evaluate_1.0.1
    ## [121] readr_2.1.5                 GenomicFeatures_1.58.0
    ## [123] mvtnorm_1.3-1               cli_3.6.3
    ## [125] locfit_1.5-9.10             compiler_4.4.1
    ## [127] Rsamtools_2.22.0            rlang_1.1.4
    ## [129] crayon_1.5.3                rngtools_1.5.2
    ## [131] nor1mix_1.3-3               mclust_6.1.1
    ## [133] emdbook_1.3.13              plyr_1.8.9
    ## [135] stringi_1.8.4               mcr_1.3.3.1
    ## [137] BiocParallel_1.40.0         nnls_1.6
    ## [139] assertthat_0.2.1            munsell_0.5.1
    ## [141] Biostrings_2.74.0           Matrix_1.7-1
    ## [143] hms_1.1.3                   sparseMatrixStats_1.18.0
    ## [145] bit64_4.5.2                 ggplot2_3.5.1
    ## [147] Rhdf5lib_1.28.0             KEGGREST_1.46.0
    ## [149] methylKit_1.32.0            statmod_1.5.0
    ## [151] SummarizedExperiment_1.36.0 fastseg_1.52.0
    ## [153] memoise_2.0.1               bslib_0.8.0
    ## [155] bit_4.5.0

    stopCluster(cl)