Structure and content of RAVmodel
November 01, 2022
Contents
1 Citing GenomicSuperSignature
Please cite GenomicSuperSignature as follows:
Oh, S., Geistlinger, L., Ramos, M. et al. GenomicSuperSignature facilitates interpretation of RNA-seq experiments through robust, efficient comparison to public databases. Nat Commun 13, 3695 (2022). https://doi.org/10.1038/s41467-022-31411-3
2 Setup
2.1 Install and load package
if (!require("BiocManager"))
install.packages("BiocManager")
BiocManager::install("GenomicSuperSignature")library(GenomicSuperSignature)2.2 Download RAVmodel
You can download GenomicSuperSignature from Google Cloud bucket using
GenomicSuperSignature::getModel function. Currently available models are
built from top 20 PCs of 536 studies (containing 44,890 samples) containing
13,934 common genes from each of 536 study’s top 90% varying genes based on
their study-level standard deviation. There are two versions of this RAVmodel
annotated with different gene sets for GSEA: MSigDB C2 (C2) and three
priors from PLIER package (PLIERpriors). In this vignette, we are showing
the C2 annotated model.
Note that the first interactive run of this code, you will be asked to allow
R to create a cache directory. The model file will be stored there and
subsequent calls to getModel will read from the cache.
RAVmodel <- getModel("C2", load=TRUE)
#> [1] "downloading"3 Content of RAVmodel
RAVindex is a matrix containing genes in rows and RAVs in columns. colData
slot provides the information on each RAVs, such as GSEA annotation and
studies involved in each cluster. metadata slot stores model construction
information. trainingData slot contains the information on individual
studies in training dataset, such as MeSH terms assigned to each study.
RAVmodel
#> class: PCAGenomicSignatures
#> dim: 13934 4764
#> metadata(8): cluster size ... version geneSets
#> assays(1): RAVindex
#> rownames(13934): CASKIN1 DDX3Y ... CTC-457E21.9 AC007966.1
#> rowData names(0):
#> colnames(4764): RAV1 RAV2 ... RAV4763 RAV4764
#> colData names(4): RAV studies silhouetteWidth gsea
#> trainingData(2): PCAsummary MeSH
#> trainingData names(536): DRP000987 SRP059172 ... SRP164913 SRP188526
version(RAVmodel)
#> [1] "1.1.1"
geneSets(RAVmodel)
#> [1] "MSigDB C2 version 7.1"
3.1 RAVindex
Replicable Axis of Variation (RAV) index is the main component of
GenomicSuperSignature. It serves as an index connecting new datasets and the
existing database. You can access it through GenomicSuperSignature::RAVindex
(equivalent of SummarizedExperiment::assay). Rows are genes and columns are
RAVs.
Here, RAVmodel consists of 13,934 genes and 4,764 RAVs.
class(RAVindex(RAVmodel))
#> [1] "matrix" "array"
dim(RAVindex(RAVmodel))
#> [1] 13934 4764
RAVindex(RAVmodel)[1:4, 1:4]
#> RAV1 RAV2 RAV3 RAV4
#> CASKIN1 0.002449532 -2.845471e-03 0.001601715 -0.009366156
#> DDX3Y 0.006093438 1.870528e-03 -0.004346019 -0.006094526
#> MDGA1 0.009999975 2.610302e-03 -0.002831447 -0.006710757
#> EIF1AY 0.007923652 8.507021e-05 0.002322275 -0.0078507843.2 Metadata for RAVmodel
Metadata slot of RAVmodel contains information related to the model building.
names(metadata(RAVmodel))
#> [1] "cluster" "size" "k" "n" "MeSH_freq"
#> [6] "updateNote" "version" "geneSets"cluster: cluster membership of each PCs from the training dataset
size: an integer vector with the length of clusters, containing the number of PCs in each cluster
k: the number of all clusters in the given RAVmodel
n: the number of top PCs kept from each study in the training datasetgeneSets: the name of gene sets used for GSEA annotation
MeSH_freq: the frequency of MeSH terms associated with the training dataset. MeSH terms like ‘Humans’ and ‘RNA-seq’ are top ranked (which is very expected) because the training dataset of this model is Human RNA sequencing data.updateNote: a brief note on the given model’s specificationversion: the version of the given model
head(metadata(RAVmodel)$cluster)
#> DRP000987.PC1 DRP000987.PC2 DRP000987.PC3 DRP000987.PC4 DRP000987.PC5
#> 1 2 3 4 5
#> DRP000987.PC6
#> 6
head(metadata(RAVmodel)$size)
#> RAV1 RAV2 RAV3 RAV4 RAV5 RAV6
#> 6 21 4 7 3 3
metadata(RAVmodel)$k
#> [1] 4764
metadata(RAVmodel)$n
#> [1] 20
geneSets(RAVmodel)
#> [1] "MSigDB C2 version 7.1"
head(metadata(RAVmodel)$MeSH_freq)
#>
#> Humans RNA-Seq Transcriptome
#> 1163 591 576
#> RNA Whole Exome Sequencing Sequencing, RNA
#> 372 284 277
updateNote(RAVmodel)
#> [1] "GSEA with set.seed"
metadata(RAVmodel)$version
#> [1] "1.1.1"3.3 Studies in each RAV
You can find which studies are in each cluster using studies method. Output is
a list with the length of clusters, where each element is a character vector
containing the name of studies in each cluster.
length(studies(RAVmodel))
#> [1] 4764
studies(RAVmodel)[1:3]
#> $Cl4764_01
#> [1] "DRP000987" "SRP059172" "SRP059959" "SRP118741" "SRP144178" "SRP183186"
#>
#> $Cl4764_02
#> [1] "DRP000987" "ERP022839" "ERP104463" "ERP106996" "ERP109789" "ERP114435"
#> [7] "SRP059039" "SRP059172" "SRP065317" "SRP071547" "SRP073061" "SRP074274"
#> [13] "SRP075449" "SRP092158" "SRP096736" "SRP107337" "SRP111402" "SRP125008"
#> [19] "SRP133442" "SRP135684" "SRP155483"
#>
#> $Cl4764_03
#> [1] "DRP000987" "SRP001540" "SRP056840" "SRP125396"You can check which PC from different studies are in RAVs using PCinRAV.
PCinRAV(RAVmodel, 2)
#> [1] "DRP000987.PC2" "ERP022839.PC2" "ERP104463.PC1" "ERP106996.PC2"
#> [5] "ERP109789.PC2" "ERP114435.PC1" "SRP059039.PC3" "SRP059172.PC2"
#> [9] "SRP065317.PC1" "SRP071547.PC3" "SRP073061.PC2" "SRP074274.PC5"
#> [13] "SRP075449.PC2" "SRP092158.PC2" "SRP096736.PC2" "SRP107337.PC3"
#> [17] "SRP111402.PC1" "SRP125008.PC2" "SRP133442.PC1" "SRP135684.PC4"
#> [21] "SRP155483.PC2"3.4 Silhouette width for each RAV
Silhouette width ranges from -1 to 1 for each cluster. Typically, it is
interpreted as follows:
- Values close to 1 suggest that the observation is well matched to the
assigned cluster
- Values close to 0 suggest that the observation is borderline matched
between two clusters
- Values close to -1 suggest that the observations may be assigned to the
wrong cluster
For RAVmodel, the average silhouette width of each cluster is a quality control measure and suggested as a secondary reference to choose proper RAVs, following validation score.
x <- silhouetteWidth(RAVmodel)
head(x) # average silhouette width of the first 6 RAVs
#> 1 2 3 4 5 6
#> -0.05470163 0.06426256 -0.01800335 -0.04005584 0.05786189 -0.025209733.5 GSEA on each RAV
Pre-processed GSEA results on each RAV are stored in RAVmodel and can be
accessed through gsea function.
class(gsea(RAVmodel))
#> [1] "list"
class(gsea(RAVmodel)[[1]])
#> [1] "data.frame"
length(gsea(RAVmodel))
#> [1] 4764
gsea(RAVmodel)[1]
#> $RAV1
#> Description NES
#> REACTOME_MITOCHONDRIAL_TRANSLATION REACTOME_MITOCHONDRIAL_TRANSLATION 2.503359
#> REACTOME_TRANSLATION REACTOME_TRANSLATION 2.271857
#> CROONQUIST_STROMAL_STIMULATION_UP CROONQUIST_STROMAL_STIMULATION_UP -3.634893
#> pvalue qvalues
#> REACTOME_MITOCHONDRIAL_TRANSLATION 1.00000e-10 9.218794e-08
#> REACTOME_TRANSLATION 1.00000e-10 9.218794e-08
#> CROONQUIST_STROMAL_STIMULATION_UP 1.20798e-10 9.218794e-083.6 MeSH terms for each study
You can find MeSH terms associated with each study using mesh method.
Output is a list with the length of studies used for training. Each element of
this output list is a data frame containing the assigned MeSH terms and the
detail of them. The last column bagOfWords is the frequency of the MeSH term
in the whole training dataset.
length(mesh(RAVmodel))
#> [1] 536
mesh(RAVmodel)[1]
#> $DRP000987
#> score identifier concept name explanation
#> 35822 1000 DRP000987 C0042333 Genetic Variation Forced N....
#> 58627 18575 DRP000987 C0017431 Genotype Forced L....
#> 149067 37162 DRP000987 C0021247 Indonesia RtM via:....
#> 216848 74145 DRP000987 C0030498 Parasites RtM via:....
#> 716101 22634 DRP000987 C4760607 RNA-Seq RtM via:....
#> 761253 55509 DRP000987 C0024530 Malaria RtM via:....
#> 832289 96999 DRP000987 C0024535 Malaria, Falciparum RtM via:....
#> 1116527 161422 DRP000987 C0032150 Plasmodium falciparum RtM via:....
#> 1230230 7197 DRP000987 C0020969 Innate Immune Response Entry Te....
#> 1468242 162422 DRP000987 C0003062 Animals Parasite....
#> 1573542 19938 DRP000987 C0035668 RNA RtM via: RNA
#> 1596889 162422 DRP000987 C0086418 Humans Forced L....
#> 1627781 36315 DRP000987 C0752248 Transcriptome Analysis Entry Te....
#> 1689323 57176 DRP000987 C3178810 Transcriptome RtM via:....
#> bagOfWords
#> 35822 11
#> 58627 20
#> 149067 2
#> 216848 7
#> 716101 591
#> 761253 6
#> 832289 4
#> 1116527 5
#> 1230230 8
#> 1468242 209
#> 1573542 372
#> 1596889 1163
#> 1627781 66
#> 1689323 5763.7 PCA summary for each study
PCA summary of each study can be accessed through PCAsummary method. Output
is a list with the length of studies, where each element is a matrix containing
PCA summary results: standard deviation (SD), variance explained by each PC
(Variance), and the cumulative variance explained (Cumulative).
length(PCAsummary(RAVmodel))
#> [1] 536
PCAsummary(RAVmodel)[1]
#> $DRP000987
#> DRP000987.PC1 DRP000987.PC2 DRP000987.PC3 DRP000987.PC4
#> SD 42.6382804 21.97962707 12.94848367 11.89265640
#> Variance 0.3580557 0.09514629 0.03302091 0.02785537
#> Cumulative 0.3580557 0.45320196 0.48622287 0.51407824
#> DRP000987.PC5 DRP000987.PC6 DRP000987.PC7 DRP000987.PC8
#> SD 10.89460616 10.53655583 9.47278967 8.97268098
#> Variance 0.02337622 0.02186495 0.01767287 0.01585607
#> Cumulative 0.53745446 0.55931942 0.57699228 0.59284836
#> DRP000987.PC9 DRP000987.PC10 DRP000987.PC11 DRP000987.PC12
#> SD 8.43652010 7.90810771 7.60865968 7.076401846
#> Variance 0.01401774 0.01231676 0.01140165 0.009862254
#> Cumulative 0.60686609 0.61918285 0.63058449 0.640446748
#> DRP000987.PC13 DRP000987.PC14 DRP000987.PC15 DRP000987.PC16
#> SD 6.89881271 6.766783788 6.703417100 6.6042884
#> Variance 0.00937346 0.009018116 0.008850009 0.0085902
#> Cumulative 0.64982021 0.658838324 0.667688332 0.6762785
#> DRP000987.PC17 DRP000987.PC18 DRP000987.PC19 DRP000987.PC20
#> SD 6.44257163 6.245257583 6.042236748 5.992461574
#> Variance 0.00817466 0.007681604 0.007190294 0.007072317
#> Cumulative 0.68445319 0.692134796 0.699325091 0.7063974084 Other relevant code
The workflow to build the RAVmodel is available from https://github.com/shbrief/model_building which is archived in Zenodo with the identifier https://doi.org/10.5281/zenodo.6496552. All analyses presented in the GenomicSuperSignatures manuscript are reproducible using code accessible from https://github.com/shbrief/GenomicSuperSignaturePaper/ and archived in Zenodo with the identifier [https://doi.org/10.5281/zenodo.6496612].
5 Session Info
sessionInfo()
#> R version 4.2.1 (2022-06-23)
#> Platform: x86_64-pc-linux-gnu (64-bit)
#> Running under: Ubuntu 20.04.5 LTS
#>
#> Matrix products: default
#> BLAS: /home/biocbuild/bbs-3.16-bioc/R/lib/libRblas.so
#> LAPACK: /home/biocbuild/bbs-3.16-bioc/R/lib/libRlapack.so
#>
#> locale:
#> [1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
#> [3] LC_TIME=en_GB LC_COLLATE=C
#> [5] LC_MONETARY=en_US.UTF-8 LC_MESSAGES=en_US.UTF-8
#> [7] LC_PAPER=en_US.UTF-8 LC_NAME=C
#> [9] LC_ADDRESS=C LC_TELEPHONE=C
#> [11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C
#>
#> attached base packages:
#> [1] stats4 stats graphics grDevices utils datasets methods
#> [8] base
#>
#> other attached packages:
#> [1] GenomicSuperSignature_1.6.0 SummarizedExperiment_1.28.0
#> [3] Biobase_2.58.0 GenomicRanges_1.50.0
#> [5] GenomeInfoDb_1.34.0 IRanges_2.32.0
#> [7] S4Vectors_0.36.0 BiocGenerics_0.44.0
#> [9] MatrixGenerics_1.10.0 matrixStats_0.62.0
#> [11] BiocStyle_2.26.0
#>
#> loaded via a namespace (and not attached):
#> [1] bitops_1.0-7 bit64_4.0.5 filelock_1.0.2
#> [4] httr_1.4.4 doParallel_1.0.17 RColorBrewer_1.1-3
#> [7] tools_4.2.1 backports_1.4.1 bslib_0.4.0
#> [10] irlba_2.3.5.1 utf8_1.2.2 R6_2.5.1
#> [13] DBI_1.1.3 colorspace_2.0-3 GetoptLong_1.0.5
#> [16] withr_2.5.0 tidyselect_1.2.0 curl_4.3.3
#> [19] bit_4.0.4 compiler_4.2.1 cli_3.4.1
#> [22] DelayedArray_0.24.0 bookdown_0.29 sass_0.4.2
#> [25] scales_1.2.1 rappdirs_0.3.3 stringr_1.4.1
#> [28] digest_0.6.30 rmarkdown_2.17 XVector_0.38.0
#> [31] pkgconfig_2.0.3 htmltools_0.5.3 dbplyr_2.2.1
#> [34] fastmap_1.1.0 rlang_1.0.6 GlobalOptions_0.1.2
#> [37] RSQLite_2.2.18 shape_1.4.6 jquerylib_0.1.4
#> [40] generics_0.1.3 jsonlite_1.8.3 dplyr_1.0.10
#> [43] car_3.1-1 RCurl_1.98-1.9 magrittr_2.0.3
#> [46] GenomeInfoDbData_1.2.9 Matrix_1.5-1 Rcpp_1.0.9
#> [49] munsell_0.5.0 fansi_1.0.3 abind_1.4-5
#> [52] lifecycle_1.0.3 stringi_1.7.8 yaml_2.3.6
#> [55] carData_3.0-5 zlibbioc_1.44.0 BiocFileCache_2.6.0
#> [58] blob_1.2.3 grid_4.2.1 parallel_4.2.1
#> [61] crayon_1.5.2 lattice_0.20-45 circlize_0.4.15
#> [64] knitr_1.40 ComplexHeatmap_2.14.0 pillar_1.8.1
#> [67] ggpubr_0.4.0 rjson_0.2.21 ggsignif_0.6.4
#> [70] codetools_0.2-18 glue_1.6.2 evaluate_0.17
#> [73] BiocManager_1.30.19 png_0.1-7 vctrs_0.5.0
#> [76] foreach_1.5.2 gtable_0.3.1 purrr_0.3.5
#> [79] tidyr_1.2.1 clue_0.3-62 assertthat_0.2.1
#> [82] cachem_1.0.6 ggplot2_3.3.6 xfun_0.34
#> [85] broom_1.0.1 rstatix_0.7.0 tibble_3.1.8
#> [88] iterators_1.0.14 memoise_2.0.1 cluster_2.1.4