# miRmine dataset as RangedSummarizedExperiment

Dusan Randjelovic

dusan.randjelovic@sbgenomics.com

#### 28 April 2020

#### Abstract

miRmine is data package built for easier use of miRmine dataset (Panwar et al (2017) miRmine: A Database of Human miRNA Expression. Bioinformatics, btx019. doi: 10.1093/bioinformatics/btx019). In it’s current version miRmine contains 304 publicly available experiments from NCBI SRA. Annotations used are from miRBase v21 (miRBase: Annotating high confidence microRNAs using deep sequencing data. Kozomara A, Griffiths-Jones S. NAR 2014 42:D68-D73).

#### Package

miRmine 1.10.0

# Contents

# 1 Data preparation

miRmine dataset contains rich metadata around 304 selected publicly available, miRNA-Seq experiments. Authors’ processed the data with miRdeep2 using annotation files from miRBase v21. Mentioned metadata is used as colData and miRBase annotations as GRanges are used as rowRanges while preparing this dataset as RangedSummarizedExperiment. Data used for preprocessing and constructing the `miRmine` RangedSummarizedExperiment are available in `extdata` folder. Details of this proccess could be followed in data help file: `?miRmine`.

    #library(GenomicRanges)
    #library(rtracklayer)
    #library(SummarizedExperiment)
    #library(Biostrings)
    #library(Rsamtools)

    ext.data <- system.file("extdata", package = "miRmine")
    list.files(ext.data)

    ## [1] "hsa.gff3"               "mature.fa"              "mature.fa.fai"
    ## [4] "miRmine-cell-lines.csv" "miRmine-info.txt"       "miRmine-tissues.csv"

Number of ranges from miRBase GFF and number of features output by miRdeep2 are not the same (2813 vs. 2822). After closer look it turns out that 2 rows from either tissues or cell.lines data are duplicated (with same mature miRNA and same precursor miRNA) and 7 rows don’t correspond to mirna/precursor combination existing in miRBase v21. These rows were removed for all samples, as seen in `?miRmine`.

# 2 Usage

To load this dataset use:

    library("miRmine")

    ## Loading required package: SummarizedExperiment

    ## Loading required package: GenomicRanges

    ## Loading required package: stats4

    ## Loading required package: BiocGenerics

    ## Loading required package: parallel

    ##
    ## Attaching package: 'BiocGenerics'

    ## The following objects are masked from 'package:parallel':
    ##
    ##     clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    ##     clusterExport, clusterMap, parApply, parCapply, parLapply,
    ##     parLapplyLB, parRapply, parSapply, parSapplyLB

    ## The following objects are masked from 'package:stats':
    ##
    ##     IQR, mad, sd, var, xtabs

    ## The following objects are masked from 'package:base':
    ##
    ##     Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    ##     as.data.frame, basename, cbind, colnames, dirname, do.call,
    ##     duplicated, eval, evalq, get, grep, grepl, intersect, is.unsorted,
    ##     lapply, mapply, match, mget, order, paste, pmax, pmax.int, pmin,
    ##     pmin.int, rank, rbind, rownames, sapply, setdiff, sort, table,
    ##     tapply, union, unique, unsplit, which, which.max, which.min

    ## Loading required package: S4Vectors

    ##
    ## Attaching package: 'S4Vectors'

    ## The following object is masked from 'package:base':
    ##
    ##     expand.grid

    ## Loading required package: IRanges

    ##
    ## Attaching package: 'IRanges'

    ## The following object is masked from 'package:grDevices':
    ##
    ##     windows

    ## Loading required package: GenomeInfoDb

    ## Loading required package: Biobase

    ## Welcome to Bioconductor
    ##
    ##     Vignettes contain introductory material; view with
    ##     'browseVignettes()'. To cite Bioconductor, see
    ##     'citation("Biobase")', and for packages 'citation("pkgname")'.

    ## Loading required package: DelayedArray

    ## Loading required package: matrixStats

    ##
    ## Attaching package: 'matrixStats'

    ## The following objects are masked from 'package:Biobase':
    ##
    ##     anyMissing, rowMedians

    ##
    ## Attaching package: 'DelayedArray'

    ## The following objects are masked from 'package:matrixStats':
    ##
    ##     colMaxs, colMins, colRanges, rowMaxs, rowMins, rowRanges

    ## The following objects are masked from 'package:base':
    ##
    ##     aperm, apply, rowsum

    data(miRmine)
    miRmine

    ## class: RangedSummarizedExperiment
    ## dim: 2813 304
    ## metadata(0):
    ## assays(1): counts
    ## rownames(2813): hsa-let-7a-2-3p.hsa-let-7a-2 hsa-let-7a-3p.hsa-let-7a-1
    ##   ... hsa-miR-99b-3p.hsa-mir-99b hsa-miR-99b-5p.hsa-mir-99b
    ## rowData names(10): source type ... UniqueName mirna_seq
    ## colnames(304): DRX003170..Lung. DRX003171..Lung. ... SRX666573..C8166.
    ##   SRX666574..C8166.
    ## colData names(17): Experiment.Accession Tissue ... Total.RUNs
    ##   Total.Bases

You may want to further subset data on some of many colData features (Tissue, Cell Line, Disease, Sex, Instrument) or output some specifics of particular experiment(s) (Accession #, Description, Publication):

    adenocarcinoma = miRmine[ , miRmine$Disease %in% c("Adenocarcinoma")]
    adenocarcinoma

    ## class: RangedSummarizedExperiment
    ## dim: 2813 30
    ## metadata(0):
    ## assays(1): counts
    ## rownames(2813): hsa-let-7a-2-3p.hsa-let-7a-2 hsa-let-7a-3p.hsa-let-7a-1
    ##   ... hsa-miR-99b-3p.hsa-mir-99b hsa-miR-99b-5p.hsa-mir-99b
    ## rowData names(10): source type ... UniqueName mirna_seq
    ## colnames(30): ERX358444..Hair.follicle. ERX358445..Hair.follicle. ...
    ##   SRX290601..Pancreas. SRX290617..Pancreas.
    ## colData names(17): Experiment.Accession Tissue ... Total.RUNs
    ##   Total.Bases

    as.character(adenocarcinoma$Sample.Accession)

    ##  [1] "DRS016321" "DRS016322" "DRS016297" "DRS016298" "DRS016299" "DRS016300"
    ##  [7] "DRS016301" "DRS016302" "DRS016303" "DRS016304" "DRS016305" "DRS016306"
    ## [13] "DRS016307" "DRS016308" "DRS016309" "DRS016310" "DRS016311" "DRS016312"
    ## [19] "DRS016313" "DRS016314" "DRS016315" "DRS016316" "DRS016317" "DRS016318"
    ## [25] "DRS016319" "DRS016320" "DRS016289" "DRS016289" "DRS016293" "DRS016293"

rowRanges data is also rich in metadata, containing all the features from miRBase hsa.gff3, with addition of actual miRNA sequence as DNAString instance. For example to read the sequence of top expressed miRNA over a subset of samples:

    top.mirna = names(sort(rowSums(assays(adenocarcinoma)$counts))[1])
    rowRanges(adenocarcinoma)$mirna_seq[[top.mirna]]

    ## Loading required package: Biostrings

    ## Loading required package: XVector

    ##
    ## Attaching package: 'Biostrings'

    ## The following object is masked from 'package:base':
    ##
    ##     strsplit

    ## 16-letter DNAString object
    ## seq: CAAGCCCGACAAGCGC

`miRmine` could be directly used in DESeq2 (note that expression values are RPM not raw reads):

    library("DESeq2")

    mirmine.subset = miRmine[, miRmine$Tissue %in% c("Lung", "Saliva")]
    mirmine.subset = SummarizedExperiment(
        assays = SimpleList(counts=ceiling(assays(mirmine.subset)$counts)),
        colData=colData(mirmine.subset),
        rowRanges=rowRanges(mirmine.subset),
        rowData=NULL
    )

    ddsSE <- DESeqDataSet(mirmine.subset, design = ~ Tissue)

    ## converting counts to integer mode

    ## factor levels were dropped which had no samples

    ddsSE <- ddsSE[ rowSums(counts(ddsSE)) > 1, ]

    dds <- DESeq(ddsSE)

    ## estimating size factors

    ## estimating dispersions

    ## gene-wise dispersion estimates

    ## mean-dispersion relationship

    ## final dispersion estimates

    ## fitting model and testing

    res <- results(dds)
    res

    ## log2 fold change (MLE): Tissue Saliva vs Lung
    ## Wald test p-value: Tissue Saliva vs Lung
    ## DataFrame with 1110 rows and 6 columns
    ##                               baseMean log2FoldChange     lfcSE      stat
    ##                              <numeric>      <numeric> <numeric> <numeric>
    ## hsa-let-7a-3p.hsa-let-7a-1     1.36878       -5.06122  4.407717  -1.14826
    ## hsa-let-7a-3p.hsa-let-7a-3     1.36878       -5.06122  4.407717  -1.14826
    ## hsa-let-7a-5p.hsa-let-7a-1 70832.41524       -6.66070  0.905381  -7.35679
    ## hsa-let-7a-5p.hsa-let-7a-2 70816.38028       -6.68916  0.910762  -7.34458
    ## hsa-let-7a-5p.hsa-let-7a-3 70796.45382       -6.69707  0.907304  -7.38128
    ## ...                                ...            ...       ...       ...
    ## hsa-miR-98-3p.hsa-mir-98       2.74944      -6.067066  4.104334 -1.478210
    ## hsa-miR-98-5p.hsa-mir-98     147.19291      -2.999533  1.451895 -2.065944
    ## hsa-miR-99a-5p.hsa-mir-99a    44.51050       3.346607  2.165446  1.545459
    ## hsa-miR-99b-3p.hsa-mir-99b    11.66482      -0.850215  3.003986 -0.283029
    ## hsa-miR-99b-5p.hsa-mir-99b   213.01781       1.560347  0.923541  1.689526
    ##                                 pvalue        padj
    ##                              <numeric>   <numeric>
    ## hsa-let-7a-3p.hsa-let-7a-1 2.50860e-01          NA
    ## hsa-let-7a-3p.hsa-let-7a-3 2.50860e-01          NA
    ## hsa-let-7a-5p.hsa-let-7a-1 1.88380e-13 5.44588e-12
    ## hsa-let-7a-5p.hsa-let-7a-2 2.06413e-13 5.70776e-12
    ## hsa-let-7a-5p.hsa-let-7a-3 1.56776e-13 4.74806e-12
    ## ...                                ...         ...
    ## hsa-miR-98-3p.hsa-mir-98     0.1393517    0.227835
    ## hsa-miR-98-5p.hsa-mir-98     0.0388338    0.088643
    ## hsa-miR-99a-5p.hsa-mir-99a   0.1222354    0.211830
    ## hsa-miR-99b-3p.hsa-mir-99b   0.7771547    0.836329
    ## hsa-miR-99b-5p.hsa-mir-99b   0.0911187    0.168955

# Session info

    ## R version 4.0.0 (2020-04-24)
    ## Platform: x86_64-w64-mingw32/x64 (64-bit)
    ## Running under: Windows Server 2012 R2 x64 (build 9600)
    ##
    ## Matrix products: default
    ##
    ## locale:
    ## [1] LC_COLLATE=C
    ## [2] LC_CTYPE=English_United States.1252
    ## [3] LC_MONETARY=English_United States.1252
    ## [4] LC_NUMERIC=C
    ## [5] LC_TIME=English_United States.1252
    ##
    ## attached base packages:
    ## [1] parallel  stats4    stats     graphics  grDevices utils     datasets
    ## [8] methods   base
    ##
    ## other attached packages:
    ##  [1] DESeq2_1.28.0               Biostrings_2.56.0
    ##  [3] XVector_0.28.0              miRmine_1.10.0
    ##  [5] SummarizedExperiment_1.18.0 DelayedArray_0.14.0
    ##  [7] matrixStats_0.56.0          Biobase_2.48.0
    ##  [9] GenomicRanges_1.40.0        GenomeInfoDb_1.24.0
    ## [11] IRanges_2.22.0              S4Vectors_0.26.0
    ## [13] BiocGenerics_0.34.0         BiocStyle_2.16.0
    ##
    ## loaded via a namespace (and not attached):
    ##  [1] locfit_1.5-9.4         Rcpp_1.0.4.6           lattice_0.20-41
    ##  [4] assertthat_0.2.1       digest_0.6.25          R6_2.4.1
    ##  [7] RSQLite_2.2.0          evaluate_0.14          pillar_1.4.3
    ## [10] ggplot2_3.3.0          zlibbioc_1.34.0        rlang_0.4.5
    ## [13] annotate_1.66.0        blob_1.2.1             Matrix_1.2-18
    ## [16] rmarkdown_2.1          splines_4.0.0          BiocParallel_1.22.0
    ## [19] geneplotter_1.66.0     stringr_1.4.0          RCurl_1.98-1.2
    ## [22] bit_1.1-15.2           munsell_0.5.0          compiler_4.0.0
    ## [25] xfun_0.13              pkgconfig_2.0.3        htmltools_0.4.0
    ## [28] tidyselect_1.0.0       tibble_3.0.1           GenomeInfoDbData_1.2.3
    ## [31] bookdown_0.18          XML_3.99-0.3           dplyr_0.8.5
    ## [34] crayon_1.3.4           bitops_1.0-6           grid_4.0.0
    ## [37] xtable_1.8-4           gtable_0.3.0           lifecycle_0.2.0
    ## [40] DBI_1.1.0              magrittr_1.5           scales_1.1.0
    ## [43] stringi_1.4.6          genefilter_1.70.0      ellipsis_0.3.0
    ## [46] vctrs_0.2.4            RColorBrewer_1.1-2     tools_4.0.0
    ## [49] bit64_0.9-7            glue_1.4.0             purrr_0.3.4
    ## [52] survival_3.1-12        yaml_2.2.1             AnnotationDbi_1.50.0
    ## [55] colorspace_1.4-1       BiocManager_1.30.10    memoise_1.1.0
    ## [58] knitr_1.28