vignettes/browsing-object.Rmd
browsing-object.RmdAbstract
bcbioSmallRna package version: 0.0.1
library(BiocStyle)
knitr::opts_chunk$set(tidy=FALSE,
dev="png",
message=FALSE, error=FALSE,
warning=TRUE)
library(knitr)
library(ggplot2)
# Set seed for reproducibility
set.seed(1454944673L)
theme_set(
theme_light(base_size = 11L))
theme_update(
legend.justification = "center",
legend.position = "bottom")library(isomiRs)
library(DEGreport)
library(bcbioSmallRna)
data(sbcb)
# bcbioSmallRnaDataSet
bcb <- sbcbYou can get all the count matrix with the method mirna, isomir, cluster:
# for miRNAs
head(mirna(bcb))## ERR187490 ERR187494 ERR187664 ERR187665
## hsa-let-7a-3p 26 102 25 197
## hsa-let-7a-5p 15396 88290 30838 111189
## hsa-let-7b-3p 8 0 0 39
## hsa-let-7b-5p 400 229 106 1067
## hsa-let-7c-5p 58 58 93 115
## hsa-let-7d-3p 124 560 265 848# for clusters
head(cluster(bcb))## ERR187490 ERR187494 ERR187664 ERR187665
## cluster:1 32 190 126 55
## cluster:2 1033 6675 2283 8369
## cluster:3 313 1077 564 2132
## cluster:4 2996 16959 15000 48050
## cluster:5 465 2470 729 2259
## cluster:6 9 43 14 47# for isomir
head(isomir(bcb))## ERR187490 ERR187494 ERR187664 ERR187665
## hsa-let-7a-3p 5 26 5 36
## hsa-let-7a-3p;iso_3p:c 3 8 3 27
## hsa-let-7a-3p;iso_3p:C 0 2 0 3
## hsa-let-7a-3p;iso_3p:tc 3 0 0 0
## hsa-let-7a-3p;iso_add:A 0 4 0 6
## hsa-let-7a-3p;iso_add:T 15 54 17 116By default this is the raw count data, however you can access a pre-computed normalized data using the second positional parameter log:
head(mirna(bcb, "log"))## ERR187490 ERR187494 ERR187664 ERR187665
## hsa-let-7a-3p 6.215516 6.219226 5.698424 6.803659
## hsa-let-7a-5p 15.086041 15.638169 15.467636 15.723060
## hsa-let-7b-3p 5.021667 3.046144 3.046144 5.063780
## hsa-let-7b-5p 9.844976 7.212310 7.424764 9.064181
## hsa-let-7c-5p 7.199906 5.603116 7.254332 6.159710
## hsa-let-7d-3p 8.210866 8.408052 8.663905 8.744015There are some important metris stored in the object that can be gotten with the following methods:
These section shows how to get general stats for the adapter removal step.
To get the numbers of adapters removed at each position:
head(adapter(bcb)[["reads_by_pos"]])## size reads sample colorby
## 1 17 155324 ERR187490 BRITISH
## 2 18 293195 ERR187490 BRITISH
## 3 19 155948 ERR187490 BRITISH
## 4 20 187603 ERR187490 BRITISH
## 5 21 211411 ERR187490 BRITISH
## 6 22 338768 ERR187490 BRITISHAs well, the total reads with adapter can be seen with:
adapter(bcb)[["reads_by_sample"]]## # A tibble: 4 x 3
## # Groups: sample [?]
## sample colorby total
## <chr> <fct> <int>
## 1 ERR187490 BRITISH 2457059
## 2 ERR187494 FINLAND 6048597
## 3 ERR187664 USA 3759076
## 4 ERR187665 NIGERIA 5772822All the metrics performed by bcbio can be seen with:
metrics(bcb)## country group sample library_size quality_format read_pass_filter
## 1 BRITISH group1 ERR187490 30 standard 8594767
## 2 FINLAND group1 ERR187494 30 standard 11802968
## 3 USA group2 ERR187664 30 standard 9697283
## 4 NIGERIA group2 ERR187665 40 standard 8176320
## read_with_adapter reads_before_trimming sequence_length
## 1 3098670 8594767 17-28
## 2 8488581 11802968 17-28
## 3 4334146 9697283 17-28
## 4 8075701 8176320 17-42
## sequences_flagged_as_poor_quality x_gc
## 1 0 51
## 2 0 51
## 3 0 49
## 4 0 49sessionInfo()## R version 3.5.1 (2018-07-02)
## Platform: x86_64-apple-darwin15.6.0 (64-bit)
## Running under: macOS High Sierra 10.13.3
##
## Matrix products: default
## BLAS: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRblas.0.dylib
## LAPACK: /Library/Frameworks/R.framework/Versions/3.5/Resources/lib/libRlapack.dylib
##
## locale:
## [1] en_US.UTF-8/en_US.UTF-8/en_US.UTF-8/C/en_US.UTF-8/en_US.UTF-8
##
## attached base packages:
## [1] parallel stats4 stats graphics grDevices utils datasets
## [8] methods base
##
## other attached packages:
## [1] bcbioSmallRna_0.0.1 DEGreport_1.17.5
## [3] isomiRs_1.9.1 SummarizedExperiment_1.10.1
## [5] DelayedArray_0.6.5 BiocParallel_1.14.2
## [7] matrixStats_0.54.0 Biobase_2.40.0
## [9] GenomicRanges_1.32.6 GenomeInfoDb_1.16.0
## [11] IRanges_2.14.11 S4Vectors_0.18.3
## [13] BiocGenerics_0.26.0 DiscriMiner_0.1-29
## [15] ggplot2_3.0.0 knitr_1.20
## [17] BiocStyle_2.8.2
##
## loaded via a namespace (and not attached):
## [1] assertive.base_0.0-7 colorspace_1.3-2
## [3] rjson_0.2.20 rprojroot_1.3-2
## [5] circlize_0.4.4 htmlTable_1.12
## [7] XVector_0.20.0 ggdendro_0.1-20
## [9] GlobalOptions_0.1.0 base64enc_0.1-3
## [11] fs_1.2.6 rstudioapi_0.7
## [13] roxygen2_6.1.0 assertive.sets_0.0-3
## [15] MultiAssayExperiment_1.6.0 ggrepel_0.8.0
## [17] bit64_0.9-7 fansi_0.3.0
## [19] AnnotationDbi_1.42.1 xml2_1.2.0
## [21] splines_3.5.1 logging_0.7-103
## [23] mnormt_1.5-5 geneplotter_1.58.0
## [25] Formula_1.2-3 Nozzle.R1_1.1-1
## [27] broom_0.5.0 annotate_1.58.0
## [29] cluster_2.0.7-1 readr_1.1.1
## [31] compiler_3.5.1 backports_1.1.2
## [33] assertthat_0.2.0 Matrix_1.2-14
## [35] lazyeval_0.2.1 cli_1.0.0
## [37] limma_3.36.3 lasso2_1.2-19
## [39] acepack_1.4.1 htmltools_0.3.6
## [41] tools_3.5.1 bindrcpp_0.2.2
## [43] gtable_0.2.0 glue_1.3.0
## [45] GenomeInfoDbData_1.1.0 dplyr_0.7.6
## [47] Rcpp_0.12.18 pkgdown_1.1.0
## [49] gdata_2.18.0 nlme_3.1-137
## [51] psych_1.8.4 stringr_1.3.1
## [53] gtools_3.8.1 XML_3.98-1.16
## [55] edgeR_3.22.3 zlibbioc_1.26.0
## [57] MASS_7.3-50 scales_1.0.0
## [59] hms_0.4.2 RColorBrewer_1.1-2
## [61] ComplexHeatmap_1.18.1 yaml_2.2.0
## [63] memoise_1.1.0 gridExtra_2.3
## [65] rpart_4.1-13 reshape_0.8.7
## [67] latticeExtra_0.6-28 stringi_1.2.4
## [69] RSQLite_2.1.1 genefilter_1.62.0
## [71] desc_1.2.0 checkmate_1.8.5
## [73] caTools_1.17.1.1 shape_1.4.4
## [75] rlang_0.2.2 pkgconfig_2.0.2
## [77] commonmark_1.5 bitops_1.0-6
## [79] evaluate_0.11 lattice_0.20-35
## [81] purrr_0.2.5 bindr_0.1.1
## [83] htmlwidgets_1.2 cowplot_0.9.3
## [85] bit_1.1-14 tidyselect_0.2.4
## [87] GGally_1.4.0 plyr_1.8.4
## [89] magrittr_1.5 DESeq2_1.20.0
## [91] R6_2.2.2 gplots_3.0.1
## [93] Hmisc_4.1-1 DBI_1.0.0
## [95] pillar_1.3.0 foreign_0.8-71
## [97] withr_2.1.2 survival_2.42-6
## [99] RCurl_1.95-4.11 nnet_7.3-12
## [101] tibble_1.4.2 janitor_1.1.1
## [103] crayon_1.3.4 utf8_1.1.4
## [105] KernSmooth_2.23-15 rmarkdown_1.10
## [107] GetoptLong_0.1.7 locfit_1.5-9.1
## [109] grid_3.5.1 data.table_1.11.4
## [111] blob_1.1.1 ConsensusClusterPlus_1.44.0
## [113] digest_0.6.16 xtable_1.8-3
## [115] tidyr_0.8.1 munsell_0.5.0