tidySummarizedExperiment - part of tidytranscriptomics ================ <!-- badges: start --> [](https://www.tidyverse.org/lifecycle/#maturing) [](https://github.com/stemangiola/tidySummarizedExperiment/actions) <!-- badges: end --> **Brings SummarizedExperiment to the tidyverse!** website: [stemangiola.github.io/tidySummarizedExperiment/](https://stemangiola.github.io/tidySummarizedExperiment/) Another [nice introduction](https://carpentries-incubator.github.io/bioc-intro/60-next-steps/index.html) by carpentries-incubator. Please also have a look at - [tidySingleCellExperiment](https://stemangiola.github.io/tidySingleCellExperiment/) for tidy manipulation of SingleCellExperiment objects - [tidyseurat](https://stemangiola.github.io/tidyseurat/) for tidy manipulation of Seurat objects - [tidybulk](https://stemangiola.github.io/tidybulk/) for tidy analysis of RNA sequencing data - [nanny](https://github.com/stemangiola/nanny) for tidy high-level data analysis and manipulation - [tidygate](https://github.com/stemangiola/tidygate) for adding custom gate information to your tibble - [tidyHeatmap](https://stemangiola.github.io/tidyHeatmap/) for heatmaps produced with tidy principles # Introduction tidySummarizedExperiment provides a bridge between Bioconductor [SummarizedExperiment](https://bioconductor.org/packages/release/bioc/html/SummarizedExperiment.html) \[@morgan2020summarized\] and the tidyverse \[@wickham2019welcome\]. It creates an invisible layer that enables viewing the Bioconductor *SummarizedExperiment* object as a tidyverse tibble, and provides SummarizedExperiment-compatible *dplyr*, *tidyr*, *ggplot* and *plotly* functions. This allows users to get the best of both Bioconductor and tidyverse worlds. ## Functions/utilities available | SummarizedExperiment-compatible Functions | Description | |-------------------------------------------|------------------------------------------------------------------------------------| | `all` | After all `tidySummarizedExperiment` is a SummarizedExperiment object, just better | | tidyverse Packages | Description | |--------------------|---------------------------------------------| | `dplyr` | Almost all `dplyr` APIs like for any tibble | | `tidyr` | Almost all `tidyr` APIs like for any tibble | | `ggplot2` | `ggplot` like for any tibble | | `plotly` | `plot_ly` like for any tibble | | Utilities | Description | |-------------|---------------------------------------------| | `as_tibble` | Convert cell-wise information to a `tbl_df` | ## Installation ``` r if (!requireNamespace("BiocManager", quietly=TRUE)) { install.packages("BiocManager") } BiocManager::install("tidySummarizedExperiment") ``` From Github (development) ``` r devtools::install_github("stemangiola/tidySummarizedExperiment") ``` Load libraries used in the examples. ``` r library(ggplot2) library(tidySummarizedExperiment) ``` # Create `tidySummarizedExperiment`, the best of both worlds! This is a SummarizedExperiment object but it is evaluated as a tibble. So it is fully compatible both with SummarizedExperiment and tidyverse APIs. ``` r pasilla_tidy <- tidySummarizedExperiment::pasilla ``` **It looks like a tibble** ``` r pasilla_tidy ``` ## # A SummarizedExperiment-tibble abstraction: 102,193 × 5 ## # Transcripts=14599 | Samples=7 | Assays=counts ## .feature .sample counts condition type ## <chr> <chr> <int> <chr> <chr> ## 1 FBgn0000003 untrt1 0 untreated single_end ## 2 FBgn0000008 untrt1 92 untreated single_end ## 3 FBgn0000014 untrt1 5 untreated single_end ## 4 FBgn0000015 untrt1 0 untreated single_end ## 5 FBgn0000017 untrt1 4664 untreated single_end ## 6 FBgn0000018 untrt1 583 untreated single_end ## 7 FBgn0000022 untrt1 0 untreated single_end ## 8 FBgn0000024 untrt1 10 untreated single_end ## 9 FBgn0000028 untrt1 0 untreated single_end ## 10 FBgn0000032 untrt1 1446 untreated single_end ## # … with 40 more rows **But it is a SummarizedExperiment object after all** ``` r assays(pasilla_tidy) ``` ## List of length 1 ## names(1): counts # Tidyverse commands We can use tidyverse commands to explore the tidy SummarizedExperiment object. We can use `slice` to choose rows by position, for example to choose the first row. ``` r pasilla_tidy %>% slice(1) ``` ## # A SummarizedExperiment-tibble abstraction: 1 × 5 ## # Transcripts=1 | Samples=1 | Assays=counts ## .feature .sample counts condition type ## <chr> <chr> <int> <chr> <chr> ## 1 FBgn0000003 untrt1 0 untreated single_end We can use `filter` to choose rows by criteria. ``` r pasilla_tidy %>% filter(condition == "untreated") ``` ## # A SummarizedExperiment-tibble abstraction: 58,396 × 5 ## # Transcripts=14599 | Samples=4 | Assays=counts ## .feature .sample counts condition type ## <chr> <chr> <int> <chr> <chr> ## 1 FBgn0000003 untrt1 0 untreated single_end ## 2 FBgn0000008 untrt1 92 untreated single_end ## 3 FBgn0000014 untrt1 5 untreated single_end ## 4 FBgn0000015 untrt1 0 untreated single_end ## 5 FBgn0000017 untrt1 4664 untreated single_end ## 6 FBgn0000018 untrt1 583 untreated single_end ## 7 FBgn0000022 untrt1 0 untreated single_end ## 8 FBgn0000024 untrt1 10 untreated single_end ## 9 FBgn0000028 untrt1 0 untreated single_end ## 10 FBgn0000032 untrt1 1446 untreated single_end ## # … with 40 more rows We can use `select` to choose columns. ``` r pasilla_tidy %>% select(.sample) ``` ## # A tibble: 102,193 × 1 ## .sample ## <chr> ## 1 untrt1 ## 2 untrt1 ## 3 untrt1 ## 4 untrt1 ## 5 untrt1 ## 6 untrt1 ## 7 untrt1 ## 8 untrt1 ## 9 untrt1 ## 10 untrt1 ## # … with 102,183 more rows We can use `count` to count how many rows we have for each sample. ``` r pasilla_tidy %>% count(.sample) ``` ## # A tibble: 7 × 2 ## .sample n ## <chr> <int> ## 1 trt1 14599 ## 2 trt2 14599 ## 3 trt3 14599 ## 4 untrt1 14599 ## 5 untrt2 14599 ## 6 untrt3 14599 ## 7 untrt4 14599 We can use `distinct` to see what distinct sample information we have. ``` r pasilla_tidy %>% distinct(.sample, condition, type) ``` ## # A tibble: 7 × 3 ## .sample condition type ## <chr> <chr> <chr> ## 1 untrt1 untreated single_end ## 2 untrt2 untreated single_end ## 3 untrt3 untreated paired_end ## 4 untrt4 untreated paired_end ## 5 trt1 treated single_end ## 6 trt2 treated paired_end ## 7 trt3 treated paired_end We could use `rename` to rename a column. For example, to modify the type column name. ``` r pasilla_tidy %>% rename(sequencing=type) ``` ## # A SummarizedExperiment-tibble abstraction: 102,193 × 5 ## # Transcripts=14599 | Samples=7 | Assays=counts ## .feature .sample counts condition sequencing ## <chr> <chr> <int> <chr> <chr> ## 1 FBgn0000003 untrt1 0 untreated single_end ## 2 FBgn0000008 untrt1 92 untreated single_end ## 3 FBgn0000014 untrt1 5 untreated single_end ## 4 FBgn0000015 untrt1 0 untreated single_end ## 5 FBgn0000017 untrt1 4664 untreated single_end ## 6 FBgn0000018 untrt1 583 untreated single_end ## 7 FBgn0000022 untrt1 0 untreated single_end ## 8 FBgn0000024 untrt1 10 untreated single_end ## 9 FBgn0000028 untrt1 0 untreated single_end ## 10 FBgn0000032 untrt1 1446 untreated single_end ## # … with 40 more rows We could use `mutate` to create a column. For example, we could create a new type column that contains single and paired instead of single_end and paired_end. ``` r pasilla_tidy %>% mutate(type=gsub("_end", "", type)) ``` ## # A SummarizedExperiment-tibble abstraction: 102,193 × 5 ## # Transcripts=14599 | Samples=7 | Assays=counts ## .feature .sample counts condition type ## <chr> <chr> <int> <chr> <chr> ## 1 FBgn0000003 untrt1 0 untreated single ## 2 FBgn0000008 untrt1 92 untreated single ## 3 FBgn0000014 untrt1 5 untreated single ## 4 FBgn0000015 untrt1 0 untreated single ## 5 FBgn0000017 untrt1 4664 untreated single ## 6 FBgn0000018 untrt1 583 untreated single ## 7 FBgn0000022 untrt1 0 untreated single ## 8 FBgn0000024 untrt1 10 untreated single ## 9 FBgn0000028 untrt1 0 untreated single ## 10 FBgn0000032 untrt1 1446 untreated single ## # … with 40 more rows We could use `unite` to combine multiple columns into a single column. ``` r pasilla_tidy %>% unite("group", c(condition, type)) ``` ## # A SummarizedExperiment-tibble abstraction: 102,193 × 4 ## # Transcripts=14599 | Samples=7 | Assays=counts ## .feature .sample counts group ## <chr> <chr> <int> <chr> ## 1 FBgn0000003 untrt1 0 untreated_single_end ## 2 FBgn0000008 untrt1 92 untreated_single_end ## 3 FBgn0000014 untrt1 5 untreated_single_end ## 4 FBgn0000015 untrt1 0 untreated_single_end ## 5 FBgn0000017 untrt1 4664 untreated_single_end ## 6 FBgn0000018 untrt1 583 untreated_single_end ## 7 FBgn0000022 untrt1 0 untreated_single_end ## 8 FBgn0000024 untrt1 10 untreated_single_end ## 9 FBgn0000028 untrt1 0 untreated_single_end ## 10 FBgn0000032 untrt1 1446 untreated_single_end ## # … with 40 more rows We can also combine commands with the tidyverse pipe `%>%`. For example, we could combine `group_by` and `summarise` to get the total counts for each sample. ``` r pasilla_tidy %>% group_by(.sample) %>% summarise(total_counts=sum(counts)) ``` ## # A tibble: 7 × 2 ## .sample total_counts ## <chr> <int> ## 1 trt1 18670279 ## 2 trt2 9571826 ## 3 trt3 10343856 ## 4 untrt1 13972512 ## 5 untrt2 21911438 ## 6 untrt3 8358426 ## 7 untrt4 9841335 We could combine `group_by`, `mutate` and `filter` to get the transcripts with mean count \> 0. ``` r pasilla_tidy %>% group_by(.feature) %>% mutate(mean_count=mean(counts)) %>% filter(mean_count > 0) ``` ## # A tibble: 86,513 × 6 ## # Groups: .feature [12,359] ## .feature .sample counts condition type mean_count ## <chr> <chr> <int> <chr> <chr> <dbl> ## 1 FBgn0000003 untrt1 0 untreated single_end 0.143 ## 2 FBgn0000008 untrt1 92 untreated single_end 99.6 ## 3 FBgn0000014 untrt1 5 untreated single_end 1.43 ## 4 FBgn0000015 untrt1 0 untreated single_end 0.857 ## 5 FBgn0000017 untrt1 4664 untreated single_end 4672. ## 6 FBgn0000018 untrt1 583 untreated single_end 461. ## 7 FBgn0000022 untrt1 0 untreated single_end 0.143 ## 8 FBgn0000024 untrt1 10 untreated single_end 7 ## 9 FBgn0000028 untrt1 0 untreated single_end 0.429 ## 10 FBgn0000032 untrt1 1446 untreated single_end 1085. ## # … with 86,503 more rows # Plotting ``` r my_theme <- list( scale_fill_brewer(palette="Set1"), scale_color_brewer(palette="Set1"), theme_bw() + theme( panel.border=element_blank(), axis.line=element_line(), panel.grid.major=element_line(size=0.2), panel.grid.minor=element_line(size=0.1), text=element_text(size=12), legend.position="bottom", aspect.ratio=1, strip.background=element_blank(), axis.title.x=element_text(margin=margin(t=10, r=10, b=10, l=10)), axis.title.y=element_text(margin=margin(t=10, r=10, b=10, l=10)) ) ) ``` We can treat `pasilla_tidy` as a normal tibble for plotting. Here we plot the distribution of counts per sample. ``` r pasilla_tidy %>% tidySummarizedExperiment::ggplot(aes(counts + 1, group=.sample, color=`type`)) + geom_density() + scale_x_log10() + my_theme ``` <!-- -->