3 SummarizedExperiment review

Instructor: Renee

LIBD rstats club notes

3.1 Overview

The SummarizedExperiment class is used to store experimental results in the form of matrixes. Objects of this class include observations (features) of the samples, as well as additional metadata. Usually, this type of object is automatically generated as the output of other software (ie. SPEAQeasy), but you can also build them.

One of the main characteristics of SummarizedExperiment is that it allows you to handle you data in a “coordinated” way. For example, if you want to subset your data, with SummarizedExperiment you can do so without worrying your assays and metadata unsync.

3.2 Quiz

  1. How many classes does the SummarizedExperiment class has?

  2. What does features stand for?

  3. Which is the structure of the SummarizedExperiment class?

  1. What type of data can we store on an assay?

  2. What information does colData has?

3.3 Exercises

We are gonna use the same sample data set as yesterday from the airway library

library(SummarizedExperiment)
library(airway)

data(airway, package = "airway")
se <- airway

Exercise 1: a) How many genes do we have in this object? And samples? b) How many samples come from donors treated (trt) with dexamethasone (dex)? 

## For a) you could only print the summary of the object but since the idea is to understand
## how to explore the object find other function that gives you the answer.
se
#> class: RangedSummarizedExperiment 
#> dim: 63677 8 
#> metadata(1): ''
#> assays(1): counts
#> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492 ENSG00000273493
#> rowData names(10): gene_id gene_name ... seq_coord_system symbol
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(9): SampleName cell ... Sample BioSample

## Same thing for b, you could just print the colData and count the samples, but this is not
## efficient when our data consists in hundreds of samples. Find the answer using other tools.

colData(se)
#> DataFrame with 8 rows and 9 columns
#>            SampleName     cell      dex    albut        Run avgLength Experiment    Sample    BioSample
#>              <factor> <factor> <factor> <factor>   <factor> <integer>   <factor>  <factor>     <factor>
#> SRR1039508 GSM1275862  N61311     untrt    untrt SRR1039508       126  SRX384345 SRS508568 SAMN02422669
#> SRR1039509 GSM1275863  N61311     trt      untrt SRR1039509       126  SRX384346 SRS508567 SAMN02422675
#> SRR1039512 GSM1275866  N052611    untrt    untrt SRR1039512       126  SRX384349 SRS508571 SAMN02422678
#> SRR1039513 GSM1275867  N052611    trt      untrt SRR1039513        87  SRX384350 SRS508572 SAMN02422670
#>  [ reached getOption("max.print") -- omitted 4 rows ]

Exercise 2: Add another assay that has the log10 of your original counts 

## In our object, if you look at the part that says assays, we can see that at the moment
## we only have one with the name "counts"

se
#> class: RangedSummarizedExperiment 
#> dim: 63677 8 
#> metadata(1): ''
#> assays(1): counts
#> rownames(63677): ENSG00000000003 ENSG00000000005 ... ENSG00000273492 ENSG00000273493
#> rowData names(10): gene_id gene_name ... seq_coord_system symbol
#> colnames(8): SRR1039508 SRR1039509 ... SRR1039520 SRR1039521
#> colData names(9): SampleName cell ... Sample BioSample

## To see the data that's stored in that assay you can do either one of the next commands
assay(se)
#>                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 SRR1039521
#> ENSG00000000003        679        448        873        408       1138       1047        770        572
#> ENSG00000000005          0          0          0          0          0          0          0          0
#> ENSG00000000419        467        515        621        365        587        799        417        508
#> ENSG00000000457        260        211        263        164        245        331        233        229
#> ENSG00000000460         60         55         40         35         78         63         76         60
#> ENSG00000000938          0          0          2          0          1          0          0          0
#>  [ reached getOption("max.print") -- omitted 63671 rows ]
assays(se)$counts
#>                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 SRR1039521
#> ENSG00000000003        679        448        873        408       1138       1047        770        572
#> ENSG00000000005          0          0          0          0          0          0          0          0
#> ENSG00000000419        467        515        621        365        587        799        417        508
#> ENSG00000000457        260        211        263        164        245        331        233        229
#> ENSG00000000460         60         55         40         35         78         63         76         60
#> ENSG00000000938          0          0          2          0          1          0          0          0
#>  [ reached getOption("max.print") -- omitted 63671 rows ]

## Note that assay() does not support $ operator
# assay(se)$counts

## We would have to do:
assay(se, 1)
#>                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 SRR1039521
#> ENSG00000000003        679        448        873        408       1138       1047        770        572
#> ENSG00000000005          0          0          0          0          0          0          0          0
#> ENSG00000000419        467        515        621        365        587        799        417        508
#> ENSG00000000457        260        211        263        164        245        331        233        229
#> ENSG00000000460         60         55         40         35         78         63         76         60
#> ENSG00000000938          0          0          2          0          1          0          0          0
#>  [ reached getOption("max.print") -- omitted 63671 rows ]
assay(se, "counts")
#>                 SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 SRR1039521
#> ENSG00000000003        679        448        873        408       1138       1047        770        572
#> ENSG00000000005          0          0          0          0          0          0          0          0
#> ENSG00000000419        467        515        621        365        587        799        417        508
#> ENSG00000000457        260        211        263        164        245        331        233        229
#> ENSG00000000460         60         55         40         35         78         63         76         60
#> ENSG00000000938          0          0          2          0          1          0          0          0
#>  [ reached getOption("max.print") -- omitted 63671 rows ]

## If you use assays() without specifying the element you want to see it shows you the length
## of the list and the name of each element
assays(se)
#> List of length 1
#> names(1): counts

## To obtain a list of names as a vector you can do:
assayNames(se)
#> [1] "counts"

## Which can also be use to change the name of the assays
assayNames(se)[1] <- "foo"
assayNames(se)
#> [1] "foo"
assayNames(se)[1] <- "counts"

Exercise 3: Explore the metadata and add a new column that has the library size of each sample. 

## To calculate the library size use

apply(assay(se), 2, sum)
#> SRR1039508 SRR1039509 SRR1039512 SRR1039513 SRR1039516 SRR1039517 SRR1039520 SRR1039521 
#>   20637971   18809481   25348649   15163415   24448408   30818215   19126151   21164133

3.4 Solutions

Solution 1: 

## For a), dim() gives the desired answer

dim(se)
#> [1] 63677     8

## For b),

colData(se)[colData(se)$dex == "trt", ]
#> DataFrame with 4 rows and 9 columns
#>            SampleName     cell      dex    albut        Run avgLength Experiment    Sample    BioSample
#>              <factor> <factor> <factor> <factor>   <factor> <integer>   <factor>  <factor>     <factor>
#> SRR1039509 GSM1275863  N61311       trt    untrt SRR1039509       126  SRX384346 SRS508567 SAMN02422675
#> SRR1039513 GSM1275867  N052611      trt    untrt SRR1039513        87  SRX384350 SRS508572 SAMN02422670
#> SRR1039517 GSM1275871  N080611      trt    untrt SRR1039517       126  SRX384354 SRS508576 SAMN02422673
#> SRR1039521 GSM1275875  N061011      trt    untrt SRR1039521        98  SRX384358 SRS508580 SAMN02422677

Solution 2: 

## There are multiple ways to do it

assay(se, "logcounts") <- log10(assay(se, "counts"))

assays(se)$logcounts_v2 <- log10(assays(se)$counts)

Solution 3: 

## To add the library size we an use..

colData(se)$library_size <- apply(assay(se), 2, sum)

names(colData(se))
#>  [1] "SampleName"   "cell"         "dex"          "albut"        "Run"          "avgLength"    "Experiment"  
#>  [8] "Sample"       "BioSample"    "library_size"

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