hermes
hermes 1.10.0
hermes
is a successor of the Roche internal rnaseqTools
R package, and therefore many code ideas have been borrowed from it. Therefore we would like to thank the rnaseqTools
authors for their work.
In particular, we would like to acknowledge Chendi Liao and Joe Paulson for their guidance and explanations during the development of hermes
. We also discussed the class design with Valerie Obenchain, and discussed RNAseq
data standards with Armen Karapetyan. We borrowed some ideas from the Roche internal biokitr
R package and discussed them with its maintainer Daniel Marbach.
Finally, hermes
originated as part of the NEST project.
We are grateful for the entire team’s support.
Thanks a lot to everyone involved!
First let’s see how we can install the hermes
package.
BioConductor
With the development version (3.15) of BioConductor
,
you can install the current package version with:
if (!require("BiocManager")) {
install.packages("BiocManager")
}
BiocManager::install("hermes")
You can install the unstable development version from GitHub with:
if (!require("devtools")) {
install.packages("devtools")
}
devtools::install_github("insightsengineering/hermes")
The hermes
R package provides classes, methods and functions to import, quality-check, filter, normalize, analyze RNAseq
counts data. The core functionality is built on the BioConductor
ecosystem, especially SummarizedExperiment
. This is the vignette to read for new users of this package.
In this vignette you are going to learn how to:
RNAseq
count data into the hermes
ready format.BioMart
).The packages used in this vignette are:
library(hermes)
library(SummarizedExperiment)
The datasets used in this vignette are:
?expression_set
?summarized_experiment
The data for hermes
needs to be imported into the HermesData
or RangedHermesData
format.
SummarizedExperiment
The simplest way to import data is from a SummarizedExperiment
(SE) object. This is because a HermesData
object
is just a special SE, with few additional requirements and slots.
In a nutshell, the object needs to have a counts
assay, have certain
gene and sample variables, and have unique row and column names. The row names, i.e. the gene names, must
start with a common prefix GeneID
or ENSG
to enable easy annotations.
See ?HermesData
for the detailed requirements.
When the SE follows the minimum conventions, we can just call the HermesData
constructor on it:
object <- HermesData(summarized_experiment)
And we have a HermesData
object.
object
#> class: HermesData
#> assays(1): counts
#> genes(5085): GeneID:11185 GeneID:10677 ... GeneID:9087 GeneID:9426
#> additional gene information(12): HGNC HGNCGeneName ... chromosome_name
#> LowExpressionFlag
#> samples(20): 06520011B0023R 06520067C0018R ... 06520015C0016R
#> 06520019C0023R
#> additional sample information(74): Filename SampleID ... LowDepthFlag
#> TechnicalFailureFlag
Note that in this case deprecated names were used for the rowData
and colData
variables,
therefore they appear under “additional” gene and sample information. However we can
still call the default constructor because the new names will be filled with missing values, e.g.:
head(annotation(object))
#> DataFrame with 6 rows and 4 columns
#> symbol desc chromosome size
#> <logical> <logical> <logical> <logical>
#> GeneID:11185 NA NA NA NA
#> GeneID:10677 NA NA NA NA
#> GeneID:101928428 NA NA NA NA
#> GeneID:100422835 NA NA NA NA
#> GeneID:102466731 NA NA NA NA
#> GeneID:64881 NA NA NA NA
If we want to map old column names to new column names to avoid duplication with new missing value columns,
we can do this using the rename()
method. For example here:
object <- summarized_experiment %>%
rename(
row_data = c(
symbol = "HGNC",
desc = "HGNCGeneName",
chromosome = "Chromosome",
size = "WidthBP",
low_expression_flag = "LowExpressionFlag"
),
col_data = c(
low_depth_flag = "LowDepthFlag",
technical_failure_flag = "TechnicalFailureFlag"
)
) %>%
HermesData()
For example we can now see in the annotations that we successfully carried over the information since we mapped the old annotations to the new required names above:
head(annotation(object))
#> DataFrame with 6 rows and 4 columns
#> symbol desc chromosome size
#> <character> <character> <character> <integer>
#> GeneID:11185 INMT indolethylamine N-me.. 7 5468
#> GeneID:10677 AVIL advillin 12 18694
#> GeneID:101928428 LOC101928428 RNA-binding protein .. GL000220.1 138
#> GeneID:100422835 MIR3183 microRNA 3183 17 84
#> GeneID:102466731 MIR6769A microRNA 6769a 16 73
#> GeneID:64881 PCDH20 protocadherin 20 13 5838
For a bit more details we can also call summary()
on the object.
summary(object)
#> HermesData object with 20 samples of 5085 genes.
#> - Library sizes across samples: mean 5476759, median 5365970, range 4632496 to 7262374
#> - Included assays (1): counts
#> - Additional gene information (7): GeneID StartBP ... SYMBOL
#> chromosome_name
#> - Additional sample information (73): Filename SampleID ... STDSSDY
#> technical_failure_flag
#> - Low expression genes (3021): GeneID:10677 GeneID:101928428 ...
#> GeneID:9084 GeneID:9426
#> - Samples with too low depth or technical failures (20): NA NA ... NA
#> NA
For the below, let’s use the already prepared HermesData
object.
object <- hermes_data
Likewise, when we receive the error “no ‘counts’ assay found”, we can use the rename()
function to change the name of the assay in the SummarizedExperiment
object to counts
. For example, the following object of type SummarizedExperiment
would have the assay name count
, and would produce the assay name error:
object_exp <- summarized_experiment %>%
rename(assays = c(count = "counts"))
And we would use the following code to convert the assay name to counts
, making it able to convert into HermesData
object:
object_exp <- rename(object_exp,
assays = c(counts = "count")
)
object_exp <- HermesData(object_exp)
ExpressionSet
If we start from an ExpressionSet
, we can first convert it to a RangedSummarizedExperiment
and then import it to RangedHermesData
:
se <- makeSummarizedExperimentFromExpressionSet(expression_set)
object2 <- HermesData(se)
object2
#> class: RangedHermesData
#> assays(1): counts
#> genes(5085): GeneID:11185 GeneID:10677 ... GeneID:9087 GeneID:9426
#> additional gene information(12): HGNC HGNCGeneName ... chromosome_name
#> LowExpressionFlag
#> samples(20): 06520011B0023R 06520067C0018R ... 06520015C0016R
#> 06520019C0023R
#> additional sample information(74): Filename SampleID ... LowDepthFlag
#> TechnicalFailureFlag
In general we can also import a matrix of counts. We just have to pass the required gene and sample information as data frames to the constructor.
counts_matrix <- assay(hermes_data)
object3 <- HermesDataFromMatrix(
counts = counts_matrix,
rowData = rowData(hermes_data),
colData = colData(hermes_data)
)
object3
#> class: HermesData
#> assays(1): counts
#> genes(5085): GeneID:11185 GeneID:10677 ... GeneID:9087 GeneID:9426
#> additional gene information(3): GeneID SYMBOL chromosome_name
#> samples(20): 06520011B0023R 06520067C0018R ... 06520015C0016R
#> 06520019C0023R
#> additional sample information(72): Filename SampleID ... TTYPE STDSSDY
identical(object, object3)
#> [1] TRUE
Note that we can easily access the counts assay (matrix) in the final object with counts()
:
cnts <- counts(object)
cnts[1:3, 1:3]
#> 06520011B0023R 06520067C0018R 06520063C0043R
#> GeneID:11185 3 66 35
#> GeneID:10677 1668 236 95
#> GeneID:101928428 0 0 0
hermes
provides a modular approach for querying gene annotations, in order to
allow for future extensions in this or other downstream packages.
The first step is to connect to a database. In hermes
the only option is currently databases that utilize the
BioMart
software suite.
However due to the generic function design, it is simple to extend hermes
with other data base
connections.
In order to save time during vignette build, we zoom in here on a subset of the original object
containing only the first 10 genes.
small_object <- object[1:10, ]
The corresponding function takes the common gene ID prefix as argument to determine the format of the gene IDs and the filter variable to use in the query later on.
httr::set_config(httr::config(ssl_verifypeer = 0L))
connection <- connect_biomart(prefix(small_object))
Here we are using the prefix()
method to access the prefix saved in the HermesData
object.
Then the second step is to query the gene annotations and save them in the object.
annotation(small_object) <- query(genes(small_object), connection)
Here we are using the genes()
method to access the gene IDs (row names) of the HermesData
object.
Note that not all genes might be found in the data base and the corresponding rows would then be NA
in the annotations.
hermes
provides automatic gene and sample flagging, as well as manual sample flagging functionality.
For genes, it is counted how many samples don’t pass a minimum expression CPM
(counts per million reads mapped) threshold.
If too many, then this gene is flagged as a “low expression” gene.
For samples, two flags are provided. The “technical failure” flag is based on the average Pearson correlation with other samples. The “low depth” flag is based on the library size, i.e. the total sum of counts for a sample across all genes.
Thresholds for the above flags can be initialized with control_quality()
, and the flags are added with add_quality_flags()
.
my_controls <- control_quality(min_cpm = 10, min_cpm_prop = 0.4, min_corr = 0.4, min_depth = 1e4)
#> Loading required namespace: testthat
object_flagged <- add_quality_flags(object, control = my_controls)
Sometimes it is necessary to manually flag certain samples as technical failures, e.g. after looking at one of the analyses discussed below. This is possible, too.
object_flagged <- set_tech_failure(object_flagged, sample_ids = "06520011B0023R")
All flags have access functions.
head(get_tech_failure(object_flagged))
#> 06520011B0023R 06520067C0018R 06520063C0043R 06520105C0017R 06520092C0017R
#> TRUE FALSE FALSE FALSE FALSE
#> 06520103C0017R
#> FALSE
head(get_low_depth(object_flagged))
#> 06520011B0023R 06520067C0018R 06520063C0043R 06520105C0017R 06520092C0017R
#> FALSE FALSE FALSE FALSE FALSE
#> 06520103C0017R
#> FALSE
head(get_low_expression(object_flagged))
#> GeneID:11185 GeneID:10677 GeneID:101928428 GeneID:100422835
#> TRUE FALSE TRUE TRUE
#> GeneID:102466731 GeneID:64881
#> TRUE TRUE
We can either filter based on the default QC flags, or based on custom variables from the gene or sample information.
This is simple with the filter()
function. It is also possible to selectively only filter the genes or the samples using the what
argument.
object_flagged_filtered <- filter(object_flagged)
object_flagged_genes_filtered <- filter(object_flagged, what = "genes")
This can be done with the subset()
function. Genes can be filtered with the subset
argument via expressions using the gene information variables, and samples can be filtered with the select
argument using the sample information variables. In order to see which ones are available these can be queries first.
names(rowData(object_flagged))
#> [1] "symbol" "desc" "GeneID"
#> [4] "chromosome" "size" "SYMBOL"
#> [7] "chromosome_name" "low_expression_flag"
names(colData(object_flagged))
#> [1] "Filename" "SampleID" "AGEGRP"
#> [4] "AGE18" "STDDRS" "STDDRSD"
#> [7] "STDSSDT" "TRTDRS" "TRTDRSD"
#> [10] "BHDCIRC" "BHDCIRCU" "ADAFL"
#> [13] "BLANP" "BKPS" "BLKS"
#> [16] "BTANNER" "FRPST" "DURIDX"
#> [19] "DURSAF" "DURSUR" "LNTHRPY"
#> [22] "AENCIFL" "STUDYID" "USUBJID"
#> [25] "RFSTDTC" "RFENDTC" "RFXSTDTC"
#> [28] "RFXENDTC" "RFICDTC" "RFPENDTC"
#> [31] "DTHDTC" "DTHFL" "SITEID"
#> [34] "INVID" "AGE" "AGEU"
#> [37] "SEX" "RACE" "ETHNIC"
#> [40] "ARMCD" "ARM" "ACTARMCD"
#> [43] "ACTARM" "COUNTRY" "DMDTC"
#> [46] "DMDY" "BAGE" "BAGEU"
#> [49] "BWT" "BWTU" "BHT"
#> [52] "BHTU" "BBMI" "ITTFL"
#> [55] "SAFFL" "INFCODT" "RANDDT"
#> [58] "TRTSDTC" "TRTSDTM" "TRTSTMF"
#> [61] "TRTEDTM" "TRTETMF" "TRTDUR"
#> [64] "DISCSTUD" "DISCDEAT" "DISCAE"
#> [67] "DISTRTFL" "AEWITHFL" "ALIVDT"
#> [70] "COHORT" "TTYPE" "STDSSDY"
#> [73] "low_depth_flag" "tech_failure_flag"
head(rowData(object_flagged)$chromosome)
#> [1] "7" "12" "GL000220.1" "17" "16"
#> [6] "13"
head(object_flagged$ARMCD)
#> [1] "COH1" "COH1" "COH8" "COH12" "COH9O" "COH9E"
object_flagged_subsetted <- subset(
object_flagged,
subset = chromosome == "5",
select = ARMCD == "COH1"
)
Normalizing counts within samples (CPM
), genes (RPKM) or across both (TPM) can be
achieved with the normalize()
function. The normalize()
function can also transform the counts by the variance stabilizing transformation (vst
) and the regularized log transformation (rlog
) as proposed in the DESeq2
package.
object_normalized <- normalize(object_flagged_filtered)
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#> function: y = a/x + b, and a local regression fit was automatically substituted.
#> specify fitType='local' or 'mean' to avoid this message next time.
The corresponding assays are saved in the object and can be accessed with assay()
.
assay(object_normalized, "tpm")[1:3, 1:3]
#> 06520067C0018R 06520063C0043R 06520105C0017R
#> GeneID:10677 4.096418 3.323016 7.714990
#> GeneID:286205 2.985506 3.182624 3.769962
#> GeneID:8365 11.711741 12.421108 12.466491
The used control settings can be accessed afterwards from the metadata of the object:
metadata(object_normalized)
#> $control_quality_flags
#> $control_quality_flags$min_cpm
#> [1] 10
#>
#> $control_quality_flags$min_cpm_prop
#> [1] 0.4
#>
#> $control_quality_flags$min_corr
#> [1] 0.4
#>
#> $control_quality_flags$min_depth
#> [1] 10000
#>
#>
#> $control_normalize
#> $control_normalize$log
#> [1] TRUE
#>
#> $control_normalize$lib_sizes
#> NULL
#>
#> $control_normalize$prior_count
#> [1] 1
#>
#> $control_normalize$fit_type
#> [1] "parametric"
Note that also the filtering settings are saved in here. For custom normalization options,
use control_normalize()
. For example, to not use log scale but the original scale of the counts:
object_normalized_original <- normalize(
object_flagged_filtered,
control = control_normalize(log = FALSE)
)
#> -- note: fitType='parametric', but the dispersion trend was not well captured by the
#> function: y = a/x + b, and a local regression fit was automatically substituted.
#> specify fitType='local' or 'mean' to avoid this message next time.
assay(object_normalized_original, "tpm")[1:3, 1:3]
#> 06520067C0018R 06520063C0043R 06520105C0017R
#> GeneID:10677 16.105854 9.007544 209.1084
#> GeneID:286205 6.920033 8.079569 12.6418
#> GeneID:8365 3353.172671 5483.360511 5658.6256
A series of simple descriptive plots can be obtained by just calling autoplot()
on an object.
autoplot(object)