Package 'hermes'

Description

hermes facilitates preprocessing, analyzing, and reporting of RNA-seq data.

Author(s)

Maintainer: Daniel Sabanés Bové [email protected]

Authors:

• Namrata Bhatia

• Stefanie Bienert

• Benoit Falquet

• Haocheng Li

• Jeff Luong

• Lyndsee Midori Zhang [email protected]

• Alex Richardson

• Simona Rossomanno

• Tim Treis

• Mark Yan

• Naomi Chang

• Chendi Liao [email protected]

• Carolyn Zhang

• Joseph N. Paulson [email protected]

Other contributors:

• F. Hoffmann-La Roche AG [copyright holder, funder]

See Also

Useful links:

• https://insightsengineering.github.io/hermes/

• Report bugs at https://github.com/insightsengineering/hermes/issues

Description

The function add_quality_flags() adds quality flag information to a AnyHermesData object:

• low_expression_flag: for each gene, counts how many samples don't pass a minimum expression Counts per Million (CPM) threshold. If too many, then it flags this gene as a "low expression" gene.

• tech_failure_flag: first calculates the Pearson correlation matrix of the sample wise CPM values, resulting in a matrix measuring the correlation between samples. Then compares the average correlation per sample with a threshold - if it is too low, then the sample is flagged as a "technical failure" sample.

• low_depth_flag: computes the library size (total number of counts) per sample. If this number is too low, the sample is flagged as a "low depth" sample.

Separate helper functions are internally used to create the flags, and separate getter functions allow easy access to the quality control flags in an object.

Usage

add_quality_flags(object, control = control_quality(), overwrite = FALSE)

h_low_expression_flag(object, control = control_quality())

h_low_depth_flag(object, control = control_quality())

h_tech_failure_flag(object, control = control_quality())

get_tech_failure(object)

get_low_depth(object)

get_low_expression(object)

Arguments

Details

While object already has the variables mentioned above as part of the rowData and colData (as this is enforced by the validation method for AnyHermesData), they are usually still NA after the initial object creation.

Value

The input object with added quality flags.

Functions

• h_low_expression_flag(): creates the low expression flag for genes given control settings.

• h_low_depth_flag(): creates the low depth (library size) flag for samples given control settings.

• h_tech_failure_flag(): creates the technical failure flag for samples given control settings.

• get_tech_failure(): get the technical failure flags for all samples.

• get_low_depth(): get the low depth failure flags for all samples.

• get_low_expression(): get the low expression failure flags for all genes.

See Also

• control_quality() for the detailed settings specifications;

• set_tech_failure() to manually flag samples as technical failures.

Examples

# Adding default quality flags to `AnyHermesData` object.
object <- hermes_data
result <- add_quality_flags(object)
which(get_tech_failure(result) != get_tech_failure(object))
head(get_low_expression(result))
head(get_tech_failure(result))
head(get_low_depth(result))

# It is possible to overwrite flags if needed, which will trigger a message.
result2 <- add_quality_flags(result, control_quality(min_cpm = 1000), overwrite = TRUE)

# Separate calculation of low expression flag.
low_expr_flag <- h_low_expression_flag(
  object,
  control_quality(min_cpm = 500, min_cpm_prop = 0.9)
)
length(low_expr_flag) == nrow(object)
head(low_expr_flag)

# Separate calculation of low depth flag.
low_depth_flag <- h_low_depth_flag(object, control_quality(min_depth = 5))
length(low_depth_flag) == ncol(object)
head(low_depth_flag)

# Separate calculation of technical failure flag.
tech_failure_flag <- h_tech_failure_flag(object, control_quality(min_corr = 0.35))
length(tech_failure_flag) == ncol(object)
head(tech_failure_flag)
head(get_tech_failure(object))
head(get_low_depth(object))
head(get_low_expression(object))

Description

Internal function to check whether a whole vector is NA.

Usage

all_na(x)

Arguments

Value

Corresponding flag.

Description

These methods access and set the gene annotations stored in a AnyHermesData object.

Usage

## S4 method for signature 'AnyHermesData'
annotation(object, ...)

.row_data_annotation_cols

## S4 replacement method for signature 'AnyHermesData,DataFrame'
annotation(object) <- value

Arguments

Format

The annotation column names are available in the exported character vector .row_data_annotation_cols.

Value

The S4Vectors::DataFrame with the gene annotations:

• symbol

• desc

• chromosome

• size

Note

When trying to replace the required annotations with completely missing values for any genes, a warning will be given and the corresponding gene IDs will be saved in the attribute annotation.missing.genes. Note also that additional annotations beyond the required ones may be supplied and will be stored.

Examples

object <- hermes_data
head(annotation(object))

Description

The documentation to this function lists all the conventional arguments in additional checkmate assertions.

Arguments

Description

We provide additional assertion functions which can be used together with assertthat::assert_that().

We provide additional assertion functions which can be used together with the checkmate functions. These are described in individual help pages linked below.

Usage

is_class(x, class2)

is_hermes_data(x)

is_counts_vector(x)

is_list_with(x, elements)

one_provided(one, two)

is_constant(x)

Arguments

Value

Depending on the function prefix.

• assert_ functions return the object invisibly if successful, and otherwise throw an error message.

• check_ functions return TRUE if successful, otherwise a string with the error message.

• test_ functions just return TRUE or FALSE.

Functions

• is_class(): checks the class.

• is_hermes_data(): checks whether x is an AnyHermesData object.

• is_counts_vector(): checks for a vector of counts (positive integers).

• is_list_with(): checks for a list containing elements.

• one_provided(): checks that exactly one of the two inputs one, two is not NULL.

• is_constant(): checks whether the vector x is constant (only supports numeric, factor, character, logical). NAs are removed first.

See Also

assert_proportion()

Examples

# Assert a general class.
a <- 5
is_class(a, "character")

# Assert a `AnyHermesData` object.
is_hermes_data(hermes_data)
is_hermes_data(42)

# Assert a counts vector.
a <- 5L
is_counts_vector(a)

# Assert a list containing certain elements.
b <- list(a = 5, b = 3)
is_list_with(b, c("a", "c"))
is_list_with(b, c("a", "b"))

# Assert that exactly one of two arguments is provided.
a <- 10
b <- 10
one_provided(a, b)
one_provided(a, NULL)

# Assert a constant vector.
is_constant(c(1, 2))
is_constant(c(NA, 1))
is_constant(c("a", "a"))
is_constant(factor(c("a", "a")))

Description

This generates all standard plots - histogram and q-q plot of library sizes, density plot of the (log) counts distributions, boxplot of the number of number of non-zero expressed genes per sample, and a stacked barplot of low expression genes by chromosome at default setting.

Usage

## S4 method for signature 'AnyHermesData'
autoplot(object)

Arguments

Value

A list with the ggplot objects from draw_libsize_hist(), draw_libsize_qq(), draw_libsize_densities(), draw_nonzero_boxplot() and draw_genes_barplot() functions with default settings.

Examples

result <- hermes_data
autoplot(result)

Description

The calc_pca() function performs principal components analysis of the gene count vectors across all samples.

A corresponding autoplot() method then can visualize the results.

Usage

calc_pca(object, assay_name = "counts", n_top = NULL)

Arguments

Details

• PCA should be performed after filtering out low quality genes and samples, as well as normalization of counts.

• In addition, genes with constant counts across all samples are excluded from the analysis internally in calc_pca(). Centering and scaling is also applied internally.

• Plots can be obtained with the ggplot2::autoplot() function with the corresponding method from the ggfortify package to plot the results of a principal components analysis saved in a HermesDataPca object. See ggfortify::autoplot.prcomp() for details.

Value

A HermesDataPca object which is an extension of the stats::prcomp class.

See Also

Afterwards correlations between principal components and sample variables can be calculated, see pca_cor_samplevar.

Examples

object <- hermes_data %>%
  add_quality_flags() %>%
  filter() %>%
  normalize()

result <- calc_pca(object, assay_name = "tpm")
summary(result)

result1 <- calc_pca(object, assay_name = "tpm", n_top = 500)
summary(result1)

# Plot the results.
autoplot(result)
autoplot(result, x = 2, y = 3)
autoplot(result, variance_percentage = FALSE)
autoplot(result, label = TRUE, label.repel = TRUE)

Description

This function concatenates inputs like cat() and prints them with newline.

Usage

cat_with_newline(...)

Arguments

Value

None, only used for the side effect of producing the concatenated output in the R console.

See Also

This is similar to cli::cat_line().

Examples

cat_with_newline("hello", "world")

Description

This method combines AnyHermesData objects with the same ranges but different samples (columns in assays).

Arguments

Value

The combined AnyHermesData object.

Note

• Note that this just inherits SummarizedExperiment::cbind,SummarizedExperiment-method(). When binding a AnyHermesData object with a SummarizedExperiment::SummarizedExperiment object, then the result will be a SummarizedExperiment::SummarizedExperiment object (the more general class).

• Note that the combined object needs to have unique sample IDs (column names).

See Also

rbind to row bind objects.

Examples

a <- hermes_data[, 1:10]
b <- hermes_data[, 11:20]
result <- cbind(a, b)
class(result)

Description

Check whether x is a (single) proportion.

Usage

check_proportion(x, null.ok = FALSE)

assert_proportion(
  x,
  null.ok = FALSE,
  .var.name = checkmate::vname(x),
  add = NULL
)

test_proportion(x, null.ok = FALSE)

expect_proportion(x, null.ok = FALSE, info = NULL, label = vname(x))

Arguments

Value

TRUE if successful, otherwise a string with the error message.

See Also

assertions for more details.

Examples

check_proportion(0.25)

Description

This obtains the sample variables of a HermesData object together with selected gene information.

Usage

col_data_with_genes(object, assay_name, genes)

Arguments

Value

The combined data set, where the additional attribute gene_cols contains the names of the columns obtained by extracting the genes information.

Note

The class of the returned data set will depend on the class of colData, so usually will be S4Vectors::DFrame.

Examples

result <- col_data_with_genes(hermes_data, "counts", gene_spec("GeneID:1820"))
tail(names(result))
result$GeneID.1820

Description

This helper function returns the Z-score from an assay stored as a matrix.

Usage

colMeanZscores(x)

Arguments

Value

A numeric vector containing the mean Z-score values for each column in x.

Examples

object <- hermes_data %>%
  add_quality_flags() %>%
  filter() %>%
  normalize() %>%
  assay("counts")

colMeanZscores(object)

Description

This helper function returns the first principal component from an assay stored as a matrix.

Usage

colPrinComp1(x, center = TRUE, scale = TRUE)

Arguments

Value

A numeric vector containing the principal component values for each column in x.

Examples

object <- hermes_data %>%
  add_quality_flags() %>%
  filter() %>%
  normalize() %>%
  assay("counts")

colPrinComp1(object)

Description

connect_biomart() creates a connection object of class ConnectionBiomart which contains the biomaRt object of class biomaRt::Mart and the prefix of the object which is used downstream for the query.

Usage

connect_biomart(prefix = c("ENSG", "GeneID"), version = NULL)

Arguments

Details

This connects to the Ensembl data base of BioMart for human genes. A specific version can be optionally chosen to ensure reproducibility of results once a new release is available, as accessed data might then change.

Value

ConnectionBiomart object.

Examples

if (interactive()) {
  connection <- connect_biomart("ENSG")
}

Description

This control function allows for easy customization of the normalization settings.

Usage

control_normalize(
  log = TRUE,
  lib_sizes = NULL,
  prior_count = 1,
  fit_type = "parametric"
)

Arguments

Value

List with the above settings used to perform the normalization procedure.

Note

To be used with the normalize() function.

Examples

control_normalize()
control_normalize(log = FALSE, lib_sizes = rep(1e6L, 20))

Description

Control function which specifies the quality flag settings. One or more settings can be customized. Not specified settings are left at defaults.

Usage

control_quality(
  min_cpm = 1,
  min_cpm_prop = 0.25,
  min_corr = 0.5,
  min_depth = NULL
)

Arguments

Value

List with the above criteria to flag observations.

Note

To be used with the add_quality_flags() function.

Examples

# Default settings.
control_quality()

# One or more settings can be customized.
control_quality(min_cpm = 5, min_cpm_prop = 0.001)

Description

New generic function to calculate correlations for one or two objects.

Usage

correlate(object, ...)

Arguments

Value

Corresponding object that contains the correlation results.

See Also

pca_cor_samplevar and calc_cor which are the methods included for this generic function.

Examples

sample_cors <- correlate(hermes_data)
autoplot(sample_cors)

pca_sample_var_cors <- correlate(calc_pca(hermes_data), hermes_data)
autoplot(pca_sample_var_cors)

Description

The correlate() method can calculate the correlation matrix between the sample vectors of counts from a specified assay. This produces a HermesDataCor object, which is an extension of a matrix with additional quality flags in the slot flag_data (containing the tech_failure_flag and low_depth_flag columns describing the original input samples).

An autoplot() method then afterwards can produce the corresponding heatmap.

Usage

## S4 method for signature 'AnyHermesData'
correlate(object, assay_name = "counts", method = "pearson", ...)

## S4 method for signature 'HermesDataCor'
autoplot(
  object,
  flag_colors = c(`FALSE` = "green", `TRUE` = "red"),
  cor_colors = circlize::colorRamp2(c(0, 0.5, 1), c("red", "yellow", "green")),
  ...
)

Arguments

Value

A HermesDataCor object.

Functions

• autoplot(HermesDataCor): This autoplot() method uses the ComplexHeatmap::Heatmap() function to plot the correlations between samples saved in a HermesDataCor object.

Examples

object <- hermes_data

# Calculate the sample correlation matrix.
correlate(object)

# We can specify another correlation coefficient to be calculated.
result <- correlate(object, method = "spearman")

# Plot the correlation matrix.
autoplot(result)

# We can customize the heatmap.
autoplot(result, show_column_names = FALSE, show_row_names = FALSE)

# Including changing the axis label text size.
autoplot(
  result,
  row_names_gp = grid::gpar(fontsize = 8),
  column_names_gp = grid::gpar(fontsize = 8)
)

Description

This correlate() method analyses the correlations (in R2 values) between all sample variables in a AnyHermesData object and the principal components of the samples.

A corresponding autoplot() method then can visualize the results in a heatmap.

Usage

## S4 method for signature 'HermesDataPca'
correlate(object, data)

## S4 method for signature 'HermesDataPcaCor'
autoplot(
  object,
  cor_colors = circlize::colorRamp2(c(-1, 0, 1), c("blue", "white", "red")),
  ...
)

Arguments

Value

A HermesDataPcaCor object with R2 values for all sample variables.

Functions

• autoplot(HermesDataPcaCor): This plot method uses the ComplexHeatmap::Heatmap() function to visualize a HermesDataPcaCor object.

See Also

h_pca_df_r2_matrix() which is used internally for the details.

Examples

object <- hermes_data %>%
  add_quality_flags() %>%
  filter() %>%
  normalize()

# Perform PCA and then correlate the prinicipal components with the sample variables.
object_pca <- calc_pca(object)
result <- correlate(object_pca, object)

# Visualize the correlations in a heatmap.
autoplot(result)

# We can also choose to not reorder the columns.
autoplot(result, cluster_columns = FALSE)

# We can also choose break-points for color customization.
autoplot(
  result,
  cor_colors = circlize::colorRamp2(
    c(-0.5, -0.25, 0, 0.25, 0.5, 0.75, 1),
    c("blue", "green", "purple", "yellow", "orange", "red", "brown")
  )
)

Description

These methods access and set the counts assay in a AnyHermesData object.

Usage

## S4 method for signature 'AnyHermesData'
counts(object, ...)

## S4 replacement method for signature 'AnyHermesData,matrix'
counts(object, ..., withDimnames = TRUE) <- value

Arguments

Value

The counts assay.

Methods (by class)

• counts(object = AnyHermesData) <- value:

Examples

a <- hermes_data
result <- counts(a)
class(result)
head(result)
counts(a) <- counts(a) + 100L
head(counts(a))

Description

This function transforms a numeric vector into a factor corresponding to the quantile intervals. The intervals are left-open and right-closed.

Usage

cut_quantile(x, percentiles = c(1/3, 2/3), digits = 0)

Arguments

Value

The factor with a description of the available quantiles as levels.

Examples

set.seed(452)
x <- runif(10, -10, 10)
cut_quantile(x, c(0.33333333, 0.6666666), digits = 4)

x[1:4] <- NA
cut_quantile(x)

Description

This utility function converts all eligible character and logical variables in a S4Vectors::DataFrame to factor variables. All factor variables get amended with an explicit missing level. This includes both NA and empty strings.

Usage

df_cols_to_factor(data, omit_columns = NULL, na_level = "<Missing>")

Arguments

Value

The modified data.

Note

All required rowData and colData variables cannot be converted to ensure proper downstream behavior. These are automatically omitted if found in data and therefore do not need to be specified in omit_columns.

Examples

dat <- colData(summarized_experiment)
any(vapply(dat, is.character, logical(1)))
any(vapply(dat, is.logical, logical(1)))
dat_converted <- df_cols_to_factor(dat)
any(vapply(dat_converted, function(x) is.character(x) || is.logical(x), logical(1)))

Description

The diff_expression() function performs differential expression analysis using a method of preference.

A corresponding autoplot() method is visualizing the results as a volcano plot.

Usage

diff_expression(object, group, method = c("voom", "deseq2"), ...)

## S4 method for signature 'HermesDataDiffExpr'
autoplot(object, adj_p_val_thresh = 0.05, log2_fc_thresh = 2.5)

Arguments

Details

Possible method choices are:

• voom: uses limma::voom(), see h_diff_expr_voom() for details.

• deseq2: uses DESeq2::DESeq(), see h_diff_expr_deseq2() for details.

Value

A HermesDataDiffExpr object which is a data frame with the following columns for each gene in the HermesData object:

• log2_fc (the estimate of the log2 fold change between the 2 levels of the provided factor)

• stat (the test statistic, which one depends on the method used)

• p_val (the raw p-value)

• adj_p_val (the multiplicity adjusted p-value value)

Functions

• autoplot(HermesDataDiffExpr): generates a volcano plot for a HermesDataDiffExpr object.

Note

• We provide the df_cols_to_factor() utility function that makes it easy to convert the colData() character and logical variables to factors, so that they can be subsequently used as group inputs. See the example.

• In order to avoid a warning when using deseq2, it can be necessary to specify fitType = "local" as additional argument. This could e.g. be the case when only few samples are present in which case the default parametric dispersions estimation will not work.

Examples

object <- hermes_data %>%
  add_quality_flags() %>%
  filter()

# Convert character and logical to factor variables in `colData`,
# including the below used `group` variable.
colData(object) <- df_cols_to_factor(colData(object))
res1 <- diff_expression(object, group = "SEX", method = "voom")
head(res1)
res2 <- diff_expression(object, group = "SEX", method = "deseq2")
head(res2)

# Pass method arguments to the internally used helper functions.
res3 <- diff_expression(object, group = "SEX", method = "voom", robust = TRUE, trend = TRUE)
head(res3)
res4 <- diff_expression(object, group = "SEX", method = "deseq2", fitType = "local")
head(res4)

# Create the corresponding volcano plots.
autoplot(res1)
autoplot(res3)

Description

This produces a barplot of the dichotomized gene expression counts into two or three categories based on custom defined percentiles.

Usage

draw_barplot(
  object,
  assay_name,
  x_spec,
  facet_var = NULL,
  fill_var = NULL,
  percentiles = c(1/3, 2/3)
)

Arguments

Value

The ggplot barplot.

Examples

object <- hermes_data

g <- genes(object)

draw_barplot(
  object,
  assay_name = "counts",
  x_spec = gene_spec(g[1]),
  facet_var = "SEX",
  fill_var = "AGE18"
)

draw_barplot(
  object,
  assay_name = "counts",
  x_spec = gene_spec(g[1:3], colMedians, "Median"),
  facet_var = "SEX",
  fill_var = "AGE18"
)

draw_barplot(
  object,
  assay_name = "counts",
  x_spec = gene_spec(g[1:3], colMeans, "Mean"),
  facet_var = "SEX",
  fill_var = "AGE18",
  percentiles = c(0.1, 0.9)
)

Description

This produces boxplots of the gene expression values of a single gene, multiple genes or a gene signature.

Usage

draw_boxplot(
  object,
  assay_name,
  genes,
  x_var = NULL,
  color_var = NULL,
  facet_var = NULL,
  violin = FALSE,
  jitter = FALSE
)

h_draw_boxplot_df(object, assay_name, genes, x_var, color_var, facet_var)

Arguments

Value

The ggplot boxplot.

Functions

• h_draw_boxplot_df(): Helper function to prepare the data frame required for plotting.

Examples

object <- hermes_data
draw_boxplot(
  object,
  assay_name = "counts",
  genes = gene_spec(c(A = genes(object)[1])),
  violin = TRUE
)

object2 <- object %>%
  add_quality_flags() %>%
  filter() %>%
  normalize()
draw_boxplot(
  object2,
  assay_name = "tpm",
  x_var = "SEX",
  genes = gene_spec(setNames(genes(object2)[1:10], 1:10), fun = colMeans),
  facet_var = "RACE",
  color_var = "AGE18",
  jitter = TRUE
)

draw_boxplot(
  object,
  assay_name = "counts",
  x_var = "SEX",
  genes = gene_spec(genes(object)[1:3]),
  jitter = TRUE,
  facet_var = "AGE18"
)

draw_boxplot(
  object,
  assay_name = "counts",
  genes = gene_spec(c(A = "GeneID:11185", B = "GeneID:10677")),
  violin = TRUE
)

Description

This creates a barplot of chromosomes for the AnyHermesData object with the proportions of low expression genes.

Usage

draw_genes_barplot(
  object,
  chromosomes = c(seq_len(22), "X", "Y", "MT"),
  include_others = TRUE
)

Arguments

Value

The ggplot object with the histogram.

Examples

object <- hermes_data

# Display chromosomes 1-22, X, Y, and MT. Other chromosomes are displayed in "Others".
# To increase readability, we can have flip the coordinate axes.
draw_genes_barplot(object) + coord_flip()

# Alternatively we can also rotate the x-axis tick labels.
draw_genes_barplot(object) + theme(axis.text.x = element_text(angle = 90, vjust = 0.5, hjust = 1))

# Display chromosomes 1 and 2. Other chromosomes are displayed in "Others".
draw_genes_barplot(object, chromosomes = c("1", "2"))

# Display chromosomes 1 and 2 only.
draw_genes_barplot(object, chromosomes = c("1", "2"), include_others = FALSE)

Description

This produces a heatmap of the chosen assay and groups by various sample variables.

Usage

draw_heatmap(
  object,
  assay_name,
  color_extremes = c(0.01, 0.99),
  col_data_annotation = NULL,
  ...
)

Arguments

Value

The ComplexHeatmap::Heatmap heatmap

Examples

result <- hermes_data %>%
  normalize(methods = "voom") %>%
  add_quality_flags() %>%
  filter(what = "genes")

draw_heatmap(
  object = result[1:10, ],
  assay_name = "counts",
  col_data_annotation = "COUNTRY"
)

draw_heatmap(
  object = result[1:10, ],
  assay_name = "counts",
  color_extremes = c(0.001, 0.999),
  col_data_annotation = "AGEGRP"
)

Description

This creates a density plot of the (log) counts distributions of the AnyHermesData object where each line on the plot corresponds to a sample.

Usage

draw_libsize_densities(object, log = TRUE)

Arguments

Value

The ggplot object with the density plot.

Examples

result <- hermes_data
draw_libsize_densities(result)
draw_libsize_densities(result, log = FALSE)

Description

This creates a histogram of the library sizes of the AnyHermesData object.

Usage

draw_libsize_hist(object, bins = 30L, fill = "darkgrey")

Arguments

Value

The ggplot object with the histogram.

Examples

result <- hermes_data
draw_libsize_hist(result)
draw_libsize_hist(result, bins = 10L, fill = "blue")

Description

This creates a Q-Q plot of the library sizes of the AnyHermesData object.

Usage

draw_libsize_qq(object, color = "grey", linetype = "dashed")

Arguments

Value

The ggplot object with the Q-Q Plot.

Examples

result <- hermes_data
draw_libsize_qq(result)
draw_libsize_qq(result, color = "blue", linetype = "solid")

# We can also add sample names as labels.
library(ggrepel)
draw_libsize_qq(result) + geom_text_repel(label = colnames(result), stat = "qq")

Description

This draws a boxplot, with overlaid data points, of the number of non-zero expressed genes per sample.

Usage

draw_nonzero_boxplot(object, position = position_jitter(0.2), alpha = 0.25)

Arguments

Value

The ggplot object with the boxplot.

Examples

# Default boxplot.
result <- hermes_data
draw_nonzero_boxplot(result)

# Reusing the same position for labeling.
library(ggrepel)
pos <- position_jitter(0.5)
draw_nonzero_boxplot(result, position = pos) +
  geom_text_repel(aes(label = samples(result)), position = pos)

Description

This produces a scatterplot of two genes or gene signatures.

Usage

draw_scatterplot(
  object,
  assay_name,
  x_spec,
  y_spec,
  color_var = NULL,
  facet_var = NULL,
  smooth_method = c("lm", "loess", "none")
)

Arguments

Value

The ggplot scatterplot.

Examples

object <- hermes_data
g <- genes(object)

draw_scatterplot(
  object,
  assay_name = "counts",
  facet_var = NULL,
  x_spec = gene_spec(c(A = g[1])),
  y_spec = gene_spec(g[2]),
  color = "RACE"
)

object2 <- object %>%
  add_quality_flags() %>%
  filter() %>%
  normalize()
g2 <- genes(object2)

draw_scatterplot(
  object2,
  assay_name = "tpm",
  facet_var = "SEX",
  x_spec = gene_spec(g2[1:10], colMeans, "Mean"),
  y_spec = gene_spec(g2[11:20], colMedians, "Median"),
  smooth_method = "loess"
)

Description

This example data can be used to try out conversion of a Biobase::ExpressionSet object into a HermesData object.

Usage

expression_set

Format

A Biobase::ExpressionSet object with 20 samples covering 5085 features (Entrez gene IDs).

Source

This is an artificial dataset designed to resemble real data.

See Also

• SummarizedExperiment::makeSummarizedExperimentFromExpressionSet() to convert into a SummarizedExperiment::SummarizedExperiment.

• summarized_experiment which contains similar data already as a SummarizedExperiment::SummarizedExperiment.

Description

The methods access the names of the variables in colData() and rowData() of the object which are not required by design. So these can be additional sample or patient characteristics, or gene characteristics.

Usage

extraColDataNames(x, ...)

## S4 method for signature 'AnyHermesData'
extraColDataNames(x, ...)

extraRowDataNames(x, ...)

## S4 method for signature 'AnyHermesData'
extraRowDataNames(x, ...)

Arguments

Value

The character vector with the additional variable names in either colData() or rowData().

Examples

object <- hermes_data
extraColDataNames(object)
extraRowDataNames(object)

Description

This filters a AnyHermesData object using the default QC flags and required annotations.

Usage

filter(object, ...)

## S4 method for signature 'AnyHermesData'
filter(object, what = c("genes", "samples"), annotation_required = "size")

## S4 method for signature 'data.frame'
filter(object, ...)

## S4 method for signature 'ts'
filter(object, ...)

Arguments

Details

• Only genes without low expression (low_expression_flag) and samples without low depth (low_depth_flag) or technical failure (tech_failure_flag) remain in the returned filtered object.

• Also required gene annotation columns can be specified, so that genes which are not complete for these columns are filtered out. By default this is the size column, which is needed for default normalization of the object.

Value

The filtered AnyHermesData object.

Note

The internal implementation cannot use the subset() method since that requires non-standard evaluation of arguments.

Examples

a <- hermes_data
dim(a)

# Filter genes and samples on default QC flags.
result <- filter(a)
dim(result)

# Filter only genes without low expression.
result <- filter(a, what = "genes")

# Filter only samples with low depth and technical failure.
result <- filter(a, what = "samples")

# Filter only genes, and require certain annotations to be present.
result <- filter(a, what = "genes", annotation_required = c("size"))

Description

Creates a new GeneSpec object.

Usage

gene_spec(genes = NULL, fun = NULL, fun_name = deparse(substitute(fun)))

Arguments

Value

A new GeneSpec object.

Examples

gene_spec("GeneID:11185")
gene_spec(c("GeneID:11185", "GeneID:10677", "GeneID:101928428"), fun = colMeans)

Description

Access the gene IDs, i.e. row names, of a AnyHermesData object with a nicely named accessor method.

Usage

genes(object)

## S4 method for signature 'AnyHermesData'
genes(object)

Arguments

Value

The character vector with the gene IDs.

See Also

samples() to access the sample IDs.

Examples

a <- hermes_data
genes(a)

Description

A GeneSpec consists of the gene IDs (possibly named with labels), the summary function and the name of the summary function.

Methods

Public methods

• GeneSpec$new()

• GeneSpec$get_genes()

• GeneSpec$get_gene_labels()

• GeneSpec$returns_vector()

• GeneSpec$get_label()

• GeneSpec$extract()

• GeneSpec$extract_data_frame()

• GeneSpec$clone()

Method new()

Creates a new GeneSpec object.

Usage

GeneSpec$new(genes = NULL, fun = NULL, fun_name = deparse(substitute(fun)))

Arguments

Returns

A new GeneSpec object.

Method get_genes()

Returns the genes.

Usage

GeneSpec$get_genes()

Method get_gene_labels()

Returns the gene labels (substituted by gene IDs if not available).

Usage

GeneSpec$get_gene_labels(genes = self$get_genes())

Arguments

Method returns_vector()

Predicate whether the extract returns a vector or not.

Usage

GeneSpec$returns_vector()

Method get_label()

Returns a string which can be used e.g. for plot labels.

Usage

GeneSpec$get_label(genes = self$get_genes())

Arguments

Method extract()

Extract the gene values from an assay as specified.

Usage

GeneSpec$extract(assay)

Arguments

Returns

Either a vector with one value per column, or a matrix with multiple genes in the rows.

Method extract_data_frame()

Extract the gene values as a data.frame.

Usage

GeneSpec$extract_data_frame(assay)

Arguments

Returns

A data.frame with the genes in the columns and the samples in the rows.

Method clone()

The objects of this class are cloneable with this method.

Usage

GeneSpec$clone(deep = FALSE)

Arguments

Examples

# Minimal specification if only one gene is used.
x_spec <- gene_spec("GeneID:1820")

# Using multiple genes with a signature.
x_spec <- gene_spec(c("GeneID:1820", "GeneID:52"), fun = colMeans)
x_spec <- gene_spec(c("GeneID:1820", "GeneID:52"), fun = colPrinComp1)
x_spec$returns_vector()
x_spec$get_genes()
x_spec$get_gene_labels()
x_spec$get_label()

# Using multiple genes with partial labels, without a signature.
x_spec <- gene_spec(c(A = "GeneID:1820", "GeneID:52"))
x_spec$returns_vector()
x_spec$get_gene_labels()

# Use the gene specification to extract genes from a matrix.
mat <- matrix(
  data = rpois(15, 10),
  nrow = 3, ncol = 5,
  dimnames = list(c("GeneID:1820", "GeneID:52", "GeneID:523"), NULL)
)
x_spec$extract(mat)

# We can also extract these as a `data.frame`.
x_spec$extract_data_frame(mat)

Description

The difference here to duplicated() is that also the first occurrence of a duplicate is flagged as TRUE.

Usage

h_all_duplicated(x)

Arguments

Value

Logical vector flagging all occurrences of duplicate values as TRUE.

Examples

h_all_duplicated(c("a", "a", "b"))
duplicated(c("a", "a", "b"))

Description

This helper function converts all character and logical variables to factor variables in a data.frame. It also sets an explicit missing data level for all factor variables that have at least one NA. Empty strings are handled as NA.

Usage

h_df_factors_with_explicit_na(data, na_level = "<Missing>")

Arguments

Value

The modified data.

Examples

dat <- data.frame(
  a = c(NA, 2),
  b = c("A", NA),
  c = c("C", "D"),
  d = factor(c(NA, "X")),
  e = factor(c("Y", "Z"))
)
h_df_factors_with_explicit_na(dat)

Description

This helper functions performs the differential expression analysis with DESeq2::DESeq() for a given AnyHermesData input and design matrix.

Usage

h_diff_expr_deseq2(object, design, ...)

Arguments

Value

A data frame with columns log2_fc (estimated log2 fold change), stat (Wald statistic), p_val (raw p-value), adj_p_pval (Benjamini-Hochberg adjusted p-value).

References

Love MI, Huber W, Anders S (2014). “Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2.” Genome Biology, 15(12), 550. doi:10.1186/s13059-014-0550-8.

Examples

object <- hermes_data

# Create the design matrix corresponding to the factor of interest.
design <- model.matrix(~SEX, colData(object))

# Then perform the `DESeq2` differential expression analysis.
result <- h_diff_expr_deseq2(object, design)
head(result)

# Change of the `fitType` can be required in some cases.
result2 <- h_diff_expr_deseq2(object, design, fitType = "local")
head(result2)

Description

This helper functions performs the differential expression analysis with the voom method from the limma package (via limma::voom(), limma::lmFit() and limma::eBayes()) for given counts in a AnyHermesData object and a corresponding design matrix.

Usage

h_diff_expr_voom(object, design, ...)

Arguments

Value

A data frame with columns log2_fc (estimated log2 fold change), stat (moderated t-statistic), p_val (raw p-value), adj_p_pval (Benjamini-Hochberg adjusted p-value).

References

Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK (2015). “limma powers differential expression analyses for RNA-sequencing and microarray studies.” Nucleic Acids Research, 43(7), e47. doi:10.1093/nar/gkv007.

Law CW, Chen Y, Shi W, Smyth GK (2014). “voom: precision weights unlock linear model analysis tools for RNA-seq read counts.” Genome Biology, 15(2), R29. doi:10.1186/gb-2014-15-2-r29.

Examples

object <- hermes_data

# Create the design matrix corresponding to the factor of interest.
design <- model.matrix(~SEX, colData(object))

# Then perform the differential expression analysis.
result <- h_diff_expr_voom(object, design)
head(result)

# Sometimes we might want to specify method details.
result2 <- h_diff_expr_voom(object, design, trend = TRUE, robust = TRUE)
head(result2)

Description

This helper function queries BioMart to translate Ensembl to Entrez Gene IDs.

Usage

h_ensembl_to_entrez_ids(gene_ids, mart)

Arguments

Value

Character vector of Entrez gene IDs.

Examples

if (interactive()) {
  mart <- biomaRt::useMart("ensembl", dataset = "hsapiens_gene_ensembl")
  h_ensembl_to_entrez_ids(c("ENSG00000135407", "ENSG00000241644"), mart)
}

Description

Helper function to query annotations from biomaRt, for cleaned up gene IDs of a specific ID variable and given biomaRt::Mart.

Usage

h_get_annotation_biomart(gene_ids, id_var, mart)

Arguments

Value

A data frame with columns:

• id_var (depending on what was used)

• hgnc_symbol

• entrezgene_description

• chromosome_name

• size

• refseq_mrna

• refseq_peptide

Examples

if (interactive()) {
  mart <- biomaRt::useMart("ensembl", dataset = "hsapiens_gene_ensembl")
  h_get_annotation_biomart(c("11185", "10677"), id_var = "entrezgene_id", mart = mart)
}

Description

This function extracts the chromosome number, the start position and the end position of transcripts in given data.frame with coordinates as returned by biomaRt::getBM() and converts them to a GRanges object.

Usage

h_get_granges_by_id(coords, id)

Arguments

Value

GRange objects for the respective single gene ID.

Examples

if (interactive()) {
  mart <- biomaRt::useMart("ensembl", dataset = "hsapiens_gene_ensembl")
  attrs <- c(
    "ensembl_gene_id",
    "ensembl_exon_id",
    "chromosome_name",
    "exon_chrom_start",
    "exon_chrom_end"
  )
  coords <- biomaRt::getBM(
    filters = "entrezgene_id",
    attributes = attrs,
    values = c("11185", "10677"),
    mart = mart
  )
  h_get_granges_by_id(coords, "ENSG00000135407")
}

Description

This helper function queries BioMart for lengths of genes by adding up all exon lengths after reducing overlaps.

Usage

h_get_size_biomart(gene_ids, id_var, mart)

Arguments

Value

Named integer vector indicating the gene lengths.

Examples

if (interactive()) {
  mart <- biomaRt::useMart("ensembl", dataset = "hsapiens_gene_ensembl")
  h_get_size_biomart("11185", "entrezgene_id", mart)
  h_get_size_biomart("ENSG00000215417", "ensembl_gene_id", mart)
  h_get_size_biomart(c("11185", "10677"), "entrezgene_id", mart)
  h_get_size_biomart(c("ENSG00000135407", "ENSG00000215417"), "ensembl_gene_id", mart)
}

Description

This helper function determines for each gene in the object whether all required annotation columns are filled.

Usage

h_has_req_annotations(object, annotation_required)

Arguments

Value

Named logical vector with one value for each gene in object, which is TRUE if all required annotation columns are filled, and otherwise FALSE.

See Also

filter() where this is used internally.

Examples

object <- hermes_data
result <- h_has_req_annotations(object, "size")
all(result)
rowData(object)$size[1] <- NA # nolint
which(!h_has_req_annotations(object, "size"))

Description

This is used by the rename method. It wraps the assertions and the matching used several times.

Usage

h_map_pos(names, map)

Arguments

Value

Integer vector of the positions of the map values in the names.

Examples

h_map_pos(c("a", "b"), c(d = "b"))

Description

This helper function adds parentheses around each element of a character vector.

Usage

h_parens(x)

Arguments

Value

Character vector with parentheses, except when x is a blank string in which case it is returned unaltered.

Examples

h_parens("bla")
h_parens("")
h_parens(c("bla", "bli"))

Description

This function processes sample variables from AnyHermesData and the corresponding principal components matrix, and then generates the matrix of R2 values.

Usage

h_pca_df_r2_matrix(pca, df)

Arguments

Details

• Note that only the df columns which are numeric, character, factor or logical are included in the resulting matrix, because other variable types are not supported.

• In addition, df columns which are constant, all NA, or character or factor columns with too many levels are also dropped before the analysis.

Value

A matrix with R2 values for all combinations of sample variables and principal components.

See Also

h_pca_var_rsquared() which is used internally to calculate the R2 for one sample variable.

Examples

object <- hermes_data %>%
  add_quality_flags() %>%
  filter() %>%
  normalize()

# Obtain the principal components.
pca <- calc_pca(object)$x

# Obtain the `colData` as a `data.frame`.
df <- as.data.frame(colData(object))

# Correlate them.
r2_all <- h_pca_df_r2_matrix(pca, df)
str(r2_all)

# We can see that only about half of the columns from `df` were
# used for the correlations.
ncol(r2_all)
ncol(df)

Description

This helper function calculates R2 values between one sample variable from AnyHermesData and all Principal Components (PCs) separately (one linear model is fit for each PC).

Usage

h_pca_var_rsquared(pca, x)

Arguments

Details

Note that in case there are estimation problems for any of the PCs, then NA will be returned for those.

Value

A vector with R2 values for each principal component.

Examples

object <- hermes_data %>%
  add_quality_flags() %>%
  filter() %>%
  normalize()

# Obtain the principal components.
pca <- calc_pca(object)$x

# Obtain the sample variable.
x <- colData(object)$AGE18

# Correlate them.
r2 <- h_pca_var_rsquared(pca, x)

Description

This helper function makes a short list string, e.g. "a, b, ..., z" out of a character vector, e.g. letters.

Usage

h_short_list(x, sep = ", ", thresh = 3L)

Arguments

Value

String with the short list.

Examples

h_short_list(letters)
h_short_list(letters[1:3])
h_short_list(LETTERS[1:5], sep = ";", thresh = 5L)

Description

This helper function removes the prefix and possible delimiter from a vector of gene IDs, such that only the digits are returned.

Usage

h_strip_prefix(gene_ids, prefix)

Arguments

Value

Character vector that contains only the digits for each gene ID.

Note

This is currently used to strip away the GeneID prefix from Entrez gene IDs so that they can be queried from BioMart

Examples

h_strip_prefix(c("GeneID:11185", "GeneID:10677"), prefix = "GeneID")

Description

This helper function generates a set of unique labels given unique IDs and not necessarily unique names.

Usage

h_unique_labels(ids, nms = NULL)

Arguments

Value

Character vector where empty names are replaced by the IDs and non-unique names are made unique by appending the IDs in parentheses.

Examples

h_unique_labels(c("1", "2", "3"), c("A", "B", "A"))
h_unique_labels(NULL)
h_unique_labels(c("1", "2", "3"))

Description

This example HermesData is created from the underlying SummarizedExperiment::SummarizedExperiment object by renaming descriptors to align with standard specification. It already contains the required columns in rowData and colData.

Usage

hermes_data

Format

A HermesData object with 20 samples covering 5085 features (Entrez gene IDs).

Source

This is an artificial dataset designed to resemble real data.

See Also

summarized_experiment for the underlying SummarizedExperiment::SummarizedExperiment object.

Description

The HermesData class is an extension of SummarizedExperiment::SummarizedExperiment with additional validation criteria.

Usage

HermesData(object)

HermesDataFromMatrix(counts, ...)

Arguments

Details

The additional criteria are:

• The first assay must be counts containing non-missing, integer, non-negative values. Note that rename() can be used to edit the assay name to counts if needed.

• The following columns must be in rowData:

  • symbol (also often called HGNC or similar, example: "INMT")

  • desc (the gene name, example: "indolethylamine N-methyltransferase")

  • chromosome (the chromosome as string, example: "7")

  • size (the size of the gene in base pairs, e.g 5468)

  • low_expression_flag (can be populated with add_quality_flags())

• The following columns must be in colData:

  • low_depth_flag (can be populated with add_quality_flags())

  • tech_failure_flag (can be populated with add_quality_flags())

• The object must have unique row and column names. The row names are the gene names and the column names are the sample names.

Analogously, RangedHermesData is an extension of SummarizedExperiment::RangedSummarizedExperiment and has the same additional validation requirements. Methods can be defined for both classes at the same time with the AnyHermesData signature.

A Biobase::ExpressionSet object can be imported by using the SummarizedExperiment::makeSummarizedExperimentFromExpressionSet() function to first convert it to a SummarizedExperiment::SummarizedExperiment object before converting it again into a HermesData object.

Value

An object of class AnyHermesData (HermesData or RangedHermesData).

Slots

prefix

common prefix of the gene IDs (row names).

Note

• Note that we use S4Vectors::setValidity2() to define the validity method, which allows us to turn off the validity checks in internal functions where intermediate objects may not be valid within the scope of the function.

• It can be helpful to convert character and logical variables to factors in colData() (before or after the HermesData creation). We provide the utility function df_cols_to_factor() to simplify this task, but leave it to the user to allow for full control of the details.

See Also

rename() for renaming columns of the input data.

Examples

# Convert an `ExpressionSet` to a `RangedSummarizedExperiment`.
ranged_summarized_experiment <- makeSummarizedExperimentFromExpressionSet(expression_set)

# Then convert to `RangedHermesData`.
HermesData(ranged_summarized_experiment)

# Create objects starting from a `SummarizedExperiment`.
hermes_data <- HermesData(summarized_experiment)
hermes_data

# Create objects from a matrix. Note that additional arguments are not required but possible.
counts_matrix <- assay(summarized_experiment)
counts_hermes_data <- HermesDataFromMatrix(counts_matrix)

Description

This is a useful function when trying to join genetic with CDISC data sets.

Usage

inner_join_cdisc(
  gene_data,
  cdisc_data,
  patient_key = "USUBJID",
  additional_keys = character()
)

Arguments

Value

A data.frame which contains columns from both data sets merged by the keys.

Note

Columns which are contained in both data sets but are not specified as keys are taken from gene_data and not from cdisc_data.

Examples

gene_data <- col_data_with_genes(hermes_data, "counts", gene_spec("GeneID:1820"))
cdisc_data <- data.frame(
  USUBJID = head(gene_data$USUBJID, 10),
  extra = 1:10
)
result <- inner_join_cdisc(gene_data, cdisc_data)
result

Description

This method checks whether a SummarizedExperiment::SummarizedExperiment object is empty.

Usage

## S4 method for signature 'SummarizedExperiment'
isEmpty(x)

Arguments

Value

Flag whether the object is empty.

Examples

isEmpty(summarized_experiment)
isEmpty(summarized_experiment[NULL, ])
isEmpty(hermes_data)

Description

Apply a function on all experiments in an MAE.

Usage

## S4 method for signature 'MultiAssayExperiment'
lapply(X, FUN, safe = TRUE, ...)

Arguments

Value

MultiAssayExperiment object with specified function applied.

Examples

object <- multi_assay_experiment
result <- lapply(object, normalize, safe = TRUE)
# Similarly, all experiments in an MAE can be converted to HermesData class:
result <- lapply(object, HermesData, safe = TRUE)

Description

These methods access or set the metadata in a AnyHermesData object.

Arguments

Value

The metadata which is a list.

Note

Note that this just inherits S4Vectors::metadata,Annotated-method().

Examples

a <- hermes_data
metadata(a)
metadata(a) <- list(new = "my metadata")
metadata(a)

Description

This example MultiAssayExperiment::MultiAssayExperiment can be used as test data.

Usage

multi_assay_experiment

Format

A MultiAssayExperiment::MultiAssayExperiment object with 3 separate HermesData objects.

• The first object contains 5 samples and covers 1000 features (Entrez gene IDs).

• The second object contains 9 samples with 2500 features.

• The third object contains 6 samples with 1300 features.

Source

This is an artificial dataset designed to resemble real data.

Description

The normalize() method is normalizing the input AnyHermesData according to one or more specified normalization methods. The results are saved as additional assays in the object.

Possible normalization methods (which are implemented with separate helper functions):

• cpm: Counts per Million (CPM). Separately by sample, the original counts of the genes are divided by the library size of this sample, and multiplied by one million. This is the appropriate normalization for between-sample comparisons.

• rpkm: Reads per Kilobase of transcript per Million reads mapped (RPKM). Each gene count is divided by the gene size (in kilobases) and then again divided by the library sizes of each sample (in millions). This allows for within-sample comparisons, as it takes into account the gene sizes - longer genes will always have more counts than shorter genes.

• tpm: Transcripts per Million (TPM). This addresses the problem of RPKM being inconsistent across samples (which can be seen that the sum of all RPKM values will vary from sample to sample). Therefore here we divide the RPKM by the sum of all RPKM values for each sample, and multiply by one million.

• voom: VOOM normalization. This is essentially just a slight variation of CPM where a prior_count of 0.5 is combined with lib_sizes increased by 1 for each sample. Note that this is not required for the corresponding differential expression analysis, but just provided as a complementary experimental normalization approach here.

• vst: Variance stabilizing transformation. This is to transform the normalized count data for all genes into approximately homoskedastic values (having constant variance).

• rlog: The transformation to the log2 scale values with approximately homoskedastic values.

Usage

## S4 method for signature 'AnyHermesData'
normalize(
  object,
  methods = c("cpm", "rpkm", "tpm", "voom", "vst"),
  control = control_normalize(),
  ...
)

h_cpm(object, control = control_normalize())

h_rpkm(object, control = control_normalize())

h_tpm(object, control = control_normalize())

h_voom(object, control = control_normalize())

h_vst(object, control = control_normalize())

h_rlog(object, control = control_normalize())

Arguments

Value

The AnyHermesData object with additional assays containing the normalized counts. The control is saved in the metadata of the object for future reference.

Functions

• h_cpm(): calculates the Counts per Million (CPM) normalized counts.

• h_rpkm(): calculates the Reads per Kilobase per Million (RPKM) normalized counts.

• h_tpm(): calculates the Transcripts per Million (TPM) normalized counts.

• h_voom(): calculates the VOOM normalized counts.

• h_vst(): variance stabilizing transformation (vst) from DESeq2 package.

• h_rlog(): regularized log transformation (rlog) from DESeq2 package.

See Also

control_normalize() to define the normalization method settings.

Examples

a <- hermes_data

# By default, log values are used with a prior count of 1 added to original counts.
result <- normalize(a)
assayNames(result)
tpm <- assay(result, "tpm")
tpm[1:3, 1:3]

# We can also work on original scale.
result_orig <- normalize(a, control = control_normalize(log = FALSE))
tpm_orig <- assay(result_orig, "tpm")
tpm_orig[1:3, 1:3]

# Separate calculation of the CPM normalized counts.
counts_cpm <- h_cpm(a)
str(counts_cpm)

# Separate calculation of the RPKM normalized counts.
counts_rpkm <- h_rpkm(a)
str(counts_rpkm)

# Separate calculation of the TPM normalized counts.
counts_tpm <- h_tpm(a)
str(counts_tpm)

# Separate calculation of the VOOM normalized counts.
counts_voom <- h_voom(a)
str(counts_voom)

# Separate calculation of the vst transformation.
counts_vst <- h_vst(a)
str(counts_vst)

# Separate calculation of the rlog transformation.
counts_rlog <- h_rlog(a)
str(counts_rlog)

Description

Generic function to access the prefix from an object.

Usage

prefix(object, ...)

Arguments

Value

The prefix slot contents.

Examples

a <- hermes_data
prefix(a)

Description

The generic function query() is the interface for querying gene annotations from a data base connection.

Usage

query(genes, connection)

## S4 method for signature 'character,ConnectionBiomart'
query(genes, connection)

Arguments

Details

• A method is provided for the ConnectionBiomart class. However, the framework is extensible: It is simple to add new connections and corresponding query methods for other data bases, e.g. company internal data bases. Please make sure to follow the required format of the returned value.

• The BioMart queries might not return information for all the genes. This can be due to different versions being used in the gene IDs and the queried Ensembl data base.

Value

A S4Vectors::DataFrame with the gene annotations. It is required that:

• The rownames are identical to the input genes.

• The colnames are equal to the annotation columns .row_data_annotation_cols.

• Therefore, missing information needs to be properly included in the DataFrame with NA entries.

Examples

if (interactive()) {
  object <- hermes_data
  connection <- connect_biomart(prefix(object))
  result <- query(genes(object), connection)
  head(result)
  head(annotation(object))
}

Description

This method combines AnyHermesData objects with the same samples but different features of interest (rows in assays).

Arguments

Value

The combined AnyHermesData object.

Note

• Note that this just inherits SummarizedExperiment::rbind,SummarizedExperiment-method(). When binding a AnyHermesData object with a SummarizedExperiment::SummarizedExperiment object, then the result will be a SummarizedExperiment::SummarizedExperiment object (the more general class).

• Note that we need to have unique gene IDs (row names) and the same prefix across the combined object.

See Also

cbind to column bind objects.

Examples

a <- hermes_data[1:2542, ]
b <- hermes_data[2543:5085, ]
result <- rbind(a, b)
class(result)

Description

This method renames columns of the rowData and colData, as well as assays, of SummarizedExperiment::SummarizedExperiment objects. This increases the flexibility since renaming can be done before conversion to a HermesData object.

Usage

## S4 method for signature 'SummarizedExperiment'
rename(
  x,
  row_data = character(),
  col_data = character(),
  assays = character(),
  ...
)

## S4 method for signature 'data.frame'
rename(x, ...)

Arguments

Value

The SummarizedExperiment::SummarizedExperiment object with renamed contents.

Examples

x <- summarized_experiment
# Use deliberately a non-standard assay name in this example.
assayNames(x) <- "count"

# Rename `HGNC` to `symbol` in the `rowData`.
x <- rename(x, row_data = c(symbol = "HGNC"))
head(names(rowData(x)))

# Rename `LowDepthFlag` to `low_depth_flag` in `colData`.
x <- rename(x, col_data = c(low_depth_flag = "LowDepthFlag"))
tail(names(colData(x)))

# Rename assay `count` to `counts`.
x <- rename(x, assays = c(counts = "count"))
assayNames(x)

Description

Access the sample IDs, i.e. col names, of a AnyHermesData object with a nicely named accessor method.

Usage

## S4 method for signature 'AnyHermesData'
samples(object)

Arguments

Value

The character vector with the sample IDs.

See Also

genes() to access the gene IDs.

Examples

a <- hermes_data
samples(a)

Description

Setter function which allows the user to define a sample manually as a technical failure.

Usage

set_tech_failure(object, sample_ids)

Arguments

Value

AnyHermesData object with modified technical failure flags.

See Also

add_quality_flags() which automatically sets all (gene and sample) quality flags, including these technical failure flags.

Examples

# Manually flag technical failures in a `AnyHermesData` object.
object <- hermes_data
get_tech_failure(object)["06520101B0017R"]
result <- set_tech_failure(object, c("06520101B0017R", "06520047C0017R"))
get_tech_failure(result)["06520101B0017R"]

Description

A show method that displays high-level information of AnyHermesData objects.

Usage

## S4 method for signature 'HermesData'
show(object)

## S4 method for signature 'RangedHermesData'
show(object)

Arguments

Value

None (invisible NULL), only used for the side effect of printing to the console.

Note

The same method is used for both HermesData and RangedHermesData objects. We need to define this separately to have this method used instead of the one inherited from SummarizedExperiment::SummarizedExperiment.

Examples

object <- hermes_data
object

Description

This method subsets AnyHermesData objects, based on expressions involving the rowData columns and the colData columns.

Arguments

Value

The subsetted AnyHermesData object.

Note

Note that this just inherits SummarizedExperiment::subset,SummarizedExperiment-method().

Examples

a <- hermes_data
a

# Subset both genes and samples.
subset(a, subset = low_expression_flag, select = DISCSTUD == "N")

# Subset only genes.
subset(a, subset = chromosome == "2")

# Subset only samples.
subset(a, select = AGE > 18)

Description

This example SummarizedExperiment::SummarizedExperiment can be used to create a HermesData object. It already contains the required columns in rowData and colData.

Usage

summarized_experiment

Format

A SummarizedExperiment::SummarizedExperiment object with 20 samples covering 5085 features (Entrez gene IDs).

Source

This is an artificial dataset designed to resemble real data.

See Also

expression_set which contains similar data as a Biobase::ExpressionSet.

Description

Provides a concise summary of the content of AnyHermesData objects.

Usage

summary(object, ...)

## S4 method for signature 'AnyHermesData'
summary(object)

## S4 method for signature 'HermesDataSummary'
show(object)

Arguments

Value

An object of the corresponding summary class, here HermesDataSummary.

Methods (by class)

• summary(AnyHermesData): A summary method for AnyHermesData object that creates a HermesDataSummary object.

• show(HermesDataSummary): A show method prints summary description of HermesDataSummary object generated by the summary() method.

Examples

object <- hermes_data
object_summary <- summary(object)

# We can access parts of this S4 object with the `slot` function.
str(object_summary)
slotNames(object_summary)
slot(object_summary, "lib_sizes")

# Just calling the summary method like this will use the `show()` method.
summary(object)

Description

top_genes() creates a HermesDataTopGenes object, which extends data.frame. It contains two columns:

• expression: containing the statistic values calculated by summary_fun across columns.

• name: the gene names.

The corresponding autoplot() method then visualizes the result as a barplot.

Usage

top_genes(
  object,
  assay_name = "counts",
  summary_fun = rowMeans,
  n_top = if (is.null(min_threshold)) 10L else NULL,
  min_threshold = NULL
)

## S4 method for signature 'HermesDataTopGenes'
autoplot(
  object,
  x_lab = "HGNC gene names",
  y_lab = paste0(object@summary_fun_name, "(", object@assay_name, ")"),
  title = "Top most expressed genes"
)

Arguments

Details

• The data frame is sorted in descending order of expression and only the top entries according to the selection criteria are included.

• Note that exactly one of the arguments n_top and min_threshold must be provided.

Value

A HermesDataTopGenes object.

Functions

• autoplot(HermesDataTopGenes): Creates a bar plot from a HermesDataTopGenes object, where the y axis shows the expression statistics for each of the top genes on the x-axis.

Examples

object <- hermes_data

# Default uses average of raw counts across samples to rank genes.
top_genes(object)

# Instead of showing top 10 genes, can also set a minimum threshold on average counts.
top_genes(object, n_top = NULL, min_threshold = 50000)

# We can also use the maximum of raw counts across samples, by specifying a different
# summary statistics function.
result <- top_genes(object, summary_fun = rowMax)

# Finally we can produce barplots based on the results.
autoplot(result, title = "My top genes")
autoplot(result, y_lab = "Counts", title = "My top genes")

Description

These functions are used internally only and therefore not exported. They work on SummarizedExperiment::SummarizedExperiment objects, and AnyHermesData objects are defined by successfully passing these validation checks.

Usage

validate_counts(object)

validate_cols(required, actual)

validate_row_data(object)

validate_col_data(object)

validate_names(object)

validate_prefix(object)

Arguments

Value

A character vector with the validation failure messages, or NULL in case validation passes.

Functions

• validate_counts(): validates that the first assay is counts containing non-missing, integer, non-negative values.

• validate_cols(): validates that required column names are contained in actual column names.

• validate_row_data(): validates that the object contains rowData with required columns.

• validate_col_data(): validates that the object contains colData with required columns.

• validate_names(): validates that the object contains row and column names.

• validate_prefix(): validates that the object prefix is a string and only contains alphabetic characters.

Description

This helper function wraps SummarizedExperiment objects into an a MultiAssayExperiment (MAE) object.

Usage

wrap_in_mae(x, name = deparse(substitute(x)))

Arguments

Value

The MAE object with the only experiment being x having the given name.

Examples

mae <- wrap_in_mae(summarized_experiment)
mae[["summarized_experiment"]]