Skip to contents

Differential Expression Analysis

Source: R/differential.R
diff_expression.Rd

[Experimental]

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 HermesDataDiffExpr
autoplot(object, adj_p_val_thresh = 0.05, log2_fc_thresh = 2.5)

Arguments

object

(AnyHermesData)
input. Note that this function only uses the original counts for analysis, so this does not need to be normalized.

group

(string)
name of factor variable with 2 levels in colData(object). These 2 levels will be compared in the differential expression analysis.

method

(string)
method for differential expression analysis, see details below.

...

additional arguments passed to the helper function associated with the selected method.

adj_p_val_thresh

(proportion)
threshold on the adjusted p-values (y-axis) to flag significance.

log2_fc_thresh

(number)
threshold on the absolute log2 fold-change (x-axis) to flag up- or down-regulation of transcription.

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)

Details

Possible method choices are:

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)
#>                  log2_fc      stat       p_val adj_p_val
#> GeneID:344558  1.4951365  3.768257 0.001217934 0.7815544
#> GeneID:51227  -1.0670584 -3.766113 0.001224023 0.7815544
#> GeneID:10280  -0.7518694 -3.461800 0.002478362 0.7815544
#> GeneID:9435    1.8555120  3.414364 0.002764727 0.7815544
#> GeneID:51575  -0.7238103 -3.326330 0.003384750 0.7815544
#> GeneID:123036  2.8900474  3.246333 0.004064925 0.7815544
res2 <- diff_expression(object, group = "SEX", method = "deseq2")
head(res2)
#>                  log2_fc      stat        p_val   adj_p_val
#> GeneID:64344  -2.9129064 -4.864176 1.149347e-06 0.002694069
#> GeneID:9002    2.3092323  4.215740 2.489604e-05 0.020061145
#> GeneID:167681 -4.0974632 -4.201788 2.648146e-05 0.020061145
#> GeneID:51227  -1.1573425 -4.143302 3.423404e-05 0.020061145
#> GeneID:4359    2.8926465  4.004010 6.227765e-05 0.029195764
#> GeneID:10280  -0.7997803 -3.876433 1.059991e-04 0.031057749

# Pass method arguments to the internally used helper functions.
res3 <- diff_expression(object, group = "SEX", method = "voom", robust = TRUE, trend = TRUE)
head(res3)
#>                  log2_fc      stat       p_val adj_p_val
#> GeneID:51227  -1.0670584 -3.765145 0.001226726 0.7884829
#> GeneID:344558  1.4951365  3.759186 0.001243851 0.7884829
#> GeneID:10280  -0.7518694 -3.467915 0.002443545 0.7884829
#> GeneID:9435    1.8555120  3.390300 0.002922073 0.7884829
#> GeneID:51575  -0.7238103 -3.325713 0.003389426 0.7884829
#> GeneID:123036  2.8900474  3.232786 0.004230256 0.7884829
res4 <- diff_expression(object, group = "SEX", method = "deseq2", fitType = "local")
head(res4)
#>                  log2_fc      stat        p_val   adj_p_val
#> GeneID:64344  -2.9128847 -4.892128 9.975152e-07 0.002338176
#> GeneID:9002    2.3091854  4.256818 2.073572e-05 0.014702529
#> GeneID:167681 -4.0975071 -4.300519 1.703986e-05 0.014702529
#> GeneID:51227  -1.1573487 -4.213992 2.508964e-05 0.014702529
#> GeneID:4359    2.8925392  4.055798 4.996349e-05 0.021309899
#> GeneID:10280  -0.7997974 -3.958566 7.540117e-05 0.021309899

# Create the corresponding volcano plots.
autoplot(res1)

autoplot(res3)