Generate a line plot for gene expression / fold change values.

Usage

get_gene_expression_line_plot(
  x,
  samples,
  genes,
  km = NULL,
  facet_clusters = FALSE,
  summarise_replicates = TRUE,
  summarise_method = "median",
  scale_log10 = TRUE,
  line_transparency = 0.5,
  show_average_line = TRUE,
  average_line_color = "black",
  average_line_size = 1,
  average_line_summary_method = "median"
)

get_fold_change_line_plot(
  x,
  sample_comparisons,
  genes,
  km = NULL,
  facet_clusters = FALSE,
  line_transparency = 0.5,
  show_average_line = TRUE,
  average_line_color = "black",
  average_line_size = 1,
  average_line_summary_method = "median"
)

Arguments

x: an abject of class parcutils. This is an output of the function run_deseq_analysis().
samples: a character vector denotes sample names to show in the line plot.
genes: a character vector denotes genes to show in the line plot.
km: a numeric or NULL, default NULL, denotes number of clusters for k-means clustering. If NULL data will be plotted without clustering.
facet_clusters: a logical, default FALSE, denoting whether to facet clusters. Works only when km is set to a numeric value.
summarise_replicates: logical, default TRUE, indicating whether to summarise values for each gene across replicates.
summarise_method: a character string, default "median", denotes a summary method to average gene expression values across replicates. Values can be one of the "mean" or "median".
scale_log10: logical, default TRUE, denotes whether to transform scale Y on log10.
line_transparency: a numeric, default 0.5, denotes transparency of each line in the line plot.
show_average_line: logical, default TRUE, denotes whether to show a line for average gene expression.
average_line_color: a character string, default "black", denotes a color for an average line.
average_line_size: a numeric, default 1, denotes a size for an average line.
average_line_summary_method: a character string, default "median", denoting a summary method used to generate an average line. Values can be one of the "mean" or "median".
sample_comparison: a character string denoting a valid differential gene comparison. Possible comparisons can be found from x$de_comparisons.

Value

ggplot2.

Examples


count_file <- system.file("extdata","toy_counts.txt" , package = "parcutils")
count_data <- readr::read_delim(count_file, delim = "\t", show_col_types = FALSE)

sample_info <- count_data %>% colnames() %>% .[-1]  %>%
  tibble::tibble(samples = . , groups = rep(c("control" ,"treatment1" , "treatment2"), each = 3))


res <- parcutils::run_deseq_analysis(counts = count_data ,
                                     sample_info = sample_info,
                                     column_geneid = "gene_id" ,
                                     group_numerator = c("treatment1", "treatment2") ,
                                     group_denominator = c("control"))
#> ℹ Running DESeq2 ...
#> converting counts to integer mode
#> Warning: some variables in design formula are characters, converting to factors
#> estimating size factors
#> estimating dispersions
#> gene-wise dispersion estimates
#> mean-dispersion relationship
#> final dispersion estimates
#> fitting model and testing
#> ✔ Done.
#> ℹ Summarizing DEG ...
#> ✔ Done.

genes = parcutils::get_genes_by_regulation(x = res, sample_comparison = "treatment2_VS_control" , "both") %>% names()

# line plot with all replicates
get_gene_expression_line_plot(x = res,
samples = c("control", "treatment1"),
genes = genes, line_transparency = 0.5,
average_line_color = "red",
summarise_replicates = FALSE)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis


# line plot with replicate average
get_gene_expression_line_plot(x = res,
samples = c("control", "treatment1" ,"treatment2"),
genes = genes, line_transparency = 0.5,
average_line_color = "red",
summarise_replicates = TRUE)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis


# line plot with k-means clustering

get_gene_expression_line_plot(x = res,
samples = c("control", "treatment1" ,"treatment2"),
km = 4,
genes = genes, line_transparency = 0.5,
average_line_color = "black",
summarise_replicates = TRUE)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis


# line plot with k-means clustering faceted

get_gene_expression_line_plot(x = res,
samples = c("control", "treatment1" ,"treatment2"),
km = 4,facet_clusters = TRUE,
genes = genes, line_transparency = 0.5,
average_line_color = "black",
summarise_replicates = TRUE)
#> Warning: Transformation introduced infinite values in continuous y-axis
#> Warning: Transformation introduced infinite values in continuous y-axis


# fold change lineplot
get_fold_change_line_plot(x = res,
sample_comparisons = c("treatment1_VS_control", "treatment2_VS_control"),
genes = genes,km = 4,
average_line_summary_method =  "mean")