Normalize counts

Usage

normalize_counts(
  moo,
  count_type = "filt",
  norm_type = "voom",
  feature_id_colname = NULL,
  samples_to_include = NULL,
  sample_id_colname = NULL,
  group_colname = "Group",
  label_colname = NULL,
  input_in_log_counts = FALSE,
  voom_normalization_method = "quantile",
  samples_to_rename = c(""),
  add_label_to_pca = TRUE,
  principal_component_on_x_axis = 1,
  principal_component_on_y_axis = 2,
  legend_position_for_pca = "top",
  label_offset_x_ = 2,
  label_offset_y_ = 2,
  label_font_size = 3,
  point_size_for_pca = 8,
  color_histogram_by_group = TRUE,
  set_min_max_for_x_axis_for_histogram = FALSE,
  minimum_for_x_axis_for_histogram = -1,
  maximum_for_x_axis_for_histogram = 1,
  legend_font_size_for_histogram = 10,
  legend_position_for_histogram = "top",
  colors_for_plots = NULL,
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  interactive_plots = FALSE,
  plots_subdir = "norm"
)

Arguments

moo: multiOmicDataSet object (see create_multiOmicDataSet_from_dataframes())
count_type: the type of counts to use – must be a name in the counts slot (moo@counts)
norm_type: normalization type. Default: "voom" which uses limma::voom.
feature_id_colname: The column from the counts dataa containing the Feature IDs (Usually Gene or Protein ID). This is usually the first column of your input Counts Matrix. Only columns of Text type from your input Counts Matrix will be available to select for this parameter. (Default: NULL - first column in the counts matrix will be used.)
samples_to_include: Which samples would you like to include? Usually, you will choose all sample columns, or you could choose to remove certain samples. Samples excluded here will be removed in this step and from further analysis downstream of this step. (Default: NULL - all sample IDs in moo@sample_meta will be used.)
sample_id_colname: The column from the sample metadata containing the sample names. The names in this column must exactly match the names used as the sample column names of your input Counts Matrix. (Default: NULL - first column in the sample metadata will be used.)
group_colname: The column from the sample metadata containing the sample group information. This is usually a column showing to which experimental treatments each sample belongs (e.g. WildType, Knockout, Tumor, Normal, Before, After, etc.).
label_colname: The column from the sample metadata containing the sample labels as you wish them to appear in the plots produced by this template. This can be the same Sample Names Column. However, you may desire different labels to display on your figure (e.g. shorter labels are sometimes preferred on plots). In that case, select the column with your preferred Labels here. The selected column should contain unique names for each sample. (Default: NULL – sample_id_colname will be used.)
input_in_log_counts: set this to TRUE if counts are already log2-transformed
voom_normalization_method: Normalization method to be applied to the logCPM values when using limma::voom
samples_to_rename: If you do not have a Plot Labels Column in your sample metadata table, you can use this parameter to rename samples manually for display on the PCA plot. Use "Add item" to add each additional sample for renaming. Use the following format to describe which old name (in your sample metadata table) you want to rename to which new name: old_name: new_name
add_label_to_pca: label points on the PCA plot
principal_component_on_x_axis: The principal component to plot on the x-axis for the PCA plot. Choices include 1, 2, 3, ... (default: 1)
principal_component_on_y_axis: The principal component to plot on the y-axis for the PCA plot. Choices include 1, 2, 3, ... (default: 2)
legend_position_for_pca: legend position for the PCA plot
label_offset_x_: label offset x for the PCA plot
label_offset_y_: label offset y for the PCA plot
label_font_size: label font size for the PCA plot
point_size_for_pca: geom point size for the PCA plot
color_histogram_by_group: Set to FALSE to label histogram by Sample Names, or set to TRUE to label histogram by the column you select in the "Group Column Used to Color Histogram" parameter (below). Default is FALSE.
set_min_max_for_x_axis_for_histogram: whether to set min/max value for histogram x-axis
minimum_for_x_axis_for_histogram: x-axis minimum for histogram plot
maximum_for_x_axis_for_histogram: x-axis maximum for histogram plot
legend_font_size_for_histogram: legend font size for the histogram plot
legend_position_for_histogram: legend position for the histogram plot. consider setting to 'none' for a large number of samples.
colors_for_plots: Colors for the PCA and histogram will be picked, in order, from this list. If you have >12 samples or groups, program will choose from a wide range of random colors
print_plots: Whether to print plots during analysis (Defaults to FALSE, overwritable using option 'moo_print_plots' or environment variable 'MOO_PRINT_PLOTS')
save_plots: Whether to save plots to files during analysis (Defaults to FALSE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
interactive_plots: set to TRUE to make PCA and Histogram plots interactive with plotly, allowing you to hover your mouse over a point or line to view sample information. The similarity heat map will not display if this toggle is set to TRUE. Default is FALSE.
plots_subdir: subdirectory in where plots will be saved if save_plots is TRUE

Value

multiOmicDataSet with normalized counts

Examples

moo <- multiOmicDataSet(
  sample_metadata = as.data.frame(nidap_sample_metadata),
  anno_dat = data.frame(),
  counts_lst = list(
    "raw" = as.data.frame(nidap_raw_counts),
    "clean" = as.data.frame(nidap_clean_raw_counts),
    "filt" = as.data.frame(nidap_filtered_counts)
  )
) %>%
  normalize_counts(
    group_colname = "Group",
    label_colname = "Label"
  )
#> * normalizing filt counts
#> Total number of features included: 7943
#> Sample columns: A1, Sample columns: A2, Sample columns: A3, Sample columns: B1, Sample columns: B2, Sample columns: B3, Sample columns: C1, Sample columns: C2, Sample columns: C3
head(moo@counts[["norm"]][["voom"]])
#>            Gene       A1       A2       A3       B1       B2       B3       C1
#> 1 0610007P14Rik 6.532994 6.192871 5.954869 6.375896 6.275880 6.119449 6.419913
#> 2 0610009B22Rik 4.484983 5.448875 5.286875 3.445612 4.451347 5.473886 3.500359
#> 3 0610010F05Rik 4.883688 5.668494 6.537590 6.216408 5.893089 5.498884 3.845207
#> 4 0610011F06Rik 5.199684 5.374085 5.112952 5.155558 5.163359 5.650929 5.441965
#> 5 0610012G03Rik 5.368118 5.445918 5.456511 4.567138 5.274928 5.625039 5.787457
#> 6 0610037L13Rik 5.327987 5.388747 5.233520 5.450169 3.656585 4.929386 4.274944
#>         C2       C3
#> 1 6.172204 6.497050
#> 2 4.709254 5.471951
#> 3 2.685177 2.805426
#> 4 6.043492 5.490958
#> 5 6.214163 4.682896
#> 6 4.744405 5.173531

Normalize counts

Usage

Arguments

Value

See also

Examples