Filter low counts

This is often the first step in the QC portion of an analysis to filter out features that have very low raw counts across most or all of your samples.

Usage

filter_counts(
  moo,
  count_type = "clean",
  feature_id_colname = NULL,
  sample_id_colname = NULL,
  group_colname = "Group",
  label_colname = NULL,
  samples_to_include = NULL,
  minimum_count_value_to_be_considered_nonzero = 8,
  minimum_number_of_samples_with_nonzero_counts_in_total = 7,
  minimum_number_of_samples_with_nonzero_counts_in_a_group = 3,
  use_cpm_counts_to_filter = TRUE,
  use_group_based_filtering = FALSE,
  principal_component_on_x_axis = 1,
  principal_component_on_y_axis = 2,
  legend_position_for_pca = "top",
  point_size_for_pca = 1,
  add_label_to_pca = TRUE,
  label_font_size = 3,
  label_offset_y_ = 2,
  label_offset_x_ = 2,
  samples_to_rename = c(""),
  color_histogram_by_group = FALSE,
  set_min_max_for_x_axis_for_histogram = FALSE,
  minimum_for_x_axis_for_histogram = -1,
  maximum_for_x_axis_for_histogram = 1,
  legend_position_for_histogram = "top",
  legend_font_size_for_histogram = 10,
  number_of_histogram_legend_columns = 6,
  colors_for_plots = NULL,
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  interactive_plots = FALSE,
  plots_subdir = "filt"
)

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)
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.)
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.)
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.)
minimum_count_value_to_be_considered_nonzero: Minimum count value to be considered non-zero for a sample
minimum_number_of_samples_with_nonzero_counts_in_total: Minimum number of samples (total) with non-zero counts
minimum_number_of_samples_with_nonzero_counts_in_a_group: Only keeps genes that have at least this number of samples with nonzero CPM counts in at least one group
use_cpm_counts_to_filter: If no transformation has been been performed on counts matrix (eg Raw Counts) set to TRUE. If TRUE counts will be transformed to CPM and filtered based on given criteria. If gene counts matrix has been transformed (eg log2, CPM, FPKM or some form of Normalization) set to FALSE. If FALSE no further transformation will be applied and features will be filtered as is. For RNAseq data RAW counts should be transformed to CPM in order to properly filter.
use_group_based_filtering: If TRUE, only keeps features (e.g. genes) that have at least a certain number of samples with nonzero CPM counts in at least one group
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
point_size_for_pca: geom point size for the PCA plot
add_label_to_pca: label points on the PCA plot
label_font_size: label font size for the PCA plot
label_offset_y_: label offset y for the PCA plot
label_offset_x_: label offset x for the PCA plot
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
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_position_for_histogram: legend position for the histogram plot. consider setting to 'none' for a large number of samples.
legend_font_size_for_histogram: legend font size for the histogram plot
number_of_histogram_legend_columns: number of columns for the histogram legend
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 filtered counts

Details

This function takes a multiOmicDataSet containing clean raw counts and a sample metadata table, and returns the multiOmicDataSet object with filtered counts. It also produces an image consisting of three QC plots.

You can tune the threshold for tuning how low counts for a given gene are before they are deemed "too low" and filtered out of downstream analysis. By default, this parameter is set to 1, meaning any raw count value less than 1 will count as "too low".

The QC plots are provided to help you assess: (1) PCA Plot: the within and between group variance in expression after dimensionality reduction; (2) Count Density Histogram: the dis/similarity of count distributions between samples; and (3) Similarity Heatmap: the overall similarity of samples to one another based on unsupervised clustering.

Examples

moo <- create_multiOmicDataSet_from_dataframes(
  as.data.frame(nidap_sample_metadata),
  as.data.frame(nidap_clean_raw_counts),
  sample_id_colname = "Sample",
  feature_id_colname = "Gene"
) %>%
  filter_counts(
    count_type = "raw"
  )
#> * filtering raw counts
#> Number of features after filtering: 7943
head(moo@counts$filt)
#>      Gene   A1   A2   A3   B1   B2   B3   C1   C2   C3
#> 1  Mrpl15 1245 1341 1476  965 1235 1784 1058 1732 1531
#> 2  Lypla1 1483 1410 1370 1146 1422 2624  991 1101 2352
#> 3   Tcea1 1381 2044 2051 2325 2386 1893 2391  916 2261
#> 4 Atp6v1h 1033 1959 1890 2075 2702 2150 2436 1321 1018
#> 5  Rb1cc1  666 1397 1576  681 2040 1988  774 1921 2660
#> 6  Pcmtd1  798  966  407  487  455  950 1710 1995 2502

Usage

Arguments

Value

Details

See also

Examples