Package 'MOSuite'

Title: R package for differential multi-omics analysis
Description: Multi-Omics Suite provides a suite of functions to clean, filter, batch-correct, normalize, visualize, and perform differential analysis. While the package is designed for differential RNA-seq analysis and multi-omics datasets, it can be used for any data represented in a counts table. See the website for more information, documentation, and examples at <https://ccbr.github.io/MOSuite/>.
Authors: Kelly Sovacool [aut, cre] (ORCID: <https://orcid.org/0000-0003-3283-829X>), Philip Homan [aut], Vishal Koparde [aut] (ORCID: <https://orcid.org/0000-0001-8978-8495>), Samantha Chill [aut] (ORCID: <https://orcid.org/0000-0002-8734-9875>), T. Joshua Meyer [ctb], CCR Collaborative Bioinformatics Resource [cph]
Maintainer: Kelly Sovacool <[email protected]>
License: MIT + file LICENSE
Version: 0.3.0.9001
Built: 2026-04-23 15:59:37 UTC
Source: https://github.com/CCBR/MOSuite

Help Index

Perform batch correction

Description

Perform batch correction using sva::ComBat()

Usage

batch_correct_counts(
  moo,
  count_type = "norm",
  sub_count_type = "voom",
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  samples_to_include = NULL,
  covariates_colnames = "Group",
  batch_colname = "Batch",
  label_colname = NULL,
  colors_for_plots = NULL,
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "batch"
)

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)
sub_count_type

if count_type is a list, specify the sub count type within the list. (Default: "voom")
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.)
feature_id_colname

The column from the counts data 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.)
covariates_colnames

The column name(s) from the sample metadata containing variable(s) of interest, such as phenotype. Most commonly this will be the same column selected for your Groups Column. Some experimental designs may require that you add additional covariate columns here. Do not include the batch_colname here.
batch_colname

The column from the sample metadata containing the batch information. Samples extracted, prepared, or sequenced at separate times or using separate materials/staff/equipment may belong to different batches. Not all data sets have batches, in which case you do not need batch correction. If your data set has no batches, you can provide a batch column with the same value in every row to skip batch correction (alternatively, simply do not run this function).
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.)
colors_for_plots

Colors for the PCA and histogram will be picked, in order, from this list. Colors must either be names in grDevices::colors() or valid hex codes.
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_subdir

subdirectory in ⁠figures/⁠ where plots will be saved if save_plots is TRUE

Value

multiOmicDataSet with batch-corrected counts

See Also

Other moo methods: clean_raw_counts(), diff_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

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),
    "norm" = list(
      "voom" = as.data.frame(nidap_norm_counts)
    )
  )
) |>
  batch_correct_counts(
    count_type = "norm",
    sub_count_type = "voom",
    covariates_colnames = "Group",
    batch_colname = "Batch",
    label_colname = "Label"
  )

head(moo@counts[["batch"]])

Bind dataframes in named list to long dataframe

Description

The dataframes must have all of the same columns

Usage

bind_dfs_long(df_list, outcolname = contrast)

Arguments

df_list

named list of dataframes
outcolname

column name in output dataframe for the names from the named list

Value

long dataframe with new column outcolname from named list

Examples

dfs <- list(
  "a_vs_b" = data.frame(id = c("a1", "b2", "c3"), score = runif(3)),
  "b_vs_c" = data.frame(id = c("a1", "b2", "c3"), score = rnorm(3))
)
dfs |> bind_dfs_long()

Calculate counts-per-million (CPM) on raw counts in a multiOmicDataSet

Description

Calculate counts-per-million (CPM) on raw counts in a multiOmicDataSet

Usage

calc_cpm(moo, ...)

Arguments

moo

multiOmicDataSet object
...

additional arguments to pass to edgeR::cpm()

Value

multiOmicDataSet with cpm-transformed counts

Examples

sample_meta <- data.frame(
  sample_id = c("KO_S3", "KO_S4", "WT_S1", "WT_S2"),
  condition = factor(
    c("knockout", "knockout", "wildtype", "wildtype"),
    levels = c("wildtype", "knockout")
  )
)
moo <- create_multiOmicDataSet_from_dataframes(sample_meta, gene_counts) |>
  calc_cpm()
head(moo@counts$cpm)

Perform principal components analysis

Description

Perform principal components analysis

Usage

calc_pca(
  counts_dat,
  sample_metadata,
  sample_id_colname = NULL,
  feature_id_colname = NULL
)

Arguments

counts_dat

data frame of feature counts (e.g. from the counts slot of a multiOmicDataSet).
sample_metadata

sample metadata as a data frame or tibble.
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.)
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.)

Value

data frame with statistics for each principal component

See Also

Other PCA functions: plot_pca(), plot_pca_2d(), plot_pca_3d()

Examples

calc_pca(nidap_raw_counts, nidap_sample_metadata) |> head()

Clean Raw Counts

Description

This function checks the input raw counts matrix for common formatting problems with feature identifiers and sample names. If feature IDs contain multiple IDs separated by special characters (| - , or space) they will be split into multiple columns. If duplicate feature IDs are detected the counts are summed across duplicate feature ID rows within each sample. Invalid sample names will also be reported and can be automatically corrected. If your sample names are corrected here, be sure to make equivalent changes to your metadata table.

Usage

clean_raw_counts(
  moo,
  count_type = "raw",
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  samples_to_rename = "",
  cleanup_column_names = TRUE,
  split_gene_name = TRUE,
  aggregate_rows_with_duplicate_gene_names = TRUE,
  gene_name_column_to_use_for_collapsing_duplicates = "",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "clean"
)

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)
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.)
feature_id_colname

The column from the counts data 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_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
cleanup_column_names

Invalid raw counts column names can cause errors in the downstream analysis. If this is TRUE, any invalid column names will be automatically altered to a correct format. These format changes will include adding an "X" as the first character in any column name that began with a numeral and replacing some special characters ("-,:. ") with underscores ("_"). Invalid sample names and any changes made will be detailed.
split_gene_name

If TRUE, split the gene name column by any of these special characters: ⁠,|_-:⁠
aggregate_rows_with_duplicate_gene_names

If a Feature ID (from the "Cleanup Column Names" parameter above) is found to be duplicated on multiple rows of the raw counts, the Log will report these Feature IDs. Using the default behavior (TRUE), the counts for all rows with a duplicate Feature IDs are aggregated into a single row. Counts are summed across duplicate Feature ID rows within each sample. Additional identifier columns, if present (e.g. Ensembl IDs), will be preserved and multiple matching identifiers in such additional columns will appear as comma-separated values in an aggregated row.
gene_name_column_to_use_for_collapsing_duplicates

Select the column with Feature IDs to use as grouping elements to collapse the counts matrix. The log output will list the columns available to identify duplicate row IDs in order to aggregate information. If left blank your "Feature ID" Column will be used to Aggregate Rows. If "Feature ID" column can be split into multiple IDs the non Ensembl ID name will be used to aggregate duplicate IDs. If "Feature ID" column does not contain Ensembl IDs the split Feature IDs will be named 'Feature_id_1' and 'Feature_id_2'. For this case an error will occur and you will have to manually enter the Column ID for this field.
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_subdir

subdirectory in ⁠figures/⁠ where plots will be saved if save_plots is TRUE

Value

multiOmicDataSet with cleaned counts

See Also

Other moo methods: batch_correct_counts(), diff_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

Examples

moo <- create_multiOmicDataSet_from_dataframes(
  as.data.frame(nidap_sample_metadata),
  as.data.frame(nidap_raw_counts),
  sample_id_colname = "Sample",
) |>
  clean_raw_counts(sample_id_colname = "Sample", feature_id_colname = "GeneName")
head(moo@counts$clean)

Construct a multiOmicDataSet object from data frames

Description

Construct a multiOmicDataSet object from data frames

Usage

create_multiOmicDataSet_from_dataframes(
  sample_metadata,
  counts_dat,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  count_type = "raw"
)

Arguments

sample_metadata

sample metadata as a data frame or tibble. The first column is assumed to contain the sample IDs which must correspond to column names in the raw counts.
counts_dat

data frame of feature counts (e.g. expected feature counts from RSEM).
sample_id_colname

name of the column in sample_metadata that contains the sample IDs. (Default: NULL - first column in the sample metadata will be used.)
feature_id_colname

name of the column in counts_dat that contains feature/gene IDs. (Default: NULL - first column in the count data will be used.)
count_type

type to assign the values of counts_dat to in the counts slot

Value

multiOmicDataSet object

See Also

Other moo constructors: create_multiOmicDataSet_from_files(), multiOmicDataSet()

Examples

sample_meta <- data.frame(
  sample_id = c("KO_S3", "KO_S4", "WT_S1", "WT_S2"),
  condition = factor(
    c("knockout", "knockout", "wildtype", "wildtype"),
    levels = c("wildtype", "knockout")
  )
)
moo <- create_multiOmicDataSet_from_dataframes(sample_meta, gene_counts)
head(moo@sample_meta)
head(moo@counts$raw)
head(moo@annotation)

sample_meta_nidap <- readr::read_csv(system.file("extdata", "nidap",
  "Sample_Metadata_Bulk_RNA-seq_Training_Dataset_CCBR.csv.gz",
  package = "MOSuite"
))
raw_counts_nidap <- readr::read_csv(system.file("extdata", "nidap", "Raw_Counts.csv.gz",
  package = "MOSuite"
))
moo_nidap <- create_multiOmicDataSet_from_dataframes(sample_meta_nidap, raw_counts_nidap)

Construct a multiOmicDataSet object from text files (e.g. TSV, CSV).

Description

Construct a multiOmicDataSet object from text files (e.g. TSV, CSV).

Usage

create_multiOmicDataSet_from_files(
  sample_meta_filepath,
  feature_counts_filepath,
  count_type = "raw",
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  delim = NULL,
  ...
)

Arguments

sample_meta_filepath

path to text file with sample IDs and metadata for differential analysis.
feature_counts_filepath

path to text file of expected feature counts (e.g. gene counts from RSEM).
count_type

type to assign the values of counts_dat to in the counts slot
sample_id_colname

name of the column in sample_metadata that contains the sample IDs. (Default: NULL - first column in the sample metadata will be used.)
feature_id_colname

name of the column in counts_dat that contains feature/gene IDs. (Default: NULL - first column in the count data will be used.)
delim

Delimiter used in the input files. Any delimiter accepted by readr::read_delim() can be used. If the files are in CSV format, set delim = ','; for TSV format, set delim = '\t'.
...

additional arguments forwarded to readr::read_delim().

Value

multiOmicDataSet object

See Also

Other moo constructors: create_multiOmicDataSet_from_dataframes(), multiOmicDataSet()

Examples

moo <- create_multiOmicDataSet_from_files(
  sample_meta_filepath = system.file("extdata",
    "sample_metadata.tsv.gz",
    package = "MOSuite"
  ),
  feature_counts_filepath = system.file("extdata",
    "RSEM.genes.expected_count.all_samples.txt.gz",
    package = "MOSuite"
  ),
  delim = "\t"
)
moo@counts$raw |> head()
moo@sample_meta

moo_nidap <- create_multiOmicDataSet_from_files(
  system.file("extdata", "nidap",
    "Sample_Metadata_Bulk_RNA-seq_Training_Dataset_CCBR.csv.gz",
    package = "MOSuite"
  ),
  system.file("extdata", "nidap", "Raw_Counts.csv.gz", package = "MOSuite"),
  delim = ","
)

Differential expression analysis

Description

Differential expression analysis

Usage

diff_counts(
  moo,
  count_type = "filt",
  sub_count_type = NULL,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  samples_to_include = NULL,
  covariates_colnames = NULL,
  contrast_colname = NULL,
  contrasts = NULL,
  input_in_log_counts = FALSE,
  return_mean_and_sd = FALSE,
  voom_normalization_method = "quantile",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "diff"
)

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)
sub_count_type

if count_type is a list, specify the sub count type within the list. (Default: NULL)
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.)
feature_id_colname

The column from the counts data 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.)
covariates_colnames

Columns to be used as covariates in linear modeling. Must include column from "Contrast Variable". Most commonly your covariate will be group and batch (if you have different batches in your data).
contrast_colname

The column in the metadata that contains the group variables you wish to find differential expression between. Up to 2 columns (2-factor analysis) can be used.
contrasts

Specify each contrast in the format group1-group2, e.g. treated-control
input_in_log_counts

set this to TRUE if counts are already log2-transformed
return_mean_and_sd

if TRUE, return Mean and Standard Deviation of groups in addition to DEG estimates for contrast(s)
voom_normalization_method

Normalization method to be applied to the logCPM values when using limma::voom
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_subdir

subdirectory in ⁠figures/⁠ where plots will be saved if save_plots is TRUE

Value

multiOmicDataSet with diff added to the analyses slot (i.e. moo@analyses$diff)

See Also

Other moo methods: batch_correct_counts(), clean_raw_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

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)
  )
) |>
  diff_counts(
    count_type = "filt",
    sub_count_type = NULL,
    sample_id_colname = "Sample",
    feature_id_colname = "Gene",
    covariates_colnames = c("Group", "Batch"),
    contrast_colname = c("Group"),
    contrasts = c("B-A", "C-A", "B-C"),
    voom_normalization_method = "quantile",
  )
head(moo@analyses$diff)

Extract count data

Description

Extract count data

Usage

extract_counts(moo, count_type, sub_count_type = NULL)

Arguments

moo

multiOmicDataSet containing count_type & sub_count_type in the counts slot
count_type

the type of counts to use – must be a name in the counts slot (moo@counts[[count_type]])
sub_count_type

if count_type is a list, specify the sub count type within the list (moo@counts[[count_type]][[sub_count_type]]). (Default: NULL)

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),
    "norm" = list(
      "voom" = as.data.frame(nidap_norm_counts)
    )
  )
)

moo |>
  extract_counts("filt") |>
  head()

moo |>
  extract_counts("norm", "voom") |>
  head()

Filter low counts

Description

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,
  plot_corr_matrix_heatmap = TRUE,
  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 data 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. Colors must either be names in grDevices::colors() or valid hex codes.
plot_corr_matrix_heatmap

Datasets with a large number of samples may be too large to create a correlation matrix heatmap. If this function takes longer than 5 minutes to run, Set to FALSE and the correlation matrix will not be be created. Default is TRUE.
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 TRUE, 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 ⁠figures/⁠ where plots will be saved if save_plots is TRUE

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.

Value

multiOmicDataSet with filtered counts

See Also

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_diff(), normalize_counts(), plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

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"
  )
head(moo@counts$filt)

Filter features from differential analysis based on statistical significance

Description

Outputs dataset of significant genes from DEG table; filters genes based on statistical significance (p-value or adjusted p-value) and change (fold change, log2 fold change, or t-statistic); in addition allows for selection of DEG estimates and for sub-setting of contrasts and groups included in the output gene list.

Usage

filter_diff(
  moo,
  feature_id_colname = NULL,
  significance_column = "adjpval",
  significance_cutoff = 0.05,
  change_column = "logFC",
  change_cutoff = 1,
  filtering_mode = "any",
  include_estimates = c("FC", "logFC", "tstat", "pval", "adjpval"),
  round_estimates = TRUE,
  rounding_decimal_for_percent_cells = 0,
  contrast_filter = "none",
  contrasts = c(),
  groups = c(),
  groups_filter = "none",
  label_font_size = 6,
  label_distance = 1,
  y_axis_expansion = 0.08,
  fill_colors = c("steelblue1", "whitesmoke"),
  pie_chart_in_3d = TRUE,
  bar_width = 0.4,
  draw_bar_border = TRUE,
  plot_type = "bar",
  plot_titles_fontsize = 12,
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = file.path("diff", "filt")
)

Arguments

moo

multiOmicDataSet object (see create_multiOmicDataSet_from_dataframes())
feature_id_colname

The column from the counts data 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.)
significance_column

Column name for significance, e.g. "pval" or "pvaladj" (default)
significance_cutoff

Features will only be kept if their significance_column is less then this cutoff threshold
change_column

Column name for change, e.g. "logFC" (default)
change_cutoff

Features will only be kept if the absolute value of their change_column is greater than or equal to this cutoff threshold
filtering_mode

Accepted values: "any" or "all" to include features that meet the criteria in any contrast or in all contrasts
include_estimates

Column names of estimates to include. Default: c("FC", "logFC", "tstat", "pval", "adjpval")
round_estimates

Whether to round estimates. Default: TRUE
rounding_decimal_for_percent_cells

Decimal place to use when rounding Percent cells
contrast_filter

Whether to filter contrasts in or our of analysis. If "keep", only the contrast names listed in contrasts will be included. If ⁠"remove⁠, the contrast names listed by contrasts will be removed. If "none", all contrasts in the dataset are used. Options: "keep", "remove", or "none"
contrasts

Contrast names to filter by contrast_filter. If contrast_filter is "none", this parameter has no effect.
groups

Group names to filter by groups_filter. If groups_filter is "none", this parameter has no effect. Options: "keep", "remove", or "none"
groups_filter

Whether to filter groups in or out of analysis. If "keep", only the group names listed in groups will be included. If "remove", the group names listed by groups will be removed. If "none", all groups in the dataset are used.
label_font_size

Font size for labels in the plot (default: 6)
label_distance

Distance of labels from the bars (default: 1)
y_axis_expansion

Expansion of the y-axis (default: 0.08)
fill_colors

Fill colors for the bars (default: c("steelblue1", "whitesmoke"))
pie_chart_in_3d

Whether to draw pie charts in 3D (default: TRUE)
bar_width

Width of the bars (default: 0.4)
draw_bar_border

Whether to draw borders around bars (default: TRUE)
plot_type

"bar" or "pie"
plot_titles_fontsize

Font size for plot titles (default: 12)
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_subdir

subdirectory in where plots will be saved if save_plots is TRUE

See Also

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_counts(), normalize_counts(), plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

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)
  )
) |>
  diff_counts(
    count_type = "filt",
    sub_count_type = NULL,
    sample_id_colname = "Sample",
    feature_id_colname = "Gene",
    covariates_colnames = c("Group", "Batch"),
    contrast_colname = c("Group"),
    contrasts = c("B-A", "C-A", "B-C"),
    voom_normalization_method = "quantile",
  ) |>
  filter_diff()
head(moo@analyses$diff_filt)

RSEM expected gene counts

Description

RSEM expected gene counts

Usage

gene_counts

Format

gene_counts

A data frame with columns 'gene_id', 'GeneName', and a column for each sample's expected count.

Source

Generated by running RENEE v2.5.8 on the test dataset

Create named list of default colors for plotting

Description

Create named list of default colors for plotting

Usage

get_colors_lst(sample_metadata, palette_fun = grDevices::palette.colors, ...)

Arguments

sample_metadata

sample metadata as a data frame or tibble. The first column is assumed to contain the sample IDs which must correspond to column names in the raw counts.
palette_fun

Function for selecting colors. Assumed to contain n for the number of colors. Default: grDevices::palette.colors()
...

additional arguments forwarded to palette_fun

Value

named list, with each column in sample_metadata containing entry with a named vector of colors

Examples

get_colors_lst(nidap_sample_metadata)
## Not run: 
get_colors_lst(nidap_sample_metadata, palette_fun = RColorBrewer::brewer.pal, name = "Set3")

## End(Not run)

Get vector of colors for observations in one column of a data frame

Description

Get vector of colors for observations in one column of a data frame

Usage

get_colors_vctr(dat, colname, palette_fun = grDevices::palette.colors, ...)

Arguments

dat

data frame
colname

column name in dat
palette_fun

Function for selecting colors. Assumed to contain n for the number of colors. Default: grDevices::palette.colors()
...

additional arguments forwarded to palette_fun

Value

named vector of colors for each unique observation in dat$colname

Join dataframes in named list to wide dataframe

Description

The first column is assumed to be shared by all dataframes

Usage

join_dfs_wide(df_list, join_fn = dplyr::left_join)

Arguments

df_list

named list of dataframes
join_fn

join function to use (Default: dplyr::left_join)

Value

wide dataframe

Examples

dfs <- list(
  "a_vs_b" = data.frame(id = c("a1", "b2", "c3"), score = runif(3)),
  "b_vs_c" = data.frame(id = c("a1", "b2", "c3"), score = rnorm(3))
)
dfs |> join_dfs_wide()

multiOmicDataSet class

Description

multiOmicDataSet class

Usage

multiOmicDataSet(sample_metadata, anno_dat, counts_lst, analyses_lst = list())

Arguments

sample_metadata

sample metadata as a data frame or tibble. The first column is assumed to contain the sample IDs which must correspond to column names in the raw counts.
anno_dat

data frame of feature annotations, such as gene symbols or any other information about the features in counts_lst.
counts_lst

named list of data frames containing counts, e.g. expected feature counts from RSEM. Each data frame is expected to contain a feature_id column as the first column, and all remaining columns are sample IDs in the sample_meta.
analyses_lst

named list of analysis results, e.g. DESeq results object

See Also

Other moo constructors: create_multiOmicDataSet_from_dataframes(), create_multiOmicDataSet_from_files()

Batch-corrected counts for the NIDAP test dataset.

Description

Batch-corrected counts for the NIDAP test dataset.

Usage

nidap_batch_corrected_counts

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 7943 rows and 10 columns.

Batch-corrected counts for the NIDAP test dataset. The result of running batch_correct_counts() on nidap_norm_counts.

Description

Batch-corrected counts for the NIDAP test dataset. The result of running batch_correct_counts() on nidap_norm_counts.

Usage

nidap_batch_corrected_counts_2

Format

An object of class data.frame with 7943 rows and 10 columns.

Clean raw counts for the NIDAP test dataset. The result of running clean_raw_counts() on nidap_raw_counts.

Description

Clean raw counts for the NIDAP test dataset. The result of running clean_raw_counts() on nidap_raw_counts.

Usage

nidap_clean_raw_counts

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 43280 rows and 10 columns.

Differential gene expression analysis for the NIDAP test dataset.

Description

Differential gene expression analysis for the NIDAP test dataset.

Usage

nidap_deg_analysis

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 7943 rows and 25 columns.

Differential gene expression analysis for the NIDAP test dataset. The result of running diff_counts() on nidap_filtered_counts.

Description

Differential gene expression analysis for the NIDAP test dataset. The result of running diff_counts() on nidap_filtered_counts.

Usage

nidap_deg_analysis_2

Format

An object of class list of length 3.

List of differentially expressed genes from the NIDAP test dataset using default parameters with filter_diff().

Description

List of differentially expressed genes from the NIDAP test dataset using default parameters with filter_diff().

Usage

nidap_deg_gene_list

Format

An object of class data.frame with 641 rows and 16 columns.

Filtered counts for the NIDAP test dataset. The result of running filter_counts() on nidap_clean_raw_counts.

Description

Filtered counts for the NIDAP test dataset. The result of running filter_counts() on nidap_clean_raw_counts.

Usage

nidap_filtered_counts

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 7943 rows and 10 columns.

Normalized counts for the NIDAP test dataset. The result of running normalize_counts() on nidap_filtered_counts.

Description

Normalized counts for the NIDAP test dataset. The result of running normalize_counts() on nidap_filtered_counts.

Usage

nidap_norm_counts

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 7943 rows and 10 columns.

Raw counts for the NIDAP test dataset Pairs with nidap_sample_metadata.

Description

Raw counts for the NIDAP test dataset Pairs with nidap_sample_metadata.

Usage

nidap_raw_counts

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 43280 rows and 10 columns.

Sample metadata for the NIDAP test dataset

Description

Sample metadata for the NIDAP test dataset

Usage

nidap_sample_metadata

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 9 rows and 5 columns.

Output data from venn diagram. The result of running plot_venn_diagram() on nidap_volcano_summary_dat

Description

Output data from venn diagram. The result of running plot_venn_diagram() on nidap_volcano_summary_dat

Usage

nidap_venn_diagram_dat

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 3068 rows and 4 columns.

Summarized differential expression analysis for input to venn diagram

Description

Summarized differential expression analysis for input to venn diagram

Usage

nidap_volcano_summary_dat

Format

An object of class spec_tbl_df (inherits from tbl_df, tbl, data.frame) with 4929 rows and 7 columns.

Normalize counts

Description

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",
  number_of_histogram_legend_columns = 6,
  plot_corr_matrix_heatmap = TRUE,
  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 data 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.
number_of_histogram_legend_columns

number of columns for the histogram legend
plot_corr_matrix_heatmap

Datasets with a large number of samples may be too large to create a correlation matrix heatmap. If this function takes longer than 5 minutes to run, Set to FALSE and the correlation matrix will not be be created. Default is TRUE.
colors_for_plots

Colors for the PCA and histogram will be picked, in order, from this list. Colors must either be names in grDevices::colors() or valid hex codes.
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 TRUE, 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 ⁠figures/⁠ where plots will be saved if save_plots is TRUE

Value

multiOmicDataSet with normalized counts

See Also

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_counts(), filter_diff(), plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

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"
  )
head(moo@counts[["norm"]][["voom"]])

MOSuite Options

Description

Internally used, package-specific options. All options will prioritize R options() values, and fall back to environment variables if undefined. If neither the option nor the environment variable is set, a default value is used.

Checking Option Values

Option values specific to MOSuite can be accessed by passing the package name to env. 

options::opts(env = "MOSuite")

options::opt(x, default, env = "MOSuite")

Options

print_plots

Whether to print plots during analysis

default: 
FALSE
option:

moo_print_plots

envvar:

MOO_PRINT_PLOTS (evaluated if possible, raw string otherwise)

save_plots

Whether to save plots to files during analysis

default: 
TRUE
option:

moo_save_plots

envvar:

MOO_SAVE_PLOTS (evaluated if possible, raw string otherwise)

plots_dir

Path where plots are saved when moo_save_plots is TRUE

default: 
"figures/"
option:

moo_plots_dir

envvar:

MOO_PLOTS_DIR (evaluated if possible, raw string otherwise)

See Also

options getOption Sys.setenv Sys.getenv

Plot correlation heatmap

Description

Plot correlation heatmap

Usage

plot_corr_heatmap(moo_counts, ...)

Arguments

moo_counts

counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot
...

arguments forwarded to method plot_corr_heatmap_dat

Value

heatmap from ComplexHeatmap::Heatmap()

Methods

link to docs class
plot_corr_heatmap_moo multiOmicDataSet
plot_corr_heatmap_dat data.frame

Method Usage

# multiOmicDataSet
plot_corr_heatmap(moo_counts,
  count_type,
  sub_count_type = NULL,
  ...)

# dataframe
plot_corr_heatmap(moo_counts,
  sample_metadata,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  group_colname = "Group",
  label_colname = "Label",
  color_values = c(
    "#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"
  ))

See Also

Other plotters: plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), print_or_save_plot()

Other heatmaps: plot_expr_heatmap()

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

Examples

# plot correlation heatmap for a counts slot in a multiOmicDataset Object
moo <- multiOmicDataSet(
  sample_metadata = as.data.frame(nidap_sample_metadata),
  anno_dat = data.frame(),
  counts_lst = list("raw" = as.data.frame(nidap_raw_counts))
)
p <- plot_corr_heatmap(moo, count_type = "raw")

# plot correlation heatmap for a counts dataframe
plot_corr_heatmap(
  moo@counts$raw,
  sample_metadata = moo@sample_meta,
  sample_id_colname = "Sample",
  feature_id_colname = "Gene",
  group_colname = "Group",
  label_colname = "Label"
)

Plot correlation heatmap for counts dataframe

Description

Plot correlation heatmap for counts dataframe

Arguments

moo_counts

counts dataframe (Required)
sample_metadata

sample metadata as a data frame or tibble (Required)
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.)
feature_id_colname

The column from the counts data 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.)
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.)
color_values

vector of colors as hex values or names recognized by R

See Also

plot_corr_heatmap generic

Other plotters for counts dataframes: plot_histogram_dat, plot_pca_dat, plot_read_depth_dat

Plot correlation heatmap for multiOmicDataSet

Description

Plot correlation heatmap for multiOmicDataSet

Arguments

moo_counts

multiOmicDataSet containing count_type & sub_count_type in the counts slot
count_type

the type of counts to use. Must be a name in the counts slot (names(moo@counts)).
sub_count_type

used if count_type is a list in the counts slot: specify the sub count type within the list. Must be a name in names(moo@counts[[count_type]]).
...

arguments forwarded to method plot_corr_heatmap_dat

See Also

plot_corr_heatmap generic

Other plotters for multiOmicDataSets: plot_histogram_moo, plot_pca_moo, plot_read_depth_moo

Plot expression heatmap

Description

The samples (i.e. the columns) are clustered in an unsupervised fashion based on how similar their expression profiles are across the included genes. This can help identify samples that are non clustering with their group as you might expect based on the experimental design.

Usage

plot_expr_heatmap(
  moo_counts,
  count_type,
  sub_count_type = NULL,
  sample_metadata = NULL,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  group_colname = "Group",
  label_colname = NULL,
  samples_to_include = NULL,
  color_values = c("#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"),
  include_all_genes = FALSE,
  filter_top_genes_by_variance = TRUE,
  top_genes_by_variance_to_include = 500,
  specific_genes_to_include_in_heatmap = "None",
  cluster_genes = TRUE,
  gene_distance_metric = "correlation",
  gene_clustering_method = "average",
  display_gene_dendrograms = TRUE,
  display_gene_names = FALSE,
  center_and_rescale_expression = TRUE,
  cluster_samples = FALSE,
  arrange_sample_columns = TRUE,
  order_by_gene_expression = FALSE,
  gene_to_order_columns = " ",
  gene_expression_order = "low_to_high",
  smpl_distance_metric = "correlation",
  smpl_clustering_method = "average",
  display_smpl_dendrograms = TRUE,
  reorder_dendrogram = FALSE,
  reorder_dendrogram_order = c(),
  display_sample_names = TRUE,
  group_columns = c("Group", "Replicate", "Batch"),
  assign_group_colors = FALSE,
  assign_color_to_sample_groups = c(),
  group_colors = c("#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"),
  heatmap_color_scheme = "Default",
  autoscale_heatmap_color = TRUE,
  set_min_heatmap_color = -2,
  set_max_heatmap_color = 2,
  aspect_ratio = "Auto",
  legend_font_size = 10,
  gene_name_font_size = 4,
  sample_name_font_size = 8,
  display_numbers = FALSE,
  plot_filename = "expr_heatmap.png",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "heatmap"
)

Arguments

moo_counts

counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot
count_type

the type of counts to use. Must be a name in the counts slot (names(moo@counts)).
sub_count_type

used if count_type is a list in the counts slot: specify the sub count type within the list. Must be a name in names(moo@counts[[count_type]]).
sample_metadata

sample metadata as a data frame or tibble (only required if moo_counts is a dataframe)
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.)
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.)
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.)
color_values

vector of colors as hex values or names recognized by R
include_all_genes

Set to TRUE if all genes are to be included. Set to FALSE if you want to filter genes by variance and/or provide a list of specific genes that will appear in the heatmap.
filter_top_genes_by_variance

Set to TRUE if you want to only include the top genes by variance. Set to FALSE if you do not want to filter genes by variance.
top_genes_by_variance_to_include

The number of genes to include if filtering genes by variance. This parameter is ignored if "Filter top genes by variance" is set to FALSE.
specific_genes_to_include_in_heatmap

Enter the gene symbols to be included in the heatmap, with each gene symbol separated with a space from the others. Alternatively, paste in a column of gene names from any spreadsheet application. This parameter is ignored if "Include all genes" is set to TRUE.
cluster_genes

Choose whether to cluster the rows (genes). If TRUE, rows will have clustering applied. If FALSE, clustering will not be applied to rows.
gene_distance_metric

Distance metric to be used in clustering genes. (TODO document options)
gene_clustering_method

Clustering method metric to be used in clustering samples. (TODO document options)
display_gene_dendrograms

Set to TRUE to show gene dendrograms. Set to FALSE to hide dendrograms.
display_gene_names

Set to TRUE to display gene names on the right side of the heatmap. Set to FALSE to hide gene names.
center_and_rescale_expression

Center and rescale expression for each gene across all included samples.
cluster_samples

Choose whether to cluster the columns (samples). If TRUE, columns will have clustering applied. If FALSE, clustering will not be applied to columns.
arrange_sample_columns

If TRUE, arranges columns by annotation groups. If FALSE, and "Cluster Samples" is FALSE, samples will appear in the order of input (samples to include)
order_by_gene_expression

If TRUE, set gene name below and direction for ordering
gene_to_order_columns

Gene to order columns by expression levels
gene_expression_order

Choose direction for gene order
smpl_distance_metric

Distance metric to be used in clustering samples. (TODO document options)
smpl_clustering_method

Clustering method to be used in clustering samples. (TODO document options)
display_smpl_dendrograms

Set to TRUE to show sample dendrograms. Set to FALSE to hide dendrogram.
reorder_dendrogram

If TRUE, set the order of the dendrogram (below)
reorder_dendrogram_order

Reorder the samples (columns) of the dendrogram by name, e.g. “sample2”,“sample3",“sample1".
display_sample_names

Set to TRUE if you want sample names to be displayed on the plot. Set to FALSE to hide sample names.
group_columns

Columns containing the sample groups for annotation tracks
assign_group_colors

If TRUE, set the groups assigned colors (below)
assign_color_to_sample_groups

Enter each sample to color in the format: group_name: color This parameter is ignored if "Assign Colors" is set to FALSE.
group_colors

Set group annotation colors.
heatmap_color_scheme

color scheme (TODO document options)
autoscale_heatmap_color

Set to TRUE to autoscale the heatmap colors between the maximum and minimum heatmap color parameters. If FALSE, set the heatmap colors between "Set max heatmap color" and "Set min heatmap color" (below).
set_min_heatmap_color

If Autoscale heatmap color is set to FALSE, set the minimum heatmap z-score value
set_max_heatmap_color

If Autoscale heatmap color is set to FALSE, set the maximum heatmap z-score value.
aspect_ratio

Set figure Aspect Ratio. Ratio refers to entire figure including legend. If set to Auto figure size is based on number of rows and columns form counts matrix. default - Auto
legend_font_size

Set Font size for figure legend. Default is 10.
gene_name_font_size

Font size for gene names. If you don't want gene labels to show, toggle "Display Gene Names" below to FALSE
sample_name_font_size

Font size for sample names. If you don't want to display samples names, toggle "Display sample names" (below) to FALSE
display_numbers

Setting to FALSE (default) will not display numerical value of heat on heatmap. Set to TRUE if you want to see these numbers on the plot.
plot_filename

plot output filename - only used if save_plots is TRUE
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_subdir

subdirectory in ⁠figures/⁠ where plots will be saved if save_plots is TRUE

Details

By default, the top 500 genes by variance are used, as these are generally going to include those genes that most distinguish your samples from one another. You can change this as well as many other parameters about this heatmap if you explore the advanced options.

Value

heatmap from ComplexHeatmap::Heatmap()

See Also

Other plotters: plot_corr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), print_or_save_plot()

Other heatmaps: plot_corr_heatmap()

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_corr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

Examples

# plot expression heatmap for a counts slot in a multiOmicDataset Object
moo <- multiOmicDataSet(
  sample_metadata = as.data.frame(nidap_sample_metadata),
  anno_dat = data.frame(),
  counts_lst = list(
    "raw" = nidap_raw_counts,
    "norm" = list(
      "voom" = as.data.frame(nidap_norm_counts)
    )
  )
)
p <- plot_expr_heatmap(moo, count_type = "norm", sub_count_type = "voom")

# customize the plot
plot_expr_heatmap(moo,
  count_type = "norm", sub_count_type = "voom",
  top_genes_by_variance_to_include = 100
)

# plot expression heatmap for a counts dataframe
counts_dat <- moo@counts$norm$voom
plot_expr_heatmap(
  counts_dat,
  sample_metadata = nidap_sample_metadata,
  sample_id_colname = "Sample",
  feature_id_colname = "Gene",
  group_colname = "Group",
  label_colname = "Label",
  top_genes_by_variance_to_include = 100
)

Plot histogram

Description

Plot histogram

Usage

plot_histogram(moo_counts, ...)

Arguments

moo_counts

counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot
...

arguments forwarded to method

Value

ggplot object

Methods

link to docs class
plot_histogram_moo multiOmicDataSet
plot_histogram_dat data.frame

See Also

Other plotters: plot_corr_heatmap(), plot_expr_heatmap(), plot_pca(), plot_read_depth(), print_or_save_plot()

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_corr_heatmap(), plot_expr_heatmap(), plot_pca(), plot_read_depth(), run_deseq2(), set_color_pal()

Examples

# plot histogram for a counts slot in a multiOmicDataset Object
moo <- multiOmicDataSet(
  sample_metadata = nidap_sample_metadata,
  anno_dat = data.frame(),
  counts_lst = list("raw" = nidap_raw_counts)
)
p <- plot_histogram(moo, count_type = "raw")

# customize the plot
plot_histogram(moo,
  count_type = "raw",
  group_colname = "Group", color_by_group = TRUE
)

# plot histogram for a counts dataframe directly
counts_dat <- moo@counts$raw
plot_histogram(
  counts_dat,
  sample_metadata = nidap_sample_metadata,
  sample_id_colname = "Sample",
  feature_id_colname = "GeneName",
  label_colname = "Label"
)

Plot histogram for counts dataframe

Description

Plot histogram for counts dataframe

Arguments

moo_counts

counts dataframe (required)
sample_metadata

sample metadata as a data frame or tibble (required)
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.)
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.)
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.)
color_values

vector of colors as hex values or names recognized by R
color_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

whether to override the default for ggplot2::xlim() (default: FALSE)
minimum_for_x_axis

value to override default min for ggplot2::xlim()
maximum_for_x_axis

value to override default max for ggplot2::xlim()
x_axis_label

text label for the x axis ggplot2::xlab()
y_axis_label

text label for the y axis ggplot2::ylab()
legend_position

passed to in legend.position ggplot2::theme()
legend_font_size

passed to ggplot2::element_text() via ggplot2::theme()
number_of_legend_columns

passed to ncol in ggplot2::guide_legend()
interactive_plots

set to TRUE to make the plot 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.

See Also

plot_histogram generic

Other plotters for counts dataframes: plot_corr_heatmap_dat, plot_pca_dat, plot_read_depth_dat

Examples

# plot histogram for a counts dataframe directly
plot_histogram(
  nidap_clean_raw_counts,
  sample_metadata = nidap_sample_metadata,
  sample_id_colname = "Sample",
  feature_id_colname = "Gene",
  label_colname = "Label"
)

# customize the plot
plot_histogram(
  nidap_clean_raw_counts,
  sample_metadata = nidap_sample_metadata,
  sample_id_colname = "Sample",
  feature_id_colname = "Gene",
  group_colname = "Group",
  color_by_group = TRUE
)

Plot histogram for multiOmicDataSet

Description

Plot histogram for multiOmicDataSet

Arguments

moo_counts

counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot
count_type

Required if moo_counts is a multiOmicDataSet: the type of counts to use – must be a name in the counts slot (moo@counts).
sub_count_type

Used if moo_counts is a multiOmicDataSet AND if count_type is a list, specify the sub count type within the list
...

arguments forwarded to method: plot_histogram_dat

See Also

plot_histogram generic

Other plotters for multiOmicDataSets: plot_corr_heatmap_moo, plot_pca_moo, plot_read_depth_moo

Examples

# plot histogram for a counts slot in a multiOmicDataset Object
moo <- multiOmicDataSet(
  sample_metadata = nidap_sample_metadata,
  anno_dat = data.frame(),
  counts_lst = list("raw" = nidap_raw_counts)
)
p <- plot_histogram(moo, count_type = "raw")

# customize the plot
plot_histogram(moo,
  count_type = "raw",
  group_colname = "Group", color_by_group = TRUE
)

Perform and plot a Principal Components Analysis

Description

Perform and plot a Principal Components Analysis

Usage

plot_pca(moo_counts, principal_components = c(1, 2), ...)

Arguments

moo_counts

counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot
principal_components

vector with numbered principal components to plot. Use 2 for a 2D pca with ggplot, or 3 for a 3D pca with plotly. (Default: c(1,2))
...

additional arguments forwarded to method (see Details below)

Details

See the low-level function docs for additional arguments depending on whether you're plotting 2 or 3 PCs:

  • plot_pca_2d - used when there are 2 principal components

  • plot_pca_3d - used when there are 3 principal components

Value

PCA plot (2D or 3D depending on the number of principal_components)

Methods

link to docs class
plot_pca_moo multiOmicDataSet
plot_pca_dat data.frame

See Also

Other plotters: plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_read_depth(), print_or_save_plot()

Other PCA functions: calc_pca(), plot_pca_2d(), plot_pca_3d()

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_read_depth(), run_deseq2(), set_color_pal()

Examples

# multiOmicDataSet
moo <- multiOmicDataSet(
  sample_metadata = nidap_sample_metadata,
  anno_dat = data.frame(),
  counts_lst = list(
    "raw" = nidap_raw_counts,
    "clean" = nidap_clean_raw_counts
  )
)
plot_pca(moo, count_type = "clean", principal_components = c(1, 2))

# 3D
plot_pca(moo, count_type = "clean", principal_components = c(1, 2, 3))

# dataframe
plot_pca(nidap_clean_raw_counts,
  sample_metadata = nidap_sample_metadata,
  principal_components = c(1, 2)
)

Perform and plot a 2D Principal Components Analysis

Description

Perform and plot a 2D Principal Components Analysis

Usage

plot_pca_2d(
  moo_counts,
  count_type = NULL,
  sub_count_type = NULL,
  sample_metadata = NULL,
  sample_id_colname = NULL,
  feature_id_colname = NULL,
  group_colname = "Group",
  label_colname = "Label",
  samples_to_rename = NULL,
  color_values = c("#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"),
  principal_components = c(1, 2),
  legend_position = "top",
  point_size = 1,
  add_label = TRUE,
  label_font_size = 3,
  label_offset_x_ = 2,
  label_offset_y_ = 2,
  interactive_plots = FALSE,
  plots_subdir = "pca",
  plot_filename = "pca_2D.png",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots")
)

Arguments

moo_counts

counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot
count_type

type to assign the values of counts_dat to in the counts slot
sub_count_type

used if count_type is a list in the counts slot: specify the sub count type within the list. Must be a name in names(moo@counts[[count_type]]).
sample_metadata

sample metadata as a data frame or tibble.
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.)
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.)
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_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_values

vector of colors as hex values or names recognized by R
principal_components

vector with numbered principal components to plot
legend_position

passed to in legend.position ggplot2::theme()
point_size

size for ggplot2::geom_point()
add_label

whether to add text labels for the points
label_font_size

label font size for the PCA plot
label_offset_x_

label offset x for the PCA plot
label_offset_y_

label offset y for the PCA plot
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 ⁠figures/⁠ where plots will be saved if save_plots is TRUE
plot_filename

plot output filename - only used if save_plots is TRUE
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')

Value

ggplot object

See Also

plot_pca generic

Other PCA functions: calc_pca(), plot_pca(), plot_pca_3d()

3D PCA for counts dataframe

Description

3D PCA for counts dataframe

Usage

plot_pca_3d(
  moo_counts,
  count_type = NULL,
  sub_count_type = NULL,
  sample_metadata = NULL,
  feature_id_colname = NULL,
  sample_id_colname = NULL,
  samples_to_rename = NULL,
  group_colname = "Group",
  label_colname = "Label",
  principal_components = c(1, 2, 3),
  point_size = 8,
  label_font_size = 24,
  color_values = c("#5954d6", "#e1562c", "#b80058", "#00c6f8", "#d163e6", "#00a76c",
    "#ff9287", "#008cf9", "#006e00", "#796880", "#FFA500", "#878500"),
  plot_title = "PCA 3D",
  plot_filename = "pca_3D.html",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "pca"
)

Arguments

moo_counts

counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot
count_type

type to assign the values of counts_dat to in the counts slot
sub_count_type

used if count_type is a list in the counts slot: specify the sub count type within the list. Must be a name in names(moo@counts[[count_type]]).
sample_metadata

sample metadata as a data frame or tibble.
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.)
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
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.)
principal_components

vector with numbered principal components to plot
point_size

size for ggplot2::geom_point()
label_font_size

label font size for the PCA plot
color_values

vector of colors as hex values or names recognized by R
plot_title

title for the plot
plot_filename

plot output filename - only used if save_plots is TRUE
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_subdir

subdirectory in ⁠figures/⁠ where plots will be saved if save_plots is TRUE

Value

plotly::plot_ly figure

See Also

Other PCA functions: calc_pca(), plot_pca(), plot_pca_2d()

Plot 2D or 3D PCA for counts dataframe

Description

Plot 2D or 3D PCA for counts dataframe

Arguments

moo_counts

counts dataframe
sample_metadata

Required if moo_counts is a data.frame: sample metadata as a data frame or tibble.
principal_components

vector with numbered principal components to plot. Use 2 for a 2D pca with ggplot, or 3 for a 3D pca with plotly. (Default: c(1,2))
...

additional arguments forwarded to plot_pca_2d() (if 2 PCs) or plot_pca_3d() (if 3 PCs).

See Also

plot_pca generic

Other plotters for counts dataframes: plot_corr_heatmap_dat, plot_histogram_dat, plot_read_depth_dat

Plot 2D or 3D PCA for multiOmicDataset

Description

Plot 2D or 3D PCA for multiOmicDataset

Arguments

moo_counts

multiOmicDataSet containing count_type & sub_count_type in the counts slot
count_type

the type of counts to use. Must be a name in the counts slot (names(moo@counts)).
sub_count_type

used if count_type is a list in the counts slot: specify the sub count type within the list. Must be a name in names(moo@counts[[count_type]]).
principal_components

vector with numbered principal components to plot. Use 2 for a 2D pca with ggplot, or 3 for a 3D pca with plotly. (Default: c(1,2))
...

additional arguments forwarded to plot_pca_2d() (if 2 PCs) or plot_pca_3d() (if 3 PCs).

Value

PCA plot

See Also

plot_pca generic

Other plotters for multiOmicDataSets: plot_corr_heatmap_moo, plot_histogram_moo, plot_read_depth_moo

Plot read depth as a bar plot

Description

The first argument can be a multiOmicDataset object (moo) or a data.frame containing counts. For a moo, choose which counts slot to use with count_type & (optionally) sub_count_type.

Usage

plot_read_depth(moo_counts, ...)

Arguments

moo_counts

counts dataframe or multiOmicDataSet containing count_type & sub_count_type in the counts slot
...

arguments forwarded to method

Value

ggplot barplot

Methods

link to docs class
plot_read_depth_moo multiOmicDataSet
plot_read_depth_dat data.frame

See Also

Other plotters: plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), print_or_save_plot()

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), run_deseq2(), set_color_pal()

Examples

# multiOmicDataSet
moo <- multiOmicDataSet(
  sample_metadata = nidap_sample_metadata,
  anno_dat = data.frame(),
  counts_lst = list(
    "raw" = nidap_raw_counts,
    "clean" = nidap_clean_raw_counts
  )
)

plot_read_depth(moo, count_type = "clean")

# dataframe
plot_read_depth(nidap_clean_raw_counts)

Plot read depth for data.frame

Description

Plot read depth for data.frame

Arguments

moo_counts

counts dataframe

Value

ggplot barplot

See Also

plot_read_depth generic

Other plotters for counts dataframes: plot_corr_heatmap_dat, plot_histogram_dat, plot_pca_dat

Examples

# dataframe
plot_read_depth(nidap_clean_raw_counts)

Plot read depth for multiOmicDataSet

Description

Plot read depth for multiOmicDataSet

Arguments

moo_counts

multiOmicDataSet containing count_type & sub_count_type in the counts slot
count_type

the type of counts to use. Must be a name in the counts slot (names(moo@counts)).
sub_count_type

used if count_type is a list in the counts slot: specify the sub count type within the list. Must be a name in names(moo@counts[[count_type]]).

Value

ggplot barplot

See Also

plot_read_depth generic

Other plotters for multiOmicDataSets: plot_corr_heatmap_moo, plot_histogram_moo, plot_pca_moo

Examples

# multiOmicDataSet
moo <- multiOmicDataSet(
  sample_metadata = nidap_sample_metadata,
  anno_dat = data.frame(),
  counts_lst = list(
    "raw" = nidap_raw_counts,
    "clean" = nidap_clean_raw_counts
  )
)

plot_read_depth(moo, count_type = "clean")

Plot a venn diagram, UpSet plot, or table of intersections

Description

generates Venn diagram of intersections across a series of sets (e.g., intersections of significant genes across tested contrasts). This Venn diagram is available for up to five sets; Intersection plot is available for any number of sets. Specific sets can be selected for the visualizations and the returned dataset may include all (default) or specified intersections.

Usage

plot_venn_diagram(
  moo_diff_summary_dat,
  feature_id_colname = NULL,
  contrasts_colname = "Contrast",
  select_contrasts = c(),
  plot_type = "Venn diagram",
  intersection_ids = c(),
  venn_force_unique = TRUE,
  venn_numbers_format = "raw",
  venn_significant_digits = 2,
  venn_fill_colors = c("darkgoldenrod2", "darkolivegreen2", "mediumpurple3",
    "darkorange2", "lightgreen"),
  venn_fill_transparency = 0.2,
  venn_border_colors = "fill colors",
  venn_font_size_for_category_names = 3,
  venn_category_names_distance = c(),
  venn_category_names_position = c(),
  venn_font_size_for_counts = 6,
  venn_outer_margin = 0,
  intersections_order = "degree",
  display_empty_intersections = FALSE,
  intersection_bar_color = "steelblue4",
  intersection_point_size = 2.2,
  intersection_line_width = 0.7,
  table_font_size = 0.7,
  table_content = "all intersections",
  graphics_device = grDevices::png,
  dpi = 300,
  image_width = 4000,
  image_height = 3000,
  plot_filename = "venn_diagram.png",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "diff"
)

Arguments

moo_diff_summary_dat

Summarized differential expression analysis
feature_id_colname

The column from the counts data 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.)
contrasts_colname

Name of the column in moo_diff_summary_dat that contains the contrast names (default: "Contrast")
select_contrasts

A vector of contrast names to select for the plot. If empty, all contrasts are used.
plot_type

Type of plot to generate: "Venn diagram" or "Intersection plot". Default: "Venn diagram"
intersection_ids

A vector of intersection IDs to select for the plot. If empty, all intersections are used.
venn_force_unique

If TRUE, forces unique elements in the Venn diagram. Default: TRUE
venn_numbers_format

Format for the numbers in the Venn diagram. Options: "raw", "percent", "raw-percent", "percent-raw". Default: "raw"
venn_significant_digits

Number of significant digits for the Venn diagram numbers. Default: 2
venn_fill_colors

A vector of colors to fill the Venn diagram categories. Default: c("darkgoldenrod2", "darkolivegreen2", "mediumpurple3", "darkorange2", "lightgreen")
venn_fill_transparency

Transparency level for the Venn diagram fill colors. Default: 0.2
venn_border_colors

Colors for the borders of the Venn diagram categories. Default: "fill colors" (uses the same colors as venn_fill_colors)
venn_font_size_for_category_names

Font size for the category names in the Venn diagram. Default: 3
venn_category_names_distance

Distance of the category names from the Venn diagram circles. Default: c()
venn_category_names_position

Position of the category names in the Venn diagram. Default: c()
venn_font_size_for_counts

Font size for the counts in the Venn diagram. Default: 6
venn_outer_margin

Outer margin for the Venn diagram. Default: 0
intersections_order

Order of the intersections in the plot. Default: "by size"
display_empty_intersections

If TRUE, displays empty intersections in the plot. Default: FALSE
intersection_bar_color

Color for the intersection bars in the plot. Default: "lightgray"
intersection_point_size

Size of the points in the intersection plot. Default: 2
intersection_line_width

Width of the lines in the intersection plot. Default: 0.5
table_font_size

Font size for the table in the plot. Default: 3
table_content

Content of the table in the plot. Default: NULL
graphics_device

passed to ggsave(device). Default: grDevices::png
dpi

dots-per-inch of the output image (see ggsave()) - only used if save_plots is TRUE
image_width

output image width in pixels - only used if save_plots is TRUE
image_height

output image height in pixels - only used if save_plots is TRUE
plot_filename

plot output filename - only used if save_plots is TRUE
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_subdir

subdirectory in ⁠figures/⁠ where plots will be saved if save_plots is TRUE

Examples

plot_venn_diagram(nidap_volcano_summary_dat, print_plots = TRUE)

Enhanced Volcano Plot

Description

Uses Bioconductor's Enhanced Volcano Plot.

Usage

plot_volcano_enhanced(
  moo_diff,
  feature_id_colname = NULL,
  signif_colname = c("B-A_adjpval", "B-C_adjpval"),
  signif_threshold = 0.05,
  change_colname = c("B-A_logFC", "B-C_logFC"),
  change_threshold = 1,
  value_to_sort_the_output_dataset = "p-value",
  num_features_to_label = 30,
  use_only_addition_labels = FALSE,
  additional_labels = "",
  is_red = TRUE,
  lab_size = 4,
  change_sig_name = "p-value",
  change_lfc_name = "log2FC",
  title = "Volcano Plots",
  use_custom_lab = FALSE,
  ylim = 0,
  custom_xlim = "",
  xlim_additional = 0,
  ylim_additional = 0,
  axis_lab_size = 24,
  point_size = 2,
  image_width = 3000,
  image_height = 3000,
  dpi = 300,
  interactive_plots = FALSE,
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "diff",
  plot_filename = "volcano_enhanced.png"
)

Arguments

moo_diff

Differential expression analysis result from one or more contrasts. This must be a dataframe.
feature_id_colname

The column from the counts data 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.)
signif_colname

column name of significance values (e.g., adjusted p-values or FDR). This column will be used to determine which points are considered significant in the volcano plot.
signif_threshold

Numeric value specifying the significance cutoff for p-values (i.e. filters on signif_colname)
change_colname

column name of fold change values.
change_threshold

Numeric value specifying the fold change cutoff for significance (i.e. filters on change_colname)
value_to_sort_the_output_dataset

How to sort the output dataset. Options are "fold-change" or "p-value".
num_features_to_label

Number of top features/genes to label in the volcano plot. Default is 30.
use_only_addition_labels

If TRUE, only the additional labels specified in additional_labels will be used for labeling in the volcano plot, ignoring the top features.
additional_labels

comma-separated string of feature names or IDs to include in the volcano plot.
is_red

Logical. If TRUE, highlights points in red.
lab_size

Size of the labels in the volcano plot.
change_sig_name

Name for the significance column in the plot. Default is "p-value".
change_lfc_name

Name for the fold change column in the plot. Default is "log2FC".
title

Title of the plot. Default is "Volcano Plots".
use_custom_lab

If TRUE, uses custom labels for the plot (set by change_sig_name and change_lfc_name)
ylim

Y-axis limits for the plot.
custom_xlim

Custom X-axis limits for the plot.
xlim_additional

Additional space to add to the X-axis limits.
ylim_additional

Additional space to add to the Y-axis limits.
axis_lab_size

Size of the axis labels.
point_size

Size of the points in the plot.
image_width

output image width in pixels - only used if save_plots is TRUE
image_height

output image height in pixels - only used if save_plots is TRUE
dpi

dots-per-inch of the output image (see ggsave()) - only used if save_plots is TRUE
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.
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_subdir

subdirectory in ⁠figures/⁠ where plots will be saved if save_plots is TRUE
plot_filename

plot output filename - only used if save_plots is TRUE

Examples

plot_volcano_enhanced(nidap_deg_analysis, print_plots = TRUE)

Volcano Plot - Summary

Description

Produces one volcano plot for each tested contrast in the input DEG table. It can be sorted by either fold change, t-statistic, or p-value. The returned dataset includes one row for each significant gene in each contrast, and contains columns from the DEG analysis of that contrast as well as columns useful to the Venn diagram template downstream.

Usage

plot_volcano_summary(
  moo_diff,
  feature_id_colname = NULL,
  signif_colname = "pval",
  signif_threshold = 0.05,
  change_threshold = 1,
  value_to_sort_the_output_dataset = "t-statistic",
  num_features_to_label = 30,
  add_features = FALSE,
  label_features = FALSE,
  custom_gene_list = "",
  default_label_color = "black",
  custom_label_color = "green3",
  label_x_adj = 0.2,
  label_y_adj = 0.2,
  line_thickness = 0.5,
  label_font_size = 4,
  label_font_type = 1,
  displace_feature_labels = FALSE,
  custom_gene_list_special_label_displacement = "",
  special_label_displacement_x_axis = 2,
  special_label_displacement_y_axis = 2,
  color_of_signif_threshold_line = "blue",
  color_of_non_significant_features = "black",
  color_of_logfold_change_threshold_line = "red",
  color_of_features_meeting_only_signif_threshold = "lightgoldenrod2",
  color_for_features_meeting_pvalue_and_foldchange_thresholds = "red",
  flip_vplot = FALSE,
  use_default_x_axis_limit = TRUE,
  x_axis_limit = 5,
  use_default_y_axis_limit = TRUE,
  y_axis_limit = 10,
  point_size = 2,
  add_deg_columns = c("FC", "logFC", "tstat", "pval", "adjpval"),
  graphics_device = grDevices::png,
  image_width = 15,
  image_height = 15,
  dpi = 300,
  use_default_grid_layout = TRUE,
  number_of_rows_in_grid_layout = 1,
  aspect_ratio = 0,
  plot_filename = "volcano_summary.png",
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_subdir = "diff"
)

Arguments

moo_diff

Differential expression analysis result from one or more contrasts. This must be a dataframe.
feature_id_colname

The column from the counts data 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.)
signif_colname

column name of significance values (e.g., adjusted p-values or FDR). This column will be used to determine which points are considered significant in the volcano plot.
signif_threshold

Numeric value specifying the significance cutoff for p-values (i.e. filters on signif_colname)
change_threshold

Numeric value specifying the fold change cutoff for significance (i.e. filters on change_colname)
value_to_sort_the_output_dataset

How to sort the output dataset. Options are "fold-change" or "p-value".
num_features_to_label

Number of top features/genes to label in the volcano plot. Default is 30.
add_features

Add custom_gene_list To Labels. Set TRUE when you want to label a specific set of features (features) in the "custom_gene_list" parameter" IN ADDITION to the number of features you set in the "Number of Features to Label" parameter.
label_features

Select TRUE when you want to label ONLY a specific list of features(features) given in the "custom_gene_list" parameter.
custom_gene_list

Provide a list of features (comma separated) to be labeled on the volcano plot. You must toggle one of the following ON to see these labels: "Add features" or "Label Only My Feature List".
default_label_color

Set the color for the text used to add feature (gene) name labels to points.
custom_label_color

Set the color for the specific list of features (features) provided in the "Feature List" parameter.
label_x_adj

adjust position of the labels on the x-axis. Default: 0.2
label_y_adj

adjust position of the labels on the y-axis. Default: 0.2
line_thickness

Set the thickness of the lines in the plot. Default: 0.5
label_font_size

Set the font size of the labels. Default: 4
label_font_type

Set the font type of the labels. Default: 1
displace_feature_labels

Set to TRUE to displace gene labels. Default: FALSE. Set TRUE if you want to displace the feature (gene) label for a specific set of features. Make sure to use custom x- and y- limits and give sufficient space for displacement; otherwise other labels than the desired ones will appear displaced.
custom_gene_list_special_label_displacement

Provide a list of features (comma separated) for which you want special displacement of the feature label.
special_label_displacement_x_axis

Displacement of the feature label on the x-axis. Default: 2
special_label_displacement_y_axis

Displacement of the feature label on the y-axis. Default: 2
color_of_signif_threshold_line

Color of the significance threshold line. Default: "blue"
color_of_non_significant_features

Color of the non-significant features. Default: "black"
color_of_logfold_change_threshold_line

Color of the log fold change threshold line. Default: "red"
color_of_features_meeting_only_signif_threshold

Color of the features that meet only the significance threshold. Default: "lightgoldenrod2"
color_for_features_meeting_pvalue_and_foldchange_thresholds

Color of the features that meet both the p-value and fold change thresholds. Default: "red"
flip_vplot

Set to TRUE to flip the fold change values so that the volcano plot looks like a comparison was B-A. Default: FALSE
use_default_x_axis_limit

Set to TRUE to use the default x-axis limit. Default: TRUE
x_axis_limit

Custom x-axis limit. Default: c(-5, 5)
use_default_y_axis_limit

Set to TRUE to use the default y-axis limit. Default: TRUE
y_axis_limit

Custom y-axis limit. Default: c(0, 10)
point_size

Size of the points in the plot. Default: 1
add_deg_columns

Add additional columns from the DEG analysis to the output dataset. Default: ⁠"FC", "logFC", "tstat", "pval", "adjpval"⁠
graphics_device

passed to ggsave(device). Default: grDevices::png
image_width

output image width in pixels - only used if save_plots is TRUE
image_height

output image height in pixels - only used if save_plots is TRUE
dpi

dots-per-inch of the output image (see ggsave()) - only used if save_plots is TRUE
use_default_grid_layout

Set to TRUE to use the default grid layout. Default: TRUE
number_of_rows_in_grid_layout

Number of rows in the grid layout. Default: 1
aspect_ratio

Aspect ratio of the output image. Default: 4/3
plot_filename

Filename for the output plot. Default: "volcano_plot.png"
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_subdir

subdirectory in ⁠figures/⁠ where plots will be saved if save_plots is TRUE

Examples

plot_volcano_summary(nidap_deg_analysis, print_plots = TRUE)

Print and/or save a ggplot

Description

If save_plots is TRUE, the plot will be saved as an image to the path at file.path(plots_dir, filename). If plot_obj is a ggplot, ggplot2::ggsave() is used to save the image. Otherwise, graphics_device is used (grDevice::png() by default).

Usage

print_or_save_plot(
  plot_obj,
  filename,
  print_plots = options::opt("print_plots"),
  save_plots = options::opt("save_plots"),
  plots_dir = options::opt("plots_dir"),
  graphics_device = grDevices::png,
  ...
)

Arguments

plot_obj

plot object (e.g. ggplot, ComplexHeatmap...)
filename

name of the output file. will be joined with the plots_dir option.
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 TRUE, overwritable using option 'moo_save_plots' or environment variable 'MOO_SAVE_PLOTS')
plots_dir

Path where plots are saved when moo_save_plots is TRUE (Defaults to "figures/", overwritable using option 'moo_plots_dir' or environment variable 'MOO_PLOTS_DIR')
graphics_device

Default: grDevice::png(). Only used if the plot is not a ggplot.
...

arguments forwarded to ggplot2::ggsave()

Value

invisibly returns the path where the plot image was saved to the disk

See Also

Other plotters: plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth()

Read a multiOmicDataSet from disk

Description

Read a multiOmicDataSet from disk

Usage

read_multiOmicDataSet(filepath)

Arguments

filepath

Path to an RDS file produced by write_multiOmicDataSet()

Value

multiOmicDataSet

Set color palette for a single group/column

Description

This allows you to set custom palettes individually for groups in the dataset

Usage

set_color_pal(moo, colname, palette_fun = grDevices::palette.colors, ...)

Arguments

moo

multiOmicDataSet object (see create_multiOmicDataSet_from_dataframes())
colname

group column name to set the palette for
palette_fun

Function for selecting colors. Assumed to contain n for the number of colors. Default: grDevices::palette.colors()
...

additional arguments forwarded to palette_fun

Value

moo with colors updated at moo@analyses$colors$colname

See Also

Other moo methods: batch_correct_counts(), clean_raw_counts(), diff_counts(), filter_counts(), filter_diff(), normalize_counts(), plot_corr_heatmap(), plot_expr_heatmap(), plot_histogram(), plot_pca(), plot_read_depth(), run_deseq2()

Examples

moo <- create_multiOmicDataSet_from_dataframes(
  sample_metadata = as.data.frame(nidap_sample_metadata),
  counts_dat = as.data.frame(nidap_raw_counts)
)
moo@analyses$colors$Group
moo <- moo |> set_color_pal("Group", palette_fun = RColorBrewer::brewer.pal, name = "Set2")
moo@analyses$colors$Group

Write a multiOmicDataSet to disk as an RDS file

Description

Write a multiOmicDataSet to disk as an RDS file

Usage

write_multiOmicDataSet(moo, filepath = "moo.rds")

Arguments

moo

multiOmicDataSet object to serialize
filepath

Path to the RDS file to write (default: "moo.rds")

Value

Invisibly returns filepath

Write multiOmicDataSet properties to disk as CSV files

Description

Writes the properties of a multiOmicDataSet object to disk as separate files in output_dir. Properties that are data frames are saved as CSV files, while all other objects are saved as RDS files.

Usage

write_multiOmicDataSet_properties(moo, output_dir = "moo")

Arguments

moo

multiOmicDataSet object to write properties from
output_dir

Directory where the properties will be saved (default: "moo")

Value

Invisibly returns the output_dir where the files were saved