sparseDOSSA: Sparse Data Observations for Simulating Synthetic Abundance

In biobakery/sparseDOSSA: Sparse Data Observations for Simulating Synthetic Abundance

Description

Sparse Data Observations for Simulating Synthetic Abundance

Usage

sparseDOSSA( strNormalizedFileName = "SyntheticMicrobiome.pcl",
             strCountFileName = "SyntheticMicrobiome-Counts.pcl",
             parameter_filename = "SyntheticMicrobiomeParameterFile.txt",
             bugs_to_spike = 0,
             spikeFile = NA,
             calibrate = NA,
             datasetCount = 1,
             read_depth = 8030,
             number_features = 300,
             bugBugCorr =  "0.5",
             spikeCount = "1",
             lefse_file = NULL,
             percent_spiked = 0.03,
             minLevelPercent = 0.1,
             number_samples = 50,
             max_percent_outliers = 0.05,
             number_metadata = 5,
             spikeStrength = "1.0",
             seed =  NA,
             percent_outlier_spikins = 0.05,
             minOccurence =  0,
             verbose =  TRUE,
             minSample =  0,
             scalePercentZeros = 1,
             association_type =  "linear",
             noZeroInflate =  FALSE,
             noRunMetadata = FALSE, 
             runBugBug =  FALSE,
             UserMetadata = NA,
             Metadatafrozenidx = NA )

Arguments

strNormalizedFileName	This output file records the synthetic microbiome data for null community (no spike-in and outliers), outlier-added community without spike-in and final spiked data. We put samples in columns and features in rows. The first chunk of the file is metadata, with row names Metadata_. The second chunk is for null community, with row names Feature_Lognormal_. The third chunk is for outlier-introduced community, with row names Feature_Outlier_*. The last chunk is for spiked data, with row names Feature_spike. This file records relative abundance data.
strCountFileName	This output file has the same organization as the file strNormalizedFileName but records raw counts data.
parameter_filename	This output file records diagnostic information and values of model parameters as well as the spike-in assignment. The most part of this file is used only for debugging. Users can focus on lines after Minimum Spiked-in Samples. Those lines record which metadata are correlated with which feature. The format is all metadata that are correlated with a specific features are listed under the name of the feature.
bugs_to_spike	Number of bugs to correlate with others. A non-negative integer value is expected.
spikeFile	The name of the file where the correlation values are stored. Should have fields 'Domain', 'Range', and 'Correlation'.
calibrate	Calibration file for generating the random log normal data. TSV file (column = feature).
datasetCount	The number of bug-bug spiked datasets to generate. A positive integer value is expected.
read_depth	Simulated read depth for counts. A positive integer value is expected.
number_features	The number of features per sample to create. A positive integer value is expected.
bugBugCorr	A vector of string separated values for the correlation values of the pairwise bug-bug associations. This is the correlation of the log-counts. Values are comma-separated; for example: 0.7,0.5. Default is 0.5.
spikeCount	Counts of spiked metadata used in the spike-in dataset - These values should be comma delimited values, in the order of the spikeStrength values (if given), Can be one value, in this case the value will be repeated to pair with the spikeCount values (if multiple are present). For example 1,2,3.
lefse_file	Folder containing lefSe inputs.
percent_spiked	The percent of features spiked-in. A real number between 0 and 1 is expected.
minLevelPercent	Minimum percent of measurements out of the total a level can have in a discontinuous metadata (rounded up to the nearest count). A real number between 0 and 1 is expected.
number_samples	The number of samples to generate. A positive integer greater than 0 is expected.
max_percent_outliers	The maximum percent of outliers to spike into a sample. A real number between 0 and 1 is expected.
number_metadata	Indicates how many metadata are created, number_metadata*2 = number continuous metadata, number_metadata = number binary metadata, number_metadata = number quaternary metadata, A positive integer greater than 0 is expected.
spikeStrength	Strength of the metadata association with the spiked-in feature, These values should be comma delimited and in the order of the spikeCount values (if given),Can be one value, in this case the value will be repeated to pair with the spikeStrength values (if multiple are present). For example 0.2,0.3,0.4.
seed	A seed to freeze the random generation of counts/relative abundance,If left as default (NA), generation is random - If seeded, data generation will be random within a run but identical if ran again under the same settings,an integer is expected.
percent_outlier_spikins	The percent of samples to spike in outliers. A real number between 0 to 1 is expected.
minOccurence	Minimum counts a bug can have for the occurrence quality control filter used when creating bugs (filtering minimum number of counts in a minimum number of samples). A positive integer is expected.
verbose	If True logging and plotting is made by the underlying methodology. This is a flag, it is either included or not included in the command line, no value needed.
minSample	Minimum samples a bug can be in for the occurrence quality control filter used when creating bugs (filtering minimum number of counts in a minimum number of samples). A positive integer is expected.
scalePercentZeros	A scale used to multiply the percent zeros of all features across the sample after it is derived from the relationships with it and the feature abundance or calibration file. Requires a number greater than 0. A number greater than 1 increases sparsity, a number less than 1 decreases sparsity, O removes sparsity, 1 (default) does not change the value and the value.
association_type	The type of association to generate. Options are 'linear' or 'rounded_linear'.
noZeroInflate	If given, zero inflation is not used when generating a feature. This is a flag, it is either included or not included in the command line, no value needed.
noRunMetadata	If given, no metadata files are generated, This is a flag, it is either included or not included in the command line, no value needed.
runBugBug	If given, bug-bug interaction files are generated in addition to any metadata files. This is a flag, it is either included or not included in the command line, no value needed.
UserMetadata	If given, it should be a numeric matrix containing metadata information. Notice discrete variable should be first converted into dummy variables. Default is to generate metadata matrix randomly.
Metadatafrozenidx	If given, it should be a vector of integers. It contains the row indices of metadata matrix that will be used in all metadata spike-in. The length of this vector should be equal to spikeCount.

Value

A list with four fields. output_files contains the names of the output files (count data, normalized data and truth file). OTU_count is a list of character matrices, containing all datasetCount simulated OTU count tables. OTU_norma is a list of character matrices, containing all datasetCount nomralized simulated OTU tables. truth is list of character matrices, containing the ground truth of each of all datasetCount simulations.

Author(s)

Boyu Ren<[e-mail]>, Emma Schwager<[e-mail]>, Timothy Tickle<[e-mail]>, Curtis Huttenhower <[e-mail]>

Examples

sparseDOSSA(strNormalizedFileName = "SyntheticMicrobiome.pcl",
	strCountFileName = "SyntheticMicrobiome-Counts.pcl",
	parameter_filename = "SyntheticMicrobiomeParameterFile.txt",
	bugs_to_spike = 0,
	calibrate = NA,
	datasetCount = 1,
	read_depth = 8030,
	number_features = 300,
	spikeCount = "1",
	lefse_file = NA,
	percent_spiked = 0.03,
	minLevelPercent =  0.1,
	number_samples = 50, 
	max_percent_outliers = 0.05,
	number_metadata = 5,
	spikeStrength =  "1.0",
	seed =  1,
	percent_outlier_spikins = 0.05,
	minOccurence =  0,
	verbose =  TRUE,
	minSample =  0,
	association_type =  "linear",
	noZeroInflate =  FALSE,
	noRunMetadata = FALSE,
	runBugBug =  FALSE)

biobakery/sparseDOSSA documentation built on March 29, 2021, 3:06 p.m.