BioC 3.5: BUILD report for sparseDOSSA on malbec2

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### Running command:
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### /home/biocbuild/bbs-3.5-bioc/R/bin/R CMD build --keep-empty-dirs --no-resave-data sparseDOSSA
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* checking for file ‘sparseDOSSA/DESCRIPTION’ ... OK
* preparing ‘sparseDOSSA’:
* checking DESCRIPTION meta-information ... OK
* installing the package to build vignettes
* creating vignettes ... ERROR
sparseDOSSA package:sparseDOSSA R Documentation

_S_p_a_r_s_e _D_a_t_a _O_b_s_e_r_v_a_t_i_o_n_s _f_o_r _S_i_m_u_l_a_t_i_n_g _S_y_n_t_h_e_t_i_c _A_b_u_n_d_a_n_c_e

_D_e_s_c_r_i_p_t_i_o_n:

Sparse Data Observations for Simulating Synthetic Abundance

_U_s_a_g_e:

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 )

_A_r_g_u_m_e_n_t_s:

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.

_V_a_l_u_e:

A list contains the names of the output files.

_A_u_t_h_o_r(_s):

Boyu Ren<bor158@mail.harvard.edu>, Emma
Schwager<eschwager@hsph.harvard.edu>, Timothy
Tickle<ttickle@hsph.harvard.edu>, Curtis Huttenhower
<chuttenh@hsph.harvard.edu>

_E_x_a_m_p_l_e_s:

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)

Warning in sparseDOSSA::sparseDOSSA() :
number of associations = 0, and no spike file specified; no bug-bug spike-ins will be done.
Parameters BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 300
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 2.39794758911363 LogSD 1.33357395233333 Threshold 26.178898826878
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.3792048696371650.5114782028284780.6291781720965290.6427351137112290.7581370409308820.978795940576947
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
Start funcShuffleMatrix
stop func_generate_random_lognormal_matrix
start func_generate_random_lognormal_with_outliers
Stop func_generate_random_lognormal_with_outliers
start func_generate_random_lognormal_with_multivariate_spikes
stop func_generate_random_lognormal_with_multivariate_spikes
Warning in sparseDOSSA::sparseDOSSA(number_features = n.microbes, number_samples = n.samples, :
number of associations = 0, and no spike file specified; no bug-bug spike-ins will be done.
Parameters BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 150
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 0.423465540160656 LogSD 2.66714790466667 Threshold 59.1226199198229
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Changing low SDs to a little more than 0. # occurences = 20
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.1519073841952140.5866075074462880.8664115182376170.7661556770358360.9967477645598250.999984482714654
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
Start funcShuffleMatrix
stop func_generate_random_lognormal_matrix
start func_generate_random_lognormal_with_outliers
Stop func_generate_random_lognormal_with_outliers
start func_generate_random_lognormal_with_multivariate_spikes
stop func_generate_random_lognormal_with_multivariate_spikes
Warning in sparseDOSSA::sparseDOSSA(spikeStrength = "2.0", spikeCount = "2") :
number of associations = 0, and no spike file specified; no bug-bug spike-ins will be done.
Parameters BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 300
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 2.39794758911363 LogSD 1.33357395233333 Threshold 26.178898826878
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.3662529477633120.5153690271571280.6377603808008880.6438865122582410.7647978310241540.977765606336258
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
Start funcShuffleMatrix
stop func_generate_random_lognormal_matrix
start func_generate_random_lognormal_with_outliers
Stop func_generate_random_lognormal_with_outliers
start func_generate_random_lognormal_with_multivariate_spikes
stop func_generate_random_lognormal_with_multivariate_spikes
Parameters BEFORE Calibration File
Length exp NA Length vdMu NA length vdSD NA length vdPercentZero NA Read depth 8030
Parameters AFTER Calibration File (if no calibration file is used, defaults are shown)
Length exp 1 Length vdMu 1 length vdSD 1 length vdPercentZero 1 Read depth 8030 Feature Count 300
func_generate_random_lognormal_matrix START
start funcGenerateFeatureParameters
funcGenerateFeatureParameters: Generating vdExp Vector.
LogMu 2.39794758911363 LogSD 1.33357395233333 Threshold 26.178898826878
funcGenerateFeatureParameters: Generating vdSD Vector.
funcGenerateFeatureParameters: Generating vdMu Vector.
funcGenerateFeatureParameters: Generating vdPercentZero Vector.
***Scale***
1
***vdPercentZero***
0.3565056208862230.5236188907058240.6424672546809520.6422328155928110.7619314396161650.9740564286435
stop funcGenerateFeatureParameters
func_generate_random_lognormal_matrix: START Making features
Start funcShuffleMatrix
stop func_generate_random_lognormal_matrix
start func_generate_random_lognormal_with_outliers
Stop func_generate_random_lognormal_with_outliers
start func_generate_random_lognormal_with_multivariate_spikes
Quitting from lines 134-136 (sparsedossa-vignette.Rmd)
Error: processing vignette 'sparsedossa-vignette.Rmd' failed with diagnostics:
funcSample:: Can not sample from length of 0 vector.
Execution halted

BioC 3.5: BUILD report for sparseDOSSA on malbec2

Summary

Command output