DESeq2_l2fc: Calculating log2 fold change for HTS-SIP data.
In HTSSIP: High Throughput Sequencing of Stable Isotope Probing Data Analysis

Description Usage Arguments Details Value Examples

The phyloseq object will be filtered to 1) just OTUs that pass the sparsity cutoff 2) just samples in the user-defined 'heavy' fractions. The log2 fold change (l2fc) is calculated between labeled treatment and control gradients.

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DESeq2_l2fc(physeq, density_min, density_max, design,
  l2fc_threshold = 0.25, sparsity_threshold = 0.25,
  sparsity_apply = "all", size_factors = "geoMean")

`physeq`	Phyloseq object
`density_min`	Minimum buoyant density of the 'heavy' gradient fractions
`density_max`	Maximum buoyant density of the 'heavy' gradient fractions
`design`	`design` parameter used for DESeq2 analysis. See `DESeq2::DESeq` for more details.
`l2fc_threshold`	log2 fold change (l2fc) values must be significantly above this threshold in order to reject the hypothesis of equal counts.
`sparsity_threshold`	All OTUs observed in less than this portion (fraction: 0-1) of gradient fraction samples are pruned. A a form of indepedent filtering, The sparsity cutoff with the most rejected hypotheses is used.
`sparsity_apply`	Apply sparsity threshold to all gradient fraction samples ('all') or just heavy fraction samples ('heavy')
`size_factors`	Method of estimating size factors. 'geoMean' is from (Pepe-Ranney et. al., 2016) and removes all zero-abundances from the calculation. 'default' is the default for estimateSizeFactors. 'iterate' is an alternative when every OTU has a zero in >=1 sample.

The 'use_geo_mean' parameter uses geometric means on all non-zero abundances for estimateSizeFactors instead of using the default log-tranformed geometric means.

dataframe of HRSIP results

data(physeq_S2D2)
## Not run: 
df_l2fc = DESeq2_l2fc(physeq_S2D2, density_min=1.71, density_max=1.75, design=~Substrate)
head(df_l2fc)

## End(Not run)