HTSSIP (version 1.4.1)

DESeq2_l2fc: Calculating log2 fold change for HTS-SIP data.

Description

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.

Usage

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

Arguments

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.

Value

dataframe of HRSIP results

Details

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

Examples

# NOT RUN {
data(physeq_S2D2)
# }
# NOT RUN {
df_l2fc = DESeq2_l2fc(physeq_S2D2, density_min=1.71, density_max=1.75, design=~Substrate)
head(df_l2fc)
# }
# NOT RUN {
# }