Prepare data in proper format for use in PLN model and its variants. The function (i) merges a count table and a covariate data frame in the most comprehensive way and (ii) computes offsets from the count table using one of several normalization schemes (TSS, CSS, RLE, GMPR, Wrench, etc). The function fails with informative messages when the heuristics used for sample matching fail.
prepare_data(
counts,
covariates,
offset = "TSS",
call = rlang::caller_env(),
...
)Required. An abundance count table, preferably with dimensions names and species as columns.
Required. A covariates data frame, preferably with row names.
Optional. Normalization scheme used to compute scaling factors used as offset during PLN inference. Available schemes are "TSS" (Total Sum Scaling, default), "CSS" (Cumulative Sum Scaling, used in metagenomeSeq), "RLE" (Relative Log Expression, used in DESeq2), "GMPR" (Geometric Mean of Pairwise Ratio, introduced in Chen et al., 2018), Wrench (introduced in Kumar et al., 2018) or "none". Alternatively the user can supply its own vector or matrix of offsets (see note for specification of the user-supplied offsets).
Optional. The execution environment in which to set the local error call.
Additional parameters passed on to compute_offset()
A data.frame suited for use in PLN() and its variants with two specials components: an abundance count matrix (in component "Abundance") and an offset vector/matrix (in component "Offset", only if offset is not set to "none")
User supplied offsets should be either vectors/column-matrices or have the same number of column as the original count matrix and either (i) dimension names or (ii) the same dimensions as the count matrix. Samples are trimmed in exactly the same way to remove empty samples.
Chen, L., Reeve, J., Zhang, L., Huang, S., Wang, X. and Chen, J. (2018) GMPR: A robust normalization method for zero-inflated count data with application to microbiome sequencing data. PeerJ, 6, e4600 doi:10.7717/peerj.4600
Paulson, J. N., Colin Stine, O., Bravo, H. C. and Pop, M. (2013) Differential abundance analysis for microbial marker-gene surveys. Nature Methods, 10, 1200-1202 doi:10.1038/nmeth.2658
Anders, S. and Huber, W. (2010) Differential expression analysis for sequence count data. Genome Biology, 11, R106 doi:10.1186/gb-2010-11-10-r106
Kumar, M., Slud, E., Okrah, K. et al. (2018) Analysis and correction of compositional bias in sparse sequencing count data. BMC Genomics 19, 799 doi:10.1186/s12864-018-5160-5
Robinson, M.D., Oshlack, A. (2010) A scaling normalization method for differential expression analysis of RNA-seq data. Genome Biol 11, R25 doi:10.1186/gb-2010-11-3-r25
compute_offset() for details on the different normalization schemes
data(trichoptera)
proper_data <- prepare_data(
counts = trichoptera$Abundance,
covariates = trichoptera$Covariate,
offset = "GMPR",
scale = "count"
)
proper_data$Abundance
#> Che Hyc Hym Hys Psy Aga Glo Ath Cea Ced Set All Han Hfo Hsp Hve Sta
#> 1 0 0 5 0 17 0 0 0 0 2 0 1 0 1 2 0 1
#> 2 0 0 3 0 8 0 0 0 0 0 0 2 0 0 0 0 0
#> 3 0 0 1 0 32 0 0 0 0 0 0 4 0 0 1 0 0
#> 4 0 0 3 0 176 4 0 0 0 1 0 3 0 0 3 0 2
#> 5 0 0 4 0 69 2 0 0 0 0 0 1 0 0 1 0 2
#> 6 0 0 2 0 14 1 0 0 0 0 0 1 0 0 0 0 0
#> 7 0 0 2 0 4 0 0 0 0 2 0 0 0 0 0 0 0
#> 8 0 0 1 0 29 1 0 0 0 0 0 0 0 0 0 0 3
#> 9 0 0 4 0 8 0 0 0 0 0 0 0 0 0 0 0 0
#> 10 0 0 2 0 2 0 0 0 0 0 0 0 0 0 0 0 0
#> 11 0 0 1 0 3 0 0 0 0 0 0 0 0 0 0 0 0
#> 12 0 0 0 0 3 0 0 0 0 0 0 0 0 0 0 0 0
#> 13 1 0 1 0 40 2 0 1 0 2 0 1 0 0 0 1 0
#> 14 0 0 4 0 20 2 1 0 0 1 0 0 0 0 1 0 4
#> 15 0 0 3 0 500 19 0 2 0 17 5 2 0 0 2 1 49
#> 16 0 0 2 0 142 9 0 0 0 3 3 2 0 0 0 0 11
#> 17 0 0 2 0 44 1 0 0 1 4 3 0 0 0 0 0 3
#> 18 0 0 3 1 31 4 0 0 0 1 7 0 0 0 0 0 4
#> 19 0 0 6 0 30 6 0 0 0 2 5 0 0 0 0 0 7
#> 20 0 1 7 0 71 5 1 1 0 5 18 0 0 1 5 0 12
#> 21 0 0 5 0 20 0 0 0 0 5 1 0 0 0 0 0 4
#> 22 0 0 3 0 5 0 0 0 0 2 0 0 0 0 2 0 1
#> 23 0 0 0 1 8 0 0 0 0 1 0 0 0 1 4 0 2
#> 24 0 0 1 0 2 0 0 0 0 0 0 0 0 0 0 0 0
#> 25 0 0 3 0 10 1 1 0 0 0 2 0 0 2 7 0 1
#> 26 0 0 1 0 9 0 0 0 0 1 2 1 3 3 14 0 6
#> 27 0 0 1 0 14 0 0 0 0 5 3 0 7 0 10 0 4
#> 28 0 0 0 0 2 0 0 1 0 0 0 0 0 1 3 0 1
#> 29 0 0 0 0 4 0 0 0 0 0 1 0 0 0 3 0 1
#> 30 0 1 0 0 1173 2 3 1 2 9 71 0 27 69 227 1 13
#> 31 0 0 12 0 2671 1 4 1 3 7 49 1 15 52 161 1 2
#> 32 0 0 7 0 33 4 0 0 0 0 0 3 33 1 0 0 7
#> 33 0 0 12 0 62 8 0 0 0 3 1 15 23 0 5 0 6
#> 34 0 0 15 0 220 20 0 0 0 2 0 5 27 0 5 0 33
#> 35 0 0 6 0 29 6 0 0 0 3 0 2 3 0 0 0 17
#> 36 0 0 4 0 60 3 0 0 0 0 0 0 2 0 0 0 21
#> 37 0 0 18 1 21 2 0 0 0 2 1 2 5 0 0 0 11
#> 38 0 0 4 0 3 0 0 0 0 4 0 0 1 0 0 0 3
#> 39 0 0 5 0 5 0 0 0 0 1 0 0 1 0 0 0 2
#> 40 0 0 1 0 18 0 0 0 0 0 0 1 0 0 0 0 0
#> 41 0 0 4 0 49 0 0 0 0 3 0 1 0 0 0 2 11
#> 42 0 0 3 1 33 0 0 2 0 1 0 0 2 0 0 0 11
#> 43 0 0 3 0 71 1 0 0 0 11 1 0 3 0 0 0 5
#> 44 0 0 6 1 28 1 2 1 0 3 0 1 0 0 0 0 0
#> 45 0 0 5 1 37 3 1 2 0 7 1 1 0 0 0 1 3
#> 46 1 0 4 0 103 1 1 0 0 2 10 2 0 2 3 2 18
#> 47 0 0 2 0 11 0 0 1 0 1 1 0 0 0 0 0 0
#> 48 1 0 0 0 17 0 0 1 1 2 0 0 3 0 0 0 6
#> 49 0 1 2 0 27 0 0 0 0 1 4 0 36 0 11 0 4
proper_data$Offset
#> [1] 30.084856 29.463845 37.495950 57.505735 49.888839 29.513765
#> [7] 19.949548 31.864374 32.075396 12.733865 9.104921 4.550832
#> [13] 41.915322 36.609494 249.546262 100.457779 46.401773 47.994943
#> [19] 69.219635 106.791751 48.521274 19.577977 18.898231 7.790568
#> [25] 23.345452 25.239568 34.703381 9.006205 10.116829 542.364607
#> [31] 243.692871 82.500856 108.703715 241.444947 77.274416 95.984236
#> [37] 62.017749 29.133040 22.133947 25.942671 75.746825 51.798296
#> [43] 67.640234 52.596146 57.108005 83.702490 22.685087 43.059821
#> [49] 41.384158