Takes idea from Wu et al. (citation below) and calculates IQLR for
Lambda, potentially useful if you believe there is an invariant group of
categories (e.g., taxa / genes) that are not changing (in absolute abundance)
between samples. IQLR is defined as
$$IQLR_x = log(x_i/g(IQVF))$$
for i in 1,...,D.
IQVF are the CLR coordinates whose variance is within the inter-quantile range
(defined by probs argument to this function).
A different IQVF is fit for each posteior sample as the IQVFs are calculted
based on posterior estimates for Lambda. The variance of a CLR coordinate
is defined as the norm of each row of Lambda[,focus.cov] (i.e.,
the covariation in Eta, explained by those covariates). This definition of
variance allows uses to exclude variation from technical / trivial sources
in calculation of IQVF/IQLR.
Usage
lambda_to_iqlr(m, focus.cov = NULL, probs = c(0.25, 0.75))Arguments
- m
object of class pibblefit (e.g., output of
pibble)- focus.cov
vector of integers or characters specifying columns (covariates) of Lambda to include in calculating IQLR (if NULL, default, then uses all covariates)
- probs
bounds for categories (i.e., features / genes / taxa) to include in calculation of iqlr (smaller bounds means more stringent inclusion criteria)
Value
array of dimension (D, Q, iter) where D is number of taxa, Q is number of covariates, and iter is number of posterior samples.
Details
Primarily intended for doing differential expression analysis under assumption that only small group of categories (e.g., taxa / genes) are changing
References
Jia R. Wu, Jean M. Macklaim, Briana L. Genge, Gregory B. Gloor (2017) Finding the center: corrections for asymmetry in high-throughput sequencing datasets. arxiv:1704.01841v1
Examples
sim <- pibble_sim()
fit <- pibble(sim$Y, sim$X)
# Use first two covariates to define iqlr, just show first 5 samples
lambda_to_iqlr(fit, 1:2)[,,1:5]
#> , , 1
#>
#> [,1] [,2]
#> [1,] 2.71086966 5.7756342
#> [2,] -2.83387197 -2.8177118
#> [3,] 6.21734877 -1.0205003
#> [4,] 1.03795398 -1.1817226
#> [5,] 0.02936279 0.6105819
#> [6,] 0.56491065 -1.4853419
#> [7,] -0.35171465 -0.2038065
#> [8,] 0.99487170 2.1857393
#> [9,] -2.59773634 0.4813253
#> [10,] 1.00534847 -0.3817622
#>
#> , , 2
#>
#> [,1] [,2]
#> [1,] 3.0282808 6.150287737
#> [2,] -2.3594906 -2.576907294
#> [3,] 6.5439513 -0.302916382
#> [4,] 1.2905173 -1.711260645
#> [5,] 0.7489538 0.465223279
#> [6,] 1.1754987 -1.210338979
#> [7,] 0.3202066 -0.008866017
#> [8,] 0.5439563 3.397195594
#> [9,] -3.0099723 -0.475595970
#> [10,] 1.5254589 -0.142063900
#>
#> , , 3
#>
#> [,1] [,2]
#> [1,] 2.5343542 5.5237336
#> [2,] -2.8942428 -2.2415649
#> [3,] 7.2954993 -0.3862755
#> [4,] 1.0831178 -0.4090549
#> [5,] 0.3286953 0.6520604
#> [6,] 1.1259718 -1.2595486
#> [7,] 0.1886304 0.2571886
#> [8,] 0.5532129 2.6440282
#> [9,] -2.7623024 -0.9754247
#> [10,] 1.2980611 -0.2556193
#>
#> , , 4
#>
#> [,1] [,2]
#> [1,] 3.52305266 5.21132802
#> [2,] -3.00855521 -3.21624947
#> [3,] 6.30345752 -0.46103455
#> [4,] 1.11136601 -0.40460251
#> [5,] 0.81671390 0.49443808
#> [6,] 1.37591213 -1.08911184
#> [7,] 0.06188396 -0.03022373
#> [8,] 0.57146984 2.31820778
#> [9,] -2.00926594 -1.19887222
#> [10,] 1.61737686 -0.11045263
#>
#> , , 5
#>
#> [,1] [,2]
#> [1,] 2.7257996 5.9445044
#> [2,] -3.2776972 -3.5446320
#> [3,] 5.0955870 -0.1133081
#> [4,] 0.9483930 -2.2406642
#> [5,] -0.1463391 0.5437634
#> [6,] 0.9236704 -1.3396334
#> [7,] -0.4409935 -0.3799092
#> [8,] 0.8102189 2.5869159
#> [9,] -2.6822822 0.9933817
#> [10,] 1.0285488 -0.2413530
#>