1) Metadata (Required)

Metadata must include sample IDs and the experimental design

# here just an example
groupinformation <- data.frame(
    group = c(rep("a", 10), rep("b", 10)),
    factor1 = rnorm(10),
    factor2 = rnorm(mean = 100, 10),
    subject = factor(c(1:10, 1:10))
  )
head(groupinformation)

##   group    factor1   factor2 subject
## 1     a -0.1533463  99.88965       1
## 2     a  1.2855310 100.86515       2
## 3     a  0.5674519 101.01802       3
## 4     a -0.7097412 100.06131       4
## 5     a  0.4745012  99.07435       5
## 6     a -2.2891605  98.36897       6

Note: The ordering of samples in the metadata must match the ordering in the feature table

2 & 3) Feature Table and Taxonomy Table (Required)

These are commonly provided together in QIIME-style outputs.

# Load the example dataset

data(testotu)

# Extract feature table (numeric abundance matrix)

feature_table <- testotu[, -c(1, 22)]
head(feature_table)

##    X1  X2  X3 X4 X5 X6  X7  X8  X9 X10 X11 X12 X13 X14 X15 X16 X17 X18 X19 X20
## 1 202 146 120 48 30 49   9  14  13  11   9   9  52  47  36  51  55  83 535 309
## 2   0   2   0 14  7  5 135 190 143 149  73 107  11  34  34 165 176 448  18  25
## 3  89 100 103 47 45 27 150 188 131 285 358 369   9  12  12  40  34  47  52  57
## 4   0   0   0  3  2  0   3   1   2   3   2   2   0   1   0   0   0   0   1   5
## 5  15  47  12  9  2  9  46  73  54 115  92 109  12   7   4  13  15  12  29  39
## 6   2   3   3  0  0  1   2   3   2   6   5  11   1   0   0   4   2   1   0   2

# Extract taxonomy table

tax_anno <- testotu[, c(1, 22)]
head(tax_anno)

##   OTU.ID
## 1   OTU1
## 2   OTU2
## 3   OTU3
## 4   OTU4
## 5   OTU5
## 6   OTU6
##                                                                                                                                                   taxonomy
## 1 d__Bacteria; p__Gemmatimonadota; c__Gemmatimonadetes; o__Gemmatimonadales; f__Gemmatimonadaceae; g__uncultured; s__uncultured Gemmatimonadetes bacterium
## 2                         d__Bacteria; p__Armatimonadota; c__Fimbriimonadia; o__Fimbriimonadales; f__Fimbriimonadaceae; g__norank; s__uncultured bacterium
## 3                                   d__Bacteria; p__Actinobacteriota; c__Thermoleophilia; o__Gaiellales; f__uncultured; g__norank; s__uncultured bacterium
## 4                                           d__Bacteria; p__Proteobacteria; c__Alphaproteobacteria; o__Micavibrionales; f__Micavibrionaceae; g__Micavibrio
## 5                                    d__Bacteria; p__Gemmatimonadota; c__S0134 terrestrial group; o__norank; f__norank; g__norank; s__uncultured bacterium
## 6                                                               d__Bacteria; p__Chloroflexi; c__Chloroflexia; o__Thermomicrobiales; f__AKYG1722; g__norank

Notes:

1.The feature table should contain only numeric values.

2.Column names in the taxonomy table are flexible.

3.The taxonomy column must contain all hierarchical taxonomic annotations.

Optional Input: Phylogenetic Tree

A phylogeny tree

Alternatively: Import Existing Objects

LorMe can directly import from:

phyloseq

microeco

objects, allowing seamless integration with existing workflows.

Option I: Direct encapsulation

 # Simple mode

test_object <- tax_summary(
groupfile = groupinformation,
inputtable = feature_table,
taxonomytable = tax_anno
)

# Complete mode

test_object <- tax_summary(
groupfile = groupinformation,
inputtable = feature_table,
taxonomytable = tax_anno,
reads = TRUE,
into = "standard",
sep = ";",
outputtax = c("Phylum", "Genus")
)

Notes:

1.If your metagenomic annotations contain both Kingdom and Domain, set into = "complete".

2.To output all taxonomic levels, use outputtax = "standard".

Option II: Converting from phyloseq or microeco object

# Convert from phyloseq

if (requireNamespace("phyloseq", quietly = TRUE)) {
data("GlobalPatterns", package = "phyloseq")
gp_obj <- Trans_from_phylo(GlobalPatterns, outputtax = "standard")
}

# Convert from microeco

if (requireNamespace("microeco", quietly = TRUE)) {
data("dataset", package = "microeco")
dataset_obj <- Trans_from_microeco(dataset, outputtax = "standard")
}

Note: The phyloseq or microeco object must include complete taxonomy information.

Basic Configuration

test_object_plan1 <- object_config(
taxobj = test_object,
treat_location = 1  # Automatically assigns aesthetic colors
)

## Color scheme generated, see in your plot interface

Advanced Configuration (Optional)

my_col <- color_scheme("Plan1", names = c("a", "b"))

## Color scheme generated, see in your plot interface

my_order <- c("b", "a")

test_object_plan1 <- object_config(
taxobj = test_object,
treat_location = 1,
rep_location = 4,
treat_col = my_col,
treat_order = my_order
)

More details in colors color_scheme