iCAMP (version 1.5.12)

icamp.cate: Summarize iCAMP result for different categories of taxa

Description

This function is to calculate various statistic index to assess relative importance of each process on different categories of taxa. The categories can be defined in various ways. For example, core, consistently and occasionally rare taxa; or different phyla; or various particular functional groups.

Usage

icamp.cate(icamp.bins.result, comm, cate, treat = NULL,
           silent = FALSE, between.group = FALSE)

Arguments

icamp.bins.result

list object, the output from icamp.bins

comm

matrix or data.frame, community data, each row is a sample or site, each colname is a taxon (a species or OTU or ASV), thus rownames should be sample IDs, colnames should be taxa IDs.

cate

matrix or data.frame, indicating the category of each taxon, rownames are taxa IDs. If the matrix has multiple columns, only the first column will be used.

treat

matrix or data.frame, indicating the group or treatment of each sample, rownames are sample IDs. Allow to input multi-column matrix, different columns represent different ways to group the samples.

silent

logic, if FALSE, some messages will show during calculation.

between.group

logic, whether to analyze between-treatment turnovers.

Value

Output is a list object.

Ptuvx

Relative importance of each process in governing each category's turnover between each pair of communities (samples).

Ptx

Relative importance of each process in governing each category's turnovers among a group of samples.

Details

This function simply sums up the relative abundance of taxa of a category in different bins governed by a process to summarize the relative importance of the process on the category.

References

Ning, D., Yuan, M., Wu, L., Zhang, Y., Guo, X., Zhou, X. et al. (2020). A quantitative framework reveals ecological drivers of grassland microbial community assembly in response to warming. Nature Communications, 11, 4717.

See Also

icamp.bins

Examples

# NOT RUN {
data("icamp.out")
data("example.data")
comm=example.data$comm
treatment=example.data$treat
classification=example.data$classification
rand.time=20 # usually use 1000 for real data.
# 1 # summarize each bin
icampbin=icamp.bins(icamp.detail = icamp.out, treat = treatment,
                    clas = classification, boot = TRUE,
                    rand.time = rand.time, between.group = TRUE)

# 2 # define category
cate=data.frame(type=rep("others",ncol(comm)),stringsAsFactors = FALSE)
rownames(cate)=colnames(comm)
tax.frequency=colSums(comm>0)/nrow(comm)
tax.relative.ab=colMeans(comm/rowSums(comm))
cate[which(tax.frequency>0.75 & tax.relative.ab>0.05),1]="core"
cate[which(tax.relative.ab<0.02),1]="rare"

# 3 # summarize each category
icampcate=icamp.cate(icamp.bins.result = icampbin, comm = comm, cate = cate,
                     treat = treatment, silent = FALSE, between.group = TRUE)
# }