CMStatistics: Statistics to one or multipe community matrix
In walterxie/ComMA: Community Matrix Analysis

Description Usage Arguments Details Examples

Statistics to one or multipe community matrix, such as number of reads, OTUs, etc. Jost diversity is also included in the summary.

summaryCM.Vector(community.matrix)

summaryCM(community.matrix, most.abund, has.total = 1, digits = 2,
  pretty.numbers = TRUE, x.lab = "sample", y.lab = "OTU",
  abundance.lab = "read")

summaryOTUs(..., digits = 2, input.list = FALSE, pretty.numbers = TRUE,
  x.lab = "sample", y.lab = "OTU", abundance.lab = "read")

summaryDiversity(..., row.order = c(), digits = 2, input.list = FALSE,
  pretty.numbers = TRUE, verbose = TRUE, row.names = c("$^0D_\\gamma$",
  "$^0D_\\alpha$", "$^0D_\\beta$", "$^1D_\\gamma$", "$^1D_\\alpha$",
  "$^1D_\\beta$", "$^2D_\\gamma$", "$^2D_\\alpha$", "$^2D_\\beta$"))

summaryTaxaGroup(..., input.list = FALSE, unclassified = 3,
  pretty.numbers = TRUE, taxa.group = c("ARCHAEA", "BACTERIA", "CHROMISTA",
  "PROTOZOA", "FUNGI", "PLANTAE", "ANIMALIA"), group.rank = "kingdom",
  count.rank = "phylum", digits = 0)

`most.abund`	The threshold to define the number of the most abundent OTUs.
`has.total`	If 0, then only return abudence by samples (columns) of community matrix. If 1, then only return toal abudence. If 2, then return abudence by samples (columns) and total. Default to 1.
`digits`	The digits to `round` decimal places if number is not interger. Default to 2.
`pretty.numbers`	Default to TRUE to make numbers look pretty, but they will be hard to convert to numeric type.
`x.lab, y.lab, abundance.lab`	The default text for "sample", "OTU", and "read".
`input.list`	Default to TRUE to unwrap list(...) to get the actual list if the input is a list of cm.
`row.order`	The same row indices of `row names`, but by a given order. Default to c().
`row.names`	The row names in the summary. Default to "reads", "OTUs", "samples", "singletons", "doubletons", "max.OTU.abun","min.OTU.abun","max.sample.abun","min.sample.abun".
`unclassified`	Refere to `assignTaxaByRank`, default to 3 to remove every rows containing "unclassified".
`taxa.group`	The row names in the summary. Default to "ARCHAEA", "BACTERIA", "CHROMISTA", "PROTOZOA", "FUNGI", "PLANTAE", "ANIMALIA".
`group.rank`	The rank of given taxa groups, which determines the column name (rank) to create the subset.
`count.rank`	The lower rank to be counted for each taxa group. Set NA to ignore this count.
`row.names`	The row names in the summary. Default to "$^0D_\gamma$","$^0D_\alpha$","$^0D_\beta$", "$^1D_\gamma$","$^1D_\alpha$", "$^1D_\beta$", "$^2D_\gamma$","$^2D_\alpha$","$^2D_\beta$".

summaryCM.Vector return a named vector of summary of the community matrix, where community.matrix can be one column only. The vector is c("reads","OTUs","samples","Shannon","singletons","doubletons").

summaryCM summarizes only single community matrix.

summaryOTUs returns a data frame of OTU clustering summary given one or multiple community matrix(matrices). The summary is created by summaryCM.Vector.

summaryDiversity returns a data frame of OTU clustering summary given a list of community matrix. The community matrix is transposed to an input of d vegetarian, and the summary is created by diversityTable.

summaryTaxaGroup returns two data frames of taxonomic composition of given one or multiple merged community matrix(matrices) with taxa table, which is created by mergeCMTaxa. The 1st data frame otus summarizes the OTUs assigned to each taxa group. The 2nd data frame rank.count summarizes the count.rank assigned to each taxa group.

The group.rank determines which taxonomic rank is based on summary. It has to be consistent with given taxa.group. But there is a trick, if you want to count the number of OTUs that are identified as EUKARYOTA but aren't identified to Kingdom level, for example, you can set group.rank="kingdom" to get that number.

summary.cm.vector <- summaryCM.Vector(community.matrix)

summary.cm <- summaryCM(community.matrix)

otu.stats <- summaryOTUs(cm)
# cm.list is a list of cm
otu.stats <- ComMA::summaryOTUs(cm.list, input.list=T)

div.stats <- summaryDiversity(cm, row.order=c(2,5,8,3,6,9,1,4,7))

ta.gr.stats <- summaryTaxaGroup(cm.taxa)
ta.gr.stats$otus
ta.gr.stats$rank.count 
# OTUs that were only identified to high-level EUKARYOTA ranks
ta.gr.stats <- summaryTaxaGroup(cm.taxa, group.rank="kingdom")