classifyDD {scDD} | R Documentation

## classifyDD

### Description

Classify significantly DD genes into the four categories (DE, DP, DM or DB) based on posterior distributions of cluster mean parameters

### Usage

    classifyDD(pe_mat, condition, sig_genes, oa, c1, c2, alpha, m0, s0, a0, b0,
      log.nonzero = TRUE, adjust.perms = FALSE, ref, min.size = 3)

### Arguments

`pe_mat` | Matrix with genes in rows and samples in columns. Column names indicate condition.
`condition` | Vector of condition indicators (with two possible values).
`sig_genes` | Vector of the indices of significantly DD genes (indicating the row number of `pe_mat`)
`oa` | List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in the overall (pooled) fit.
`c1` | List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in condition 1 only fit
`c2` | List item with one item for each gene where the first element contains the cluster membership for each nonzero sample in condition 2 only fit
`alpha` | Value for the Dirichlet concentration parameter
`m0` | Prior mean value for generating distribution of cluster means
`s0` | Prior precision value for generating distribution of cluster means
`a0` | Prior shape parameter value for the generating distribution of cluster precision
`b0` | Prior scale parameter value for the generating distribution of cluster precision
`log.nonzero` | Logical indicating whether to perform log transformation of nonzero values.
`adjust.perms` | Logical indicating whether or not to adjust the permutation tests for the sample detection rate (proportion of nonzero values). If true, the residuals of a linear model adjusted for detection rate are permuted, and new fitted values are obtained using these residuals.
`ref` | one of two possible values in condition; represents the referent category.
`min.size` | a positive integer that specifies the minimum size of a cluster (number of cells) for it to be used during the classification step. Any clusters containing fewer than `min.size` cells will be considered an outlier cluster and ignored in the classfication algorithm. The default value is three.

### Value

cat Character vector of the same length as `sig_genes` that indicates which category of DD each significant gene belongs to (DE, DP, DM, DB, or NC (no call))

### References

Korthauer KD, Chu LF, Newton MA, Li Y, Thomson J, Stewart R, Kendziorski C. A statistical approach for identifying differential distributions in single-cell RNA-seq experiments. Genome Biology. 2016 Oct 25;17(1):222. https://genomebiology.biomedcentral.com/articles/10.1186/s13059-016-1077-y

[Package scDD version 1.18.0 Index]