merge_cl | R Documentation |
Merge clusters based on pairwise differential expressed genes.
merge_cl(
norm.dat,
cl,
rd.dat = NULL,
rd.dat.t = NULL,
de.param = de_param(),
merge.type = c("undirectional", "directional"),
max.cl.size = 300,
de.method = "limma",
de.genes = NULL,
return.markers = FALSE,
pairBatch = 40,
verbose = 0
)
norm.dat |
normalized expression data matrix in log transform, using genes as rows, and cells and columns. Users can use log2(FPKM+1) or log2(CPM+1) |
cl |
A vector of cluster membership with cell index as names, and cluster id as values. |
rd.dat |
Reduced dimensions for cells. Used to determine which clusters are close to each other. Clusters are merged among nearest neighbors first. |
rd.dat.t |
Transpose of rd.dat |
de.param |
The DE gene criteria. See de_param for details. |
merge.type |
Determine if the DE gene score threshold should be applied to combined de.score, or de.score for up and down directions separately. |
max.cl.size |
Sampled cluster size. This is to speed up limma DE gene calculation. Instead of using all cells, we randomly sampled max.cl.size number of cells for testing DE genes. |
de.method |
Use limma by default. We are still testing "chisq" mode. |
de.genes |
If not null, use DE genes computated prevoiusly by DE_genes_pw or DE_genes_pairs to avoid recomputing. |
return.markers |
If TRUE, compute the DE genes between very pairs of clusters as markers |
pairBatch |
The number of pairs to be tested for merging in one batch. Avoid compairing many pairs at the same time to reduce memory comsumption. Default 40 |
sampled |
For big dataset, norm.dat may not include all cells from cl. If TRUE, norm.dat is the data matrix for downsampled cells, and no need for further down sampling. |
A list with cl (cluster membership), de.genes (differentially expressed genes), sc (cluster pairwise de.score), markers (top cluster pairwise markers)
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.