plotexpression {FateID} | R Documentation |
Plotting of pseudo-temporal expression profiles
Description
This function allows plotting pseudo-temporal expression profiles for single genes or groups of genes.
Usage
plotexpression(
x,
y,
g,
n,
logsc = FALSE,
col = NULL,
name = NULL,
cluster = FALSE,
alpha = 0.5,
types = NULL,
cex = 3,
ylim = NULL,
map = TRUE,
leg = TRUE,
seed = 12345,
ylab = NULL
)
Arguments
x |
expression data frame with genes as rows and cells as columns. Gene IDs should be given as row names and cell IDs should be given as column names. |
y |
clustering partition. A vector with an integer cluster number for each cell. The order of the cells has to be the same as for the columns of |
g |
a gene ID corresponding to one of the rownames of |
n |
ordered vector of cell IDs to be included. Cell IDs need to be column names of |
logsc |
logical value. If |
col |
optional vector of valid color names for all clusters in |
name |
optional character string. This argument corresponds to a title for the plot. Default value is |
cluster |
logical value. If |
alpha |
positive real number. Pseudo-temporal expression profiles are derived by a local regression of expression values across the ordered cells using the function |
types |
optional vector with IDs for different subsets of cells in |
cex |
size of data points. Default value is 3. |
ylim |
vector of two numerical values: lower and upper limit of values shown on the y-axis. Default value is |
map |
logical. If |
leg |
logical. If |
seed |
integer number. Random seed for determining colour scheme. Default is 12345. |
ylab |
Optional label for the y-axis. Default is |
Value
None
Examples
x <- intestine$x
y <- intestine$y
v <- intestine$v
fcol <- intestine$col
tar <- c(6,9,13)
fb <- fateBias(x,y,tar,z=NULL,minnr=5,minnrh=10,nbfactor=5,use.dist=FALSE,seed=NULL,nbtree=NULL)
dr <- compdr(x,z=NULL,m="cmd",k=2,tsne.perplexity=30)
pr <- prcurve(y,fb,dr,k=2,m="cmd",trthr=0.4,start=NULL)
n <- pr$trc[["t6"]]
fs <- filterset(v,n,minexpr=2,minnumber=1)
s1d <- getsom(fs,nb=1000,alpha=.5)
ps <- procsom(s1d,corthr=.85,minsom=3)
# plot average profile of all genes of node 1 in the self-organizing map
g <- names(ps$nodes)[ps$nodes == 1]
plotexpression(v,y,g,n,col=fcol,name="Node 1",cluster=FALSE,alpha=.5,types=NULL)