ct.plot.grouping: Plot cell tree with grouping information

In cellTree: Inference and visualisation of Single-Cell RNA-seq data as a hierarchical tree structure

Description

Plots a backbone tree (or MST) that was computed with compute.backbone.tree, displaying each cell's grouping.

Usage

ct.plot.grouping(tree, file.output = NULL, show.labels = FALSE,
  force.recompute.layout = FALSE, height = 20, width = 10,
  vertebrae.distance = 0, backbone.vertex.size = 0, vert.vertex.size = 0)

Arguments

tree	An igraph tree, as returned by compute.backbone.tree
file.output	String (optional). Path of a file where the plot should be saved in PDF format (rendered to screen if omitted).
show.labels	Boolean (optional). Whether to write each cell's row number next to its vertex.
force.recompute.layout	Boolean (optional). If set to TRUE, recomputes the graph's layout coordinates even when present.
height	Numeric (optional). Height and width (in inches) of the plot.
width	Numeric (optional). Height and width (in inches) of the plot.
vertebrae.distance	Numeric (optional). If non-zero: forces a specific plotting distance (in pixels) between backbone cells and related peripheral cells (‘vertebrae’).
backbone.vertex.size	Numeric (optional). Diameter (in pixels) of backbone and vertebrae cell vertices.
vert.vertex.size	Numeric (optional). Diameter (in pixels) of backbone and vertebrae cell vertices.

Value

An updated igraph object with x and y vertex coordinate attributes.

Examples

# Load pre-computed LDA model for skeletal myoblast RNA-Seq data from HSMMSingleCell package:
data(HSMM_lda_model)

# Recover sampling time (in days) for each cell:
library(HSMMSingleCell)
data(HSMM_sample_sheet)
days.factor = HSMM_sample_sheet$Hours
days = as.numeric(levels(days.factor))[days.factor]

# Compute near-optimal backbone tree:
b.tree = compute.backbone.tree(HSMM_lda_model, days)
# Plot resulting tree with sampling time as a vertex group colour:
ct.plot.grouping(b.tree)

Example output

Loading required package: topGO
Loading required package: BiocGenerics
Loading required package: parallel

Attaching package: 'BiocGenerics'

The following objects are masked from 'package:parallel':

    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,
    clusterExport, clusterMap, parApply, parCapply, parLapply,
    parLapplyLB, parRapply, parSapply, parSapplyLB

The following objects are masked from 'package:stats':

    IQR, mad, sd, var, xtabs

The following objects are masked from 'package:base':

    Filter, Find, Map, Position, Reduce, anyDuplicated, append,
    as.data.frame, basename, cbind, colMeans, colSums, colnames,
    dirname, do.call, duplicated, eval, evalq, get, grep, grepl,
    intersect, is.unsorted, lapply, lengths, mapply, match, mget,
    order, paste, pmax, pmax.int, pmin, pmin.int, rank, rbind,
    rowMeans, rowSums, rownames, sapply, setdiff, sort, table, tapply,
    union, unique, unsplit, which, which.max, which.min

Loading required package: graph
Loading required package: Biobase
Welcome to Bioconductor

    Vignettes contain introductory material; view with
    'browseVignettes()'. To cite Bioconductor, see
    'citation("Biobase")', and for packages 'citation("pkgname")'.

Loading required package: GO.db
Loading required package: AnnotationDbi
Loading required package: stats4
Loading required package: IRanges
Loading required package: S4Vectors

Attaching package: 'S4Vectors'

The following object is masked from 'package:base':

    expand.grid


Loading required package: SparseM

Attaching package: 'SparseM'

The following object is masked from 'package:base':

    backsolve


groupGOTerms: 	GOBPTerm, GOMFTerm, GOCCTerm environments built.

Attaching package: 'topGO'

The following object is masked from 'package:IRanges':

    members

Loading required namespace: maptpx
Using start group: 0 (1)
Using rooting method: center.start.group
Using root vertex: 4
Adding branch #1:
 [1]  65  53  45   2  55  47  57  48  44   7  19  25  69  66   9  63  18  62  51
[20]  56  16  70 136 133 143  89  78 140  94 100 177 194 141 199 201 181 161 204
[39] 225 236 255 247 246 233 229 259 258 146 235 159 185 191 216 166 149  83 168
[58] 158   8
Using branch width: 0.927 (width.scale.factor: 1.2)
Outliers: 1
Total number of branches: 1 (forks: 0)
Backbone fork merge (width: 0.927): 60 ->  60 
Ranking all cells...
Computing tree layout...
IGRAPH 6c9ebac DNW- 271 270 -- 
+ attr: grouping.colors (g/c), grouping.labels (g/n), ordered.branches
| (g/x), ratio (g/n), is.backbone (v/l), is.root (v/l), name (v/n),
| color (v/c), group.idx (v/n), grouping.label (v/n), pie (v/x), shape
| (v/c), cell.name (v/c), x (v/n), y (v/n), size (v/n), weight (e/n),
| arrow.mode (e/n)
+ edges from 6c9ebac (vertex names):
 [1]  2-> 20  2-> 26  2-> 50  2-> 55  4->  1  4-> 10  4-> 13  4-> 15  4-> 24
[10]  4-> 28  4-> 30  4-> 31  4-> 34  4-> 40  4-> 41  4-> 49  4-> 58  4-> 59
[19]  4-> 60  4-> 64  4-> 65  4->150  4->196  4->234  4->242  4->266  4->267
[28]  7-> 19  7-> 73  7->211  8-> 42  9-> 27  9-> 63  9-> 68 16-> 70 18-> 22
+ ... omitted several edges

cellTree documentation built on Nov. 8, 2020, 5:05 p.m.