Description Usage Arguments Details Value Author(s) References See Also Examples
Determines states using hierarchical spectral clustering with a post-hoc test.
1 | findStates(sce, min_size = 0.01, min_feat = 5, max_pval = 1e-04, min_fc = 2)
|
sce |
A |
min_size |
The initial cluster dedrogram is cut at an height such that
the minimum cluster size is at least |
min_feat |
Minimum number of differentially expressed features between siblings. If this number is not reached, two neighboring clusters (siblings) in the pruned dendrogram get joined. (default: 5) |
max_pval |
Maximum P-value for differential expression computation. (default: 1e-4) |
min_fc |
Mimimum fold-change for differential expression computation (default: 2) |
To identify cellular subpopulations, CellTrails performs
hierarchical clustering via minimization of a square error criterion
(Ward, 1963) in the lower-dimensional space. To determine the cardinality
of the clustering, CellTrails conducts an unsupervised post-hoc
analysis. Here, it is assumed that differential expression of assayed
features determines distinct cellular stages. First, Celltrails identifies
the maximal fragmentation of the data space, i.e. the lowest cutting height
in the clustering dendrogram that ensured that the resulting clusters
contained at least a certain fraction of samples. Then, processing from
this height towards the root, CellTrails iteratively joins siblings if
they did not have at least a certain number of differentially expressed
features. Statistical significance is tested by means of a two-sample
non-parametric linear rank test accounting for censored values
(Peto & Peto, 1972). The null hypothesis is rejected using the
Benjamini-Hochberg (Benjamini & Hochberg, 1995) procedure for
a given significance level.
Since this methods performs pairwise comparisons, the fold change threshold
value is valid in both directions: higher and lower
expressed than min_fc
. Thus, input values < 0 are interpreted as a
fold-change of 0. For example, min_fc=2
checks for features
that are 2-fold differentially expressed in two given states (e.g., S1, S2).
Thus, a feature can be either 2-fold higher expressed in state S1 or two-fold
lower expressed in state S2 to be validated as differentially expressed.
Please note that this methods only uses the set of defined trajectory
features in a SingleCellExperiment
object; spike-in controls are
ignored and are not listed as trajectory features.
Diagnostic messages
An error is thrown if the samples stored in the SingleCellExperiment
object were not embedded yet (ie. the SingleCellExperiment
object
does not contain a latent space matrix object; latentSpace(object)
is
NULL
).
A factor
vector
Daniel C. Ellwanger
Ward, J.H. (1963). Hierarchical Grouping to Optimize an Objective Function. Journal of the American Statistical Association, 58, 236-244.
Peto, R., and Peto, J. (1972). Asymptotically Efficient Rank Invariant Test Procedures (with Discussion). Journal of the Royal Statistical Society of London, Series A 135, 185<e2><80><93>206.
Benjamini, Y., and Hochberg, Y. (1995). Controlling the false discovery rate: a practical and powerful approach to multiple testing. Journal of the Royal Statistical Society Series B 57, 289<e2><80><93>300.
latentSpace
trajectoryFeatureNames
1 2 3 4 5 6 | # Example data
data(exSCE)
# Find states
cl <- findStates(exSCE, min_feat=2)
head(cl)
|
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