Up: Component summary Component


Currently it uses Wanderlust to calculate developmental trajectories within a population of single cell data. The input is a matrix of single-cell measurements for multiple features, and the output is a value for each cell representing its developmental stage within the population evolution timeline.

Cells more similar to the initial cell (seed) will have a lower score than cells that have matured for longer time than the seed. It is useful to try several seed cells, or use existing knowledge of stemness-like markers. If the seed cell is in a mature stage it will still predict the further stages.

When using this component, please cite Wanderlust: http://www.c2b2.columbia.edu/danapeerlab/html/wanderlust.html The code in folder wanderlust is original from the CYT package.

See paper for details of the parameters. In brief; first generates num_graphs klNN graphs, then, using seed as starting point, choose num_landmarks random landmarks. Continues by weighting landmark-node pairs by distance, metric, to the power of k and adjusting each cell's position until convergence.

Recommended parameters to tweak are: num_landmarks, metric, num_graphs, k, and l.

Version 1.0
Bundle tools
Categories Analysis
Authors Julia Casado (julia.casado@helsinki.fi)
Issue tracker View/Report issues
Requires Matlab
Source files component.xml timeline.m
Usage Example with default values


Name Type Mandatory Description
in CSV Mandatory High-dimensional single-cell data table. Columns represent features or markers, and rows represent cells or datapoints. First column contains Event or Cell ID, and first row contains the column names


Name Type Description
out CSV One column with the stage for each data point, the stage is part of a continuum of the hypothetical distance between the most immature and the most mature cell in the population.


Name Type Default Description
band_sample boolean false Whether the landmarks are equidistant or randomly distributed.
branch boolean false Whether wanderlust will seek for a branch.
k int 5 Truncate the neighbors to random k out of l.
l int 30 Size of neighborhood l>k closest points.
metric string "euclidean" Distance metric for constructing the nearest neighbor graphs.
num_graphs int 20 Number of random selections of k out of l, that is, number of repetitions.
num_landmarks int 20 Number of waypoints\landmarks.
partial_order string "" A list of integers that represent datapoints in data. If non-empty, the indices point to landmarks and their order is forced.
seed int -1 Index of starting cell. If -1 the algorithm will choose a random cell.
snn int 1 Number of shared nearest neighbors.
voting_scheme string "exponential" How to weigh each point's contribution. Valid values: uniform, exponential, linear, inverse-linear.

Test cases

Test case Parameters IN
case1 properties in (missing)

num_graphs = 10,
voting_scheme = exponential,
snn = 1,
l = 30,
k = 8,
num_landmarks = 20,
num_graphs = 10,
metric = euclidean,
seed = 22

Generated 2019-02-08 07:42:19 by Anduril 2.0.0