Visualize your fitted
non-linear dimension reduction model
in the high-dimensional data space

Jayani P. G. Lakshika

Joint work with Prof Dianne Cook, Dr Paul Harrison, Dr Michael Lydeamore, Dr Thiyanga S. Talagala

Motivation

Single-cell gene expression: same data, different NLDR + hyper-parameters

Which is the most reasonable representation of the structure(s) present in the
high-dimensional data?

How do you decide which is the most reasonable representation?

This is the published figure.

Peripheral Blood Mononuclear Cells (PBMC)

Here is the \(9\text{-}D\) data viewed using a grand tour, linear projections into \(2\text{-}D\).

Software: langevitour

Show “model-in-the-data-space”

data-in-the-model-space

What is the model?

data-in-the-model-space

model-in-the-data-space

Overview of method

1. Construct the \(2\text{-}D\) model

2. Lift the model into high-dimensions

Steps of the algorithm

1. Construct the \(2\text{-}D\) model

1. NLDR layout, b. hexagon bins (hex_binning() and geom_hexgrid()), c. bin centroids (extract_hexbin_centroids()), d. triangulated centroids (tri_bin_centroids(), gen_edges() and geom_trimesh()).

Steps of the algorithm

2. Lift the model into high-dimensions

avg_highd_data()

show_langevitour()

RMSE of candidates

Chosen fit for PBMC data set

tSNE with perplexity: 30

Clusters with small separations, non-linear clusters

Densed points, filled out clusters

quollr

questioning how a high-dimensional object looks in low-dimensions using r

R package

Draft paper

Jayani P.G. Lakshika

Collaborators: Prof Dianne Cook, Dr Paul Harrison, Dr Michael Lydeamore, Dr Thiyanga S. Talagala