Pcaso is an interactive web tool for exploring point cloud data through multiple orthogonal views and smoothly zooming and highlighting points by metadata. It allows users to post datasets as shareable multiplot views, and smoothly switch between axes to track how points shift across dimensions. Future features will include zooming within plots, autocomplete search, and updating or deleting posts. Pcaso is intended to help interpret diverse complex datasets projected into multiple planes.

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Pcaso: Share and fluidly explore point-cloud data
Nathaniel M. Pearson1, Robert Aboukhalil2, Carmel Dudley3, John Greally4
1New York Genome Center, New York USA; 2Cold Spring Harbor Laboratory, Cold Spring Harbor USA;
3Weizmann Institute of Science, Rehovot Israel; 4Albert Einstein College of Medicine, New York USA
Background
People see and think in too few dimensions to easily grok data with many variates.
Flattening and freezing such data, to view as dots in a plane or box, can reveal some key
patterns, but hide others.
To help plumb many kinds of point-cloud data, in genomics and other fields, we built the
Point cloud analysis stereopticon (Pcaso), a mobile device-friendly way to collaboratively
explore plane-projected (e.g., PCA, MDS, comparative abundance) point clouds.
Distinctively, Pcaso lets users
• post and explore point-cloud datasets as interactive small-multiplots with stable URLs
(e.g., in preprint or publication)
• switch smoothly among orthogonal views, to track how each point shifts relative to others,
among potentially many dimensions
• smoothly zoom, to resolve point clumping
• highlight points by hand, stably colored metaclass (e.g., deme, tissue, platform, sex), or
metadata text search.
These and coming features, elaborated by us and others via CC0-licensed d3+ code, may help
interpret diverse complex, plane- projected datasets.
Pcaso beats paper.
(And the original beats a poster.)
Point your device to pcaso.io/mundo
Using Pcaso
To explore posted data
At the URL for a posted dataset, click any small plot to see it big. Hover on a point in the big plot
to see its metadata. And text search, or click a metaclass, to highlight point(s) by metadata.
To post your own data
At pcaso.io, upload a .csv file, then pick fields to show as axes or metadata, and save. You can
then explore away.
To share your post for others to explore
Share the post’s URL by email, IM, (pre)print text, tweet, carrier pigeon, &c.
What you’ll see
Next features
We hope to soon let users zoom within the main plot, search with autofill, update and/or delete posts, and do other handy
stuff. Please send any thoughts to npearson@nygenome.org – and look soon for source code and updates to Pcaso, along
with other tools, via the budding Open Genomics Visualization Initiative (OGVI).
Anticipated questions
What kinds of data does Pcaso help show? Basically, anything with many numeric dimensions. Could be principal components, of course – but
lots of other data can benefit from smoothly switchable, metadata-responsive planar views. If you’ve got such data, try it out.
What about parallel-coordinate plots? They too can help a lot, by showing many dimensions (especially qualitative or naturally ordered ones) at
once. But dense, criss-crossing point-specific curves can, of course, be hard to follow.
Why not show a 3d box instead of planes? We started with planes that mean something real from underlying data, and that our brains can easily
track when switching axes. (Plus, for data with many axes, a box shows little more of the whole than a plane does.)
But doesn't a stereopticon show 3d images? That’s a stereoscope ;-) Back in steampunk days, stereopticons just let people switch from one slide
to another. We were also inspired by cubists (like Picasso, of course) who splintered complex real-world shapes into many-planar views..
How robust is Pcaso? We’ve started with a beta hosted on a good standard web server, meant first to show key features and spark collaborative
insights and refinement – not (yet) to manage private or versioned data, or stop determined bad actors. Please use Pcaso responsibly.