Warning: file_get_contents(https://eutils.ncbi.nlm.nih.gov/entrez/eutils/elink.fcgi?dbfrom=pubmed&id=28251868
&cmd=llinks): Failed to open stream: HTTP request failed! HTTP/1.1 429 Too Many Requests
in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 215
Warning: imagejpeg(C:\Inetpub\vhosts\kidney.de\httpdocs\phplern\28251868
.jpg): Failed to open stream: No such file or directory in C:\Inetpub\vhosts\kidney.de\httpdocs\pget.php on line 117 BMC+Bioinformatics
2017 ; 18
(Suppl 2
): 63
Nephropedia Template TP
gab.com Text
Twit Text FOAVip
Twit Text #
English Wikipedia
Unboxing cluster heatmaps
#MMPMID28251868
Engle S
; Whalen S
; Joshi A
; Pollard KS
BMC Bioinformatics
2017[Feb]; 18
(Suppl 2
): 63
PMID28251868
show ga
BACKGROUND: Cluster heatmaps are commonly used in biology and related fields to
reveal hierarchical clusters in data matrices. This visualization technique has
high data density and reveal clusters better than unordered heatmaps alone.
However, cluster heatmaps have known issues making them both time consuming to
use and prone to error. We hypothesize that visualization techniques without the
rigid grid constraint of cluster heatmaps will perform better at
clustering-related tasks. RESULTS: We developed an approach to "unbox" the
heatmap values and embed them directly in the hierarchical clustering results,
allowing us to use standard hierarchical visualization techniques as alternatives
to cluster heatmaps. We then tested our hypothesis by conducting a survey of 45
practitioners to determine how cluster heatmaps are used, prototyping
alternatives to cluster heatmaps using pair analytics with a computational
biologist, and evaluating those alternatives with hour-long interviews of 5
practitioners and an Amazon Mechanical Turk user study with approximately 200
participants. We found statistically significant performance differences for most
clustering-related tasks, and in the number of perceived visual clusters. Visit
git.io/vw0t3 for our results. CONCLUSIONS: The optimal technique varied by task.
However, gapmaps were preferred by the interviewed practitioners and outperformed
or performed as well as cluster heatmaps for clustering-related tasks. Gapmaps
are similar to cluster heatmaps, but relax the heatmap grid constraints by
introducing gaps between rows and/or columns that are not closely clustered.
Based on these results, we recommend users adopt gapmaps as an alternative to
cluster heatmaps.
free
free
free
