Yes !! I can understand. Check the below data and the conditions.
Sample Dataset:
TEST UNIPROT_ID VALUES RELEVANCE
16847 P12821 0.150202199 YES
10964 P00918 0.289074042 YES
36315 P41145 0.203689575 NO
55033 P43088 0.183951524 NO
80965 P47869 0.156262678 YES
27639 P06276 0.130653334 YES
17170 Q72874 0.112942393 NO
15451 P25101 0.162308309 YES
27370 P04150 0.183241007 NO
27370 P04150 0.132568467 YES
27370 P10276 0.183241585 NO
39857 P00918 0.302647449 YES
33216 P10276 0.192524252 NO
Conditions:
TEST and UNIPROTID are the parameters for x & y-axis. VALUES are for those pairs - which should be on the map represented by colour gradient. RELEVANCE is the correctness of the TEST and UNIPROTID pair.
There might be case where data in TEST and UNIPROT_ID might be same and data in VALUES are different which are differentiated by RELEVANCE data.
Example:
33216 P10276 0.192524252 NO
33216 P10276 0.126589451 YES
So to differentiate these values I need to assign a specific colour to RELEVANCE and it's gradient to represent those VALUES. Other important thing is, The colour gradient should increase with increase in VALUE.
An example for your reference:
Assigned Red colour for RELEVANCE - NO data and the red colour density depends on the VALUES for that data.
FOR THE EXAMPLES SPECIFIED BY YOU
My dataset doesn't have negative values. So I cannot use gradient of two colours pointing one end for negative and the other for positive.
Example 2: The idea of "no plot colour" and text 'NA' is good. I will use that.
Appreciates,
RDS




In my lab, 3 people out of 60 are color-blind. This figure would be unintelligible for them.
ColorBrewer (http://colorbrewer2.org) is an excellent resource for choosing color schemes for scientific data, and addresses issues like color blindness.
I'm red/green blind and I can perfectly interpret the figure.