This webpage provides supplementary materials for Clustering and browser for "To Be Decided", Peshkin et al., To Be Decided, 2019.

Table of gene sets features

GeneSet / Clusters Median Dynamicity In Set Total In Genome Total Detected Total unDetected Unique in Genome Unique Detected Median concentration (nM) Tissue Specificity
DNA REPLICATION
0.0011 23 30 28 0 23 23 334.8 1.7
ALL PROTEINS
0.00184 5649 11095 7546 8 5536 5641 70.5 1.6
FATTY ACID METABOLISM
0.00066 28 49 40 2 28 26 1033.6 1.62
TRICARBOXYLIC ACID CYCLE
0.0009 26 48 44 2 26 24 1790.5 1.73
PENTOSE PHOSPHATE PATHWAY
0.00067 12 20 14 1 12 11 434.6 1.98
PROTEOSOME
0.00077 35 70 49 5 35 30 843.8 1.5
RNA DEGRADATION
0.00129 33 61 43 6 33 27 196 1.6
SPLICEOSOME
0.00215 108 188 113 24 103 84 116.8 1.58
KEGG PEROXISOME
0.00095 78 128 75 21 76 57 221.4 1.52
CHROMOSOMAL PASSENGER COMPLEX
0.0088 11 18 15 3 8 8 60.1 2.15
RNA POLYMERASE
0.00115 21 34 16 6 20 15 98.8 1.48
MITO CARTA MOUSE V2
0.00112 974 1395 882 309 823 665 273.7 1.59
HALLMARK MITOTIC SPINDLE
0.00312 200 405 192 69 193 131 57.8 1.68
CELL CYCLE
0.00319 101 178 89 41 90 60 59.2 1.76
RIBOSOME
0.00163 76 172 70 33 75 43 2817.2 1.13
OXIDATIVE PHOSPHORYLATION
0.00111 152 207 118 73 104 79 980.6 1.6
LYSOSOME
0.00098 78 126 49 39 68 39 155.9 1.48
GLYCOLYSIS
0.00166 55 87 49 28 39 27 1940.9 1.93
CHROMATIN
0.0023 146 234 94 82 116 64 42.1 1.61
KEGG ENDOCYTOSIS
0.0024 183 332 113 106 158 77 74.4 1.72
HALLMARK XENOBIOTIC METABOLISM
0.00128 200 403 111 125 174 75 273.6 1.6
GO PHOSPHATASE
0.00177 180 320 98 117 159 63 57.4 1.64
E3S
0.00214 624 1160 199 470 489 154 24.4 1.6
KINOME
0.00263 694 1172 209 542 426 152 29.6 1.66
COLLAGENS
0.00323 60 123 11 52 52 8 22.7 1.49
EXTRA CELLULAR MATRIX
0.00356 363 766 63 325 316 38 34.4 1.48
LIGANDS
0.00244 722 1353 134 647 541 75 73.9 1.49
RECEPTORS
0.00393 898 1862 89 833 722 65 35.1 1.59
TRANSCRIPTION FACTORS
0.00344 1412 2269 123 1313 1080 99 18.3 1.67
SECRETED FACTORS
0.00474 321 517 11 312 222 9 18 1.73

Zip files for Clustering Data

Zip files of MATLAB source code for clustering data