Open MarziehHaghighi opened 1 year ago
This sounds great! I just want to clarify about the 2nd step where we compare WT and MT using MAP which you described by email:
Each MT profile will try to retrieve its WT profiles against a pool of what? (its own MT replicates, or the whole experiment of profiles? if it's the former I could imagine that almost all MT/WT pairs will look different enough to pass this threshold, such that offering it the whole experiment or plate of profiles provides better resolution of the ability to retrieve?)
Makes sense!
Using MAP (by @yhan8)
- [ ] Replicate correlation + null distributions
- [ ] list of map scores for each pair
Drafting my steps here. In the metadata , column Metadata_Sample_Unique
includes the wild type and mutant names. Two kinds of replicability will be calculated using evalzoo:
Metadata_Sample_Unique
are replicates. There are no controls in the data (i.e., remove all 516 -TC), so it will be replicate against non-replicates, by plate. Need to discuss with @shntnu on editing the evalzoo script to accomodate this study.
Based on protein channel features
Based on non-protein channel features
Gene | Metadata_Sample_Unique | cc_p | wt_RepCor_p | cc_np | wt_RepCor_np | RepCor_p | Rand90Perc_p | Rep10Perc_p | RepCor_np | Rand90Perc_np | Rep10Perc_np | |
---|---|---|---|---|---|---|---|---|---|---|---|---|
0 | DOLK | DOLK Tyr441Ser | 0.812154 | 0.303609 | 0.549158 | 0.246678 | 0.459496 | 0.236407 | 0.359196 | 0.32718 | 0.183632 | 0.206219 |
1 | EMD | EMD Ala56Thr | 0.400925 | 0.663506 | 0.141576 | 0.108136 | 0.541148 | 0.236407 | 0.359196 | 0.638897 | 0.183632 | 0.206219 |
2 | EMD | EMD Asp72Val | 0.496056 | 0.663506 | 0.261438 | 0.108136 | 0.305962 | 0.236407 | 0.359196 | 0.265102 | 0.183632 | 0.206219 |
3 | EMD | EMD Met1Val | 0.689438 | 0.663506 | 0.358089 | 0.108136 | 0.212016 | 0.236407 | 0.359196 | 0.248557 | 0.183632 | 0.206219 |
4 | EMD | EMD Pro183His | 0.101571 | 0.663506 | 0.263846 | 0.108136 | 0.361373 | 0.236407 | 0.359196 | 0.235395 | 0.183632 | 0.206219 |
5 | EMD | EMD Pro183Thr | 0.290396 | 0.663506 | 0.10283 | 0.108136 | 0.338601 | 0.236407 | 0.359196 | 0.506725 | 0.183632 | 0.206219 |
6 | EMD | EMD Ser54Phe | 0.543792 | 0.663506 | 0.156373 | 0.108136 | 0.360591 | 0.236407 | 0.359196 | 0.30438 | 0.183632 | 0.206219 |
7 | IMPDH1 | IMPDH1 Arg309Pro | 0.354869 | 0.545031 | 0.242057 | 0.0643272 | 0.226381 | 0.236407 | 0.359196 | 0.170917 | 0.183632 | 0.206219 |
8 | IMPDH1 | IMPDH1 Asp311Asn | -0.209642 | 0.545031 | 0.458999 | 0.0643272 | 0.542843 | 0.236407 | 0.359196 | 0.0557816 | 0.183632 | 0.206219 |
9 | AIPL1 | AIPL1 Arg270His | 0.273113 | 0.810226 | 0.605787 | 0.546889 | 0.792534 | 0.236407 | 0.359196 | 0.542621 | 0.183632 | 0.206219 |
10 | AIPL1 | AIPL1 Arg302Leu | 0.862609 | 0.810226 | 0.717986 | 0.546889 | 0.830929 | 0.236407 | 0.359196 | 0.356391 | 0.183632 | 0.206219 |
11 | AIPL1 | AIPL1 Met79Thr | 0.154947 | 0.810226 | 0.491816 | 0.546889 | 0.693704 | 0.236407 | 0.359196 | 0.260006 | 0.183632 | 0.206219 |
12 | AIPL1 | AIPL1 Thr114Ile | 0.965001 | 0.810226 | 0.939664 | 0.546889 | 0.840271 | 0.236407 | 0.359196 | 0.661714 | 0.183632 | 0.206219 |
13 | EIF2B4 | EIF2B4 Ala228Val | 0.369129 | 0.399015 | 0.618664 | 0.481428 | 0.75831 | 0.236407 | 0.359196 | 0.210716 | 0.183632 | 0.206219 |
14 | EIF2B4 | EIF2B4 Ala391Asp | 0.510709 | 0.399015 | -0.172932 | 0.481428 | 0.636257 | 0.236407 | 0.359196 | 0.435466 | 0.183632 | 0.206219 |
15 | EIF2B4 | EIF2B4 Arg306Gly | 0.626046 | 0.399015 | 0.555183 | 0.481428 | 0.443025 | 0.236407 | 0.359196 | 0.233263 | 0.183632 | 0.206219 |
16 | ALAS2 | ALAS2 Ala135Thr | 0.957832 | 0.942857 | 0.74251 | 0.509418 | 0.919249 | 0.236407 | 0.359196 | 0.382541 | 0.183632 | 0.206219 |
17 | ALAS2 | ALAS2 Arg374Cys | 0.92887 | 0.942857 | 0.584661 | 0.509418 | 0.839 | 0.236407 | 0.359196 | 0.516608 | 0.183632 | 0.206219 |
18 | ALAS2 | ALAS2 Asp122Asn | 0.96137 | 0.942857 | 0.628351 | 0.509418 | 0.892947 | 0.236407 | 0.359196 | 0.588997 | 0.183632 | 0.206219 |
19 | ALAS2 | ALAS2 Asp153Val | 0.950552 | 0.942857 | 0.576244 | 0.509418 | 0.918158 | 0.236407 | 0.359196 | 0.532146 | 0.183632 | 0.206219 |
20 | ALAS2 | ALAS2 Cys358Tyr | 0.94791 | 0.942857 | 0.566148 | 0.509418 | 0.947057 | 0.236407 | 0.359196 | 0.846761 | 0.183632 | 0.206219 |
21 | ALAS2 | ALAS2 Gly254Ser | 0.973936 | 0.942857 | 0.579645 | 0.509418 | 0.947381 | 0.236407 | 0.359196 | 0.842696 | 0.183632 | 0.206219 |
22 | ALAS2 | ALAS2 Lys262Gln | 0.97146 | 0.942857 | 0.633109 | 0.509418 | 0.941559 | 0.236407 | 0.359196 | 0.716846 | 0.183632 | 0.206219 |
23 | ALAS2 | ALAS2 Phe128Leu | 0.899358 | 0.942857 | 0.665138 | 0.509418 | 0.892667 | 0.236407 | 0.359196 | 0.307553 | 0.183632 | 0.206219 |
24 | ALAS2 | ALAS2 Ser531Gly | 0.854929 | 0.942857 | 0.59907 | 0.509418 | 0.798461 | 0.236407 | 0.359196 | 0.491536 | 0.183632 | 0.206219 |
25 | ALAS2 | ALAS2 Thr351Ser | 0.957671 | 0.942857 | 0.610488 | 0.509418 | 0.876557 | 0.236407 | 0.359196 | 0.528331 | 0.183632 | 0.206219 |
26 | ALAS2 | ALAS2 Tyr549Phe | 0.961277 | 0.942857 | 0.621325 | 0.509418 | 0.932656 | 0.236407 | 0.359196 | 0.418941 | 0.183632 | 0.206219 |
27 | CLCNKA | CLCNKA Trp80Cys | -0.471066 | 0.37997 | -0.0133432 | 0.244383 | 0.696592 | 0.236407 | 0.359196 | 0.76506 | 0.183632 | 0.206219 |
28 | FBP1 | FBP1 Ala177Asp | -0.168784 | 0.836435 | -0.200173 | 0.392719 | 0.247128 | 0.236407 | 0.359196 | 0.154469 | 0.183632 | 0.206219 |
29 | CTRC | CTRC Arg246Cys | 0.634972 | 0.797619 | 0.111161 | 0.216912 | 0.801093 | 0.236407 | 0.359196 | 0.455059 | 0.183632 | 0.206219 |
30 | CTRC | CTRC Arg37Gln | 0.765336 | 0.797619 | 0.265112 | 0.216912 | 0.796699 | 0.236407 | 0.359196 | 0.334993 | 0.183632 | 0.206219 |
31 | CTRC | CTRC Gln178Arg | -0.340483 | 0.797619 | 0.353732 | 0.216912 | 0.867971 | 0.236407 | 0.359196 | 0.568933 | 0.183632 | 0.206219 |
32 | CTRC | CTRC Glu225Ala | 0.87933 | 0.797619 | 0.599191 | 0.216912 | 0.776341 | 0.236407 | 0.359196 | 0.24573 | 0.183632 | 0.206219 |
33 | DCX | DCX Ala251Ser | -0.208084 | 0.790209 | -0.524234 | 0.305862 | -0.00795505 | 0.236407 | 0.359196 | 0.331395 | 0.183632 | 0.206219 |
34 | DCX | DCX Ala71Ser | 0.889039 | 0.790209 | 0.862022 | 0.305862 | 0.666018 | 0.236407 | 0.359196 | 0.477633 | 0.183632 | 0.206219 |
35 | DCX | DCX Arg102Cys | 0.857378 | 0.790209 | 0.778809 | 0.305862 | 0.812403 | 0.236407 | 0.359196 | 0.509653 | 0.183632 | 0.206219 |
36 | DCX | DCX Arg186His | 0.935348 | 0.790209 | 0.786348 | 0.305862 | 0.818851 | 0.236407 | 0.359196 | 0.407649 | 0.183632 | 0.206219 |
37 | DCX | DCX Arg186Leu | 0.147867 | 0.790209 | 0.469696 | 0.305862 | 0.816109 | 0.236407 | 0.359196 | 0.440123 | 0.183632 | 0.206219 |
38 | DCX | DCX Arg196Cys | 0.814541 | 0.790209 | 0.643877 | 0.305862 | 0.670303 | 0.236407 | 0.359196 | 0.421047 | 0.183632 | 0.206219 |
39 | DCX | DCX Arg196His | 0.679362 | 0.790209 | 0.724891 | 0.305862 | 0.6509 | 0.236407 | 0.359196 | 0.283375 | 0.183632 | 0.206219 |
40 | DCX | DCX Arg59His | 0.93895 | 0.790209 | 0.886552 | 0.305862 | 0.794319 | 0.236407 | 0.359196 | 0.462963 | 0.183632 | 0.206219 |
41 | DCX | DCX Arg78Cys | 0.60454 | 0.790209 | 0.353412 | 0.305862 | 0.771091 | 0.236407 | 0.359196 | 0.378881 | 0.183632 | 0.206219 |
42 | DCX | DCX Arg78His | 0.870223 | 0.790209 | 0.864304 | 0.305862 | 0.835428 | 0.236407 | 0.359196 | 0.287032 | 0.183632 | 0.206219 |
43 | DCX | DCX Arg89Gly | 0.735337 | 0.790209 | 0.375841 | 0.305862 | 0.77287 | 0.236407 | 0.359196 | 0.461303 | 0.183632 | 0.206219 |
44 | DCX | DCX Ile214Thr | 0.680948 | 0.790209 | 0.56692 | 0.305862 | 0.640235 | 0.236407 | 0.359196 | 0.60222 | 0.183632 | 0.206219 |
45 | DCX | DCX Lys174Glu | 0.666268 | 0.790209 | 0.529493 | 0.305862 | 0.780097 | 0.236407 | 0.359196 | 0.58299 | 0.183632 | 0.206219 |
46 | DCX | DCX Lys50Asn | 0.885656 | 0.790209 | 0.673236 | 0.305862 | 0.812871 | 0.236407 | 0.359196 | 0.266286 | 0.183632 | 0.206219 |
47 | DCX | DCX Met1Thr | 0.36452 | 0.790209 | 0.568294 | 0.305862 | 0.730808 | 0.236407 | 0.359196 | 0.559341 | 0.183632 | 0.206219 |
48 | DCX | DCX Pro191Arg | -0.279817 | 0.790209 | -0.514462 | 0.305862 | 0.259648 | 0.236407 | 0.359196 | 0.328803 | 0.183632 | 0.206219 |
49 | DCX | DCX Ser129Leu | 0.686765 | 0.790209 | 0.669147 | 0.305862 | 0.634991 | 0.236407 | 0.359196 | 0.427628 | 0.183632 | 0.206219 |
50 | DCX | DCX Thr203Ala | 0.957748 | 0.790209 | 0.82135 | 0.305862 | 0.860252 | 0.236407 | 0.359196 | 0.387076 | 0.183632 | 0.206219 |
51 | DCX | DCX Thr203Arg | 0.907237 | 0.790209 | 0.729365 | 0.305862 | 0.761325 | 0.236407 | 0.359196 | 0.399816 | 0.183632 | 0.206219 |
52 | DCX | DCX Tyr125His | 0.609427 | 0.790209 | 0.618964 | 0.305862 | 0.574572 | 0.236407 | 0.359196 | 0.333535 | 0.183632 | 0.206219 |
53 | CRADD | CRADD Arg185Gln | 0.371742 | 0.821747 | -0.31208 | 0.285804 | 0.814273 | 0.236407 | 0.359196 | 0.284464 | 0.183632 | 0.206219 |
54 | CRADD | CRADD Gly128Arg | -0.10655 | 0.821747 | -0.225103 | 0.285804 | 0.2063 | 0.236407 | 0.359196 | 0.131331 | 0.183632 | 0.206219 |
55 | ACSF3 | ACSF3 Ala197Thr | -0.348342 | 0.733033 | 0.293595 | 0.304979 | 0.571281 | 0.236407 | 0.359196 | 0.201265 | 0.183632 | 0.206219 |
56 | ACSF3 | ACSF3 Arg10Trp | 0.131518 | 0.733033 | 0.374758 | 0.304979 | 0.770124 | 0.236407 | 0.359196 | 0.541322 | 0.183632 | 0.206219 |
57 | ACSF3 | ACSF3 Arg471Trp | -0.3424 | 0.733033 | -0.174532 | 0.304979 | 0.498657 | 0.236407 | 0.359196 | 0.366746 | 0.183632 | 0.206219 |
58 | ACSF3 | ACSF3 Arg558Trp | -0.314254 | 0.733033 | -0.099857 | 0.304979 | 0.550779 | 0.236407 | 0.359196 | 0.328222 | 0.183632 | 0.206219 |
59 | ACSF3 | ACSF3 Asp236Asn | -0.01745 | 0.733033 | -0.214152 | 0.304979 | 0.32766 | 0.236407 | 0.359196 | 0.541452 | 0.183632 | 0.206219 |
60 | ACSF3 | ACSF3 Asp457Asn | -0.223768 | 0.733033 | 0.294623 | 0.304979 | 0.497507 | 0.236407 | 0.359196 | 0.27002 | 0.183632 | 0.206219 |
61 | ACSF3 | ACSF3 Glu359Lys | -0.344937 | 0.733033 | 0.159046 | 0.304979 | 0.51067 | 0.236407 | 0.359196 | 0.246498 | 0.183632 | 0.206219 |
62 | ACSF3 | ACSF3 Gly119Asp | -0.309312 | 0.733033 | -0.128686 | 0.304979 | 0.575937 | 0.236407 | 0.359196 | 0.208485 | 0.183632 | 0.206219 |
63 | ACSF3 | ACSF3 Gly225Arg | -0.289699 | 0.733033 | 0.433965 | 0.304979 | 0.634696 | 0.236407 | 0.359196 | 0.349261 | 0.183632 | 0.206219 |
64 | ACSF3 | ACSF3 Ile200Met | -0.31487 | 0.733033 | 0.039181 | 0.304979 | 0.534015 | 0.236407 | 0.359196 | 0.293977 | 0.183632 | 0.206219 |
65 | ACSF3 | ACSF3 Met198Arg | -0.316022 | 0.733033 | 0.078226 | 0.304979 | 0.566512 | 0.236407 | 0.359196 | 0.217637 | 0.183632 | 0.206219 |
66 | ACSF3 | ACSF3 Met266Val | -0.315708 | 0.733033 | -0.298251 | 0.304979 | 0.531917 | 0.236407 | 0.359196 | 0.185549 | 0.183632 | 0.206219 |
67 | ACSF3 | ACSF3 Pro243Leu | -0.279633 | 0.733033 | -0.163358 | 0.304979 | 0.601404 | 0.236407 | 0.359196 | 0.40753 | 0.183632 | 0.206219 |
68 | ACSF3 | ACSF3 Pro285Leu | -0.368099 | 0.733033 | 0.0446327 | 0.304979 | 0.629503 | 0.236407 | 0.359196 | 0.3804 | 0.183632 | 0.206219 |
69 | ACSF3 | ACSF3 Ser431Tyr | 0.202273 | 0.733033 | 0.388348 | 0.304979 | 0.414894 | 0.236407 | 0.359196 | 0.199084 | 0.183632 | 0.206219 |
70 | ACSF3 | ACSF3 Thr358Ile | -0.310495 | 0.733033 | 0.0161624 | 0.304979 | 0.630992 | 0.236407 | 0.359196 | 0.365734 | 0.183632 | 0.206219 |
71 | FA2H | FA2H Arg143Cys | 0.497748 | 0.261103 | 0.25313 | 0.266671 | 0.560055 | 0.236407 | 0.359196 | 0.0899539 | 0.183632 | 0.206219 |
72 | FA2H | FA2H Arg62Cys | 0.561424 | 0.261103 | 0.669551 | 0.266671 | 0.510462 | 0.236407 | 0.359196 | 0.46593 | 0.183632 | 0.206219 |
73 | FA2H | FA2H Phe144Ser | 0.362088 | 0.261103 | 0.0193911 | 0.266671 | 0.19953 | 0.236407 | 0.359196 | 0.203714 | 0.183632 | 0.206219 |
74 | FAM161A | FAM161A Leu269Arg | 0.404807 | 0.66421 | 0.0746539 | 0.554056 | 0.208519 | 0.236407 | 0.359196 | 0.477677 | 0.183632 | 0.206219 |
75 | ASNS | ASNS Ala6Glu | -0.393304 | 0.86979 | -0.0282066 | 0.552998 | 0.777259 | 0.236407 | 0.359196 | 0.467151 | 0.183632 | 0.206219 |
76 | BCL10 | BCL10 Ala5Ser | 0.87405 | 0.838855 | 0.831978 | 0.632199 | 0.85294 | 0.236407 | 0.359196 | 0.71313 | 0.183632 | 0.206219 |
77 | BCL10 | BCL10 Leu8Leu | -0.207115 | 0.838855 | -0.0647577 | 0.632199 | 0.783252 | 0.236407 | 0.359196 | 0.622756 | 0.183632 | 0.206219 |
78 | CREB1 | CREB1 Asp116Gly | -0.399006 | 0.845436 | 0.678742 | 0.364229 | 0.877664 | 0.236407 | 0.359196 | 0.60409 | 0.183632 | 0.206219 |
79 | CRYAB | CRYAB Asp109His | -0.036847 | 0.927002 | 0.334968 | 0.691911 | 0.798813 | 0.236407 | 0.359196 | 0.43454 | 0.183632 | 0.206219 |
80 | CRYAB | CRYAB Gly154Ser | 0.974476 | 0.927002 | 0.69362 | 0.691911 | 0.901846 | 0.236407 | 0.359196 | 0.365807 | 0.183632 | 0.206219 |
81 | DES | DES Ala135Val | 0.498567 | 0.96098 | -0.137358 | 0.666678 | 0.900919 | 0.236407 | 0.359196 | 0.492537 | 0.183632 | 0.206219 |
82 | DES | DES Ala213Val | 0.918395 | 0.96098 | 0.287841 | 0.666678 | 0.933563 | 0.236407 | 0.359196 | 0.392204 | 0.183632 | 0.206219 |
83 | DES | DES Ala237Thr | 0.555215 | 0.96098 | -0.256816 | 0.666678 | 0.647107 | 0.236407 | 0.359196 | 0.461733 | 0.183632 | 0.206219 |
84 | DES | DES Ala337Pro | -0.472672 | 0.96098 | -0.320047 | 0.666678 | 0.400892 | 0.236407 | 0.359196 | 0.397876 | 0.183632 | 0.206219 |
85 | DES | DES Ala357Pro | 0.0334099 | 0.96098 | -0.26341 | 0.666678 | 0.241328 | 0.236407 | 0.359196 | 0.516166 | 0.183632 | 0.206219 |
86 | DES | DES Ala397Thr | 0.500556 | 0.96098 | -0.255255 | 0.666678 | 0.802505 | 0.236407 | 0.359196 | 0.548423 | 0.183632 | 0.206219 |
87 | DES | DES Arg127Pro | 0.39693 | 0.96098 | -0.120309 | 0.666678 | 0.698106 | 0.236407 | 0.359196 | 0.316707 | 0.183632 | 0.206219 |
88 | DES | DES Arg150Gln | 0.436826 | 0.96098 | -0.128675 | 0.666678 | 0.705245 | 0.236407 | 0.359196 | 0.525045 | 0.183632 | 0.206219 |
89 | DES | DES Arg16Cys | 0.974271 | 0.96098 | 0.895651 | 0.666678 | 0.916468 | 0.236407 | 0.359196 | 0.582325 | 0.183632 | 0.206219 |
90 | DES | DES Arg212Gln | 0.876609 | 0.96098 | 0.257424 | 0.666678 | 0.939741 | 0.236407 | 0.359196 | 0.602379 | 0.183632 | 0.206219 |
91 | DES | DES Arg222His | 0.476384 | 0.96098 | -0.139527 | 0.666678 | 0.784399 | 0.236407 | 0.359196 | 0.319717 | 0.183632 | 0.206219 |
92 | DES | DES Arg227Cys | 0.909985 | 0.96098 | 0.47929 | 0.666678 | 0.880824 | 0.236407 | 0.359196 | 0.398954 | 0.183632 | 0.206219 |
93 | DES | DES Arg278Pro | 0.339447 | 0.96098 | -0.203738 | 0.666678 | 0.717046 | 0.236407 | 0.359196 | 0.523847 | 0.183632 | 0.206219 |
94 | DES | DES Arg350Pro | 0.838215 | 0.96098 | 0.334518 | 0.666678 | 0.940407 | 0.236407 | 0.359196 | 0.53797 | 0.183632 | 0.206219 |
95 | DES | DES Arg355Pro | -0.262917 | 0.96098 | -0.0822246 | 0.666678 | 0.0933792 | 0.236407 | 0.359196 | 0.00857638 | 0.183632 | 0.206219 |
96 | DES | DES Arg37Trp | 0.379265 | 0.96098 | -0.0111762 | 0.666678 | 0.454628 | 0.236407 | 0.359196 | 0.325438 | 0.183632 | 0.206219 |
97 | DES | DES Asn342Asp | -0.292271 | 0.96098 | -0.234666 | 0.666678 | 0.651685 | 0.236407 | 0.359196 | 0.497773 | 0.183632 | 0.206219 |
98 | DES | DES Asp312Ala | 0.613102 | 0.96098 | 0.101297 | 0.666678 | 0.671385 | 0.236407 | 0.359196 | 0.473157 | 0.183632 | 0.206219 |
99 | DES | DES Asp343Asn | 0.581868 | 0.96098 | -0.212229 | 0.666678 | 0.798856 | 0.236407 | 0.359196 | 0.31538 | 0.183632 | 0.206219 |
100 | DES | DES Gln131Lys | 0.845324 | 0.96098 | 0.0421202 | 0.666678 | 0.956003 | 0.236407 | 0.359196 | 0.552383 | 0.183632 | 0.206219 |
101 | DES | DES Gln389Pro | 0.948128 | 0.96098 | 0.485484 | 0.666678 | 0.954953 | 0.236407 | 0.359196 | 0.59401 | 0.183632 | 0.206219 |
102 | DES | DES Gln99Glu | -0.0921232 | 0.96098 | -0.312078 | 0.666678 | 0.294887 | 0.236407 | 0.359196 | 0.397539 | 0.183632 | 0.206219 |
103 | DES | DES Glu245Asp | -0.112741 | 0.96098 | -0.248482 | 0.666678 | 0.476539 | 0.236407 | 0.359196 | 0.419267 | 0.183632 | 0.206219 |
104 | DES | DES Glu413Lys | 0.688131 | 0.96098 | -0.183831 | 0.666678 | 0.660136 | 0.236407 | 0.359196 | 0.445209 | 0.183632 | 0.206219 |
105 | DES | DES Gly20Arg | 0.830217 | 0.96098 | 0.302616 | 0.666678 | 0.838598 | 0.236407 | 0.359196 | 0.610163 | 0.183632 | 0.206219 |
106 | DES | DES Gly44Ser | 0.489784 | 0.96098 | -0.0703174 | 0.666678 | 0.560471 | 0.236407 | 0.359196 | 0.371118 | 0.183632 | 0.206219 |
107 | DES | DES Gly84Ser | 0.456559 | 0.96098 | -0.133157 | 0.666678 | 0.575886 | 0.236407 | 0.359196 | 0.306097 | 0.183632 | 0.206219 |
108 | DES | DES His243Tyr | 0.832262 | 0.96098 | 0.452825 | 0.666678 | 0.882084 | 0.236407 | 0.359196 | 0.249457 | 0.183632 | 0.206219 |
109 | DES | DES His441Leu | 0.691012 | 0.96098 | -0.102578 | 0.666678 | 0.832119 | 0.236407 | 0.359196 | 0.454825 | 0.183632 | 0.206219 |
110 | DES | DES Leu136Pro | 0.910889 | 0.96098 | 0.390357 | 0.666678 | 0.949513 | 0.236407 | 0.359196 | 0.663502 | 0.183632 | 0.206219 |
111 | DES | DES Leu274Pro | 0.558595 | 0.96098 | -0.338633 | 0.666678 | 0.824172 | 0.236407 | 0.359196 | 0.379396 | 0.183632 | 0.206219 |
112 | DES | DES Leu338Arg | -0.118026 | 0.96098 | -0.113017 | 0.666678 | 0.312462 | 0.236407 | 0.359196 | 0.328504 | 0.183632 | 0.206219 |
113 | DES | DES Leu345Pro | -0.488238 | 0.96098 | -0.275704 | 0.666678 | 0.442715 | 0.236407 | 0.359196 | 0.209938 | 0.183632 | 0.206219 |
114 | DES | DES Met349Ile | 0.50701 | 0.96098 | -0.233906 | 0.666678 | 0.837868 | 0.236407 | 0.359196 | 0.516332 | 0.183632 | 0.206219 |
115 | DES | DES Pro419Ser | 0.352441 | 0.96098 | -0.190622 | 0.666678 | 0.412984 | 0.236407 | 0.359196 | 0.37414 | 0.183632 | 0.206219 |
116 | DES | DES Pro433Thr | 0.671607 | 0.96098 | -0.266801 | 0.666678 | 0.769299 | 0.236407 | 0.359196 | 0.499264 | 0.183632 | 0.206219 |
117 | DES | DES Ser298Leu | 0.59923 | 0.96098 | -0.165427 | 0.666678 | 0.753565 | 0.236407 | 0.359196 | 0.550371 | 0.183632 | 0.206219 |
118 | DES | DES Ser424Phe | 0.680491 | 0.96098 | -0.240006 | 0.666678 | 0.856886 | 0.236407 | 0.359196 | 0.507256 | 0.183632 | 0.206219 |
119 | DES | DES Ser46Tyr | 0.15768 | 0.96098 | -0.221751 | 0.666678 | 0.141532 | 0.236407 | 0.359196 | 0.172794 | 0.183632 | 0.206219 |
120 | DES | DES Thr219Ile | 0.856067 | 0.96098 | 0.479039 | 0.666678 | 0.918727 | 0.236407 | 0.359196 | 0.574198 | 0.183632 | 0.206219 |
121 | DES | DES Thr445Ala | 0.769654 | 0.96098 | 0.233077 | 0.666678 | 0.899982 | 0.236407 | 0.359196 | 0.206988 | 0.183632 | 0.206219 |
122 | DES | DES Thr453Ile | 0.507269 | 0.96098 | -0.286501 | 0.666678 | 0.783687 | 0.236407 | 0.359196 | 0.290476 | 0.183632 | 0.206219 |
123 | DES | DES Tyr122Asp | 0.966974 | 0.96098 | 0.6171 | 0.666678 | 0.906981 | 0.236407 | 0.359196 | 0.470362 | 0.183632 | 0.206219 |
124 | DES | DES Tyr331Asn | 0.0531358 | 0.96098 | 0.149425 | 0.666678 | 0.622804 | 0.236407 | 0.359196 | 0.415561 | 0.183632 | 0.206219 |
125 | DES | DES Val126Leu | 0.961887 | 0.96098 | 0.636213 | 0.666678 | 0.907963 | 0.236407 | 0.359196 | 0.526978 | 0.183632 | 0.206219 |
126 | DES | DES Val394Met | 0.42363 | 0.96098 | -0.139181 | 0.666678 | 0.736489 | 0.236407 | 0.359196 | 0.404362 | 0.183632 | 0.206219 |
127 | DES | DES Val469Met | -0.165913 | 0.96098 | -0.272643 | 0.666678 | 0.251948 | 0.236407 | 0.359196 | 0.216013 | 0.183632 | 0.206219 |
128 | DES | DES Val56Leu | 0.547154 | 0.96098 | -0.155579 | 0.666678 | 0.781271 | 0.236407 | 0.359196 | 0.337276 | 0.183632 | 0.206219 |
129 | CA8 | CA8 Arg237Gln | 0.392047 | 0.766353 | 0.738549 | 0.678179 | 0.925869 | 0.236407 | 0.359196 | 0.561532 | 0.183632 | 0.206219 |
130 | CDKN1A | CDKN1A Arg67Leu | 0.883155 | 0.860748 | 0.580158 | 0.638411 | 0.711187 | 0.236407 | 0.359196 | 0.638306 | 0.183632 | 0.206219 |
131 | CDKN1A | CDKN1A Arg84Gln | 0.879257 | 0.860748 | 0.599658 | 0.638411 | 0.698247 | 0.236407 | 0.359196 | 0.590087 | 0.183632 | 0.206219 |
132 | CDKN1A | CDKN1A Asp149Gly | 0.952675 | 0.860748 | 0.919989 | 0.638411 | 0.892883 | 0.236407 | 0.359196 | 0.834276 | 0.183632 | 0.206219 |
133 | CDKN1A | CDKN1A Ser31Arg | -0.143582 | 0.860748 | 0.242012 | 0.638411 | 0.587451 | 0.236407 | 0.359196 | 0.576635 | 0.183632 | 0.206219 |
134 | EFHC1 | EFHC1 Arg159Trp | 0.442311 | 0.353609 | 0.65473 | 0.308837 | 0.278683 | 0.236407 | 0.359196 | 0.286627 | 0.183632 | 0.206219 |
135 | EFHC1 | EFHC1 Asp210Asn | 0.710313 | 0.353609 | 0.314558 | 0.308837 | 0.647675 | 0.236407 | 0.359196 | 0.484588 | 0.183632 | 0.206219 |
136 | EFHC1 | EFHC1 Asp253Tyr | 0.720759 | 0.353609 | 0.558822 | 0.308837 | 0.459286 | 0.236407 | 0.359196 | 0.133959 | 0.183632 | 0.206219 |
137 | EFHC1 | EFHC1 Cys259Tyr | 0.55515 | 0.353609 | -0.388751 | 0.308837 | 0.844412 | 0.236407 | 0.359196 | 0.555367 | 0.183632 | 0.206219 |
138 | EFHC1 | EFHC1 Ile174Val | 0.662932 | 0.353609 | 0.620766 | 0.308837 | 0.390085 | 0.236407 | 0.359196 | 0.27129 | 0.183632 | 0.206219 |
139 | EFHC1 | EFHC1 Met448Thr | 0.554476 | 0.353609 | -0.328411 | 0.308837 | 0.774648 | 0.236407 | 0.359196 | 0.478676 | 0.183632 | 0.206219 |
140 | EFHC1 | EFHC1 Phe229Leu | 0.700251 | 0.353609 | 0.275369 | 0.308837 | 0.706964 | 0.236407 | 0.359196 | 0.477644 | 0.183632 | 0.206219 |
141 | BAG3 | BAG3 Arg218Trp | 0.866316 | 0.839816 | 0.36141 | 0.500452 | 0.798331 | 0.236407 | 0.359196 | 0.52026 | 0.183632 | 0.206219 |
142 | BAG3 | BAG3 Arg258Trp | -0.346637 | 0.839816 | 0.163514 | 0.500452 | 0.937087 | 0.236407 | 0.359196 | 0.203287 | 0.183632 | 0.206219 |
143 | BAG3 | BAG3 Arg477His | 0.862916 | 0.839816 | 0.723351 | 0.500452 | 0.852504 | 0.236407 | 0.359196 | 0.484095 | 0.183632 | 0.206219 |
144 | BAG3 | BAG3 Leu462Pro | 0.657287 | 0.839816 | 0.274224 | 0.500452 | 0.773469 | 0.236407 | 0.359196 | 0.349204 | 0.183632 | 0.206219 |
145 | BAG3 | BAG3 Pro380Ser | 0.946702 | 0.839816 | 0.798054 | 0.500452 | 0.920722 | 0.236407 | 0.359196 | 0.659353 | 0.183632 | 0.206219 |
146 | CSNK1D | CSNK1D His46Arg | 0.489742 | 0.652241 | 0.0320267 | 0.600537 | 0.771899 | 0.236407 | 0.359196 | 0.401865 | 0.183632 | 0.206219 |
147 | BFSP2 | BFSP2 Ala407Asp | 0.188576 | 0.86151 | 0.0961269 | 0.525939 | 0.451919 | 0.236407 | 0.359196 | 0.365802 | 0.183632 | 0.206219 |
148 | BFSP2 | BFSP2 Arg287Trp | -0.203345 | 0.86151 | 0.112663 | 0.525939 | 0.827873 | 0.236407 | 0.359196 | 0.672278 | 0.183632 | 0.206219 |
149 | BFSP2 | BFSP2 Arg339His | 0.766487 | 0.86151 | 0.498634 | 0.525939 | 0.663021 | 0.236407 | 0.359196 | 0.434368 | 0.183632 | 0.206219 |
150 | FADD | FADD Cys105Trp | 0.333091 | 0.369791 | 0.218344 | 0.158193 | 0.324666 | 0.236407 | 0.359196 | 0.621813 | 0.183632 | 0.206219 |
151 | AGXT | AGXT Ala186Val | 0.40574 | 0.849535 | 0.20802 | 0.664522 | 0.881702 | 0.236407 | 0.359196 | 0.151812 | 0.183632 | 0.206219 |
152 | AGXT | AGXT Ala210Pro | 0.339954 | 0.849535 | 0.46798 | 0.664522 | 0.692378 | 0.236407 | 0.359196 | 0.14402 | 0.183632 | 0.206219 |
153 | AGXT | AGXT Ala248Ser | 0.599298 | 0.849535 | 0.407043 | 0.664522 | 0.736866 | 0.236407 | 0.359196 | 0.276903 | 0.183632 | 0.206219 |
154 | AGXT | AGXT Ala248Val | 0.919494 | 0.849535 | 0.884055 | 0.664522 | 0.767134 | 0.236407 | 0.359196 | 0.548562 | 0.183632 | 0.206219 |
155 | AGXT | AGXT Ala280Val | 0.846497 | 0.849535 | 0.672835 | 0.664522 | 0.651513 | 0.236407 | 0.359196 | 0.52764 | 0.183632 | 0.206219 |
156 | AGXT | AGXT Ala295Thr | 0.964667 | 0.849535 | 0.853996 | 0.664522 | 0.846539 | 0.236407 | 0.359196 | 0.607658 | 0.183632 | 0.206219 |
157 | AGXT | AGXT Ala85Asp | 0.335741 | 0.849535 | 0.186985 | 0.664522 | 0.808038 | 0.236407 | 0.359196 | 0.238338 | 0.183632 | 0.206219 |
158 | AGXT | AGXT Arg111Gln | 0.639816 | 0.849535 | 0.762217 | 0.664522 | 0.756375 | 0.236407 | 0.359196 | 0.518734 | 0.183632 | 0.206219 |
159 | AGXT | AGXT Arg118Cys | 0.676583 | 0.849535 | 0.563772 | 0.664522 | 0.702174 | 0.236407 | 0.359196 | 0.611143 | 0.183632 | 0.206219 |
160 | AGXT | AGXT Arg197Gln | 0.896846 | 0.849535 | 0.580685 | 0.664522 | 0.64914 | 0.236407 | 0.359196 | 0.410531 | 0.183632 | 0.206219 |
161 | AGXT | AGXT Arg289His | 0.374419 | 0.849535 | 0.452246 | 0.664522 | 0.807065 | 0.236407 | 0.359196 | 0.572713 | 0.183632 | 0.206219 |
162 | AGXT | AGXT Arg301Cys | 0.634586 | 0.849535 | 0.281424 | 0.664522 | 0.741832 | 0.236407 | 0.359196 | 0.450398 | 0.183632 | 0.206219 |
163 | AGXT | AGXT Arg36Cys | 0.095247 | 0.849535 | -0.256261 | 0.664522 | 0.68908 | 0.236407 | 0.359196 | 0.212972 | 0.183632 | 0.206219 |
164 | AGXT | AGXT Arg381Lys | 0.887036 | 0.849535 | 0.689149 | 0.664522 | 0.781288 | 0.236407 | 0.359196 | 0.54443 | 0.183632 | 0.206219 |
165 | AGXT | AGXT Asn22Ser | 0.773378 | 0.849535 | 0.594779 | 0.664522 | 0.580641 | 0.236407 | 0.359196 | 0.462521 | 0.183632 | 0.206219 |
166 | AGXT | AGXT Asp129His | 0.927968 | 0.849535 | 0.750045 | 0.664522 | 0.717215 | 0.236407 | 0.359196 | 0.335258 | 0.183632 | 0.206219 |
167 | AGXT | AGXT Asp201Asn | -0.153482 | 0.849535 | -0.238843 | 0.664522 | 0.3578 | 0.236407 | 0.359196 | 0.466604 | 0.183632 | 0.206219 |
168 | AGXT | AGXT Asp341Glu | 0.0697666 | 0.849535 | -0.0565573 | 0.664522 | 0.356182 | 0.236407 | 0.359196 | 0.276413 | 0.183632 | 0.206219 |
169 | AGXT | AGXT Glu274Asp | 0.607957 | 0.849535 | 0.58081 | 0.664522 | 0.577989 | 0.236407 | 0.359196 | 0.5882 | 0.183632 | 0.206219 |
170 | AGXT | AGXT Gly116Arg | 0.41309 | 0.849535 | 0.752629 | 0.664522 | 0.708328 | 0.236407 | 0.359196 | 0.568779 | 0.183632 | 0.206219 |
171 | AGXT | AGXT Gly156Arg | 0.216318 | 0.849535 | 0.228235 | 0.664522 | 0.583163 | 0.236407 | 0.359196 | 0.281612 | 0.183632 | 0.206219 |
172 | AGXT | AGXT Gly161Arg | 0.278343 | 0.849535 | 0.67403 | 0.664522 | 0.73253 | 0.236407 | 0.359196 | 0.47044 | 0.183632 | 0.206219 |
173 | AGXT | AGXT Gly161Ser | 0.728239 | 0.849535 | 0.520919 | 0.664522 | 0.781395 | 0.236407 | 0.359196 | 0.327061 | 0.183632 | 0.206219 |
174 | AGXT | AGXT Gly41Arg | 0.664937 | 0.849535 | 0.807193 | 0.664522 | 0.381512 | 0.236407 | 0.359196 | 0.204492 | 0.183632 | 0.206219 |
175 | AGXT | AGXT Gly41Glu | 0.814439 | 0.849535 | 0.611575 | 0.664522 | 0.745704 | 0.236407 | 0.359196 | 0.4382 | 0.183632 | 0.206219 |
176 | AGXT | AGXT Gly82Arg | 0.915432 | 0.849535 | 0.927311 | 0.664522 | 0.869626 | 0.236407 | 0.359196 | 0.668718 | 0.183632 | 0.206219 |
177 | AGXT | AGXT Ile202Asn | 0.203934 | 0.849535 | 0.514362 | 0.664522 | 0.717426 | 0.236407 | 0.359196 | 0.34812 | 0.183632 | 0.206219 |
178 | AGXT | AGXT Ile279Met | 0.078649 | 0.849535 | 0.686356 | 0.664522 | 0.824106 | 0.236407 | 0.359196 | 0.437371 | 0.183632 | 0.206219 |
179 | AGXT | AGXT Ile279Thr | -0.204764 | 0.849535 | -0.26962 | 0.664522 | 0.207085 | 0.236407 | 0.359196 | 0.198754 | 0.183632 | 0.206219 |
180 | AGXT | AGXT Ile340Met | 0.903956 | 0.849535 | 0.644107 | 0.664522 | 0.691263 | 0.236407 | 0.359196 | 0.635978 | 0.183632 | 0.206219 |
181 | AGXT | AGXT Leu298Pro | 0.235721 | 0.849535 | 0.26251 | 0.664522 | 0.602883 | 0.236407 | 0.359196 | 0.226917 | 0.183632 | 0.206219 |
182 | AGXT | AGXT Lys12Arg | 0.937222 | 0.849535 | 0.939283 | 0.664522 | 0.787612 | 0.236407 | 0.359196 | 0.686885 | 0.183632 | 0.206219 |
183 | AGXT | AGXT Met195Leu | 0.670139 | 0.849535 | 0.443538 | 0.664522 | 0.674409 | 0.236407 | 0.359196 | 0.311371 | 0.183632 | 0.206219 |
184 | AGXT | AGXT Met49Leu | 0.666889 | 0.849535 | 0.43624 | 0.664522 | 0.765939 | 0.236407 | 0.359196 | 0.509871 | 0.183632 | 0.206219 |
185 | AGXT | AGXT Phe152Ile | 0.704864 | 0.849535 | 0.857269 | 0.664522 | 0.363803 | 0.236407 | 0.359196 | 0.142884 | 0.183632 | 0.206219 |
186 | AGXT | AGXT Pro10Ala | 0.906964 | 0.849535 | 0.89344 | 0.664522 | 0.809542 | 0.236407 | 0.359196 | 0.572227 | 0.183632 | 0.206219 |
187 | AGXT | AGXT Pro11His | 0.689958 | 0.849535 | 0.591079 | 0.664522 | 0.753937 | 0.236407 | 0.359196 | 0.314062 | 0.183632 | 0.206219 |
188 | AGXT | AGXT Pro11Leu | 0.33432 | 0.849535 | 0.158312 | 0.664522 | 0.659494 | 0.236407 | 0.359196 | 0.11871 | 0.183632 | 0.206219 |
189 | AGXT | AGXT Pro319Leu | 0.59928 | 0.849535 | 0.328312 | 0.664522 | 0.733404 | 0.236407 | 0.359196 | 0.291737 | 0.183632 | 0.206219 |
190 | AGXT | AGXT Ser187Phe | 0.59755 | 0.849535 | 0.759807 | 0.664522 | 0.804119 | 0.236407 | 0.359196 | 0.642991 | 0.183632 | 0.206219 |
191 | AGXT | AGXT Ser218Leu | 0.64313 | 0.849535 | 0.881151 | 0.664522 | 0.826705 | 0.236407 | 0.359196 | 0.762984 | 0.183632 | 0.206219 |
192 | AGXT | AGXT Ser221Pro | 0.248213 | 0.849535 | 0.672312 | 0.664522 | 0.686656 | 0.236407 | 0.359196 | 0.368276 | 0.183632 | 0.206219 |
193 | AGXT | AGXT Val162Met | 0.65846 | 0.849535 | 0.557249 | 0.664522 | 0.304567 | 0.236407 | 0.359196 | 0.244205 | 0.183632 | 0.206219 |
194 | AGXT | AGXT Val326Ile | 0.684498 | 0.849535 | 0.577242 | 0.664522 | 0.612692 | 0.236407 | 0.359196 | 0.536806 | 0.183632 | 0.206219 |
195 | COQ8A | COQ8A Gly272Asp | 0.674594 | 0.317254 | 0.469798 | 0.243587 | 0.672593 | 0.236407 | 0.359196 | 0.215357 | 0.183632 | 0.206219 |
196 | COQ8A | COQ8A Gly549Ser | 0.34926 | 0.317254 | -0.0127499 | 0.243587 | 0.732797 | 0.236407 | 0.359196 | 0.394383 | 0.183632 | 0.206219 |
197 | COQ8A | COQ8A His80Tyr | -0.376851 | 0.317254 | -0.444023 | 0.243587 | 0.755664 | 0.236407 | 0.359196 | 0.572319 | 0.183632 | 0.206219 |
198 | CHN1 | CHN1 Glu313Lys | 0.823906 | 0.497718 | 0.649934 | 0.537013 | 0.737574 | 0.236407 | 0.359196 | 0.527971 | 0.183632 | 0.206219 |
199 | CHN1 | CHN1 Ile126Met | 0.742673 | 0.497718 | 0.509495 | 0.537013 | 0.721022 | 0.236407 | 0.359196 | 0.707186 | 0.183632 | 0.206219 |
200 | CHN1 | CHN1 Pro141Leu | 0.762687 | 0.497718 | 0.537037 | 0.537013 | 0.831193 | 0.236407 | 0.359196 | 0.675641 | 0.183632 | 0.206219 |
201 | CHN1 | CHN1 Pro252Ser | -0.1729 | 0.497718 | 0.415451 | 0.537013 | 0.844275 | 0.236407 | 0.359196 | 0.771959 | 0.183632 | 0.206219 |
202 | CHN1 | CHN1 Tyr143His | 0.733163 | 0.497718 | 0.640303 | 0.537013 | 0.722927 | 0.236407 | 0.359196 | 0.774065 | 0.183632 | 0.206219 |
203 | CDC73 | CDC73 Met1Ile | -0.682187 | 0.765012 | 0.11803 | 0.527967 | 0.817551 | 0.236407 | 0.359196 | 0.434372 | 0.183632 | 0.206219 |
204 | COMP | COMP Ala171Thr | 0.0519353 | 0.48337 | -0.0979181 | 0.565594 | 0.757712 | 0.236407 | 0.359196 | 0.479403 | 0.183632 | 0.206219 |
205 | COMP | COMP Arg718Pro | 0.014536 | 0.48337 | -0.427918 | 0.565594 | 0.870755 | 0.236407 | 0.359196 | 0.377358 | 0.183632 | 0.206219 |
206 | COMP | COMP Asn523Lys | 0.0469446 | 0.48337 | -0.370681 | 0.565594 | 0.735884 | 0.236407 | 0.359196 | 0.171554 | 0.183632 | 0.206219 |
207 | COMP | COMP Asn555Lys | 0.278054 | 0.48337 | 0.291873 | 0.565594 | 0.888965 | 0.236407 | 0.359196 | 0.870107 | 0.183632 | 0.206219 |
208 | COMP | COMP Asp271His | 0.448496 | 0.48337 | 0.305684 | 0.565594 | 0.214483 | 0.236407 | 0.359196 | 0.487396 | 0.183632 | 0.206219 |
209 | COMP | COMP Asp319Val | 0.482311 | 0.48337 | 0.112505 | 0.565594 | 0.42538 | 0.236407 | 0.359196 | 0.328242 | 0.183632 | 0.206219 |
210 | COMP | COMP Asp342Tyr | 0.0366465 | 0.48337 | -0.564795 | 0.565594 | 0.741217 | 0.236407 | 0.359196 | 0.421236 | 0.183632 | 0.206219 |
211 | COMP | COMP Asp408Asn | 0.652258 | 0.48337 | 0.683344 | 0.565594 | 0.159243 | 0.236407 | 0.359196 | 0.407203 | 0.183632 | 0.206219 |
212 | COMP | COMP Asp408His | 0.0460424 | 0.48337 | 0.137493 | 0.565594 | 0.318446 | 0.236407 | 0.359196 | 0.192894 | 0.183632 | 0.206219 |
213 | COMP | COMP Asp511Glu | 0.098606 | 0.48337 | -0.178495 | 0.565594 | 0.690808 | 0.236407 | 0.359196 | 0.512038 | 0.183632 | 0.206219 |
214 | COMP | COMP Asp530Glu | -0.0878529 | 0.48337 | -0.311885 | 0.565594 | 0.914823 | 0.236407 | 0.359196 | 0.29386 | 0.183632 | 0.206219 |
215 | COMP | COMP Asp605Asn | -0.0132953 | 0.48337 | -0.343115 | 0.565594 | 0.815929 | 0.236407 | 0.359196 | 0.347799 | 0.183632 | 0.206219 |
216 | COMP | COMP Cys348Arg | 0.1296 | 0.48337 | -0.320824 | 0.565594 | 0.879598 | 0.236407 | 0.359196 | 0.574866 | 0.183632 | 0.206219 |
217 | COMP | COMP Gly207Asp | -0.163465 | 0.48337 | -0.386234 | 0.565594 | 0.768955 | 0.236407 | 0.359196 | 0.388437 | 0.183632 | 0.206219 |
218 | COMP | COMP His189Arg | -0.309563 | 0.48337 | -0.339264 | 0.565594 | 0.793272 | 0.236407 | 0.359196 | 0.364794 | 0.183632 | 0.206219 |
219 | COMP | COMP His441Arg | -0.271095 | 0.48337 | -0.36719 | 0.565594 | 0.753647 | 0.236407 | 0.359196 | 0.433715 | 0.183632 | 0.206219 |
220 | COMP | COMP His587Arg | -0.0776071 | 0.48337 | -0.475992 | 0.565594 | 0.865808 | 0.236407 | 0.359196 | 0.471006 | 0.183632 | 0.206219 |
221 | COMP | COMP Ser681Cys | 0.654227 | 0.48337 | 0.675944 | 0.565594 | 0.266211 | 0.236407 | 0.359196 | 0.207867 | 0.183632 | 0.206219 |
222 | COMP | COMP Thr529Ile | -0.0165323 | 0.48337 | 0.100917 | 0.565594 | 0.634356 | 0.236407 | 0.359196 | 0.47933 | 0.183632 | 0.206219 |
223 | COMP | COMP Thr585Arg | 0.278612 | 0.48337 | -0.0138169 | 0.565594 | 0.833655 | 0.236407 | 0.359196 | 0.415051 | 0.183632 | 0.206219 |
224 | COMP | COMP Thr585Lys | 0.37571 | 0.48337 | 0.13773 | 0.565594 | 0.520079 | 0.236407 | 0.359196 | 0.371695 | 0.183632 | 0.206219 |
225 | COMP | COMP Thr585Met | 0.0867655 | 0.48337 | -0.326112 | 0.565594 | 0.848373 | 0.236407 | 0.359196 | 0.363455 | 0.183632 | 0.206219 |
226 | AMPD2 | AMPD2 Glu697Asp | 0.766463 | 0.81178 | 0.849455 | 0.470379 | 0.772086 | 0.236407 | 0.359196 | 0.249831 | 0.183632 | 0.206219 |
227 | CORO1A | CORO1A Val397Ile | 0.0615145 | 0.852053 | 0.306829 | 0.385279 | 0.81264 | 0.236407 | 0.359196 | 0.64429 | 0.183632 | 0.206219 |
228 | APOA1 | APOA1 Ala188Ser | -0.092556 | 0.596283 | -0.152395 | 0.463634 | 0.484829 | 0.236407 | 0.359196 | 0.358139 | 0.183632 | 0.206219 |
229 | APOA1 | APOA1 Ala199Pro | -0.00654168 | 0.596283 | 0.693163 | 0.463634 | 0.886548 | 0.236407 | 0.359196 | 0.0456008 | 0.183632 | 0.206219 |
230 | APOA1 | APOA1 Arg197Cys | 0.0274639 | 0.596283 | -0.173555 | 0.463634 | 0.875393 | 0.236407 | 0.359196 | 0.228524 | 0.183632 | 0.206219 |
231 | APOA1 | APOA1 Arg34Leu | -0.261367 | 0.596283 | 0.698514 | 0.463634 | 0.861617 | 0.236407 | 0.359196 | 0.448169 | 0.183632 | 0.206219 |
232 | APOA1 | APOA1 Leu114Pro | -0.0300296 | 0.596283 | 0.840867 | 0.463634 | 0.838112 | 0.236407 | 0.359196 | 0.455979 | 0.183632 | 0.206219 |
233 | APOA1 | APOA1 Leu198Ser | -0.0397486 | 0.596283 | -0.0862165 | 0.463634 | 0.748647 | 0.236407 | 0.359196 | 0.0596162 | 0.183632 | 0.206219 |
234 | APOA1 | APOA1 Leu84Arg | -0.0390537 | 0.596283 | 0.230308 | 0.463634 | 0.859377 | 0.236407 | 0.359196 | 0.246977 | 0.183632 | 0.206219 |
235 | APOA1 | APOA1 Trp74Arg | -0.157724 | 0.596283 | 0.747219 | 0.463634 | 0.873373 | 0.236407 | 0.359196 | 0.448962 | 0.183632 | 0.206219 |
236 | APOA1 | APOA1 Val180Glu | -0.124348 | 0.596283 | 0.887283 | 0.463634 | 0.834699 | 0.236407 | 0.359196 | 0.426655 | 0.183632 | 0.206219 |
237 | CFP | CFP Tyr414Asp | -0.457254 | 0.862304 | 0.264033 | 0.204986 | 0.691247 | 0.236407 | 0.359196 | 0.423041 | 0.183632 | 0.206219 |
238 | DIABLO | DIABLO Ala3Gly | 0.406146 | 0.544288 | 0.406309 | 0.36727 | 0.328608 | 0.236407 | 0.359196 | 0.255339 | 0.183632 | 0.206219 |
239 | DIABLO | DIABLO Gly224Arg | 0.71521 | 0.544288 | 0.395186 | 0.36727 | 0.461083 | 0.236407 | 0.359196 | 0.504745 | 0.183632 | 0.206219 |
240 | DIABLO | DIABLO Ile59Val | 0.426364 | 0.544288 | 0.211285 | 0.36727 | 0.787706 | 0.236407 | 0.359196 | 0.479351 | 0.183632 | 0.206219 |
241 | DIABLO | DIABLO Ser126Leu | 0.55814 | 0.544288 | 0.42785 | 0.36727 | 0.651042 | 0.236407 | 0.359196 | 0.166687 | 0.183632 | 0.206219 |
Exciting! I'm confused though because it looks like 80% of the red pairs are to the right of the dotted lines here and 10-20% of pairs are on the left of the lines, am I missing something? Neither is around 40% so I must be misinterpreting something. Also I wasn't sure what "the replicate correlate dist" means?
These distributions are the regular replicate correlation distributions (along with their corresponding null - blue dist). 40% is not captured here. The only number from this figure with influences the 40% number is where the red dotted line falls (10th percentile of the red -distribution of correlation coef values among replicates- dist). Impact scores distribution is not placed on this figure. But you can check per-WT/MT-pair values in the table. For example, if you look at the "cc_np" column in that table, 40% of the values should be less than red dotted line value for the figure you copied for non-protein channel dists which is 0.2.
I see! It would be nice to visually see the distribution of the WT-MT pairs (because IIUC the histograms are only showing WT replicates and MT replicates in red, or scrambled replicates in blue) but I am following the logic now.
Drafting my steps here. In the metadata , column
Metadata_Sample_Unique
includes the wild type and mutant names. Two kinds of replicability will be calculated using evalzoo:
- technical replicability-whether replicates based on
Metadata_Sample_Unique
are replicates. There are no controls in the data (i.e., remove all 516 -TC), so it will be replicate against non-replicates, by plate.
All analysis was done by Copairs, three plates were combined together and all 516-TC were removed, which gave us 1077 samples. We define replicates as those who have the same Metadata_Sample_Unique
, To see if we can retrieve replicates from non replicates, the following parameters were implemented into Copairs: pos_sameby = ['Metadata_Sample_Unique'] neg_diffby = ['Metadata_Sample_Unique']
. We got a p value for each individual sample, so I then aggregated the result using Metadata_Sample_Unique
. The table and figure show the unique Metadata_Sample_Unique
that passed the significance threshold.
Is this plot for all reagents (WT and MUT) being able to retrieve replicates of themselves against a background of all samples on the plates? And we are seeing roughly half do so?
- biological replicability-whether mutant for the same wild type can be retrieved from from the wild type itself.
To see if we can retrieve mutants from its own WT, the following parameters were implemented into Copairs:
pos_sameby = ['Metadata_Gene'], pos_diffby = ['Metadata_type'], neg_diffby = ['Metadata_Gene']
. This is to say we match mutants to its WT (a particular gene name) against the rest of the gene names including both their WTs and MTs. I got a p value for each individual sample, given the fact that each WT has different mutants, it is interesting to see which particular mutant has impact on its WT. Thus, I removed all the WTs from the Copair results, and then aggregated the results usingMetadata_Sample_Unique
, which in this case corresponded to each unique mutant. The table and figure below show whether those unique MT passed the significance threshold, however, in this case, we care about those who did not pass the threshold, meaning the MT had an effect on its WT.
@MarziehHaghighi has generated correlation score for each Metadata_Sample_Unique
to demonstrate if we can retrieve MT from its WT, the equivalence to this analysis. I plotted the mAP score for each Metadata_Sample_Unique
using Copairs on the same plot with correlation score. I noticed that there are 12 unique Metadata_Sample_Unique
included in my mAP score, but not in @MarziehHaghighi's correlation score.
['CTH Gln240Glu', 'BLMH Ile443Val', 'AP2S1 Arg15Cys', 'CLDN19 Arg200Gln', 'CUL3 Lys459Arg', 'CTNNA3 Val94Asp', 'AP2S1 Arg15His', 'BLK Ala71Thr', 'CCBE1 Gly136Arg', 'CLDN19 Gln57Glu', 'CTH Thr67Ile', 'CTH Ser403Ile']
IIUC the samples that correlate highly but have low average precision must be getting mixed up with lots of other samples in the experiment. That is, they have a strong phenotype that is similar to many other samples so it's hard to retrieve. (does anyone have an alternate explanation?) I am surprised it happens so often - the top left quadrant is much more full than I would have guessed.
@yhan8 please put your data stats as I have done in my report comment to make sure they are consistent as the first thing to start with. Here it would be the number of samples and the level of profiles you used and number of features for each "p" and "np". The pattern for "np" is weird so I guess there might be some discrepancy in "np". If you checkout the short script I used to generate my analysis, you can figure out what I have filtered and the reason behind extra samples you have.
That's a good idea - I agree Yu that it's good to record those stats so we can reality-check that everything looks sensible.
100 uniques WTs 254 unique pairs Used feature selected level of profiles 101 protein channel features and 584 non-protein channel features corresponding to the rest of channels went for analysis Note: all stats are consistent except I have 6 more protein features.
I am unclear on why @MarziehHaghighi's profile filtered out additional 12 pairs, I'll leave that to her.
Regarding the x and y scales, I am not sure if we want to visualize it like this?
I should add a comment, Marzieh - I think the two plots are really similar - I don't see np as problematic (except those two weird points!) They are both showing a gentle curve but with lots in the top left.
@AnneCarpenter well the unexpected pattern is much bolder for np to me. ~50 samples for MAP>0.3 for protein but ~15 for non-protein plot although in cc they look the same (many high cc points exist for both).
Thanks for checking @Yu. I cloned the repo (as it was among the repos I lost) but realized I cant regenerate the results since I was using functions from the main rare_disease repo which I lost in EC2 termination incident! I had done major refactoring on that repo during the past few months which are all gone :((! Anyway, I wanted to check the reason behind the missing variants, but since you have more variants I skip doing further checking as we want to switch to your way of analysis anyway!
Next steps: Plot nlogp value, x-axis is biological, y-axis is technical. The quadrant we care about is where the WT/MT that passed the significant technical threshold, but did not pass the biological threshold, meaning there is real signal when a MT has an effect on WT, not just random noise.
And to clarify, we probably want to reverse the axes so biological is on the y axis. Also, we couldn't decide if the technical should be retrieval for WT or for MUT of each pair. We waffled between making two copies of this chart, one with each on that axis, or if both ought to be plotted (with a line connecting them, even better). Depends how complex it is to plot all this. Yu is going to read the lung allele paper to understand better the concepts and take a look at the sparkler plot which aims to address this plotting conundrum (but is not very intuitive!).
In checkin we talked about different visualizations that could work here: 1) 3D with WT and MUT tech retrieval on separate axes (though hard to publish unless the data cooperates to be nicely viewable at a good angle), 2) drawing a straight line between paired WT and MUT dots on x axis (with bio value being the same for both, such that all lines would be horizontal), could get messy but shows all the info we need. 3) I realized another approach that may be ideal and also easy to implement: just plot the maximum of the WT or MUT tech retrieval for each pair on the x axis (and maybe have a 3 color legend for the dots, where the color indicate whether it was (a) WT tech retrieval is above the threshold, (b) MUT is above the threshold, or (c) both).
- I realized another approach that may be ideal and also easy to implement: just plot the maximum of the WT or MUT tech retrieval for each pair on the x axis (and maybe have a 3 color legend for the dots, where the color indicate whether it was (a) WT tech retrieval is above the threshold, (b) MUT is above the threshold, or (c) both).
Here I am showing you the plot, that includes 254 WT+MT pairs (i.e., excluding all WTs), their biological retrieval, that is, does each unique MT have an effect on its WT. The x-axis is average precision score, the y-axis is nlogp value. The dotted redline is the p=0.05 threshold, then I color coded the scatters based on whether 1) the WT passed the technical retrieval threshold (p<0.05) 2). the MT but not the WT passed the technical retrieval threshold 3). both WT and MT passed the technical retrieval threshold. 4) False is neither WT and MT passed the technical retrieval threshold. This is for us to determine whether the signal we see is noise, and where the noise is from.
Note that we care about WT+MT pairs that do not pass the significance threshold, meaning we cannot retrieve its MT from its WT, hence the MT has an effect.
Awesome, could you provide zooms of both where the x axis ends around 0.1? And can you make the legend the same in both so we don't re-learn the colors' meanings? (also good to use colorblind friendly palette, IIRC one of our labmates has trouble w red/green)
Check in on 6/22: We have come to the conclusion that the distance metric (mAP) may not be the best approach because of the common protein properties across different genes. @shntnu will discuss with Marzieh and see if we shall try a classifier, and we will go from there.
We have thus far used the mean average precision (mAP) framework for hit calling. In this method, a variant is deemed a 'hit' if it cannot retrieve wild-type replicates efficiently against all other wells on the same plate. To be specific:
One replicate of a variant is queried against all different-gene wells on the same plate; these serve as negative connections (typical n = 384 - number of same-gene wells on the plate). It is also compared to the corresponding (same-gene) wild-type wells across all plates; these serve as positive connections (typical n = 4, due to the typical four replicates of everything).
An mAP score and a corresponding p-value is derived for each variant using these query results. If the FDR-adjusted p-value is less than the prespecified alpha, we infer that we can retrieve the wild-type replicates well, implying that the wild-type and the mutant are practically indistinguishable (w.r.t. to other perturbations -- this is a key point). If the mAP score falls below this threshold, the variant is considered a hit. (Note, this is somewhat convoluted as we're essentially stating that we designate it as a hit if we can't reject the null hypothesis, but this is not a major issue)
Problematic Scenario:
Complications arise when the wild-type and the mutant only have subtle differences. For instance, if the wild-type elongates the cell nucleus and the mutant further enhances this phenotype, it will likely result in a high retrieval score due to the rarity of nuclear elongation as a phenotype. This implies the variant would not be tagged as a 'hit' despite the minor yet significant difference between the wild-type and the mutant.
Potential Solution (long-term):
The primary focus should be determining whether a screenable phenotype exists. In this context, we should aim to train a classifier capable of differentiating between the wild-type and the mutant. If the classifier exhibits good accuracy, it suggests the presence of a screenable phenotype.
Previously, this method was not explored due to insufficient replicates. Despite four replicates being inadequate even now, we can execute this approach at a single-cell level, which is the most promising direction moving forward.
Stop-gap solution:
The current mAP-based approach is a reasonable stop-gap solution. We may miss some hits (lower sensitivity), but the called hits will likely be true (high specificity).
Additional notes:
Marzieh and I evaluated whether her previous method of hit calling (as discussed above in this GitHub issue) was fundamentally distinct. It is not remarkably different. Her approach also designates a 'hit' based on the similarity between the wild-type and the mutant, providing it's under a certain threshold. This threshold is based on the similarities between replicates of the same perturbation.
Hence, both methodologies depend on similarities amongst other perturbations to set a threshold. However, we truly need a strategy focusing mainly on the phenotype of the wild-type and the mutant rather than their comparison to other perturbations. A targeted approach like this could yield more precise results when determining 'hits'.
Marzieh and I also discussed the broader concern about the application of supervised methods in profiling, with primary concerns in two specific areas:
Phenotypic Determination: A classifier could be built at the single-cell level to distinguish between the profiles of a perturbation and those of negative controls. A high classification score on a held-out test set suggests the presence of a detectable phenotype. However, while this method could help identify whether a perturbation has a phenotype, it is not conducive to creating a profile for the perturbation that could be used for clustering. Previous approaches using SVM classifiers have yielded profiles but are not wide-ranging enough, as they focus narrowly on the general perturbation effect.
Mechanisms of Action (MOA) Classification: Although there's no inherent issue with constructing a classifier for MOA classification, we risk failing to predict the classes of novel mechanisms. We prefer addressing this problem in a more unsupervised manner.
However, supervised learning can be a perfectly acceptable approach to predicting whether a variant has an impact, because that is the endpoint of the analysis.
One final aspect is whether we'd recommend a supervised (single-cell) approach, even for studies such as LUAD. For instance, should we recommend building a classifier to distinguish between the variant and the reference in the example below, using the accuracy of that classifier to declare whether the variant is a hit? I'm inclined to say yes, but this is open for debate (but we needn't debate that right now)
I thought through what makes sense to me and perhaps you can cross check if it’s the same as the long-term solution you propose - I think it is!
Anne’s plan:
train a classifier to distinguish a particular variant from its WT? WTgene1_well1 vs VARgene1_well1 and so on for all combinations of replicate wells for each. (I guess at the single cell level?)
caveat: such classifiers may always seem effective (due to plate layout, slight changes in infection efficiency/expression, etc). So, we want to get a sense of what level of classification accuracy is significant.
to create such a null we can use replicates of all samples as the baseline. So the null pairings would be WTgene2_well1 vs WTgene2_well2 and so on for all the WT genes and similarly for all the variants?
(fancy extra detail) We could exclude the query gene itself in creating this null baseline - maybe that’s unnecessary if there are ~382 other samples on the plate. If there are lots of variants of one gene on a plate then we may want to do this step.
(ruled out alternative) for each gene’s null we could instead try to train a classifier to distinguish replicates of only the query gene itself: WTgene1_well1 vs WTgene1_well2 but this will likely always yield ‘successful’ classifiers due to plate layout effects.
Furthermore, I don’t see why the LUAD case is any different than the WT/VAR case of Variant Painting experiments so I would also say Yes to your query there that the same approach is appropriate there.
I made a schematic. Sort of obvious now that I drew it out so I don't expect to spark major insight here, but adding a link to google slide in case it helps anyone think through things.
- train a classifier to distinguish a particular variant from its WT? WTgene1_well1 vs VARgene1_well1 and so on for all combinations of replicate wells for each. (I guess at the single cell level?)
We will do this at the single-cell level but will build a single model, so I wasn't sure what you mean by "all combinations of replicate wells". Maybe you are referring to the way we do train-test splits? If so, yes, we'd want to factor in the experimental hierarchy in some way when splitting.
- caveat: such classifiers may always seem effective (due to plate layout, slight changes in infection efficiency/expression, etc). So, we want to get a sense of what level of classification accuracy is significant.
- to create such a null we can use replicates of all samples as the baseline. So the null pairings would be WTgene2_well1 vs WTgene2_well2 and so on for all the WT genes and similarly for all the variants?
This sounds sensible
- (fancy extra detail) We could exclude the query gene itself in creating this null baseline - maybe that’s unnecessary if there are ~382 other samples on the plate. If there are lots of variants of one gene on a plate then we may want to do this step.
No need to exclude the query gene when creating the null in this manner, because our null hypothesis is that the wells are arbitrarily assigned WT and MUT labels. The fancy thing to do would be to have a separate null for each WT-MUT pair, where we only consider the wells of the WT-MUT pair and shuffle their labels. But that is an overkill
- (ruled out alternative) for each gene’s null we could instead try to train a classifier to distinguish replicates of only the query gene itself: WTgene1_well1 vs WTgene1_well2 but this will likely always yield ‘successful’ classifiers due to plate layout effects.
Oh, maybe this is the same as my fancy idea right above. We can pay closer attention to the design of the splits when we actually do the experiment.
Furthermore, I don’t see why the LUAD case is any different than the WT/VAR case of Variant Painting experiments so I would also say Yes to your query there that the same approach is appropriate there.
I think you are right
I made a schematic. Sort of obvious now that I drew it out so I don't expect to spark major insight here, but adding a link to google slide in case it helps anyone think through things.
This makes sense
To address your first comment, I agree it's sensible to build a single model that distinguishes WTgene1 (all wells) vs VARgene1 (all wells) but what I was proposing was different :D I propose actually training a bunch of small classifiers on each pair, like single cells in WTgene1_well1 vs VARgene1_well1 and so on with well2, well3, etc. I should've been more clear and said "all pairs of replicate wells" instead of "all combinations of replicate wells".
One reason to do this pairwise across individual wells is to put error bars on classification accuracy, I guess. But mainly to make it easier to calculate a realistic null because now we can use two replicates of a sample that we KNOW should look alike (WTgene2_well1 vs WTgene2_well2 and so on for all the WT genes and similarly for all the variants). I guess a downside of this approach, though, is that WTgene2_well1 vs WTgene2_well2 is likely to always be same-well-position whereas the query test WTgene1_well1 vs VARgene1_well1 is not :(
Still, if we instead make a single classifier for each WT-MUT, those values will almost certainly always seem to be accurate classifiers (due to technical variations) so to decide if they are significant, we need a suitable null. To make its null we need to get WTs with a similar number of replicates and similar number of single cells to be fair (?). And maybe choose those having similar plate positions? I dunno.
Some questions/notes:
My understanding is that we don't really care about overall phenotype impact score on the space of whole perturbations in the experiment. Instead, we care about the score on "if there is any consistent (across all cells) signature for WT versus mutant"? For example a 100% score means that there exist a phenotype that exists for all single cells of WT versus mutant (In contrast to the previous way of unbiased score given the full space, in which 100% (or 1) meant a signature that is in average the most distinct across all WTs and VARs of an experiment). Let me know if there is any flaw in my understanding.
If the above is correct and we indeed care about a phenotype that is consistent across single cells for WT versus VAR, we should be careful of the following:
The heterogeneity of the samples: we had a huge amount of heterogeneity across the samples of the Taipale Lab rare diseases datasets. I have not looked into subpopulation analysis data for the new VarChamp batches to have an idea of if this has changed in the new experiments. But wanted to give you a heads up given this prior knowledge.
Number of cells versus number of features for the classification problem, again this maybe is something that doesn't hold in the new batches of data as all the cells are now transfected but in the old batches we had small number of single cells for many wells and we should be careful about (n of features)>> (n of samples) which cause overfitting.
Position effect: in the first pilot batch of data that we analysed for VarChamp the plate layout was the same. If the position effect is strong, it is problematic for any method of scoring, but more problematic for a classifier which cares about all single cells in a well having a phenotype that other well dont have.
The overall amount of computational complexity we add to the problem (by using single cells) versus what we gain.
I make comments on the null after I fully understand your suggestions but for now wanted to give my two cents on this thread.
Yes, your understanding is correct!
Your bullet points are beautifully helpful. Yes, we do anticipate heterogeneity. Essentially every MUT will be distinguishable (classifiable) from its WT just due to technical variations so we have to be careful how to set the null to know which ones are really distinguishable.
I agree with the rest of the points too.
Goal:
Basic Analysis using mean profiles:
Using correlation coefficients (by Marzieh)
Using MAP (by @yhan8)