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![rcdk
• Idioma<c
R
interface
to
the
CDK
library
– I/O
support
for
chemical
file
formats
– Manipula<on
of
atoms,
bonds,
molecules
– Generate
molecular
descriptors,
fingerprints
library(rcdk)
mol <- parse.smiles(‘CCCC’)[[1]]
mols <- load.molecules(‘http://www.rguha.net/mipe100.smi’)](https://image.slidesharecdn.com/bdcon-140913140845-phpapp02/85/Robots-Small-Molecules-R-17-320.jpg)
![rcdk
• rcdk
works
with
references
to
Java
objects
– Can’t
save
them
in
a
workspace
(trivially)
> mol
[1] "Java-Object{AtomContainer(2040919865, #A:4, Atom(2131361171, S:C, H:3,
AtomType(2131361171, FC:0, Isotope(2131361171, Element(2131361171, S:C, AN:6)))),
Atom(1759969037, S:C, H:2, AtomType(1759969037, FC:0, Isotope(1759969037,
Element(1759969037, S:C, AN:6)))), Atom(359851081, S:C, H:2, AtomType(359851081, FC:0,
Isotope(359851081, Element(359851081, S:C, AN:6)))), Atom(703168415, S:C, H:3,
AtomType(703168415, FC:0, Isotope(703168415, Element(703168415, S:C, AN:6)))), #B:3,
Bond(549041464, #O:SINGLE, #S:NONE, #A:2, Atom(2131361171, S:C, H:3,
AtomType(2131361171, FC:0, Isotope(2131361171, Element(2131361171, S:C, AN:6)))),
Atom(1759969037, S:C, H:2, AtomType(1759969037, FC:0, Isotope(1759969037,
Element(1759969037, S:C, AN:6)))), ElectronContainer(549041464EC:2)), Bond(2654289,
#O:SINGLE, #S:NONE, #A:2, Atom(1759969037, S:C, H:2, AtomType(1759969037, FC:0,
Isotope(1759969037, Element(1759969037, S:C, AN:6)))), Atom(359851081, S:C, H:2,
AtomType(359851081, FC:0, Isotope(359851081, Element(359851081, S:C, AN:6)))),
ElectronContainer(2654289EC:2)), Bond(1660962283, #O:SINGLE, #S:NONE, #A:2,
Atom(359851081, S:C, H:2, AtomType(359851081, FC:0, Isotope(359851081,
Element(359851081, S:C, AN:6)))), Atom(703168415, S:C, H:3, AtomType(703168415, FC:0,
Isotope(703168415, Element(703168415, S:C, AN:6)))), ElectronContainer(1660962283EC:
2)))}"
>](https://image.slidesharecdn.com/bdcon-140913140845-phpapp02/85/Robots-Small-Molecules-R-18-320.jpg)
![Calcula<ng
Fingerprints
• Methods
to
compute
similari<es,
generate
summaries
&
manipulate
fingerprints
> fps[[1]]
Fingerprint object
name =
length = 1024
folded = FALSE
source = CDK
bits on = 15 18 45 73 77 78 79 85 87 96 107 109 129 139 149 159
162 166 172 179 194 209 214 223 225 227 239 254 266 272 301 312 327
335 350 354 359 392 393 395 397 415 435 455 486 491 492 499 534 535
541 543 544 545 546 559 575 600 605 618 621 622 626 635 638 644 645
647 690 723 728 742 743 753 754 800 819 831 832 889 893 913 922 930
936 954 985 988 1005 1008 1016
>](https://image.slidesharecdn.com/bdcon-140913140845-phpapp02/85/Robots-Small-Molecules-R-21-320.jpg)
![• Comparison
• Simply
e.g.:
Compare
~
800
solubles
with
>
30k
insolubles
1.0
Use
Case
-‐
Bit
Spectrum
of
two
datasets
is
now
O(n)
take
the
difference
of
the
two
bit
spectra
Frequency
0.5
Normalized 0.0
-0.5
Δ -1.0
Bit Position 0 50 100 150
## make two subsets and generate bit spectra
sol.idx <- which(sol$label == 'high')
insol.idx <- which(sol$label != 'high')
sol.bs <- bit.spectrum(fps[sol.idx])
insol.bs <- bit.spectrum(fps[insol.idx])
## display a difference plot
bsdiff <- sol.bs - insol.bs
d <- data.frame(x=1:length(sol.bs), y=bsdiff)
ggplot(d, aes(x=x,y=y))+geom_line()+
xlab('Bit Position')+
ylab('Normalized Frequency')+
ylim(c(-1,1))](https://image.slidesharecdn.com/bdcon-140913140845-phpapp02/85/Robots-Small-Molecules-R-25-320.jpg)
Uploaded byRajarshi Guha
Robots, Small Molecules & R
This document discusses high-throughput screening (HTS) workflows for identifying biologically active small molecules. It describes how robots are used to rapidly screen large libraries of compounds in assays and generate large datasets. Statistical and machine learning methods in R can then be used to build predictive models from these datasets to identify promising leads and guide the screening of additional compounds. Caveats regarding the applicability of models to new chemical spaces are also discussed.
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Robots, Small Molecules & R
- 1. Robots, Small Molecules & R Ingredients for Exploring and Predic<ng Biological Effects Rajarshi Guha September 13, 2014 hEp://blog.rguha.net/
- 2. Target Iden<fica<on Lead Discovery Lead Op<miza<on Clinical Development • Sensi<vity • Scaling Assay Op<miza<on Primary Screening • Fluorescence • High Content • Select subset to follow up • Diversity Cherry Picking Confirma<on • Counter screen • Explore SAR HTS Hun<ng for Leads
- 3. High Throughput Screening • Test thousands to hundreds of thousands of compounds in one or more assays • Employs a robo<c plaXorm • Rapidly iden<fy novel modulators of biological systems – Infec<ous agents – Cellular basis of diseases
- 4.
- 5.
- 6. HTS Workflow •Rapidly screen large compound collec<ons • Efficiently iden<fy real ac<ves – Test them in slower, accurate, expensive screens • Use the data to learn what types of compounds tend to be ac<ve • Use the model to suggest more compounds to screen 300K HTS 1000 300 Number of Molecules Cherry Picks
- 7. Data Science Problems • Predic<ve models for highlight imbalanced datasets • Global versus local models? • Feature selec<on – data driven? Domain driven? • Clustering & enrichment • Similarity – defini<on, computa<on, performance • Integra<on – chemical structures, numerical data, text (papers, patents), images
- 8. The Roles of R Data Access ROracle RMyQSL RPostgreSQL rpubchem chemblr Chemistry rcdk ChemmineR fingerprint HTS QC displayHTS spdep Imaging EBImage rflowcyt ripa raster Visualization grid ggplot Shiny ggvis igraph Data Analysis drc igraph randomForest svm ... Also see ChemPhys CRAN Task View
- 9. HTS Data Types – Single Point 100 75 50 25 0 9.50 9.75 10.00 10.25 10.50 Concentration Response
- 10. HTS Data Types – Dose Response 120 90 60 30 0.01 1.00 log10 Concentration Response y = S0 + Sinf − S0 1+10(log AC50−x)H
- 11. HTS Data Types – Mul<ple Readouts (and have this at mul<ple doses!)
- 12. HTS Data Types -‐ Combina<ons +
- 13.
- 14. Features, Features, Features • How do we “quan<fy” a chemical structure?
- 15. Features, Features, Features Charges Dipole moments Topological invariants Surface proper<es 1 0 1 1 0 0 0 1 0
- 16. Working with Molecules in R • A number of OSS libraries are available • ChemmineR and rcdk are the main packages that allow you to manipulate molecules in R • Uses rJava to interface with JOELib and CDK respec<vely
- 17. rcdk • Idioma<c R interface to the CDK library – I/O support for chemical file formats – Manipula<on of atoms, bonds, molecules – Generate molecular descriptors, fingerprints library(rcdk) mol <- parse.smiles(‘CCCC’)[[1]] mols <- load.molecules(‘http://www.rguha.net/mipe100.smi’)
- 18. rcdk • rcdk works with references to Java objects – Can’t save them in a workspace (trivially) > mol [1] "Java-Object{AtomContainer(2040919865, #A:4, Atom(2131361171, S:C, H:3, AtomType(2131361171, FC:0, Isotope(2131361171, Element(2131361171, S:C, AN:6)))), Atom(1759969037, S:C, H:2, AtomType(1759969037, FC:0, Isotope(1759969037, Element(1759969037, S:C, AN:6)))), Atom(359851081, S:C, H:2, AtomType(359851081, FC:0, Isotope(359851081, Element(359851081, S:C, AN:6)))), Atom(703168415, S:C, H:3, AtomType(703168415, FC:0, Isotope(703168415, Element(703168415, S:C, AN:6)))), #B:3, Bond(549041464, #O:SINGLE, #S:NONE, #A:2, Atom(2131361171, S:C, H:3, AtomType(2131361171, FC:0, Isotope(2131361171, Element(2131361171, S:C, AN:6)))), Atom(1759969037, S:C, H:2, AtomType(1759969037, FC:0, Isotope(1759969037, Element(1759969037, S:C, AN:6)))), ElectronContainer(549041464EC:2)), Bond(2654289, #O:SINGLE, #S:NONE, #A:2, Atom(1759969037, S:C, H:2, AtomType(1759969037, FC:0, Isotope(1759969037, Element(1759969037, S:C, AN:6)))), Atom(359851081, S:C, H:2, AtomType(359851081, FC:0, Isotope(359851081, Element(359851081, S:C, AN:6)))), ElectronContainer(2654289EC:2)), Bond(1660962283, #O:SINGLE, #S:NONE, #A:2, Atom(359851081, S:C, H:2, AtomType(359851081, FC:0, Isotope(359851081, Element(359851081, S:C, AN:6)))), Atom(703168415, S:C, H:3, AtomType(703168415, FC:0, Isotope(703168415, Element(703168415, S:C, AN:6)))), ElectronContainer(1660962283EC: 2)))}" >
- 19. Calcula<ng Molecular Features • Evaluate a matrix of numerical features mols <- load.molecules("mipe100.smi") dnames <- get.desc.names('topological') descs <- eval.desc(mols, dnames) • End up with a rectangular data.frame > str(descs) 'data.frame': 99 obs. of 195 variables: $ nRings7 : num 1 0 1 0 0 0 0 0 0 0 ... $ nRings8 : num 0 0 0 0 0 0 0 0 0 0 ... $ nRings9 : num 0 0 0 0 0 0 0 0 0 0 ... $ tpsaEfficiency : num 0.1856 0.2035 0.0118 0.0602 ...
- 20. Calcula<ng Fingerprints •Binary string representa<on of molecular structure – Objec<vely defined, fast to calculate – Good for searching, clustering, predic<on library(fingerprint) fps <- lapply(mols, get.fingerprint) • The fingerprint package is used to represent them as S4 objects
- 21. Calcula<ng Fingerprints •Methods to compute similari<es, generate summaries & manipulate fingerprints > fps[[1]] Fingerprint object name = length = 1024 folded = FALSE source = CDK bits on = 15 18 45 73 77 78 79 85 87 96 107 109 129 139 149 159 162 166 172 179 194 209 214 223 225 227 239 254 266 272 301 312 327 335 350 354 359 392 393 395 397 415 435 455 486 491 492 499 534 535 541 543 544 545 546 559 575 600 605 618 621 622 626 635 638 644 645 647 690 723 728 742 743 753 754 800 819 831 832 889 893 913 922 930 936 954 985 988 1005 1008 1016 >
- 22. Use Case -‐ SAR • Cluster molecules by structure and examine whether clusters are enriched in ac<vity library(chemblr); library(rcdk) d <- get.activity(chembl.id='CHEMBL857155', type='assay') cmpds <- lapply(d$ingredient_cmpd_chemblid, get.compound, type='chemblid') cmpds <- do.call(rbind, lapply(cmpds, function(x) data.frame(x$chemblId, x$smiles, stringsAsFactors=FALSE))) mols <- parse.smiles(cmpds$x.smiles) fps <- lapply(mols, get.fingerprint) sm <- fp.sim.matrix(fps) rownames(sm) <- cmpds$x.chemblId dm <- as.dist(1-sm) clus <- hclust(dm)
- 23. Use Case -‐ SAR CHEMBL331502 CHEMBL328164 CHEMBL52551 CHEMBL331120 CHEMBL120497 CHEMBL331759 CHEMBL120547 CHEMBL324064 CHEMBL318208 CHEMBL328627 CHEMBL99803 CHEMBL317562 CHEMBL332678 CHEMBL100312 CHEMBL119963 CHEMBL334031 CHEMBL323657 CHEMBL118406 CHEMBL118162 CHEMBL120137 CHEMBL331722 CHEMBL120078 CHEMBL121953 CHEMBL331783 CHEMBL333066 CHEMBL116832 CHEMBL316512 CHEMBL318471 CHEMBL98153 CHEMBL95827 CHEMBL119932 CHEMBL99037 CHEMBL120355 CHEMBL430574 CHEMBL120941 CHEMBL299756 CHEMBL317964 CHEMBL98501 CHEMBL317150 CHEMBL120030 CHEMBL99779 CHEMBL98554 CHEMBL318911 CHEMBL97844 CHEMBL316485 CHEMBL296586 CHEMBL100309 CHEMBL98360 CHEMBL316940 CHEMBL120664 CHEMBL419054 CHEMBL119989 CHEMBL121958 CHEMBL121957 CHEMBL329505 CHEMBL121543 CHEMBL121492 CHEMBL333894 CHEMBL333006 CHEMBL50894 CHEMBL116545 CHEMBL331190 CHEMBL325403 CHEMBL99423 CHEMBL330398 CHEMBL95477 CHEMBL545053 CHEMBL329063 CHEMBL331000 CHEMBL319373 CHEMBL431634 CHEMBL325654 CHEMBL332359 CHEMBL334084 CHEMBL328194
- 24. 1.00 0.75 0.50 0.25 0.00 0 250 500 750 Bit Position Normalized Frequency Use Case -‐ Bit Spectrum • Vector summary of the fingerprints for a dataset • Defined as the frac<on of <mes a bit posi<on is set to 1, for each bit posi<on 0 0 1 0 1 0 1 1 1 1 0 1 0.5 0.5 0.75 ... ... ... ... ... ~ 10K molecules
- 25. • Comparison •Simply e.g.: Compare ~ 800 solubles with > 30k insolubles 1.0 Use Case -‐ Bit Spectrum of two datasets is now O(n) take the difference of the two bit spectra Frequency 0.5 Normalized 0.0 -0.5 Δ -1.0 Bit Position 0 50 100 150 ## make two subsets and generate bit spectra sol.idx <- which(sol$label == 'high') insol.idx <- which(sol$label != 'high') sol.bs <- bit.spectrum(fps[sol.idx]) insol.bs <- bit.spectrum(fps[insol.idx]) ## display a difference plot bsdiff <- sol.bs - insol.bs d <- data.frame(x=1:length(sol.bs), y=bsdiff) ggplot(d, aes(x=x,y=y))+geom_line()+ xlab('Bit Position')+ ylab('Normalized Frequency')+ ylim(c(-1,1))
- 26. PREDICTIVE MODELS -‐ CAVEATS
- 27. Building Models is the Easy Part • Given a descriptor data.frame or fingerprint list we’re ready to build models – caret, caretEnsemble • Ques<on is whether the model(s) can generalize • Applicability is a key considera<on when predic<ng bioac<vity – Has economic & safety ramifica<ons in regulatory enviroments
- 28. Domain Applicability •How Training Set Test Set dissimilar to the training set do you have to be before the predic<on is meaningless? – Distance to training set? Inside/outside convex hull – Comparison of bit spectra
- 29. Global vs Local Models • Bioassay data is not really big data • Can big data be too big? • AID 1996 – 57K measurements of aqueous solubility • Do we build one model? • Or mul<ple local models? PCA of 166 Binary Features
- 30.
- 31. Screening Drug Combina<ons • Increased efficacy • Delay resistance • AEenuate toxicity • Inform signaling pathway connec<vity • Iden<fy synthe<c lethality • Polypharmacology Transla'onal Interest Basic Interest
- 32. How to Test Combina<ons • Many procedures described in the literature – Fixed dose ra<o (aka ray) – Ray contour – Checkerboard – Gene<c algorithm C5,D5 C5 C4,D4 C4 C3,D3 C3 C2,D2 C2 C1,D5 C1,D4 C1,D3 C1,D2 C1,D1 C1 D5 D4 D3 D2 D1 0
- 33. How to Test Combina<ons • Many procedures described in the literature – Fixed dose ra<o (aka ray) – Ray contour – Checkerboard – Gene<c algorithm Vargatef DCC-2036 PD-166285 GDC-0941 PI-103 GDC-0980 Bardoxolone methyl AATT-77551199 SNS-032 NCGC00188382-01 Lestaurtinib CNF-2024 ISOX Belinostat PF-477736 AZD-7762
- 34. • Vargatef Why Similarity? exhibited anomalous matrix response compared to other VEGFR inhibitors Vargatef Linifanib Axitinib Sorafenib Vatalanib Motesanib Tivozanib Brivanib Telatinib Cabozantinib Cediranib BMS-794833 Lenvatinib OSI-632 Foretinib Regorafenib
- 35. When are Combina<ons Similar? • Differences and their aggregates such as RMSD can lead to degeneracy • Instead we’re interested in the shape of the surface • How to characterize shape? – Parametrized fits – Distribu<on of responses 0.010 0.005 0.000 0 25 50 75 100 0.06 0.04 0.02 0.00 0 25 50 75 100 0.15 0.10 0.05 0.00 0 50 100 D, p value
- 36. Similarity via the Syrjala Test 10.0 7.5 5.0 2.5 0.0 0.00 0.25 0.50 0.75 D density • Syrjala test used to compare popula<on distribu<ons over a spa<al grid – Invariant to grid orienta<on – Provides an empirical p-‐value • Less degenerate than just considering 1D distribu<ons Syrjala, S.E., “A Sta<s<cal Test for a Difference between the Spa<al Distribu<ons of Two Popula<ons”, Ecology, 1996, 77(1), 75-‐80
- 37. Clustering Response Surfaces 0.0 0.2 0.4 0.6 0.8 C1 (24) C3(35) C2(47) C4(24)
- 38. Working in “Combina<on Space” • Each cell line is represented as a vector of response matrices • “Distance” between two cell lines is a func<on of the distance between component response matrices • F can be min, max, mean, … L1 L2 = d1 = d2 = d3 = d4 = d5 D L1, L2 ( ) = F({d1, d2,…, dn}) , , , , ,
- 39. Many Choices to Make 0 1 2 3 4 KMS-34 INA-6 L363 OPM-1 XG-2 FR4 AMO-1 XG-6 MOLP-8 ANBL-6 KMS-20 XG-7 OCI-MY1 XG-1 8226 EJM U266 KMS-11LB SKMM-1 MM-MM1 sum 0.0 0.1 0.2 0.3 0.4 0.5 0.6 L363 OPM-1 XG-2 KMS-20 XG-1 XG-7 ANBL-6 OCI-MY1 U266 XG-6 INA-6 MOLP-8 AMO-1 KMS-34 KMS-11LB SKMM-1 MM-MM1 EJM FR4 8226 max 0.00 0.05 0.10 0.15 0.20 0.25 INA-6 MM-MM1 8226 XG-1 U266 ANBL-6 SKMM-1 EJM OPM-1 XG-2 OCI-MY1 KMS-20 L363 KMS-11LB AMO-1 XG-6 FR4 KMS-34 MOLP-8 XG-7 min 0.0 0.2 0.4 0.6 0.8 1.0 1.2 L363 OPM-1 XG-2 KMS-34 INA-6 KMS-11LB SKMM-1 EJM U266 MM-MM1 FR4 AMO-1 XG-6 8226 MOLP-8 ANBL-6 OCI-MY1 XG-1 KMS-20 XG-7 euc
- 40.
- 41. Networks & Integra<on • Network models of molecules, and targets are common – Allows for the incorpora<on of lots of associated informa<on – Diseases, pathways, OTE’s, • When linked with clinical data & outcomes, we can generate massive networks – Adverse events (FDA AERS) – Analysis by Cloudera considered > 10E6 drug-‐drug-‐ reac<on triples Yildirim, M.A. et al
- 42. Networks & integra<on • SAR data can be viewed in a network form – SALI, SARI based networks – Usually requires pairwise calcula<ons of the metric • Current studies have focused on small datasets (< 1000 molecules) • Hadoop + Giraph could let us apply this to HTS-‐ scale datasets Peltason, L et al hEp://sali.rguha.net/
- 43. Networks & integra<on • When we apply a network view we can consider many interes<ng applica<ons & make use of cloud scale infrastructure – Network based similarity – Community detec<on (aka clustering) – PageRank style ranking (of targets, compounds, …) – Generate network metrics, which can be used as input to predic<ve models (for interac<ons, effects, …) Bauer-‐Mehren et al
- 44. Combina<ons as Networks Combina<on screens lend themselves naturally to network representa<ons ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Δ Bliss+ 0.0 −0.5 −1.0 −1.4 −1.9 −2.4 −2.9 −3.3 −3.8 −4.3 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● Δ Bliss+ 0.0 −0.4 −0.8 −1.2 −1.5 −1.9 −2.3 −2.7 −3.1 −3.4 immune system process apoptotic process transcription from RNA polymerase II promoter protein phosphorylation cell communication immune response
- 45. Combina<ons as Networks • Things get more interes<ng when we have n m screens • Can be simplified using a variety of methods – Neighborhoods – Minimum ● ● ● Spanning Tree ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ×
- 46. Comparing Neighborhoods Combina<ons that have DBSumNeg < 1st quar<le value for that strain 3D7 DD2 HB3
- 47. Iden<fying the Most Synergis<c Pairs ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●
- 48. Summary • The HTS workflow presents mul<ple data science problems involving (unique) data types • R can play a role at several stages, but model building is straighXorward • Representa<on is key and guides the types and nature of analyses