E xpe rime nts De sig n a nd Ana lysis F o tis E . Pso mo po ulo - - PowerPoint PPT Presentation

E xpe rime nts De sig n a nd Ana lysis

F

• tis E

. Pso mo po ulo s

CODAT A-RDA Advanc e d Bio info rmatic s Wo rksho p, 20-24 Aug ust 2018, T rie ste , I taly

A sho rt intro … to me 

Mo nda y, Aug ust 20th 2018 E xpe rime nt De sig n a nd Ana lysis

2 Bio info rma tic s Clo ud Co mputing Da ta Mining

 Bio info rma tic s a nd Da ta Mining

 to o ls a nd pipe line s to a ddre ss

do ma in-spe c ific q ue stio ns

• g e no me -a wa re me tho ds

 Bio info rma tic s a nd Clo ud

Co mputing

 wo rkflo ws a nd pipe line s o n

c lo ud infra struc ture s

• sta nda rdiza tio n a nd re usa b ility

Bio a na lytic s Gro up @ I NAB|CE RT H

• Ma ria K
• to uza , PhD Stud e nt
• Ma ria T

sa yo po ulo u, PhD Stud e nt

• Atha na ssio s K

intsa kis, PhD Stud e nt

Mo nda y, Aug ust 20th 2018 E xpe rime nt De sig n a nd Ana lysis

3

CE RT H Main Building

NGS Da ta Ana lysis using Clo ud Co mputing (Oc t 2015) 1st So ftwa re Ca rpe ntry Wo rksho p (Oc t 2016)

 NGS Wo rkflo ws  Omic s Da ta I

nte g ra tio n

 Da ta Mining

T ra ining Re se a rc h Pe o ple

Why do we pe rfo rm e xpe rime nts?

Mo nda y, Aug ust 20th 2018 E xpe rime nt De sig n a nd Ana lysis

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Go to www.me nti.c om and use the c ode 45 89 48

Wha t is a n e xpe rime nt?

• 1. E

xpe rime nts a llo w us to se t up a dire c t c o mpa riso n b e twe e n the tre a tme nts

• f inte re st.
• 2. We c a n de sig n e xpe rime nts to minimize a ny b ia s in the c o mpa riso n.
• 3. We c a n de sig n e xpe rime nts so tha t the e rro r in the c o mpa riso n is sma ll.
• 4. Mo st impo rta nt, we a re in c o ntro l o f e xpe rime nts, a nd ha ving tha t c o ntro l

a llo ws us to ma ke stro ng e r infe re nc e s a b o ut the na ture o f diffe re nc e s tha t we se e in the e xpe rime nt. Spe c ific a lly, we ma y ma ke infe re nc e s a b o ut c a usa tio n.

Mo nda y, Aug ust 20th 2018 E xpe rime nt De sig n a nd Ana lysis

5 An e xpe rime nt is c ha ra c te rize d b y the tr

e atme nts a nd e xpe r ime ntal units to b e use d, the wa y tre a tme nts a re assigne d to units, a nd the r e sponse s tha t a re me a sure d.

Co mpo ne nts o f a n E xpe rime nt

 An a lte rna tive de finitio n is:

 “tre a tme nt de sig n” is the se le c tio n o f tre a tme nts to b e use d  “e xpe rime nt de sig n” is the se le c tio n o f units a nd a ssig nme nt o f tre a tme nts

 No te tha t the re is no me ntio n o f a me tho d fo r a na lyzing the re sults.

 a na lysis is not pa rt o f the de sig n  Ho we ve r: it is o fte n use ful to c o nside r the a na lysis whe n pla nning a n e xpe rime nt.

Mo nda y, Aug ust 20th 2018 E xpe rime nt De sig n a nd Ana lysis

6 T re a tme nts, units, a nd a ssig nme nt me tho d spe c ify the e xpe rime nta l de sig n

Why T hink Ab o ut E xpe rime nta l De sig n?

Mo nda y, Aug ust 20th 2018 E xpe rime nt De sig n a nd Ana lysis

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Crisis in Re pro duc ib le Re se a rc h

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http://ne ilfws.g ithub .io /Pub Me d/pmre trac t/pmre trac t.html

Co nse q ue nc e s o f Po o r E xpe rime nta l De sig n…

 Cost o f e xpe rime nta tio n.

We ha ve a re spo nsib ility to do no rs!

 L

imite d & Pr e c ious ma te ria l

e sp. c linic a l sa mple s.

 Immor

talization o f da ta se ts in pub lic da ta b a se s a nd me tho ds in the

lite ra ture . Our b a d sc ie nc e b e g e ts mo re b a d sc ie nc e .

 E

thic al c onc e r ns o f e xpe rime nta tio n: a nima ls a nd c linic a l sa mple s.

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Slide s adapte d fro m “De sig ning F unc tio nal Ge no mic s E xpe rime nts fo r Suc c e ssful Analysis”, b y Ro ry Stark, 18/09/2017, CRUK-CI

So , wha t is a g o o d e xpe rime nta l de sig n?

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Go to www.me nti.c om and use the c ode 45 89 48

A g o o d e xpe rime nt de sig n

 No t a ll e xpe rime nta l de sig ns a re c re a te d e q ua l!  A g o o d e xpe rime nta l de sig n must

1. Avo id syste ma tic e rro r 2. Be pre c ise 3. Allo w e stima tio n o f e rro r 4. Ha ve b ro a d va lidity

 L

e t’ s se e the se a spe c ts o ne a t a time !

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Slide s adapte d fro m Gary W. Oe hle rt, “A F irst Co urse in De sig n and Analysis o f E xpe rime nts”, 2010 - I SBN 0-7167-3510-5

• 1. De sig n to a vo id syste ma tic e rro r

 Co mpa ra tive e xpe rime nts e stima te diffe re nc e s in re spo nse b e twe e n

tre a tme nts.

 I

f a n e xpe rime nt ha s syste ma tic e rro r, the n the c o mpa riso ns will b e b ia se d, no ma tte r ho w pre c ise o ur me a sure me nts a re o r ho w ma ny e xpe rime nta l units we use .

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I f re spo nse s fo r units re c e iving tr

e atme nt one a re me a sure d with instr ume nt A

a nd re spo nse s fo r tr

e atme nt two a re me a sure d with instr ume nt B,

the n we do n’ t kno w if a ny o b se rve d diffe re nc e s a re due to tre a tme nt e ffe c ts o r instrume nt misc a lib ra tio ns.

• 2. De sig n to inc re a se pre c isio n

 E

ve n witho ut syste ma tic e rro r, the re will b e ra ndo m e rro r in the re spo nse s, a nd this will le a d to ra ndo m e rro r in the tre a tme nt c o mpa riso ns.

 E

xpe rime nts a re pre c ise whe n this ra ndo m e rro r in tre a tme nt c o mpa riso ns is sma ll.

 Pre c isio n de pe nds o n the size o f the ra ndo m e rro rs in the re spo nse s, the

numb e r o f units use d, a nd the e xpe rime nta l de sig n use d.

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• 3. De sig n to e stima te e rro r

 E

xpe rime nts must b e de sig ne d so tha t we ha ve a n e stima te o f the size o f ra ndo m e rro r.

 T

his pe rmits sta tistic a l infe re nc e :

 fo r e xa mple , c o nfide nc e inte rva ls o r te sts o f sig nific a nc e .

 We c annot do infe r

e nc e without an e stimate of e r r

• r

! Sa dly, e xpe rime nts

tha t c a nno t e stima te e rro r c o ntinue to b e run.

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We will se e those in pra c tic e la te r.

• 4. De sig n to wide n va lidity

 T

he c o nc lusio ns we dra w fro m a n e xpe rime nt a re a pplic a b le to the e xpe rime nta l units we use d in the e xpe rime nt.

 I

f the units a re a c tua lly a sta tistic a l sa mple fro m so me po pula tio n o f units, the n the c o nc lusio ns a re a lso va lid fo r the po pula tio n.

 Be yo nd this, we a re e xtra po la ting , a nd the e xtra po la tio n mig ht o r mig ht no t

b e suc c e ssful.

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We c o mpa re two diffe re nt drug s fo r tre a ting a tte ntio n de fic it diso rde r a nd o ur sub je c ts a re pr

e - adole sc e nt boys fro m our c linic .

• We mig ht ha ve a fa ir c a se tha t o ur re sults wo uld ho ld fo r pre -a do le sc e nt b o ys e lse whe re ,

b ut e ve n tha t mig ht no t b e true if o ur c linic ’ s po pula tio n o f sub je c ts is unusua l in so me wa y.

• T

he re sults a re e ve n le ss c o mpe lling fo r o lde r b o ys o r fo r g irls.

K e e ping a c o mmo n vo c a b ula ry

• 1. T

re a tme nts

• 2. E

xpe rime nta l units

• 3. Re spo nse s
• 4. Me a sure me nt units
• 5. Ra ndo miza tio n
• 6. Co ntro l
• 7. F

a c to rs

• 8. Co nfo unding
• 9. E

xpe rime nta l E rro r 10.Blinding

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T e rms a nd c o nc e pts (1/ 5)

1. T r e atme nts a re the diffe re nt pro c e dure s we wa nt to c o mpa re .  diffe re nt kinds o r a mo unts o f fe rtilize r in a g ro no my  diffe re nt lo ng dista nc e ra te struc ture s in ma rke ting  diffe re nt te mpe ra ture s in a re a c to r ve sse l in c he mic a l e ng ine e ring 2. E xpe r ime ntal units a re the thing s to whic h we a pply the tre a tme nts.  plo ts o f la nd re c e iving fe rtilize r  g ro ups o f c usto me rs re c e iving diffe re nt ra te struc ture s  b a tc he s o f fe e dsto c k pro c e ssing a t diffe re nt te mpe ra ture s

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T e rms a nd c o nc e pts (2/ 5)

3. Re sponse s a re o utc o me s tha t we o b se rve a fte r a pplying a tre a tme nt to a n

e xpe rime nta l unit (a me a sure o f wha t ha ppe ne d in the e xpe rime nt; we o fte n ha ve mo re tha n o ne re spo nse )

 nitro g e n c o nte nt o r b io ma ss o f c o rn pla nts  pro fit b y c usto me r g ro up  yie ld a nd q ua lity o f the pro d uc t pe r to n o f ra w ma te ria l 4. Me a sur e me nt units (o r re spo nse units) a re the a c tua l o b je c ts o n whic h the

re spo nse is me a sure d. T he se ma y diffe r fro m the e xpe rime nta l units.

 (e .g . in d iffe re nt fe rtilize rs o n the nitro g e n c o nte nt o f c o rn pla nts) Diffe re nt fie ld plo ts

a re the e xpe rime nta l units, b ut the me a sure me nt units mig ht b e a sub se t o f the c o rn pla nts o n the fie ld plo t, o r a sa mple o f le a ve s, sta lks, a nd ro o ts fro m the fie ld plo t.

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T e rms a nd c o nc e pts (3/ 5)

5. Ra ndomiza tion is the use o f a kno wn, unde rsto o d pro b a b ilistic me c ha nism fo r

the a ssig nme nt o f tre a tme nts to units.

 Othe r a spe c ts o f a n e xpe rime nt c a n a lso b e ra nd o mize d : fo r e xa mple , the o rd e r in

whic h units a re e va lua te d fo r the ir re spo nse s.

6. Contr

• l ha s se ve ra l diffe re nt use s in de sig n.

 An e xpe rime nt is c o ntro lle d b e c a use we a s e xpe rime nte rs a ssig n tre a tme nts to

e xpe rime nta l units. Othe rwise , we wo uld ha ve a n o b se rva tio na l stud y.

 A c o ntro l tre a tme nt is a “sta nd a rd ” tre a tme nt tha t is use d a s a b a se line o r b a sis o f

c o mpa riso n fo r the o the r tre a tme nts.

 T

his c o ntro l tre a tme nt mig ht b e the tre a tme nt in c o mmo n use , o r it mig ht b e a null tre atme nt (no tre a tme nt a t a ll).

 e .g . a study o n the e ffic a c y o f fe rtilize r c o uld g ive so me fie lds no fe rtilize r a t a ll.

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T e rms a nd c o nc e pts (4/ 5)

7. F ac tor s c o mb ine to fo rm tre a tme nts.  the b a king tre a tme nt fo r a c a ke invo lve s a g ive n time a t a g ive n te mpe ra ture . T he tre a tme nt is the c o mb ina tio n o f time a nd te mpe ra ture , b ut we c a n va ry the time a nd te mpe ra ture se pa ra te ly. T hus we spe a k o f a time fa c to r a nd a te mpe ra ture fa c to r.  I ndividua l se tting s fo r e a c h fa c to r a re c a lle d le ve ls o f the fa c to r. 8. Confounding o c c urs whe n the e ffe c t o f o ne fa c to r o r tre a tme nt c a nno t b e

disting uishe d fro m tha t o f a no the r fa c to r o r tre a tme nt.

 E

xc e pt in ve ry spe c ia l c irc umsta nc e s, c o nfo unding sho uld b e a vo ide d.

 e .g . pla nting c o rn va rie ty A in Minne so ta a nd c o rn va rie ty B in I

• wa . I

n this e xpe rime nt, we c a nno t disting uish lo c a tio n e ffe c ts fro m va rie ty e ffe c ts—the va rie ty fa c to r a nd the lo c a tio n fa c to r a re c o nfo unde d.

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T e rms a nd c o nc e pts (5/ 5)

9. E xpe r ime nta l E r r

• r

is the ra ndo m va ria tio n pre se nt in a ll e xpe rime nta l re sults.  Diffe re nt e xpe rime nta l units will g ive d iffe re nt re spo nse s to the sa me tre a tme nt, a nd it

is o fte n true tha t a pplying the sa me tre a tme nt o ve r a nd o ve r a g a in to the sa me unit will re sult in d iffe re nt re spo nse s in d iffe re nt tria ls.

 E

xpe rime nta l e rro r d o e s no t re fe r to c o nd uc ting the wro ng e xpe rime nt o r d ro pping te st tub e s.

• 10. Blinding o c c urs whe n the e va lua to rs o f a re spo nse do no t kno w whic h

tre a tme nt wa s g ive n to whic h unit.

 he lps pre ve nt b ia s in the e va lua tio n, e ve n unc o nsc io us b ia s fro m we ll-inte ntio ne d

e va lua to rs.

 Do ub le b lind ing o c c urs whe n b o th the e va lua to rs o f the re spo nse a nd the (huma n

sub je c t) e xpe rime nta l units d o no t kno w the a ssig nme nt o f tre a tme nts to units.

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Ok, le t’ s g o b a c k to o ur initia l q ue stio n:

What is a g o o d e xpe rime ntal de sig n?

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A We ll-De sig ne d E xpe rime nt

 Sho uld ha ve :

1. Cle a r Ob je c tive s 2. F

• c us a nd Simplic ity

3. Suffic ie nt Po we r 4. Ra nd o mize d Co mpa riso ns

 And b e :

1. Pre c ise 2. Unb ia se d 3. Ame na b le to sta tistic a l a na lysis 4. Re pro d uc ib le

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So und re aso nab le … Ho w are the se applie d in prac tic e tho ug h?

Aspe c ts o f E xpe rime nta l De sig n

 E

xpe rime nta l F a c to rs

 Va ria b ility

1. So urc e s o f Va ria nc e 2. Re plic a te s

 Bia s

1. Co nfo unding fa c to rs 2. Ra ndo miza tio n whe re ve r a de c isio n is to b e ma de

• Co ntro ls fo r both me a sure d a nd unme a sure d fa c to rs

3. Co ntro ls

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E xpe rime nta l F a c to rs



F a c to rs: Aspe c ts o f E xpe rime nt tha t c ha ng e a nd influe nc e the o utc o me o f the e xpe rime nt



e .g . time , we ig ht, d rug , g e nd e r, e thnic ity, c o untry, pla te , c a g e e tc .



Va ria b le type de pe nds o n type o f me a sure me nt



Ca te g o ric a l (nominal) , e .g . g e nd e r



Ca te g o ric a l with o rd e ring (or

dinal), e .g . tumo r g ra d e



Disc r e te, e .g . sho e size , numb e r o f c e lls



Continuous, e .g . b o d y we ig ht in kg , he ig ht in c m



I nde pe nde nt o r De pe nde nt Va ria b le s



Ind e pe nd e nt va ria b le (IV): wha t you c ha ng e



De pe nd e nt va ria b le (DV): wha t c ha ng e s due to IV



“I f (inde pe nde nt variab le ), the n (de pe nde nt variab le )”

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So urc e s o f Va ria tio n

 Bio lo g ic a l “No ise ”

 Bio lo g ic a l pro c e sse s a re inhe re ntly sto c ha stic  Sing le c e lls, c e ll po pula tio ns, individua ls, o rg a ns, spe c ie s….  T

ime po ints, c e ll c yc le , sync hro nize d vs. unsync hro nize d

 T

e c hnic a l No ise

 Re a g e nts, a ntib o die s, te mpe ra ture s, po llutio n  Pla tfo rms, runs, o pe ra to rs

 Co nside r in a dva nc e a nd c o ntro l re plic a tio n re q uire d to c a pture va ria nc e

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T ype s o f Re plic a tio n

 Bio lo g ic a l Re plic a tio n

 In vivo

 Pa tie nts  Mic e

 In vitro

 Diffe re nt c e ll line s  Re -g ro wing c e lls (pa ssa g e s)

 T

e c hnic a l Re plic a tio n

 E

xpe rime nta l pro to c o l

 Me a sure me nt pla tfo rm (i.e . se q ue nc e r)

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Ho w ma ny sa mple s? Why do yo u ne e d re plic a te s?



Ca lc ula ting a ppro pria te sa mple size s  Po we r c a lc ula tio ns  Pla nning fo r pre c isio n  Re so urc e e q ua tio n



Po we r: the pr

• ba bility o f d e te c ting a n e ffe c t o f a spe c ifie d size if pre se nt.

 I

de ntify a nd c o ntro l the so urc e s o f va ria b ility

 Bio lo g ic a l va ria b ility  T

e c hnic a l va ria b ility

 Using a ppro pria te numb e rs o f sa mple s (sa mple size / re plic a te s)  Po we r c a lc ula tio ns e stima te sa mple size re q uire d to de te c t a n e ffe c t if de g re e o f variab ility

is kno wn

 Depends on δ, n, sd , a , H

A

 I

f a dding sa mple s inc re a se s va ria b ility, tha t a lo ne wo n’ t a dd po we r!

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Co nfo unding F a c to rs



Aka E xtra ne o us, hidde n, lurking o r ma sking fa c to rs, o r the third va ria b le o r me dia to r va ria b le .



Ma y ma sk a n a c tua l a sso c ia tio n o r fa lse ly de mo nstra te a n a ppa re nt a sso c ia tio n b e twe e n the inde pe nde nt a nd de pe nde nt va ria b le s.



Hypo the tic a l e xa mple wo uld b e a study o f c o ffe e drinking a nd lung c a nc e r.

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Othe r fa c tor

smo king (c o nfo unding variab le )

E ffe c t/ Outc ome

L ung c a nc e r (de pe nde nt variab le )

F AL SE ASSOCIAT ION

Ca use

Drinking Co ffe e (inde pe nde nt variab le )

Co nfo unding fa c to rs

 I

na de q ua te ma na g e me nt a nd mo nito ring o f c o nfo unding fa c to rs

 o ne o f the mo st c o mmo n c a use s o f re se a rc he rs wro ng ly a ssuming tha t a

c o rre la tio n le a ds to a c a usa lity.

 I

f a study do e s no t c o nside r c o nfo unding fa c to rs, do n’ t b e lie ve it!

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So lutio ns!

 Ra ndo miza tio n

 Sta tistic a l a na lysis a ssume ra ndo mize d c o mpa riso ns  Ma y no t se e issue d c a use d b y no n-ra ndo mize d c o mpa riso ns  Ma ke e ve ry de c isio n ra ndo m no t a rb itra ry

 Blinding

 E

spe c ia lly impo rta nt whe re sub je c tive me a sure me nts a re ta ke n

 E

ve ry e xpe rime nt sho uld re a c h its po te ntia l de g re e o f b linding

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T e c hnic a l Co nfo unding F a c to rs: Ba tc h E ffe c ts

 RNA E

xtra c tio n

 T

he diffe re nc e b e twe e n Co ntro l, T re a tme nt 1 a nd T re a tme nt 2 is c o nfo unde d b y da y a nd pla te .

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Contr

• l

T r e atme nt 1 T r e atme nt 2 Day 1, Plate 1 Day 2, Plate 2 Day 3, Plate 3

Ra ndo mize d Blo c k De sig n



Blo c king is the a rra ng ing o f e xpe rime nta l units in g ro ups (b lo c ks) tha t a re simila r to o ne a no the r



RBD a c ro ss pla te s so tha t e a c h pla te c o nta ins spa tia lly ra nd o mize d e q ua l pro po rtio ns o f:

1. Co ntro l 2. T re a tme nt 1 3. T re a tme nt 2

c o ntro lling pla te e ffe c ts.

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All g o o d in the o ry, b ut in pra c tic e ?

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E xpe rime nta l De sig n Pra c tic a l Que stio ns I

• 1. Wha t a re yo ur o b je c tive s?
• 2. Wha t a re yo u me a suring ?
• 3. Wha t a re yo ur prima ry sa mple g ro ups o f inte re st?
• 4. Wha t c o ntro ls will yo u use e a c h type o f sa mple g ro up?
• 5. Wha t c o nstitute s a re plic a te in this e xpe rime nt? Are the y b io lo g ic a l o r

te c hnic a l? Ho w ma ny sa mple s/ re plic a te s sho uld b e c o lle c te d?

• 6. Ske tc h o ut the de sig n a s a ma trix, with sa mple numb e rs
• 7. Wha t sa mple g ro up c o mpa riso ns (c o ntra sts) will yo u ma ke with the da ta ?

Whic h g e ne se t(s) will yo u use fo r pa thwa y a na lysis?

• 8. Wha t a re po ssib le c o nfo unding fa c to rs a nd so urc e s o f b ia s?

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E xpe rime nta l De sig n Pra c tic a l Que stio ns I I

• 9. Ho w will yo u c o nfirm e ffe c tive sile nc ing ?

10.Wha t info rma tio n a b o ut yo ur e xpe rime nt sho uld b e re c o rde d to he lp ide ntify a ny pro b le ms sho uld the re b e a ny? 11.Will yo u b e multiple xing sa mple s? Ho w will yo u a ssig n b a rc o de s? Will yo u use po o le d lib ra rie s? Ho w ma ny po o ls? Ho w will sa mple s b e a ssig ne d to po o ls? 12.Wha t a re the se q ue nc ing pa ra me te rs yo u ne e d to b e a wa re o f (e .g . se q ue nc ing type a nd de pth)? 13.Wha t o the r type s o f da ta mig ht b e use ful to a ssa y, a nd ho w mig ht the se q ue nc ing pa ra me te rs ne e d to c ha ng e to a c c o mmo da te this? 14.Ca n yo u think o f a ny o the r de sig n re la te d issue s tha t c o uld/ sho uld b e a ddre sse d?

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And no w, q ue stio ns a nd a c o ffe e b re a k!

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L e t’ s do a n e xpe rime nt!

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T he se tup

 150 individua ls  50 o f e a c h tre a tme nt  T

re a tme nt la sts 1 we e k

 We ha ve 3 inc ub a to rs/ g re e nho use s/ ta nks/ c a g e s whic h e a c h ho ld 50

individua ls

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Split pe r we e k?

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We e k 1 We e k 2 We e k 3

Split a c ro ss we e ks?

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We e k 1 We e k 2 We e k 3

T he twist!

 L

e t’ s do the b lue tre a tme nt in we e k 1, g re e n tre a tme nt in we e k 2 a nd re d tre a tme nt in we e k 3

 b e c a use … re a so ns!

 Yo u ha ve 3 unde rg ra ds. Ho w sho uld the y split the da ta c o lle c tio n wo rk?  T

he y a re a lso a va ila b le fo r just two da ys to do the lib ra ry pre p.

 And! Yo u just ha ve 2 la ne s pe r Se q ue nc e r a va ila b le

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Disc uss in gr

• ups!

L e t’ s a c tua lly do this in R/ RStudio

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