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Topics in Microbiology

by: Reed McGlynn

Topics in Microbiology MMG 991

Reed McGlynn
GPA 3.89


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Class Notes
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This 44 page Class Notes was uploaded by Reed McGlynn on Saturday September 19, 2015. The Class Notes belongs to MMG 991 at Michigan State University taught by Staff in Fall. Since its upload, it has received 38 views. For similar materials see /class/207347/mmg-991-michigan-state-university in Microbiology at Michigan State University.

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Date Created: 09/19/15
Session 9 Classical multidimiensional scaling Concepts 39STPlus implementation Microarrays Looking at Khan s cancer data Unanswered questions Some thoughts on data filtration Are there alternative solutions Compare the output Classification of cancers Selection of genes Binary recursive partitioning Chapter 10 in MASS Zhang et al PNAS 98 6730 6735 Projects Updates from each group Mu ltiidi39miensi nal ling An ordination technique Represent data in lower dimensional space Seeks to reduce spatial distortion Require distance matrix as input Size limitations on input Output 2D or 3D plots Visual assessment of relationships No classification produced o SPlus implementation Classical multidimensional scaling 0 Equivalent to PCA when Euclidean distances are used cmdscaled k2 eigF addF d distance matrix k number of output dimensions eig vector of k eigenvalues add additive constant T 1d Tumor classmcation ldiagnostic prediction httpnhgrihihgov 39 Khan et al Nature Medicine 7 673 o The dataset 63 training samples25 test samples fourtumorcell types 8 13 tumors10 cell lines BL 8 cell lines 12 cell lines RMS 10 tumors10 cell lines Filtering the data Minimum red intensity of 20 Relative red index 39 rri mean spot intensitymean intensity of filtered genes Expression measured as lnrri Clustering and MDS As defined in Khan et al Cancer Research 585009 highly expressed compared to reference probe solutlo Setting up the model nhgrilt supplve mental data c f 1 2 genelistlt matchanngenes2 supplementaldata11 nhgrismalllt lognhgrigEnelist 0 Estimating the distances nhgrismallcorlt l cornhgrismall nhgrismalltcorlt l cortnhgrismall Clustering nhgrismallclustlt hclustnhgrismallcor metquotavequot nhgrismallclustlt clordernhgrismallclust applynhgrismall 2 mean nhgrismalltclustlt hclustnhgrismalltcor metquotavequot nhgrismalltclustlt clordernhgrismalltclust applytnhgrismall 2 mean plclustnhgrismallclust labelsdimnamesnhgrismall2 cex06 plclustnhgrismalltclust cex06 The heat map templt nhgrismallnhgriqsmalltclust rderhnhgrismallclustdrder imagelistxldimtemp11 yl dimtemp21 zasmatrixtemp imagelegendasmatrixtemp Xnrowtempl075 yncoltempl05 sizec125 61 horF cex066 tck 00l mgpc0050 Multidimensional scaling tempmdslt cmdscaledistttemp metquotmanquot addT parptyquotsquot plottempmdspointsl tempmdspoints2 pointstempmdspointsewsltempmdspointsews2 col2 pointstempmdspointsblltempmdspointsbl2 col3 pointstempmdspointsnbltempmdspointsnb2 col4 pointstempmdspointsrmsltempmdspointsrms2 col5 tempdistlt distttemp mdsdistlt disttempmdspoints stress sumtempdist mdsdistA2sumtempdistA2 T hart mp 80 l 60 I 40 l 20 100 Multidimensional scaling Stress 2106991 W 39639 E o o 1 w 395 E o E a tempmdspoints 1 Lbservaiticns ccfm ments Solution doesn t exactly agree with Khan s Comparing the output Heat maps similar Plots of experiments similar Source of differences Experimental noise Scaling of experiments Setting up the model nhgrilt supplementaldata cl2 genelistlt matchanngenes2 supplementaldatal nhgrismalllt lognhgrigenelist nhgrismalllt applynhgrismall 2 scale gt nhgr1small1ews EWST EWST2 EWST3 EWST4 EWST6 EWST7 EWST9 EWST11 EWST12 187 01031 01874 08495 03268 02642 02582 05286 02313 02128 EWST13 EWST14 EWST15 EWST19 EWSC8 EWSC3 EWSC2 EWSC4 EWSC6 187 47662 03677 04004 01646 00465 01044 00943 01032 01353 EWSC9 EWSC7 EWSC1 EWSC11 EWSC10 187 01217 00563 00325 0099 00568 1gt nhgr1small1b1 3 BLC8 BLC1 BL BL 4 187 01056 00415 01059 0093 01195 01539 01081 01736 NBC4 NBC5 NBC10 C3 NBC6 NBC12 NBC7 01774 16303 1 NB 2 NE 187 38588 28489 88936 00365 00183 0618 03828 NBC11 NBC9 NBC8 187 03314 0196 15315 gt SL39lSEL L39lSEJ 9 C d E 39E d D gtlt G q 0 O C 39E d H D 3 0 Multidimensional scaling Stress 1682979 W 39539 E o o 1 w 395 E o E a tempmdspoints 1 So me quotthU g htS on filtering data Khan s objective quot hi ghy expressed genes Need to identify subsets within the data Tumorcell type Search data frame for differentially expression Criteria Arbitrary Difference in expression exceeds threshold value Means medians trimmed means Population based Subsets within the data groupings Scaled vs unscaled Experiment 11 Setup nhgfilt supplementaldata Cl2 nhgrilt l gnhgri Candidate genes genelistlt NULL genelistlt cgenelist applynhgriews l median applynhgribl l median gt2 genelistlt cgenelist apply nhgriews l median applynhgrinb l median gt2 genelistlt cgenelist apply nhgriews l median applynhgrirms l median gt nhgribl l median 2 gt applynhgrinb l median genelistlt cgenelist apply nhgribl l median gt2 genelistlt cgenelist apply nhgrinb l median applynhgrirms l mediangt2 genelistlt uniquenamesgenelistgenelist 2 applynhgrirms l median genelistlt cgenelist apply nhgrismalllt nhgrigenelist Th Multidimensional scaling Stress 2026463 N 39639 E o o 19 w 39o E o E a tempmdspoints 1 SetLIp ewsmatlt matrix0 nrownhgri 8 blmatlt matrix0 nrOWKthri 8 nbmatlt matrix0 nrdwnhgri 8 rmsmatlt matrix0 nrownhgri 8 fori in lnrownhgri ewsmatilt revsortasmatrixnhgriiews18 fori in lnrownhgri blmatilt revsortasmatrixnhgriibl18 fori in lnrownhgri nbmatilt revsortasmatrixnhgriinb18 fori in lnrownhgri rmsmatilt revsortasmatrixnhgriirms18 Candidate genes genelistlt NULL genelistlt cgenelist applyewsmat l median applyblmat l mediangtl75 genelistlt cgenelist applyewsmat l median applynbmat l mediangtl75 genelistlt cgenelist applyewsmat l median applyrmsmat l mediangtl75 genelistlt cgenelist applyblmat l median applynbmat l mediangtl75 genelistlt cgenelist applyblmat l median applyrmsmat l mediangtl75 genelistlt cgenelist applynbmat l median applyrmsmat l mediangtl75 genelistlt uniquereplnrownhgri6genelist 1 nhgrismalllt nhgrigenelist SO39SME BO39SME OLO39SIVE ZO39SWH E39lSEJ 1831 171831 LLl39SWH ZL39lSEi Z39lSEi LL39lSEJ EO39SWH 1736 1381 1489 1227 2231 Th Multidimensional scaling Stress 68 52 684 W 39639 E o o 19 w 39o E o E a l 100 tempmdspoints 1 nt 3 Data are scaled nhgrilt Supplementaldata c12 nhgrilt applylognhgri2scale 17139 8M3 9 C d E 39E d D gtlt d q 0 O C 39E d H D 3 0 Th Multidimensional scaling Stress 100181 N 39639 E o o 19 w 39o E o E a l 100 tempmdspoints 1 the 3910 genesllt intersectigenelistl39geneglista uniquegenesllt setdiffgenelistl genelist uniquegeneslltesetdiffgenelist genelistl c0mmongenes2lt intersectgenelist2 genelist uniquegenes2lt setdiffgenelist2 genelist uniquegenes2lt setdiffgenelist genelist2 commongenes3lt intersectgenelist3 genelist uniquegenes3lt setdiffgenelist3 genelist uniquegenes3lt setdiffgenelist genelist3 201111110171 supplementaldata common supplemental supplemental supplemental supplemental supplemental supplemental supplemental supplemental dataunique dataunique data dataunique COIUII LOI l data unique data data unique data unique COIUII LOI l Cutting the trees model l lt Cut r39eetnhgri small olust h0 6 model 1 tree lt nhgri small c lust model lt cutreenhgrismallclust h06 model tree lt nhgrismallclust model lt cutreenhgrismallclust hO6 model tree lt nhgriSmallclust model lt cutreenhgrismallclust hO6 model tree lt nhgrismallclust modelks lt cutreenhgrismallclust hO6 modelkstree lt nhgrismallclust Identifying the groups Nhgrigroups lt cbinddimnamesnhgri2 modell model2 model3 modelk modelks 1 nhgrigroups n 0 u a G HS cw 4 0 n 0 LS n wa hr m7 0 by gt tablenhgrigr0up8r 2 nhgrigroups 8 O 6 7 O 7 O O 11 O 4 2345 014301 0 0000 0 nm 0 HM HAS Paw CH fw n0 00 S 0 W3 m7 0 gt table hgri groups l 10 ll 12 35 nhgrigrOUps O O O O 11 O O No a G S 4 n 0 LS n a m7 0 gt tablenhgri groups O 5 nhgrigrOUps No a G S 4 n 0 LS n a m7 0 gt tablenhgri groups Mm 2 l 150 l 100 ow ow 0v ON 0 Mm 3 Khmggs w m m Khmgs Smmim m aw


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