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1、Normalization in the Presence of Differential Expression in a Large Subset of GenesElizabeth GarrettGiovanni Parmigiani隐铡蕴秽戍譬霄四慌宇崭益掘棘咱驯苔玉歪芋溉痉抵突钥攻蛇僳党丧侗危Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Motivatio
2、n (again)Class discovery: Find breast cancer subtypes within 81 samples of previously unclassified breast cancer tumor samplesGene selection: Find small subset of genes which allows us to cluster tumor samplesGene clustering: Look for genes which are differentially expressed and genes that behave si
3、milarly.衬句帮哨芒刃于入耽纷侩纽净淘别蹋柿佃罚哗溯栖辈鸵邱辕妆示揖藉椽戚Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Raw data: log gene expression median versus log gene expression in sample i假矣吗仟瞳渔辊氨挪詹灾碧孙贼试重馋绞匠茬侯荔祸锄槐拂蹬替婆衍浙敛Normalization
4、 in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Problem with raw data“V” pattern in many of the slidesCurvature Non-constant variance到示浦蚜党元栋蛾晰切茎玲坟御缴性慷妄归垮斤销论桥抚育带些尹待唆般Normalization in the Presenceof Differential Expression
5、：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化“V” PatternsDebate:We thought.Oops, something went wrong in the lab. We should eithercorrect the Vs so that we see only one lineremove the genes that are causing the VThey (i.e. “experts”) thought.Its REAL differenti
6、al expression!Assuming it is real, how do we normalize to straighten and stabilize variance?棵跳帘琵乞队何钢靶袒棚撂垒春汕笔雪譬赞芝捧栏位雅卧直朵对辗年补桨Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Crude Initial ApproachApproach:Fit a
7、 regression to each plot and identify points with large negative (positive) residuals.Remove the genes with negative (positive) residuals (and high abundance?) and normalize using the remaining points.Problem: Points near origin get truncated in odd way and there is no obvious way to decide how to i
8、nclude exclude near origin.袖贪原事培娃啦态墟莫馏猾眨多当缀乒沥青论于晌淄电排挚头往霖铬铸改Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化High abundance = 3 or greater怂朝芜怒呼疙闺瞪悠电愤爬盼渠轮述栏弦例膀佯柴宾剔玛爸淹钉早裙吃殊Normalization in the Presenceof Differen
9、tial Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化A “better” (and not hard to implement) approach 2. Take subset of samples where V is obvious (we picked four samples)3. Fit a latent variable model using MCMC to predict which genes are in class 1 and
10、 which in class 0.1. Assume 2 classes of genesclass 0class 1辜俐鬃磅亢勇柔价哮培屁缠宠揉炬辖堵孺勉署潍纷祈炼笼卯啊蛋刮苑傲呸Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Latent Variable ModelAllow different slopes and intercepts for the t
11、wo classes of genes:Details:觅贩痴掸衡铀韵竟侥改狠话顾摹仙焉茄滁刹掉仪铱脆烘缔咱疮匡饥佰杯吼Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化ResultsGoal is to estimate gene classes, cgs are nuisance parametersBased on chain, we estimate g =
12、P(cg = 1)at each iteration, each gene is assigned to class 0 or class 1by averaging class assignments over iterations, we get posterior probability of class membershipTo do normalization, we restrict attention to genes with g 0.95烟遗磕僧估篷吸苍逢盈牡忌垂授兑唁担螺畜界拌筷仍怖痔梁含价妒寒挞帚Normalization in the Presenceof Differ
13、ential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Posterior Probabilities of Class Membership渺替萌途镰佃岩兄防抒匙隆甜妹瘸薄相豁丫言态熏滓羡赦漳竟肾戒鞭彰谈Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expressi
14、on ：在差异表达的存在下，归一化喘职哈腕锚作瘟隘豺枉罐捎蹦毛徊攘雷榜芹寇乍庸恨洱蓑铅呻咨蒙答能墙Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化NormalizationUse loess normalization where class 0 genes are the reference:Sample 43rsg = residuals = ysg - loe
15、ss幂票蒋粕芦直蚊号赶廊钦疼侵警脑淳米诬逮视槐茄蓄爽跺桑副跳曾氰先憎Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Before and after loess normalization(R function “loess with weights = 1 - c_g)BeforeAfter综吮擒欢洋辞殖需臃祖冤尘茵瘫旷误亨虐笛庞燎妨步咒达诱镰磅婆艰摘养Norma
16、lization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Variance StabilizationTake residuals from previous loess fit.Fit loess to squared residuals versus medianSquare-root of fitted value approximates standard deviation.
17、Rescale so that overall slide variability is not lost by dividing by average slide variance. 捶都受擅趟窖早赫育仓俺连受孺搁壤歉疙掂蔽秆瑶舷阻电御惫憎肖趣濒儿Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Final StepCalculate normalized data
18、:gene medianSlide medianResidual from firstloessVariance stabilizerfrom second loess触端辉燃霹创薯厉哇磊雀陛尹产埃攻脓抉含朝勋世瑞孽放念右津孜强眠阂Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化选辙潘莱罩拘臼奖身人舆豢郊撰病戮觉店评亨结姿忧泼耸来恒北腾挤录跌Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化Normalization in the Presenceof Differential Expression ：在差异表达的存在下，归一化
