TTCA: an R package for the identification of differentially expressed genes in time course microarray data

Transcription

1 Albrecht et al. BMC Bonformatcs (2017) 18:33 DOI /s METHODOLOGY ARTICLE Open Access TTCA: an R package for the dentfcaton of dfferentally expressed genes n tme course mcroarray data Marco Albrecht 1,2*, Daman Stchel 1,3, Benedkt Müller 4, Ruth Merkle 5,6, Carsten Stcht 7, Norbert Gretz 7, Ursula Klngmüller 5,6, Ka Breuhahn 4 and Franzska Matthäus 1,8 Abstract Background: The analyss of mcroarray tme seres promses a deeper nsght nto the dynamcs of the cellular response followng stmulaton. A common observaton n ths type of data s that some genes respond wth quck, transent dynamcs, whle other genes change ther expresson slowly over tme. The exstng methods for detectng sgnfcant expresson dynamcs often fal when the expresson dynamcs show a large heterogenety. Moreover, these methods often cannot cope wth rregular and sparse measurements. Results: The method proposed here s specfcally desgned for the analyss of perturbaton responses. It combnes dfferent scores to capture fast and transent dynamcs as well as slow expresson changes, and performs well n the presence of low replcate numbers and rregular samplng tmes. The results are gven n the form of tables ncludng lnks to fgures showng the expresson dynamcs of the respectve transcrpt. These allow to quckly recognse the relevance of detecton, to dentfy possble false postves and to dscrmnate early and late changes n gene expresson. An extenson of the method allows the analyss of the expresson dynamcs of functonal groups of genes, provdng a quck overvew of the cellular response. The performance of ths package was tested on mcroarray data derved from lung cancer cells stmulated wth epdermal growth factor (EGF). Concluson: Here we descrbe a new, effcent method for the analyss of sparse and heterogeneous tme course data wth hgh detecton senstvty and transparency. It s mplemented as R package TTCA (transcrpt tme course analyss) and can be nstalled from the Comprehensve R Archve Network, CRAN. The source code s provded wth the Addtonal fle 1. Keywords: Dfferental expresson, Tme seres, EGF, Stmulaton experments, Gene ontology, Gene set analyss Background Tme course mcroarray experments are frequently conducted to study the dynamcs of gene expresson at several consecutve tme ponts. Assocated data sets often requre own custom-made analyss strateges, and cannot been adequately exploted wth standard methods whch were establshed to compare groups. The varablty of the dynamcs, spannng from fast and transent to slower, *Correspondence: marco.albrecht@posteo.de 1 Complex Bologcal Systems Group (BIOMS/IWR), Hedelberg, Im Neuenhemer Feld 294, Hedelberg, Germany 2 Systems Bology Group, Unversté du Luxembourg, 7, avenue du Swng, L-4367 Belvaux, Luxembourg Full lst of author nformaton s avalable at the end of the artcle long-lastng changes, s a challenge for the analyss of tme seres mcroarray data. In perturbaton experments, samplng frequency s often adapted to reflect the expected changes n gene expresson. Ths knd of expermental desgn leads to rregularly sampled data sets. Irregular tme samplng may also arse when tme ponts are chosen to be omtted after qualty control, for nstance when the respectve arrays represent outlers wth respect to the global trajectory resultng from prncpal component analyss (PCA) as shown n Fg. 1. If replcates are consdered, ther number may also vary due to the expermental desgn or qualty ssues. Often tme course-data provde only one replcate per tme pont. TheAuthor(s). 2017Open Access Ths artcle s dstrbuted under the terms of the Creatve Commons Attrbuton 4.0 Internatonal Lcense ( whch permts unrestrcted use, dstrbuton, and reproducton n any medum, provded you gve approprate credt to the orgnal author(s) and the source, provde a lnk to the Creatve Commons lcense, and ndcate f changes were made. The Creatve Commons Publc Doman Dedcaton waver ( apples to the data made avalable n ths artcle, unless otherwse stated.

2 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 2 of 11 Fg. 1 Trajectory of the transcrptomes: Axes represent prncpal components explanng 95.7% of the varablty n the data. Measurement ponts represent the entre transcrptome under three dfferent stmulaton experments projected onto the frst three prncpal components. For early tme perods, all three transcrptomes correlate very well wth each other. Over tme, the transcrptomes develop stmulus dependent. Stmulus 1 leads to a strong change n the transcrptome, whle stmulus 2 has a much smaller effect. Possble outlers are measurement ponts that show a large dstance from the trajectory or from related replcates The frst methods appled on tme course mcroarrays ncludng SAM [1], ANOVA [2] and Lmma [3] where extensons of methods for contrasts between states and do not nclude the order of tme ponts nto the analyss [4]. EDGE was one of the frst methods takng the tme sequence nto account [5, 6]. EDGE nvolves a ft of natural cubc splnes to gene expresson profles, and a bootstrap approach provdng a reference dstrbuton. MaSgPro (Mcroarray Sgnfcant Profles) operates n a smlar manner [7]. Sohn et al. have modfed EDGE by usng a permutaton-approach and controllng the famly wse error rate [8]. Later, they appled the FWER as a sgnfcance threshold and made the method more robust usng quantle regresson [9]. These methods have three drawbacks when used to analyse sparse data contanng sharp transent expresson changes. Frst, the nformaton of the tme course measurements s underestmated. Bologcally meanngful peaks mght be overlooked when the related measurement ponts are rejected as outlers. Second, the nformaton of the permuted reference tme course s overestmated. The permutaton of the measurement ponts wthn the tme sequence s often used to produce reference data of the same dstrbuton, but wthout the orgnal ordered pattern of dynamc changes. Ths estmaton of the error rate can fal n sparse data sets when the expresson dynamcs exhbt a sharp peak. Here, permutaton of the tme ponts merely shfts but does not wpe out the peak. Wth ths method, the sgnal-tonose rato of genes dsplayng fast varatons n expresson can be underestmated and related genes are erroneously removed from analyss. The thrd problem s that a large number of computatonally expensve permutatons s requred, to avod granularty n the resultng rankng [4]. Granularty refers n ths case to hundreds of genes wth exactly the same p-value. Repeated applcaton of the methodmayshftageneto anotherp-value cluster, whch mpedes reproducblty of the results. An alternatve method usng multvarate emprcal Bayes statstcs and one-sample Hotellng T 2 statstcs s mplemented n the R package tmecourse [10]. Ths package does not provde a sgnfcance threshold and requres a mnmum number of replcates. Also, BETR (Bayesan Estmaton of Temporal Regulaton) [11], whch uses random-effects models and consders co-expresson, reles on tme pont replcates. Network-based methods combne cluster analyss wth detecton of dfferental expresson and focus also on co-expresson [12, 13]. But co-expresson s a very strct assumpton for the extracton of dfferentally expressed genes from tme course data. In tghtly regulated and dynamc gene regulatory networks, t seems to be very unlkely that cells do not regulate ther genes at any of the sampled tme ponts. Some of the target genes could have a negatve feedback loop and could block ther own expresson, whch could explan fast transent dynamc changes, whle other target genes could have a postve feedback loop and therefore mantan gene expresson longer. Addtonal regulaton could happen after a longer tme or very fast wthout proten translaton,.e. wth functonal large non-codng RNAs [14]. Longtudnal co-expresson mght overlook target genes that are affected by the stmulus, but whch are addtonally regulated by other dynamc mechansms. Moreover, the longer the sampled tme perod s, the hgher s the rsk that ntally unaffected genes show co-expresson behavour due to completely dfferent mechansms wthout relaton to the stmulus. The rsk s hgher to detect false postve target genes. Methods based on Gaussan processes select dfferentally expressed genes from one channel experments [15] and from two channel experments [16], mplemented n

3 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 3 of 11 the R package gprege. However, the mplemented Gaussan processes suffer from massve computatonal cost and the requred tme pont replcaton. An alternatve for two channel experments s BATS (Bayesan Analyss of Tme Seres) [17, 18]. Another class of tme course methods s based on prncpal component analyss (PCA) [19]. Inspred by atrendnthedataanalysstoftthetrue underlyng functons [20, 21], methods based on functonal PCA (FPCA) were developed [22, 23]. The most recent method [23] can handle sngle replcated tme course data, predct ndvdual dynamcs wth PACE (Prncpal Component Analyss through Condtonal Expectaton) [24] and yelds reasonable results for moderately slow expresson dynamcs. Ths method was successfully appled to clncal data derved from mmune response studes [25]. For the data set consdered n our study, nvolvng perturbaton experments on cell cultures wth fast expresson changes, ths method dd not perform relably. In partcular, we observed counterntutve dfferences between our orgnal data and the orgnal data beng dsplayed by ths method after prelmnary transformaton by PACE. Frst, the method transforms flat gene profles nto profles exhbtng strong temporal changes, shown n Addtonal fle 2: Fgure S1A. Second, the transformed trajectores are too stff to follow sharp peak behavour lke n Addtonal fle 2: Fgure S1B. Ths happens before the actual tme course analyss method s appled. Fnally, even smple methods can yeld good results for sparse data, for nstance by computng dstances or the area between curves [26, 27]. Also, a sldng wndow, capturng a small subset of consecutve measurement ponts, was dscussed, but cannot be appled to non-equdstant measurements [4]. To sum up, most exstng methods cannot relably analyse sparse and rregularly sampled tme course gene expresson data sets. Further detals and a method comparson are provded n the Addtonal fles. A method overvew s gven n Addtonal fle 2: Table S1. Method TTCA The method TTCA (transcrpt tme course analyss) ncludes dfferent scores to dentfy genes showng dfferental expresson dynamcs of varous knds. The dynamcs score D captures slow gene expresson dynamcs, the peak score P selects fast transent expresson changes, the ntegral score I accounts for absolute changes n mrna producton level n dfferent tme perods, and a relevance score R provdes nformaton on exstng references n the lterature. A further opton allows for gene ontology groups to be processed n a smlar manner as ndvdual genes. Addtonally, the mnmum overlap score s computed to dentfy gene ontology groups wth maxmal separaton of the group specfc expresson bandwdths between two condtons. Sgnfcance threshold and effect sze are calculated for each score and the consensus score C combnes the dfferent scores for a fnal rankng. For the detecton of dfferental gene expresson based on two channel mcroarray data, we recommend to create a constant gene expresson profle as control profle. Ths control profle mght start wth the expresson value of the frst tme pont, or could be set to the average expresson value of the expermentally derved gene expresson profle. The gene expresson level s based on an assembled set of detected probes of 25 bp length. In ths artcle, we focus on the expresson dynamcs of genes, however, those probe level sgnals can also be mapped to related transcrpts or other longer olgonucleotdes. These can equally be analysed wth TTCA. In the followng secton, preprocessng for mcroarray tme seres data s addressed. Next, all relevant scores and components of the proposed method are explaned brefly. Pre-processng of mcroarray tme course data Mcroarray data are usually afflcted by batch effects,.e. unwanted varablty n the samples arsng from ther expermental, techncal and dgtal processng hstory. Batch effects can be ntroduced when samples are processed on dfferent hybrdsaton batches (maxmum 6-12 samples at once), or when a subset of the samples experenced slghtly dfferent expermental condtons (tme of the day, new meda, etc.). Many batch effects can be techncally detected and can be removed f enough replcates are avalable. Mcroarray tme course data sets are frequently sparse and the number of replcatespertmepontslow.insuchdatatsmpossble to detect batch-effects [28]. Moreover, the frequently used quantle-normalsaton, mplemented n RMA [29], s based on the assumpton that the majorty of the genes shows a constant expresson level. However, for tme seres experments ths mght not be the case. Especally, cancer cells are known to have a hgh varablty n ther gene expresson profles [30]. Perturbaton experments mght nduce secondary gene responses that eventually result n consderable expresson dynamcs for a broad range of genes. It has been shown that thousands of genes can change ther expresson over tme after stmulaton [6]. Instead of usng mult-array normalsatonmethodslkermafortmecourseanalyses, we recommend to use wthn-array normalsaton methods whch process each array separately, ndependent of arrays taken at other tme ponts. In partcular, we recommend ndvdual array standardsaton wth SCAN [31], whch s robust aganst GC-content bas and some batch effects.

4 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 4 of 11 Dynamcs score We defne a dynamcs score n three steps based on the method EDGE [6] and ts extenson usng quantleregresson [9]. The null hypothess H 0 s that the stmulus does not sgnfcantly alter the expresson level of gene. Thus,the measurements of the respectve condtons (.e. treatment vs. control) are derved from the same expresson pattern and can be combned for a sngle functon ft. In Fg. 2a, the null hypothess s represented by the ft to all measurement ponts wthout dstncton between the condtons (dashed lne). The alternatve hypothess H 1 s that the measurements are derved from dfferent expresson patterns, and that the two condtons have to be treated separately. Hence, the data s splt nto the two condtons, and each tme course s ftted to an ndvdual functon (see the sold lnes n Fg. 2a). The sum of the resduals of the two ndvdual functon fts should be smaller than the sum of the resduals of the sngle functon ft to fulfl the alternatve hypothess H 1. The ft s based on quantle regresson [32]. The ftted functon g(t) and the resduals r j are obtaned by mnmsng n ρ 0.5 (y j g(t j )) j=1 λ g (t) dt }{{} smoothes the functon. (1) The quantle regresson algorthm s symbolsed by ρ 0.5, and mplemented n the R-package Quantreg [33] n functonrqss().thendex0.5ndcatestheuseofthemedan to provde the most robust curve ft. The contnuous functon g s ftted to the measurements y j, j {1,..., n} taken at tme ponts t j, j {1,..., n} wth n measurements n total. The frst term of Eq. (1) represents the absolute, not the quadratc dstance between the measurements y j and the functon g(t j ). Mcroarrays are nflcted wth a certan proporton of outlers [9]. If these outlers are weghted quadratcally by least-square approaches, as most methods do, a Gaussan dstrbuted error model s assumed. However, a Gaussan error model s not a good choce for the characterstcs of frequent outlers, as ths approach bases the ft stronger than the absolute dstance. The second term of Eq. (1) penalses the absolute number of drectonal changes n the gene expresson dynamcs to avod over-fttng. The penalsaton term s weghted by Fg. 2 Score characterstcs: a) Dynamcs score. The alternatve hypothess s represented by the sold lne. The dashedlne represent the null hypothess (Pcture source [6]).b) Peak score. Is based on the largest dstance (arrow) between measurement ponts for two dfferent stmul. The sold lne represents the ft acheved vaquantle regressonwtheq. (1).c) Integralscore. The area between two dynamcs ndcates the absolute mrna producton change. Ths value can be computed for dfferent tme ntervals. d) Dfferent score dstrbutons after z-transformaton and the merged consensus score dstrbuton

5 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 5 of 11 the scalng factor λ. Weestmatedλ = 0.6 for SCANprocessed data wth the help of real-tme PCR profles from genes that are known to be dfferentally expressed after the stmulaton. The obtaned resdual-vectors R are modfed by weghtng vectors. These weghts account for the uneven expermental desgn n the followng way: Frst, each tme pont should have the same weght ndependent from the number of replcates. Second, more values n one condton than n the other result n hgher resduals wthout a better ft. TTCA balances the uneven desgn. Thrd, to reduce the unwanted bas by ths vector, the sum of all vector elements of the weghtng vector s forced to the same value. The scalar product of the resdual and weghtng vector yelds a scalar value for each gene. The dynamcs score D s then defned by D := H 0 H 1 = < stm, R stm < H 0, R H 0 > > + < ctrl, R ctrl >. The relaton H 0 /H 1 quantfes how much worse the null-hypothess fts n comparson to the alternatve hypothess and s easy to nterpret. Peak score Perturbaton experments may nvoke fast and transent peak dynamcs n a gene subset, where the peak mght be captured by only a small number of measurements. In ths case, peaks, although bologcally meanngful, may be overlooked by mcroarray analyss methods. To account for ths, we ntroduce the peak score. LetT = {t 1,..., t n } denote the set of the measurement tme ponts. For each tme-pont t T,wedefneF stm t and F ctrl t as the averages of all replcates for the stmulated and control condtons, respectvely. The peak score s then gvenby P := max t T F stm t F ctrl. t The success of ths approach has been ponted out by D Camllo et al. [26]. To test whether dfferences between the expresson profles are sgnfcant, we use the robust 0.95 quantle of all avalable standard devatons, for a mnmum of 1000 genes and multple replcated measurement ponts as a nose-threshold. A gene s consdered as sgnfcant, f P s more than twce the nose-threshold (see Fg. 2b). To account for a possble correlaton between the standard devaton and mean of gene expresson, TTCAsortsthegeneswthrespecttothermeanvalues and dvdes them nto a mnmum of 8 groups, each contanng at least 1000 genes. The nose-threshold s then computed separately for each group. TTCA can ether use replcated tme ponts to provde a nose threshold or the dstrbuton of the score values to provde a sgnfcance threshold. Replcates are not requred but can be used. If less than 4 measurement ponts are replcated, the program wll provde only a rankng and the sgnfcance wll be calculated as n the other scores as descrbed below. Instablty score Some genes, found hghly sgnfcant n the prevous scores, exhbt an extreme varance between replcates. If the medan of the standard devaton of replcated measurements of gene s two-fold larger than the gene group nose threshold, these genes are classfed as unstable. The nstablty score s bnary and appears n the results table together wth a relatve effect sze, explaned below. TRUE ndcates nstable genes that are lkely false postves, and FALSE ndcates genes wth acceptable varance between replcates. For an example see the gene SNORA11 n Table 1 and Fg. 4. Integral score The ntegral score s ntended to quantfy the area between the expresson profles for control and treatment. To compute the ntegral between the two expresson dynamcs of each gene we frst lnearly nterpolate the mssng values of the quantle regresson at measured tme ponts t and at tme ponts where the curves ntersect. We then estmate the area between the two dynamcs D applyng the trapezum rule. Ths ntegral I := t2 D stm t 1 (t) D ctrl (t) dt for each gene serves as a measure for the dfference n the mrna producton between the two condtons. Fgure 2 C llustrates the ntegral score, whch can be computed for dfferent tme ntervals. Hereby, four separate scores are computed (I early, I ntermedate, I late, I complete )to dstngush between the early response, the ntermedate response, the late response, and the response over the whole perod. The frst three scores are defned for subsequent tme-ntervals, whch can be defned by the user. These scores allow to dstngush between slowly and rapdly respondng genes, and mght also be used to dstngush a secondary response from the drect response to the stmulus. By usng a z- score transformaton and averagng of all three ntegral scores the combned ntegral score I comb s obtaned. The combned ntegral score emphasses the largest changes n gene expresson for each perod stronger than the more outbalancng complete ntegral score I complete.

6 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 6 of 11 Table 1 Compendous result table. The nstablty of SNORA11 s confrmed and the effect sze s hgh, whch ndcates a false postve result. The plotted SNORA11 profle n Fg. 4 confrms ths suspcon. The effect sze of the peak score covers up to 26% of the detecton range Consensus rank Gene name Consensus score Consensus score p-value PubMed Instablty score Effect sze of peak score 1 CTGF E EGR E SNORA E PTGS JUN GLIPR FOS AREG MIR F IL EGR PCNA DUSP MYC ROS HIF1A MIR IL TGFB TGFB JUNB Relevance score By usng the R package RISmed [34] we query the PubMed database of publcatons for records that match both the gene name and the condton. For each gene ths yelds a number of publcatons p.weusealog-transformaton to normalse p between 0 and 1, and obtan the relevance score R := log pmax (p ), where p max := max (p ). Ths score ndcates whether a gene s already well known to be assocated wth the condton or potentally a new target. Consensus score The consensus score s used for the fnal rankng of the genes and combnes the four scores. By mergng the dynamcs score wth the peak score, combned ntegral score andrelevance score, and normalsng the result to be between 0 and 1, we obtan C := D + P + Ĭ comb 4 + R, whereby score S s z-transformed S before the average s computed. Fgure 2d shows the z-transformed dstrbutons of the score values. To better centre the relevance score dstrbuton, only non-zero values are consdered for the z-transformaton. Sgnfcance Except for the peak score we dd not defne any sgnfcance threshold, yet. For the other scores a sgnfcance level can be computed by a one-sded, one-group hypothess test. The program fts the Cauchy, Gamma, lognormal, logstc, normal, Posson and Webull dstrbuton to the emprcal dstrbuton of score values usng the functon ftdstr() provded by the R package MASS [35]. The log-normal dstrbuton s only defned for strctly postve values, however, by shftng the x axs t can be ftted n the negatve part as well. The obtaned sgnfcance threshold s transformed back afterwards. The dstrbuton functon provdng the best ft of the dstrbuton of score values s automatcally selected and plotted. To estmate the sgnfcance for a dfferentally expressed gene we provde the p-value as well as the effect sze [36]. The effect sze of the peak score s defned as the

7 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 7 of 11 dstance between the expresson dynamcs, normalsed by the maxmum dstance possble,.e. the hghest expresson value wthn the data set mnus the lowest expresson value wthn the data set. The largest observed expresson change n our data set covers 25.9% of the whole detecton range and represents the effect sze. The same normalsaton s used for the nstablty score and also for the ntegral score, where the maxmum area s gven as the maxmal dstance multpled by the tme perod. In the consensus score, a gene s consdered to be sgnfcant, f t s consdered sgnfcant n at least two scores. Method extenson for gene set analyss To nvestgate the behavour of functonal groups, the genes are lnked to gene ontology groups usng the BomaRt-package [37, 38]. Then the expresson level at the ntal tme pont s subtracted from the gene expresson profle of each gene. Thus, all profles are ntally zero and only the expresson change wth respect to the frst value s observed (Fg. 3a). Second, the average expresson together wth the upper sd u and lower sd l standard devaton of all genes wthn each ontology group are calculated for each tme pont. The upper standard devaton hereby accounts for all measurement ponts above the group mean and the lower standard devaton accounts for all measurement ponts below the average. Separaton nto upper and lower standard devaton helps to better recognse when the subset of the functonal group shows ncreased (or decreased) expresson. Ths would lead to enlarged upper (or lower) standard devatons, where the classcal standard devaton does not allow such dstncton. We then consder gene groups dfferentally expressed f ther expresson bandwdths are separated by the condton,.e., that the varablty between genes n the same ontology group are small n contrast to changes caused by dfferent treatments. To test, whether the expresson bandwdths are separated by condton, we dstngush two dfferent cases, as shown n Fg. 3b. On the one hand, the band of the control can be hgher than the band of the stmulus (case A), on the other hand, the stuaton can be reversed (case B). We search for the mnmum overlap Case A {}}{ max (mean(c) sd l (C)) (mean(s) + sd u (S)); Case B {}}{ (mean(s) sd l (S)) (mean(c) + sd u (C)) θ j = 1 2 (sd u(s) + sd l (S) + sd u (C) + sd l (C)) }{{} Bandwth of both bandwdths for a combnaton of tme ponts j {1,..., n} and genes, where n ndcates the total number of measurements per gene. We are Fg. 3 Gene set analyss.a) Example analyss result wth the average of n genes.b) Scheme for mnmal overlap calculaton. The contnuous lnes represent the average expresson of the gene group at one tme pont for ether the stmulated sample (S) or the control (C). The dotted lnes represent the average wth upper (Sd u )orlower (Sd l ) standard devaton only nterested n the maxmum dstance between the bands or the mnmum mutual overlap for the score = max ( θ 1,..., θ j,..., θ n ) at each tme pont. Postve values ndcate a separaton of the bands and negatve values ndcate overlap. The average expresson profle for each gene group and treatment s then used to calculate the other scores as descrbed above. Hence, TTCA ranks functonal groups hgh f they contan genes wth smlar expresson pattern over tme wthn a condton and f they clearly change the expresson dynamcs from one condton to the other. Although we dd not compare the performance of the gene set module, the applcaton on real data seems promsng. Alternatvely, the user can use the rankng of the ndvdual genes to apply other methods for gene set analyss. Computaton tme and further packages TTCA s computatonally fast usng about 1 h for one contrast. Ths ncludes the analyss of expresson dynamcs

8 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 8 of 11 and the generaton of relevant fgures on a standard laptop ( GHz, memory 12 GB). Furthermore, TTCA uses the R-package tcltk2 [39] for a progress bar and the R-package VennDagram [40] to show automatcally the overlap of sgnfcant genes across scores. Methods for lung cancer data set Cell seedng, growth factor stmulaton and mcroarray processng The cell lne H1975 (NCI-H1975; ATCC: CRL-5908) were obtaned from LGC Standards (Teddngton, UK). The cell lne was authentcated by STR-analyss (DSMZ, Braunschweg, Germany) and routnely checked for mycoplasma contamnaton. H1975 NSCLC cells were seeded n 6-well-plates wth cells per well. After ncubaton for 3 days, cells were washed 3 tmes and supplemented wth DMEM wthout FCS for overnght starvaton. On the followng day, cells were stmulated wth 50 ng/ml of EGF dluted n starvaton medum. Sampleswereharvestedafter0,0.5,1,2,4,6,8,12, 24 and 48 hours. Subsequently RNA was extracted, as descrbed below. Total cellular RNA was solated wth the NucleoSpn RNA II kt accordng to the manufacturers nstructons. RNA concentratons were determned by measurng the absorbance ( nm) usnga NanoDrop ND-1000 spectrometer. The purty of the RNA was determned through the rato of the absorbance at 260nm and 280nm. RNA wth a rato 1.8 was used for further analyss. After assessng RNA ntegrty usng the Aglent Boanalyzer, 100 ng n 3 μl per sample were handed over. After amplfcaton, labellng wth botn and fragmentaton of the RNA, hybrdsaton wth GeneChp Human Gene 2.0 ST Array was performed for 16 h at 45 C. Subsequently, washng and stanng was performed usng an Affymetrx Fludcs Staton 450 and the mcroarray was scanned usng an Affymetrx GeneArray Scanner Mcroarray preprocessng The method Sngle Channel Array Normalsaton (SCAN) [31] was used for the preprocessng. For the mappng of probes to genes we used the Netaffx.v.34 annotaton fle whch s avalable from the array manufacturer. For the transcrpt-level we used Branarray-Ensembl-Tv [41] for annotaton. The qualty was addtonally assessed before and after preprocessng wth the R- package ArrayQualtyMetrcs [42]. Four possble outlers were vsble n the 3D-PCA-plot generated wth pcamethods [43]. They were nvestgated n contrast to other replcates or to the closest measurement ponts wth Lmma [44] and Pano [45] under use of BoMart [46] for GOmappng. We assumed a problem wth the magnesum concentraton and excluded the affected arrays from the analyss. Results and dscusson The approach presented here allows the dentfcaton of bologcally relevant genes from nosy, sparse, and possbly ncomplete tme course gene expresson data sets from perturbaton experments. In the case presented n our study, the admnstraton of the potent mtogen EGF led to the dentfcaton of numerous known EGF/EGFR nduced target genes as ndcated by the relevance score, such as CTGF (Fg. 4), EGR1, PTGS2/COX2, and transcrpton factors of the AP1 famly ncludng JUN and FOS (Table 1). The top-ranked genes represent key factors nvolved n the ntaton and mantenance of a mtogenc response n tumour cells. Interestngly, many of the mmedate EGFdependent targets lsted n Table 1 represented transcrptonal regulators, for nstance EGR1, EGR2, JUN, FOS, or MYC,andsecretedchemokneslkeCTGF,IL8(Fg.4),or KITLG/SCF, llustratng that EGF s a central nducer of pro-prolferatve gene expresson and paracrne regulaton n lung cancer. These results are confrmed by prevous publcatons descrbng for example, that actvaton of the PI3K/AKT pathway, whch typcally stmulates the transcrpton factor AP1 consstng of JUN/FOS heterodmers, can stmulate IL8 producton and secreton n NSCLC cells [47]. However, our approach not only confrmed fndngs from other studes. Even more mportant, we dentfed a long lst of prevously un-publshed downstream effectors (Addtonal fle 3: Table S4; 18/79 (23%) sgnfcantly regulated genes have not been descrbed n the context of EGF/EGFR sgnallng). For example, the target gene IL24 (Relevance Score: 0.32) has been shown to nhbt NSCLC cell mgraton suggestng that EGF-nduced IL24 mght shft tumour cells from a mgratory to a mtotc phenotype [48] (Fg. 4). The hgh ranked gene GLIPR1 (Fg. 4) has recently been dentfed as tumour suppressor n lung cancer [49], however, the relatonshp between GLIPR1 and EGF was yet unknown. In addton, the sgnfcant regulaton of the mcro-rnas mr-4320 (Relevance Score: 0.44; Fg. 4) and mr554 (Relevance Score: 0.34) suggests that EGF supports the oncogenc propertes of NSCLC cells va mrna-dependent mechansms [50]. We compared TTCA wth Lmma, EDGE and MaSg- Pro (see Addtonal fle 2). We assume, that the number of PubMed publcatons, lnkng EGF stmulaton wth ndvdual genes, can be used to generate a rankng of expected target genes. Addtonal fle 2: Table S2 shows the rankng of the top 100 expected genes, determned by TTCA, Lmma, EDGE and MaSgPro. Addtonal fle 3: Table S3 shows the top 100 gene names dsplayed by each method nvestgated. Addtonal fle 2: Fgures S2-S8 show the top ten expresson profles of each method nvestgated and a p-value dstrbuton provded by EDGE. The code for the method comparson s n Addtonal fle 2.

9 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 9 of 11 Fg. 4 Tme course profles of genes consdered sgnfcant. Red: Wth EGF stmulaton. Blue: Control. Lne: Quantle regresson. Ponts: Measurements. SNORA11 s ranked hghly sgnfcant, but the nstablty score s hgh and dentfes ths fndng as false postve The source code of the TTCA method s n Addtonal fle 1. Concluson We have presented a new method for mcroarray tme-seres data analyss, specfcally ntended for dffcult expermental desgns wth sparse measurements. Even when the expermental desgn nvolves a unform data collecton, expermental problems can lead to the excluson of ndvdual arrays, and thus to the loss of measurement ponts after qualty control. Suffcent replcates are mportant for proper mcroarray data analyss [51] and reman mportant n more accurate next-generaton sequencng [52]. However, even f such data are dffcult, they nonetheless contan helpful hnts for further nvestgatons. TTCA s able to detect dfferent characterstcs of the changes n expresson dynamcs and always provdes not only p- values but also effect szes for an optmal sgnfcance nterpretaton [36]. Our method can also be appled for data sets wth less complcated desgns (regular samplng ntervals, large number of replcates) and yeld very good results, comparable wth other tools. It should be noted, however, that the scores ncluded n TTCA detect specfcally expresson patterns arsng after perturbaton or stmulaton experments. For detectng specfc dynamcal behavours, e.g. oscllatons, we recommend specalsed methods lke Lomb-Scargle perodograms [53], JTK-CYCLE [54] or GeneCycle [55]. We beleve that the developed TTCA package s a valuable and effcent tool for the dssecton of mportant nformaton that s usually concealed by expermental and bologcal varatons leadng to data heterogenety. The connecton wth the number of PubMed publcatons has to our knowledge never been ncluded n other packages and supports the user n dstngushng between new and already known genes affected by the appled perturbaton. Further new features (at least to our knowledge) are the automatc detecton of the best densty functon, the approach to detect false postves (the nstablty score), or the dstncton between early, mddle and late response. Also, the outbalancng of the samplng desgn usng weghtng factors s an mportant new feature. Moreover, we provde a new gene set sgnfcance approach, whch pools genes nto gene ontology groups whch expresson bandwdths are separated (mnmal overlap score). TTCA provdes automatcally qualty checks and plots the gene expresson profles. Thus, the user can easly judge the performance of the package for any ncluded data set. Strong advantages of TTCA are the hgh degree of transparency, the multtude of vsual output for qualty assessment, search flexblty and senstvty also n cases where other methods cannot be appled. Addtonal fles Addtonal fle 1: R code of TTCA (EUPL). (TXT 816 kb)

10 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 10 of 11 Addtonal fle 2: Method comparson. A table summarses the methods mentoned n the ntroducton, method shortcomngs are further dscussed and TTCA s compared wth some applcable methods. Includes Table S1-S3 and Fgures S1-S8. (PDF 2165kb) Addtonal fle 3: The complete result table s gven n Table S4. (XLSX 816 kb) Abbrevatons AKT: AKT serne/threonne knase 1; ANOVA: Analyss of varance; AP1: Actvator proten 1; AREG: Amphreguln; BATS: Bayesan analyss of tme seres; BETR: Bayesan estmaton of temporal regulaton; COX2: Cytochrome C oxdase subunt II (see new name: PTGS2); CRAN: Comprehensve R archve network; CTGF: Connectve tssue growth factor; DMEM: Dulbecco s modfed eagle medum; DUSP5: Dual specfcty phosphatase 5; EDGE: Extracton of dfferental gene expresson; EGF: Epdermal growth factor; EGFR: Epdermal growth factor receptor; EGR1: Early growth response 1; EGR2: Early growth response 2; EUPL: European unon publc lcence; F3: Coagulaton factor III (thromboplastn, tssue factor); FCS: Fetal calf serum; FOS: FBJ murne osteosarcoma vral oncogene homolog; FPCA: Functonal prncple component analyss; FWER: Famly wse error rate; GLIPR1: Gloma pathogeness-related proten 1; GO: Gene ontology; HIF1A: Hypoxa-nducble factor 1-alpha; IL8: Interleukn 8; IL24: Interleukn 24; JUN: Jun proto-oncogene; JUNB: Transcrpton factor jun-b; KIT: V-Kt Hardy-Zuckerman 4 felne sarcoma vral oncogene homolog; KITLG: KIT lgand; Lmma: Lnear models for mcroarray data; MaSgPro: Mcroarray sgnfcant profles; MIR4320: McroRNA 4320; MIR554: McroRNA 554; MYC: V-myc avan myelocytomatoss vral oncogene homolog; NSCLC: Non-small cell lung cancer; PACE: Prncple component analyss through condtonal expectaton; PCA: Prncple component analyss; PCNA: Prolferatng cell nuclear antgen; PCR: Polymerase chan reacton; PI3K: Phosphatdylnostol 3-knase; Pano: Platform for ntegratve analyss of omcs data; PTGS2: Prostaglandn-endoperoxde synthase 1; RMA: Robust mult-array average; RNA: Rbonuclec acd; ROS1: Proto-oncogene tyrosne-proten knase ROS; SAM: Sgnfcance analyss of mcroarrays; SCAN: Sngle-channel array normalsaton; SCF: Stem cell factor (see new name KITLG); SNORA11: Small nucleolar RNA, H/ACA box 11; TGFB1: Transformng growth factor beta 1; TGFB2: Transformng growth factor beta 2; TTCA: Transcrpt tme course analyss Acknowledgements MA thanks Erc Koncna (Neuro Inflammaton group, Unversty of Luxembourg) for hs support for translatng the code to a user-frendly R-package at CRAN. MA thanks Sébasten de Landtsheer (Systems Bology group, Unversty of Luxembourg) for proofreadng the manuscrpt. Fundng MA acknowledges currently the Horzon 2020 MSCA grant agreement, No , MA, DS and FM were supported by a grant from the Center for Modellng and Smulaton n the Boscences (BIOMS) of the Hedelberg Unversty. KB was supported by a grant from the BMBF (LungSysII, FKZ B). RM and UK were supported by the German Center for Lung Research (DZL, 82DZL00404). The fundng body was not nvolved n the desgn of the study and collecton, analyss, and nterpretaton of data or n wrtng the manuscrpt. Avalablty of data and materals The program s freely dstrbuted under European Unon Publc Lcence (EUPL) and can drectly be nstalled from CRAN [cran.rstudo.com/web/packages/ TTCA], the offcal R package archve. The source code s provded n Addtonal fle 1 and the current verson s avalable upon request. Mcroarray data sets GSE84094 and GSE84095 have been uploaded to Gene Expresson Omnbus (GEO) database at NCBI [ncb.nlm.nh.gov/geo/query/acc.cg?acc=gse84094; ncb.nlm.nh.gov/geo/query/acc.cg?acc=gse84095]. Authors contrbutons MA created the method concept, wrote the code and performed the statstcal analyses. BM, RM, KB and UK performed the expermental desgn, conducted the experments and KB nterpreted the analyss results. NG and CS are responsble for mcroarray handlng and the publc avalablty of the data set. MA, DS, KB and FM wrote the manuscrpt. All authors read and approved the fnal manuscrpt. Competng nterests The authors declare that they have no competng nterests. Consent for publcaton Not applcable. Ethcs approval and consent to partcpate Not applcable. Author detals 1 Complex Bologcal Systems Group (BIOMS/IWR), Hedelberg, Im Neuenhemer Feld 294, Hedelberg, Germany. 2 Systems Bology Group, Unversté du Luxembourg, 7, avenue du Swng, L-4367 Belvaux, Luxembourg. 3 CCU Neuropathology Group, German Cancer Research Center (DKFZ), Im Neuenhemer Feld 221, Hedelberg, Germany. 4 Insttute of Pathology, Hedelberg Unversty Hosptal, Im Neuenhemer Feld 672, Hedelberg, Germany. 5 Systems Bology of Sgnal Transducton Group, German Cancer Research Center (DKFZ), Im Neuenhemer Feld 280, Hedelberg, Germany. 6 Translatonal Lung Research Center (TLRC), Member of the German Center for Lung Research (DZL), Im Neuenhemer Feld 430, Hedelberg, Germany. 7 Medcal Research Center, Medcal Faculty Mannhem, Unversty of Hedelberg, Theodor-Kutzer-Ufer 1-3, Mannhem, Germany. 8 Frankfurt Insttute for Advanced Studes (FIAS), Goethe Unversty Frankfurt, Ruth-Moufang-Straße 1, Frankfurt am Man, Germany. Receved: 8 July 2016 Accepted: 21 December 2016 References 1. Tusher VG, Tbshran R, Chu G. Sgnfcance analyss of mcroarrays appled to the onzng radaton response. Proc Natl Acad Sc. 2001;98(9): Kerr MK, Martn M, Churchll GA. Analyss of varance for gene expresson mcroarray data. J Comput Bol. 2000;7(6): S GK. Lmma: lnear models for mcroarray data In: Gentleman R, Carey V, Dudot S, Irzarry R, Huber W, edtors. Bonformatcs and Computatonal Bology Solutons Usng R And Boconductor. New York: Sprnger; p Mutarell M, Ccatello L, Ferraro L, Grober OMV, Ravo M, Facchano AM, Angeln C, Wesz A. Tme-course analyss of genome-wde gene expresson data from hormone-responsve human breast cancer cells. BMC Bonforma. 2008;9(Suppl 2): Leek JT, Monsen E, Dabney AR, Storey JD. EDGE: extracton and analyss of dfferental gene expresson. Bonformatcs. 2006;22(4): Storey JD, Xao W, Leek JT, Tompkns RG, Davs RW. Sgnfcance analyss of tme course mcroarray experments. Proc Natl Acad Sc USA. 2005;102(36): Conesa A, Nueda MJ, Ferrer A, Talón M. masgpro: a method to dentfy sgnfcantly dfferental expresson profles n tme-course mcroarray experments. Bonformatcs. 2006;22(9): Sohn I, Owzar K, George SL, Km S, Jung SH. A permutaton-based multple testng method for tme-course mcroarray experments. BMC Bonforma. 2009;10(1): Sohn I, Owzar K, George SL, Km S, Jung SH. A permutaton-based multple testng method for tme-course mcroarray experments. BMC Bonforma. 2009;10(1): Ta YC, Speed TP, et al. A multvarate emprcal Bayes statstc for replcated mcroarray tme course data. Ann Stat. 2006;34(5): Aryee MJ, Gutérrez-Pabello JA, Kramnk I, Mat T, Quackenbush J. An mproved emprcal bayes approach to estmatng dfferental gene expresson n mcroarray tme-course data: BETR (Bayesan Estmaton of Temporal Regulaton). BMC Bonforma. 2009;10(1): Cheng C, Ma X, Yan X, Sun F, L LM. MARD: a new method to detect dfferental gene expresson n treatment-control tme courses. Bonformatcs. 2006;22(21): Huang W, Cao X, Zhong S. Network-based comparson of temporal gene expresson patterns. Bonformatcs. 2010;26(23): Moran VA, Perera RJ, Khall AM. Emergng functonal and mechanstc paradgms of mammalan long non-codng rnas. Nuclec Acds Res. 2012;40(14): Stegle O, Denby KJ, Cooke EJ, Wld DL, Ghahraman Z, Borgwardt KM. A robust Bayesan two-sample test for detectng ntervals of dfferental

11 Albrecht et al. BMC Bonformatcs (2017) 18:33 Page 11 of 11 gene expresson n mcroarray tme seres. J Comput Bol. 2010;17(3): Kalatzs AA, Lawrence ND. A smple approach to rankng dfferentally expressed gene expresson tme courses through gaussan process regresson. BMC Bonforma. 2011;12(1): Angeln C, De Candts D, Mutarell M, Pensky M. A Bayesan approach to estmaton and testng n tme-course mcroarray experments. Stat Appl Genet Mol Bol. 2007;6(1). 18. Angeln C, Cutllo L, De Candts D, Mutarell M, Pensky M. BATS: a Bayesan user-frendly software for analyzng tme seres mcroarray experments. BMC Bonforma. 2008;9(1): Jonnalagadda S, Srnvasan R. Prncpal components analyss based methodology to dentfy dfferentally expressed genes n tme-course mcroarray data. BMC Bonforma. 2008;9(1): Ramsay JO. Functonal Data Analyss. Hoboken: John Wley & Sons, Inc; Coffey N, Hnde J. Analyzng tme-course mcroarray data usng functonal data analyss-a revew. Stat Appl Genet Mol Bol. 2011;10(1): Lu X, Yang MCK. Identfyng temporally dfferentally expressed genes through functonal prncpal components analyss. Bostatstcs. 2009;10(4): Wu S, Wu H. More powerful sgnfcant testng for tme course gene expresson data usng functonal prncpal component analyss approaches. BMC Bonforma. 2013;14(1): Yao F, Müller HG, Wang JL. Functonal data analyss for sparse longtudnal data. J Am Stat Assoc. 2005;100(470): Henn AD, et al. Hgh-resoluton temporal response patterns to nfluenza vaccne reveal a dstnct human plasma cell gene sgnature. Sc Rep. 2013;3(2327). do: /srep D Camllo B, Toffolo G, Nar SK, Greenlund LJ, Cobell C. Sgnfcance analyss of mcroarray transcrpt levels n tme seres experments. BMC Bonforma. 2007;8(Suppl 1): Mnas C, Waddell SJ, Montana G. Dstance-based dfferental analyss of gene curves. Bonformatcs. 2011;27(22): Leek JT, Scharpf RB, Bravo HC, Smcha D, Langmead B, Johnson WE, Geman D, Baggerly K, Irzarry RA. Tacklng the wdespread and crtcal mpact of batch effects n hgh-throughput data. Nat Rev Genet. 2010;11(10): Irzarry RA, Hobbs B, Colln F, Beazer-Barclay YD, Antonells KJ, Scherf U, Speed TP. Exploraton, normalzaton, and summares of hgh densty olgonucleotde array probe level data. Bostatstcs. 2003;4(2): Stevens JB, Horne SD, Abdallah BY, Chrstne JY, Heng HH. Chromosomal nstablty and transcrptome dynamcs n cancer. Cancer Metastass Rev. 2013;32(3-4): Pccolo SR, Sun Y, Campbell JD, Lenburg ME, Bld AH, Johnson WE. A sngle-sample mcroarray normalzaton method to facltate personalzed-medcne workflows. Genomcs. 2012;100(6): Koenker R, Vol. 38. Quantle Regresson. New York: Cambrdge Unversty Press; Koenker R, Portnoy S, Ng PT, Zeles A, Grosjean P, Rpley BD. Quantreg: Quantle Regresson R package verson org/web/packages/quantreg/ndex.html. 34. Kovalchk S. RISmed: Download Content from NCBI Databases R package verson Venables WN, Rpley BD. Modern Appled Statstcs wth S, 4th edn. New York: Sprnger; ISBN MASS Nuzzo R. Statstcal errors: P values, the gold standard of statstcal valdty, are not as relable as many scentsts assume. Nature. 2014; : Durnck S, Moreau Y, Kasprzyk A, Davs S, De Moor B, Brazma A, Huber W. BoMart and Boconductor: a powerful lnk between bologcal databases and mcroarray data analyss. Bonformatcs. 2005;21(16): Smedley D, Hader S, Ballester B, Holland R, London D, Thorsson G, Kasprzyk A. BoMart bologcal queres made easy. BMC Genomcs. 2009;10(1): Grosjean P. ScVews-R: A GUI API for R. MONS, Belgum: UMONS; UMONS Chen H, Boutros PC. VennDagram: a package for the generaton of hghly-customzable Venn and Euler dagrams n R. BMC Bonforma. 2011;12(1): Da M, Wang P, Boyd AD, Kostov G, Athey B, Jones EG, Bunney WE, Myers RM, Speed TP, Akl H, et al. Evolvng gene/transcrpt defntons sgnfcantly alter the nterpretaton of genechp data. Nuclec Acds Res. 2005;33(20): Kauffmann A, Gentleman R, Huber W. arrayqualtymetrcs-a boconductor package for qualty assessment of mcroarray data. Bonformatcs. 2009;25(3): Stackles W, Redestg H, Scholz M, Walther D, Selbg J. pcamethods - a boconductor package provdng PCA methods for ncomplete data. Bonformatcs. 2007;23(9): Smyth GK. Lnear models and emprcal bayes methods for assessng dfferental expresson n mcroarray experments. Stat Appl Genet Mol Bol. 2004;3(1): Väremo L, Nelsen J, Nookaew I. Enrchng the gene set analyss of genome-wde data by ncorporatng drectonalty of gene expresson and combnng statstcal hypotheses and methods. Nuclec Acds Res. 2013; Durnck S, Spellman PT, Brney E, Huber W. Mappng dentfers for the ntegraton of genomc datasets wth the r/boconductor package bomart. Nat Protoc. 2009;4(8): Zhang Y, Wang L, Zhang M, Jn M, Ba C, Wang X. Potental mechansm of nterleukn-8 producton from lung cancer cells: An nvolvement of egf egfr p3k akt erk pathway. J Cell Physol. 2012;227(1): Panneerselvam J, Jn J, Shanker M, Lauderdale J, Bates J, Wang Q, Zhao YD, Archbald SJ, Hubn TJ, Ramesh R. Il-24 nhbts lung cancer cell mgraton and nvason by dsruptng the sdf-1/cxcr4 sgnalng axs. PloS one. 2015;10(3): Sheng X, Bowen N, Wang Z. GLI pathogeness-related 1 functons as a tumor-suppressor n lung cancer. Mol Cancer. 2016;15(1): Sngh DK, Bose S, Kumar S. Role of mcrorna n regulatng cell sgnalng pathways, cell cycle, and apoptoss n non-small cell lung cancer. Curr Mol Med. 2016;16(5): Nguyen TT, Almon RR, DuBos DC, Jusko WJ, Androulaks IP. Importance of replcaton n analyzng tme-seres gene expresson data: cortcosterod dynamcs and crcadan patterns n rat lver. BMC Bonforma. 2010;11(1): Hansen KD, Wu Z, Irzarry RA, Leek JT. Sequencng technology does not elmnate bologcal varablty. Nat Botechnol. 2011;29(7): Glynn EF, Chen J, Mushegan AR. Detectng perodc patterns n unevenly spaced gene expresson tme seres usng lomb scargle perodograms. Bonformatcs. 2006;22(3): Hughes ME, Hogenesch JB, Kornacker K. Jtk_cycle: an effcent nonparametrc algorthm for detectng rhythmc components n genome-scale data sets. J Bol Rhythm. 2010;25(5): Ahdesmäk M, Lähdesmäk H, Gracey A, Yl-Harja O, et al. Robust regresson for perodcty detecton n non-unformly sampled tme-course gene expresson data. BMC Bonforma. 2007;8(1):233. Submt your next manuscrpt to BoMed Central and we wll help you at every step: We accept pre-submsson nqures Our selector tool helps you to fnd the most relevant journal We provde round the clock customer support Convenent onlne submsson Thorough peer revew Incluson n PubMed and all major ndexng servces Maxmum vsblty for your research Submt your manuscrpt at