dataesr/scientific-paragraphs-categorization huggingface dataset:
S7 Analysis of synchronicity and rhythmicity To measure synchronicity amongst individuals we used the approach using Singular Value Decomposition (SVD) as explained in section S6. We describe this in the context of the Zhang et al. data. After fRMA processing, the data contains 35,556 probe values for m = 8 organs at T time points which was structured into 35,556 T × m matrices M g , and normalised so that each T -dimensional column has mean 0 and standard deviation 1. The columns thus correspond to normalised timeseries and we have one for each organ. Then if the SVD of these matrices has the form M g = U g D g V * g as in Note S6, the optimal projection, as in Note S6. 1 is given by using as basis the columns U g,i of U g which correspond to timeseries. Thus, the first principal component U g,1 describes the dominant temporal shape found across the various organs for gene g and the other principal components U g,i , i > 1, describe how this is modified across organs in a graded way with the strength given by the singular values σ g,k.
To characterise the relative strength of the first principal component we use S 2 g = σ 2 g,1 󰁓 k σ 2 g,k and this is our synchronicity score. See Table  S1. For JTK analysis the package at https://github. com/mfcovington/jtk-cycle was used. Using this we classified which genes optimise both rhythmicity and synchronicity. To choose the set of probes to make up the training set for TimeTeller a scoring of 1-50 is assigned to each probe according to its rank, and then these are simply summed up to combine results. This is shown in Table  S1    Much of the estimation "error" is due to chronotype variation. In each plot the x-coordinate is the difference between the time estimated by TimeTeller and the real time and the y-coordinate is the phase p g of the indicated gene g. We plot the mean TimeTeller error for each individual (averaging over times) against the phase of the given gene for each individual.
The horizontal line marks the mean phase of that gene. We then for each gene use the MATLAB function fit to fit a straight line y = q g x + p g to the points as plotted. Thus, if all the "error" was due to chronotype variation the points would lie on a fitted line y = q g x + p g which is the indicated sloping line. The closeness of the points to this line indicates that much of the "error" is due to gene-phase variation rather than TimeTeller error. Indeed, when we correct for the phase of the genes the mean absolute error for the genes (in the same order as in the figure) is 0. 6423, 0. 3777, 0. 4075, 0. 5643, 0. 6928, 0. 5342, 0. 2705, 0. 5871, 0. 7500, 0. 7658, 0. 5657, 0. 5769, 0. 3858, 0. 5856, 0. 7813, and 0. 5318 giving an average of 0. 5637h. We have coloured the points to indicate which individual they correspond to (as in Figs.
1  & S3 ) and this demonstrates the high coherence of the (x, y) position for each individual across genes. There is a strong positive correlation between estimated time difference from real clock time in most of the 16 probes (and some interesting differences for some). This shows that TimeTeller can measure chronotype and predict the phase of genes such as Bmal1 (ARNTL) or REV-ERBα (NR1D1) with good accuracy if the real clock time of the sample is known. In fact, these two genes show very high correlations. S9 Further supplementary information about human & mouse datasets The timings for the UK data (blue) appear to be realistic values reflecting our expectations. For the 3 Sri Lankan samples the Θ values are significantly larger (with two MLEs at around 2am), indicating that there is something wrong with these samples.
The reasons behind this can only be speculated upon. However, this suggests that TimeTeller may have a useful function in terms of quality control. The log threshold l thresh used here is -12. B,C. TimeTeller's analysis of the Boyle et al. data  (9). B. A scatter plot of Θ values against estimated time (non-smokers (black) and smokers (red)). Most of the estimated times are between 8am and 4pm. The majority of the 79 non-smoker and smoker samples have Θ < Θ GCF = 0. 1, which are in the GCF range. C. However, the boxplot shows that the median Θ for non-smoker samples is smaller than the median Θ for the smoker samples, with a significant Wilcoxon test statistic of p = 0. 004. These results provide further evidence that human datasets show reliable well-defined likelihood functions and that one can observe differences in Θ between WT and perturbed clocks.
The log threshold l thresh used here is -12. D,E. Timing and Θ values for the Feng et al. dataset  (10). D. Estimates for the timing MLE T plotted against Θ values from TimeTeller for the Feng et al. dataset  (10). This uses l thresh = -12. The estimates for the normal data (blue) are generally between 9 am and 6 pm, where the majority of the estimates outside of this time range are for cancer samples (red). The green points indicate the times of the second peak for those samples with T < 7. This suggests that the mistimed samples are primarily so because the wrong peak has a higher likelihood. E. The distribution of Θ values by group. There is a highly significant difference in median values between the cancer group (n = 167) and both the normal group (n = 45) and the combined normal/dysplasia group (n = 62), with the p-values indicated.
F. Scatter plot of real times against estimated times for the Martelot et al. dataset  (5)   7 ) published a dataset that uses all WT mice, but half of the mice were sleep deprived when they would usually be sleeping. See Note S5. 3. This is an interesting example where the clock is perturbed rather than structurally dysfunctional. In this dataset all the mice are WT, but half of them were sleep deprived when they would normally be sleeping. See Note S5. 2 for more details. Time-Teller accurately estimated the time of the normal condition liver samples, shown as the blue circles on the left plot in Fig. S4(G). The Θ values for these samples have a median Θ value around 0. 03 (Fig. S4(H) ). There is also a good level of estimation accuracy within the normal sleep schedule group for the adipose data, with the exception of an ∼10 hour error for three data points for the adipose tissue at 07.
S9. 2 Comparing rhythmic datasets Because of the nature of the TimeTeller algorithm we were unable to find a normalisation technique to allow comparison of data from different microarray technologies with our training data without removing the key multidimensional aspects determining functionality. The time-dependent expression ratios between the genes in the rhythmic expression profile varied significantly from one technology to another. For example, we studied circadian timecourse data from two independent datasets comparing the raw expression in the liver of mice from Hughes et al. (15)  (Affymetrix Mouse Genome 430 2. 0 GeneChip) and Zhang et al. (Affymetrix MoGene 1. 0ST GeneChip) after fRMA normalisation. These use two different Affymetrix genechips. Different microarrays use slightly different probe designs and quantify the expression differently because of this.
S19 S10 Further supplementary information about the REMAGUS data set Table  S2 : Patients' characteristics. While the cause of death for 4 patients is unknown, for the others it was breast cancer Abbreviations: LVI, lymphovascular invasion; pCR, pathological complete response. **n = number of patients. Missing data: HER2-CT alone: Menopausal status: 2, Nodal status: 1, Grade: 4, PR status: 1, pCR: 2. HER2-CT+celo: Grade: 1, LVI 1,pCR: 2. HER2+ CT alone: Nodal status:1,Grade: 1, pCR: 1. HER2+ CT + trastuzumab: Grade: 1, LVI : 1.
For each prognostic stratum the HR 1/10 , its confidence limits, the number of events and individuals, and the corresponding p-value are shown. The p-values associated with each factor concern the HR for that factor where one compares the hazard for Θ against the hazard for the alternative, conditional on the other prognostic factors being the same. The prognostic groups shown for multivariate analysis are those for which this univariate analysis p-values is ≤ 0. 05. This analysis was implemented using the R package coxph. The other factors included in the multivariate analysis were ER, PR and HER2 status, tumour grade and size of tumour. The mistimed points appear corrected if the second peak is used to define time T. The red points show timing for the samples where TimeTeller gave seemingly unreasonable times and we replace the estimated time with the time given by the second peak.
S10. 4 Comparison of dysfunction metric with ZeitZeiger for the REMAGUS data It was suggested to us that we should compare our results on the REMAGUS data with Zeitzeiger ( (  16 ), Note S13. 2) using Zeitzeiger's likelihood function, noting that Zeitzeiger's authors never claimed it could calculate dysfunction in individual samples. However, we have now included an analysis in Fig. S5(E, F ). The only way we could see to do this was by using the Zeitzeiger likelihood in a similar way to TimeTeller's. The Bjarnason et al. data was used to train ZeitZeiger which seemingly accurately estimated the times of the training data in a cross-validation study as seen in Fig. S3E  (parameters sumsabsv = 1 and nSpc = 2 were chosen). We used the ZeitZeiger package (https://github. com/hugheylab/zeitzeiger).
The ZeitZeiger timings and likelihoods were then calculated for the rhythmic expression profiles of the REMAGUS data. As well as comparing Θ values (Fig. S5E ) we compared TimeTeller Θ values with the maximum value of the ZeitZeiger likelihood curve (Fig. S5F ). This is because in  (16)  it was suggested that this might be characteristic of a dysfunctional clock as they observed that in four of seven datasets studied in that paper, the likelihood of the predicted timing was significantly lower in mutant than in wild-type.
S25 S11 Supplementary information about synthetic datasets: 2). This helps to overcome problems with symmetry as described in Fig. S1  and seen in (C). (E) Scatter plot showing estimated versus real time for low noise (Ω = 1000) simulations, using local and global PCA. Correlation is generally very high, with some obvious symmetry issues with the global PC estimations. The red point at  (8, 13)  is the estimate resulting from the dotted likelihood in (C). The inset table shows R 2 values for linear fit to x = y of estimated versus real time, for Ω = 1000 simulated data. Values are generally accurate due to low noise. R 2 s are similar except for runs 1 and 4, where the accuracy of the local PC method is higher than for global PC method. (F) As (E) but for the high noise Ω = 100 simulations.
S12 Stochastic modelling There have been a number of stochastic models of the circadian clocks (e. g. (19) (20) (21) ). In particular, an early detailed stochastic mammalian model of circadian rhythms was published by Forger et al. (22) , who adapted his previous 74 equation ODE model. Instead of attempting to replicate this existing stochastic model of mammalian circadian rhythms, we have created a stochastic version of the Relogio model  (23)  with the adaptation based on methods in  (19). This stochastic model incorporates a system size parameter, Ω. We consider it has units L/nM and a value in the range 100-1000 based on typical cell size estimates  (24). The individual trajectories were calculated in MATLAB, using an implementation of the Gillespie algorithm or the pc-LNA  (24).
S13. 5 CYCLOPS Anafi et al. (30)  published CYCLOPS : a neural network that finds circadian patterns in datasets  (31). This study does not present an algorithm that can (yet) tell the time of single samples (and does not claim to be able to), but it has clear value in this field. The study uses a (quasi) unsupervised machine learning algorithm (an autoencoder) to construct a cyclic periodic timecourse, using unordered large datasets of expression measurements according to the clock genes, by mapping their eigengenes to ellipses. These known rhythmic genes are given as prior information. The number of eigengenes retained was set so that > 85% of variance was captured. CYCLOPS optimally weights and combines the eigengenes, in order to create the closest thing to an ellipse. These points are subsequently ordered along that ellipse using a neural network.
S13. 6 PLSR Laing et al. (32)  published a study that uses a partial least squares regression method to estimate melatonin cycles from transcriptome data. They used a large, novel human transcriptome dataset, which consisted of timecourses of melatonin and gene expression in sleep deprived states. The study is not strictly speaking presenting a "timetelling" model. The authors instead attempt to predict melatonin phase from the transcriptome of blood samples from human volunteers. There is no new method developed in this study, instead a new application of an existing and established supervised ML method, Partial Least Squares Regression  (33).
J-L. The curves for the REMAGUS data with Θ < 0. 1 (J),0. 1 < Θ < 0. 3 (K) and 0. 3 < Θ (L). For the last dataset the flat part of the LR curves intersect the C(t|T ) curve (black) and therefore the Θ values are significantly affected by this. This occurs because the maximum value of the corresponding likelihood curve is close to the threshold value l thresh used. To overcome this one can decrease l thresh.
Most of the estimated times are between 8am and 4pm. The majority of the 79 non-smoker and smoker samples have Θ < Θ GCF = 0. 1, which are in the GCF range. C. However, the boxplot shows that the median Θ for non-smoker samples is smaller than the median Θ for the smoker samples, with a significant Wilcoxon test statistic of p = 0. 004. These results provide further evidence that human datasets show reliable well-defined likelihood functions and that one can observe differences in Θ between WT and perturbed clocks. The log threshold l thresh used here is -12. D,E. Timing and Θ values for the Feng et al. dataset(10). D. Estimates for the timing MLE T plotted against Θ values from TimeTeller for the Feng et al. dataset(10). This uses l thresh = -12. The estimates for the normal data (blue) are generally between 9 am and 6 pm, where the majority of the estimates outside of this time range are for cancer samples (red).
The green points indicate the times of the second peak for those samples with T < 7. This suggests that the mistimed samples are primarily so because the wrong peak has a higher likelihood. E. The distribution of Θ values by group. There is a highly significant difference in median values between the cancer group (n = 167) and both the normal group (n = 45) and the combined normal/dysplasia group (n = 62), with the p-values indicated. F. Scatter plot of real times against estimated times for the Martelot et al. dataset(5). (Inset) Boxplot of the corresponding Θ values. G,H Timing and Θ distribution for the Barclay et al. data on sleep stress. See Sects. S5. 2 & S9. 1.
S1 Probability Model & Likelihood curves L g (t). S4 S1. 1 Probability model. S4 S1. 2 L g (t) and the log threshold l thresh. S4 S2 Definition of Θ. S6 S2. 1 Parameters ε and η. S6 S2. 2 Examples showing likelihood ratio curves, the curve C(t|T ) and the corresponding Θ. S7 S3 Outline pseudocode of algorithm. S9 S4 Role of gene-to-gene correlations in the likelihood function. S9 Real vs. estimated times for REMAGUS breast cancer samples, comparison with ZeitZeiger, correlation with PCNA, & the distribution of Θ values for the main prognostic strata. S22 S10. 4 Comparison of dysfunction metric with ZeitZeiger for the REMAGUS data. S24 S10. 5 Relevance of Venet et al. S24 S10. 6 Which aspects of TimeTeller likelihood curves contain biological information? S24 S11 Supplementary information about synthetic datasets: 1.
We thank  Ida Iurisci  ( INSERM, Villejuif ) and  Anne-Sophie Hamy  (for help with complementary information regarding the breast cancer database, and  Meritxell Saez  and  Laura Usselmann  ( University of Warwick ) for a critical reading. DAR and DV thank the  Engineering & Physical Sciences Research Council (EPSRC)  for a PhD studentship through the  MOAC Doctoral Training Centre  grant number  EP/F500378/1. DAR was also supported by  Biotechnology and Biological Sciences Research Council (BBSRC)  Grant  BB/K003097/1  and  EPSRC  Grant  EP/P019811/1. FL & DAR were supported by a grant from  Cancer Research UK  and  EPSRC  ( C53561/A19933 ). GAB was supported by  The Anna-Liisa Farquharson Chair in Renal Cell Cancer Research. Author Contribution Statement: FL and DAR conceived the project and supervised it.
The metric Θ discussed above is one way to do this which is naturally suggested by statistics and information theory but there are likely to be other interesting metrics (such as the maximum value of P(g|t) as t varies). It depends upon three parameters. one of which, lthresh. is used to facilitate the consolidation of the local likelihood curves into a global one (Note S1. 2, Figs. S1 & S6), while the other two, ε and η, are involved in the definition of Θ (Note S2). When comparing the values of Θ across different datasets it is important to use the same probability model and therefore the same values for these parameters.
Local PCs versus global PCs, 2. Θ dependence on the efficiency of a Bmal1 knockdown. S26 S12 Stochastic modelling. S28 CYCLOPS. S33 S13. 6 PLSR. S33 S13. 7 ∆CCD. S33 S4. 1 Θ contains an estimate of a direct measure of clock precision. S9 S4. 2 P (t|g) is estimated using P (g|t). S10 S4. 3 Θ depends crucially on the covariance structure of P (g|t). S10 S5 Data sets. S10 S5. 1 Training datasets. S10 S5. 2 Other mouse datasets. S11 S5. 3 Other human datasets. S12 S5. 4 Batch Effects. S12 S6 Singular Value Decomposition. S13 S6. 1 Optimal projections via SVD and the projection U d,i. S13 S7 Analysis of synchronicity and rhythmicity. S14 S8 Extra data on timing, distribution of Θ values and variable gene phase. S16 S9 Further supplementary information about human & mouse datasets. S18 S9. 1 Timing for the Barclay et al.
dataset. S19 S9. 2 Comparing rhythmic datasets. S19 S10 Further supplementary information about the REMAGUS data set. S20 S10. 1 Patients' characteristics. S20 S10. 2 Survival analysis for disease free survival (DFS) & overall survival (OS). S21 S10. 3 S12. 1 The stochastic Relogio model. S28 S13 Previous time-telling algorithms. S32 S13. 1 Molecular timetabling method (MTTM). S32 S13. 2 ZeitZeiger. S32 S13. 3 BodyTime as a diagnostic clinical tool. S33 S13. 4 BIO CLOCK. S33 S13. 5 S3 S1 Probability Model & Likelihood curves L g (t).
summary of the existing time-telling or clock dysfunction measuring studies is given in this section. All methods are for calculating time (internal or external) in some way from transcriptomes, except for ∆CCD which attempts to calculate clock dysfunction from whole datasets (not single samples as TimeTeller's Θ can). S13 Previous time-telling algorithms Parameters Parameter Value Description aa 12 Per d 12 Cry g 5 Rev-Erb h 5 Ror i 12 Bmal b 5 Per-activation c 7 Per-inhibition e 6 Cry-activation rate f 4 Cry-inhibition f 1 1 Cry-inhibition v 6 Rev-Erb-activation w 2 Rev-Erb-inhibition p 6 Ror-activation q 3 Ror-inhibition n 2 Bmal-activation m 5 Bmal-inhibition y 10 0*omega Per y 20 0*omega Cry y 30 0*omega Rev-Erb y 40 0*omega Ror y 50 0*omega Bmal d z1 0. 23 CRYC d z2 0. 25 PERC d z3 0.
Since ISCs are the primary dividing cell type in the midgut, we then assessed ISC proliferation using phospho-Histone H3 (PH3) as a marker for mitotic cells. 7. 6% of the control clones had at least one and never more than one mitotic cell per clone (Figures  2C-2E 0  ). In contrast, 21. 4% of kismet mutant clones had at least one mitotic cell, and clones with more than one dividing cell were detected (Figures 2C, 2D, 2F, and 2F 0 ). We assessed cell cycle parameters using the Fly-FUCCI system  (Zielke et al. , 2014)  and found that kismet mutants had an increased proportion of ISCs in S phase and G2/early M at the expense of the G1 fraction (Figures  2G-2I ). Together, these data further support the idea that kismet mutant ISCs have deregulated proliferation.
This argues against early activation of Jak/Stat and Jun Kinase pathways being initiating defects in kismet mutant responsible for ISC proliferation. Therefore, our data suggest that an initial enhancement of EGFR signaling occurs, followed by activation of additional pathways during the following 10 days of clone growth and further driving mutant clone growth. We conclude that Kismet is required to limit EGFR signaling in ISCs. Kismet Localizes to Chromatin Enriched in RNA Pol II and Brahma and Depleted for HP1, Histone H1, and Polycomb To further understand how Kismet regulates ISC self-renewal, we identified Kismet-bound regions of the ISC genome using a targeted DamID-seq strategy (STAR Methods)  (Marshall et al. , 2016). A Dam-Kismet (Dam-Kis) fusion protein construct was expressed in ISCs using esg ts , NRE-GAL80 for 1 day and significantly methylated GATC sites as compared to Dam-alone control expression were determined.
Kismet Localizes to Transcriptionally Active Genomic Regions and Developmental Enhancers We then assessed how Kismet localized relative to active genes and enhancers. We established a list of expressed or ''active'' ISC genes based on Dam-RNA Pol II gene occupancy, a good proxy for gene expression  (Marshall and Brand, 2015; Southall et al. , 2013 ) (STAR Methods). 4,539 genes had significant occupancy suggesting that they are expressed, including many known ISC-enriched genes (esg, sox21a, Delta, and spdo; Table  S3 ). Accordingly, these genes strongly overlap with published RNA sequencing (RNA-seq) data for ISC express genes  (Dutta et al. , 2015)  (Figure  4H ). We found that 67. 0% of the genes with Kismet peaks were also occupied by RNA Pol II (Figure  4I ). Genes enriched for Kismet and RNA Pol II or enriched for one but not the other were also detected (examples in Figures  S5B-S5H ).
, 2017). The GTF file was filtered to retain only 3 0 UTR coding, 5 0 UTR coding, exon and gene features. The file was then split into a single file per genomic feature and overlapping features were merged using bedtools. Using bedtools subtract, exonic regions were subtracted from genic regions to obtain intronic regions, and exonic regions were subtracted from overlapping 3 0 UTR and 5 0 UTR coding regions. Significantly bound GATC sites were classified as belonging to one of these regions using bedtools intersect. RNA Pol II occupancy was determined by considering mean ratios (Dam-RNA Pol II/Dam-only) across annotated transcripts using ''polii. gene. call'' script and false discovery rates (FDR) were assigned  (Marshall and Brand, 2015; Southall et al. , 2013). Genes with an FDR < 0. 01 were used as genes active in ISC  (Figures 4J and 6D).
QUANTIFICATION AND STATISTICAL ANALYSIS Image acquisition was followed by data processing with Fiji software and assembled using Adobe Photoshop. Images were processed with a median filter of 1-pixel width before applying Z-stack max projections. All quantification of clonal analysis was limited to the posterior midgut and only clones containing two or more cells (stem cell clones) were scored except for Figure  S3J  where 1 cell clones were included. Contiguous cells (GFP+ or RFP+) were considered as part of one discrete clone for quantifications. All graphs are scatterplots of raw data to present the full distribution of values observed and all statistical analysis were performed using Prism software. PH3+ cells number per gut was evaluated on the entire midgut (Figure  2D ). In Figures  2J  and 5H , Kis and Utx staining intensity were quantified within the posterior midgut.