dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistical analysis was performed using SAS version 9. 2. Stepwise multivariate analyses were used to investigate factors predictive for hormonal control at week 48/LVA, focusing on T2 signal intensity at baseline. Univariate logistic regression analyses were first conducted to identify suggestive associations (P < 0. 2). Multivariate logistic regression models were then used, in which T2 signal intensities were retained and to which 'significant' factors from the univariate analyses were added (no interaction was tested). Factors investigated comprised baseline variables: T2 signal intensity, GH level, IGF-1 level, tumor volume and EEPs. P values (chi-square) were generated using likelihood ratio tests, and odds ratios were estimated, with Wald 95% CIs. Similar logistic regression was used to investigate factors predictive for tumor response at week 48/LVA.
Factors predictive for treatment response In univariate analyses, T2 signal intensity at baseline was not significantly associated with hormonal control at week 48/LVA (for all methods of assigning intensity category, data not shown) (primary endpoint). The presence of any EEPs at baseline was also not associated with hormonal control. Univariate associations of T2 signal intensity were also investigated with GH levels and IGF-1 levels as separate endpoints. T2 signal intensity at baseline was significantly associated with GH ≤2. 5 μg/L at week 48/LVA, as defined by visual assessment (P < 0. 001; OR (hypo-vs iso-intense) = 7. 27; 95% CI: 2. 57-20. 56) and signal-ratio methods (P = 0. 004; OR (hypo-vs iso-intense) = 4. 67; 95% CI: 1. 39-15. 66), with no significant association using the three-tissue method.
Acknowledgments The authors thank the investigators and patients participating in this study, including the  PRIMARYS Study Group. PRIMARYS Study Group: Belgium: L Van Gaal ; Czech Republic:  J Marek ; Finland:  P Nuutila ,  M Välimäki ; France:  C Ajzenberg ,  F Borson-Chazot ,  T Brue ,  P Caron ,  O Chabre ,  P Chanson ,  C Cortet Rudelli ,  B Delemer ,  J-M Kuhn ,  A Tabarin ; Germany:  K Badenhoop ,  C Berg ,  S Petersenn ,  C Schöfl ,  J Schopohl ; Italy:  S Cannavò ,  A Colao ,  L De Marinis ; The Netherlands:  A Stades , A J van der Lely; Turkey: P Kadıoğlu; UK:  J S Bevan ,  D Flanagan ,  P Trainer. Medical writing and submission support was provided by  Emma Leah  and  Richard McDonald of Watermeadow Medical , an Ashfield company, part of  UDG Healthcare plc , funded by  Ipsen  ( Emma Leah  is now an employee of Ipsen).
ISoLDE learns the distribution of a specific test statistic from the data and calls genes 'allelically imbalanced', 'bi-allelically expressed' or 'undetermined'. Depending on the number of replicates, predefined thresholds or permutations are used to make calls. We benchmarked ISoLDE against published methods, and showed that ISoLDE compared favorably with respect to sensitivity, specificity and robustness to the number of replicates. Using ISoLDE on different RNA-seq datasets generated from hybrid mouse tissues, we did not discover novel imprinted genes (IGs), confirming the most conservative estimations of IG number.
The number of bona fide IGs was estimated to be approximately  150-200 (DeVeale et al. , 2012; Kelsey and Bartolomei, 2012). In 2015, Dulac's group used a novel statistical method and found 160 IGs, including 41 new ones that displayed a weak to moderate parental bias  (Perez et al. , 2015). This study suggested that a number of IGs remained to be identified in specific tissues or cell types. Similarly, Gregg's group designed a new statistical method and identified 407 IGs, substantially more than the previous estimates, including 142 autosomal IGs that were not previously called 'imprinted'  (Bonthuis et al. , 2015). Babak et al. re-analyzed published human and mouse datasets that covered many different organs and found only a handful of new IGs  (Babak et al. , 2015). To circumvent the limitations of the modeling approach, we designed Integrative Statistics of alleLe Dependent Expression (ISoLDE), a method that uses a nonparametric approach based on data resampling.
We designed a specific nonparametric test statistic to take into account data specificities and make the best of biological replicates. ISoLDE identifies both biased and unbiased genes, leaving some genes as undetermined. It may be applied when at least two replicates of reciprocal crosses are available. We benchmarked ISoLDE against 7 other methods using 13 experimental datasets and 2 simulated datasets; we concluded that ISoLDE is more sensitive, specific and robust to the number of replicates than currently available methods. Using ISoLDE on different RNA-seq datasets generated from hybrid mouse tissues, we did not discover novel IGs, confirming the most conservative estimations of IG number.
ISoLDE relied on two predefined thresholds when only two biological replicates in at least one cross were available In such experimental situations, there was not enough information to obtain reliable distributions of S g under the null hypotheses for Q1 and Q2. We then selected two thresholds for the S g statistic based on the analysis of 10 different experimental datasets comprising two or more replicates for each cross: • five datasets from  Bouschet et al. (2017)   The majority of these datasets had few replicates, and we excluded other datasets with many replicates to avoid biasing the thresholds toward values that were sensible only when many replicates were available. Moreover, the selected datasets included very different biological conditions and sequencing depth to cover a range of experimental situations.
We used the resampling version of ISoLDE when datasets had more than two replicates. For two-replicate datasets, we applied the Storer-Kim test to each gene, or its normal approximation when appropriate. To prevent false positives, we set a Storer-Kim test P-value threshold using mock reciprocal crosses  (DeVeale et al. , 2012)  with a false discovery rate (FDR) threshold set at 0. 05. We applied two additional filters based on biological assumptions. The first filter selected only AI candidates whose parental bias was the same for both replicates. The second filter selected those genes for which the Storer-Kim test was significant for most replicates taken independently. We used again the mock reciprocal crosses approach to set a threshold on significance agreement. Then, the statistic S g was computed for all genes of a dataset.
A first threshold was defined as the fifth percentile of the absolute value of the statistic distribution among allelically imbalanced candidates identified in the previous step. Genes whose statistic absolute value was higher than this threshold were considered allelically imbalanced. Finally, a second threshold was defined as the 99th percentile of the absolute value of the statistic distribution among genes not identified as allelically imbalanced candidates in the previous step. Genes whose statistic absolute value was lower than this threshold were considered bi-allelically expressed. Genes whose statistic absolute value was between the two thresholds were called 'undetermined' ('UN'). This method was applied to the 10 different datasets mentioned above. The two selected thresholds were the median values of the thresholds obtained among the 10 datasets: genes with a S g absolute value lower than 0.
5 and P0 murine cortices  (Bouschet et al. , 2017). Read counts were normalized using the RLE method as implemented in the edgeR package  (Robinson et al. , 2010). Each gene was projected according to its S g denominator value (ordinate) and numerator value (abscissa), which corresponds to the allelic bias (Fig. 1A  and Supplementary Fig. S2A ). The ordinate scales are not the same in Figure  1A  and Supplementary Figure  S2A  due to the different number of replicates in both experiments; three replicates for each cross at E13. 5 and three and four replicates for the crosses at P0. A total of 37 genes displayed significant parent-of-origin dependent expression at E13. 5 (Supplementary Fig. S2A ) or P0 (Fig. 1A ), all of which have already been identified in previous AI studies. The allelic expression of nine genes was confirmed by other methods  (Bouschet et al.
Benchmarking of the ISoLDE method We used simulated datasets to compare ISoLDE specificity and sensitivity to those of other methods. We generated two simulated datasets (see Supplementary File, Section S1. 2 for details). The first dataset consisted of 10 882 genes, including 59 imprinted ones, with three replicates in each cross. The second dataset consisted of 11 889 genes, including 68 imprinted ones, with five replicates in each cross. We also used experimental datasets to further compare ISoLDE and other methods (Table  1 ). We occasionally did not succeed to match gene IDs between the published article and the corresponding data submitted to GEO. When we did not solve such discrepancies by manual curation and/or by contacting the authors, we discarded the corresponding genes from the comparisons.
As the actual status of genes was unknown for experimental datasets, we attempted to make a decision on the discordant calls by observing data distribution of individual genes and by comparing the calls to the lists of published IGs. We organized the benchmarking according to the modeling used by the published methods. We first compared ISoLDE to methods based on Poisson, binomial and beta-binomial modeling and representing a variety of significance testing. For the simulated datasets, we chose the method by DeVeale et al. (  2012 ) that accounted for the most classical approaches based on binomial modeling, v 2 test and mock reciprocal crosses to correct for false positives and filter for consistency in the bias direction. This method has been used in several other reports  (Babak et al. , 2015; Lorenc et al.
, 2014). We also tested the method by  Pirinen et al. (2015) , which explored an alternative modeling based on beta-binomial and Bayesian estimation. For the experimental datasets, we used the method by DeVeale et al. as well as those by  Babak et al. (2008)  and  Lorenc et al. (2014). We also tested the method by Perez et al. , which used Poisson-based modeling and Bayesian estimation. We then benchmarked ISoLDE against two methods based on negative-binomial modeling and GLM. We tested the method by  Bonthuis et al. (2015)  on simulated and experimental datasets, and the method by  Hasin-Brumshtein et al. (2014)  on the corresponding experimental dataset. Finally, we tested the behavior of ISoLDE with respect to replicate number, and compared it to that of the methods by  Perez et al. (2015)  and  Bonthuis et al.
3. 2. 1 ISoLDE is more conservative than methods based on Poisson, binomial and beta-binomial distributions 3. 2. 1. 1 Comparison of ISoLDE, DeVeale and Pirinen methods on simulated datasets. Table  2  summarizes the results of the analysis of the two simulated datasets by the different methods. The results for two additional datasets with 7 and 10 replicates are displayed in Supplementary Table  S2. ISoLDE resulted in five possible calls; allelic imbalance ('AI'), undetermined call with consistency flag ('UN_flag'), undetermined call with significance flag ('UN_flag'), unflagged undetermined call ('UN') and bi-allelically expressed ('BA'). To compute sensitivity and specificity, we considered 'AI' and 'UN_flag' as 'positive' calls and 'UN' and 'BA' as 'negative' calls. The analysis of the simulated three-replicate dataset by ISoLDE resulted in 99.
4% sensitivity and a 99% specificity; we obtained only three genes called 'BA' whereas they were imprinted. Moreover, the 10 remaining IGs were undetermined and flagged for consistency. On the other hand, 66 biallelically expressed genes were flagged for consistency and 40 other ones were called undetermined. We concluded that, in this application, flags did not provide specific information. The analysis of the five-replicate dataset by ISoLDE resulted in 94. 1% sensitivity and 100% specificity. Only 13 genes were considered as undetermined, 7 of which were flagged, 5 true IGs and only 2 true BA genes. In this case, the flags were informative. ROC curves and other performance indices can be found in Supplementary Figure  S4. Analysis of the same datasets with the DeVeale method resulted in 52.
5% sensitivity (28 false negatives) and 99. 8% specificity (21 false positives) on the three-replicate dataset, and 45. 6% sensitivity (37 false negatives) and 99. 8% specificity (27 false positives) on the five-replicate one. The Pirinen method provided six calls that we grouped in the following way to compare with ISoLDE calls: • genes called 'moderately' or 'strongly' biased by the Pirinen method were considered 'AI'; • genes called 'heterogeneous' across replicates by the Pirinen method were considered 'UN'; • genes called 'unbiased' by the Pirinen method were considered 'BA'. As for ISoLDE, we considered 'undetermined' calls as 'negative' calls to compute sensitivity and specificity. This method achieved 79. 7% sensitivity and 98. 7% specificity. The undetermined genes comprised 8 IGs and 1694 BA genes.
Comparison of ISoLDE and DeVeale method on data from P0 and E13. 5 cortex. DeVeale method was also applied to the P0 and E13. 5 murine cortex datasets (Figs  1C  and 2 , Supplementary Figs  S5  and S6 ). One gene called 'AI' by the method of DeVeale in the E13. 5 dataset has been filtered out by ISoLDE in the P0 and E13. 5 samples due to very low expression (see Section 2 for more details about filtering). Similarly, three and two genes called 'AI' by ISoLDE in the P0 and E13. 5 datasets, respectively, were filtered out by the method of DeVeale. The other genes called 'AI' by ISoLDE in both datasets were also called 'AI' by the method of DeVeale. Sixteen and nine genes were called 'AI' by the method of DeVeale alone in the P0 and E13. 5 datasets, respectively (Figs 1C and 2 and Table  3 ).
Comparison of ISoLDE and Babak method on Babak's dataset. The dataset from  Babak et al. (2008)  contains four replicates of E9. 5 mouse embryos from reciprocal crosses between CAST/EiJ and C57BL/6J. Forty genes were called 'AI' using a binomial modeling combined with signal consistency filter and mock reciprocal crosses to assess the FDR  (Babak et al. , 2008). The resampling version of ISoLDE confirmed 24 of these and called two additional ones (Fig. 2  and Supplementary Fig. S7  and Table 3 ). Among the 16 genes not called 'AI' by ISoLDE, 6 were filtered out due to low expression levels, 7 were called 'UN', including one flagged for consistency and 3 'BA'. Supplementary Figure  S8  showed that six of the undetermined genes (Rian, Asb4, Neurabin, Usp29, Peg1 and Dlk1) exhibited strain-biased expression as, depending on the gene, either the C57BL or the CAST allele was systematically overexpressed.
Comparison of ISoLDE and Perez method on Perez's datasets. As previously mentioned, Perez et al. proposed a method based on a complex generalized linear model assuming a Poisson distribution of variability  (Perez et al. , 2015). This method uses MMSEQ to estimate the real expression level of each gene along with the variability. P8 and P60 cerebella of 24 mice derived from the reciprocal crosses between CAST/EiJ and C57BL/6J strains were RNA sequenced. As raw data were not available, we ran ISoLDE on MMSEQ average expression outputs (Supplementary Fig. S10 ). In the P8 cerebella, 91 out of 143 genes called 'AI' by Perez were also called 'AI' by ISoLDE. Among the 52 discordant genes, 19 were flagged by ISoLDE including 15 significance flags (Fig. 2  and Supplementary Fig. S11 , Table  3 ).
Ten genes called AI by Perez were called bi-allelically expressed by ISoLDE (Fig. 2 ); the corresponding maternal and paternal numbers of reads did not support the calls by Perez method (Supplementary Fig. S12 ). Five genes (FR0149454, NM 009225. 2-13, NM 011241. 4-55, NM 013762. 2-23 and NM 027275. 3-3) were considered biased by Fig. 2. Comparison of ISoLDE to other published methods on a series of experimental datasets. The indicated datasets were analyzed in parallel by ISoLDE and by one of the indicated published methods. For each method comparison, the genes called parentally biased ('AI') by either method were classified according to the calls of the two methods. 'AI', allelic imbalance. 'BA', bi-allelically expressed. 'UN', undetermined. 'UN-flag', undetermined and flagged by ISoLDE.
Comparison of ISoLDE and Lorenc method on Lorenc's dataset. Three tissues were investigated: VNO, HYP and LIV  (Lorenc et al. , 2014). They were collected from reciprocal crosses of WSB and PWD strains with three to six biological replicates in each cross. The method of Lorenc used a v 2 test, which involved the normal approximation of the binomial distribution, followed by signal consistency filter. Mock reciprocal crosses were used to control FDR in a way similar to the one used by  DeVeale et al. (2012). Lorenc et al. first identified significant SNPs and then annotated transcripts called 'AI' using Mus musculus GRCm38. 68 (ENSEMBL) and named UCSC genes from the mm10 reference annotation. As ISoLDE worked at the transcript/gene level, we first annotated SNPs using ANNOVAR and mm10 UCSC refFlat file and then summed all allele-specific sense read bases across a gene before running ISoLDE.
As we could not use the annotation database of Lorenc, discrepancies between gene symbols might have occurred. We analyzed all three datasets with ISoLDE (Supplementary Figs S13  -S15 ). The vast majority of 'AI' calls were identical between ISoLDE and Lorenc method (Fig. 2  and Table  3 ). Among the seven genes called 'AI' in at least one tissue by ISoLDE only (Supplementary Figs  S17  and S19 ), six were previously called 'AI' by others  (Babak et al. , 2008; Chiesa et al. , 2012; Perez et al. , 2015; Silva-Santiago et al. , 2012; Tremblay et al. , 1995; Wu et al. , 2012; Xiao et al. , 2006). Of note, DQ267100 and Snord64 are snoRNAs that might not be annotated in Lorenc's database. DQ267100 is located in an intron of the maternally expressed Rian gene. Snord64 is located in an intron of the paternally expressed Snrpn gene and overlaps AK139082, an unclassifiable mRNA from GenBank that was called paternal in HYP by Lorenc.
Furthermore, Kcnq1ot1, a long noncoding RNA, might not be present in Lorenc's database. Hence, misannotation may be the source of most of the observed discrepancies. Genes called 'AI' by the Lorenc method only displayed high variability with regards to bias (Adam23, Bcl2l1, Ppp1r9a, Asb4, Igf2, Klhdc10 and Wars), discrepancies in the bias direction depending on the replicate (Ddc, Supplementary Fig. S16 ) or allelic bias in only one cross (Nnat, Supplementary Fig. S18 ). Four of those genes were called 'UN' and flagged by ISoLDE with consistency across replicates in bias direction. ISoLDE called Wars 'BA' whereas the visual inspection of the read counts showed a weak and constant bias in favor of paternal reads (Supplementary Fig. S16 ). In short, the inspection of the data challenged some of the calls made by the Lorenc method.
Comparison of ISoLDE and Bonthuis method on simulated datasets. The method by  Bonthuis et al. (2015)  is based on the edgeR package  (Robinson et al. , 2010; Zhou et al. , 2014)  and uses a negative binomial modeling combined with GLM for significance testing. The results obtained with this method on the three-and fivereplicate datasets are given in Table  2 ; 28. 8% sensitivity and 100% specificity on the three-replicate dataset; 64. 7% sensitivity and 100% specificity on the five-replicate dataset. Compared to ISoLDE, the method by Bonthuis et al. displayed a marginally improved specificity (100% versus 99. 39% or 99. 98%) at the expense of sensitivity (28. 8% versus 94. 9%, three-replicate dataset; 64. 7% versus 94. 1%, five-replicate dataset). 2 ). In both datasets, all genes called 'AI' by the Bonthuis method were also called 'AI' by ISoLDE (Table  3 ).
Four, respectively six, additional genes were called 'AI' by ISoLDE only in P0 (Zdbf2, Copg2, Impact and Adam23), respectively E13. 5 (Snrpn, Adam23, Phactr2, Slc38a4, Impact and Asb4). Those genes have already been shown to be imprinted and their normalized read counts (Supplementary Figs  S22  and S23 ) supported this conclusion. Hence, those results were consistent with the simulation results and confirmed the low sensitivity of the Bonthuis method. In our cortex datasets, the number of replicates was low (3-4 per cross), which caused edgeR and DESeq to be restrictive. In this context, ISoLDE was still able to make the most of the available data to highlight genes whose specific characteristics allow for positive decisions. (Anders and Huber, 2010). Because this dataset included two replicates only, we used the predefined thresholds version of ISoLDE.
ISoLDE is more robust to variations in replicate number The comparison described above suggested that Perez method displayed overconfidence when the number of replicates increased. To test how ISoLDE behaved with respect to replicate number, we tested ISoLDE on the Bonthuis datasets that had nine biological replicates for each reciprocal cross in four different tissues: ARN, DRN, muscle and liver (Figs  2  and 3 ). Surprisingly, this comparison revealed many probable false positives among the Bonthuis calls. Examples of discordant calls are shown in Figure  3C. Ddc was called 'BA' by ISoLDE and 'AI' by the Bonthuis method. Inspection of the read counts supported a strain bias; the average number of paternal reads was marginally lower than the average number of maternal reads, but the same number of samples displayed a maternal or a paternal bias.
These methods have been specifically designed to avoid variability underestimation with few replicates and rapidly increase confidence with replicate number. Consequently, their sensitivity is adequate when many replicates are available but is limited for few replicates. On the other hand, their good specificity with few replicates decreases with bigger datasets. The data-driven framework of ISoLDE seems to be more flexible and automatically adapts to the sample size. Another important benefit of ISoLDE is its capacity to provide more informative results. Biological variability often prevents drawing definitive conclusions. The expression of some genes may be neither clearly allelically imbalanced nor clearly bi-allelic. ISoLDE classifies genes into three categories: allelically biased, bi-allelically expressed and undetermined.
3. 2. 2. 3Comparison ofISoLDE and Hasin-Brumshtein method on Hasin-Brumshtein's dataset. This dataset (Hasin-Brumshtein et al. , 2014) comprised two replicate experiments of mouse adipose tissue from reciprocal crosses of C57BL/6J and DBA/2J strains. It used negative binomial modeling of data combined with GLM as implemented in the DESeq package.
Funding The authors acknowledge the financial support from  CNRS, INSERM, Universitede Montpellier  and the  Agence Nationale pour la Recherche (EpiNet ,  ANR-07-BLAN-52 ). MGX acknowledge the financial support from  France Genomique National Infrastructure , funded as part of the ' Investissement d'Avenir' program  managed by the  Agence Nationale pour la Recherche  ( ANR-10-INBS-09 ).
Laboratory regime All crabs were processed on the day of collection. The following biometric data for each crab were taken: carapace width (CW; mm); sex; moult stage [inter-moult (hard) or post-moult (soft)]; fouling (presence of epibionts); pigment loss or shell disease; limb loss; and carapace colour (green, yellow or orange/red). In addition, c. 350 μl of haemolymph was withdrawn using a 23-gauge hypodermic needle fitted with a sterile 1-ml syringe and haemolymph appearance was categorised as clear or milky. Haemolymph was fixed 1:1 with 25 µl of 5% formaldehyde (v/v) in 3% NaCl (w/v) solution and total haemocyte counts were recorded using an improved Neubauer haemocytometer under phase contrast microscopy. A further 25 µl of haemolymph was placed onto a microscope slide for primary screening using the phase contrast optics of a BX41 microscope (Olympus, Tokyo, Japan), and 100 µl was stored at -80 °C for subsequent DNA extraction.