dataesr/scientific-paragraphs-categorization huggingface dataset:
Outputs for a multiple analysis To analyze more than one trajectory, the user has to put all his trajectories in a single directory (compressed into. zip file for a web interface which the program will decompress for analysis). Let's call this directory "DirName. " The results of the trajectories' analysis are saved in this directory. For such analyses, GaTewAY provides global and local results, i. e. results for the dynamics observed at each trajectory and the dynamics for the whole trajectories. Hence, we will find graphical and statistical representations like for the single analysis but with more details.
Statistical representations • Conformations occurrences. A file is generated by GaTewAY to detail the occurrences of these conformations. For each conformation, one finds the list of trajectories where it has been identified. This provides an idea about the dominant conformation. • Transitions occurrences. Many trajectories can have the same graph of transitions (the molecular system has the same behaviour). Hence, one would know only the list of the different graphs without redundancy. For each graph of transitions, the code provides the list of trajectories where it appears in an output file. This allows identifying the most frequent paths taken by the molecular system,.
Outputs for interface analysis For the interface module, GaTewAY provides the same graphical outputs as the single analysis module. In addition, it provides more statistical results. Hereafter we describe the most relevant. The illustrations used in this section are provided from the trajectory analysis of 375 snapshots (composed of an average of 48 water molecules and 144 atoms). • Distribution of the connected components. GaTewAY provides, in a schematic representation (see figure  13 ), the global distribution of the connected components according to the size. The X-axis represents the size of the connected components, and the Y-axis represents their number. The connected components are obtained by browsing the mixed graph of each conformation using graph theory algorithms. This shows the size of the network formed in the molecular system (if it is almost connected or disconnected).
Conclusion In this paper, we present a new graph-based software, GaTewAY, that we have developed to identify automatically and efficiently the conformations that occur over time in molecular dynamic simulations. Our software has been tested and validated on DFT-MD and QM-MM trajectories of gas phase molecules and clusters. We designed our algorithm so that it can be extended and take into account any types of bonds and parameters and then can be applied to any molecular system. Ongoing work aims to analyze reaction networks in clusters for catalysis. The software can detect many events of importance in catalysis. Another paper has just been submitted, which illustrates these results. From a software point of view, we are working on a new feature which aims to analyze fragments that occur along the trajectory.
This feature is of interest for CID trajectories or clusters for catalysis, where the user would like more details about the behaviour of the systems. Finally, we are working on adding a login mode for the user web interface to allow public access because now it is only available using the laboratory wired network. In addition, the user can save his results using authentification, which is not the case in the current version. The GaTewAY code will be publicly available upon the acceptance of this paper.
Copy-number variations (CNVs) are an essential component of genetic variation distributed across large parts of the human genome. CNV detection from next-generation sequencing data and artificial intelligence algorithms have progressed in recent years. However, only a few tools have taken advantage of machine-learning algorithms for CNV detection, and none propose using artificial intelligence to automatically detect probable CNV-positive samples. The most developed approach is to use a reference or normal dataset to compare with the samples of interest, and it is well known that selecting appropriate normal samples represents a challenging task that dramatically influences the precision of results in all CNV-detecting tools. With careful consideration of these issues, we propose here ifCNV, a new software based on isolation forests that creates its own reference, available in R and python with customizable parameters.
ifCNV combines artificial intelligence using two isolation forests and a comprehensive scoring method to faithfully detect CNVs among various samples. It was validated using targeted next-generation sequencing (NGS) datasets from diverse origins (capture and amplicon, germline and somatic), and it exhibits high sensitivity, specificity, and accuracy. ifCNV is a publicly available open-source software (https://github. com/SimCab-CHU/ifCNV) that allows the detection of CNVs in many clinical situations.
12 Focal copy-number increases are biomarkers predictive of responses to particular therapies; for example, patients with oncogenic ERBB2 amplification in breast cancer respond well to trastuzumab, and acquired resistance to tyrosine kinase inhibitors is exhibited in patients with MET-amplified non-small cell lung carcinomas. 13, 14 ] [17] [18] [19] [20] [21] [22] [23] [24] [25] [26] [27]  Most of these studies use the read-depth approach, relying on the hypothesis that the number of reads aligned to a genomic region is proportional to the copy number of the region. In multiple sample methods, CNVs are detected by comparing the read counts of the sample of interest to the read counts of a reference sample. The proper building of the reference is one of the main difficulties. To that end, two main solutions exist: (1) to gather a database of normal samples or (2) to add normal samples into the NGS run.
Nevertheless, both solutions come with issues, mainly the presence of a batch effect and a high cost, respectively. To avoid these problems, use of the single sample method was previously proposed, which consists of statistical modelling of the target read counts within the sample of interest to detect CNVs. Recent advances in artificial intelligence and, in particular, the availability of accessible machine-learning packages  28  have made it possible for developers to improve their algorithms in many areas. To date, only a few studies have taken advantage of these recent developments in the field of CNV detection from targeted NGS data. 15, 16, 26 th careful consideration of these issues, we present ifCNV, a novel machine-learning-based software, provided as a python and R pack-age (https://github.
com/SimCab-CHU/ifCNV and https://github. com/SimCab-CHU/ifCNV-R, respectively). This approach combines several advantages, among which is that it allows detection of CNVs without the need for a reference sample and it is low resource consuming. To validate our model and explore its limitations in clinical practice, we tested ifCNV on different synthetic datasets mimicking relevant clinical situations and on datasets obtained from amplicon-or capture-based DNA library preparation technologies.
Performance of ifCNV Detection of CNV pos samples To quantify the ability of ifCNV to detect CNV pos samples, we created a synthetic dataset of 1,500 targets and 30 samples in which we inserted one CNV pos sample. It is noteworthy that if the copy-number ratio (CNR) or the modified target ratio (MTR; i. e. , the number of altered targets, located on CNV regions, divided by the total number of targets in the panel) are low, the CNV pos samples will resemble the CNV neg samples and therefore will be difficult to detect. Thus, the performance of a CNV detector directly depends on the CNR and the MTR. Taking this fact into consideration, we iterated the CNR and MTR (from 0 to 10 and 0 to 0. 1, respectively) and performed 1,000 simulations for each iteration (Figures  2A  and 2B ). The analysis of the attribution of CNV pos samples for a CNR greater than 1 is shown in Figure  2A.
Identification of the CNV neg samples To quantify the ability of ifCNV to detect CNV neg samples, we used the same synthetic dataset, and we also iterated the CNR and the MTR (from 0 to 10 and 0 to 0. 1, respectively) and performed 1,000 simulations for each iteration (Figures  2C  and 2D ). ifCNV was able to correctly identify the CNV neg samples in 99. 87% of simulations, regardless of the CNR and MTR. For CNRs over 2, this reached 99. 9%. Interestingly, for CNRs under 1, ifCNV identified the CNV neg samples in 98. 57% of the simulations. Finally, a value of 99. 69% was obtained for CNRs under 0. 5.
Detection of one CNV pos sample in a set of several CNV neg samples Conventional hospital and research laboratories must determine the CNV status of numerous samples. The number of samples in a sequencing run can vary from a few to several dozen. We therefore assessed the ability of ifCNV to correctly find a unique CNV pos sample in a set of several negative samples. We created synthetic datasets of 2 to 100 samples in which we inserted a CNV pos sample harboring an amplification with a CNR of 5 and an MTR of 0. 03. We performed 100 simulations for each and calculated the sensitivities (Se), specificities (Sp), and accuracies (Acc) of the algorithm (Figure  2E ). For one CNV pos sample in a set of two, ifCNV failed to label any sample as positive, leading to one true negative (TN) and one false negative (FN) (Se = 0, Sp = 1, and Acc = 0.
5). Interestingly, for one CNV pos sample in three, ifCNV correctly labeled the positive sample in every simulation and found one FP in less than 50% of simulations. When increasing the number of samples in the set, the Se remained close to 1 (0. 992 from 3 to 100 samples), and the Sp and the Acc tended to 0. 9. As several CNV pos can be present in the same sequencing run, we tested the performance of ifCNV in such situations. We randomly chose 2 to 25 samples in a synthetic dataset of 50 samples. We then added random CNRs (from 2 to 6) to 3% of the targets of these samples and performed 100 iterations to determine the Se, Sp, and Acc of our algorithm (Figure  2F ). ifCNV exhibited relatively high Se, Sp, and Acc (around 0. 85, 0. 7, and 0. 75, respectively), regardless of the number of CNV pos samples in the dataset.
Detection of altered targets The second step of ifCNV consists of labeling the targets that are modified among the CNV pos samples. To assess its performance, we created a synthetic dataset of 30 samples and 300 targets. One sample was CNV pos with one modified target randomly chosen at each iteration, with a CNR from 0 to 5. We then performed 1,000 iterations and calculated the Se, Acc, positive predictive value (PPV), and Matthews correlation coefficient (MCC) (Figure  3 ). On the one hand, ifCNV exhibited a Se very close to 1 for CNRs from both 0 to 0. 3 (deletions) and 3 to 5 (amplification), meaning that the modified target was accurately labeled in almost every simulation. On the other hand, the PPV and the MCC were low ($0. 02 and $0. 1, respectively), reflecting a high number of FPs.
Thresholding on the localization score To test the ability of the score to discriminate the FPs from the TPs, we used the same synthetic dataset as before and grouped targets (from 2 to 15) together to mimic regions. We then modified all the targets corresponding to a randomly chosen region. Finally, we computed the localization score and calculated the Se, Acc, PPV, and MCC before (Figure  4A  As Figure  3  shows, before thresholding, the Se was close to 1, and the PPV and MCC were low (around 0. 1 and 0. 2, respectively). Acc was lower using the grouped targets (around 0. 4) because, by construction, the total number of regions in the dataset is lower than the number of targets, and therefore the number of TN is smaller. After thresholding, we observed that the PPV, MCC, and Acc increased to reach values very close to 1 for CNRs over 2, while the Se had only slightly decreased, meaning that the score thresholding enables the TPs to be kept while the FPs are discarded.
ICR96 dataset To compare the performance of ifCNV wtih that of other methods,  17, [23] [24] [25] 29  we compared our results with those obtained by Moreno-Cabrera et al. ,  30  who benchmarked five widely used tools with the ICR96 dataset. 31 This dataset, however, only possess one target per exon, rendering the advantage of the ifCNV score thresholding strategy not practicable in this case. As an alternative strategy, we decided to take advantage of the ability to change the contamination parameter of the IF. This main actionable parameter is the proportion of outliers in the dataset. By default, it can be set to a value between 0 and 0. 5 and to "auto," for an automatic detection of the proportion of outliers. In ifCNV, we added the ability to set this parameter to "none"; it is then calculated as 1 on the number of samples in the dataset.
TSCA dataset Next, we aimed to validate the performance of ifCNV on an in-house dataset (Table  2 ). Its particularities are (1) it is composed of tumor samples containing variable percentages of altered cells, and (2) it possesses a small number of targets per region (range: 1-40, median: 4). Using this dataset, we found that ifCNV correctly labeled 19 of the 21 amplifications present in the dataset; the 2 FNs were measured as a gain of two copies (CNR = 2) by aCGH. In addition, 14 of 17 deletions were detected with no FPs. The 3 undetected deletions were from samples that have a lower percentage of tumor cells.
Juno dataset Finally, we also assessed our tool on a distinct library preparation approach with a larger number of targets per region composed of a larger panel than the TSCA dataset (range: 1-164, median: 17) from tumor samples (Table  2 ). Interestingly, we could detect all amplifications with no FPs and 17 out of the 19 deletions, leading to an overall Acc of 0. 96 and MCC of 0. 93. Interestingly, the 2 missed deletions are on a gene that represents only 0. 4% of the panel (MTR = 0. 004).
The read-depth data obtained from targeted NGS do not follow a Gaussian distribution and can be of high dimensionality depending on the number of features of the panel and the sequencing run. Thus, the IF algorithm appears to be the most suitable to process this data. Therefore, we describe here ifCNV, a bioinformatics tool using the IF algorithm, that allows detection of CNVs without the need for a reference sample. Moreover, in routine clinical practice, the variety of pathologies involving specific molecular alterations leads to a broad diversity in the datasets generated. Thus, in general, most CNV software that is developed for a specific data type has suboptimal reliability for use in routine practice with diversified samples. In addition, most of the genetic workflows are either developed in Python or R, and, to our knowledge, no existing CNV detection tool is available in both languages.
3ifCNV is available in both languages, making it more easily to implement in pre-existing pipelines. Also, by successfully creating its own normal reference inside each analyzed NGS run, ifCNV frees itself from any batch effect inherent to tools using external references. It also avoids the need for reference samples that are copynumber neutral to be sequenced in the same batch, which is an efficient but not cost-effective solution. ifCNV also has a simple framework: it is made up of only three main steps on which the user has full control through tunable parameters. Furthermore, its efficiency makes it possible to run on hardware with limited computing resources. Using simulated data, we demonstrate that ifCNV is highly reliable and adapts to several relevant clinical situations including (1) when one CNV pos sample is present in a set of several CNV neg samples, (2) when multiple CNV pos samples are present in a set of several CNV neg samples, (3) when only one target is altered, and (4) when the CNRs are close to one, which can correspond to small alterations or to mixtures of normal and altered cells.
ifCNV also performed well using datasets generated from amplicon-or capture-based libraries prepared from germline or somatic clinical samples. Analyses of real data also demonstrated that ifCNV's performance was comparable to that of other widely used tools  30  but with substantial specific advantages. Our solution has a tuneable control of the FPR thanks to localization score thresholding and to the contamination parameter, which can both be optimized according to the dataset by an entry-level user. ifCNV was also able to accurately detect CNVs in difficult samples, such as those composed of a mixture of normal cells and tumoral cells, which dilutes the CNR of samples. Even if we demonstrated that ifCNV can process various targeted dataset, as is, it cannot be applied to whole-genome sequencing (WGS) and third-generation sequencing (TGS) datasets.
IF algorithm The IF algorithm was developed by Liu et al. 34  It "isolates" observations using a binary tree structure called an isolation tree. In this isolation tree, anomalies are more likely to be isolated closer to the root, whereas normal points are more likely to be isolated at the deeper end. The IF algorithm builds its isolation trees for a given dataset by randomly selecting a feature and then randomly selecting a split value between the maximum and minimum values of the selected feature. The number of splittings required to isolate a sample is equivalent to the path length from the root node to the terminating node. This path length is then averaged over a forest of such isolation trees to produce a decision value. The smaller the value, the more likely it is that the sample represents an anomaly.
Synthetic datasets To create synthetic datasets that reproduce faithfully those obtained in routine clinical practice, we selected 1,910 samples from in-house targeted NGS data with no CNVs. We extracted the total reads on each target from the aligned. bam files with the bedtools  36  (https:// bedtools. readthedocs. io) multicov function and created a reference reads matrix ordered with samples as columns and targets as rows. This reference reads matrix was then normalized by dividing each column by its median. All medians were used to create a median reads distribution that was needed for the rescaling process. Next, we created a normalized target reads distribution from each row of the normalized matrix. Thus, to generate synthetic datasets, we filled each line by taking a normalized target reads distribution, in which we randomly picked a value for each column.
ICR96 dataset The dataset ICR96 exon CNV validation series  31  was downloaded from the European Genome-phenome Archive (EGA) (EGA: EGAD00001003335). This dataset consists of the read counts of 96 germline samples sequenced on a targeted panel for which the copy number, at the exon level, was validated using high-resolution multiplex ligation-dependent probe amplification (MLPA) experiments (Table  3 ). In-house datasets DNA extracted from clinical somatic samples was analyzed alongside by two molecular approaches: aCGH as a reference method, and NGS using two different library preparation protocols. DNA extraction of formalin-fixed paraffin-embedded samples DNA was extracted from tissue samples using the Maxwell RSC DNA FFPE Kit (Promega, Madison, WI, USA) according to the manufacturer's recommendations.
Comparative genomic hybridization aCGH profiling was performed with the Human Agilent Sureprint G3 8 Â 60 K Microarray Kit (Agilent Technologies, Santa Clara, CA, USA). Tumor DNA was labeled with cyanine 5 (Cy5), while reference DNA from an individual of the same sex as the patient was labeled with Cy3. Sample and reference DNAs were pooled and hybridized for 24 h at 67 C on the arrays. The fluorescence was read by an Agilent SureScan Microarray scanner, and the Cy5/Cy3 fluorescence ratios were converted into log2-transformed values with Cytogenomics software (Agilent). The threshold of the absolute value of the log2 fluorescence ratio retained to define a chromosomal anomaly was 0. 25. A mean log2 ratio was calculated when, for at least three probes located on contiguous positions on the chromosome, a log2 ratio absolute value greater than 0.
25 and of the same sign was measured. The minimum size of the anomalies considered in the interpretation of the results was set at 1 Mb. The different chromosomal anomalies were defined by the Cytogenomics software according to the mean log2 ratio values, as follows: homozygous deletion for a value <À1, loss of one gene copy for a value between À0. 25 and À1, gain of one gene copy for a value between 0. 25 and 1, and amplification (gain of at least five copies) for a value >2. TruSeq Custom Amplicon library preparation assay: TSCA dataset Library preparation was performed as previously described. 37 Briefly, extracted DNA was qualified using KAPA Sybr Fast qPCR (Kapa Biosystems, Boston, MA, USA). A home-made panel targeting specific exons of 35 clinically relevant cancer genes was used for amplification of regions of interest.
For each sample, dual-strand libraries were prepared using a TruSeq Custom Amplicon protocol, as described by the manufacturer (Illumina, San Diego, CA, USA). After amplification, PCR products were purified using AMPure XP beads (Beckman Coulter, Brea, CA, USA) and quantified, normalized, and pair-end sequenced on a MiSeq instrument (2 Â 150 cycles, Illumina). This dataset is composed of 81 samples from 59 different sequencing runs, with 25 CNV pos samples (Table  1 ). Specific regions of biological significance (gene or exon) can be covered by several targets. In the event that a region is altered, all the targets in the region should be modified. By contrast, if only one target in the region is modified, it is likely to be an FP. We integrated this reasoning to develop a localization score in order to reduce the number of FPs.
After amplification, pooled harvested samples were purified using AMPure XP beads (Beckman Coulter), and a second PCR was performed to integrate the sequencing adapters. Libraries were then quantified, normalized, and pair-end sequenced on a NextSeq instrument (2 Â 150 cycles, Illumina). In this dataset, there are 43 samples with 26 CNV pos from 20 different sequencing runs (Table1).
After amplification, pooled harvested samples were purified using AMPure XP beads (Beckman Coulter), and a second PCR was performed to integrate the sequencing adapters. Libraries were then quantified, normalized, and pair-end sequenced on a NextSeq instrument (2 Â 150 cycles, Illumina). In this dataset, there are 43 samples with 26 CNV pos from 20 different sequencing runs (Table1). Advanta Solid Tumor NGS library preparation assay: Juno dataset Libraries were prepared using the Advanta Solid Tumor NGS Library Prep Assay with the automated Juno system on integrated fluidic circuits (LP 8. 8. 6 IFC) (Fluidigm, San Francisco, CA, USA) following the manufacturer's procedure. The panel is developed to allow the detection of somatic mutations in 53 oncology-relevant genes (234 kb, 1,508 assays).
g. , data likelihood), and (optionally) a recipe for how latent variables can be sampled given the data. The Module class has a generic structure consisting of three functions. First, the generative function returns the parameters of the data generating distribution, as a function of latent variables, model parameters, and observed covariates. In the case of a VAE (e. g. , scVI, totalVI, etc. ), the generative function takes samples of the latent variables as input and returns the underlying data distribution encoded according to the generative (decoder) neural network (Figure  4b ). In the case of maximum a posteriori (MAP) inference (as in Stereoscope) or EM (as in CellAssign), the generative function maps the model parameters to the data generating distribution or returns the components of the expected joint log likelihood, respectively.