dataesr/scientific-paragraphs-categorization huggingface dataset:
DNA methylations, and gene expression levels) of large panels of cell lines prior to being tested with hundreds of drugs. Those datasets have been growing ever since; for example, the latest GDSC study 5 employed 265 drugs characterized in 1001 cell lines, with 990 cell line drug responses being readily available. CCLE/CTRP and GDSC have expanded their cell line panels by more than 16-fold compared to the pioneering NCI-60 panel  [6] , enabling the study of over 30 cancer types in detail, albeit restricting their screening effort to a smaller set of clinical and pre-clinical compounds. These screening efforts describe the heterogeneous landscape of cancer and explore pharmacological responses to drug treatments, which ultimately enables Precision Oncology. The resulting pharmacogenomics databases have in turn spurred the generation of a range of computational models for drug response prediction in cancer cell lines  [7] [8] [9].
However, XGB adds gradient boosting and is known to require substantially less training time than RF on most problem instances  [24]. XGB has also been found to provide predictions almost as good as those of deep neural nets on the related problem of QSAR (Quantitative Structure-Activity Relationship) modeling, while running in less than one-third of its wall-clock time  [24]. In addition, XGB has excelled at predicting drug-target binding affinities  [25] , where it outperformed widely used regularized least-squares techniques on the studied datasets. As its constituent tree models ignore by construction irrelevant features, XGB has embedded feature selection, which makes it an algorithm suitable to analyze high-dimensional data to some extent. Due to these qualities, some aspects of the application of the off-the-shelf XGBoost algorithm have been investigated using GDSC datasets  [26].
Data Description We defined each case as a drug and cancer type pair, where cell lines can be described by four omics profiles. In terms of genomic profiles, we analyzed Single-Nucleotide Variants (SNV) and CNA data. Both were encoded as binary features, each feature indicating whether a mutation was detected in the gene or if a chromosomal region had copy-number alterations, respectively. On the other hand, gene expression intensities (RMA normalized mapped to gene) and methylation ß-values of the CpG islands were used as real-valued features. Regarding drug sensitivity data, we found 990 cell lines with 212,774 log IC50 values for 265 drugs in 30 cancer types in the GDSC (49,576 measurements did not have an associated cancer type and were subsequently discarded). Once we restricted the dataset to the cancer types with at least 45 cell lines, 3167 unique triplets of drug, cancer type, and tumor profile type remained.
Each of these triplets had between 45 and 62 fully profiled data instances, comprising 219 drugs across five cancer types (BRCA: breast invasive carcinoma, COAD/READ: colorectal adenocarcinoma/rectum adenocarcinoma, LUAD: lung adenocarcinoma, SCLC: small cell lung cancer, and SKCM: skin cutaneous melanoma). Drug response is quantified with the concentration required to reduce cell viability by half, i. e. , the IC50 value. The log IC50 of an instance is the dependent continuous variable for which regression models will be built. Further description of the data and their pre-processing is available in the Methods section. To assess the predictive power of the generated models (Figure  1 ), we split the cell lines of each case into a training set (used also for model selection via cross-validation) and an external test set.
To promote that the OMC generalizes well, we required that the minimum Spearman correlation (Rs) of all five validation folds exceeded an Rs of 0. 25 (this is a cut-off previously used to model these datasets  [5] ); if this minimum performance level is not reached, that OMC is no longer considered. Once the OMC is determined, p-values are calculated using the entire training set, and only the top k_opt features are employed to train the XGB-OMC model, which is applied to predict drug sensitivities in the test set (see Figure  1 ). To summarize, we obtain combinations of a drug, cancer type, and omics level. These datasets were each separately analyzed. If we find complexities generalizing well across all five folds, we keep Once the optimal k (k_opt) has been determined, the top k_opt features on the entire training set are identified.
25 (this is a cut-off previously used to model these datasets  [5] ); if this minimum performance level is not reached, that OMC is no longer considered. Once the OMC is determined, p-values are calculated using the entire training set, and only the top k_opt features are employed to train the XGB-OMC model, which is applied to predict drug sensitivities in the test set (see Figure  1 ). To summarize, we obtain combinations of a drug, cancer type, and omics level. These datasets were each separately analyzed. If we find complexities generalizing well across all five folds, we keep this complexity and use it to build the final model. The final model is trained on the full training set and uses only the top-k elements that best correlate with the drug's response.
To further assess whether these 77 models are truly predictive, we evaluated each selected OMC subset by performing 10 five-fold cross-validation runs on the full dataset (i. e. , merging training and test sets for the considered case), in which each run was initialized with a different random seed. We employed the full dataset of each case for this validation in order to use all instances available to that case, instead of validating partially because of using the much smaller test set. Then, we permutated (randomly shuffled) log IC50s across the same data instances and carried out 10 other five-fold cross-validations per OMC subset. In Figure  2 , we compared the performances of XGB-OMC models trained on the measured log IC50 values with those from XGB-OMC models trained using case-specific randomly permutated log IC50 values.
This y-randomization  [29]  validation shows what proportion of the model accuracy comes from the signal in the data. As specified before, we only present the best case, i. e. , the most predictive combination of drug, cancer type, and omics type. All omics types and cancer types were analyzed separately to be able to compare them. We can see a substantial difference in Figure  2 , even with just 10 cross-validation runs, between the models trained with the unaltered data (blue) and those trained using randomly permutated log IC50s (green). Comparison using a paired t-test for R s from the predictive model and their corresponding permutated variant indicated a highly significant difference between both model types (p-value < 2. 2 × 10 -16 across cases), indicating that these model predictions are extremely unlikely to be due to chance.
The cases for which XGB with all features did generate less predictive models are also of interest. From a practical perspective, it is interesting to see that for over half of the cases for which a prediction using the standard all features predictions is possible, these can just as well be described with the OMC subset, as hypothesis testing can then be performed in targeted research experiments, while avoiding the need for costly exome-or genome-wide experiments  [30]  and qualified personnel that may not be required. The cases for which XGB with all features did generate less predictive models are also of interest. In each plot in Figure  3 , these cases are shown above the dashed diagonal line. Therefore, it is important to consider using the OMC approach as a complement to traditional machine learning, as doing so will add 22 cases that were poorly predicted using the same dataset.
In contrast to omics profiles, the distribution of the 118 predictive models across cancer types is much more even, complementing their equivalent overall performance characteristics, which suggest we are not biasing toward a single cancer type. The two cancer types with the largest number of cell lines (LUAD and SCLC) are also those for which the largest number of predictive models was obtained, despite the predictive models from some cancer types coming from a similar number of analyzed cases: LUAD (864), SCLC (647), BRCA (600), COAD/READ (362), and SKCM (694). This further supports the importance of larger datasets. An ANOVA analysis performed in R could not find a statistically significant effect of the cancer type, indicating that no cancer type was better predicted than the rest. This was in stark contrast to real-valued profiles, which did indeed seem to be much more informative: 60 predictive models using gene expression data (56 best models for the case) and 32 predictive models' DNA methylation data (31 best model for the case).
However, gene expression (GEX) was characterized by high predictive performance, and it resulted in the largest number of predictive models. In panel b (right), we visualize the predictive performance of all 118 XGB-OMC and XGB-all models when grouped per cancer type. As in panel a, no cancer type is significantly better predicted than another (p-values > 0. 05, t-test). Both plots show that predictive models can be built for each omics profile and cancer type. Figure  5  compiles test R s values between predicted and observed logIC50 of the best cases from each cancer type. In the majority of cancer types, gene expression results in substantially more predictive models than the other three molecular profiles. This is consistent with the highest proportion of predictive models arising from gene expression datasets (60 of the 118 models).
On the other hand, SNV seems to be more relevant for SKCM. Although this relation between predictive omics data types and cancer type depends strongly on the drugs tested, extensive reviews have been presented describing the importance of a given data type to specific cancers, such as the role of DNA methylation in SCLC  [31]. It is noteworthy that of the 12 cases best predicted with CNA features, 7 are found in BRCA. Similarly, SCLC seems to have a proportionally larger presence of drugs that can be predicted with methylation information (13 of the 35 models), while CNA does not seem to play as much of a role as it does in BRCA. On the other hand, SNV seems to be more relevant for SKCM. Although this relation between predictive omics data types and cancer type depends strongly on the drugs tested, extensive reviews have been presented describing the importance of a given data type to specific cancers, such as the role of DNA methylation in SCLC  [31].
Enrichment analysis using KEGG annotation indicated a slight enrichment of BRCA (p = 0. 029, ID: hsa05224), thanks to the presence of APC2 (APC regulator of Wnt signaling pathway 2), a Wnt signaling pathway regulator, E2F3 (E2F transcription factor 3, also involved in Wnt/β-catenin regulation), WNT5B (Wnt family member 5), EGFR (epidermal growth factor), and a large number of fibroblast growth factors being present. The presence of several Wnt signaling-related features in the dataset and the model's performance in a cancer type in which the drug has been found to have an effect increases confidence in the polygenic complex hypothesis embodied by the XGB-OMC model. The third case is the gene expression model to predict the response of LUAD cell lines to Mitomycin, which again seems to be in a clinically relevant tissue for the drug.
Thus far, 11 clinical trials evaluate Mitomycin to treat lung cancers (clinicaltrials. gov). In fact, this drug is already indicated for certain lung cancer types (Drugbank ID: DB00305), e. g. , it has been used since 1984 for non-small cell lung cancers  [36]. Mitomycin is an antibiotic produced by amongst others the actinobacteria Streptomyces caespitosus. Our OMC subset contained 12 gene expression features: NCOR2_expr, MCM3_expr, AMOT_expr, POLR2B_expr, ARFGEF2_expr, U2AF1_expr, NDRG1_expr, MYD88_expr, NTRK2_expr, ATF1_expr, SHMT1_expr, and HNRPDL_expr. Interestingly, MYD88 (MYD88 innate immune signal transduction adaptor) works in close relation with members of the TNF (tumor necrosis factor) family  [37] , which has been frequently linked to Mitomycin and its Fas/FasL (Fas cell surface death receptor)-dependent apoptosis in cervical carcinoma  [38].
KDR (kinase insert domain receptor) associates with the known targets PDGFB (platelet-derived growth factor subunit B, 0. 559), FLT1 (vascular endothelial growth factor receptor 1, 0. 963) and VEGFR3 (vascular endothelial growth factor receptor 3, 0. 943). VHL (Von Hippel-Lindau syndrome), also in our OMC set, associates with both KDR and FLT1. Other than finding immediate connections in a protein-protein association network, we could find that a clinical trial involving Pazopanib for stage IV cutaneous melanoma was completed in 2010 and reported one patient of the 13 tested with a partial or complete response (https://clinicaltrials. gov/ct2/show/results/NCT00861913). It was also further tested in combination with paclitaxel for advanced melanoma, with the authors describing the potential as interesting  [47].
GDSC All datasets used were made publicly available by the authors of the 2016 GDSC study through their publication-specific download portal, which can be found at http://www. cancerrxgene. org/ gdsc1000/GDSC1000_WebResources/Home. html. This page provides several Excel sheets referred to in the Iorio et al. paper  [5]  as Supplementary Materials. All datasets containing cell line phenotypic data, drug response, and information regarding primary tumors used in this study were downloaded through this web portal. The pre-processed datasets were used, as the steps taken by Iorio et al. [5]  result in a well-annotated list of features with known functional role in cancer.
Drug Response (Continuous Variable) Drug responses were provided as the natural logarithm of half the maximal inhibitory concentration in a data matrix (TableS4A. xlsx, tab "TableS4A-IC50s"), which we subsequently converted to logarithms with base 10. Cell line information was then merged with the drug response and other phenotypic properties (genomics, transcriptomics, methylomics). Pre-processing steps for the four molecular profile types (SNV, CNA, gene expression, methylation) are further described below.
Copy Number Alterations (CNA, Binary Feature) CNA data for cell lines were obtained from TableS2G. xlsx (tab "TableS2G-CellLinesRACSs_CNA"). CNAs were identified by Iorio et al. [5]  through ADMIRE  [49]  analysis over 27 cancer types. Note that no CNA data were released for SCLC. From TableS2G. xlsx, we extracted all segments listed under the column header "Region identifier". These segments were annotated with the prefix cna, the cancer type, and a number (e. g. , cnaLUAD1). Contained genes are also indicated between brackets where applicable (e. g. , cnaLUAD35 (MECOM)). In total, we obtained 558 unique segments for cell lines. From TableS2F. xlsx, which contains the altered segments in primary tumors, we again filtered out everything but the contents of the "Region identifier" column. This resulted in 851 altered segments for primary tumors.
Transcriptome (GEX, Continuous Feature) RMA-normalized pre-processed expression information (sanger1018_brainarray_ensemblgene _rma. txt) was also available throughout the article download portal. However, in this case, it links out to the main GDSC portal. We used the latest dataset version of 2 March 2017. The file contains the Ensembl gene identifiers for the genes and COSMIC identifiers for the cell lines. Out of the 1018 cell lines in the file, we retained only those with a COSMIC identifier part of the 1001 cell lines presented in the original GDSC study  [5]. The list of genes we reduced from the 17,737 Ensembl gene identifiers to the list of 466 genes to represent the information also covered in SNV. To this end, we mapped the identifiers to the HGNC gene name using the mapping files provided by Ensembl BioMart [50] (Supplementary file 2).
Generation of Non-Overlapping Training and Test Sets Starting from the pre-processed data, we compiled a dataset for each cancer type with at least 45 treated cell lines annotated with response to a given drug. This modeling choice takes as reference the lowest training set size successfully used in the literature  [11] , where 28 drugs with 35 profiled cell lines were used as a training set. To be on the safe side, we only considered cases with at least 35 training cell lines. Since we also required an external test set of at least 10 cell lines, only cancer types with at least 45 cell lines in the GDSC  [3]  were analyzed. Per case (i. e. , a combination of drug, cancer type, and omics data type), an external test containing 10 cell lines was created. Since we intended to create a test set representative of the full drug response range, we ranked all log IC50 values in ascending order and placed the second most extreme values (i.
Pre-Ranking Feature Input The OMC strategy starts by first ranking the input features based on the strength of their association with the log IC50 for a particular case. For discrete inputs (SNV, CNA), this strength is calculated as the p-value of R's  [51]  wilcox. test function, as the corresponding Python implementation uses approximations. R objects were imported in Python using rpy2 (https://rpy2. bitbucket. io). p-values for continuous input were calculated using a Spearman rank-correlation test  [51].
XGBoost (XGB) We downloaded the code for XGB (https://github. com/dmlc/xgboost). We first trained and tested all models using default parameters and all features to investigate if XGB captured some of the underlying biology and performed better than random. For the actual model complexity estimation, we opted to use a more conservative learning rate (0. 05) than the default value of the implementation, a maximal tree depth of 6, and a larger number of trees (700), as described by Sheridan and co-workers  [24]. Subsampling of row (cell line) and column (feature) features per boosting iteration without replacement was set as 0. 8 for both in the OMC estimation model. The 80% denotes the fraction of features and observations that was used to build a tree. A case-specific comprehensive grid search for optimal hyperparameter values is expected to improve results further, but it is impractical as the number of cases is too large.
For many drugs, only much smaller amounts of pharmacogenomics datasets, if any, has been released using more patient-relevant in vivo models  [59] [60] [61] [62]. Consequently, often the only data that can be used to predict drug sensitivity comes from cell lines, thus having their niche in guiding precision oncology efforts  [55]. Our results indicate that XGB, combined with a strategy searching for the Optimal Model Complexity (OMC), can generate very predictive models using only concise sets of tumor features. The latter means that only a fraction of the initially considered features need to be determined in additional cell lines, which is far less resource-intensive. We further demonstrate that the predictive models are very unlikely to arise by chance (Figure  2 ). In addition, they are largely complementary to those generated by XGB using all features (Figure  3 ).
Supplementary Materials: The following are available online at http://www. mdpi. com/2218-273X/10/6/963/s1, Code supporting the conclusions of this paper and its instructions of use are available at http://ballester. marseille. inserm. fr/GDSC_XGB-OMC. zip. The instructions also specify which input data files are used and hence have to be downloaded from https://www. cancerrxgene. org/ XGB models along with their predictive accuracies and OMC feature sets are specified in Supplementary File 1, Correspondence between Ensembl identifiers and HGNC gene names as provided by Ensembl BioMart in Supplementary file 2.
We present Clustering and Lineage Inference in Single-Cell Transcriptional Analysis (CALISTA), a numerically efficient and highly scalable toolbox for an end-to-end analysis of single-cell transcriptomic profiles. CALISTA includes four essential single-cell analyses for cell differentiation studies, including single-cell clustering, reconstruction of cell lineage specification, transition gene identification, and cell pseudotime ordering, which can be applied individually or in a pipeline. In these analyses, we employ a likelihood-based approach where single-cell mRNA counts are described by a probabilistic distribution function associated with stochastic gene transcriptional bursts and random technical dropout events. We illustrate the efficacy of CALISTA using single-cell gene expression datasets from different single-cell transcriptional profiling technologies and from a few hundreds to tens of thousands of cells.
The pseudotime of a cell represents the relative position of the cell along the developmental path and is typically normalized to be between 0 and 1. By plotting the gene expression against the pseudotime of the cells along a developmental path, one obtains a dynamic trajectory of the gene expression based on which the gene regulations driving the cell fate decision-making can be inferred. Numerous algorithms are available for singlecell transcriptional data analysis for cell lineage inference and cell ordering, notably DPT  (Haghverdi et al. , 2016) , MONOCLE 2  (Trapnell et al. , 2014; Qiu et al. , 2017) , and PAGA (  (Wolf et al. , 2019) ; for a more complete list, see a recent review by  Cannoodt et al. (2016). In this work, we developed Clustering and Lineage Inference in Single Cell Transcriptional Analysis (CALISTA), a numerically efficient and highly scalable toolbox for an end-to-end analysis of single-cell transcriptomics data.
CALISTA is capable of and has been tested for analyzing datasets from major single-cell transcriptional profiling technologies, including scRT-qPCR and scRNA-sequencing with both plate-based (e. g. , SMART-seq) and droplet-based platforms (scDropseq). CALISTA enables four essential analyses of single-cell transcriptomics in stem cell differentiation studies, namely single-cell clustering, reconstruction of lineage progression, transition gene identification and cell pseudotime ordering. In existing literature, these analyses are typically carried out by stringing several task-specific tools together in a bioinformatics pipeline. But, the basic assumptions behind different tools (e. g. , regarding the distribution of data noise) maybe incompatible, an issue that has not been delved into more carefully in the literature.
, 2016) , where the single-cell clustering is carried out in two steps as illustrated in Figure  1b:  (1) independent runs of maximum likelihood clustering, and (2) consensus clustering. SABEC has a high computational requirement due to the implementation of simulated annealing in the maximum likelihood step. This requirement prohibits the application of SABEC to large scRNA datasets with >10 K cells, such as from scDrop-Seq. CALISTA offers a substantial numerical speed-up over SABEC thanks to the implementation of a greedy algorithm and the reduction in the model parameter space (see Supplementary Note S1 and Supplementary Table  S1 ). CALISTA offers a parallel computing option which enables running the analysis over multiple computing cores for further speed-up. The rest of the single-cell analyses in CALISTA represent new contributions of this work.