dataesr/scientific-paragraphs-categorization huggingface dataset:
Based on these analyses, we conclude the complementarity value of the SWATH proteotype with genomic features. Exploration of the SWATH data can also reveal secondary predictors of drug response. The protein kinase inhibitor vemurafenib (VEM, NSC 761431) yielded a multivariate model where the most prominent SWATH feature was the expression level of LAMTOR3. Although the BRAF V600E mutation is a highly significant predictor of vemurafenib activity in the NCI-60  (Abaan et al. , 2013) , we speculate that LAMTOR3 may be a secondary drug response predictor, although further studies outside of the scope of the current work are necessary for validation. LAMTOR3 (MP1) is part of an endosomal scaffolding complex interacting with components of the RAF/MEK/ERK mitogenic signaling pathway. LAMTOR3 binds MEK1 and ERK1, facilitating activation of the latter protein  (Schaeffer et al.
DISCUSSION Complementarity of protein and transcript data  (Liu et al. , 2016; Mertins et al. , 2016; Zhang et al. , 2014 Zhang et al. , , 2016) )  can be expected to reveal new biological insights that are not apparent from the commonly used mutation and transcriptome profiles and which could be applied to enhance precision medicine. However, due to technical limitations, the acquisition of proteomic cohort datasets has been challenging. Here, using the NCI-60 cell line compendium, we demonstrate the ability of the PCT-SWATH proteomic technique to consistently quantify over 3,000 proteins across each of the NCI-60 cell lines measured in duplicate with a realistic turnaround. The data were acquired in approximately 30 working days on a single mass spectrometer, and for each sample measurement ca.
1 microgram of total peptide mass was consumed. This has been enabled by the pressure cycling technology, which minimizes sample consumption and the data-independent MS data acquisition using SWATH-MS  (Guo et al. , 2015). Because of their extensive omic annotation and drug databases including over 21,000 publicly accessible compounds, the NCI-60 are a unique platform for testing new technologies and exploring determinants and predictors of drug response  (Abaan et al. , 2013; Monks et al. , 2018; Rajapakse et al. , 2018; Reinhold et al. , 2012 Reinhold et al. , , 2019;; Zoppoli et al. , 2012). In addition, most of the NCI-60 cell lines are widely used for cell biology and pharmacology (MCF-7, MDA-MB231, HCT116, HCT15, HT29, HL60, CCR-CEM, and K562 to name a few). Hence, providing MS data for over 3,000 proteins and making the data available and easy to mine through CellMinerCDB will provide a unique resource for the scientific community worldwide.
, 2016; Wang et al. , 2012) , which are dysregulated in many diseases, especially cancer  (Le, 2015). Our high-quality proteomic data allowed a systematic investigation of the composition of 101 protein complexes in 60 cell lines. We expect that this represents a proof-of-principle for a generic, high-throughput approach, applicable to larger cancer cell line databases and clinical specimens  (Guo et al. , 2015) , for exploring biological networks and the association of defective protein complexes with diseases and drug responses. In addition, using CellMinerCDB (discover. nci. nih. gov/cellminercdb) casual users can test the stoichiometric expression of proteins belonging to small complexes. Several examples are provided in the manuscript including dimeric complexes (XRCC6/KU70 and XRCC5/KU80; Figures  2E  and S36 ), trimeric complexes (RPA1, RPA2 and RPA3), larger complexes such as the chromatin remodeling NuRD complex (Figures S37, S41, and S47), the b-catenin plasma membrane complexes (Figure  S43 ), and replication complexes including MCM helicases, RPAs, PCNA, RFC4, RFC2, and FEN1 (Figures S37-S39), as well as stoichiometrically coordinated RNA binding protein complexes, such FUS and the RNase A DHX9 (Figures  S35  and S40 ).
Yet, it has been challenging to identify such signatures, and the combination of proteomic, transcriptomic, and mutation data will likely be necessary to generate predictive signatures for precision medicine. Likely, a limitation at the current technical level is the number of proteins identified, and their skewing toward the higher expressed proteins. As technology improves, and a broader group of proteins is identified; it can be anticipated that the predictive utility of the protein data will increase rapidly. Effort was put into making our work publicly available and easily accessible through data submission to the NCI-60 CellMiner database and an accompanying R package, rcellminer  (Luna et al. , 2015). We expect that the analyses developed, including those based on the widely used LASSO and elastic net methods, will continue to evolve and enable future studies on additional datasets and phenotypes.
Limitations of the Study This study and the resulting dataset invite further investigation of this unique proteomic data resource. The analysis of protein complexes in the current study highlights the value of mining beyond transcriptomic data, at the functionally critical proteomic level. The proteomic data of this study were acquired by the SWATH-MS technology, a massively parallel targeting method, at an early stage in its development. Over the last years the technology has rapidly advanced in terms of the proteomic depth that it can achieve. Our observation of the common lack of correlation between mRNA and protein expression has been similarly made in tumor samples across multiple tissue types  (Kosti et al. , 2016). Understanding these differences should help drive future studies in the development of mathematical and experimental models that leverage these -omics datasets effectively.
Future investigations considering different approaches to handling the data, providing increased penetrance in the number of genes assessed, extending the functional implications of the data, and assessing perturbed proteomes in these cells will push back the current limitations of the field. Additionally, we have strived to make our work publicly available and easily accessible through data submission to the NCI-60 CellMiner databases (http://discover. nci. nih. gov/and http://discover. nci. nih. gov/cellminercdb) and an accompanying R package, rcellminer  (Luna et al. , 2015). Although the strengths of these methods over other related methods have been previously described  (Jang et al. , 2014; Papillon-Cavanagh et al. , 2013) , the resulting models still require scrutiny. The interpretation of the models developed here, and by others using our pipeline, should be guided by an understanding of the biological activities of the associated predictors in the context of the mechanisms of action and survival assays used for the input drugs.
(F) The correlation densities for protein pairs derived from the same complex are significantly shifted relative to those from random pairs across different resources. In each plot, the light blue density is for correlation values from random protein pairs. Densities associated with specific resources for protein complexes or stable protein interactions are indicated with different colors. The relatively lighter resource-colored density plot shows the distribution of correlation values for true protein interactors, whereas the darker one is derived from random pairs present in that database. The upper panel shows correlation values for the measured protein quantities, whereas the lower one corresponds to the mRNA levels of the same proteins. The vertical dashed red line indicates a value of the Spearman's coefficient of correlation of 0.
ACKNOWLEDGMENTS We thank  Margot Sunshine  who developed the early versions of CellMiner and  Jeffrey Wang  within the  CCR CellMiner team  for developing the current versions of CellMiner and CellMinerCDB and the  NCI-DTP  team ( Dr. Jerry Collins  and  Dr. James H. Doroshow ) for sharing the drug data and supporting for data acquisition from the  DTP  to the CellMiner team. We also thank  Emanuel Gonc  ¸alves for comments on the manuscript. The work was supported by the  SystemsX. chproject PhosphoNetX PPM (to R. A. ), the  Swiss National Science Foundation  (grant no. 3100A0-688 107679  to R. A. ), the  European Research Council  (grants no. ERC-2008-AdG 233226  and  ERC-20140AdG 670821  to R. A. ), the  Ruth L. Kirschstein National Research Service Award  (grant no. F32 CA192901  to A.
Supplementary Figures Supplementary Figure  1 , Related to Figure  1. Workflow for generating the NCI-60 proteome maps and predicting phenotypes. (A) Flowchart of experimental design. The NCI-60 cell pellets were divided into 12 batches, lysed and digested using the PCT method. The peptides were first analyzed in DDA mode to build a SWATH assay library. We also included DDA files from U2OS and HeLa cell digests. In total we performed 63 DDA injections either from whole cell lysate or fractionated samples. Each sample was analyzed in SWATH mode twice. The SWATH data were processed using software tools including OpenSWATH and DIA-expert in sequence. Our data were deposited in several public databases including CellMiner 2. 0. Subsequently, we developed a computational workflow to model drug responsiveness using multiple layers of molecular data.
Spectral and assay library generation All raw instrument data were centroided using Proteowizard msconvert (version 2. 0). The assay library was generated using an established protocol  (Schubert et al. , 2015). In short, the shotgun data sets were searched individually using X!Tandem  (Craig and Beavis, 2003)  (2011. 12. 01. 1) with k-score plugin  (MacLean et al. , 2006) , Myrimatch  (Tabb et al. , 2007)  (2. 1. 138), OMSSA  (Geer et al. , 2004)  (2. 1. 8) and Comet  (Eng et al. , 2013 (Eng et al. , ) (2013. 02r2). 02r2) against the reviewed UniProtKB/Swiss-Prot (2014_02) protein sequence database containing 20,270 proteins appended with 11 iRT peptides and decoy sequences. Carbamidomethyl was used as a fixed modification and oxidation as the variable modification. Maximally two missed cleavages were allowed.
Peptide mass tolerance was set to 50 ppm, fragment mass error to 0. 1 Da. The search identifications were combined and statistically scored using PeptideProphet  (Keller et al. , 2002)  and iProphet  (Shteynberg et al. , 2011)  available within the Trans-Proteomics Pipeline (TPP) toolset (version 4. 7. 0) (Keller et al. , 2005). MAYU  (Reiter et al. , 2009) (v. 1. 07 ) was used to determine the iProphet cutoff (0. 999354) corresponding to a protein FDR of 1. 03%. SpectraST was used in library generation mode with CID-QTOF settings and iRT normal-isation at import against the iRT Kit  (Escher et al. , 2012)  peptide sequences (-c_IRTirt. txt -c_IRR) and a consensus library was consecutively generated. An in-house python script, spec-trast2tsv. py31 (msproteomicstools 0. 2. 2) was then used to generate the assay library with the following settings: -l 350,2000 -s b,y -x 1,2 -o 6 -n 6 -p 0.
05 -d -e -w swath32. txt -k openswath (fragment ions between 350 and 2000 m/z, b and y ions authorized, fragment charges 1+ and 2+, 6 most intense transitions, precision of fragment ion retrieved 0. 05 Da, exact fragment ion mass calculated, exclude fragments in the swath window). The OpenSWATH tool, ConvertTSVToTraML converted the TSV file to TraML format; Open-SwathDecoyGenerator generated the decoy assays in shuffle mode and appended them to the TraML assay library. In this study, we built a SWATH assay library containing 86,209 proteotypic peptide precursors in 8,056 proteotypic SwissProt proteins. This library is supplied in PRIDE project PXD003539.
Protein identification using OpenSWATH We analyzed the SWATH data using OpenSWATH software  (Rost et al. , 2014)  using parameters as described previously  (Ori et al. , 2016). We identified 48,374 peptides from 6,556 protein groups from the NCI-60 panel with < 1% false discovery rate at both peptide and protein level evaluated by OpenSWATH  (Rost et al. , 2014) and Mayu  (Reiter et al. , 2009)  (supplied in PRIDE project PXD003539).
All pairwise comparisons were classified into two categories: either two proteins were members of the same complex or not. Average abundance, standard deviation and average Pearson correlation of each complex were calculated based on the abundance of complex members in the NCI-60 proteotypes. An extended list of protein-protein interactions (PPIs) was generated based on information acquired from 6 resources: 1) 17,556 PPIs were retrieved from the CORUM database of human protein complexes  (Ruepp et al. , 2010) ; 2) 16'345 PPIs were composed from the interaction pairs annotated as 'complex' members in the Reactome database  (Fabregat et al. , 2018) ; 3) 12,664 PPIs were retrieved from the STRING database  (Szklarczyk et al. , 2015)  considering only high confidence interactions (score ≥ 700).
4) 1'378 interaction pairs were obtained from Interactome3D  (Mosca et al. , 2013). These interactors corresponded to experimentally observed interactions with a support in the form of structural data or structural models. 5) 309 PPIs were obtained by considering interactions identified in at least 3 independent APMS experiments. For this, we included studies deposited in the BioGrid database  (Chatr-Aryamontri et al. , 2017) , interactions listed in the BioPlex portal  (Huttlin et al. , 2015) , and interactions observed in the large-scale Polycomb  (Hauri et al. , 2016)  and Kinome studies (manuscript in preparation). 6) 122 PPIs were retrieved from the EMBL-EBI complex portal  (Park et al. , 2017). The latter (smallest) set of interactions is manually curated and of high confidence. Combining information from the different databases, a list of 35,693 unique interactions (encompassing 1,766 proteins) was generated.
The Spearman coefficient of correlation of protein abundances (Spearman's r) and the associated p-value were calculated for all the 5,026,035 protein pairs that can be formed from the 3171 proteins measured in the proteomics dataset. For this, the cor. mtest function from the package corrplot was applied with the Benjamini-Hochberg correction for multiple testing. Distribution of pairwise correlation values for three different sets was visualized with the density plots. The sets represented pairs found to interact in the respective database, all background NCI60 pairs (common to all analysis) and protein pairs that were both measured by NCI60 and present in the respective database, but not reported as interacting. The mean correlation values between the datasets were compared with the Wilcoxon test in R.
, 2017) , with missing values imputed using the impute. knn function (with default parameters) of the Bioconductor impute package. Gene-level mutation profiles were available for 9,307 genes, and were obtained from CellMiner exome sequencing data, with values indicating the percent conversion to a variant form for the case of expected function-impacting alterations (frameshift, nonsense, splice-sense, missense mutations by SIFT/PolyPhen2 analysis). SWATH-MS based protein expression data from the current study was also included. Elastic net analysis was done using the glmnet R package  (Friedman et al. , 2010). The elastic net analysis was conducted using a multi-step pipeline involving cross-validations performed in a nested manner. The "outer" cross-validation is a leave-one-out cross validation that is conducted over all computational steps present in the "inner" pipeline, and it is used to validate model performance.
The appropriate k was set to the smallest value yielding a cross-validation error within one standard deviation of the minimum cross-validation error. To obtain a robust estimate of performance on unseen data, leave-one-out cross-validation was applied to the overall procedure as part of the "outer" pipeline. Specifically, drug response for each cell line was predicted using an elastic net model derived using the remaining held out data (and the steps outlined above). The vector of predicted response values was then correlated with the actual response values, with the Pearson's correlation coefficient providing an estimate of the predictive value of the applied input data combination. More details of the elastic net algorithm are provided in Supplementary Note 6. Elastic net analysis was done using the rcellminerElasticNet R package (https://bitbucket.
docker. com/r/cannin/swath/). Key dependencies installed, include: RStudio Server (https://www. rstudio. com/), rcellminer/rcellminerData  (Luna et al. , 2016) , and rcellminerElasticNet. With these installed dependencies, readers have the opportunity to 1) rerun analysis for specific drug compounds and modify the code in order to extend the analysis using RStudio Server, a web-based version of the RStudio R editor, and 2) use an R Shiny app web-based data explorer to further understand described results. Instructions on the usage of the Docker container are located at the rcellminerElasticNet project page (https://bitbucket. org/cbio_mskcc/rcellminerelasticnet).
Introduction In the era of data science, clustering various kinds of objects (documents, genes, customers) has become a key activity and many high quality packaged implementations are provided for this purpose by many popular packages such as stats (R Core Team 2013), skmeans  (Hornik, Feinerer, Kober, and Buchta 2012) , kernlab  (Karatzoglou, Smola, Hornik, and Zeileis 2004) , NbClust  (Charrad, Ghazzali, Boiteau, and Niknafs 2014) , Cluto  (Karypis 2003) , scikitlearn  (Pedregosa, Varoquaux, Gramfort, Michel, Thirion, Grisel, Blondel, Prettenhofer, Weiss, Dubourg, Vanderplas, Passos, Cournapeau, Brucher, Perrot, and Duchesnay 2011) , SciPy (scipy. cluster module)  (Jones, Oliphant, and Peterson 2001-) , nltk (nltk. cluster module)  (Bird, Klein, and Loper 2009) , Weka  (Hall, Frank, Holmes, Pfahringer, Reutemann, and Witten 2009) , etc.
While quite a large number of implementations of co-clustering algorithms 1 have been developed for gene expression data, such as biclust  (Kaiser and Leisch 2008) , bicat  (Barkow, Bleuler, Prelić, Zimmermann, and Zitzler 2006)  and bibench  (Eren et al. 2013) , in contrast, not so many implementations are available for co-clustering co-occurrence matrices such, for example, as document-term matrices used in text mining applications. The CoClust package presented in this paper therefore provides implementations of algorithms designed to efficiently handle such matrices. Depending on the method used, algorithms for co-clustering co-occurrence matrices can broadly be divided into several categories: -Spectral methods: Spectral co-clustering methods treat the input data matrix as a bipartite graph between documents and words, and approximate the normalized cut of this graph using a real relaxation.
Both models have been extended to valued networks with optional covariates on the edges. -Matrix factorization based methods: Matrix factorization based methods are also used in the clustering and co-clustering fields. However while packages exist for document clustering based on non-negative matrix factorization (e. g. , the NMF R package (Gaujoux and Seoighe 2010) which includes different NMF methods) leading to clustering (see for instance  (Ding, Li, Peng, and Park 2006; Ding and Li 2007 )), there is unfortunately no package on Non Negative Matrix Trifactorization Factorization for co-clustering. -Information theoretic based methods: Information theoretic based methods are used to co-cluster two-way contingency tables. In this approach, a joint probability distribution is first derived from the two-way contingency matrix.
2015) , they are clearly outperformed by the other cited methods when co-clustering document-term matrices in terms of accuracy. Also, as indicated above, co-clustering spectral methods are already available in scikit-learn. Probabilistic co-clustering methods deliver better accuracy, but several open-source implementations already exist in the form of the above-mentioned blockcluster and blockmodels R. 2  The CoClust package presented in this paper therefore provides scikit-learn-compatible implementations of two modularity-based methods and one information theoretic based methods. It is freely available at https://pypi. python. org/pypi/coclust. The first two algorithms, CoclustMod  (Ailem et al. 2015 (Ailem et al. , 2016) )  and CoclustSpecMod  (Labiod and Nadif 2011) , are recent representatives of the family of block-diagonal co-clustering algorithms.
Modularity-based, block-diagonal co-clustering The first family of algorithms implemented in the CoClust package consists of two algorithms (CoClustMod and CoclustSpecMod) that seek an optimal block diagonal clustering, meaning that objects and features have the same number of clusters and that, after proper permutation of the rows and columns, the algorithm produces as result a block diagonal matrix (see Figure  1 ). In the context of document-term matrices, this co-clustering model has the advantage of directly producing interpretable descriptions of the resulting document clusters. A notable block-diagonal co-clustering algorithm is the bipartite spectral graph partitioning algorithm described in  (Dhillon 2001). Inspired by previous work on spectral graph clustering, this algorithm finds the optimal minimum cut partitions in a bipartite document-term graph by computing the second left and right singular vector of the normalized documentterm matrix, thus using a real relaxation of the discrete optimization problem.
CoclustMod: Co-clustering by alternated maximization of BGM In this section we describe the theory underlying CoClustMod, one of the two block-diagonal algorithms provided by the CoClust package  (Ailem et al. 2015 (Ailem et al. , 2016)). Proposition 1: Let X be a (n × d) matrix with non-negative entries and c be a (n × d) matrix defining a block seriation, the modularity measure Q(X, c) can be rewritten as: 1. Q(X, c) = 1 x. T race[(X w -δ w ) z] = Q(X w , z) where X w = (x w ik ) 1≤i≤n 1≤k≤g and δ w = (δ w ik ) 1≤i≤n 1≤k≤g respectively defined by x w ik = d j=1 w jk x ij and δ kj = x i. x w. k x. where x w. k = d j=1 w jk x. j. 2. Q(X, c) = 1 x. T race[(X z -δ z ) w] = Q(X z , w) where X z = (x z kj ) 1≤k≤g 1≤j≤d and δ z = (δ z kj ) 1≤k≤g 1≤j≤d respectively defined by x z kj = n i=1 z ik x ij and δ z kj = x z k.
Information-theoretic co-clustering In this section we describe the notions underlying the third algorithm, CoclustInfo, provided by the CoClust package. In contrast to the previously described algorithms, CoclustInfo takes an information-theoretic approach and uses mutual information to define its criterion (Govaert  and Nadif 2013) 4. Another important difference is that this algorithm does not seek to discover a block-diagonal structure like the previously described algorithms. The requested number of row clusters can be different from the requested number of column clusters. A representative example of the kind of matrix obtained when using CoclustInfo is shown in Figure  2 ). Figure  2 : Typical matrix obtained when using CoclustInfo to co-cluster a dataset. This matrix is to be compared to the kind of block-diagonal matrix obtained when using one of the two previously described graph-based algorithms.
Running a co-clustering algorithm: the "coclust" script The coclust script can be used to run an algorithm on a data matrix. It also provides parameters for the evaluation of the results. The user has to select an algorithm which is given as a first argument to coclust. The choices are: • modularity; • specmodularity; • info. These choices correspond to the CoclustMod, CoclustSpecMod, and CoclustInfo algorithms respectively. The other options that have to be given depend on the algorithm. Some of them are however common to all of them: • those describing the input matrix; • those used for the evaluation; • some of the output and algorithm parameters. The input matrix can be given as a MATLAB file or a text file. For the MATLAB file, the key corresponding to the matrix must be given. For the text file, each line should describe an entry of a matrix with three columns: the row index, the column index and the value.