dataesr/scientific-paragraphs-categorization huggingface dataset:
Individual visits We identify systematics using a visit-specific white light analysis, integrating the entire spectrum to create each individual data point. This assumes that the instrumental systematics behave similarly over the integrated range  (Kreidberg et al. 2014). We fit the raw light curves (F λ (t)) with a parametric transit and systematics models similar to  Cubillos et al. (2023)  as a function of time (t), CU T E orbital phase (ϕ), and jitter vector (j i ): F λ (t) = T λ (t) S λ (t, ϕ, j i ), (1) where T λ (t) is a  Mandel & Agol (2002)  transit model and S λ (t, ϕ, j i ) is a model of the CU T E instrumental systematics similar to the scheme used in  Cubillos et al. (2023). We obtain the best-fitting parameters and uncertainties from a Levenberg-Marquardt optimization and a Markov-chain Monte Carlo (MCMC) sampling, respectively, employing the open-source mc3 package 2   (Cubillos et al.
2017) , which implements the Snooker Differential-evolution MCMC algorithm of ter  Braak & Vrugt (2008). This enables us to arrive at statistically robust parameter estimations. Our transit model fits the input data using the planet orbital parameters, planet-star radius ratios R p /R s (λ), outof-transit stellar fluxes F s (λ), and limb-darkening coefficients. We used the planet orbital parameters computed by  Lendl et al. (2020)  using CHEOPS observations. These parameters, except for mid-transit time, R p /R s (λ), and F s (λ), were kept fixed during the fitting and MCMC process. The mid-transit time is fitted by applying a Gaussian prior according to the measured value from  Lendl et al. (2020). Uncertainties are propagated according to the epoch of the CU T E observations. The limb-darkening parameters were computed based on the stellar properties using the open-source code of  Espinoza & Jordán (2015).
Acknowledgments: CU T E is supported by  NASA  grant  NNX17AI84G  and  80NSSC21K1667  ( PI -K. France) to the  University of Colorado Boulder. This project was funded in part by the  Austrian Science Fund (FWF )  J4595-N  and  J4596-N. L. F. acknowledge financial support from the  Austrian Forschungsförderungsgesellschaft FFG  project  CARNIVALS P885348. AV acknowledges funding from the  European Research Council (ERC)  under the  European Union 's  Horizon 2020 research and innovation programme  (grant agreement No  817540 ,  ASTROFLOW ). This research has made use of  NASA 's Astrophysics Data  System Bibliographic Services.
Methods Strains, their whole genome sequences, and phylogenetic tree construction The main collection of S. sonnei genomes (n = 164 isolates, Table  1 ) used in this study originated from the archive of isolates from the national reference laboratory of UK Health Security Agency collected from 2008 to 2014 and has been described before. 16  Sequencing data for all isolates is deposited in the European Nucleotide Archive and individual accession numbers are provided in Supplementary Table  S1. An additional isolate, CIP 106374, which was also previously described was included in the bioinformatic analyses and sequenced as mentioned in Supplementary appendix along with the full details of phylogenetic tree construction and genotyping. In addition to S. sonnei, several E. coli strains were used in this study including the laboratory strain MG1655, and three diverse isolates received at the National Reference Laboratory during 2023: including a commensal ST10 (O15:H16), an ST6199 (O1:H7), and a pathogenic ST131 (O25:H4) (Table  2 ).
Colicin database construction and detection To investigate the presence or absence of specific colicins within the isolates, a large quantity of colicin sequences were collated. Over 10,000 colicins from over 50 species of bacteria were collated from the European Nucleotide Archive as well as including some isolates from previously published sources. 53  A multi-FASTA file containing the collated colicin sequences was utilised to generate a custom database via the prepareref command of ARIBA v2. 14. 6 where prepareref removes erroneous data and runs cd-hit to cluster the sequences based on a user-defined similarity threshold (90% in our case). ARIBA v2. 14. 6 was then run with the FASTQ files of all isolates, a percentage sequence ID cut-off of 95% and the colicin database to report which sequences were observed in each isolate.
Exploring the presence of the T6SS The 18 components of the full T6SS were extracted from the annotated Nanopore sequence of S. sonnei CIP 106347 and compiled into a singular multi-FASTA file. SRST2 v0. 2. 0 54 was then utilised for the purpose of T6SS component gene detection as a custom database. The T6SS components within the multi-FASTA were not pre-clustered and therefore were assigned to gene clusters based on 90% nucleotide similarity via CD-HIT v4. 8. 1. 55  Python scripts provided as part of the SRST2 package were utilised to parse the clusters and generate a SRST2 compatible database. SRST2 gene detection was then run utilising the custom T6SS database.
Specifically, we used cell sorting to measure the proportion (%) of green-fluorescent E. coli remaining after overnight competition with S. sonnei in a 96-well format. As a cell sorter is a specialist equipment, we also correlated these results with a binary read out of green fluorescence following a simple plate-reader based growth assay which is more broadly available. Introducing a threshold of positivity (20%) among the cell sorting results of these assays led to 99% concordance between the two approaches (Fig. 1 , Supplementary Table  S1 , see methods). We found that most 82% (131/159) of S. sonnei displayed E. coli killing and that phenotype clustered with genotype (Fig. 1 ). The continuous data (from cell sorting) revealed a spectrum of difference in the phenotype with the proportion of E.
Various colicins are widely distributed in S. sonnei To determine the distribution of colicins in S. sonnei we screened the genomes against a custom database of >10,000 colicin sequences that grouped into 145 clusters (methods). Although this revealed that widespread distribution of colicins (all isolates contained ≥1 colicin), no single colicin or cluster showed perfect concordance with the presence of the killing phenotype across S. sonnei (Supplementary Table  S1 , Fig. 1 ), suggesting no single protein was responsible for the phenotype. To determine which colicin clusters were most likely responsible for the killing phenotype, we determined the association of colicin clusters with the binary E. coli killing phenotype and ordered cluster inclusion based on statistical support for the association with the phenotype (i.
sonnei killed E. coli MG1655 while those from the two non-killing isolates did not (Supplementary Figure  S2 ). Mass spectrometry on filtered supernatants (details in Supplementary Appendix) from all isolates (100%, 15 of 15) that displayed E. coli killing contained at least one contributing colicin in the total determined by plate reading is depicted in the colour strip closest to the tree followed by S. sonnei genotype, assignment to co-circulating MSM clades, and key colicins clusters associated with the killing phenotype. Individual colicins are shown by different colours labelled, by their European Nucleotide Archive accession numbers) and are grouped (and in columns) by arbitrarily named genetic clusters generated by the ARIBA package (see methods and Supplementary Table  S2 ). peptides identified in the samples and, for 87% (13 of 15) of isolates, the proteins matched to a unique peptide sequence of an individual colicin (Supplementary Table  S3 ).
Mass spectrometry also confirmed that, the supernatants from the two isolates that did not display E. coli killing did not yield any matches to these colicin sequences further confirming the role of colicins in E. coli killing (Supplementary Table  S3 ). To interpret our varied killing results across E. coli strains we also screened these strains against the colicin database. The screened genomes of the E. coli challenge strains revealed that while the MG1655 and ST10 strains both contained only two (and identical) colicin and colicin related proteins, the ST6199 and ST131 strains contained an additional five proteins each (Supplementary Table  S6 , see below and methods).
E. coli killing in vitro in S. sonnei is not mediated by T6SS Our results highlight the critical role of colicins in mediating in vitro E. coli killing in S. sonnei. As a previous study described a functional T6SS in S. sonnei clinical isolate CIP 106347 that was hypothesised to contribute to competition with E. coli and S. flexneri, we also explored the possible role of the putative T6SS in E. coli killing. 24  To do this we re-sequenced CIP106347 and extracted the gene sequences for predicted proteins in the region of the T6SS system. Predicted proteins were used as the reconstituted locus was inconsistent with that originally described  24  and contained multiple interruptions that would render the apparatus nonfunctional (Supplementary Figure  S4 ). The assembly is available in GenBank under accession number CP109775.
Acknowledgements P. M. D. S and K. S. B. are supported by a  Biotechnology and Biological Sciences Research Council  project grant  BB/V009184/1. R. J. B is funded by a  Biotechnology and   Biological Sciences Research Council Doctoral Training Partnership studentship   BB/M011186/1. K. S. B was also supported by a  Wellcome Trust   Clinical Research Career Development Award   106690/A/14/Z  and drew on funding from  Medical Research Council   MR/R020787/1. K. S. B is affiliated to the  National Institute for Health Research Health Protection Research Unit (NIHR-HPRU) in Gastrointestinal Infections at University of Liverpool  in partnership with the  United Kingdom Health Security Agency (UKHSA) , in collaboration with  University of Warwick. The views expressed are those of authors and not necessarily those of the  NHS ,  NIHR ,  Department of Health and Social Care or UKHSA.
Authors acknowledge the staff and facilities of  GeneMill foundry ,  Shared Research Facilities, University of Liverpool  for their help in setting up and the use of cell sorting facilities. The mass spectrometry instrumentation was funded by the  LABEX :  ANR-10-LABX-0036 NETRNA , a funding from the state managed by the  French National Research Agency  as part of the investments for the future program. Authors would like to thank  Michael Bottery  ( University of Manchester ) for his kind gift of E. coli MG1655 strain and  Serge Mostowy  ( London School of Hygiene and Tropical Medicine ) for kindly providing the GFP reporter plasmid used in this study.
Cell-type deconvolution methods aim to infer cell-type composition and the cell abundances from bulk transcriptomic data. The proliferation of currently developed methods, coupled with the inconsistent results obtained in many cases, highlights the pressing need for guidance in the selection of appropriate methods. Previous proposed tests have primarily been focused on simulated data and have seen limited application to actual datasets. The growing accessibility of systematic single-cell RNA sequencing datasets, often accompanied by bulk RNA sequencing from related or matched samples, makes it possible to benchmark the existing deconvolution methods more objectively. Here, we propose a comprehensive assessment of 29 available deconvolution methods, leveraging single-cell RNAsequencing data from different tissues.
We offer a new comprehensive framework to evaluate deconvolution across a wide range of simulation scenarios and we show that single-cell regression-based deconvolution methods perform well but their performance is highly dependent on the reference selection and the tissue type. We validate deconvolution results on a gold standard bulk PBMC dataset with well known cell-type proportions and suggest a novel methodology for consensus prediction of cell-type proportions for cases when ground truth is not available. Our study also explores the signi cant impact of various batch effects on deconvolution, including those associated with sample, study, and technology, which have been previously overlooked. The evaluation of cell-type prediction methods is provided in a modularised pipeline for reproducibility (https://github.
Furthermore, conventional methods, such as ow cytometry and immunohistochemistry which can quantify cell abundances, rely on prior knowledge of cell markers, depend on interpretation bias and are not easily scalable  6, 7. Additionally, there are only a handful of these types of datasets available, mostly from non-solid tissues such as PBMCs  8, 9. Consequently, cell-type deconvolution methods show a great advantage, since cell proportions from hundreds of samples can be quanti ed computationally in minutes. Deconvolution methods aim to derive cell type compositions and cell abundances from bulk RNA-seq data using large databases such as GTEx  10  , TCGA  11  , TARGET (https://ocg. cancer. gov/programs/target) and GEO  12. Speci cally, cell-type deconvolution can assist in the study interaction between cell abundances and various disease phenotypes or clinical features, coupled with expression data within large databases  13.
CIBERSORT  16  , FARDEEP  18  , NNLS (nonnegative least squares) and EpiDISH  19  are some examples of commonly used bulk deconvolution methods. Contemporaneous deconvolution methods leverage available single-cell datasets to extract celltype speci c features in order to perform deconvolution. Bseq-sc  17  , DWLS  20  and MuSiC  21  and others  22- 24  utilise single-cell data to deconvolve bulk RNA-seq samples. Unlike the supervised methods, unsupervised methods like deconf  25  and CDSeq 26 only require a bulk sample to be deconvolved and the number of components (number of cell types to be found) as an input. Finally, semi-supervised methods such as ssKL and ssFrobenius, simultaneously estimate the cell-speci c matrix and cell proportions, using marker information and a block-descent method that estimates both matrices  27.
Even though deconvolution remains a very promising and cost-effective approach for estimating cell-type abundances in a tissue, there are still many challenges to overcome. A plethora of methods have been developed, yet there are no clear guidelines on method selection and a systematic large-scale benchmark across tissues is missing. There are additional components such as the pre-processing of the input matrices, the selection of reference and the batch effects between data inputs that complicate deconvolution  22. However, it remains uncertain whether the proportions of closely related cell types or cell states can be accurately resolved 26. Previous benchmarking studies have evaluated the performance of deconvolution methods on a small number of datasets and on limited organs and tissues, with a focus on data from pancreatic islets and blood tissue  [28] [29] [30].
Moreover, there is a dearth of formal guidelines for the generation of suitable simulation data to drive new method development. Μost benchmarks have focused on the technical aspects of deconvolution and lack biological interpretation of real tissue data  29. Here, we have designed a curated, large-scale, detailed assessment of 29 available deconvolution methods by utilising sc-RNA-sequencing datasets from both human and mouse tissues. We rst suggest a framework for simulating realistic bulk samples from single-cell methodologies, suitable for evaluating diverse methods. Next, we provide guidelines on the use of normalisation and transformation to reduce the effect of library size and the differences in expression between bulk and single-cell references respectively. Furthermore, we study the impact of batch effects in deconvolution and provide suggestions for the optimal selection of references.
Computational framework for large-scale analysis of deconvolution (CATD) We developed a computational framework that enables us to systematically investigate various factors that in uence the deconvolution process, in order to provide clear guidelines for the decomposition of bulk tissue expression data. (Fig. 1 ). To benchmark the 29 available deconvolution methods, we collected scRNA-sequencing published datasets from different human and mouse organs (Table  S1 ). In our pipeline, each scRNA-seq dataset is split into a training dataset, used as a "reference input" for deconvolution, and a testing dataset that was composed of pseudo-bulk mixtures to be deconvolved (Methods). The use of pseudo-bulk mixtures allows us to obtain ground truth against which we can evaluate the deconvolution methods.
The collected scRNA-seq experiments were generated by numerous protocols including Smart-Seq2 31 , 10X  32  , CEL-seq  33  , Seq-well  34  , Drop-Seq  35  , inDrop 36 and originate from different organs including brain, pancreas, lung, blood(PBMC) and placenta. Moreover, datasets of different granularity were included in the benchmark to account for the effect of cell-type numbers in deconvolution (Table  S1 ). As a result, the generated pseudo-bulks encompass a wide range of real data scenarios. Additionally, different levels of noise were added to the pseudo-bulks to mimic realistic bulk samples. In addition, 11 different normalisation and 2 transformation methods (Methods, TableS2) were tested on both pseudo-bulk and reference matrices to select the most suitable combination of preprocessing to be performed prior to deconvolution.
After the preprocessing step, we evaluated the performance of the 29 collected deconvolution methods (Table  S3 ). To compare the performance of deconvolution methods we implemented various evaluation metrics in the CATD pipeline. Pearson correlation coe cient (r/pcor), root mean square error (RMSE) and average cosine similarity (avgcos) are the ones used across this study to evaluate the performance of the methods(Methods). We select the best performing methods taking into consideration the methods that have both low RMSE and high R values consistently across different simulation scenarios, datasets and tissues. All the steps of the benchmarking have been modularised in a snakemake pipeline for the reproducibility of the results (https://github. com/Functional-Genomics/CATD_snakemake). The pipeline also allows the evaluation of the scalability of each benchmarking module using additional metrics (Methods).
We considered two different approaches in this study. In the rst approach, pseudo-bulks are generated by randomly sampling cells from three pool sizes (p1 = 100, p2 = 10K, p3 = 100K) from a single-cell dataset and generating 1,000 samples. In the second approach, we generate an array of pseudo-bulks by setting the following parameters: the number of cell types sampled (nCT) from single-cell, the cell proportion assignment method (variance of proportions) along with the pool size (p) and the number of samples (s) (Methods, Fig. 2a ). We generated 5 pseudo-bulk datasets based on a 10X single-cell-RNA-seq from the mouse brain  41  (Table  S1 , Supplementary Fig. 1a-b ) and compared them with real bulk samples derived from the same study (Fig. 2b ). Real bulk and generated pseudo-bulk samples exhibit a negative binomial distribution, as expected, for expression data  42  however, random sampling pseudo-bulk deviates the most from real bulk, with less overdispersion observed (Fig.
The selection of pre-processing methods yields varied deconvolution results Previous studies have reported that pre-processing of input data in deconvolution impacts the deconvolution performance  22, 28  , but it is yet unclear which methods should be used for each deconvolution method selected. Standard preprocessing of bulk and single-cell data includes the transformation and normalisation of the expression matrices. Transformation methods account for the mean-variance dependencies as well as the extreme count values present in a dataset. This is a crucial pre-processing step for most downstream analyses of both single-cell and bulk RNA-seq data. For instance, both the identi cation of DEGs and PCA analysis requires the transformation of raw count values. The same principle applies to single-cell data for performing dimensionality reduction and identi cation of marker genes.
Lastly, we tested 11 different normalisation methods across 3 different brain datasets that have been produced from different single-cell technologies (Hochgerner1 and Hochegerner 3 = 10X, Darmanis = SS2). In all three datasets, row normalisation seems to perform worse across all the deconvolution methods (lower Pearson Correlation values), while the other normalisation methods appear to perform differently depending on the dataset. The majority of normalisation methods yield good results for the rst dataset (Darmani2017). On the other hand, for the other two 10X datasets, none, global_min_max, column-zscore and mean normalisation approaches appear to yield better results. (Fig. 3d-f ).
5% of the variance between the bulk and the singlecell (Supplementary Fig. 4a, b ). First, we generated pseudo-bulk pro les and single-cell references by randomly splitting the same dataset in half and then performing deconvolution afterwards. This task is a very simple case of deconvolution since both the bulk and the reference contain the same gene expression information. We name this 'self-reference deconvolution'. While self-reference gives the opportunity to study deconvolution with simulations, it oversimpli es the problem. For this reason, we introduced cross-sample deconvolution to more systematically study the impact of batch effects in celltype deconvolution. For this task, we split the same pancreatic single-cell dataset in half and we selected cells from two individuals to generate the reference while the other two were used to build the bulk pro les.
In this way, we arti cially introduced batch effects in cellular decomposition. Deconvolution results from one "bulk" and one "single-cell" algorithm (FARDEEP, DWLS) show a signi cant drop in deconvolution performance in the cross-sample task compared to the self-reference task (Fig. 5a , Supplementary Fig. 5a ). These results suggest that cell-type deconvolution is severely affected by batch effects caused by expression differences across samples. Furthermore, since the deconvolution of real bulk samples typically entails expression data from completely different studies and the use of different technologies, we next studied how cross-studies (same tissue) and cross-technology tasks can impact the performance of deconvolution algorithms. For the cross-study deconvolution, we selected two human pancreatic single-cell datasets  17, 50  (TableS1) that were generated from two different laboratories.
We observed that deconvolution across studies can impact deconvolution performance severely when compared to self-reference tasks (Fig. 4b , Supplementary Fig. 5b ). Next, we compared the performance of deconvolution when datasets from different technologies are involved. For this task, we focused on 3 independent dataset pairs from the placenta, brain and lung tissue (Supplementary Fig. 6 , Supplementary Fig. 7 ). Each dataset pair was generated from the same laboratory but with two different single-cell technologies (10X, Smart-Seq2). Given that 10X and Smart-Seq2 are the most common single-cell protocols available, this test will help reveal which references are suitable in deconvolution. Results show that self-reference deconvolution with 10X sc-RNASeq data yields better results overall (higher Pearson correlation, lower RMSE values Fig.