dataesr/scientific-paragraphs-categorization huggingface dataset:
In this work, we hypothesized that by using deep learning, we could capture the intrinsic relationship between cellular composition of tissue and its bulky gene expression profile without the need for manual feature selection or marker gene identification. To test this hypothesis, we developed a deep learning-based cell type fraction prediction model with in silico mixing training data from multiple microarray and RNA-seq (Table  S12 ), as well as scRNA-seq, datasets. We tested its performance on our testing sets comprised of nine public datasets (GSE64385, GSE65133, GSE106898, GSE107011, GSE64655, GSE127813, SDY67, GSE59654, GSE107990) and datasets from three public leaderboard rounds. We found that the algorithm shows better accuracy and stability in comparison with existing methods on our testing sets.
Training models We mixed the purified expressions of 8 cell types in random proportions, and at the same time mixed a part of the unknown cell lines (e. g. , cancer cell line). These purified expression samples came from microarray, RNA-seq, scRNA-seq. The expression of a simulated sample was calculated as , where is the number of cell types involved in mixing, , are random variables with Dirichlet distribution that determined the fractions of different cells in the in silico mixed sample, and is the expression profile of a randomly selected purified sample of cell type from the respective RNA-seq or microarray dataset. For scRNA-seq dataset, is given by , where is the number of cells of type extracted randomly from scRNA-seq datasets for mixing, and denote their expression profiles. Note that in this case were further TPM-normalized before used as training data.
In this way, the expression of heterogeneous samples in the real-world tumor microenvironment is simulated, and large number of samples can be generated as training data. In addition, we also used a previously-described  33  in silico mixing method to expand a large number of training samples through mixing RNA-seq dataset SDY67 with ground truth and scRNA-seq data. We used bagging to select 30%-100% training datasets for model training. In model training, we used cross-validation through selecting 20% training data as a validation set to evaluate the training degree of the model. We used early stopping such that training is stopped when the loss on the validation set is not reduced after 10 epochs.
sub-Challenge 2 We used a similar approach as in sub-Challenge 1 to create our prediction model. The only exception is that in our third submission, xCell  24  was used to create prediction of cell type myeloid dendritic cells as it performs better than other methods on this cell type on our testing sets. In the coarse cell type track, we made two submissions. In the first submission, we use a deep learning-based prediction model as described above on all cell types. For each cell type, we chose the best model that achieved the highest Pearson correlation results on our testing datasets. The grand mean of the Pearson correlation over all cell types was 88. 3%.
Table: Pearson correlation results of the coarse-grained cell type deconvolution track We noticed that on our testing datasets, the deep learning-based model did not outperform marker gene-based models such as MCP-counter on some cell types, namely, neutrophils, fibroblasts, and endothelial cells. We believe that these cell types have highly specific marker genes, therefore it is relatively easy to predict using marker gene-based models. Therefore, we replaced the method with MCP-counter on these three cell types to profile the abundance of the cells in the second submission. The grand mean of the Pearson correlation over all cell types increased to 92% in this submission. Table : Pearson correlation results of the fine-grained cell type deconvolution track In the fine cell type track, we made three submissions.
Conclusion/Discussion Looking forward, we believe that the following points can be further elaborated to improve the cell fraction deconvolution performance. First, we used all the gene expression data for genes available in both training and testing datasets as the input of our prediction model. However, further analysis showed that different genes contribute differently to the prediction results. Therefore, it is possible to significantly reduce the number of genes used as prediction input by identifying a small subset of genes that contribute most to the prediction results. Secondly, we found that although the deep learning-based model performs relatively well on all cell types, for some cell types, shallow models such as MCP-counter or xCell perform better. Therefore, it seems that both deep and shallow models provide complementary information to some extent, and a late fusion strategy to combine both models could potentially be used to further improve the performance.
Note that although shallow models such as MCP-counter or xCell perform better on those cell types, they do not produce absolute cell fractions as the deep learning-based model does. Hence, the late fusion strategy could potentially bring additional advantage to unify different kinds of output scores to absolute scores that can be compared across cell types and samples. Finally, as suggested in reported results,  33  it may be challenging to produce a "one-size-fits-all" prediction model that consistently performs well across datasets of different sources due to the existence of batch effects and technical variations from different experiment sites. A better solution seems to be training a "dataset-specific prediction model". Preliminary results on datasets with a small number of calibration samples with ground truth cell fractions are very promising in our experiments.
Background/Intro Usually, RNA-Seq gene expression analysis is approached by applying a plethora of scaling and normalization methods to the sample's raw counts to apply different data analysis methods on the processed estimates. Although current techniques have proven to be solid and robust, many rely on inter-sample information gathering to perform their normalization procedures such as quantile normalization or using the library size to scale the expression counts (Voom normalization). This presents great obstacles when these methods are applied to samples that share the same biological origin but are from different technical contexts. These issues represent a great obstacle not only when we want to train a model using multiple datasets from different research centers, but also when we wish to apply our prediction models to new unseen samples from different centers or as isolated cases (i.
e. , when we desire to apply the model as a product to aid medical diagnosis and decide treatment). Here we propose a normalization method to adjust count values measured on different scales into a common scale disregarding the sample counts' prior probability distribution or library size, utilizing a transformed ranking system under the assumption that the features expression order in a given sample would be unaltered under different scales and probability distributions. In particular, this approach allowed us to mix RNA-Seq experiments of purified cell lines into complex admixtures to train different machine learning models to predict cell-type proportions in new unseen cases. To test our hypothesis we trained three mainstream Machine Learning models for each cell type using the admixtures created from 23 different datasets of bulk RNA-Seq experiments of purified cell types.
Normalization/pre-processing data We believe that the most important step in our framework was the data pre-processing and normalization. To train our model 23 different sequencing datasets were manually curated and download from public repositories where purified immune, stromal, and cancer cell types from different origins and conditions were sequenced using different sequencing platforms (Genome Analyzer II, NextSeq500, Hiseq2000, 2500, 4000, Novaseq600, etc). The selection criteria to choose the different experiments were: • Human isolated stromal, immune or cancer cell types • No molecular modifications of the cells were performed on the samples • The samples represent biologically plausible conditions Once downloaded, samples were manually labeled in a unified coarse-grain or fine-grain classification.
For the coarse-grain class the subtypes "cancer", "fibroblast", "endothelial", "NK cells", "Neutrophils", "monocytic lineage", "B cells", "CD4 T cells", "CD8 T cells" and "Adipocytes" were considered. Other cell types present in the used datasets were classified as "others". For the fine-grain labels, immune cells were further categorized. "Monocytic lineage" was divided into "monocytes", "macrophages" and "myeloid dendritic cells". B, CD4, and CD8 cells were further classified into "naïve" and "memory", and the "regulatory T cell" label was also included for CD4 cells. Also, the "others" label was used. The metadata of the datasets is included in Table  S13. Since some cell types might be similar or related in their origin (for example CD4 and CD8 T cells), we used a community detection clustering approach to detect communities of samples from the same origin.
Using these two sets a Random Forest (RF) for feature selection was performed. 53 e RF algorithm consists of a collection of tree-structured regressors. In general, each tree grows with respect to a random subset of the input dataset independent and identically distributed. The strategy known as Random Input Selection (RIS) is used to generate different trees. The algorithm chooses randomly a subset S with M features from the original set of n features and seeks within S the best feature to split a node of the tree according to some purity measure. Therefore a regression tree is found with M feature subset for each subset S. The final output for a given input data point is calculated using the average of all the regression trees predicted values. The parameters considered during the tuning process of the algorithm are the number of trees (ntree) and the number of features considered on each tree (mrty).
Data collection and preprocessing The publicly available data used to generate the signature matrix were taken from the following datasets: PRJEB14751, PRJEB36933, PRJNA218851, PRJNA272556, PRJNA327180, PRJNA338944, PRJNA352224, PRJNA418779, PRJNA430418, PRJNA449980, PRJNA471906, PRJNA483877, PRJNA484735, PRJNA489270, PRJNA490870, PRJNA495625, PRJNA540256, PRJNA559359, PRJNA562113, PRJNA598222 (Table  S15 ). The samples were selected independently of the condition or tissue they were derived from and for some datasets only a subset of samples were downloaded and processed for this challenge.
Variations in 2nd and 3rd submission The variation included in the 2nd and 3rd submission do not affect the method, but only the implementation of batch effect correction and the inclusion and exclusion of various datasets. For the 2nd and 3rd submission, the outlier samples to exclude were evaluated visually through a PCA analysis and were excluded from subsequent analyses. The data were also corrected for batch effects using Combat. Fig. S3  shows that after batch effect correction of some immune cell datasets, the samples do not cluster according to dataset anymore. Regarding specific datasets that allowed us to achieve better performance, we noticed that we could improve the scores obtained on T regulatory cells only after adding the dataset PRJEB36933. Moreover, to improve the results obtained for macrophages, we excluded the samples from the datasets PRJNA483877 and PRJNA540256, and we added samples from the datasets PRJNA449980 and PRJNA559359.
IZI deconvolution method The method aims to utilize a probabilistic description of the cell-type specific transcription composition. We gathered thousands of replicates for gene expression measurements per cell type from 75,720 different samples in hundreds of publicly available GEO datasets. The replicates include single cell RNA-seq, bulk RNA-seq and bulk expression microarray data. To harmonize the datasets, we selected genes and samples such that the amount of recovered samples and genes that are shared between all samples are maximized. This resulted in a total of 21,992 harmonized RNA-expression vectors for the coarse-grained sub-Challenge and 13,317 for the fine-grained sub-Challenge. We then applied a pipeline of transformations and dimensional reduction that are both reversible and differentiable to the expression data.
The algorithm aims to make very little assumptions about the data and could potentially achieve absolute cell mass quantifications of individual samples without knowing the context of the cohort. The main limitations are the approximation of the posterior through ADVI and inaccuracies of the cell type characterization through mislabeling or lack of transcriptome samples, e. g. , we used only 42 memory CD8 T-cells. Pearson correlation is first averaged over validation dataset and then (n=1,000) over bootstraps (Methods). Results from latest submission up to second for those methods with fewer than two submissions. Black entry indicates cell type not predicted by corresponding method. Bottom two rows ("mean" and "max") are the mean and maximum correlation, respectively, for corresponding cell type across methods.
Code to generate the (unconstrained and biological) in vitro validation admixtures is in analysis/admixtures/new-admixtures/gen-admixtures-061819. R. The admixture expression data (i. e. , represented as TPM and as read counts) are in Synapse folder syn21821096 and the (ground truth) admixtures are in Synapse folder syn21820011. They are the datasets designated 'DS1', 'DS2', 'DS3', and 'DS4. ' Participants were told the cancer cell type included in each dataset (BRCA or CRC), which was BRCA for DS1 and DS3 and CRC for DS2 and DS4.
Acknowledgements : B. S. W, A. L. , X. G. , T. Y. , and J. G. were supported by an  NCI Coordinating Center for the Cancer Systems Biology Consortium and the Physical Sciences-Oncology Network   U24  grant ( CA209923 ). A. J. G. , A. M. N. ( CA209971 ) and L. M. H (CA209988) were supported by  NCI Cancer Systems Biology  Consortium  U54  grants, and J. S. R. by the  German Federal Ministry of Education and Research (BMBF, Computational Life Sciences  grant no. 031L0181B ). The experimental data were generated under an administrative supplement from the  CSBC  to A. J. G. and  J. G. S. D. , T. B. and C. P. were supported by  National Institutes of Health , grant no. R01GM122085. F. P and A. d. R were supported by the  Programme Cartes d'Identité des Tumeurs (CIT)  from the  Ligue Nationale Contre le Cancer.
Data and Code Availability: The data discussed in this publication have been deposited in NCBI's Gene Expression Omnibus  39  and are accessible through GEO Series accession number GSE199324 (https://www. ncbi. nlm. nih. gov/geo/query/acc. cgi?acc=GSE199324). Additionally, they are hosted on the Synapse data-sharing platform at syn21557721 and syn21571479, respectively. Round one code implementations are available for Aginome-XMU (https://github. com/xmuyulab/DCTD_Team_Aginome-XMU), DA_505 (https://github. com/martinguerrero89/Dream_Deconv_Challenge_Team_DA505), mitten_TDC19 (https://github. com/sdomanskyi/mitten_TDC19), and Biogem (https://github. com/giannimonaco/DREAMChallenge_Deconvolution).
Taking these observations into consideration, we propose a new transformation method to normalize sample counts across datasets of different origins; Where is the count matrix of a given sample with dimensions , with being the number of features in the count matrix. With this method, we only take into account the ranks of the expressed features using the 'max' form to account for ties in the rankings. Once samples were collected and normalized, a sample selection procedure was performed to discard possible low quality or mislabeled purified samples. Briefly, a penalized multinomial classifier was trained with the complete dataset. Then, we selected the non-zero coefficients of the model and used this subset of features to calculate Pearson's distance between samples.
Background/Intro The key idea of the method was to focus on identifying new markers that are predictive of the relative cell type fractions. This was achieved by first identifying bulk and single-cell datasets of pure cell populations. For single-cell data, we also used non-pure populations and extracted pure populations using previously-published methods. 34, 35 We then used a Monte Carlo procedure to create random mixtures and identified genes that correlated better with the generated fractions. This took into account the overall distribution of each candidate marker in correlating with the cell fraction.
Methods Fourteen pure cell type RNA-seq datasets were collected from GEO (Table  S14 ). Only control samples, i. e. , samples from healthy human donors without treatment, were retained, except for two datasets including samples with breast and colon cancer. Gene expression values were converted to TPM if not in TPM originally. To convert Raw data to TPM, gene length information was obtained from Gencode v31. We used the above datasets of pure-cell types to create training datasets where are genes and are cell types. We then constructed samples of randomly mixed pure cell type populations, where is the in silico sample index with fractions. The random fractions were drawn from a non-uniform distribution enriched for smaller fractions. Using the ensemble, we calculated the Pearson correlation coefficient between the concentration of cell type (i.
Background/Intro The approach used in this challenge is based on work that was previously published. 36 The feature selection of the approach is done through differential expression and filtering for specific and low-noisy genes. The deconvolution method is robust linear model, which is robust to collinearity and outliers. The signature matrix was normalized by mRNA abundance to take into account the different mRNA yields of the various immune cell types. The main variation in respect to the published paper  36  is the utilization of a large harmonized dataset for the generation of the signature matrix. The benefit of this approach over other.
36,49astq files were downloaded from the ENA archive and kallisto (version 0. 46. 2)36,49was used to obtain gene expression data. The pseudo-alignment was done against the GENCODE transcriptome (release 33, genome assembly GRCh38). PRJNA540256 Cell type Macrophages Condition from Platform Illumina HiSeq Submissions 1, 2 endometrium 4000 PRJEB14751 Endothelial cells HUVEC cell line Illumina HiSeq 1, 2, 3 PRJNA559359 Macrophages healthy 2500 Illumina HiSeq 3 2500 PRJEB36933 Tregs healthy donors Illumina HiSeq 2, 3 PRJNA562113 CRC primary and 4000 Illumina HiSeq 1, 2, 3 metastatic tumor 2500 PRJNA218851 CRC primary tumor Illumina HiSeq 1, 2, 3 PRJNA598222 BRCA primary tumor 2000 Illumina HiSeq 1, 2, 3 2000 PRJNA272556 CRC cell lines Illumina HiSeq 1, 2, 3 1000 PRJNA327180 Fibroblasts cell lines Illumina HiSeq 1, 2, 3 2000 PRJNA338944 Fibroblasts human cancer Illumina HiSeq 1, 2, 3 2500 PRJNA352224 mDCs healthy Illumina HiSeq 2, 3 stimulated and 2000 non PRJNA418779 various immune healthy Illumina HiSeq 1, 2, 3 cells 2000 PRJNA430418 Endothelial cells stimulated and Illumina HiSeq 1, 2, 3 non 2500 PRJNA449980 Macrophages healthy Illumina HiSeq 3 simulated and 2500 non PRJNA471906 Endothelial cells healthy Illumina HiSeq 1, 2, 3 2500 PRJNA483877 Monocytes breast cancer Illumina HiSeq 1, 2 2500 PRJNA484735 Various immune healthy Illumina 1, 2, 3 cells stimulated and NovaSeq 6000 non PRJNA489270 BRCA cell lines Illumina HiSeq 1, 2, 3 2000 PRJNA490870 CRC primary tumor Illumina HiSeq 1, 2, 3 4000 PRJNA495625 BRCA cell lines Illumina HiSeq 1, 2, 3 2000.
Effective population size (N e ) is a pivotal evolutionary parameter with crucial implications in conservation practice and policy. Genetic methods to estimate N e have been preferred over demographic methods because they rely on genetic data rather than time-consuming ecological monitoring. Methods based on linkage disequilibrium, in particular, have become popular in conservation as they require a single sampling and provide estimates that refer to recent generations. A recently developed software based on linkage disequilibrium, GONE, looks particularly promising to estimate contemporary and recent-historical N e (up to 200 generations in the past). Genomic datasets from non-model species, especially plants, may present some constraints to the use of GONE, as linkage maps and reference genomes are seldom available, and SNPs genotyping is usually based on reduced-representation methods.
In this study, we use empirical datasets from four plant species to explore the limitations of plant genomic datasets when estimating N e using the algorithm implemented in GONE, in addition to exploring some typical biological limitations that may affect N e estimation using the linkage disequilibrium method, such as the occurrence of population structure. We show how accuracy and precision of N e estimates potentially change with the following factors: occurrence of missing data, limited number of SNPs/individuals sampled, and lack of information about the location of SNPs on chromosomes, with the latter producing a significant bias, previously unexplored with empirical data.
However, as other methods to estimate N e , the LD method is not devoid of biases and drawbacks, mostly relating to the assumption that the population is isolated, which is rarely satisfied  (Hill 1981; England et al. 2010; Waples and England 2011) , and to the occurrence of agestructure in populations  (Nunney 1991; Yonezawa 1997; Waples and Do 2010; Robinson and Moyer 2013; Waples et al. 2014; Hössjer et al. 2016; Ryman et al. 2019). In this study, we aimed to explore the limitations of plant genomic datasets when estimating contemporary N e. We mostly focused on estimating N e using the software GONE  (Santiago et al. 2020 ), but we also provide N e estimates obtained in NeEstimator  (Do et al. 2014). These programmes provide recent historical and contemporary N e estimates, respectively, using the LD method, though they differ mostly in the data requirement and timescales of estimates provided.
In particular, we explored the technical requirements of the software GONE by conducting power analyses aimed at testing how the number of SNPs, the proportion of missing data, the number of individuals, the lack of information about the location of SNPs on chromosomes, and the occurrence of population structure could affect N e estimation. The N e estimates obtained in GONE were then compared to the ones obtained in NeEstimator and discussed in light of the biological and ecological features of the species. Our findings help better understand the limitations and potentialities of genomic datasets when estimating LD-based, one-sample N e , providing new insights on how to use current methods.
Datasets We selected four datasets obtained with different high-throughput sequencing techniques from different plant taxa (Symphonia globulifera L. f. (Clusiaceae), Mercurialis annua L. (Euphorbiaceae), Fagus sylvatica L. (Fagaceae), Prunus armeniaca L. (Rosaceae)), to represent different botanical groups, ecosystems, generation times and reproductive strategies. Sampling strategies in the datasets encompassed different sample sizes for markers and individuals, and datasets featured distinct levels of population genetic structure (Table  1 ). For boarwood, S. globulifera s. l. , a widespread and predominantly outcrossing evergreen tree typical of mature rainforests in Africa and the Neotropics  (Degen et al. 2004; Torroba-Balmori et al. 2017) , we used the targeted sequence capture dataset described in Schmitt et al.