dataesr/scientific-paragraphs-categorization huggingface dataset:
Consequently, DE analysis produces even more erroneous results with more genes being mis-characterized from up-regulation to down-regulation while the rest having much reduced fold changes, as demonstrated in the DE result. Besides DE analysis, Gene Set Enrichment Analyses (GSEA) is also sensitive to total mRNA differences between cell types and states. For instance, when analyzing the "defense response to virus" gene set of an in-house naïve-and-activated CD4+ T cell scRNA-seq dataset, we found that the GSEA result is highly dependent on the normalization scheme. Such dependency is illustrated in Figure  1B : when the total gene mRNA counts of both cell types are normalized to the same level, not only half of the genes within the gene set are designated as down-regulated in activated T cells, the entire gene set is given a negative enrichment score with an inconclusive running-sum statistic curve (containing notable net-positive and net-negative segments at once).
However, without the precise knowledge about the total mRNA differences between naïve and activated T cells, it remains a question as which result is the closest to the truth. Total mRNA differences are present in many scRNA-seq datasets. Figure  1C  portraits the captured total mRNA count distributions of different cell types from three public scRNA-seq datasets, including a PBMC dataset [17], a B cell dataset  [18]  and a hepatocellular carcinoma (HCC) dataset  [19]. In the PBMC dataset, cell types are annotated and curated via surface markers and are considered of high annotation accuracy. In the B cell and the HCC datasets, cell types are annotated with reference marker genes. In all three examples, it is evident that cell types exhibit differences in total mRNA molecule counts (manifested as UMI counts in the plot)reaching to multiple folds.
Occasionally, two or more cells are enclosed into a single droplet, forming doublets (or multiplets if there are more than two cells). When the two cells are of different types, the doublet becomes a heterotypic doublet. The percentage of doublets in a dataset grows super-linearly as the number of input cells increases and is expected to be a common element in future scRNA-seq experiments as the scRNA-seq assay size increases. Through in silico deconvolution of the mRNA molecules in a heterotypic doublet, it is plausible to estimate the total mRNA ratios between the two member cell types for subsequent total-mRNA bias corrections in DE analysis. In silico deconvolution of heterotypic doublets is complicated by the fact that all mRNA molecules, regardless of their origins, share the same molecular identifiers (also called cell barcodes) and cannot be easily partitioned by cell types.
After total-mRNA bias correction by TOMAS, however, all converted to positive enrichment in activated T cells with typical running-sum statistic curves, which are in accordance with our anticipation. Finally, our work challenges the common perception that doublets are despicable technical noise in scRNA-seq and should be avoided even at the cost of limiting the experiment scale. Instead, we support the narrative that doublets are in fact "diamonds"  [20]  which retain unique and irreplaceable information. We believe that they should be embraced as an essential integral part in future scRNA-seq. Overall, TOMAS offers accurate scRNA-seq analysis in spite of inherent total mRNA content size differences between cell types; identifies the unique value of heterotypic doublets as total mRNA content size controls and alleviates the anxiety over their presence in the dataset; and paves the way for future large-scale, low per-cell cost, doublet rich scRNA-seq experiments and analysis.
In the DE results, increased expressions (positive log fold-changes) are highlighted in red and decreased expressions (negative log-fold changes) are colored in black. (B) The GSEA results of the defense response to virus gene ontology (GO) term in CD4+ T cell activation under different total mRNA ratio assumptions, when analysing an in-house naïve-andactivated CD4+ T cell dataset. Naïve and activated CD4+ T cells are extracted an incubated from a lab mouse. From left to right shows the GSEA results of the defense response to virus GO term comparing activated CD4+ T cells against naïve CD4+ T cells, under the assumption that activated T cells have, on average, 1X, 3X and 6X total mRNA content of naïve CD4+ T cells, respectively. The final enrichment score under each assumption is highlighted in a red dashed line.
Any deviation from this assumed invariant, therefore, must be products of technical variations and are subject to corrections. However, the cell-type specific total UMI count distribution of a public PBMC dataset challenges this assumption  [17]. In this dataset, droplets are manually curated, with the help of CITE-sort  [21] , based on surface marker protein expressions. The surface marker expression data are obtained through joint single cell sequencing of transcriptomes and surface proteins via CITE-seq. Details about the cell type annotation process is provided in the Supplementary Materials section S4. Figure  3A  shows the total UMI count distributions of NK, DC and monocyte droplets. Two points become evident from this figure: 1) the total UMI counts for a specific cell type follows a log-Normal distribution, and 2) droplets of different cell types follow different distributions with apparent gaps between their centers.
Such observation contradicts the belief that all droplets share the same total UMI count distributions and shakes the foundation of its derivative scRNA-seq analytical methods, such as normalization, DE and GSEA analytical methods. Further analysis, presented in the Supplementary Materials section S9, shows that total UMI variations between cell types is an important contributor to gene expression variations, which must be taken into consideration during normalization. Otherwise, it could lead to serious data distortions and might confound downstream analysis (Supplementary Materials section S9). A similar trend can also be observed in our in-house naïve and activated T cell scRNA-seq dataset, shown in Figure  3B. In this dataset, naïve and activated T cells are prepared and subsequently sorted into separate assays.
7/48 and DemuxEM  [22]  can be used to remove triplets and homotypic doublets. Alternatively, homotypic droplets and heterotypic doublets of interest can also be identified with a combination of high fidelity clustering algorithms, such as CITE-sort for CITE-seq datasets, and heterotypic doublet authentication algorithms, for instance, GMM-Demux. Also illustrated in Figure  4A , this approach does not require a pre-library-prep cell sorting run. Instead, the cell assay is partitioned into equally-sized sub-samples, which are then labeled with sub-sample-specific hash tags, following the general cell hashing protocol. With the help of high fidelity clustering algorithms, homotypic droplets of individual cell types, together with heterotypic doublets of distinctive cell type combinations, are clustered into respective droplet groups.
TOMAS accurately estimates the total mRNA ratio between cell types We benchmark the performance of TOMAS through real-data-guided scRNA-seq simulation. This simulation is guided by our in-house CD4+-naïve-and-activated T cell dataset. In our simulation, we first extract the Dirichlet-Multinomial probability signatures Ψ naive and Ψ activated from the naïve and activated homotypic populations from the real dataset. We further assume that the total mRNA count of a cell follows log-Normal distributions, denoted as log(N naive ) ∼ N (µ naive , σ naive ) for naïve T cells and log(N activated ) ∼ N (µ activated , σ activated ) for activated T cells, respectively. Among them, µ naive and µ activated are simulation parameters while σ naive and σ activated are estimated from the real dataset. The total mRNA ratio r a/n between activated and naïve CD4+ T cells, therefore, follow log(r a/n ) ∼ N (µ r , σ r ), with µ r = µ activated -µ naive.
Quantifying the total mRNA ratio between naïve and activated T cells We prepared and FACS-sorted naïve and activated CD4+ T cells from a mouse spleen. Then we divide the naïve and the activated assays each equally into three partitions, for a total of six partitions. Each partition is assigned a unique cell-hashing barcode. The overall workflow is presented in Figure  6A. Based on their cell-hashing barcodes, droplets are classified into naïve (G naive ), activated (G activated ) and naïve-activated heterotypic doublet (D) droplets, via GMM-Demux. We also used GMM-Demux to further purify the dataset by removing homotypic doublets and heterotypic triplets. Details are provided in the Methods section. We used TOMAS to derive the Dirichlet-Multinomial probability signatures Ψ naive and Ψ activated from G naive and G activated , respectively.
The inferred activated-to-naïve total mRNA ratio (r activated/naive ) distribution is provided in Figure  6B. Since TOMAS models both the naïve and the activated CD4+ T cells with log-Normal distributions, the distribution of r activated/naive should also be log-Normal. As illustrated in Figure  6B , after removing outliers using interquartile range (IQR), the distribution of r activated/naive matches a log-Normal distribution of e µ r = 4. 3 and e σ r = 2. 8. This suggests that on average activated CD4+ T cell contains 4. 3X mRNA molecules of an average naïve CD4+ T cell. To benchmark the efficacy of TOMAS on this dataset, we simulated an scRNA-seq dataset with the real-data-derived Ψ naive , Ψ activated and r activated/naive distribution parameters. The simulated dataset is then mixed with the original real dataset and are jointly visualized in Figure  6C , via UMAP transformation.
The RPKM (Reads Per Kilobase of exon per Million) was then computed for each gene as well as for each synthetic spike-in RNA. We used the loess. normalize function from the affy package in R to normalize gene expressions with regard to ERCC counts. The final result is shown in Figure  6E. As evident in the figure, most genes have higher RPKM counts in activated T cells than in naïve T cells. Such result is in line with our scRNA-seq result, where after correcting the total-mRNA bias, activated T cells in general have higher expressions in most genes than naïve T cells. Overall, measured in RPKM, activated CD4+ T cells have a 5. 3X total mRNA of naïve T cells. While 5. 3 is greater than the TOMAS-inferred e µ r = 4. 3, this deviation can be explained by multiple factors. First of all, e µ r = 4.
Assessing the Total mRNA Ratios with TOMAS in a Multi-Cell-Type Scenario It has been hypothesized that total mRNA differences among cell types is widespread in organisms  [34]  but is not always thoroughly examined in scRNA-seq analysis. An analysis of a public PBMC data from  [17]  reveals the broad presence of total-mRNA bias among common immune cell types. The results are summarized in Figure  8. In this analysis, we isolated four cell types, including CD8+ T cells, B cells, mature NK cells (abbreviated as mNK) and classical monocytes (abbreviated as CMono), through unsupervised clustering and supervised annotation based on their surface marker expressions with CITEsort  [21]. CITE-sort also isolates their corresponding heterotypic doublet populations, which are authenticated (confirmation that they are indeed heterotypic doublets) by GMM-Demux.
Total mRNA ratios between cell subtypes that are not separated by surface markers can still be computed by examining their mRNA expressions. Figure  9  presents the workflow of applying TOMAS to the CD4+ T helper cell population extracted from  [17] , which is estimated to consist mostly naïve and memory subtypes as well as their heterotypic doublets. This dataset does not include surface markers that are capable of differentiating naïve and memory CD4+ T helper cell subtypes; hence all analyses including cell type classification and doublet identification must be performed in the mRNA realm. To obtain the total mRNA ratio between the naïve and the memory subtypes, we trained a prior-work-inspired Dirichlet-Multinomial-based cell type classifier  [35]  from the surface-marker-annotated Seurat V4 dataset  [36] ; then obtained the Dirichlet-Multinomial distribution parameters for the naïve and memory CD4+ T cell subtypes.
We applied the cell type classifier to the CD4+ homotypic droplet population and identified the naïve and the memory subtype populations (Figure  9A  step 1). Genes that only express in a specific subtype are classified as subtype exclusive genes, which can be found by comparing the Dirichlet-Multinomial distribution parameters of the two subtypes. Heterotypic doublets are identified as droplets that co-express exclusive genes of both subtypes (Figure  9A  step 2). Details about CD4+ T cell subtyping process is provided in the Supplementary Materials section S5. The TOMAS-inferred total mRNA ratio distributions between memory and naïve CD4+ T cells are demonstrated in 9A step 3, where we estimate the total mRNA content of memory CD4+ T cells is 1. 707X of naïve CD4+ T cells on average. To verify the correctness of TOMAS, we simulated naïve CD4+ T cells, memory CD4+ T cells and their heterotypic doublets; then compared their distribution with the real dataset.
Such profound changes in DE analysis provide a partial explanation for why some of the gene enrichment analyses produce inconclusive and/or counter-intuitive results in conventional analytic pipelines. With conventional analytic pipelines, the non-top and nonbottom ranking DE genes are most susceptible to the influence by the total-mRNA bias. Gene sets that contain large fractions of such middle-ranking DE genes are more likely to have an undermined running-sum statistic curve without bias correction. Our analysis also exposed that the expression levels of housekeeping genes between cell types are not as steady as they were anticipated to be. We advocate for a thorough re-evaluation of their reliability as reference anchors in RT-qPCR experiments. Overall, TOMAS empowers accurate scRNA-seq analysis for datasets with large total mRNA content differences between cell types.
It identifies the unique value of heterotypic doublets and supports their acceptance as an essential integral part in future scRNA-seq experiments and analyses. By demonstrating the utility of heterotypic doublets, TOMAS alleviates the anxiety over their presence in the dataset and paves the way for future large-scale, low per-cell cost, doublet-rich droplet-based scRNA-seq experiments, which improves the sensitivity and the statistical power in detecting and studying rare cell types.
The idea of digitally synthesizing doublets is not unique to TOMAS, and has been explored by a number of doublet detection methods, including DoubletFinder  [77] , scrublet  [78] , solo  [79] , DoubletDetection  [80] , bcds  [81]  and DoubletDecon  [82]. A common goal shared by the above methods is to identify and remove doublets from scRNA-seq datasets. Doublets are very common in scRNA-seq. Its rate increase super-linearly as the cell population increases  [17]. Previous work has reported doublet rates greater than 40%  [79]. However, all of the above methods, with the exception of DoubletDecon, either assume that the mRNA content of a doublet can be evenly distributed by its two member cells; or synthesize a doublet by directly adding the two UMI vectors of the two member cells. Both schools of thoughts circumvent the inference of true mRNA content ratios between cell types.
For computational-based approaches, mRNA-based cell type classifiers might have compromised performance in separating closely related cell types that share highly similar expression patterns, such as Th1 and Th2 cells, and could be negatively impacted by batch effects that widely exist between scRNA-seq datasets. Although it is beyond the scope of this work, we believe that cell type classification, feature selection, dimension reduction, gene and protein expression modeling, total mRNA ratio inference, doublet detection and differential expression analysis are all intertwined. We plan to explore a unified model that serves all above purposes in our future work. Second, TOMAS assumes that the expression profile of a cell remains unchanged, either being in a singlet or being part of a heterotypic doublet.
Flow Cytometry Analysis for CD27 Naïve and activated CD4+ T cells were prepared the same way as described above. Cells were then fixed and permeabilized with Cytofix/Cytoperm (BD Pharmingen, San Diego, CA) and stained intracellularly with anti-CD27. Samples were acquired on an LSR-II flow cytometer and data analysis was conducted using FlowJo software (Treestar).
25/48 of the ERCC synthetic spike-in RNA sequences (https://tools. thermofisher. com/content/sfs/manuals/ERCC92. zip) had been added. Genes were annotated with GENECODE GRCm39 and ERCC transcripts were quantified with the featureCounts function in the Subread package v2. 0. 0  [92]. We computed the RPKM (reads per kilobase of exon per million) for both genes and synthetic spike-in RNAs. The RPKM values are normalized by the loess. normalize function from the affy R package. The loess. normalize function performs the loess regression on the global expression RPKM matrix, such that the ERCC spike-in RPKM values from both naïve and activated CD4+ T cells are aligned. We set the subset parameter to all ERCC spike-ins and used default values for all other parameters. After normalization, we ranked all genes by their RPKM values.
(B) The UMI distributions of an in-hosue naïve-and-activated CD4+ T cell scRNA-seq dataset. The top plot shows the overall distributions of the entire dataset. The bottom plot individually portraits the distributions of the two cell types, pre-sorted and annotated with flow cytometry and cell hashing. Evident from the plot, activated CD4+ T cells have elevated expression levels than naïve CD4+ T cells. (C) The UMI distributions of singlets, homotypic doublets and simulated homotypic doublets of both naïve and activated CD4+ T cells. The left plot shows the box plot of the UMI distributions of naïve CD4+ T cell singlets, homotypic doublets and simulated doublets. The right plot shows the UMI distributions of activated CD4+ T cell singlets, homotypic doublets and simulated doublets. In both cases, maximum and minimum are set to Q3 + 1.
Evident from the plots, real and simulated data exhibit good coalescence. (D) The scatter plot comparing the TOMAS-inferred ratios against the simulation ground truth. The scatter plot is presented at the center, where each dot represents a heterotypic doublet. Both the x and the y axes are log-scaled. A linear regression curve of the data is provided as the dashed line in the plot, with a Pearson correlation of ρ = 0. 96. Above and right of the scatter plot, are histograms of the simulation ground truth and the TOMAS inference results, respectively. Log-normal distribution curves are fitted separately in both curves with their medians highlighted with dashed lines. (E) The gene expression scatter plot of naïve and activated CD4+ T cells in an ERCC-controlled bulk-RNA sequencing dataset. The x axis measures the expression level in activated CD4+ T cells and the y axis measures the expression level in naïve CD4+ T cells.
Figure8. Illustrations of total mRNA differences between selected cell types in a public PBMC dataset. (A) The UMAP plot of four selected cell types, including some of their heterotypic doublets, retained from a public PBMC cell-hashed CITE-seq dataset. Cell types are classified by their surface marker expressions. Droplets that jointly express marker proteins of multiple cell types are identified as heterotypic doublets. Five selected heterotypic doublet groups are underscored in the plot with gray ellipse backgrounds.
Data availability Raw and pre-processed scRNA-seq and bulk-RNA-seq data of murine CD4 T cells will be publicly released to GEO upon manuscript acceptance. Temporary access for raw and pre-processed fastq data (during the reviewing phase) is granted through: https://tinyurl. com/2hu8d465. CITE-seq human PBMC dataset  [35]  can be found at the GEO (GSE152981).
16 / 48 Figure 8. 16488 Figure8. Illustrations of total mRNA differences between selected cell types in a public PBMC dataset. (A) The UMAP plot of four selected cell types, including some of their heterotypic doublets, retained from a public PBMC cell-hashed CITE-seq dataset. Cell types are classified by their surface marker expressions. Droplets that jointly express marker proteins of multiple cell types are identified as heterotypic doublets. Five selected heterotypic doublet groups are underscored in the plot with gray ellipse backgrounds.
at inclusion, the selected subjects are invited to fill a questionnaire and to attend a health Screening center (hSc) for a comprehensive health examination: weight, height, blood pressure, electrocardiogram, vision, auditory, spirometry, and biological parameters; for those aged 45 years and older, a specific work-up of functional, physical, and cognitive capacities is performed. a biobank will be set up. The follow-up includes a yearly self-administered questionnaire, and a periodic visit to an hSc. Social and work-related events and health data are collected from the French national retirement, health and death databases. The data include social and demographic characteristics, socioeconomic status, life events, behaviors, and occupational factors. The health data cover a wide spectrum: self-reported health scales, reported prevalent and incident diseases, long-term chronic diseases and hospitalizations, sick-leaves, handicaps, limitations, disabilities and injuries, healthcare utilization and services provided, and causes of death.
To take into account non-participation at inclusion and attrition throughout the longitudinal follow-up, a cohort of non-participants was set up and will be followed through the same national databases as participants. rESulTS: a field-pilot was performed in 2010 in seven hScs, which included about 3 500 subjects; it showed a satisfactory structure of the sample and a good validity of the collected data. concluSionS: the constitution of the full eligible sample begun in 2012 and the cohort will be completed by the end of 2017. a public call for ancillary research projects will be launched in 2014.
Active participation by self-administered questionnaire is high: after 25 years of follow-up, only 3. 1% of the subjects who participated at inclusion never returned an annual questionnaire. The number of subjects truly lost to follow-up, that is, whom we can no longer locate in the databases, is tiny: 107, or approximately 0. 5%  [9]. It is reasonable to think that CONSTANCES, which will apply similar methods, will also have very high follow-up rates. As one of the main objectives of the study is to provide information on the health status and disease burden of this large part of the French adult population, the CONSTANCES cohort was designed as a representative sample of the general French population insured by CNAMTS in terms of age (18 to 69 years at inception), sex, and social category.
To obtain a representative sample of the target population and to minimize the bias associated with selection effects at inclusion and during follow-up in CONSTANCES, we took the following steps. The sampling base at inclusion is composed of all persons aged 18 to 69 years and covered by CNAMTS in the catchment areas of the 17 CONSTANCES HSCs. Sampling is done within the database of the National Retirement Insurance Fund administered by the Caisse nationale d'assurance vieillesse  [30]  which includes exhaustively all the persons in France affiliated to the CNAMTS and permanently collects professional and social data compulsory for establishing the retirement benefits of all individuals in France from different sources (see below). The random drawing is stratified according to unequal inclusion probabilities, based on data from participation in previous surveys involving invitations to the HSC  [31].