dataesr/scientific-paragraphs-categorization huggingface dataset:
This balance can be hard to strike, as discussed in Zhao et al. [17]. Proper experimental design -such as having several batches per condition -or limiting batch effects as much as possiblefor example, sequencing a mix of conditions together -can help lessen this impact. Still, some amount of confounding is sometimes inherent to the nature of the problem under study. The tests used in the workflow (e. g. , Kolmogorov-Smirnov test) assume that the pseudotime and weight vector are known and independent observations for each cell. However, this is not the case: they are estimated using TI methods which use all samples to infer the trajectory, and each estimate inherently has some uncertainty. Here, we ignore this dependence, as is the case in other differential abundance methods, which assume that the reduced-dimensional coordinates are observed independent random variables even thought they are being estimated using the full dataset.
Moreover, results on simulations show that all methods considered keep excellent control of the false discovery rate despite the violation of the independence assumptions. This issue of "double-dipping" therefore seems to have a limited impact. The two issues raised in the previous paragraphs highlight the need for independent benchmarking. Simulation frameworks such as dyngen  [22]  are crucial. They also need to be complemented by real-world case studies, which will become easier as more and more datasets that study dynamic systems under multiple conditions are being published. condiments has thus been developed to be a general and flexible workflow that will be of use to researchers asking complex and ever-changing questions.
slingshot additionally fits simultaneous principal curves. A cell's pseudotime along a lineage is determined by its projection onto a particular curve and its weight of assignment is determined by its distance from the curve. We therefore construct the MST on the full dataset (i. e. , using all the cells regardless of their condition label), based on user-defined cluster labels. Then, we keep the same graph structure as input to either TI method: the nodes are the centers of the clusters, but restrained to cells of a given condition. This way, the path and graph structure are preserved. Note however, that there no guarantee that the graph remains the MST when it is used for TI on a subset of cells.
Simulating datasets The simulation study relies on the dyngen framework of Cannoodt et al. [22]  and all datasets are simulated as follows. 1/ A common trajectory is generated, with an underlying gene network that drives the differentiation along the trajectory. 2/ A set of N W T cells belonging to the wild-type condition (i. e. , with no modification of the gene network) is generated. 3/ One master regulator that drives differentiation into one of the lineages is impacted, by multiplying the wild-type expression rate of that gene by a factor m. If m = 1, there is no effect; if m > 1, the gene is over-expressed; and if m < 1, the gene is under-expressed, with m = 0 amounting to a total knock-out. 4/ A set of N KO = N W T cells is generated using the common trajectory with the modified gene network.
5/ A common reduced-dimensional representation is computed. We For T 1 and T 2 , we use values of m ∈ {. 5,. 8,. 9,. 95, 1, 1/. 95, 1/. 9, 1/. 8, 1/. 5}, such that at the extremes the KO cells fully ignore some lineages. Values of. 95 and 1/. 95 represent the closest to no condition effect (m = 1), where the effect was still picked out by some tests. For T 3 , since the simulation is symmetrical in m, we pick m ∈ {. 5,. 8,. 9,. 95, 1}. We have one large dataset per value of m and per trajectory type. We use those large datasets to generate smaller ones of size n, by sampling 10% of the cells from each condition 50 times and applying the various tests on the smaller datasets. The reason for first generating a large dataset and then smaller ones by subsampling instead of generating small ones straightaway are computational: the generation of the datasets is time-consuming and the part that scales with N W T can be parallelized.
Hence, it is almost as fast to generate a large dataset than a small one with dyngen. We pick N W T = 20, 000 (for the large dataset) and thus n = 2, 000. Since we generate many datasets with true effect (m = 1) but only one null dataset, the size of N W T for m = 1 is doubled to 40, 000. To be comparable, the fraction of cells sampled is decreased to 5% so that n = 2, 000 and we perform 100 subsampling. Table  2  recapitulates all this. To run the condiments workflow, we first estimate the trajectories using slingshot with the clusters provided by dyngen. Then, we run the progressionTest and the differentiationTest with default arguments. We compare condiments to two other methods. milo  [16]  and DAseq  [17]  both look at differences in proportions within local neighborhoods, using k-nearest neighbor graphs to define this locality.
Then, milo uses a negative binomial GLM to compare counts for each neighborhood, while DAseq uses a logistic classifier test. Therefore, both methods test for differential abundance in multiple regions. To account for multiple testing, we adjust the p-values using the Benjamini, Yoav ; Hochberg  [21]  FDR-controlling procedure. We select two adjusted p-value cutoffs,. 01 and. 05, which amount to controlling the FDR at nominal level 1% and 5%, respectively. For a given cutoff c and a given dataset, we can look at the results of each test on all simulated datasets for all values of m. ). The diffTopoTest rejects the null and separate trajectories are fitted for each condition (e. ). After mapping the lineages, there is also differential progression: the pseudotime distribution along the trajectory are not identical (f.
S-4 Figure  S4 : TCDD dataset: Differential expression. The tradeSeq gene expression model is fitted using the trajectory computed with slingshot. Differential expression between conditions is assessed using the conditionTest and genes are ranked according to the test statistics. The genes with the highest (a. ), second highest (b. ), and smallest (c. ) test statistics are displayed. After adjusting the p-values to control the FDR at a nominal level of 5%, we display genes in both conditions using a pseudocolor image (d. ). The tradeSeq gene expression model is fitted using the trajectory computed with slingshot. Differential expression between conditions is assessed using the conditionTest and genes are ranked according to the test statistics. The genes with the highest (a. ), second highest (b.
generate three types of datasets, over a range of values of m: a simple trajectory with L = 2 lineages and C = 2 conditions (WT and KO) named T 1 ; a trajectory with two consecutive branchings with L = 3 lineages and C = 2 conditions (WT and KO) named T 2 ; and a simple trajectory with L = 2 lineages and C = 3 conditions (WT, KO, and UP) named T 3. For the latter case, Steps 3-4/ are repeated twice, with values of m for KO and 1/m for UP.
For each test, the number of true positives (TP) is the number of simulated datasets where m = 1 and the adjusted p-value is smaller than c, the number of true negatives (TN) is the number of simulated datasets where m = 1 and the adjusted p-value is larger than c, the number of false positives (FP) is the number of simulated datasets where m = 1 and the adjusted p-value is smaller than c, and the number of false negatives (FN) is the number of subsampled datasets where m = 1 and the adjusted p-value is larger than c. We then examine 5 metrics built on these four.
Summary of all case study datasets. We report the name, number of cells n, number of conditions C, number of lineages L of each dataset, as well as the p-value resulting from testing for differential topology, progression and differentiation and the number of differentially expressed genes between conditions according to the conditionTest ≤ 2. 2 × 10 -16 ≤ 2. 2 × 10 -16 ≤ 2. 2 × 10 -16 363.
As the capacity for generating large-scale molecular profiling data continues to grow, the ability to extract meaningful biological knowledge from it remains a limitation. Here, we describe the development of a new fixed repertoire of transcriptional modules, BloodGen3, that is designed to serve as a stable reusable framework for the analysis and interpretation of blood transcriptome data. The construction of this repertoire is based on co-clustering patterns observed across sixteen immunological and physiological states encompassing 985 blood transcriptome profiles. Interpretation is supported by customized resources, including module-level analysis workflows, fingerprint grid plot visualizations, interactive web applications and an extensive annotation framework comprising functional profiling reports and reference transcriptional profiles.
Our group has previously developed fixed repertoires of transcriptional modules which have been employed to support the analysis and interpretation of blood transcriptome data  [16] [17] [18]. Notably, we have developed such repertoires as reusable analytic frameworks-i. e. with the intent of employing these preestablished and well-characterized module sets for the analysis of newly generated transcriptome datasets. 0] [21] [22]  ]. With the construction of a new repertoire ("BloodGen3"), we aimed first to increase the range of immunological states upon which the definition of modules would be based, which was limited to seven in our earlier attempt, and second, to improve the resources that would support downstream data analysis and interpretation. Specifically, 16 input datasets were employed for the construction of the BloodGen3 repertoire, comprising 985 unique blood transcriptome profiles from: patients with autoimmune, infectious, or inflammatory diseases; cancer patients; liver transplant recipients; and pregnant women.
Secondly, we developed a comprehensive bioinformatics ecosystem specifically adapted to the BloodGen3 repertoire, to support downstream analysis, visualization, and interpretation of blood transcriptome profiling data. The custom resources that have been developed include an R package which permit to run analysis workflows for group comparison and individual molecular fingerprinting and to visualize module-level data as custom module fingerprint grids and heatmaps. In addition, extensive functional annotations and reference transcriptome profiles for each of the 382 modules comprising the repertoire have been made available via interactive circle packing plots. Finally, web applications have been deployed that give users the ability to dynamically generate fingerprint plots for different collections of reference datasets.
Results Generation of a collection of datasets covering a wide range of immune states. The development of transcriptional module repertoires relies on identifying gene co-expression events using transcriptome profiling data as a starting point. For this new blood transcriptome module repertoire, we used 16 datasets (GEO ID: GSE100150) that encompassed 985 individual whole blood transcriptome profiles. Each dataset corresponds to a different pathological or physiological state (Table  1 ). These datasets were generated from whole blood samples processed in the same facility using Illumina HT12 BeadArrays (details are provided in the Methods section). Similar to our first two repertoires (Supplementary Table  1 ), we included data from patients (adult and pediatric) with: systemic lupus erythematosus (SLE), systemic onset juvenile idiopathic arthritis (SoJIA), liver transplants, and receiving maintenance immunosuppressive therapy, metastatic melanoma, and infectious diseases [with an expanded range that now includes infections caused by influenza, respiratory syncytial virus (RSV), human immunodeficiency virus (HIV) infections, Mycobacterium tuberculosis, Staphylococcus aureus, and Burkholderia pseudomallei (which causes melioidosis) and sepsis caused by other bacteria (Streptococcus pneumoniae, Salmonella spp.
, and Pseudomonas aeruginosa)]. We also added six new conditions to our framework: inflammatory conditions of the skin (juvenile dermatomyositis), lung [chronic obstructive pulmonary disease (COPD)] and circulation (Kawasaki disease); multiple sclerosis (MS); primary immune (B-cell) deficiency; and  13  33   Sixteen distinct datasets were used as the input for module repertoire construction. Each dataset corresponds to a different condition or physiological state and comprises both cases and matched controls. Each dataset was processed as a single batch at the same facility with the data generated using Illumina HumanHT-12 v3. 0 Gene Expression BeadChips. The collection comprises a total of 985 individual transcriptome profiles. pregnancy (serving as a physiological variant). Patients with type 1 diabetes or with an Escherichia coli infection included in previous repertoires were not included this time.
In summary, the sixteen datasets that were assembled capture a wide range of immunological responses. This should permit the construction of a transcriptional module repertoire that will prove useful as a generic framework for blood transcriptome data analyses. Implementation of a stepwise approach to blood transcriptional module repertoire construction. After collating the 16 input datasets, we next followed a stepwise process to construct the BloodGen3 module repertoire and identify co-expression networks (Fig. 1 ). We used the module construction algorithm we implemented for the selection of the previous repertoire (the second generation; the code is provided in the Supplementary Material). This approach comprised four main steps: (1) clustering of each individual dataset; (2) recording the number of instances where two genes are included in the same cluster, with the values ranging from 0 to 16 (i.
e. reflecting the range of coclustering in none or all 16 of the datasets); (3) construction of a weighted co-expression network, where the edges between the genes represent at least one co-clustering event in one of the input datasets and the weight is assigned based on the total number of co-clustering events; (Supplementary Fig. 1 ); and (4) mining the resulting network to identify highly inter-connected sub-networks that form the modules. The construction of a module repertoire in this manner is thus entirely data-driven and does not rely on any a priori information about gene interactions or functions. In total, we identified 382 modules comprising 14,168 transcripts (95. 8% of the transcripts detected in this dataset collection).
Development of module-level analysis workflows and visualizations. A key characteristic of the gene sets collected via the process described above is that, by construction, changes in abundance of the corresponding transcripts within a given module will tend to be coordinated. As such, it should be possible to use these modules as a "framework" to: (1) identify functional convergences among the genes that comprise each set, and (2) summarize changes in overall transcript abundance related to pathological processes or therapeutic interventions. We determined the gene composition of each of the 382 modules comprising the BloodGen3 repertoire (Supplementary Data 1). The average number of genes per module was 37. 1, the median was 26. 5 and the range was 12-169. Functional profiling and enrichment results were generated using multiple tools (GSAn, Literature Lab, IPA, DAVID, KEGG, BioCarta, OMIM, and GOTERM).
2 ). For this, we then performed a second tier of clustering to group the 382 modules into 38 "aggregates", with each row on the grid displaying the modules corresponding to one such aggregate. Segregation into distinct aggregates was based on similarities in abundance levels observed across the collection of 16 datasets. Using this approach, we derived two levels of granularity (i. e. module level vs. module aggregate level) with the number of variables for interpretation at the least granular module aggregate level constrained to a more manageable number. As a result of this process, the changes in expression levels for each row in the fingerprint grid tend to be coordinated, which was not the case for prior iterations of such fingerprint grids (Fig. 3 ). Some degree of functional convergence can thus be observed within a given row of modules.
Fingerprints of other reference disease cohorts employed for module construction are included to provide additional context. Data interpretation is facilitated by tiered dimension reduction. The first vertical reading of the fingerprint grid permits visualization of changes across the aggregates, while the horizontal reading permits visualization of changes within an aggregate and across modules. In this illustrative case, we compared the transcriptome profiles among 55 pediatric patients with SLE and 14 healthy control subjects (Fig. 3 ). We identified an interferon-dominated signature (A28) accompanied by modules associated with cell cycle (A27 and A29, including antibody production). We observed an increase in the abundance of modules associated with inflammation and neutrophils (A35), which is a hallmark of the SLE transcriptome signature.
These changes were accompanied by a decrease in transcript abundance, which was more apparent for some modules belonging to aggregates A1, A2, and A3, which are arrayed across the first three rows of the fingerprint grid. More specifically, for the module aggregate A1, the most marked decreases were observed for modules associated with protein synthesis (dark purple color at positions 1, 5, 11, and 19 on row A1). It is also possible to "aggregate" the changes observed by row, thereby reducing the dimensions for a given dataset even further. In this illustrative case, we reduced the dataset from 382 modules to 27 "aggregates" (Fig. 3a ). The decision to take this extra step depends on the desired level of resolution. Mapping changes at the aggregate level will produce the most reducted and simplest fingerprint possible.
We also found differences in the intensities of sets of modules associated with inflammation between these two diseases (A33-A35). Interferon signatures (A28) were a salient trait in patients with SLE (Fig. 3a ) and were present in patients infected with viral pathogens (e. g. HIV, in Fig. 3 ), as well as patients with active tuberculosis (consistently with one of our previous reports  20 ). Taken together, this illustrative case demonstrates the use of "fingerprint" representations. Notably, such fingerprint grid , where the entries indicate in how many datasets, out of the 16, co-clustering was observed for a given gene pair. d The co-clustering table served as the input to build a weighted co-clustering graph (see also Supplementary Fig. 1 ), where the nodes represent genes and the edges represent co-clustering events.
However, while this representation can be used as a complement, it does not replace the more traditional heatmap representations. In  Fig. 2  The development of the BloodGen3 module fingerprint grids. a Rows on this heatmap correspond each to changes in transcript abundance for a given dataset and for a given direction (i. e. increase or decrease in transcript abundance). These values, the percentages of constitutive probes either increased or decreased within a module, are computed for the 16 datasets used as input for module construction (Table  1 ). Increases in transcript abundance compared to a healthy baseline are depicted in red and decreases are depicted in blue. Therefore, in total 32 rows are displayed on this heatmap. Columns correspond to modules comprising the BloodGen3 repertoire (N = 382).
The colors shown on the bottom track are associated with module aggregate ID and only serve to illustrate the strategy that was employed for organization of modules on the fingerprint grid plot. b The modules were arranged onto the grid as follows: the master set of 382 modules was partitioned into 38 clusters (or aggregates) based on similarities among their module activity profiles across the sixteen reference datasets (A1-A38). A subset of 27 aggregates comprising two or more modules in turn occupied a line on the grid. The length of each line was adapted to accommodate the number of modules assigned to each cluster. The format of the grid was fixed for all analyses carried out using the BloodGen3 module repertoire. c When performing downstream analyses of blood transcriptome datasets using the BloodGen3Module R package changes in transcript abundance at the module level are mapped onto this grid and represented by colored spots of varying intensity.
spanning several years. We therefore focused on comprehensive functional annotation of the BloodGen3 repertoire, and for this used two main approaches: 1 concurrent ontology, pathway or literature-term profiling analyses; and 2 determination for the constitutive genes for each module of expression patterns in select reference transcriptome datasets. We compiled the resulting information for BloodGen3 modules and made it accessible via interactive circle packing plots. These interactive plots make it possible to zoom in and out, determine spatial relationships and interactively browse the very large compendium of analysis reports and heatmaps generated as part of our annotation efforts. (Fig. 4 ; links are listed in Supplementary Table  2  and are also accessible via the BloodGen3 web application: https://drinchai.
d. available at: https://youtu. be/db58FBUua-g). Below, we describe briefly how we conducted our annotations (see also Experimental Procedures): Step 1-Functional profiling: We conducted gene ontology profiling for each of the 382 modules using DAVID  25  , GOTERM, and GSAn  26. GSAn interactive reports were uploaded to a custom web portal (https://ayllonbe. github. io/modulesV3/index. html). We also performed pathway enrichment analyses using the KEGG, BioCarta and the Ingenuity Pathway Analysis application, as well as literature-term enrichment with Literature Lab. Finally, we also used the RcisTarget tool in the R package to identify transcription factor binding motifs over-represented among the transcript constituents of each module  27. We synthesized and compiled the results of the analyses (Supplementary Data 1) to identify convergences and attribute functional titles to the different modules.
Functional titles could not be attributed in all cases due to a lack of convergence or poor enrichment in one or more of the analyses. Step 2-Expression patterns in reference transcriptome datasets: We used transcriptome datasets as a reference to improve characterization and biological interpretation of the BloodGen3 module repertoire. Three different datasets were used. The first was contributed by Novershtern et al. and comprised the transcriptome profiles of 38 human hematopoietic cell populations  28. The second was contributed by Speake et al. and comprised the RNA-seq profiles of six circulating leukocyte populations from patients with various immune-associated diseases  29. The third was contributed by Monaco et al. and comprised RNA-seq profiles of 29 leukocyte populations isolated from healthy donors  30.
We generated heatmaps for each module to visualize the abundance patterns of the constituent transcripts for each dataset. Overall, this resource serves two purposes. First, it provides access to information required to interpret the transcriptome fingerprints generated via module-level analyses. Second, it helps us to improve the accuracy with which functional titles and roles are attributed to the different modules and aggregates. Indeed, although the transcriptional module repertoire is fixed over time, we anticipate that the functional annotations will continue to evolve over its lifespan. Measuring inter-individual variability for the molecular stratification of patient cohorts. The analysis and visualization steps presented so far focus on characterizing differences between groups of subjects (e.