dataesr/scientific-paragraphs-categorization huggingface dataset:
Statistics and Ontology Analyses Statistical analyses were performed with Prism (GraphPad Software, Inc. ). Data are represented as mean ± standard error of mean (SEM). After performing a normality test on the numeric variables, we used two-tailed Student's t-test for parametric data and Mann-Whitney U test for non-parametric data, when two groups were compared. The statistical tests performed in each quantification are detailed in the corresponding figure legend. To calculate the statistical significance of the overlap between two groups of genes we used an online tool 2. This statistical test calculates the probability (p-value) of finding the computed overlap if the genes were present at random in both groups, as well as the representation factor: fold-change between overlap observed and number expected by chance  (Kim et al.
We then asked whether the splicing profiles of DMSXL astrocytes provided evidence of impaired splicing transitions during differentiation, similar to the expression changes in DMSXL oligodendroglia. To address this question, we used previously published datasets, which included transcriptomic data from mouse NSC, immature astrocytes (collected at 1 DIV) and differentiated astrocytes (collected at 7 DIV)  (Han et al. , 2014). We generated a list of 244 physiological splicing variations associated with astroglia differentiation and maturation involving single exons, multiple exons or mutually exclusive exons (Figure  6A ): 202 changes occurred during the NSC -7 DIV transition and an additional 95 took place during the final 1 DIV -7 DIV maturation (with an overlap of 53 splicing events between both).
ACKNOWLEDGMENTS We thank  Solenne Chardonnet  and  Soumia Hamada  for their ideas and support to perform the two-dimensional western blot experiments. We are grateful to the personnel of  CERFE (Centre d'Exploration et de Recherche Fonctionelle Expérimentale, Genopole, Evry, France)  and  LEAT (Laboratoire d'Expérimentation Animale ,  Imagine Institute, Paris, France ) for attentively caring for the mice;  Ludovic Arandel  and  Denis Furling  for providing the secondary antibody for the analysis of CAPZB; all members of the " Repeat Expansions & Myotonic Dystrophy -REDs" group  at the  Centre de Recherche en Myologie (Paris, France ) for insightful ideas and discussions. We thank the support provided by the  Genomics Platform  ( Imagine Institute, France ) on the RNA sequencing analysis, and the  PSMN (Pôle Scientifique de Modélisation Numérique) of the ENS de Lyon  for computing resources.
FUNDING This study was supported by grants from the  Agence Nationale de Recherche (ANR, France ,  Project Grant DM_Neuroglia) , the  AFM-Téléthon  ( France, Project Grant   19920 ), the  INSERM (France) , the  Université Paris Descartes (France) , as well as Ph. D. fellowships from the  Ministère Français de la Recherche et Technologie (France) , the  Imagine Foundation (France) , the  Bettencourt Schuller Foundation (France) , and the  AFM-Téléthon (France). This work was supported by  Paris Ile-de-France Region. Our research received a state subsidy managed by the  National Research Agency , under the Investments or the Future program bearing the reference  ANR-10-IAHU-01  and under the program  ANR-10 BLAN-1121-01.
min -1. Slices were visualized under an Olympus BX51WI microscope (Olympus, Rungis, France), with a 4 × /0. 13 objective for the placement of the stimulating electrode and a 40 × /0. 80 water-immersion objective for the localization of cells for whole-cell recordings. Current-clamp recordings were sampled at 2. 5 kHz and voltage-clamp recordings were sampled at 10 kHz, with the Patchmaster v2 × 32 program (HEKA Elektronik). Glutamate transmission blockers 6-Cyano-7-nitroquinoxaline-2,3-dione (CNQX, 10 µM, Tocris, Ellisville, MO, United States) and DL-2-amino-5-phosphono-pentanoic acid (D-AP5, 50 µM, Tocris) were bath-applied and responses were measured after 5 min of application.
Electrophysiological Data Analysis Off-line analysis was performed using Patchmaster (Heka Elektronik), Igor-Pro 6. 0. 3 (Wavemetrics, Lake Oswego, OR, United States) and MATLAB (The Mathworks). Pyramidal neurons were identified in slices by their morphology and basic electrophysiological characteristics to distinguish them from interneurons  (Cohen et al. , 2009; Mueller et al. , 2020) : patched cells were pre-selected by their large soma with triangular shape through a visual inspection using infrared DIC video-microscopy. Offline analysis of AP features was then used to exclude interneurons, namely if the spike half-width was < 0. 7 ms and/or the rise/decay slope ratio was < 1 in the absence of an ADP. Only cells that had a resting membrane potential (RMP) of less than -50 mV (unless spontaneously active), and an AP overshoot were included.
Clustering Analysis Clustering algorithms were used on standardized data (centered and reduced) from 50 neurons using 18 electrophysiological parameters. The parameters were: (1) RMP, (2) Membrane resistance, (3) Membrane time constant, (4) Rheobase, (5) Sag index, (6) AP threshold, (7) AP amplitude, (  8 ) AP duration at half-width, (9) AP rise slope, (10) AP decay slope, (11) AHP amplitude, (12) fast AHP amplitude, (13) AHP amplitude, (  14 ) AHP duration, (15) Firing rate at + 40 pA from rheobase, (16) SFA early , (17) SFA late , (18) I/O gain. The average absolute correlation coefficient between these 19 parameters was 0. 30, with only five pairs of parameters for which the absolute value of the correlation coefficient was superior to 0. 7. To reduce the dimensionality of the dataset and remove correlations between these parameters, a principal component analysis (PCA) was performed.
The first three principal components, explaining 60. 5% of the variance, were retained for classification by cluster analysis (Figure  1B ). The projection of the 18 electrophysiological parameters onto the first two principal components is indicated in Figure  1D. Clustering analysis was implemented using the statistics toolbox of Matlab using the k-means algorithm, based on the squared Euclidean distance. We computed the Calinski-Harabasz index, which corresponds to the normalized ratio between the overall between-cluster variance and the overall within-cluster variance. Silhouette scores, a measure of similarity of a sample to points of its cluster when compared to points in other clusters, were measured using the squared Euclidean distance. High average silhouette scores, close to 1, indicate that the clusters are compact and distinct from each other.
Additional tests of the robustness of the clustering results were performed, by varying the number of principal components used or by directly applying the clustering algorithms on the normalized dataset. A comparison of the number of mismatches, average silhouette values and Calinski-Harabasz index is presented in Tables  1, 2. Overall, the clustering results were highly consistent, with only one mismatch. Furthermore, the number of clusters was deduced from the Calinski-Harabasz index and silhouette scores (Figure  1C ): in most cases, the optimal number of clusters was 2. Yet, a total of 4 clusters was also found to be an optimal solution when computing these indices on the first three principal components dataset. We also verified that the quality of the recordings did not affect the clustering results, by estimating the series resistance (R series ) in 47 out of the 50 cells (Supplementary Figure  1 ).
Statistics Statistical analysis was performed using Matlab 2019 (The Mathworks) or the R-based Jamovi software (The jamovi project, 2021). In all cases "n" refers to a single cell experiment from a single slice. When comparing electrophysiological features between the two groups, reported p-values were calculated using a nonparametric Mann-Whitney test. Chi-square tests were used to compare the proportions of resonant neurons, or spontaneously active neurons between clusters. A 2-way repeated measures ANOVA was used to compare the I-V curves (injected current X cluster identity). A linear mixed model (LMM) was used to compare the f-I curves (fit by REML, random effect of the neuron identity and fixed effects of injected current X cluster identity tested using omnibus F-test with Satterthwaite method for the degrees of freedom).
Validity of the Classification and Analysis for Four Clusters The segregation obtained for two clusters was robust since only one mismatch could be detected when modifying the number of principal components used in the k-means algorithm or when performing the clustering directly on the whole normalized dataset (Table  2 ). In addition, when performing k-means classification on scrambled data, on average 7 electrophysiological parameters reached significance level (with only 2 below 0. 001 and 2 between 0. 01 and 0. 05), while in our dataset we obtained high significance levels for 12 parameters out of 18 (with 8 below 0. 001 and 4 between 0. 01 and 0. 05). Since four clusters also appeared to be an optimal classification for some indicators, we also calculated the mean electrophysiological properties related to each cluster (Table  5 ), as well as the indices quantifying the robustness of the clustering method for four clusters (Tables  1 2 3 4 5 ).
Introduction Clinical studies of neurodegenerative disease (ND) progression, including that of Parkinson's and Huntington's disease (HD), provide evidence that progression to more advanced disease stages is often associated with a loss of compensatory processes that enable neural circuits to retain robust function even in the presence of some level of cellular dysfunction or loss  (Lee et al. , 2000; Dubois et al. , 2018; Gregory et al. , 2018). However, the dynamics of such changes remain poorly characterized on a molecular systems level. Precisely inferring how such biological processes are coordinated at a molecular systems level can be aided by the analysis of multidimensional datasets, for example, transcriptomic time-series data collected across genotypes or cell types. Such datasets are increasingly available to ND research  (Langfelder et al.
, 2015). We applied this approach to the analysis of the cell-type-specific and temporal features of molecular responses in mouse models of Huntington's disease (HD), an ND that is caused by CAG repeat expansion in the huntingtin gene (HTT;  Zuccato et al. , 2010)  and that is characterized by large changes to gene expression in the striatum and cortex  (Moumneet al. , 2013). Cell-type-specific studies are critical to understanding how each cell type responds to mutant HTT (mHTT) as, for example, striatal D2 dopamine receptor (Drd2)-expressing medium spiny neurons (MSNs) show enhanced vulnerability to the disease  (Reiner et al. , 1988) , while some other striatal cell types are much less affected. We used Geomic to integrate three datasets including (i) a multidimensional RNA-seq data characterizing gene expression changes in bulk striatal RNA of the allelic series of HD knock-in mice (Hdh mice), currently the largest RNA-seq reference dataset (six CAG repeat lengths: Q20 to Q175; three age points: 2 months, 6 months, 10 months), which was created to study how molecular responses may develop on a systems level in HD mouse models  (Langfelder et al.
, 2016) , (ii) a celltype-specific RNA-seq dataset obtained from Hdh mice that were crossed with BAC-translating ribosome affinity purification (TRAP) mice  (Doyle et al. , 2008; Heiman et al. , 2008)  prior to cell-typespecific mRNA capture and sequencing, encompassing four striatal cell types and five CAG repeat lengths  (20, 50, 111, 170, or 175 CAG repeats)  at 6 months of age  (Lee et al. , 2020) , and (iii) a neuron survival screening dataset obtained in the Hdh-Q175 mice upon infection of the striatum with genome-wide shRNA pools subcloned into lentivirus that preferentially transfect neurons  (Wertz et al. , 2020). As described below, our approach identifies the temporal subtypes of pathogenic and compensatory responses, and the transitions thereof, as detected in Drd1-expressing MSNs, Drd2-expressing MSNs, striatal cholinergic (ChAT) interneurons, and striatal astrocytes, implicating stress response and cellular resilience pathways in all cell types.
These responses involve genes that were previously shown to modulate HD pathogenesis in experimental models and/or to modify HD onset age, as well as genes previously unknown to modify disease course. A major feature of the Geomic map of molecular responses in the striatal cells of Hdh mice indicates that HD progression may be primarily driven by the loss of homeostatic responses, highlighting the alteration of similar homeostatic responses in different cell types, notably that of endosome biogenesis and mitochondrial quality control in Drd1-expressing neurons and striatal astrocytes. These data highlight that shape deformation analysis is an approach to help prioritize HD targets for prolonging compensatory responses and delaying HD progression in one or more cell types. These data also highlight the value of shape deformation analysis for accurate analysis of complex genomic datasets.
Results Cell-type-specific assignment of whole-striatum gene deregulation surfaces in the Hdh model mice To dissect the nature and evolution of the cell-type-specific molecular responses that may develop in the striatum of the 'allelic series' Hdh mice (a set of HD models that all possess a humanized exon one knock-in Htt allele and differ only by a series of increasing CAG repeat lengths;  Wheeler et al. , 1999; Menalled et al. , 2003; Heikkinen et al. , 2012; Menalled et al. , 2012; Langfelder et al. , 2016; Franich et al. , 2019) , we designed Geomic, a workflow in which the shape deformation principle is applied to the analysis of complex genomic data (Figure  1 , see also Materials and methods). We used Geomic to integrate three datasets: (i) a multidimensional RNA-seq dataset characterizing gene expression changes in total RNA of the allelic series knock-in Hdh model mice, currently the largest reference dataset (six CAG repeat lengths: Q20 to Q175; three age points: 2 months, 6 months, 10 months), which was created to study how CAG length-dependent molecular responses to mHTT develop on a systems level in HD mouse models  (Langfelder et al.
, 2016) , (ii) a cell-typespecific RNA-seq dataset obtained from some of the same allelic series knock-in Hdh mice that were crossed with BAC-TRAP mice  (Doyle et al. , 2008; Heiman et al. , 2008)  prior to cell-type-specific mRNA capture and sequencing, encompassing four striatal cell types and several CAG repeat lengths  (20, 50, 111, 170, 175 repeats)  at 6 months of age  (Lee et al. , 2020) , and (iii) an in vivo neuronal survival genetic screening dataset that identified genome-wide modifiers of striatal neuron survival in the context of mHtt  (Wertz et al. , 2020). We focused our Geomic analysis on a group of 4310 genes of high interest, that is, those for which data on whole-striatum gene expression deregulation  (Langfelder et al. , 2016) , striatal celltype-specific gene expression  (Lee et al.
, 2020) , and effect on striatal neuron survival upon in vivo shRNA screen  (Wertz et al. , 2020)  were available in the Hdh mice. As a first step of Geomic analysis (Figure  1 ), we compared the whole-striatum gene deregulation curves defined by the evolution of the log2-fold-change (LFC) across expanded CAG repeats at the 6 months of age model timepoint -which is part of the gene deregulation surface (GDS) defined by RNA-seq data across 15 CAG repeats and three age points in the striatum of Hdh mice  (Langfelder et al. , 2016 ; see http://www. broca. inserm. fr/geomic/index. php; the database might take some time to load at first consultation)and striatal cell-type-specific curves defined by the evolution of gene expression changes (LFC) across four expanded CAG repeats. A linear interpolation on the data was made in order to make the curves comparable.
Discussion Highly dimensional genomic datasets  (Langfelder et al. , 2016; Maniatis et al. , 2019; Megret et al. , 2020)  offer the possibility of dissecting the complexity of biological processes on a molecular level, for example, the context-dependent features of molecular regulation dynamics. Here, we report the first application of the shape deformation formalism to the analysis of omics data. Our results suggest that this approach precisely identifies the cell-type-specific and temporal features of molecular responses to mHTT in the mouse brain. To ensure reliability and accuracy in mapping cell-type-specific and temporal features of molecular responses to mHTT, we used a shape deformation analysis workflow that is completely datadriven. Reliability was ensured by permutation analysis for the key analytical step of the workflow used herein, namely gene deregulation curve matching (see Figure  1 , step 1).
Differential expression analysis To identify deregulated genes in either the whole striatum  (Langfelder et al. , 2016)  or specific striatal cell types (this study) of Hdh model mice, significant read count (>10 counts in at least one of the samples) data for eight individuals (four males and four females) as available in the RNA-seq data in the allelic series of Hdh model mice was fed into Deseq2 implemented in the R package DESeq2 (version 3. 8 on R 3. 5. 2; Love et al. , 2014)  in order to obtain an LFC vector for each condition (CAG repeat length >20, age) and a vector indicating if the genes are significantly deregulated (p<0. 05) or unchanged (p!0. 05) for each condition. The set age k, Q20 is used as reference for each condition at age k and Q>20. In this series, we have 10 individuals for each condition (males).
Linear interpolation The whole-striatum gene deregulation data and the cell-type-specific data in Hdh mice are not fully comparable because the CAG repeat data points are not exactly the same between these two datasets. To make these two datasets fully comparable, we used linear interpolation to transform CAG repeat data points in the whole-striatum gene deregulation curves at 6 months into CAG repeat data points as available in the cell-type-specific gene deregulation curves. This interpolation was not applied to the Q111 data point as it is common to the two datasets. For Q170, we took the value LFðQ140Þ þ ðLFCðQ175Þ À LFCðQ140ÞÞ 170 À 140 175 À 140 which corresponds to the intersection of the axis x = Q170 and the segments connecting the LFC at Q140 and Q175 in the whole striatum. For Q50, we take the value LFðQ80Þ 92 À 50 92 À 80 À LFCðQ90Þ 80 À 50 92 À 80 which corresponds to the intersection of the axis x = Q50 and the prolongation of the segments connecting LFC at Q80 and at Q92 in the whole striatum.
Calculation of weighted deformation distance For each gene, we compute the deformation distance between the interpolated whole-striatum gene deregulation curve and each striatal cell-type-specific gene deregulation curve in Hdh model mice at 6 months of age. Here, a confounding factor is that curves with a high amplitude may be further far away from each other than the ones with a small amplitude, which is true even though the resulting distances are small compared to amplitudes. To overcome this problem, we transformed the deformation distance (see above) into a weighted deformation distance that takes into account the LFC amplitude (maximum LFC value across CAG repeats). The weighted deformation distance between the curve for gene G in the whole-striatum data and the curve for the gene G in a given cell-type-specific dataset is computed as: 100 Â d G striatal ; G cell-type À Á 0:5 þ min amp G striatal ð Þ; amp G cell-type À Á À Á ; where d(G striatal ,G cell-type ) is the deformation distance between A and B, amp(G striatal ) is the maximum LFC of G in the whole-striatum data, and amp(G cell-type ) is the maximum LFC of G in the cell-type-specific data.
This situation might then lead to selecting a threshold that is typical of any one distance between two curves in the data space and that could miss too many true hypotheses or retain too many false hypotheses. We thus reasoned that a better approach is to select the threshold so that a maximum of hypotheses (gene deregulation mapped to a cell type), obtained by using this threshold, does not survive permutations. To this end, we tested several thresholds from the minimum up to the third quantile (integers) of the distribution of distance values, here ranging from 2 to 23 (Figure  1 -figure supplement 2). We then selected the one threshold that shows the highest number of assignments with a false discovery rate (FDR) < 0. 1, as detailed below. To estimate the number of assignments that may be obtained by chance for a given threshold, we computed deformation distances upon 10,000 permutations of the LFC values across the five expanded CAG repeat lengths in the whole-striatum gene deregulation data.
Of note, permutating only the curves between genes to perform permutations is not enough as several whole-striatum LFC curves are very close to each other (which is expected assuming that the expression of several genes may be controlled by the same block of transcriptional modulators). We thus independently permuted the LFC for each CAG repeat length between genes. The p-value for each hypothesis on a threshold is given by the number of trials for which this hypothesis is obtained using this threshold. We then used the vector of p-values associated to each threshold to compute an FDR using the Benjamini and Hochberg method  (Benjamini Y, 1995)  and the function p. adjust of the package StatsModels (https://www. statsmodels. org/0. 8. 0/#) and statsmodels. stats written in Python. The FDR is a method for conceptualizing the rate of false positives when conducting multiple comparisons.
cluster in python using the cost distance. More precisely, the K-means algorithm was applied to the tables of transformed LFCs, obtained by multiplying the original LFCs by the matrix (mId+A t A À1 ) t , as defined in Equation (  3 ). This analysis allowed us to use the classical K-means algorithm to be used in a simple way as the cost distance on original LFC is equal to the Euclidean distance on the transformed LFC. One important parameter when running a K-means algorithm is the number of clusters. Here, this number is chosen independently for each cell type, based on the evolution of the sum of squared errors and the Rand index  (Rand, 1971 ) computed using the function adjustedrandscore of sklearn. cluster. Running the algorithm 10 times, we verified that all series of clusters have an adjusted rand score higher than 0.
Biological significance of Geomic data To assess the biological significance of gene deregulation effects that are associated to specific striatal cell types (cell-type-specific GDS clusters) and to molecular response families as inferred from Geomic analysis, we subjected gene lists to STRING version 11. 0 analysis  (Szklarczyk et al. , 2017;  https://string-db. org/) in which stringent settings were used, namely information from 'Databases' and 'Experiments' only, a STRING confidence score !0. 7 and 40-60 additional interactors allowed on the first shell. To assess the biological significance of molecular response categories, we applied the same approach, however allowing additional first-shell interactors in the range of greater than twice the number of seeds to a maximum of 80 interactors added.
To assess network convergence of the increased pathogenic and lost compensatory responses, we allowed additional five times more first-shell interactors compared to the number of seeds, using a STRING confidence score !0. 4 and information from 'Databases,'  'Experiments,' and 'co-expression. ' In all types of analysis, the resulting networks were tested for the top annotations (smallest p-values for the largest numbers of nodes) provided by GO (KEGG pathways, biological processes). PubMed searches were also used to assess the biological significance of gene deregulation effects with a cellular assignment.