dataesr/scientific-paragraphs-categorization huggingface dataset:
Stability with respect to technical variations in the input data: number of profiled cells, sequencing platform and cell type annotation In the experiments performed above, we tested the reproducibility of the network inference algorithms by using two independent datasets for each biological condition (e. g. human retina). A limitation of this approach comes from the technical differences between the protocols followed to generate these datasets: different sequencing platforms, different procedures used for the annotation of the cell types, and different number of cells. All these technical differences could impact our results. To evaluate the stability of the results against technical variations, we used the largest dataset, from  (Menon et al. , 2019) , encompassing 20,091 cells. We splitted this dataset into two subsets, keeping the proportions of the various cell types constant.
We then applied the five network inference algorithms independently to the two subsets, and we evaluated the reproducibility of the algorithms using perINT and WJS, as in the previous tests. To further assess the effect of the number of cells on network This is a provisional file, not the final typeset article Running Title inference, we split the same scRNAseq dataset generated by Menon et al. three times to obtain couples of datasets encompassing decreasing number of cells: 10,000, 1,000 and 100. Note that for all these comparisons, the sequencing platform and/or the method/technique used to annotate the cells are identical for all subsets. PPCOR inferred networks for 10,000 and 1,000 cells, but failed at 100 cells (see Supplementary Table  2 ). For details on the number of links before and after thresholding (K=100,000) refer to Supplementary Table  2.
The scNET Jupyter notebook To foster the reproducibility of all the results and figures presented in this study, we implemented the corresponding code in a Jupyter notebook, which is available at https://github. com/ComputationalSystemsBiology/scNET, together with a Conda package containing all the required libraries. Importantly, scNET can be used to benchmark new network inference algorithms based on their reproducibility, or further test GENIE3, PPCOR, GRNBoost2, CLR and GeneNet on user-provided datasets. 4 Discussion Starting from the benchmark of Paratapa et al. (  2020 ), we evaluated the network inference algorithms from a complementary perspective by assessing their reproducibility. We were thus interested to test if the algorithms would infer similar networks when applied to pairs of independent datasets from the same biological condition (e.
g. T-cells in colorectal cancer). Our benchmark focused on real patientderived data spanning three biological contexts: human retina, T-cells in CRC, and human hematopoiesis cells. We thus consider highly different biological contexts, going from cancer tissue, to isolated healthy immune cells, and to a mixture of normal retina cells combined in a single dataset. Importantly, we aimed at inferring networks involving a much higher number of genes compared to previous works. In agreement with previous benchmarks, all network inference algorithms generated networks having low intersections with ground-truth. Of note the ground-truth considered here, based on RcisTarget and regulatory circuits, is different and complementary to those used in previous benchmarks. This disappointing result might arise for different reasons, potentially adding up.
Limitations can be present in the input data, as scRNAseq may not provide sufficient resolution for reliable network inference, and technical and experimental factors present in the input data might affect information content. Turning to the inference algorithm, limitations may arise from underlying statistical assumptions and the documented lack of uniqueness in the solution of the network inference problem. Finally, the ground-truth network considered here and in previous benchmarks may not be sufficiently comprehensive. PPCOR provided weights outside the normal range of correlation values ([-1,1]) for datasets having less than 1,000 cells. Such inconsistencies are likely due to numerical problems arising when the input dataset encompasses many more genes than cells. PIDC was the algorithm that suffered the most when applied to high numbers of genes.
We tried to make the inferred networks comparable by fixing the number of links in all network to a certain value K, thus obtaining networks with the same density. However, in principle, methods inferring only TF-target links will have higher chances to be reproducible in our comparison. At the same time, once the links of PPCOR and GeneNet are restricted to only TF-target links, the dimension of the networks drastically decreases (sometimes empty networks are obtained) and their performances do not improve. The main limitation of this benchmark is the number of considered network inference algorithms. Future extensions of this study could include pseudotime-based network inference methods, once adequate datasets will become available. To date, available independent datasets relevant for pseudotime-based network inference algorithms (e.
The results are reported for all the tested datasets (A) retina, (B) CRC T-cells, (C ) CLPs, (D) Dendritic cells, (E) Erythrocytes, (F) HSCs, (G) Monocytes. Figure 4. Stability of the network inference performances with respect to technical variations in the input data. Reproducibility scores of GRENBoost2 (red), GENIE3 (black) PPCOR (yellow), CLR (gray) and GeneNet (green) across different splittings of the Menon retina dataset. Panels A and B report the percentage of intersection (perINT) and Weighted Jaccard Similarity (WJS), respectively.
Datasets employed in this benchmark Data Name Biological context Sequencing technology Number of cells Cell type annotation strategy Associated publication Number of genes after preprocessing. Network Medicine in the Age of Biomedical Big Data. Frontiers in Genetics 10, 294. doi:10. 3389/fgene. 2019. 00294. Tantardini, M. , Ieva, F. , Tajoli, L. , and Piccardi, C. (2019). Comparing methods for comparing networks. Scientific Reports 9, 17557. doi:10. 1038/s41598-019-53708-y. The DREAM5 Consortium, Marbach, D. , Costello, J. C. , Küffner, R. , Vega, N. M. , Prill, R. J. , et al. (2012). Wisdom of crowds for robust gene network inference. Nat Methods 9, 796-804. doi:10. 1038/nmeth. 2016. Verny, L. , Sella, N. , Affeldt, S. , Singh, P. P. , and Isambert, H. (2017). Learning causal networks with latent variables from multivariate information in genomic data.
, 2012). Additionally, post-mortem MRI is valuable as an adjunct to, or instead of, traditional autopsy, providing similar diagnostic accuracy  (Thayyil et al. , 2013; Kang et al. , 2020)  and to improve our understanding of congenital neurodevelopmental defects  (Vaneckova et al. , 2010). While post-mortem infant MRI overcomes several issues inherent to in vivo MRI scans, it comes with its own unique and complex set of challenges and limitations. The ethical and logistical challenges of subject recruitment results in small sample sizes and the collection of data across restricted age ranges. Furthermore, the acquisition of high resolution post-mortem data is primarily achieved via long scan times. Due to natural tissue degradation during prolonged acquisitions, tissue integrity must be preserved.
A key challenge for longer scans is the need to minimise degradation of unfixed tissue, requiring that the body be maintained at a low temperature similar to mortuary conditions. Temperature control may be a particular challenge at ultra-high field due to higher radio-frequency energy deposition during imaging. In this paper, we present the Forget-Me-Not (FMN) study that aims to provide highresolution dMRI data for connectivity and microstructural estimates in the post-mortem infant. As a substudy of the dHCP, FMN primarily aims to acquire and openly distribute high-quality dMRI data. We use the in vivo dHCP study as a useful comparison throughout, although the aim is for these studies to be both compatible and complementary. For the remainder of the paper, we refer to the previously acquired in vivo dHCP data as the "in vivo" dataset and the novel postmortem dHCP data collected during the FMN study as the "post-mortem" dataset.
We propose an approach that directly addresses the technical challenges involved in using 7T dMRI to image the post-mortem infant brain at high resolution, in situ and unfixed. The acquisition protocol utilizes spin echo excitation and echo-planar readout to enable straightforward comparison with the in vivo dHCP  (Hutter et al. , 2018)  while optimizing acquisition parameters for best image quality based on field strength and the different conditions of in vivo and post-mortem examination. Achieving high resolution imaging data of the intact brain can act as a link beween in vivo dMRI microstructural and connectivity analyses and post-mortem histological findings. The post-mortem diffusion data were acquired using a high b-value multishell protocol to allow the fitting of complex microstructural models, and a relatively large number of diffusion encoding directions to allow high angular resolution tractography.
In addition to logistical and technical challenges, the FMN study required careful consideration regarding participant recruitment and research governance; we detail the sensitive recruitment and scanning procedure we developed to enable data to be collected in a single overnight visit shortly after death. We present results from the first analysable subject recruited to the study. Microstructural properties of the post-mortem cortical plate, subplate, and major white matter tracts were contrasted with previously published findings, both in vivo dMRI and post-mortem histology, to assess the influence of high spatial resolution, low acquisition temperature, and postmortem tissue degredation effects. Our findings demonstrate that ultra-high field scanning can improve the SNR, and therefore spatial resolution, of neonatal post-mortem imaging.
We provide raw data for both infants, but data analysis is only presented in this paper for one infant. The current structural analysis pipeline has not yet been optimised for the other infant, where the significant pathology makes it difficult to use the age-matched brain atlas. The infant presented (male) was born at 23+0 weeks GA, weighing 545g. He had bilateral grade 3 IVH and post-haemorrhagic ventricular dilatation. Following a decision to transition to palliative care, he died on postnatal day 46 at 29+4 weeks PMA and weighing 580g. The infant was scanned within 18h of death. Parents expressed several reasons for their participation in the FMN study, one of which was the acceptability of the non-invasive nature of the MRI method. Additionally, parents cited altruistic reasons, they commented on their awareness that advances in neonatal care allowing their infants the chance at survival had only come about through research.
Data acquisition setup To facilitate a direct comparison between post-mortem and in vivo data, FMN uses spin echo excitation with EPI readout for dMRI acquisitions, which is consistent with the in vivo dHCP study. Other aspects of the post-mortem study were optimised to provide the best quality data, which resulted in acquisitions that are different to the in vivo dHCP protocols, but is intended to maximise the total information content from the study as a whole. This led to a number of specific decisions regarding acquisition, and a range of technical challenges. The main challenge for high resolution imaging is obtaining sufficiently high SNR, which scales proportionally to voxel volume. This challenge led to a first key decision for the study, which was to image at higher field strength than the in vivo dHCP study (7T vs 3T).
The imaging protocol used to acquire the first two infants' data includes T1-and T2weighted and dMRI protocols. Here, we present the T2w and dMRI data. T2-weighted structural data were acquired at 0. 4 mm isotropic resolution (compared to 0. 8 mm resolution for in vivo dHCP data). dMRI data were acquired at 0. 8mm isotropic resolution (compared to 1. 5mm resolution for in vivo dHCP data). Similarly to the in vivo dHCP data, we acquired dMRI data with three shells but with evenly spaced b-values adjusted to account for reduced diffusivities: b=3000, 6000 and 9000 s/mm 2 (compared to b=400, 1000 and 2600 s/mm 2 for in vivo dHCP). The protocol was optimised for data quality achievable in an overnight scan session, which is described in Supplementary Information S2.
Structural and diffusion data quality assessment Cranial ultrasound images of the infant taken prior to death reveal evidence of bilateral IVH grade 3, post-haemorrhagic ventricular dilatation, and parenchymal extension in the left hemisphere (Fig. 3  Ultrasound). These pathological details are visible with finer detail and improved contrast on the post-mortem structural T2-weighted images at 7T (Fig. 3  Postmortem T2w). The gain in structural detail in the post-mortem structural images compared to in vivo is highlighted by comparison to an age-matched subject from the in vivo dHCP dataset (Fig. 3  In vivo T2w). This reflects the higher spatial resolution (0. 4 mm post-mortem vs 0. 8 mm in vivo), achievable due to the higher field strength and longer scan time, and lack of motion-related artefacts.
, 1996; Xiao et al. , 2020). In the results that follow, we analyse the microstructural properties of the cortical plate, subplate, and white matter tracts. An over-arching goal is to assess the value that the postmortem dHCP data adds to visualising and analysing infant brain microstructure, the suitability of this data to act as a bridge between histological findings and the in vivo dHCP dMRI data and the potential to use the post-mortem data to investigate pathology and death related tissue changes. To facilitate interpretability and cross-dataset comparisons, we restricted analyses to the right hemisphere only, to mitigate the influence of significant overt pathology in the left hemisphere of the post-mortem subject (Fig. 3 ).
7a ). Fig. 7b  summarises DKI and NODDI parameter estimates for white matter tracts. Comparing spatial patterns across tracts, the post-mortem data and in vivo data have similar tract-to-tract variability for fractional anisotropy, mean diffusivity and intra-neurite fraction with coefficients of determination R 2 >0. 5. Comparing global parameter scaling between datasets, the NODDI results suggest that compartment fractions are globally inverted between the datasets, with the post-mortem data having consistently larger intra-neurite (∆((post-mortem -in vivo)/post-mortem)=0. 66) and lower extra-neurite volume fractions (∆= -1. 34) compared to the in vivo data. This would be consistent with cellular swelling (cytotoxic edema) reducing the extracellular space. Orientation dispersion parameters are generally higher in the post-mortem data than the in vivo data, which may also reflect death related tissue changes.
In these cases, random permutations will also be overfit and achieve high LDR values, which then results in higher p-values. For example, in the dHCP data, the DKI (3-parameter) and DKI+NODDI (7-parameter) models have similar LDR values (3. 15 and 3. 27, respectively) but quite different p-values (0. 01 and 0. 12, respectively). It is important to note that even when comparing post-mortem and in vivo clustering derived from the same set of diffusion metrics (e. g. DKI parameters) the actual discriminants (LDA feature vectors) are derived from the two datasets separately and are unlikely to reflect the same information.
Discussion The post-mortem Forget-Me-Not dHCP study aims to acquire and openly distribute highquality diffusion imaging of unfixed post-mortem infant brains that will provide insight into developing brain connectivity and microstructure. Furthermore, the data are designed to enable direct comparison with the in vivo dHCP dataset. The post-mortem data appeared to resolve fine brain structures and to detect cortical radiality in regions with high curvature, results that suggest a direct benefit of the increased spatial resolution. While the post-mortem data have greatly improved spatial resolution relative to the in vivo data, the post-mortem brain features macroscopically visible pathologies, and our dMRI analysis results suggest that there are further microstructural changes, such as cell swelling and autolysis, which are expected consequences of pathological and death-related processes.
, 2013). While this data was not usable in the first babies due to image artefacts, these will be straightforward to address and we aim to include this protocol in future scans. We thus anticipate future datasets providing T1, T2*, proton density and magnetic transfer saturation at 0. 8mm isotropic resolution in a ~1. 5 hour scan. A major aim in many post-mortem MRI studies is the direct comparison with histological data, enabling validation of imaging results. To date, FMN has not attempted to recruit brain donation, which would enable histology data to be collected, but might affect parents' willingness to participate. While we feel it is unlikely that many parents would consent to brain donation, we will aim to explore this possibility in the future. Acquisition of histological data along with imaging data in a subset of future recruits may be possible, and would greatly improve the value of the resource.
These results demonstrate the advantage of high resolution data despite long scan hours, which enables more detailed depiction of small structures. The patterns in the downsampled postmortem data were not seen in the in vivo data, despite matched spatial resolution. This difference could relate to tissue changes due to death and pathology, but as yet we do not have sufficient data to determine the impact of pathology and death-related processes on these observations. Further data collected for the post-mortem dHCP study will provide insight. Quantitative linear discriminant analyses separated limbic tracts from both the association and projection tracts in the in vivo and post-mortem data using either DKI or NODDI model parameters (Fig. 8 ). However, the subsequent subdivision of the isocortical tracts into projection and association tracts is less well defined in both datasets.
, 2014). This suggests the higher orientation dispersion in the post-mortem data may be influenced by post-mortem cell swelling. Digging deeper into these initial promising findings and tackling these interesting challenges is an ongoing endeavour for the FMN study. Due to the successful implementation of the care pathway for this infant post-mortem research study, which was well received by the clinical team, research imaging centre, and the parents participating, further data collection is ongoing. We hope collaborative research on this open dataset will accelerate advances in understanding of developing human brain as well as pathology and death during the term and preterm periods.
, 2003; Smith et al. , 2004). The total acquisition time for dMRI data is just under 7 hours. In the Supplementary Information S2, we provide an overview of this process with justifications for key decisions made. A comparison between post-mortem and in vivo dMRI protocols is shown in Table  1. dMRI images were reconstructed off-line in MATLAB (Mathworks, Natick, MA) from raw k space data. The split slice-GRAPPA algorithm  (Cauley et al. , 2014)  was used to separate aliased slices, followed by GRAPPA reconstruction of segmented EPI data using similar pipeline as in previous work  (Frost et al. , 2015). This large volume of raw data (~300G) was incompatible with the reconstruction computer's native storage, requiring data to be streamed from the scanner to an offline computer using the iceNIH_RawSend tool developed by Jacco A.
Subplate microstructure Regions-of-interest (ROIs) for the gray and white matter in the right hemisphere were generated using the dHCP structural pipeline segmentation outputs. The white matter ROI was fed into FSL's distancemap command to generate a voxelwise map of the distance from the nearest gray-white matter boundary. This was used to characterise diffusion parameters as a function of distance value. In addition, diffusion parameters were averaged over the cortical plate and taken to be the value at zero distance. As distance values are discretised, the median parameter value was taken for a given distance value, and a smoothing spline curve fit to the data using MATLAB fit function (model type = smoothing spline, smoothing parameter = 0. 99). The group median for the in vivo dHCP data was calculated as the median parameter value across all subjects for a given distance.
For the post-mortem dHCP subject, the ultrasound images were acquired prior to death, while the T2w structural MRI is post-mortem. The in vivo T2w data are from an age-matched infant from the in vivo dHCP database. The post-mortem and in vivo T2w images were acquired at 0. 4x0. 4x0. 4mm 3 and 0. 8x0. 8x0. 8mm 3 spatial resolutions, respectively. Note the in vivo T2w images were interpolated to 0. 5x0. 5x0. 5mm 3 in post-processing. The postmortem images were acquired using 3D T2w-SPACE sequence and the in vivo images were acquired using 2D multi-slice FSE sequence with motion corrected reconstruction.
Acknowledgements The research leading to these results has received funding from the  European Research Council  under the  European Union   Seventh Framework Programme  ( FP/2007-2013 )/ERC Grant Agreement no. 319456. We are grateful to the families who generously supported this trial. The  Wellcome Centre for Integrative Neuroimaging  is supported by core funding from the  Wellcome Trust  ( 203139/Z/16/Z ). WW is supported by the  Royal Academy of Engineering  ( RF\201819\18\92 ). KM is supported by the  Wellcome Trust  ( WT202788/Z/16/A ). LB, FA, REF, VM, FM, and RS are supported by the  Wellcome Trust  ( 207457/Z/17/Z ). J. S. is supported by a  IDEXLYON "IMPULSION 2020  grant ( IDEX/IMP/2020/14 ) and the  Labex CORTEX   ANR-11-LABX-0042  of  Université de Lyon. AFDH is funded by the  Engineering and Physical Sciences Research Council (EPSRC ,  EP/L016052/1 ) and  Medical Research Council (MRC , grant  MR/L009013/1 ).
Code availability FSL tools for dMRI processing and analysis are available via FSL (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki). FSL Eddy tool for dMRI data preprocessing are available via https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/eddy. DKI fitting was performed with FSL's dtifit tool (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FDT). Bias field correction was performed with FSL's fsl_anat tool (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/fsl_anat). Linear image registration was performed with FSL FLIRT (https://fsl. fmrib. ox. ac. uk/fsl/fslwiki/FLIRT). Cudimot was used for NODDI fitting (https://users. fmrib. ox. ac. uk/~moisesf/cudimot/index. html). Tractography was performed with Baby Autoptx tool developed for the dHCP (https://doi. org/10. 1016/j. neuroimage. 2018. 05. 064). Processing pipeline for T2w structural data is available online (https://github.
5mm and 0. 8mm isotropic, respectively. For clear visulisation, T2w, mean diffusivity, and mean kurtosis values are plotted using separate scales per dataset, with in vivo data scaled according to the left y-axis and post-mortem data scaled according to the right y-axis. Abbreviations: Post-mortem HR = Post-mortem high resolution; Post-mortem LR = Post-mortem low resolution; In vivo group = group median of in vivo dHCP data, In vivo individual = individual in vivo dHCP subject data. The mean diffusivity is expressed in mm 2 /s.