• Embargo close
• Research software close

The circadian clock drives the oscillatory expression of thousands of genes across all tissues and bears significant implications for human health. RNA-seq time-series experiments interrogate the mechanistic links between transcriptional rhythms and phenotypic outcomes. Analysis methods must overcome the challenges of sparse temporal sampling, noisy data, and non-strictly periodic dynamics. Moreover, there remains a need for differential cycling analysis methods that can identify complex changes in rhythmicity for 2-sample comparisons across experimental conditions. We present TimeChange -- a non-parametric and model-free method for the quantification of differential dynamics across experimental conditions. The method leverages a data transformation technique known as time-delay embedding to reconstruct the underlying state space for each gene-of-interest. Takens' embedding theorem implies that rhythmic dynamics will exhibit circular patterns in the embedded space. TimeChange non-parametrically compares the distributions of points in the embedded space via the Fasano-Franceschini test to assess whether the topological structures differ significantly between phenotypes, thereby quantifying differences in transcriptional dynamics without requiring knowledge of the underlying model. Application of TimeChange to synthetic data shows that it accurately identifies changes in transcriptomic dynamics, including differences in amplitude/peaked-ness; changes in sawtooth asymmetries; and trending oscillatory drifts (e.g. linear, damped, and contractile). We further show that the method has potential utility beyond circadian dynamics. For instance, initial tests of TimeChange using erg rowing-machine data shows accurate classification of differential stroke dynamics in real time, suggesting potential uses of TimeChange in the field of sports science and medicine.

Python package and web app to retrieve and visualize data contained in SCKAN (e.g., across species, relationship to spinal segments) to highlight similarities and differences in neuronal pathways.

Pathogens were cultured with food samples and their genomes sequenced. The same food samples had their metagenomes sequenced. This repository contains the R script used to rarefy the food metagenomes and identify differences amongst food commodities using Aldex2. It also contains the linear regression model used to compare the ability of metagenomes to detect pathogens from food with the type of pathogen, the number of microbial reads sequenced, and the relative abundance of the food pathogens in the metagenomes.

Updated version An up-to-date version of this repository can be found at: https://gitlab.com/fabreges/fabreges-2024 Abstract How living systems achieve precision in form and function despite their intrinsic stochasticity is a fundamental yet open question in biology. Here, we establish a quantitative morphomap of pre-implantation embryogenesis in mouse, rabbit and monkey embryos, which reveals that although blastomere divisions desynchronise passively without compensation, 8-cell embryos still display robust 3D structure. Using topological analysis and genetic perturbations in mouse, we show that embryos progressively change their cellular connectivity to a preferred topology, which can be predicted by a simple physical model where noise and actomyosin-driven compaction facilitate topological transitions lowering surface energy. This favours the most compact embryo packing at the 8- and 16-cell stage, thus promoting higher number of inner cells. Impairing mitotic desynchronisation reduces embryo packing compactness and generates significantly more cell mis-allocation and a lower proportion of inner-cell-mass-fated cells, suggesting that stochasticity in division timing contributes to achieving robust patterning and morphogenesis. This repository In this repository, you will find the R-scripts used to generate the figures, along with the data necessary to make the plots. You will also find the Python-scripts used to compute the pair-wise distance between embryos, to estimate the surface, the volume, the α-parameter, to identify the closest rigid packing and to generate the morphomap. Additionally, you have access to the base model used with Plantseg to predict and label the cells at the 8-cell stage. Note that a more accurate and more specific model was generated for each embryo based on the curation of at least 5 timepoints. Figures The plots in Figure 1-7 and Figure S1-S7 can be generated with the respective files. The code is written in R and requires RStudio to work out-of-the-box. The R scripts are located in figures/. Figures and supplemental figures are generated with the same file (e.g., Figure 1.R generates all the computed panels of Figure 1 and Figure S1). Data required to generate the plots are located in figures/data/. Note that the code to work with the morphomap and the tracking data is located in local packages figures/fabreges.morphomap and figures/fabreges.tracking. An additional package figures/fabreges.generic contains utility functions. Python packages For the needs of this study, we developed a series of package for Python located in python-packages/. The packages can be directly imported within your scripts. Along with the packages, you will find 6 scripts used to compute, extract or estimate the data from the source. The folder is organised as follow: package interfaces is located in python-packages/interfaces/ and can be tested by running the scripts to extract the angles an estimate the surface of contact (compute-angles.py and estimate-contacts.py respectively) in folder python-packages, package morphomap is located in python-packages/morphomap/ and can be tested by running the script to compute the geometrical distances between embryos, project the data in 2D and compute the volume of the cells (compute-distances.py, process-distances.py and compute-volumes.py respectively) in folder python-packages, package splinefit is included in morphomap in python-packages/morphomap/splinefit/, but can function independently, package rbb is located in python-packages/rbb/ and can be tested by running the script to identify the closest rigid packing of an embryo at the 8-cell stage (identify-packing.py) in folder python-packages. We provide example of segmented data in python-packages/samples/. CNN We provide a convolutional neural network (CNN) used in our study, located in cnn. Please note that we optimised the CNN further for each dataset by doing manual curation of at least 5 time points and by retraining the network with those ground truth. The CNN were trained using pytorch-3dunet. The CNN can be used with Plantseg. Currently (plantseg v.1.6.0 and pytorch-3dunet v.1.6.0), the output model from pytorch-3dunet must currently be modified to work with plantseg: # pythonimport torchfor model in ('best_checkpoint.pytorch', 'last_checkpoint.pytorch'): state = torch.load(model, map_location='cpu') state = state['model_state_dict'] torch.save(state, model) Requierements Figures The figures were generated with R (v. 4.4.0) and RStudio (v. 2024.04.1+748). The following packages must be installed prior to execution: alphashape3d (v. 1.3.2) devtools (v. 2.4.5) ggplot2 (v. 3.5.1) ggrepel (v. 0.9.5) rstudioapi (v. 0.16.0) To install all these packages, run the following command in the RStudio console: # Rinstall.packages(c("alphashape3d@1.3.2", "devtools@2.4.5", "ggplot2@3.5.1", "ggrepel@0.9.5", "rstudioapi@0.16")) More recent version of R, RStudio and the packages may also work (not tested). Python packages Python 3.12.3 was used to analyse the data. The scripts may work with more recent version of Python and with Python 3.8 or earlier (not tested). To run the code, you must first install the following packages: numpy (v. 1.26.4) imageio (v. 2.34.1) scipy (v. 1.13.1) scikit-image (v. 0.23.2) scikit-learn (v. 1.5.0) Alternatively, you can create a conda environment: # Shell scriptconda create -y --name fabreges2024 python=3.12.3conda activate fabreges2024python -m pip install numpy==1.26.4 imageio==2.34.1 scipy==1.13.1 scikit-image==0.23.2 scikit-learn==1.5.0 More recent version of the dependencies may work (not tested). Usage Figures Open one of the figure script in RStudio and run the entire code. You may have to create a folder output in figures/ if it does not exist. The code has local dependencies that will be installed on your computer when encountering them for the first time. They start with fabreges.* and are necessary to generate the figures. Python packages (example) Compute angles # Shell script# Compute a summary (mean, median, s.d.) of the angles in embryo C1 at time 40python compute-angles.py -s -r 15 -R 20 samples/C1_t040.tif.gz output/C1_t040_angles.csv# Compute a detailed list of angle valuespython compute-angles.py -r 15 -R 20 samples/C1_t041.tif.gz output/C1_t041_angles.csv Compute distances # Shell script# Compute the distance between two embryospython compute-distances.py -c 5 -s 5 samples/C1_t040.tif.gz samples/C1_t041.tif.gz output/distances.csv# Append another distance measurementpython compute-distances.py -a -c 5 -s 5 samples/C1_t040.tif.gz samples/C1_t042.tif.gz output/distances.csv Compute volumes # Shell script# Compute the volume in isotropic datapython compute-volumes.py -r 0.416 0.416 0.416 samples/C1_t040.tif.gz output/volumes.csv Estimate contacts # Shell script# Estimate the contacts and the free surface areapython estimate-contacts.py -r 0.416 0.416 0.416 samples/C1_t040.tif.gz output/contacts.csv Identify the closest rigid packing # Shell script# Assuming the file `output/contacts.csv` exists and contains the output# from command `estimate-contacts.py`.# Identify the closest rigid packingspython identify-packing.py output/contacts.csv output/backbones.csv Generate the seeds for Surface Evolver # Shell script# Assuming the file `output/contacts.csv` exists and contains the output# from command `estimate-contacts.py`, and the file `output/volumes.csv`# exists and contains the output from command `compute-volumes.py`# Generate the seeds for Surface Evolver using automatic thresholding# Automatic threshold will determine the highest cell-cell contact threshold# that preserves rigidity of the embryo (or 0 if not rigid)python generate-seed.py output/contacts.csv output/volumes.csv output/seed.fe# Generate the seeds for Surface Evolver with custom thresholdpython generate-seed.py --threshold 0.1 output/contacts.csv output/volumes.csv output/seed.fe Project in 2D and generate a distance matrix # Shell script# Assuming the folder `foo` only contains one or more CSV# from command `compute-distances.py`. # Compute the tSNE 2D projectionpython process-distances.py -D2 -t output/tsne_2d.csv foo/ # Compute the tSNE 3D projectionpython process-distances.py -D3 -t output/tsne_2d.csv foo/ # Generate the distance matrixpython process-distances.py -m output/dismat.csv foo/

This document presents all codes used in Diverging trends in plant phenology across European mountainsin a warming world by Andreatta et al. All the data used for this study are available online, and links tosources are reported in this document. Full details of the methods are available in the manuscript. Forfurther questions, contact davide.andreatta@fmach.it.

Code for analyzing the data for manuscript for Global Ecology and Biogeography

Jupyter notebooks, python scripts, and R scripts for the analysis of human ovarian cancer single-cell and bulk profiling data.

Matlab script to compute fULM activation maps

This repository contains all the analysis source code for Hirsch et al., 2019 (2019JD030665) and can be used to reproduce the analysis of the manuscript. Note that all directory paths will need to be updated for successful implementation of the code. Datasets to which the code applies: CORDEX AustralAsia domain on a 0.44˚ x 0.44˚ rotated coordinate system. All data is available from ESGF Australian Gridded Climate Data (AGCD) daily precipitation and temperature dataset [Jones et al. 2009 Australia. Aust. Meteor. Mag.] Global Land surface Evaporation: the Amsterdam Methodology (GLEAM) dataset [Miralles et al. 2011, doi:10.5194/hess-15-453-2011; Martens et al. 2017, doi:10.5194/gmd-10-1903-2017] ERA-Interim (ERAINT) dataset produced by the European Centre for Medium-Range Weather Forecasts (ECMWF) [Dee et al. 2011, doi:10.1002/qj.828] Modern-Era Retrospective Analysis for Research and Applications version 2 (MERRA2) [Reichle et al. 2017, doi:10.1175/JCLI-D-16-0720.1] This repository provides all the tools to process each datasets, calculate diagnostics describing heatwave characteristics using the Excess Heat Factor (EHF), scripts to extract all surface energy balance fluxes corresponding to the identified heatwave events and the month preceding the event, and scripts to evaluate land-atmosphere coupling. Further information on the datasets and calculations are available from: Hirsch, A. L., J. P. Evans, G. Di Virgillio, S. Perkins-Kirkpatrick, D. Argüeso, A. J. Pitman, C. Carouge, J. Kala, J. Andrys, P. Petrelli, and B. Rockel (2019), Amplification of Australian heatwaves via local land-atmosphere coupling. Journal of Geophysical Research Atmospheres, accepted 1.12.2019. Scripts used to prepare the data regrid_AWAP.sh - interpolates the AGCD data to the CORDEX AUS-44i domain regrid_ERAINT.sh - interpolates the ERA-Interim data to the CORDEX AUS-44i domain regrid_GLEAM.sh - interpolates the GLEAM data to the CORDEX AUS-44i domain regrid_MERRA2.sh - interpolates the MERRA2 data to the CORDEX AUS-44i domain Interpolate_to_regular_grid_rev_28042018.py - interpolates a time-varying field from the native WRF grid mesh (rotated pole) to a regular lat-lon grid mesh. splitup_DATA_MERRA.sh - retrieves energy balance fluxes and splits according to heatwave season per year splitup_DATA_MODEL.sh - retrieves energy balance fluxes and splits according to heatwave season per year for all model datasets splitup_EHF_MERRA.sh - splits the EHF index and spell index into one file per heatwave season splitup_EHF_MODEL.sh - splits the EHF index and spell index into one file per heatwave season for all model datasets Shell scripts Calc_two_legged_coupling.sh - Shell script that calculates Paul Dirmeyer's two-legged coupling metrics http://cola.gmu.edu/dirmeyer/Coupling_metrics_V2-7_TwoLeg.pdf using cdo Python scripts Run_allmodels_EHF_HWproject.py - this script takes the code template HWproject_WRF.deck as input and set it up depending on the experiment, then runs the final code to calculate the Excess Heat Factor for each of the model datasets HWproject_WRF.deck - template for a script that takes postprocessed WRF files (maximum and minimum temperature) and five netcdf files for each of the characteristics at yearly frequency, as well as the EHF index at daily frequency, plus other metrics for EHF analysis HWproject_AWAP.py - calculates EHF heatwave diagnostics from AWAP HWproject_ERAINT.py - calculates EHF heatwave diagnostics from ERAINT HWproject_MERRA2.py - calculates EHF heatwave diagnostics from MERRA2 compute_EHFheatwaves.py - contains the function to calculate Excess Heat Factor indices from the average daily temperature using maximum and minimum daily temperatures of each dataset HWvariables_info.py - VariablesInfo class definition, each object describe a variable and its attributes constants.py - class that contains most used atmospheric constant values Python notebooks get_gridded_timeseries.ipynb - extract the time series for all data in the lead up and post HW initiation Notebooks to plot results: plot_topo.ipynb - Contour map of the domain topography and subdomains using plot_EHF_OBS_vs_MOD_BIAS.ipynb - Contour maps of the model biases for the EHF threshold and diagnostics plot_EHF_Skill.ipynb - Plots a summary of the model skill for each of the EHF diagnostics and models plot_gridded_anomaly_timeseries.ipynb - Plot of the anomaly time series of different climate variables aggregated for different regions of interest. plot_gridded_anomaly_timeseries_abbrev.ipynb provides an abbreviated version of the same figure for the manuscript. plot_pr_negative_dXdt.ipynb - Plots the PDFs for the latent heat flux trend split according to antecedent soil moisture conditions plot_distributions_kde.ipynb - Plots the PDFs of the temperature data split according to antecedent soil moisture conditions for either the first heatwave day, the second, third or fourth heatwave days individually plot_two-legged_coupling.ipynb - Contour maps of the coupling diagnostics plot_two-legged_coupling_components.ipynb - Contour maps of the component terms for each of the coupling diagnostics plot_EHF_vs_two-legged_coupling.ipynb - Checks the relationship between each of the EHF diagnostics and the and coupling diagnostics plot_EHF_ranked_by_two-legged_coupling.ipynb - Checks the KDE distributions between strong land-driven coupling and atmospheric-driven coupling (using the IA coupling diagnostic) for all models and grid cells pooled together, regional sensitivity and model sensitivity plot_LR_EHF_vs_two-legged_coupling.ipynb - Compares the tail of the KDE distributions using the same methodology as plot_EHF_ranked_by_two-legged_coupling.ipynb for each EHF diagnostic and model difference using the likelihood ratio plot_distributions_kde_dQEdt_split.ipynb - Similar to plot_distributions_kde.ipynb but splits the temperature data according to the sign of the latent heat flux trend Table files Climate.indices.csv - List of climate indices and their definitions

INVALID CODE