Hung Chak Ho

DiffuCpG 1. Introduction In this study, we used a generative AI diffusion model to address missing methylation data. We trained the model with Whole-Genome Bisulfite Sequencing data from 26 acute myeloid leukemia samples and validated it with Reduced Representation Bisulfite Sequencing data from 93 myelodysplastic syndrome and 13 normal samples. Additional testing included data from the Illumina 450k methylation array and Single-Cell Reduced Representation Bisulfite Sequencing on HepG2 cells. Our model, DiffuCpG, outperformed previous methods by integrating a broader range of genomic features, utilizing both short- and long-range interactions without increasing input complexity. It demonstrated superior accuracy, scalability, and versatility across various tissues, diseases, and technologies, providing predictions in both binary and continuous methylation states. In this repository, we deposit the code used to build the diffusion models along with necessary example datasets to train and test a diffusion model for methylation imputation purposes. Docker Usage Install Docker Install Docker using the following link:https://docs.docker.com/engine/install/Recommended system specs: Debian 12 bookworm with 16GB RAM or more.Make sure you have the latest Nvidia GPU driver installed and docker can access your Nvidia GPU. Run Docker images with Tissue-specific Models docker pull yay135/diffucpg_tssUse our example to generate input samples with Hi-C matrix and CIS (Confidence Interval Cross Sample) data.docker run -it yay135/diffucpg_tssthenpython generate_train_test_samples.py The tissue-specific models (pytorch) are for CD34+ cells, GBM and BRCA, they are stored in folders named "model*" in the image. Run the Tissue specific modelsdocker run -it yay135/diffucpg_tssthenpython batch_run.py Run Docker images Example Models docker pull yay135/diffucpgIf you do not have a GPU enabled system, pull a CPU-only imagedocker pull yay135/diffucpg_cpuprepare your input data directory, use the following command to print a example input data directorydocker run --rm yay135/diffucpg -e trueassume your data directory name is "input_data"in windowsdocker run --gpus all -v .\input_data\:/data --rm yay135/diffucpgin unix or linuxdocker run --gpus all -v ./input_data:/data --rm yay135/diffucpg Other docker options -d or --device : select which cuda device to run with, default is 0-m or --mingcpg : scan your methyl array, limit only imputing windows with at least m non-missing methyl values, default is m=10-o or --overlap : set number of impute epochs, shift window locations between epochs, get mean imputed values for each CpG location, default is 2example:docker run --gpus all -v ./input_data:/data --rm yay135/diffucpg -d 1 -m 5 -o 3use cuda device 1, min number of non-missing methyl values in a window is 5, overlap epochs 3 The following tutorials are for non-docker usages. 2. Data and Models Example datasets are available for download using "gdown.sh". The example datasets only contain WGBS methylation data. The model is the DDPM diffusion model, the repository contains a complete implementation for 1-dimensional input. Please refer to https://arxiv.org/abs/2006.11239 and https://huggingface.co/blog/annotated-diffusion for more details. 3. How to use 3.1 System Requirements The number of steps in the diffusion process is set to 2000. Imputing a sample requires 2000 steps. Gpu acceleration is preferred. 16GB of RAM is required. The code is fully tested and operational on the following platform: Distributor ID: DebianDescription: Debian GNU/Linux 12 (bookworm)Release: 12Codename: bookworm 3.2 Clone the Current Project Run the following command to clone the project.git clone https://github.com/yay135/DiffuCpG.git 3.4 Configure Environment Make sure you have the following software installed in your system:Python 3.9+Pytorch 2.0.1+ 3.4 Run Training and Testing python run.pyThe script will download necessary data and install dependencies automatically. 4 Data and Script Details 4.1 RAW Data The methylation arrays downloaded are in the folder "raw", each file is a methylation array. The first 2 columns are "chromosome" and "location". The assembly used for mapping in our project is the "GRCH37 primary assembly". It is also downloaded automatically. The rest of the columns in each file are methylation levels(required) and other biological data (optional) you wish to incorporate to enhance the model. These files in the raw folder are the initial inputs for pipeline,if you wish to use your own data, it must be configured as such before running the pipeline. 4.2 Generate Sample Use script "generate_samples.py" to generate samples for training and testing.The model can not directly read and impute a methylation array file. Instead, each methylation array is divided into windows, each window is 1kb (1000 base pairs) in length, and each training testing sample is generated from a window. Each sample contains at least 5 channels. the first 4 is the sequence one-hot encoding, the 5th is the methylation data. If a base pair location is not a CpG location, the methylation data value for it is "-1". If a CpG's methylation data is missing or waiting for imputaion, its value is also "-1". Other biological data can be added as extra channels. Check out example raw files in the folder "raw" to form your own datasets for training and testing sample generation.For each raw file in the "raw" folder, the first 3 columns are chr, loc, and methylation.The rest of the columns are treated as additional channels and will be added to each sample during generation. '-d' or '--folder': specify raw data folder'-i' or '--index' : which column in a raw file is the methylation array'-t' or '--tol' : how many missing methylation value is tolerated(we recommend 0 for generating training samples and -1 for generating testing samples, 0 will force the script to only select from windows with no missings, -1 will tolerate missing as much as possible.)'-c' or '--chr' : limit which chromosome to use, default is "chr#" to use all chromosomes'-w' or '--winsize' : what window size to use, default is 1000 '-m' or '--mincpg': force generate from window to have a minimum number of CpGs, default is 10 '-n' or '--nsample': number of samples to generate per chromosome '-p' or '--output': samples output folder, default is "out" Use script "generate_samples_concat.py" to generate samples from long-range interacting windows such as Hi-C interactions or computed correlation.Check out the example long range file in the folder "data" to form your own long-range interacting windows for sample generation and concatenation. 4.3 Training Script Use diffusion.py to train and test a DDPM model using the generated samples'-t' or '--train_folder' : the folder containing the training samples'-f' or '--model_folder' : the model folder, will be created if it does not exist'-w' or '--win_size' : window size of each sample, default is 1000'-c' or '--channel': channel size of each sample'-d' or '--cuda_device' : if you have multiple cuda gpus, select which gpu to use, default is 0"-e" or "--epoch" : how many epochs for training, default is 2000"-s" or "--earlystop" : whether to use "early stopping" during training, default is False"-p" or "--patience" : patience for early stopping, default is 10 4.4 Imputation Use diffusion_inpainting.py to perform imputation on generated samples.'-t' or '--test_folder' : the folder containing samples for imputation'-o' or '--out_folder': imputed output folder name, default="inpainting_out"'-w' or '--win_size' : window size of each sample, default is 1000'-c' or '--channel': channel size of each sample'-d' or '--cuda_device' : if you have multiple cuda gpus, select which gpu to use, default is 0 Team If you have any questions or concerns about the project, please contact the following team member: Fengyao Yan fxy134@miami.edu

IMPORTANCE: Cancer is the second leading cause of death worldwide. Current estimates on the burden of cancer are needed for cancer control planning. OBJECTIVE: To estimate mortality, incidence, years lived with disability (YLDs), years of life lost (YLLs), and disability-adjusted life-years (DALYs) for 32 cancers in 195 countries and territories from 1990 to 2015. EVIDENCE REVIEW: Cancer mortality was estimated using vital registration system data, cancer registry incidence data (transformed to mortality estimates using separately estimated mortality to incidence [MI] ratios), and verbal autopsy data. Cancer incidence was calculated by dividing mortality estimates through the modeled MI ratios. To calculate cancer prevalence, MI ratios were used to model survival. To calculate YLDs, prevalence estimates were multiplied by disability weights. The YLLs were estimated by multiplying age-specific cancer deaths by the reference life expectancy. DALYs were estimated as the sum of YLDs and YLLs. A sociodemographic index (SDI) was created for each location based on income per capita, educational attainment, and fertility. Countries were categorized by SDI quintiles to summarize results. FINDINGS: In 2015, there were 17.5 million cancer cases worldwide and 8.7 million deaths. Between 2005 and 2015, cancer cases increased by 33%, with population aging contributing 16%, population growth 13%, and changes in age-specific rates contributing 4%. For men, the most common cancer globally was prostate cancer (1.6 million cases). Tracheal, bronchus, and lung cancer was the leading cause of cancer deaths and DALYs in men (1.2 million deaths and 25.9 million DALYs). For women, the most common cancer was breast cancer (2.4 million cases). Breast cancer was also the leading cause of cancer deaths and DALYs for women (523 000 deaths and 15.1 million DALYs). Overall, cancer caused 208.3 million DALYs worldwide in 2015 for both sexes combined. Between 2005 and 2015, age-standardized incidence rates for all cancers combined increased in 174 of 195 countries or territories. Age-standardized death rates (ASDRs) for all cancers combined decreased within that timeframe in 140 of 195 countries or territories. Countries with an increase in the ASDR due to all cancers were largely located on the African continent. Of all cancers, deaths between 2005 and 2015 decreased significantly for Hodgkin lymphoma (-6.1% [95% uncertainty interval (UI), -10.6% to -1.3%]). The number of deaths also decreased for esophageal cancer, stomach cancer, and chronic myeloid leukemia, although these results were not statistically significant. CONCLUSION AND RELEVANCE: As part of the epidemiological transition, cancer incidence is expected to increase in the future, further straining limited health care resources. Appropriate allocation of resources for cancer prevention, early diagnosis, and curative and palliative care requires detailed knowledge of the local burden of cancer. The GBD 2015 study results demonstrate that progress is possible in the war against cancer. However, the major findings also highlight an unmet need for cancer prevention efforts, including tobacco control, vaccination, and the promotion of physical activity and a healthy diet.