From f8cf49ddfc4c7761e21907721b74aa3a3974b160 Mon Sep 17 00:00:00 2001 From: Nicolas HOMBERG Date: Wed, 31 Jul 2024 14:42:36 +0200 Subject: [PATCH] create bundle for the soothie challenge --- README.md | 1 + bundle/overview.md | 4 +- ingestion_program/sub_ingestion.R | 3 +- phase-0-smoothie/bundle/FAQ.md | 1 + phase-0-smoothie/bundle/HADACA3_com.png | Bin 0 -> 949562 bytes .../bundle/aim-of-the-challenge.md | 24 ++ phase-0-smoothie/bundle/baseline.md | 11 + phase-0-smoothie/bundle/competition.yaml | 84 ++++++ phase-0-smoothie/bundle/data.md | 76 +++++ .../bundle/evaluation-and-scoring.md | 3 + phase-0-smoothie/bundle/how-do-i-start.md | 13 + phase-0-smoothie/bundle/organization.md | 37 +++ phase-0-smoothie/bundle/overview.md | 9 + phase-0-smoothie/bundle/sponsors.md | 11 + phase-0-smoothie/bundle/submission.md | 59 ++++ phase-0-smoothie/bundle/terms.md | 8 + phase-0-smoothie/generate_bundle_smoothie.sh | 38 +++ .../ingestion_program/ingestion.R | 115 ++++++++ phase-0-smoothie/ingestion_program/metadata | 1 + .../ingestion_program/sub_ingestion.R | 95 ++++++ .../ingestion_program/sub_ingestion.py | 131 ++++++++ phase-0-smoothie/prepare2score_locally.sh | 12 + .../scoring_program/detailed_results.Rmd | 211 +++++++++++++ phase-0-smoothie/scoring_program/metadata | 1 + phase-0-smoothie/scoring_program/scoring.R | 279 ++++++++++++++++++ .../starting_kit/submission_script.R | 271 +++++++++++++++++ .../starting_kit/submission_script.py | 272 +++++++++++++++++ .../starting_kit/submissions/program.R | 26 ++ scoring_program/scoring.R | 4 - 29 files changed, 1793 insertions(+), 7 deletions(-) create mode 100644 phase-0-smoothie/bundle/FAQ.md create mode 100644 phase-0-smoothie/bundle/HADACA3_com.png create mode 100755 phase-0-smoothie/bundle/aim-of-the-challenge.md create mode 100644 phase-0-smoothie/bundle/baseline.md create mode 100644 phase-0-smoothie/bundle/competition.yaml create mode 100644 phase-0-smoothie/bundle/data.md create mode 100755 phase-0-smoothie/bundle/evaluation-and-scoring.md create mode 100755 phase-0-smoothie/bundle/how-do-i-start.md create mode 100644 phase-0-smoothie/bundle/organization.md create mode 100644 phase-0-smoothie/bundle/overview.md create mode 100644 phase-0-smoothie/bundle/sponsors.md create mode 100755 phase-0-smoothie/bundle/submission.md create mode 100644 phase-0-smoothie/bundle/terms.md create mode 100644 phase-0-smoothie/generate_bundle_smoothie.sh create mode 100644 phase-0-smoothie/ingestion_program/ingestion.R create mode 100644 phase-0-smoothie/ingestion_program/metadata create mode 100644 phase-0-smoothie/ingestion_program/sub_ingestion.R create mode 100644 phase-0-smoothie/ingestion_program/sub_ingestion.py create mode 100644 phase-0-smoothie/prepare2score_locally.sh create mode 100644 phase-0-smoothie/scoring_program/detailed_results.Rmd create mode 100644 phase-0-smoothie/scoring_program/metadata create mode 100644 phase-0-smoothie/scoring_program/scoring.R create mode 100644 phase-0-smoothie/starting_kit/submission_script.R create mode 100644 phase-0-smoothie/starting_kit/submission_script.py create mode 100644 phase-0-smoothie/starting_kit/submissions/program.R diff --git a/README.md b/README.md index 5867ec2..48b0ed4 100644 --- a/README.md +++ b/README.md @@ -105,6 +105,7 @@ sudo docker login -u hombergn #upload on dockerhub sudo docker push hombergn/hadaca3_light:latest +sudo docker push hombergn/hadaca3_pyr:latest #Single command to build and push. sudo docker build -t hombergn/hadaca3_light . && sudo docker push hombergn/hadaca3_light:latest diff --git a/bundle/overview.md b/bundle/overview.md index fd38d2f..f88a092 100644 --- a/bundle/overview.md +++ b/bundle/overview.md @@ -1,7 +1,9 @@ -Health data challenge (HADACA) is a serie of data challenge aiming to contribute to scientific crowdsourced benchmarking in the field of data analysis in health. +Health data challenge (HADACA) is a series of data challenge aiming to contribute to scientific crowdsourced benchmarking in the field of data analysis in health. The aim of a scientific data challenge is to improve the state-of-the-art from a quantitative reference point. In the field of methodological development for health data analysis, HADACA is seeking to provide a formal comparison of performance between new algorithms and state-of-the-art methods. To carry out these methodological assessments, HADACA brings together scientists from a variety of disciplines to tackle a specific challenge. During the week-long conference, participants brainstorm and work together to solve the problem posed by the organisers. Teams compete against each other and then share their solution publicly, so that all the participants can move on to the next stage together. Contrarily to classical workshops, HADACA challenges result in guidelines and scientific publications which are of use to the community. Offering authorship to competing teams, along with participation in manuscript design and writing, is a strong incentive that provides international visibility and recognition to participants. HADACA challenges is a reccurent event, 1st edition occurred in 2018 in partnership with the Data Institute of University Grenoble-Alpes, 2nd edition occurred in 2019 in partnership with the Ligue contre le Cancer and sponsored by the EIT Health. 3rd edition organization was delayed by the COVID pandemic. It is now scheduled to November 2024, in partnership with the M4DI project, an axis of the PEPR Santé Numérique of the Plan Innovation Santé 2030. + +The official website: [hadaca3.sciencesconf.org](https://hadaca3.sciencesconf.org/) \ No newline at end of file diff --git a/ingestion_program/sub_ingestion.R b/ingestion_program/sub_ingestion.R index 4f2d15c..9ae8d68 100644 --- a/ingestion_program/sub_ingestion.R +++ b/ingestion_program/sub_ingestion.R @@ -54,8 +54,7 @@ total_time <- 0 predi_list = list() for (dataset_name in 1:nb_datasets){ - # dir_name = dir_name = paste0(input,.Platform$file.sep,"input_data", .Platform$file.sep,"input_data_",toString( dataset_name),.Platform$file.sep) - dir_name = dir_name = paste0(input,.Platform$file.sep,"input_data_",toString( dataset_name),.Platform$file.sep) + dir_name = paste0(input,.Platform$file.sep,"input_data_",toString( dataset_name),.Platform$file.sep) print(paste0("generating prediction for dataset:",toString(dataset_name) )) diff --git 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samples (average profile of millions of cells), with all cells present in the tissue contributing to the recorded signal. Despite great promise, conventional computational approaches to quantifying cellular heterogeneity from mixtures of cells have encountered difficulties in providing robust and biologically relevant estimates. + +Here, our focus will be on reference-based approaches, which are gaining increasing popularity. While each method presents its own set of advantages and limitations, all are inherently constrained by the quality of the reference data employed. We hypothesize that existing algorithms could be enhanced by leveraging multimodal data integration to improve the quality of references. + +The objective of the HADACA3 challenge will be **to enhance existing cell-type deconvolution models by integrating multimodal datasets as reference data.** + +## Program + +**Phase 0:** Toy deconvolution challenge to test the Codabench framework and familiarize with the platform. A toy dataset respresenting smoothie is used. + +**Phase 1:** Second toy deconvolution challenge aiming to handle a dataset close to chalenge target dataset. + + +**Phase 2:** Estimation of cell type heterogeneity from pancreatic adenocarcinoma matching bulk methylomes and transcriptomes using the following references profiles, for five different cell types (endothelial cells, fibroblasts, immune cells, cancer cells basal-like, cancer cells classic-like): + +- bulk RNA-seq references +- DNAm references +- single-cell RNA-seq profiles + +**Phase 3:** Auto-migration from phase 2 best methods and evalution on previously unseen validation dataset. diff --git a/phase-0-smoothie/bundle/baseline.md b/phase-0-smoothie/bundle/baseline.md new file mode 100644 index 0000000..116673b --- /dev/null +++ b/phase-0-smoothie/bundle/baseline.md @@ -0,0 +1,11 @@ +Baseline can be found on the `submission_script.R` contained in the `starting_kit`. + +## NNLS baseline + +We propose a baseline that executes the following steps: + +[1] Run the NNLS deconvolution algorithm on RNA mix using bulk RNA-seq reference data to generate an estimate of the proportion matrix. + +[2] Run the NNLS deconvolution algorithm on methylation mix using bulk methylation reference data to generate an estimate of the proportion matrix. + +[3] Average the two estimates to generate a prediction of the proportion matrix. diff --git a/phase-0-smoothie/bundle/competition.yaml b/phase-0-smoothie/bundle/competition.yaml new file mode 100644 index 0000000..895727a --- /dev/null +++ b/phase-0-smoothie/bundle/competition.yaml @@ -0,0 +1,84 @@ +version: '2' +title: Smoothie decovolution +# docker_image: hombergn/hadaca3_light +docker_image: hombergn/hadaca3_pyr +queue: null +description: HADACA3 +registration_auto_approve: true +enable_detailed_results: true +image: HADACA3_com.png +terms: terms.md +make_programs_available: true +make_input_data_available: true +pages: +- title: Overview + file: overview.md +- title: Aim of the challenge + file: aim-of-the-challenge.md +- title: How do I start? + file: how-do-i-start.md +- title: Data + file: data.md +- title: Baseline + file: baseline.md +- title: Submission + file: submission.md +- title: Evaluation and scoring + file: evaluation-and-scoring.md +- title: FAQ + file: FAQ.md +- title: Organization + file: organization.md +- title: Sponsors + file: sponsors.md +tasks: +- index: 0 + name: LIGHT Cell type proportion estimation from transcriptome data1 + description: LIGHT COMETH data challenge + reference_data: ground_truth.zip #rename to ground_truth! + input_data: input_data.zip + scoring_program: scoring_program.zip + ingestion_program: ingestion_program.zip +solutions: [] +phases: +- index: 0 + name: Cell type proportion estimation from transcriptome and methylome data, using multimodal references. + description: good luck + start: '2024-03-20 ' + # end: '2024-07-20 11:00' + max_submissions_per_day: 5 + max_submissions: 100 + execution_time_limit: 600 + auto_migrate_to_this_phase: false + hide_output: false + starting_kit: starting_kit.zip + tasks: + - 0 + +# Fact sheets to add more information in the leaderboard +fact_sheet: { + "method_name": { + "key": "method_name", + "type": "text", + "title": "Method name", + "selection": "", + "is_required": "false", + "is_on_leaderboard": "true" + } +} +leaderboards: +- index: 0 + title: Scores + key: score + hidden: false + columns: + - title: Accuracy_mean + key: Accuracy_mean + index: 1 + sorting: desc + hidden: false + - title: Excecution Time global + key: Time + index: 2 + sorting: desc + hidden: false diff --git a/phase-0-smoothie/bundle/data.md b/phase-0-smoothie/bundle/data.md new file mode 100644 index 0000000..6ad2e5a --- /dev/null +++ b/phase-0-smoothie/bundle/data.md @@ -0,0 +1,76 @@ +## Data source + +Public data : *to be completed later* + +Private data : *to be completed later* + +## Data generation + +The cell-type proportion matrices (ground truth) are simulated by a Dirichlet distribution. +Simulated mixes were obtained using a convolution of the cell-type proportion matrix with the reference matrix of the corresponding omic data. +Finally, Gaussian noise was added to the matrix of convoluted methylation profiles. + + # R function to generate cell-type proportion matrices using Dirichlet distribution + ground_truth = gtools::rdirichlet(n = n, alpha = alpha) # with n the number of sample to generate, and alpha a vector of targeted proportion + # Convolution of references and proportion + mix_rna = reference_rna %*% ground_truth + # Function to generate gaussian noise + noise = matrix(rnorm(prod(dim(data)), mean = mean, sd = sd), nrow = nrow(data)) # with data corresponding to the simulated mixes, and mean and standard deviation (sd) representing the parameters of the noise + +## Data description + +### Phase 1 : + + *to be completed later* + +### Phase 2 : + +- mixes_data.rds, a list of matching DNAmethylation and RNAseq bulk data, for 30 samples + + # read mixes data + mixes = readRDS("mixes_data.rds") + dim(mixes$mix_rna) + [1] 18749 30 + dim(mixes$mix_met) + [1] 824678 30 + + +- reference_data.rds, a list of 2 bulk references : RNA and Met and 1 single cell count data and associated metadata . + + # read reference data + references = readRDS("reference_data.rds") + + # format of bulk RNA references + colnames(reference$ref_bulkRNA) + [1] "endo" "fibro" "immune" "classic" "basal" + dim(reference$ref_bulkRNA) + [1] 18749 5 + + # format of methylome references + > colnames(reference$ref_met) + [1] "endo" "fibro" "immune" "classic" "basal" + > dim(reference$ref_met) + [1] 824678 5 + + # format of scRNAseq references + > dim(reference$scRNAseq$counts) # 23376 gene expression for 20146 cells + [1] 23376 20146 + > dim(reference$scRNAseq$metadata) # cell labels + [1] 20146 1 + > table(reference$scRNAseq$metadata[,1]) + [1] basal classic endo fibro immune + 2036 2178 8874 3946 3112 + + +### Phase 3 : + +The validation dataset of phase 3 are ketp private to avoid overfitting. + +## Data Download + +To download the dataset for this project, follow these steps : + + - Go on the challenge page, + - Go the *Get started* menu, + - Click on the *Files* tab, + - Download the `starting_kit`. diff --git a/phase-0-smoothie/bundle/evaluation-and-scoring.md b/phase-0-smoothie/bundle/evaluation-and-scoring.md new file mode 100755 index 0000000..14e7c55 --- /dev/null +++ b/phase-0-smoothie/bundle/evaluation-and-scoring.md @@ -0,0 +1,3 @@ +## How is the scoring metric computed? + +The **score** (discriminating metric) is computed on the estimated proportion matrix. The metric is a combination of row-correlation, column-correlation and mean absolute error between the ground_truth and the estimate provided by the participants. diff --git a/phase-0-smoothie/bundle/how-do-i-start.md b/phase-0-smoothie/bundle/how-do-i-start.md new file mode 100755 index 0000000..4fac9c1 --- /dev/null +++ b/phase-0-smoothie/bundle/how-do-i-start.md @@ -0,0 +1,13 @@ +## Overview of the processs + +[1] Go to the challenge webpage on Codabench platform, navigate to the *Get started* menu, and select the *Files* tab. Download the `starting_kit` corresponding to the phase you are participating in. + +[2] Get familiar with the dataset description (*Data* tab) and the baseline methods suggested by the organizer (*Baseline* tab). + +[3] Make a first submission using the baseline method (follow the guidelines in the *Submission* tab) + +[4] Check you score (more information on the scoring and ranking pipelines can be found on the *Evaluation and scoring* tab). + +[5] Improve you method by editing the `submission_script.R` contained in the `starting_kit` and resubmit your program to enhance your score. + + diff --git a/phase-0-smoothie/bundle/organization.md b/phase-0-smoothie/bundle/organization.md new file mode 100644 index 0000000..fa4767a --- /dev/null +++ b/phase-0-smoothie/bundle/organization.md @@ -0,0 +1,37 @@ +HADACA 3rd ed. is organized in closed collaboration between the **M4DI project, an axis of the PEPR santé Numérique**, and the **ITMO Cancer Aviesan (2009-2023, project ACACIA)**. + +M4DI is dedicated to Methods and models for multimodal and multi-scale data integration. + +ACACIA (Artificial intelligence on multi-omics data to study tumor heterogeneity and develop clinical classifiers) is funded by the ITMO Cancer Aviesan call "Interdisciplinary approaches to oncogenic processes and therapeutic perspectives: Contributions of mathematics and informatics to oncology (MIC)". + +## Scientific committee : + +The role ot the scientific committee is to precisely define the scientific question, select appropriate datasets and evaluation metrics, and to design the challenge. + +- A. Baudot (researcher in computational biology, MMG, Marseille, France), +- Y. Blum (researcher in computational biology, IGDR, Rennes, France), +- D. Causeur (professor in statistics, ), IRMAR, Rennes, France), +- S. Dejean (researcher in statistic, IMT, Toulouse, France), +- C. Lecellier (researcher in genomics, IGMM, Montpellier, France), +- M. Richard (researcher in computational biology, TIMC, Grenoble, France), +- P. Roy (professor in biostatistics, LBBE, Lyon, France), +- M. Térézol (researcher in bioinformatics, MMG, Marseille, France). + + +In addition, 3 external expert have joined the committee : + +- Carl Herrmann (Professor in computational biology, Heidelberg University, Germany), +- Lionel Spinelli (researcher in computational biology), +- Franck Picard (researcher in statistics, LBMC, Lyon). + +## Technical committee : + +The organization and implementation of the data challenge is coordinated by : + +- M. Richard +- Y. Blum +- F. Chuffart (IR bioinfo INSERM) +- L. Lamothe (IR bioinfo CNRS), +- N. Homberg (IR bioinfo, Univ Grenoble-Alpes), +- M. Térézol (IE bioinfo CNRS) and +- H. Barbot (PhD student, co-supervised by M.Richard and Y. Blum). diff --git a/phase-0-smoothie/bundle/overview.md b/phase-0-smoothie/bundle/overview.md new file mode 100644 index 0000000..40284a2 --- /dev/null +++ b/phase-0-smoothie/bundle/overview.md @@ -0,0 +1,9 @@ +This toy decovolution challenge is an introduction to Health data challenge (HADACA) which is a series of data challenge aiming to contribute to scientific crowdsourced benchmarking in the field of data analysis in health. + +The aim of a scientific data challenge is to improve the state-of-the-art from a quantitative reference point. In the field of methodological development for health data analysis, HADACA is seeking to provide a formal comparison of performance between new algorithms and state-of-the-art methods. + +To carry out these methodological assessments, HADACA brings together scientists from a variety of disciplines to tackle a specific challenge. During the week-long conference, participants brainstorm and work together to solve the problem posed by the organisers. Teams compete against each other and then share their solution publicly, so that all the participants can move on to the next stage together. Contrarily to classical workshops, HADACA challenges result in guidelines and scientific publications which are of use to the community. Offering authorship to competing teams, along with participation in manuscript design and writing, is a strong incentive that provides international visibility and recognition to participants. + +HADACA challenges is a reccurent event, 1st edition occurred in 2018 in partnership with the Data Institute of University Grenoble-Alpes, 2nd edition occurred in 2019 in partnership with the Ligue contre le Cancer and sponsored by the EIT Health. 3rd edition organization was delayed by the COVID pandemic. It is now scheduled to December 2024, in partnership with the M4DI project, an axis of the PEPR Santé Numérique of the Plan Innovation Santé 2030. + +The official website: [hadaca3.sciencesconf.org](https://hadaca3.sciencesconf.org/) \ No newline at end of file diff --git a/phase-0-smoothie/bundle/sponsors.md b/phase-0-smoothie/bundle/sponsors.md new file mode 100644 index 0000000..ce87bdb --- /dev/null +++ b/phase-0-smoothie/bundle/sponsors.md @@ -0,0 +1,11 @@ +With support from ITMO Cancer of Aviesan within the framework of the 2021-2030 Cancer Control Strategy, on funds administered by Inserm + +With support from M4DI, PEPR Santé Numérique + +With support from LabEx PERSYVAL-2 (Grenoble-Alpes University) + +With support from RT Math Bio Santé (CNRS) + +With support from GRICAD Mesocentre(Grenoble-Alpes University) + +With support from RIS (CNRS MITI) diff --git a/phase-0-smoothie/bundle/submission.md b/phase-0-smoothie/bundle/submission.md new file mode 100755 index 0000000..4d23dd8 --- /dev/null +++ b/phase-0-smoothie/bundle/submission.md @@ -0,0 +1,59 @@ +## How to generate a prediction of the data? + +[1] On your local machine, unzip the starting_kit.zip. Then open R in the starting_kit directory, (e.g. open submission_script.R with RStudio). + +The unziped starting_kit directory contains now: + +- A `submission_script.R` -> *to modify and to use to submit your code* +- The `reference_data.rds` -> *reference data, i.e. typical molecular profiles of expected cell types* +- The `mixes_data.rds` -> *mixes from which you will estimate cell type proportions (matching RNA and DNA methylation data)* + +[2] In the R console launch the following command (or run the `submission_script.R` in RStudio): + + source("submission_script.R") + +[3] The code of the `submission_script.R` generates the files: +- `zip_program` -> *for code submission, script format* +-` zip_results` -> *for result submission, table format* + +Edit the `submission_script.R` to replace the baseline method by the method of your choice. + +Edit the code inside the following chunk (i.e. the `program` function): + ## + ## YOUR CODE BEGINS HERE + ## + + ## + ## YOUR CODE ENDS HERE + ## + + + +## How to submit your results ? + +Now, let’s submit your code (`zip_program`) or your result (`zip_results` ) in the *My Submission* menu of the challenge. + +On the *My Submission* webpage, the STATUS of your submission will go through the following steps : + -> Submitting > Submitted > Running > Finished + +## How to see your score ? + +To view your score, go to the challenge page and navigate to the Leaderboard or Results section. Here, you can see how your submission ranks and compare your score with other participants. + +[1] Go on *My Submission* menu + +[2] When the status of your submission is finished ( don't forget to refresh the page to update the status), click on the green button 'add to leaderboard' to see your score + +By clicking on your submission in the submissions summary table, you will access to: + + - details of your submission (downloaded) + -> submitted files, + -> prediction results (ingestion output) + -> scoring results (scoring outputs) + + - some execution logs + + - a submission metadata edition menu + +[3] Check the leaderboard in the *Results* menu + diff --git a/phase-0-smoothie/bundle/terms.md b/phase-0-smoothie/bundle/terms.md new file mode 100644 index 0000000..1c2db80 --- /dev/null +++ b/phase-0-smoothie/bundle/terms.md @@ -0,0 +1,8 @@ +By participating to this challenge, you accept to publicly share your submissions. + +You may submit 5 submissions every day and 100 in total. + +This challenge is governed by the general [ChaLearn contest rules](https://www.causality.inf.ethz.ch/GeneralChalearnContestRuleTerms.html). + + + diff --git a/phase-0-smoothie/generate_bundle_smoothie.sh b/phase-0-smoothie/generate_bundle_smoothie.sh new file mode 100644 index 0000000..c2127a1 --- /dev/null +++ b/phase-0-smoothie/generate_bundle_smoothie.sh @@ -0,0 +1,38 @@ + +type Rscript >/dev/null 2>&1 || { echo >&2 "Rscript requiered but it's not installed. Aborting."; exit 1; } + + +echo "generate data" +path_data=~/projects/hadaca3_private/ + +pwd + +#Should be a seperate script ? +mkdir input_data +mkdir ground_truth +cp "$path_data"groundtruth_smoothies_fruits.rds ground_truth/groundtruth_smoothies_fruits.rds +cp "$path_data"mixes_smoothies_fruits.rds input_data/mixes_smoothies_fruits.rds +cp "$path_data"reference_fruits.rds input_data/reference_fruits.rds + +cp -r input_data/* starting_kit/ +# sh generate_data.sh $1 +# sh generate_data.sh real +echo 'data generated' + +# Zip folder +echo "create bunlde.zip" +zip -FS -j -r bundle/scoring_program.zip scoring_program/ +zip -FS -j -r bundle/ingestion_program.zip ingestion_program/ +cd starting_kit/ ; zip -FS -r ../bundle/starting_kit.zip * -x \*submissions\* ; cd .. ; + + +zip -FS -j -r bundle/ground_truth.zip ground_truth/ + +cd input_data/ +zip -FS -r ../bundle/input_data.zip * +cd .. + +zip -FS -r -j bundle-smoothie.zip bundle/ + + +echo "Bundle.zip created, upload it on Codabench, under benchmark, management and upload" diff --git a/phase-0-smoothie/ingestion_program/ingestion.R b/phase-0-smoothie/ingestion_program/ingestion.R new file mode 100644 index 0000000..ec66be5 --- /dev/null +++ b/phase-0-smoothie/ingestion_program/ingestion.R @@ -0,0 +1,115 @@ +## Authors: Alexis ARNAUD, UGA +## alexis.arnaud@univ-grenoble-alpes.fr +## +## ------------------------------------ + + + +print(x = "System information :") +print(x = Sys.info( ) ) +print(x = Sys.getenv( ) ) +print(x = "") + + +# Create symbolic link with + +try(system("ln -sf ../ingested_program/modules/ modules", intern = TRUE, ignore.stderr = TRUE)) + + +# source(file = "Detach_packages.R") + +## define ingestion_program/input/output/submission_program from command line args and remove white spaces (should in principle never be changed) +args <- commandArgs(trailingOnly = TRUE) + +## directory where the ingestion program is located : +ingestion_program <- trimws(x = args[1] ) +## input data directory : +input <- trimws(x = args[2] ) +## output directory (where predictions are written) : +output <- trimws(x = args[3] ) +## directory of the code submitted by the participants : +submission_program <- trimws(x = args[4] ) + + +print(x = "Ingestion Program :") +print(x = list.files(path = ingestion_program, all.files = TRUE, full.names = TRUE, recursive = TRUE) ) +print(x = "") +print(x = "Input :") +print(x = list.files(path = input , all.files = TRUE, full.names = TRUE, recursive = TRUE) ) +print(x = "") +print(x = "Output :") +print(x = list.files(path = output , all.files = TRUE, full.names = TRUE, recursive = TRUE) ) +print(x = "") +print(x = "Submission Program :") +print(x = list.files(path = submission_program, all.files = TRUE, full.names = TRUE, recursive = TRUE) ) +print(x = "") + +## output files +# output_program <- paste0(output, .Platform$file.sep, "output_program.txt") +# output_profiling <- paste0(output, .Platform$file.sep, "Rprof.out" ) +output_profiling_rds <- paste0(output, .Platform$file.sep, "Rprof.rds" ) +#output_dataType <- paste0(output, .Platform$file.sep, "dataType.rds" ) +# file.create(output_program, output_profiling) + +## diverting R output to a text file : +## sink(file = output_program, append = FALSE) + + +# Rprof( +# filename = output_profiling +# , append = FALSE +# , interval = 0.02 +# , memory.profiling = TRUE +# , gc.profiling = FALSE +# , line.profiling = FALSE +# ) +# start_time <- proc.time() + + +# Rprof(output_profiling,interval = 0.02) + + +#Check it is a result submission or a program submission +file_R = paste0(submission_program, .Platform$file.sep, "program.R") +file_py = paste0(submission_program, .Platform$file.sep, "program.py") +output_results <- paste0(output, .Platform$file.sep, "prediction.rds") + +if (file.exists(file_R)) { + + print("Executing a R program") + + cmd = paste("Rscript", paste0(ingestion_program, .Platform$file.sep, "sub_ingestion.R"), input, output_results, submission_program, sep = " ") + print(cmd) + system(command = paste("Rscript", paste0(ingestion_program, .Platform$file.sep, "sub_ingestion.R"), input, output_results, submission_program,output_profiling_rds, sep = " ") ) + + }else if (file.exists(file_py)) { + + print("Executing a python program") + + cmd = paste("python", paste0(ingestion_program, .Platform$file.sep, "sub_ingestion.py"), input, output_results, submission_program, sep = " ") + print(cmd) + system(command = paste("python", paste0(ingestion_program, .Platform$file.sep, "sub_ingestion.py"), input, output_results, submission_program,output_profiling_rds, sep = " ") ) + + } else { + print("no program to execute, go straight to scoring step") +} + + + + +# execution_time <- proc.time() - start_time + +# print(execution_time) + +# ## save profiling +# saveRDS( +# object = execution_time +# , file = output_profiling_rds +# ) + +## stop diverting R output to a text file +## sink(file = NULL) + +print(x = "Output :") +print(x = list.files(path = output , all.files = TRUE, full.names = TRUE, recursive = TRUE) ) +print(x = "") diff --git a/phase-0-smoothie/ingestion_program/metadata b/phase-0-smoothie/ingestion_program/metadata new file mode 100644 index 0000000..8c14a5d --- /dev/null +++ b/phase-0-smoothie/ingestion_program/metadata @@ -0,0 +1 @@ +command: Rscript $ingestion_program/ingestion.R $ingestion_program $input $output $submission_program \ No newline at end of file diff --git a/phase-0-smoothie/ingestion_program/sub_ingestion.R b/phase-0-smoothie/ingestion_program/sub_ingestion.R new file mode 100644 index 0000000..0aa9715 --- /dev/null +++ b/phase-0-smoothie/ingestion_program/sub_ingestion.R @@ -0,0 +1,95 @@ +args <- commandArgs(trailingOnly = TRUE) + + +## index of the input file : +# i <- trimws(x = args[1] ) +# print(paste0("index of the input file :", i)) +## input data directory : +input <- trimws(x = args[1] ) +print(paste0("input data directory :", input)) +## output file : +output_results <- trimws(x = args[2] ) +print(paste0("output file :", output_results)) +## directory of the code submitted by the participants : +submission_program <- trimws(x = args[3] ) +print(paste0(" directory of the code submitted by the participants :", submission_program)) +## directory of the code submitted by the participants : +output_profiling_rds <- trimws(x = args[4] ) +print(paste0(" output_profiling file:", output_profiling_rds)) + + +## read code submitted by the participants : +.tempEnv <- new.env( ) +source( +# file = paste0(submission_program, .Platform$file.sep, "program.R") + file = paste0(submission_program, .Platform$file.sep, "program.R") + , local = .tempEnv +) + +#################################### +## read input data : +##################################### + + + +# mixes_data <- readRDS(file = paste0(input, .Platform$file.sep, "mixes_data.rds") ) + +# mix_rna <- mixes_data$mix_rna +# mix_met <- mixes_data$mix_met +# names(mixes_data) + +# reference_data <- readRDS(file = paste0(input, .Platform$file.sep, "reference_data.rds") ) +# ref_rna <- as.matrix(reference_data$ref_bulkRNA) +# ref_met <- as.matrix(reference_data$ref_met) +# names(reference_data) + + + +base::set.seed(seed = 1) + +total_time <- 0 + + +mixes_data = readRDS( paste0(input,.Platform$file.sep,"mixes_smoothies_fruits.rds") ) +reference_data = readRDS( paste0(input,.Platform$file.sep,"reference_fruits.rds")) + + + # we use the previously defined function 'program' to estimate A : +start_time <- proc.time() +pred_prop <- .tempEnv$program( + mix = mixes_data, + ref = reference_data +) +elapsed_time <- proc.time() - start_time +print (paste0("Prediction has ", nrow(pred_prop), " rows and ", ncol(pred_prop), " columns")) + + +total_time <- total_time + elapsed_time["elapsed"] + + +print(total_time) +## save profiling +saveRDS( + object = total_time + , file = output_profiling_rds +) + + +print(paste0("Save predictions in .rds format")) +saveRDS( +object = pred_prop +, file = output_results +) + +# print(paste0("Save predicted A matrix from dataset ",i, " in .csv format")) +# write.csv2( +# x = prediction$A_matrix, +# file = output_A) + +# print(paste0("Save predicted T matrix from dataset ",i, " in .csv format")) + +# write.csv2( +# x = prediction$T_matrix, +# file = output_T) + + diff --git a/phase-0-smoothie/ingestion_program/sub_ingestion.py b/phase-0-smoothie/ingestion_program/sub_ingestion.py new file mode 100644 index 0000000..1b51bab --- /dev/null +++ b/phase-0-smoothie/ingestion_program/sub_ingestion.py @@ -0,0 +1,131 @@ +import argparse +import os +import numpy as np +import time +import pandas as pd +import importlib + +# from scipy.optimize import nnls + +from rpy2.robjects import pandas2ri +pandas2ri.activate() +# from rpy2.robjects.packages import importr + +import rpy2.robjects as robjects +readRDS = robjects.r['readRDS'] +saveRDS= robjects.r["saveRDS"] +import rpy2.robjects as ro + +# Parsing command-line arguments +parser = argparse.ArgumentParser(description='Process some paths.') +parser.add_argument('input', type=str, help='input data directory') +parser.add_argument('output_results', type=str, help='output file') +parser.add_argument('submission_program', type=str, help='directory of the code submitted by the participants') +parser.add_argument('output_profiling_rds', type=str, help='output_profiling_rds') + + +args = parser.parse_args() + +# Assigning the arguments to variables +input_dir = args.input.strip() +print(f"input data directory: {input_dir}") + +output_results = args.output_results.strip() +print(f"output file: {output_results}") + +submission_program = args.submission_program.strip() +print(f"directory of the code submitted by the participants: {submission_program}") + +output_profiling_rds = args.output_profiling_rds.strip() +print(f"output_profiling file: {output_profiling_rds}") + + + + +# Reading and executing the code submitted by the participants +program_file = os.path.join(submission_program, 'program.py') + +# Ensure that the file exists before attempting to read it +if os.path.isfile(program_file): + with open(program_file, 'r') as file: + program_code = file.read() + exec(program_code, globals()) +else: + print(f"File not found: {program_file}") + +# Example: calling the function 'program' if it's defined in the submitted code +if 'program' in globals(): + # print('progris in memory') + # Call the function with example arguments (these should be defined appropriately) + # result = program(arg1, arg2, ...) # Pass actual arguments required by the function + pass +else: + print("The 'program' function is not defined in the submitted code.") + + + +r_code_get_colnames_nested = ''' +get_colnames_nested <- function(reference_data) { + return (colnames(reference_data$ref_bulkRNA)) +} +''' +ro.r(r_code_get_colnames_nested) + + + + +# This should be place elsewhere. +nb_datasets = 4 + +predi_list = [] +total_time = 0 + + +for dataset_name in range(1, nb_datasets + 1): + # dir_name = f"input_data_{dataset_name}/" + # dir_name = dir_name = paste0(input,.Platform$file.sep,"input_data_",toString( dataset_name),.Platform$file.sep) + dir_name = os.path.join(input_dir, f"input_data_{dataset_name}") + print(f"generating prediction for dataset: {dataset_name}") + + + mixes_data = readRDS(os.path.join(dir_name, "mixes_data.rds")) + + mix_rna= np.array(mixes_data.rx('mix_rna')) + mix_rna = mix_rna[0] + mix_met = np.array(mixes_data.rx('mix_met')) + mix_met = mix_met[0] + + reference_data = readRDS(os.path.join(dir_name, "reference_data.rds")) + # reference_data = pandas2ri.rpy2py(reference_data) + ref_rna = np.array(reference_data.rx('ref_bulkRNA')) + ref_met = np.array(reference_data.rx('ref_met')) + ref_rna = ref_rna[0] + ref_met = ref_met[0] + + get_colnames_nested = ro.r['get_colnames_nested'] + colnames_result = get_colnames_nested(reference_data) + + #Program is in memory + start_time = time.perf_counter() + pred_prop = program( + mix_rna=mix_rna, mix_met=mix_met, + ref_rna=ref_rna, ref_met=ref_met + ) + total_time += time.perf_counter() - start_time + + pred_prop_df = pd.DataFrame(pred_prop, index=colnames_result) + + # validate_pred(pred_prop, nb_samples=mix_rna.shape[1] if mix_rna is not None else mix_met.shape[1], nb_cells=ref_rna.shape[1], col_names=reference_data['ref_met'].columns) + + predi_list.append(pred_prop_df) + +print(total_time) + +# prediction_name = "prediction.rds" +# saveRDS(predi_list, os.path.join(output_results, prediction_name)) +saveRDS(predi_list, os.path.join(output_results)) + + +saveRDS(total_time, os.path.join(output_profiling_rds)) + + diff --git a/phase-0-smoothie/prepare2score_locally.sh b/phase-0-smoothie/prepare2score_locally.sh new file mode 100644 index 0000000..5babae2 --- /dev/null +++ b/phase-0-smoothie/prepare2score_locally.sh @@ -0,0 +1,12 @@ + +rm -rf test_output +mkdir -p test_output/res/ +mkdir -p test_output/ref +cp ground_truth/* test_output/ref/ + +# cp -r starting_kit/modules starting_kit/submissions + +cp starting_kit/submissions/prediction.rds test_output/res/ + + +echo "READY" \ No newline at end of file diff --git a/phase-0-smoothie/scoring_program/detailed_results.Rmd b/phase-0-smoothie/scoring_program/detailed_results.Rmd new file mode 100644 index 0000000..bb43d45 --- /dev/null +++ b/phase-0-smoothie/scoring_program/detailed_results.Rmd @@ -0,0 +1,211 @@ +--- +title: "Detailed Results" +date: "`r Sys.Date()`" +output: + html_document: + fig_caption: yes + toc: yes + toc_depth: 5 +--- + + + +```{r label = "header", echo = FALSE, eval = TRUE} +knitr::opts_chunk$set(collapse = TRUE, comment = "", echo = FALSE) + +for ( package in c("ggplot2","viridis") ) { + if ( !{package %in% installed.packages( ) } ) { + print(x = paste0("Installation of the package : ", package) ) + install.pacakges(pckgs = package) + } +} +remove(list = "package") +library(package = "ggplot2") +``` + + +```{r} +#read output from prediction +nb_datasets<- 4 +all_prediction <- readRDS("../input/res/prediction.rds") +ground_truth <- readRDS("../input/ref/ground_truth.rds") + +``` +## Summary of the prediction + +### Heatmap of prediction + +### Heatmap of the absolute error + +```{r} +colors = viridis::viridis(5) +cols = colorRampPalette(colors)(50) + + +#image(as.matrix(pred), axes=FALSE, col = cols , ylab = colnames(pred), xlab = rownames(pred)) + + +for (dataset_name in 1:nb_datasets){ + + prediction <- all_prediction[[dataset_name]] + print(dim(prediction) ) + print(paste0("generating prediction for dataset:",toString(dataset_name) )) + + Aerr <- data.frame( + abserror = as.numeric( + x = abs(x = prediction - ground_truth ) + ) + , type = rep( + x = seq_len(length.out = nrow(x = ground_truth ) ) + , times = ncol(x = ground_truth ) + ) + , patient = rep( + x = seq_len(length.out = ncol(x = ground_truth ) ) + , each = nrow(x = ground_truth ) ) + ) + + p <- ggplot( + data = Aerr + , mapping = aes(x = patient, y = type) + ) + { + geom_tile( + mapping = aes(fill = `abserror`) + , colour = "white" + ) + } + { + theme_minimal(base_size = 16) + } + { + labs( + y = "Cell types" + , fill = "Absolute error" + ) + } + { + scale_x_discrete(expand = c(0, 0) ) + } + { + scale_y_discrete(expand = c(0, 0) ) + } + { + theme( + axis.text.x = element_text( + angle = 70 + , vjust = 1 + , size = 14 + , hjust = 1 + ) + ) + } + { + coord_fixed( ) + } + { + scale_fill_viridis_c( + option = "plasma" + , direction = -1 + ) + } + { + labs( + title = paste0("Detail of the matrice ") + , x = "Samples" + ) + } + print(x = p) + +} + + +``` + +## Summary of scores + + +- scores : +```{r scores} +# print(x = scores) +# +# print(x = "Summary :") +# print(x = summary(object = scores) ) +# +# print(x = "Standard deviation :") +# print(x = sd(x = scores) ) +# +# +# resMAE <- data.frame( +# A = seq_len(length.out = length(x = Aref) ) +# , A.MAE = scores +# , methode = rep(x = "Your method", times = length(x = Aref) ) +# , datatype = rep(x = dataType, times = length(x = Aref) ) +# , meth_type = rep(x = "Your method", times = length(x = Aref) ) +# ) +# +# allMAE <- as.data.frame(x = rbind(referenceMAE, resMAE) ) +# +# ggplot( +# data = allMAE +# , mapping = aes( +# x = methode +# , y = A.MAE +# , fill = meth_type +# , colour = datatype +# ) +# ) + { +# geom_boxplot( +# notch = FALSE +# ) +# } + { +# ## stat_summary( +# ## fun.y = mean +# ## , geom = "point" +# ## , shape = 23 +# ## , size = 3 +# ## ) +# } + { +# geom_dotplot( +# binaxis = 'y' +# , stackdir = 'center' +# , dotsize = 0.25 +# ) +# } + { +# scale_x_discrete( +# limits = levels(x = allMAE$method)[ +# order( +# sapply( +# X = levels(x = allMAE$method) +# , FUN = function( m ) { +# mean(x = allMAE[allMAE$method == m, "A.MAE"] ) +# } +# ) +# , decreasing = TRUE +# ) +# ] +# ) +# } + { +# ylim(0, 0.15) +# } + { +# theme_minimal(base_size = 16) +# } + { +# theme( +# axis.text.x = element_text( +# angle = 70 +# , vjust = 1 +# , hjust = 1 +# , size = 14 +# ) +# ) +# } + { +# scale_fill_manual( +# breaks = c("Reference", "Your method") +# , values = c("white", "#cc0000" ) +# ) +# } + { +# scale_colour_manual( +# breaks = c("both", "met", "rna", as.character(x = resMAE$datatype[ 1 ] ) ) +# , values = c("#23A900", "#4886FE", "#FED248", switch(EXPR = dataType, "both" = "#23A900", "met" = "#4886FE", "rna" = "#FED248") ) +# ) +# } + { +# labs( +# title = "" +# , x = "Methods" +# , y = "MAE" +# , colour = "Data type" +# , fill = "Method type" +# ) +# } +``` + diff --git a/phase-0-smoothie/scoring_program/metadata b/phase-0-smoothie/scoring_program/metadata new file mode 100644 index 0000000..93dd0b7 --- /dev/null +++ b/phase-0-smoothie/scoring_program/metadata @@ -0,0 +1 @@ +command: Rscript $program/scoring.R $input $output $program diff --git a/phase-0-smoothie/scoring_program/scoring.R b/phase-0-smoothie/scoring_program/scoring.R new file mode 100644 index 0000000..0cc407c --- /dev/null +++ b/phase-0-smoothie/scoring_program/scoring.R @@ -0,0 +1,279 @@ +## Authors: Alexis ARNAUD, Magali RICHARD, UGA +## alexis.arnaud@univ-grenoble-alpes.fr +## magali.richard@univ-grenoble-alpes.fr +##--------------------------------------------- + + +for ( package in c( "combinat", "rmarkdown", "clue", "infotheo") ) { + if ( !{ package %in% installed.packages( ) } ) { + install.packages(pkgs = package, repos = "https://cloud.r-project.org") + } +} +remove(list = "package") + +############################################## +### SCORING + +######################### +# homogenization function to find the best match between real and estimated A matrices + +homogeneized_cor_mat = function(A_real, A_est) { + cmat = cor(t(A_real),t(A_est)) + pvec <- c(clue::solve_LSAP((1+cmat)^2,maximum = TRUE)) + return(A_est[pvec,]) +} +######################### +# Estimated A pre-treatment +#not useful because reference based algorithm + +prepare_A <- function(A_real, A_est) { + N <- ncol(A_real) + K <- nrow(A_real) + + stopifnot(K > 1) + stopifnot(ncol(A_est) == N) + stopifnot(!anyNA(A_est)) + + ### STEP 1 : matching the number of estimated components to the real number of cell types K + + ## if estimating too few cell types + if (nrow(A_est) < K) { + #set random positive values closed to 0 for missing rows + set.seed(1) + random_data = abs(jitter(matrix(data = 0, nrow = K - nrow(A_est), ncol = N), factor = 0.01)) + A_est <- rbind(A_est, random_data) + print("Add rows of 0s to match K") + } + + ## if estimating too many cell types + + ### strategy 1: keep the most abundant cell types + if (nrow(A_est) > K) { + A_est <- A_est[order(rowSums(A_est), decreasing = TRUE)[1:K],] + print("Number of estimated cell types exceeds K, only the K most abundant cell types are kept for scoring.") + } + + ### strategy 2: clustering of similar cell types to match K components -> SLIM + + ### STEP 2 : reordering estimated components to find the best match -> SLIM + A_est_best_cor = as.matrix(homogeneized_cor_mat(A_real, A_est)) + + return(A_est_best_cor) +} + +######################### +# Computation of Pearson correlation on rows, columns and whole matrix +pearson_cor <- function(real, prediction) { + return(cor(c(as.matrix(real)), c(prediction), method="pearson")) +} + +eval_pearson = function(A_real, A_est) { + cor_matrix = pearson_cor(A_real, A_est) + cor_row = sapply(seq(nrow(A_est)), function(i) + pearson_cor(A_real[i, ], A_est[i, ])) + cor_col = sapply(seq(ncol(A_est)), function(i) + pearson_cor(A_real[, i], A_est[, i])) + return(structure(c(cor_matrix, mean(cor_row, na.rm=T), mean(cor_col, na.rm=T)), + names=c("cor_matrix", "cor_row", "cor_col"))) +} + +######################### +# Computation of RMSE + +eval_RMSE = function (A_real, A_est) { + return(sqrt(mean((A_real - A_est)^2))) +} + +######################### +# Computation of Mean Absolute Error + +eval_MAE = function (A_real, A_est){ + return(mean(abs(A_real - A_est))) +} + +######################### +# Scoring function + +scoring_function <- function(A_real, A_est) { + + # pre-treatment of estimated A + A_est_tt = prepare_A(A_real = A_real, A_est = A_est) + + # scoring + rmse = eval_RMSE(A_real, A_est_tt) + mae = eval_MAE(A_real, A_est_tt) + pearson = eval_pearson(A_real, A_est_tt) + + # scores aggregation (ADD TIME) + + # load baselines scores for normalization (data.frame of the 5 scores for each baseline on the same dataset) + # baseline_estimation = readRDS("baseline_scores.rds") + # PATCH FCh: issue #5 "baseline_scores.rds not present"" + baseline_estimation = data.frame("rmse"=rmse, + "mae"=mae, + "pearson_mat"=pearson[["cor_matrix"]], + "pearson_row"=pearson[["cor_row"]] , + "pearson_col"=pearson[["cor_col"]] + ) + + judge_candidate = rbind(baseline_estimation, + data.frame("rmse"=rmse, + "mae"=mae, + "pearson_mat"=pearson[["cor_matrix"]], + "pearson_row"=pearson[["cor_row"]] , + "pearson_col"=pearson[["cor_col"]] + ) + ) + + CenterScaleNorm <-function(x) { + tr1 = x - mean(x, na.rm=TRUE) + tr2 = tr1/sd(x, na.rm=TRUE) + return(pnorm(tr2)) + } + judge_candidate_norm = apply(judge_candidate, 2, CenterScaleNorm) + + # Merge pearson scores + judge_candidate_norm_concat = cbind(judge_candidate_norm[,-grep("pearson",colnames(judge_candidate_norm))], + rowMeans(judge_candidate_norm[,grep("pearson",colnames(judge_candidate_norm))])) + + # Average over judges (geometric mean) and keep only the score of the candidate (not the baselines) + score_aggreg = apply(judge_candidate_norm_concat, 1, function(x) exp(mean(log(x), na.rm=T)))[nrow(judge_candidate)] + + return(list(score_aggreg = score_aggreg, + rmse = rmse, + mae = mae, + "pearson_mat"=pearson[["cor_matrix"]], + "pearson_row"=pearson[["cor_row"]] , + "pearson_col"=pearson[["cor_col"]] + )) +} + +############################################## +### SESSION + +## define input/output from command line args and remove white spaces (should in principle never be changed) +args <- commandArgs(trailingOnly = TRUE) + +## input data directory : +input <- trimws(x = args[ 1 ] ) +## output directory (where predictions are written) : +output <- trimws(x = args[ 2 ] ) +## scoring program directory : +program <- trimws(x = args[ 3 ] ) + +print(x = "Input :") +print(x = list.files(path = input , all.files = TRUE, full.names = TRUE, recursive = TRUE) ) +print(x = "") +print(x = "Output :") +print(x = list.files(path = output , all.files = TRUE, full.names = TRUE, recursive = TRUE) ) +print(x = "") +print(x = "Program :") +print(x = list.files(path = program, all.files = TRUE, full.names = TRUE, recursive = TRUE) ) +print(x = "") + +### EVALUATION +output_file <- paste0(output, .Platform$file.sep, "scores.txt") + +# if(!file.exists(file = paste0(input, .Platform$file.sep, "res", .Platform$file.sep, "test_solution.rds"))){ +# print("no solution file, scoring is not executed") +# print("Scores : empty file") +# cat(paste0("Accuracy_mean: NA", "\n"), file = output_file, append = FALSE) +# cat(paste0("Accuracy_sd: NA", "\n"), file = output_file, append = TRUE ) +# cat(paste0("Time: NA", "\n"), file = output_file, append = TRUE ) +# } else { + print("execution of the info_mut scoring program") + + +## Load reference data +A_real = readRDS(file = paste0(input, .Platform$file.sep, "ref", .Platform$file.sep, "groundtruth_smoothies_fruits.rds") ) + + + +## load R profiling of the estimation of A : +profiling <- readRDS(file = paste0(input, .Platform$file.sep, "res", .Platform$file.sep, "Rprof.rds") ) + + + +############################################## + + +# validate_pred <- function(pred, nb_samples = ncol(A_real), nb_cells=nrow(A_real) , col_names=rownames(A_real) ){ + +# error_status = 0 # 0 means no errors, 1 means "Fatal errors" and 2 means "Warning" +# error_informations = '' + +# ## Ensure that all sum ofcells proportion approximately equal 1 +# if (!all(sapply(colSums(pred), function(x) isTRUE(all.equal(x, 1) )))) { +# msg = "The prediction matrix does not respect the laws of proportions: the sum of each columns should be equal to 1\n" +# error_informations = paste(error_informations,msg) +# error_status = 2 +# } + +# ##Ensure that the prediction have the correct names ! +# if(! setequal(rownames(pred),col_names) ){ +# msg = paste0( "The row names in the prediction matrix should match: ", toString(col_names),"\n") +# error_informations = paste(error_informations,msg) +# error_status = 2 +# } + +# ## Ensure that the prediction return the correct number of samples and number of cells. +# if (nrow(pred) != nb_cells | ncol(pred) != nb_samples) { +# msg= paste0('The prediction matrix has the dimension: ',toString(dim(pred))," whereas the dimension: ",toString(c(nb_cells,nb_samples))," is expected\n" ) +# error_informations = paste(error_informations,msg) +# error_status = 1 +# } + +# if(error_status == 1){ +# # The error is blocking and should therefor stop the execution. +# stop(error_informations) +# } +# if(error_status == 2){ +# print("Warning: ") +# warning(error_informations) +# } +# } + + + + + + +##Ensure some properties of the Prediction are ok. + +Aest = as.matrix(readRDS(file = paste0(input, .Platform$file.sep, "res", .Platform$file.sep, "prediction.rds") ) ) +A_real = as.matrix(A_real) + +# print(Aest) +# validate_pred( Aest ) + + +scores_full = scoring_function(A_real = A_real, A_est = Aest) + +scores = as.numeric(scores_full) + +saveRDS(scores, paste0(output,"/score.rds")) + +# stopifnot(exprs = all( !is.na(x = scores) ) ) +print(paste0("Scores names : ", paste(names(scores_full), collapse = ", ") )) +print(x = paste0("Scores dataset : ", paste(scores, collapse = ", ") ) ) +cat(paste0("Accuracy_mean: " , mean(x = scores,na.rm=TRUE), "\n"), file = output_file, append = TRUE) + +print(x = list.files(path = output, all.files = TRUE, full.names = TRUE, recursive = TRUE) ) + + + +# cat(paste0("Time :", sum( profiling$by.total$total.time ) / length(x = Aref), "\n"), file = output_file, append = TRUE ) +# cat(paste0("Time: ", profiling["elapsed"], "\n"), file = output_file, append = TRUE ) +cat(paste0("Time: ", profiling, "\n"), file = output_file, append = TRUE ) + +# rmarkdown::render( +# input = paste0(program, .Platform$file.sep, "detailed_results.Rmd") +# , envir = parent.frame( ) +# , output_dir = output +# # , output_file = "scores.html" +# , output_file = "detailed_results.html" +# ) + +print(x = "Output :") +print(x = list.files(path = output , all.files = TRUE, full.names = TRUE, recursive = TRUE) ) +print(x = "") diff --git a/phase-0-smoothie/starting_kit/submission_script.R b/phase-0-smoothie/starting_kit/submission_script.R new file mode 100644 index 0000000..0da46e3 --- /dev/null +++ b/phase-0-smoothie/starting_kit/submission_script.R @@ -0,0 +1,271 @@ +########### +# Authors +# florent.chuffard@univ-grenoble-alpes.fr +# slim.karkar@univ-grenoble-alpes.fr +# magali.richard@univ-grenoble-alpes.fr +# --- + +#to get list of function before sourcing new functions +# before <- ls() +######################################################## +### Package dependencies /!\ DO NOT CHANGE THIS PART ### +######################################################## + + +## to generate the zip files that contain the programs or the results to submit to the Codalab platform. + +if ( !exists(x = "params") ) { + params <- NULL +} +if ( is.null(x = params$package_repository) ) { + params$package_repository <- "https://cloud.r-project.org" +} +if ( is.null(x = params$install_dependencies) ) { + params$install_dependencies <- TRUE +} +print(params) +if ( params$install_dependencies ) { + installed_packages <- installed.packages( ) + for (package in c("zip","nnls") ) { + if ( !{ package %in% installed_packages } ) { + print(x = paste("Installation of ", package, sep = "") ) + install.packages( + pkgs = package + , repos = params$package_repository + ) + } else { + print(x = paste(package, " is installed.", sep = "") ) + } + library(package, character.only = TRUE) + } + remove(list = c("installed_packages", "package") ) +} +# - + +#################################################### +### Submission modes /!\ DO NOT CHANGE THIS PART ### +#################################################### + +# Participants can submit either +# - a code program, that will be executed on the challenge platform to generate the result (prediction) that will be scored against the ground truth +# - a result (prediction )file, that will be scored against the ground truth + +############################### +### Code submission mode +# Participants need make a zip file (no constrain on the namefile) that contains : +# - your code inside a *R* file named `program.R`. This file will be sourced and have to contain : +# - a function `program` with `data_test` and `input_k_value` as arguments +# - any other files that you want to access from your function `program` : during the ingestion phase (when your code is evaluated), the working directory will be inside the directory obtained by unzipping your submission. + +############################### +### Result submission mode +# Participants have to make a zip file (no constrain on the namefile), with your results as a matrix inside a rds file named `results_1.rds`. + +# if ( !( "tidyverse" %in% installed.packages() ) ) { +# install.packages(pkgs = "tidyverse", repos = "https://cloud.r-project.org") +# } + +# require(tidyverse) + + +################################################################################################## +### Submission modes /!\ EDIT THE FOLLOWING CODE BY COMMENTING/UNCOMMENTING THE REQUIRED PARTS ### +################################################################################################## + + + + +#' The function to estimate the A matrix +#' In the provided example, we use basic non-negative least squares (package "nnls"), which consists of minimizing the error term $||Mix - Ref \times Prop||^2$ with only positive entries in the prop matrix. +#' +#' @param mix a matrix of bulks (columns) and features (rows) +#' @param ref a matrix pure types (columns) and features (rows) +#' @param ... other parameters that will be ignored +#' +#' @return the estimated A matrix +#' @examples +#' +program = function(mix=NULL, ref=NULL, ...) { + + installed_packages <- installed.packages( ) + for (package in c("zip","nnls") ) { + if ( !{ package %in% installed_packages } ) { + print(x = paste("Installation of ", package, sep = "") ) + install.packages( + pkgs = package + , repos = "https://cloud.r-project.org" + ) + } else { + print(x = paste(package, " is installed.", sep = "") ) + } + library(package, character.only = TRUE) + } + remove(list = c("installed_packages", "package") ) + ## + ## YOUR CODE BEGINS HERE + ## + + idx_feat = intersect(rownames(mix), rownames(ref)) + prop = apply(mix[idx_feat,], 2, function(b, A) { + # tmp_prop = nnls::nnls(b=b, A=A)$x + tmp_prop = nnls(b=b, A=A)$x + tmp_prop = tmp_prop / sum(tmp_prop) # Sum To One + return(tmp_prop) + }, A=ref[idx_feat,]) + rownames(prop) = colnames(ref) + + return(prop) + + ## + ## YOUR CODE ENDS HERE + ## +} + + + +############################################################## +### Check the prediction /!\ DO NOT CHANGE THIS PART ### +############################################################## + +validate_pred <- function(pred, nb_samples = ncol(mix_rna) , nb_cells= ncol(ref_rna),col_names = colnames(ref_met) ){ + + error_status = 0 # 0 means no errors, 1 means "Fatal errors" and 2 means "Warning" + error_informations = '' + + ## Ensure that all sum ofcells proportion approximately equal 1 + if (!all(sapply(colSums(pred), function(x) isTRUE(all.equal(x, 1) )))) { + msg = "The prediction matrix does not respect the laws of proportions: the sum of each columns should be equal to 1\n" + error_informations = paste(error_informations,msg) + error_status = 2 + } + + ##Ensure that the prediction have the correct names ! + if(! setequal(rownames(pred),col_names) ){ + msg = paste0( "The row names in the prediction matrix should match: ", toString(col_names),"\n") + error_informations = paste(error_informations,msg) + error_status = 2 + } + + ## Ensure that the prediction return the correct number of samples and number of cells. + if (nrow(pred) != nb_cells | ncol(pred) != nb_samples) { + msg= paste0('The prediction matrix has the dimention: ',toString(dim(pred))," whereas the dimention: ",toString(c(nb_cells,nb_samples))," is expected\n" ) + error_informations = paste(error_informations,msg) + error_status = 1 + } + + if(error_status == 1){ + # The error is blocking and should therefor stop the execution. + stop(error_informations) + } + if(error_status == 2){ + print("Warning: ") + warning(error_informations) + } +} + + + +############################################################## +### Generate a prediction file /!\ DO NOT CHANGE THIS PART ### +############################################################## + + + +mixes_data = readRDS("mixes_smoothies_fruits.rds") +reference_data = readRDS("reference_fruits.rds") + +# we use the previously defined function 'program' to estimate A : +pred_prop <- program( + mix = mixes_data , + ref = reference_data +) + + + +############################### +### Code submission mode + +# we generate a zip file with the 'program' source code + +if ( !dir.exists(paths = "submissions") ) { + dir.create(path = "submissions") +} + + +## list all functions to dump them inside R the program.R. +# after <- ls() +# changed <- setdiff(after, before) +# changed_objects <- mget(changed, inherits = T) +# changed_function <- do.call(rbind, lapply(changed_objects, is.function)) +# new_functions <- changed[changed_function] +# new_functions + +# we save the source code as a R file named 'program.R' : +dump( + list = c("program") + # list = new_functions + , file = paste0("submissions", .Platform$file.sep, "program.R") +) + +date_suffix = format(x = Sys.time( ), format = "%Y_%m_%d_%H_%M_%S") + +# we create the associated zip file : +zip_program <- paste0("submissions", .Platform$file.sep, "program_", date_suffix, ".zip") +zip::zip(zipfile= zip_program + , files= paste0("submissions", .Platform$file.sep, "program.R") + , mode = "cherry-pick" + ) + +zip::zip_list(zip_program) +print(x = zip_program) + +############################### +### Result submission mode + +# Generate a zip file with the prediction +if ( !dir.exists(paths = "submissions") ) { + dir.create(path = "submissions") +} + +prediction_name = "prediction.rds" + +## we save the estimated A matrix as a rds file named 'results.rds' : +saveRDS( +object = pred_prop +, file = paste0("submissions", .Platform$file.sep, prediction_name) +) + +# write_rds(pred_prop, file = "prediction_hugo.rds") + +## we create the associated zip file : +zip_results <- paste0("submissions", .Platform$file.sep, "results_", date_suffix, ".zip") +zip::zipr( + zipfile = zip_results + , files = paste0("submissions", .Platform$file.sep, c(prediction_name) ) + ) +print(x = zip_results) + +sessionInfo( ) + +############################################################### +### How to submit the zip file? /!\ DO NOT CHANGE THIS PART ### +############################################################### +# +# The code above generates the files *`r zip_program`* and *`r zip_results`* (the 1st one for code submission, the 2nd one for result submission). +# +# Submit the zip submission file on the challenge in the `My Submission` tab, fill the metadata, select the task you want to submit to and upload your submission files +# +# On the codalab challenge web page, The *STATUS* become : +# - Submitting +# - Submitted +# - Running +# - Finished +# +# When it’s finished : +# - You refresh the page and click on the green button 'add to leaderboard' to see your score +# - If enable, details for report could be downloaded by clicking on your submission +# - Some execution logs are available to check and or download. +# - Metadata are editable when you click on your submission +# - Leader board is updated in the `Results` tab. +# + diff --git a/phase-0-smoothie/starting_kit/submission_script.py b/phase-0-smoothie/starting_kit/submission_script.py new file mode 100644 index 0000000..19a9866 --- /dev/null +++ b/phase-0-smoothie/starting_kit/submission_script.py @@ -0,0 +1,272 @@ +import os +import subprocess +import sys +import zipfile +import numpy as np +import pandas as pd +# from scipy.optimize import nnls +import inspect +import importlib + +# import rpy2.robjects as ro +from rpy2.robjects import pandas2ri +pandas2ri.activate() +# from rpy2.robjects.packages import importr + +import rpy2.robjects as ro +readRDS = ro.r['readRDS'] +saveRDS= ro.r["saveRDS"] + + +######################################################## +### Package dependencies /!\ DO NOT CHANGE THIS PART ### +######################################################## + +nb_datasets = 4 +def install(package): + subprocess.check_call([sys.executable, "-m", "pip", "install", package]) + +# List of required packages +required_packages = [ + "numpy", + "pandas", + "scipy", + "rpy2", + "zipfile", + "inspect" +] + +# Install each package if not already installed +for package in required_packages: + try: + globals()[package] = importlib.import_module(package) + except ImportError: + # print('impossible to import, installing packages',package) + install(package) + globals()[package] = importlib.import_module(package) + +#################################################### +### Submission modes /!\ DO NOT CHANGE THIS PART ### +#################################################### + +############################### +### Code submission mode +# Participants need to make a zip file (no constrain on the namefile) that contains: +# - your code inside a Python file named `program.py`. This file will be sourced and have to contain: +# - a function `program` with `data_test` and `input_k_value` as arguments +# - any other files that you want to access from your function `program`: during the ingestion phase (when your code is evaluated), the working directory will be inside the directory obtained by unzipping your submission. + +############################### +### Result submission mode +# Participants have to make a zip file (no constrain on the namefile), with your results as a matrix inside a pkl file named `results_1.pkl`. + +################################################################################################## +### Submission modes /!\ EDIT THE FOLLOWING CODE BY COMMENTING/UNCOMMENTING THE REQUIRED PARTS ### +################################################################################################## + +# Write a function to predict cell-type heterogeneity proportion matrix +# In the provided example, we use a naive method to generate the baseline prediction. + +def program(mix_rna=None, mix_met=None, ref_rna=None, ref_met=None): + """ + The function to estimate the A matrix + + Parameters: + mix_rna (numpy.ndarray): the bulk matrix associated to the transcriptome data set + mix_met (numpy.ndarray): the bulk matrix associated to the methylation data set + ref_rna (numpy.ndarray): the reference matrix associated to the transcriptome data set + ref_met (numpy.ndarray): the reference matrix associated to the methylation data set + + Returns: + numpy.ndarray: the estimated A matrix + """ + + ## + ## YOUR CODE BEGINS HERE + ## + + ###### Install requiered packages if necessary + # List of required packages + required_packages = [ + "scipy", + ] + + # Install each package if not already installed + for package in required_packages: + try: + # __import__(package) + globals()[package] = importlib.import_module(package) + except ImportError: + subprocess.check_call([sys.executable, "-m", "pip", "install", package]) + globals()[package] = importlib.import_module(package) + + + def estimate_proportions(mix, ref): + res = [scipy.optimize.nnls(ref, mix[:, i])[0] for i in range(mix.shape[1])] + props = np.array([res_i / sum(res_i) for res_i in res]) + return props.T + + if mix_rna is not None: + prop_rna = estimate_proportions(mix_rna, ref_rna) + else: + prop_rna = None + + if mix_met is not None: + prop_met = estimate_proportions(mix_met, ref_met) + else: + prop_met = None + + + if prop_rna is not None and prop_met is not None: + assert prop_rna.shape == prop_met.shape, "Dimensions of RNA and Methylation data do not match" + prop = (prop_rna + prop_met) / 2 + elif prop_rna is not None: + prop = prop_rna + elif prop_met is not None: + prop = prop_met + else: + raise ValueError("At least one of mix_rna or mix_met must be provided") + + if not np.allclose(np.sum(prop, axis=0), 1): + prop = prop / np.sum(prop, axis=0) + + return prop + + ## + ## YOUR CODE ENDS HERE + ## + + +############################################################## +### Check the prediction /!\ DO NOT CHANGE THIS PART ### +############################################################## + +def validate_pred(pred, nb_samples=None, nb_cells=None, col_names=None): + error_status = 0 # 0 means no errors, 1 means "Fatal errors" and 2 means "Warning" + error_informations = '' + + # Ensure that all sum of cells proportion approximately equal 1 + if not np.allclose(np.sum(pred, axis=0), 1): + msg = "The prediction matrix does not respect the laws of proportions: the sum of each column should be equal to 1\n" + error_informations += msg + error_status = 2 + + # Ensure that the prediction has the correct names + if not set(col_names) == set(pred.index): + msg = f"The row names in the prediction matrix should match: {col_names}\n" + error_informations += msg + error_status = 2 + + # Ensure that the prediction returns the correct number of samples and number of cells + if pred.shape != (nb_cells, nb_samples): + msg = f'The prediction matrix has the dimension: {pred.shape} whereas the dimension: {(nb_cells, nb_samples)} is expected\n' + error_informations += msg + error_status = 1 + + if error_status == 1: + # The error is blocking and should therefore stop the execution + raise ValueError(error_informations) + if error_status == 2: + print("Warning:") + print(error_informations) + + +############################################################## +### Generate a prediction file /!\ DO NOT CHANGE THIS PART ### +############################################################## + +r_code_get_colnames_nested = ''' +get_colnames_nested <- function(reference_data) { + return (colnames(reference_data$ref_bulkRNA)) +} +''' +ro.r(r_code_get_colnames_nested) + +predi_list = [] +for dataset_name in range(1, nb_datasets + 1): + dir_name = f"input_data_{dataset_name}/" + print(f"generating prediction for dataset: {dataset_name}") + + mixes_data = readRDS(os.path.join(dir_name, "mixes_data.rds")) + # mixes_data = pandas2ri.rpy2py(mixes_data) + # print(type(mixes_data)) + # print((mixes_data.rx2("mix_rna"))[0].names) + # nested_list = mixes_data.rx2("mix_rna") + # print(nested_names = nested_list.names) + + mix_rna= np.array(mixes_data.rx('mix_rna')) + mix_rna = mix_rna[0] + # print(mix_rna) + + # print(type(mix_rna[0])) + # print(pd.DataFrame({'mix_rna':mix_rna.flatten()} )) + + # mix_met = mixes_data.get('mix_met') + mix_met = np.array(mixes_data.rx('mix_met')) + mix_met = mix_met[0] + # print(mix_met) + + reference_data = readRDS(os.path.join(dir_name, "reference_data.rds")) + # reference_data = pandas2ri.rpy2py(reference_data) + ref_rna = np.array(reference_data.rx('ref_bulkRNA')) + ref_met = np.array(reference_data.rx('ref_met')) + ref_rna = ref_rna[0] + ref_met = ref_met[0] + + get_colnames_nested = ro.r['get_colnames_nested'] + colnames_result = get_colnames_nested(reference_data) + + pred_prop = program( + mix_rna=mix_rna, mix_met=mix_met, + ref_rna=ref_rna, ref_met=ref_met + ) + + pred_prop_df = pd.DataFrame(pred_prop, index=colnames_result) + + # validate_pred(pred_prop, nb_samples=mix_rna.shape[1] if mix_rna is not None else mix_met.shape[1], nb_cells=ref_rna.shape[1], col_names=reference_data['ref_met'].columns) + + predi_list.append(pred_prop_df) + +############################### +### Code submission mode + +# we generate a zip file with the 'program' source code + +if not os.path.exists("submissions"): + os.makedirs("submissions") + +# we save the source code as a Python file named 'program.py': +with open(os.path.join("submissions", "program.py"), 'w') as f: + f.write(inspect.getsource(program)) + +date_suffix = pd.Timestamp.now().strftime("%Y_%m_%d_%H_%M_%S") + +# we create the associated zip file: +zip_program = os.path.join("submissions", f"program_{date_suffix}.zip") +with zipfile.ZipFile(zip_program, 'w') as zipf: + zipf.write(os.path.join("submissions", "program.py"), arcname="program.py") + +print(zip_program) + +############################### +### Result submission mode + +# Generate a zip file with the prediction +if not os.path.exists("submissions"): + os.makedirs("submissions") + +prediction_name = "prediction.rds" + +saveRDS(predi_list, os.path.join("submissions", prediction_name)) + + + +# Create the associated zip file: +zip_results = os.path.join("submissions", f"results_{date_suffix}.zip") +with zipfile.ZipFile(zip_results, 'w') as zipf: + zipf.write(os.path.join("submissions", prediction_name), arcname=prediction_name) + +print(zip_results) + + +# diff --git a/phase-0-smoothie/starting_kit/submissions/program.R b/phase-0-smoothie/starting_kit/submissions/program.R new file mode 100644 index 0000000..dfbbd24 --- /dev/null +++ b/phase-0-smoothie/starting_kit/submissions/program.R @@ -0,0 +1,26 @@ +program <- +function (mix = NULL, ref = NULL, ...) +{ + installed_packages <- installed.packages() + for (package in c("zip", "nnls")) { + if (!{ + package %in% installed_packages + }) { + print(x = paste("Installation of ", package, sep = "")) + install.packages(pkgs = package, repos = "https://cloud.r-project.org") + } + else { + print(x = paste(package, " is installed.", sep = "")) + } + library(package, character.only = TRUE) + } + remove(list = c("installed_packages", "package")) + idx_feat = intersect(rownames(mix), rownames(ref)) + prop = apply(mix[idx_feat, ], 2, function(b, A) { + tmp_prop = nnls(b = b, A = A)$x + tmp_prop = tmp_prop/sum(tmp_prop) + return(tmp_prop) + }, A = ref[idx_feat, ]) + rownames(prop) = colnames(ref) + return(prop) +} diff --git a/scoring_program/scoring.R b/scoring_program/scoring.R index e8b5d00..2ff886f 100644 --- a/scoring_program/scoring.R +++ b/scoring_program/scoring.R @@ -261,10 +261,6 @@ validate_pred <- function(pred, nb_samples = ncol(Aref), nb_cells=nrow(Aref) , c } - - - - ##Ensure some properties of the Prediction are ok. Aest_l = readRDS(file = paste0(input, .Platform$file.sep, "res", .Platform$file.sep, "prediction.rds") )