DOI: 10.5061/dryad.cfxpnvxdh
title: Dataset for genome-wide profiling of autophagy dynamics under nutrient availability in <em>Saccharomyces cerevisiae</em>

description: This dataset provides the complete quantitative data supporting all main
 and extended figures of the study “Time-resolved functional genomics using
 deep learning reveals global hierarchical control of autophagy”. It is
 part of the AutoDRY resource, a systems-level map of the genetic network
 controlling activation and inactivation of autophagy in response to
 nitrogen availability in yeast. The dataset includes time-resolved
 measurements of autophagy across 5,919 mutants, derived from high-content
 fluorescent imaging and automated image analysis using deep learning. Data
 were further processed through UMAP latent-space embedding and Bayesian
 factor analysis to infer contributions to autophagosome formation and
 clearance. The files include autophagy quantifications, statistical
 analyses, network analyses, and cross-omics integration using random
 forest modeling. Experimental validation data, including quantitative
 Pho8Δ60 assays, GFP-Atg8 and Ape1 processing, and qPCR analyses, are also
 provided. Together, these data enable full reproducibility of the study’s
 analyses and support reuse for further computational analysis or
 comparative studies of autophagy regulation.
description: High-content fluorescent imaging
 Automatic image analysis using FIJI/Image J analysis
 Deep learning analysis Statistical
 analysis Network analysis UMAP
 latent-space and Bayes factors analysis Experimental
 validations and autophagy flux analysis using Western blotting analysis,
 quantitative Pho8Δ60 assay, and qPCR analysis
 Cross-omics analysis using Random forest
description: # **Genome-wide profiling of autophagy in** ***Saccharomyces cerevisiae***
 ## File List and Descriptions **Data_File_S1.xlsx** **Genome-wide
 libraries, strains, and primer sequences used in this study** This file
 contains multiple tabs that are divided into: **Genome-wide libraries and
 sub-libraries** *YKO_A_library*: Yeast haploid deletion collection (KO).
 List of nonessential deletion alleles screened in this study and control
 strains - crossed with JEY11511 (BG Y7092) *DAmP_A_library*: Yeast haploid
 DAmP collection (D). List of essential DAmP alleles screened in this study
 and control strains -crossed with JE11511 (BG Y7092) *Mixed_library*:
 Rearranged plates (Mix). List of nonessential and essential deletion
 strains rearranged from the rows A of YKO-A and DAmP-A to a new plate and
 position *Recovery_library*: Recovery plates (Rec). List of KO and DAmP
 strains with too low cell counts during the initial screening, and that
 were re-inoculated and subjected to an extra round of screening
 *Repetition_library*: Repetition plates (Rep). List of mutants with a
 statistically significant perturbation in autophagy or potential false
 negatives/positives that were subjected to a second measurement
 *Rapamycin_library*: Rapamycin plates (Rapa). List of mutants subjected to
 rapamycin treatment and control treatment   **Other libraries, strains,
 primers, and plasmids** ● *Strains_experimental purpose*: Lists of alleles
 that were deleted in this study for the creation of reproducibility set
 and screen controls, and for performing autophagy assays ● *Primers*:
 Sequences of oligonucleotides used in this study ● Plasmids: List of
 plasmids used in this study   **Genome-wide libraries are provided in
 tab-delimited format with the following columns** ● *ORF name* ● *Gene
 Name* ● *Plate* ● *Col* ● *Row* ● *Type* ● *Background*: yeast strain
 background ● *Source*: Number in the JE collection or plate in the
 original KO-A or DAmP-A libraries ● *Position source*: Position in the
 original KO-A or DAmP-A libraries ● *Conditions*: screening conditions
 tested   **Strains** ● *Number* ● *Genotype* ● *Source* ● *Experimental
 Purpose*   **Primers** ● *Name* ● *Sequence* ● *Provider*   **Plasmids** ●
 *Name* ● *Source*   **Data_File_S2.xlsx**
 **Autophagy_predictions_Statistics_Clustering** This file contains
 multiple tabs: *Autophagy % DNN predictions*: autophagy responses for the
 genome-wide (GW) screen ● *Time*: time point; *TimeR*: adjusted real time
 of image capture ● *P1_30*, *P1_22*: expected probability of autophagy,
 x100 (%) ● *P1_binary_30, P1_binary_22*: autophagy classification % ●
 *P_err_30, P_err_22*: expected standard deviation, x100 ● *LO_30, LO_22*:
 expected log-odds ● *LO_err_30, LO_err_22*: standard deviation in log-odds
 ● *NCells*: cell counts   *Autophagy % curvefits*: double sigmoidal
 curvefits of autophagy response % for the GW screen   *Goodness-of-fit
 measures*: RMSE and log-likelihood of double sigmoidal curvefits for the
 GW screen   *Parameter statistics*: perturbation statistics for response
 kinetic parameters computed from double sigmoidal models ● *Value*:
 estimated kinetic parameter value ● *Perturbation*: change in parameter
 values from plate medians ● *p_value, p_value_2t*: one-tailed and
 two-tailed p-values ● *FDR_BH*: FDR-adjusted p-values using the
 Benjamini-Hochberg procedure ● *w*: HMP weights ● *HMP*: harmonic mean
 p-value per mutant ● *FDR_min*: minimum FDR-adjusted p-value per mutant  
 *Parameter controls*: kinetic parameter value, perturbations from plate
 medians, and plate-wise adjusted errors for the plate controls   *Mutant
 profiles*: clustered matrix of significant mutants, with classification of
 mutant profiles   *Parameter groups*: clustering results of kinetic
 parameters   **Data_File_S3.xlsx** **GSEA GO_kinetic parameter and HMP
 enrichment** This file contains multiple tabs: *Parameter GSEA_GO-BP
 results*: GSEA results for GO-BP terms over each parameter perturbation
 statistics *Parameter GSEA_BO-BP_matrix*: matrix of signed and
 -log-transformed enrichment p-values *HMP GSEA_GO_results_emap*: GSEA
 results for GO terms over the HMP scores with enrichment map coordinates
 and autophagy perturbation profile composition per node *HMP
 GSEA_GO_results_emap*: enrichment map network coordinates 
 
 **Data_File_S4.xlsx** **SAFE analysis of the global yeast genetic
 similarity and the STRING network (v10) cutoff 990** This file contains
 multiple tabs that are divided into:   **Node and Edge column data of the
 genetic similarity network by Costanzo et al., 2016 were obtained from the
 Cytoscape session 'Yeast Genetic Interactions' (version 3.9.1)**
 *Costanzo2016_node*: This includes SAFE enrichment score per node (gene)
 *Costanzo2016_edge*: This includes pair-gene interactions and correlation
 values per each interaction   **SAFE analysis on the yeast genetic
 similarity network (Costanzo et al., 2016)** *Costanzo_SAFE_node*
 *Costanzo_SAFE_domain* *Costanzo_SAFE_attribute* *Costanzo_SAFE_autophagy
 profiles*   **Node and Edge column data from STRING network based o a
 combined score ≥ 990 of the STRING v10 database** *STRING 990_node*: This
 includes SAFE enrichment score per node (gene) *STRING 990_edge*: This
 includes pair-gene interactions and correlation values per each
 interaction   **SAFE analysis on the yeast STRING network 990 (v10)**
 *STRING 990_SAFE_node* *STRING 990_SAFE_domain* *STRING
 990_SAFE_attribute* *STRING 990_SAFE_autophagy profiles*   **Costanzo and
 STRING SAFE annotations are provided in tab-delimited format with the
 following columns**   **Node** ● *Node label* ● *Node label ORF* ● *Domain
 (predominant)*: This indicates the predominant domain (group) defined by
 SAFE score ● *Neighborhood score [max=1, min=0] (predominant)*: This
 indicates the confidence level of node annotation towards the predominant
 group from 0 to 1 ● *Total number of enriched domains*: This represents
 the count of enriched GO term attributes corresponding to each domain
 (group) for each node. ● *Number of enriched attributes per domain*: The
 domain numbers are separated by commas.   **Domain** ● *Domain number*:
 This matches to number of the predominant domain group in the tab
 "Node." ● *Domain name*: Indicates a GO term that most
 accurately characterizes each group within the predominant domain. ● *RGB
 code*: This highlights the domain in the network.   **Attribute** ●
 *Attribute Id*: GO term number ● *Attribute name*: GO term name ● *Domain
 Id*: This matches to domain number in the tab "Domain"  
 **Profiles** ● *name* ● *1: Ultrasensitive* ● *2: Hyposensitive* ● *3:
 Hyperactive* ● *4: Insufficient activation* ● *5: Failed response* ● *6:
 Null response* ● *ATG core* ● *FUSION core* 
 
 **Distance stats** Mean and SE of -log10 HMP, along with number of
 significant mutants, per path distance to core in different networks 
 
 **Shortest path** Mean and SE of shortest path length per profile in
 different networks   **Int. cor. Stats** Statistics of autophagy phenotype
 correlations within groups of gene pairs sharing edge in network or
 between genes not sharing an edge 
 
 **Fisher's stats** Number of positive and negative SGA-PCC
 similarities per correlation interval for pairs of significant genes (HMP
 ≤ 0.001) with enrichment scores and corresponding p-values for direction
 Fisher's exact test.   **SGA-PCC_GSEA stats** Statistics of two-sided
 t-tests of NES scores per mutant profiles over ATG or Fusion SGA-PCCs  
 **SGA-PCC_GSEA group stats** GSEA statistics per mutants profile against
 ATG and Fusion gene SGA-PCCs   **Distance per profile** Frequency
 distribution and enrichment of mutant profiles per causal path length to
 ATG core in the Costanzo 2016 (SGA-PCC) network. pvalue indicates the
 significance for a twp-sided Fisher’s exact test   **Data_File_S5.xlsx**
 **Autophagy perturbations across interacting protein modules spanning
 three regulatory network hierarchies** This file contains multiple tabs
 that are divided into:   **Autophagy regulatory network** *Autophagy_node*
 *Autophagy_edge*   **RNA metabolism and translation**
 *RNA_translation_node* *RNA_translation_edge*    **Gene expression
 regulation** *Gene_expression_node* *Gene_expression_edge*   **Each module
 network is provided in tab-delimited format with the following columns** ●
 *Node* ● *Shared name/ Name* ● *ORF* ● *Profile*: Autophagy perturbations
 identified in our study ● *Cluster*: Curated complexes identified through
 either the Complex Portal or the application of the MCODE algorithm ●
 *HMP*: (-log10) ● *Perturbation_starvation*: (signed -log10 p-value) ●
 *Perturbation_replenishment*: (signed -log10 p-value) ●
 *Perturbation_overall*: (signed -log10 p-value) ● *slope1*: (signed -log10
 p-value) ● *slope1Param*: (signed -log10 p-value) ● *slope2*: (signed
 -log10 p-value) ● *slope2Param*: (signed -log10 p-value) ● *A_start*:
 (signed -log10 p-value) ● *A_max*: (signed -log10 p-value) ● *A_final*:
 (signed -log10 p-value) ● *T50_1*: (signed -log10 p-value) ● *T50_2*:
 (signed -log10 p-value) ● *T_lag_1*: (signed -log10 p-value) ● *T_lag_2*:
 (signed -log10 p-value) ● *T_final_1*: (signed -log10 p-value) ●
 *T_final_2*: (signed -log10 p-value) ● *Dynamic_range_1*: (signed -log10
 p-value) ● *Dynamic_range_2*: (signed -log10 p-value)   **Edge** ●
 *interaction/shared interaction* ● *name/shared name*: gene-pair
 interaction   **Data_File_S6.xlsx** **Latent space statistics** This file
 contains multiple tabs: *UMAP statistics and entropy*: summary statistics
 of UMAP dynamics and noise and average prediction uncertainty per mutant ●
 *NCells*: average number of cells per time point ● *UMAP_30_SD,
 UMAP_22_SD*: average time-wise bivariate standard deviation for UMAP
 populations ● *UMAP_30_flux, UMAP_22_flux*: average population UMAP flux ●
 *UMAP_30_flux_norm, UMAP_22_flux_norm*: SD-normalized average population
 UMAP flux ● *Entropy_30, Entropy_22*: average binary entropy for DNN
 predictions   *LS and UMAP statistics*: population standard deviation and
 displacement (flux) per time point for latent space vectors and UMAP
 coordinates **Data_File_S7.xlsx** **Autophagy Bayes factors** This file
 contains multiple tabs: *Bayes factors overall* *Bayes factors -N* *Bayes
 factors +N* *Bayes factors per time*   **Column information** ●
 *log_BF_WT.ATG1_30, log_BF_WT.VAM6_30, log_BF_VAM6.ATG1_30,
 log_BF_WT.ATG1_22, log_BF_WT.VAM6_22, log_BF_VAM6.ATG1_22*: Time-invariant
 Bayes factors computed from time-wise reference kernel densities, averaged
 overall or per phase (-N, +N) ● *log_BFt_WT.ATG1_30, log_BFt_WT.VAM6_30,
 log_BFt_VAM6.ATG1_30, log_BFt_WT.ATG1_22, log_BFt_WT.VAM6_22,
 log_BFt_VAM6.ATG1_22*: Time-wise Bayes factors computed from fixed
 reference density kernels, averaged overall or per phase (-N, +N), or
 averaged per time point ● *NCells*: total number of cells ● *P1_adj_30,
 P1_adj_22, P1_binary_adj_30, P1_binary_adj_22*: plate median adjusted
 average autophagy % response or average binarized % class predictions ●
 *Red, Green, GxR*: mean fluorescent signal or co-occurrence per mutant
 early in the starvation protocol ● *qRed, qGreen*: outlier cutoff for mean
 fluorescent signal per mutant early in the starvation protocol ●
 *Red.late, Green.late, GxR.late*: mean fluorescent signal or co-occurrence
 per mutant late in the starvation protocol ● Bayes factors overall:
 Additonal column ● qoutliers_red, qoutliers_green: mutant flagged as
 outlier if Red &lt; qRed (outlier cutoff) and if Green &lt; qGreen
 (outlier cutoff) in Bayes factors overall **Data_File_S8.xlsx** **Bayes
 factors GSE and z-scores** This file contains multiple tabs: *BF GSE
 overall*: parametric GO enrichment statistics (using piano) for BFt
 z-scores *BF GSE phase comp.*: parametric GO enrichment statistics (using
 piano) for BF z-scores per phase or differential z-score between phases
 (starvation - replenishment) *BF z-scores overall*: z-scores of log Bayes
 factors averaged between models 30 and 22, overall *BF z-scores phase
 comp.*: z-scores for log Bayes factors averaged between models 30 and 22,
 per phase and differential between phases (starvation - replenishment)  
 **Data_File_S9.xlsx** **Nearest neighbour and GO selection** This file
 contains multiple tabs: *Nearest neighbour selection*: Dataset of summary
 statistics from the nearest neighbor enrichment of autophagy phenotypes
 using different yeast networks ● *HMP*, *FDR_min_pert*: mutant autophagy
 phenotype statistics ● *HMP_Fisher*: summary statistics for nearest
 neighbor autophagy phenotype enrichment ● *pmin_Fisher, pmin.adj_Fisher*:
 minimum p-value and adjusted p-value for nearest neighbor autophagy
 phenotype enrichment ● *Data, Set, Count*: Network, phenotype set, and
 nearest neighbor count with the most significant enrichment   *GO
 selection*: Autophagy GO term annotation of mutants ● *HMP, FDR_min_pert*:
 mutant autophagy phenotype statistics ● *N, Description*: Number and name
 of GO terms per mutant **Data_File_S10.xlsx**
 **Autophagy_reproducibility_validation** This file contains multiple tabs:
 *Repetition responses*: Autophagy response % for repetition screen mutants
 with corresponding GW screen measurements *Validation responses*:
 Autophagy response % for validation screen mutants with corresponding GW
 screen measurements *Validation statistics*: Double sigmoidal regression
 statistics for validation screen mutants   **Data_File_S11.xlsx**
 **Rapamycin screen results** This file contains multiple tabs: *Autophagy
 % DNN predictions*: autophagy response % for rapamycin screen mutants
 under the different treatment conditions *UMAP Bayes factors*: overall
 time-wise UMAP Bayes factors for rapamycin screen mutants under the
 different treatment conditions.   **Data_File_S12.xlsx** **WB, Pho8d60
 statistics, EM area, APE1 assay** This file contains multiple tabs: *GFP
 measures*: GFP cleavage % computation for all experiments. Band % is
 computed for two replicate recordings (different exposure times) and
 averaged. *GFP quant.*: GFP cleavage % quantifications across biological
 replicas *GFP stats._RTGs*: GFP cleavage statistics for RTG experiments
 using mixed effects models with replica ID as grouping variable. The table
 shows analysis results from two experiments under Experiment_id. Contrasts
 between specific conditions; the baseline reference is indicated. *GFP_BF
 comp.*: comparing average GFP cleavage (0-8 hrs) with overall Bayes
 factors from the GW screen. *GFP_BF time comp.*: comparing time-wise GFP
 cleavage with Bayes factors. *GFP_parameter comp.*: comparing average GFP
 cleavage (0-8 hrs) with response kinetic parameters from the GW screen.
 *Rps6_Act1 measures*: Rps6-p and Act1 band volume integrals. For Rps6 the
 values were averaged across two replicate recordings (different exposure
 times). *Rps6 quant.*: quantification of normalized Rps6-p signal across
 biological replicas. GFP cleavage % for equivalent experiments are also
 shown. *Rps6_BF comp.*: comparing Rps6-p with GFP cleavage and overall
 Bayes factors from the GW screen. *Pho8d60 measures WTvsMut: computation
 of normalized Pho8d60 activities for all experiments.* Pho8d60 stats.
 WTvsMut: statistics of RTG effects on normalized Pho8d60 activities using
 ANOVA and TukeyHSD test. The adjusted p-values are shown for pairwise
 comparisons between the mutants and the negative control (WT) for each
 treatment phase. *Pho8d60 measures DKOvsRTG1*: computation of normalized
 Pho8d60 activities for WT, RTG1 deletion, ATG1 deletion and DKO mutants.
 Pho8d60 stats DKOvsRTG1: statistics of RTG and ATG1 effects on normalized
 Pho8d60 activities using ANOVA and TukeyHSD test. The adjusted p-values
 are shown for pairwise comparisons between the mutants and the negative
 control (WT) for each treatment phase. EM AB area: autophagic body (AB)
 area quantifications in electron micrographs. Ape1 WB
 quant.: quantifications of normalized Ape1 band volume integrals relative
 to Act1 controls across biological replicas. Quantifications of Ape1
 processing % are also shown. For each metric, a mean and standard error
 are computed. **Data_File_S13.xlsx** **RT-qPCR statistics** This file
 contains multiple tabs: *Input data*: computation of normalized expression
 values for all experiments and performance of Combat batch corrections.
 *Statistics pairwise*: t-test statistics for pairwise comparisons between
 mutants and negative controls (WT) for each time point. *Statistics per
 phase*: statistics for comparing overall mutant effects per treatment
 phase (+N T0, -N, and +N).   **Data_File_S14.xlsx** **RF_importance
 scores** This file contains multiple tabs: Importance scores: sumGain
 values for features predicting TF autophagy responses. Input represent the
 input dataset that was used; Data indicates whether the sample labels were
 randomized (control). SHAP scores: average SHAP values computed for input
 values representing up-regulation or down-regulation of gene expression
 for a specific feature. SHAP stats: summary statistics of SHAP values
 including mean and standard error of upregulated, downregulated, and
 differential gene expression across different input dataset, autophagy
 response and stages.   **Data_File_S15.zip** **Support data (data
 folder)** This folder contains the .RData files containing compressed data
 for various analyses performed in the study.   Training data: Curated
 reference dataset used for optimization and training of DNN models. The
 dataset has been scaled. The subset of the dataset used for training of
 the DNN models is indicated as a logical column vector, and the state
 labels for this subset are indicated in a separate column (0=: no
 autophagy; 1=:autophagy). Vectors of the feature names, and mean and sd
 scale factors are also included.   Training features and scale factors:
 Contains vectors of the feature names, and mean and sd scale factors.  
 Network data: Contains datasets with the network data used in the study. ●
 df_BioGRID_PPI: protein-protein interactions.
 [Oughtred](http://paperpile.com/b/GGvNWN/CvT5e) et al. 2016 ●
 df_complexes: parsed complexome annotation data. Meldal et al. 2021 ●
 df_SGA_PCC: complete genetic similarity matrix from Costanzo et al. 2016 ●
 df_SGA_IDs: dataset for mapping alleles to ORFs in Costanzo et al. 2016 ●
 df_STRING_v10: STRING version 10 with protein-protein interactions
 predicted by a combined_score ≥ 200.
 [Szklarczyk](http://paperpile.com/b/GGvNWN/uFGou) et al. 2015 ● ORF2SYMBOL
 ● UNIPROT2ORF Note: NaN in df_SGA_PCC indicates not available numbers as
 originally provided by Costanzo et al. 2016.   Cross-omics benchmarking
 datasets: Contains cleaned and matched input omics datasets from various
 sources and output variables based on autophagy perturbation parameter
 statistics (signed -log p-values) or Bayes factors: ● *X_gex*: scaled gene
 expression data from Kemmeren et al. 2014 ● *X_prot*: differential protein
 expression levels (log2-fold) from Messner et al. 2023 ● *X_me*t:
 metabolomics dataset of amino acid levels from Mülleder et al. 2016 ●
 *X_SGA_PCC*: genetic similarity dataset from Costanzo et al. 2016 ●
 *X_STRING_PCC*: STRING v10 protein similarity dataset computed from the
 PCC correlations across the binary interaction matrix of STRING
 interactions with a combined_score ≥ 200. Szklarczyk et al. 2015 ●
 *Y_parm*: transformed kinetic perturbation p-values ● *Y_BF*: time-wise
 Bayes factors (BFt) per mutant overall or per phase   Hackett_imputed data
 paired: Contains cleaned and matched datasets of adjusted expression
 dynamics (X; Hackett et al. 2020) and autophagy % or BFt perturbation
 dynamics, annotated for TFs and matching pseudotimes.  
 **Data_File_S16.zip** **Deep learning tools: DNNs for predicting
 autophagy, pUMAP embedding of autophagic features, and density kernels for
 UMAP reference states (data folder)** This folder contains the following
 objects:   Tensorflow HDF5 files of DNNs for classifying autophagic cell
 states from image features (p=31): ● *TF_Model_WT_ID22_May_2021.h5* ●
 *TF_Model_WT_ID30_May_2021.h5*   Tensorflow HDF5 files of DNN-based fast
 pUMAP embedding of latent space feature vectors. The models take the last
 hidden layers from the corresponding autophagy classifiers as inputs and
 transform them into two-dimensional UMAP coordinates: ●
 *TF_pUMAP_Model_WT_ID22_August_2021.h5* ●
 *TF_pUMAP_Model_WT_ID30_August_2021.h5*   UMAP density kernels: .RData
 files containing lists of density kernels for UMAP reference states (WT,
 VAM6, and ATG1). Density kernels are provided for the output of each pUMAP
 model, using both fixed and time-wise reference distributions. For
 likelihood computation, use *dkde* function from the *ks* package in R,
 with *x* set to the corresponding UMAP coordinates and *fhat* set to the
 specific density kernel.   **Data_File_S17.zip** **Genome-wide library of
 single cell autophagy phenotype data (data folder)** This folder contains
 the .RData files containing compressed single-cell screen data. Each data
 file contains the results for each 96-well plate divided into four
 *R* data frames: ● *X.data*: Plate, mutant, treatment information, time
 and well coordinates for every single-cell entry corresponding to rows in
 the other data frames. ● *X*: Scaled single-cell image features used as
 input for the DNN models. ● *Y*: Predicted autophagy (P1) per cell for
 each DNN model (22 and 30) ● *Y.umap*: Predicted UMAP coordinates
 representing the latent autophagy feature space per cell for each DNN
 model (22 and 30)   **Data_File_S18.xlsx** **Vesicle tracking and
 quantification** This file contains multiple tabs: *RTG measures*: table
 of FIJI object quantifications for vesicles and cells, which have been
 tracked and assigned unique vesicle and cell trace IDs, respectively.
 Every vesicle has been assigned to a specific cell. Overlap coefficients
 per cell and vesicle have been computed. *RTG stat. area*: t-test
 statistics for pairwise comparisons of RTG1 deletion effects on log area
 (a.u.) in WT and VAM3 deletion mutants. *RTG stat. counts*: statistics for
 autophagosome counts per time point in WT, RTG1, VAM3, and DKO mutants.  
 **R_code_Chica_et_al.zip** This zip contains R scripts for data analysis
 and figures presented in *Chica et al.,*