Additional file 9 ATAC-seq peaks (coordinates, quantification, positional classification): the archive contains four folders, one for each species (bos_taurus, capra_hircus, gallus_gallus, sus_scrofa). Each folder contains the following six files:• mergedpeaks_allinfo_gn_frag.tsv• mergedpeaks_allinfo_tr_frag.tsv• mergedpeaks_allinfo_tr_ref.tsv• mergedpeaks_allinfo_gn_ref.tsv• mergedpeaks.peaknb.allexp.readnb.bed.readme.idx• mergedpeaks.peaknb.allexp.readnb.bed
In omics data integration studies, it is common, for a variety of reasons, for some individuals to not be present in all data tables. Missing row values are challenging to deal with because most statistical methods cannot be directly applied to incomplete datasets. To overcome this issue, we propose a multiple imputation (MI) approach in a multivariate framework. In this study, we focus on multiple factor analysis (MFA) as a tool to compare and integrate multiple layers of information. MI involves filling the missing rows with plausible values, resulting in M completed datasets. MFA is then applied to each completed dataset to produce M different configurations (the matrices of coordinates of individuals). Finally, the M configurations are combined to yield a single consensus solution.We assessed the performance of our method, named MI-MFA, on two real omics datasets. Incomplete artificial datasets with different patterns of missingness were created from these data. The MI-MFA results were compared with two other approaches i.e., regularized iterative MFA (RI-MFA) and mean variable imputation (MVI-MFA). For each configuration resulting from these three strategies, the suitability of the solution was determined against the true MFA configuration obtained from the original data and a comprehensive graphical comparison showing how the MI-, RI- or MVI-MFA configurations diverge from the true configuration was produced. Two approaches i.e., confidence ellipses and convex hulls, to visualize and assess the uncertainty due to missing values were also described. We showed how the areas of ellipses and convex hulls increased with the number of missing individuals. A free and easy-to-use code was proposed to implement the MI-MFA method in the R statistical environment.We believe that MI-MFA provides a useful and attractive method for estimating the coordinates of individuals on the first MFA components despite missing rows. MI-MFA configurations were close to the true configuration even when many individuals were missing in several data tables. This method takes into account the uncertainty of MI-MFA configurations induced by the missing rows, thereby allowing the reliability of the results to be evaluated.
Rural forests, i.e. farm forests and trees outside forests (TOF), are part of traditional agroforestry systems in many European regions. Yet, the industrialization of agriculture has induced the decline of rural forests and promoted a physical and functional separation between trees and agriculture. Despite the recent promotion of TOF in the Common Agriculture Policy (CAP), most farmers do not reinforce them in their farms. In order to understand farmers’ attitudes towards rural forests, we conducted 19 face-to-face interviews in southwestern France. Farmers identified 32 positive contributions, including 29 ecosystem services (ES), associated with rural forests. Similarly, they emphasized 25 negative contributions, including 21 ecosystem disservices (EDS). Contributions varied with the type of forested area. For instance, hedgerows had high levels of positive and negative contributions, while woods had high levels of positive and low levels of negative contributions. Finally, farmers identified 19 stakeholders and institutions, especially the CAP, that influenced rural forest management. In focusing on the balance between positive and negative contributions, our study enabled us to uncover the complex rationale of local rural forest management. Ecosystem disservices and CAP policies tended to discourage farmers to reinforce rural forests in their farms. Taking into account farmers’ rationale and perceptions may give invaluable information to better target public policies.
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