Additional file 2: Supplemental Figure 1. Flowcharts of the analytic samples for FOCUS 1.0 and FOCUS 2.0 survey waves. Supplemental Figure 2A. Box and whisker plots comparing FOCUS safety climate scores by size variables for FOCUSv.1.0 departments. Supplemental Figure 2B. Box and whisker plots comparing FOCUS safety climate scores by size variables for FOCUSv.2.0 departments.

RSV box-and-whisker diagram data for the search terms "malnutrition," "frailty," "sarcopenia," and "cachexia" from January 1, 2018 to January 1, 2022. The data is divided before and after the declaration of the COVID-19 pandemic.

This module utilizes a user-friendly database exploring data selection, box-and-whisker plot, and correlation analysis. It also guides students on how to make a poster of their data and conclusions.

The box and whisker plots were used to check for the variability between self reports activities and accelerometer blocks of activities

Box and whisker plots for the distribution of herbivory between native and invasive genotypes in North America, and between European genotypes in their native (EU) and invasive (NA) range.

Figure S1. CRISPR-KO screening data quality assessment. (A) Average correlation between sgRNAs read-count replicates across cell lines. (B) Receiver operating characteristic (ROC) curve obtained from classifying fitness essential (FE) and non-essential genes based on the average logFC of their targeting sgRNAs. An example cell line OVCAR-8 is shown. (C) Area under the ROC (AUROC) curve obtained for cell lines from classifying FE and non-essential genes based on the average logFC of their targeting sgRNAs. (D) Recall for sets of a priori known essential genes from MSigDB and from literature when classifying FE and non-essential genes across cell lines (5% FDR). Each circle represents a cell line and coloured by tissue type. Box and whisker plots show median, inter-quartile ranges and 95% confidence intervals. (E) Genes ranked based on the average logFC of targeting sgRNAs for OVCAR-8 and enrichment of genes belonging to predefined sets of a priori known essential genes from MSigDB, at an FDR equal to 5% when classifying FE (second last column) and non-essential genes (last column). Blue numbers at the bottom indicate the classification true positive rate (recall). Figure S2. Assessment of copy number bias before and after CRISPRcleanR correction across cell lines. sgRNA logFC values before and after CRISPRcleanR for eight cell lines are shown classified based on copy number (amplified or deleted) and expression status. Copy number segments were identified using Genomics of Drug Sensitivity in Cancer (GDSC) and Cell Line Encyclopedia (CCLE) datasets. Box and whisker plots show median, inter-quartile ranges and 95% confidence intervals. Asterisks indicate significant associations between sgRNA LogFC values (Welchs t-test, p < 0,005) and their different effect sizes accounting for the standard deviation (Cohen’s D value), compared to the whole sgRNA library. Figure S3. CN-associated effect on sgRNA logFC values in highly biased cell lines. For 3 cell lines, recall curves of non-essential genes, fitness essential genes, copy number (CN) amplified and CN amplified non-expressed genes obtained when classifying genes based on the average logFC values of their targeting sgRNAs. Figure S4. Assessment of CN-associated bias across all cell lines. LogFC values of sgRNAs averaged within segments of equal copy number (CN). One plot per cell line, with CN values at which a significant differences (Welchs t-test, p < 0.05) with respect to the logFCs corresponding to CN = 2 are initially observed (bias starting point) and start to significantly increase continuously (bias critical point). CN-associated bias is shown for all sgRNA, when excluding FE genes and histones, and for non-expressed genes only. Box and whisker plots show median, inter-quartile ranges and 95% confidence intervals. Figure S5. CRISPRcleanR correction varying the minimal number of genes required and the effect of fitness essential genes. Recall reduction of (A) amplified or (B) amplified not-expressed genes versus that of fitness essential and other prior known essential genes, when comparing CRISPRcleanR correction varying the minimal number of genes to be targeted by sgRNA in a biased segment (default parameter is n = 3). Similar results were observed when performing the analysis including or excluding known essential genes. Figure S6. CRISPRcleanR performances across 342 cell lines from an independent dataset. Recall at 5% FDR of predefined sets of genes based on their uncorrected or corrected logFCs (coordinates on the two axis) averaged across targeting sgRNAs for 342 cell lines from the Project Achilles. Figure S7. CRISPRcleanR performances in relation to data quality. The impact of data quality on recall at 5% false discovery rate (FDR) assessed following CRISPRcleanR correction for predefined set of genes. Project Achilles data (n = 342 cell lines) was binned based on the quality of uncorrected essentiality profile. This is obtained by measuring the recall at 5% FDR for predefined essential genes (from the Molecular Signature Database) and grouping the cell lines in 10 equidistant bins (1 lowest quality and 10 highest quality) when sorting them based on this value. Recall increment for fitness essential genes was greatest for the lower quality data, indicating that CRISPRcleanR can improve true signal of gene depletion in low quality data. Figure S8. Minimal impact of CRISPRcleanR on loss/gain-of-fitness effects. (A) The percentage of genes where the significance of their fitness effect (gain- or loss-of-fitness) is altered after CRISPRcleanR for Project Score and Project Achilles data. The upper row shows correction effects for all screened genes and the lower row for the subset of genes with a significant effect in the uncorrected data. Each dot is a separate cell line. Blue dots indicate the percentage of genes where significance is lost or gained post correction. Green dots indicate the percentage of genes where the fitness effect is distorted and the effect is opposite in the uncorrected data. (B) The majority of the loss-of-fitness genes impacted by correction are putative false positive effects affecting genes which are either not-expressed (FPKM < 0.5), amplified, known non-essential, or exhibit a mild phenotype in the screening data. (C) Summary of overall impact of CRISPRcleanR on fitness effects following correction when considering data for all cell lines. The colors reflect the percentage of genes with a loss-of-fitness, no phenotype or gain-of-fitness effect which are retained in the corrected data. Figure S9. CRISPRcleanR retains cancer driver gene dependencies in Project Score and Achilles data. (A) Each circle represents a tested cancer driver gene dependency (mutation or amplification of a copy number segment) and the statistical significance using MaGeCK before (x-axis) and after (y-axis) CRISPRcleanR correction, across the two screens. Plots in the first row show depletion FDR values pre/post-correction, whereas those in the second row show depletion FDR values pre-correction and enrichment FDR values post-correction. (B) Details of the tested genetic dependencies and whether they are shared before and after CRISPRcleanR correction at two different thresholds of statistical significance (5 and 10% FDR, respectively for 1st and 2nd row of plots). The third row indicates the type of alteration involving the cancer driver genes under consideration and the total number of cell lines with an alteration. (ZIP 191 kb)

The results for differentα-cut values.

% ==================================================================== %
% Data directory for 3D spherical, thermal convection models presented %
% in Fuchs and Becker (2022) with a strain-dependent weakening and %
% hardening rheology following the formulation of %
% Fuchs and Becker (2019). %
% %
% The directory contains an input_files, MATLAB_Scripts, and each %
% model directory with a certain name. %
% input_file Contains all the input files for CitcomS %
% MATLAB_Scripts Contains all the MATLAB scripts required to %
% reproduce the figures in the manuscript %
% $ModelName Contains a MATLAB directory for individual %
% data from each model, %
% a TPR directory for toroidal-poloidal data for %
% each degree, and, %
% a txt_data for data picked from CitcomS models %
% which is then visualized in MATLAB. %
% For the models discussed in the paper, surface %
% maps plots and surface grd-files are available %
% within those directories as well. %
% ==================================================================== %

MATLAB_Scripts directory:
-------------------------
To visualize certain data for each model you can run the script
Analyze_Citcom_Models in the MALTAB_Scripts directory, e.g.,

Analyze_Citcom_Models(Name,PlotParam,PlotParam2,S)

where,
Name is the model name as a string variable,
PlotParam a switch to save (1) or not save (0) the figures,
PlotParam2 a switch to activate (1) or deactivate (0) plotting,
S a switch to define the scaling of the model parameter,
no scaling (0), scaling with the diffusion time scale (1),
or scaling with the overturn time OT (2).

With this script one can visualize all the time-dependent data picked
from the CitcomS models. The CitcomS models can be reproduce with the
input-files given in the input_files directory.

To reproduce the box whisker plots in figures 2, 3, S7, and S8 one
needs to run the script CompStat. This scripts reads in the data
from all models (from the txt_files directory in the $ModelName
directory) and creates a box whisker plot for each model period and
plots them again the average lithospheric damage (gamma_L).

For more details to each MATLAB script see the help comments within the
script.

In case of any questions, do not hesitate to contact me via email:

lukas.fuchs84 at gmail dot com

% ==================================================================== %
% =============================== END ================================ %
% ==================================================================== %

The results of the six methods on VC dataset.

Supplementary Figure 1: Box and Whisker Plots of log Aldosterone to Renin Ratio, additionally adjusted for body mass index Supplementary Figure 2. Box and Whisker Plots of log Aldosterone, additionally adjusted for body mass index Supplementary Figure 3. Box and Whisker Plots of log Renin, additionally adjusted for body mass index Supplementary Figure 4. Box and Whisker Plots of log ACE activity, additionally adjusted for body mass index

The results of the six methods on PD dataset.

The number of Sbox, Smtd, M'−m, and M'+m' with N = 60.

The results of the six methods on WDBC dataset.

The results of four classifiers for the WDBC, PD, VC, and HS data set.

The results of the six methods on HS dataset.

Raincloud plot showing the duration of copulation in the virgin and mated female groups. Each box plot shows the median (second quartile) ± one quartile. Whiskers show 1.5 times the interquartile range. Each box plot is associated with its corresponding density plot. Dots represent the raw data.;Overview of pholcid species where copulatory mechanics is described up to date.;Courtship and precopulatory behaviour of Gertschiola neuquena.;Copulation and copulatory behaviour of Gertschiola neuquena emphasising pedipalp position and movements. Note the emergence of sperm at the end of the video.

A new analytical platform called PiTMaP was developed for high-throughput direct metabolome analysis by probe electrospray ionization/tandem mass spectrometry (PESI/MS/MS) using an R software-based data pipeline. PESI/MS/MS was used as the data acquisition technique, applying a scheduled-selected reaction monitoring method to expand the targeted metabolites. Seventy-two metabolites mainly related to the central energy metabolism were selected; data acquisition time was optimized using mouse liver and brain samples, indicating that the 2.4 min data acquisition method had a higher repeatability than the 1.2 and 4.8 min methods. A data pipeline was constructed using the R software, and it was proven that it can (i) automatically generate box-and-whisker plots for all metabolites, (ii) perform multivariate analyses such as principal component analysis (PCA) and projection to latent structures-discriminant analysis (PLS-DA), (iii) generate score and loading plots of PCA and PLS-DA, (iv) calculate variable importance of projection (VIP) values, (v) determine a statistical family by VIP value criterion, (vi) perform tests of significance with the false discovery rate (FDR) correction method, and (vii) draw box-and-whisker plots only for significantly changed metabolites. These tasks could be completed within ca. 1 min. Finally, PiTMaP was applied to two cases: (1) an acetaminophen-induced acute liver injury model and control mice and (2) human meningioma samples with different grades (G1–G3), demonstrating the feasibility of PiTMaP. PiTMaP was found to perform data acquisition without tedious sample preparation and a posthoc data analysis within ca. 1 min. Thus, it would be a universal platform to perform rapid metabolic profiling of biological samples.

Box–whisker plots (top three rows) and bar graphs (bottom row) showing the distribution of the modeling dataset values for the eight environmental GIS layers with respect to the dependent variable [unsuitable (U) and suitable (Su)].

A box-and-whisker plot of point estimates of error rate and figures showing results of simulations.

Two box-and-whisker-plot figures showing the range of temperature, precipitation, and elevation found in the case-study region.