Supplementary dataset for "Classification and Biological Identity of Complex Nano Shapes" - Communications Materials Paper (https://doi.org/10.1038/s43246-020-0033-2)

<b>Dataset associated with the paper "Classification and Biological Identity of Complex Nano Shapes"<br></b><br>This repository contains data reported in "Classification and Biological Identity of Complex Nano Shapes" by L. Boselli <i>et al.</i> (submission to <i>Communications Materials</i>, 2020). The main data consist of TEM images and related extracted xy contours coordinates for different shape of NPs. The files containing contours have the extension .dat and consist of two columns with the x and y coordinates of the contours. Additionally we include the code used to extract the contours from the TEM images (directory Contours_extraction_example). Finally, we include data in .xls format related to other graphs in paper (with reference to the specific figures).<br>The notation used to name the different folders corresponds to the figures reported in the paper. For example: Figure2_GNP1-4_contours contains the contours used in the calculations to produce Figure 2. We also include the codes used for the different calculations and a protocol to run these codes (see also the pdf file <b>Protocol_readme</b>). A detailed description of the folders is given below.<br><br><b>Notice that these codes are distributed in the hope that it will be useful, but WITHOUT ANY WARRANTY; without even the implied warranty of MERCHANTABILITY or FITNESS FOR A PARTICULAR PURPOSE.</b><br><b>Contours_extraction_example</b><br>EXAMPLE OF CONTOUR EXTRACTION FROM TEM IMAGES: the folder contains a TEM_input subfolder where the .tiff TEM images can be loaded and a contour_output subfolder where the file.dat of the contours will be generated by running the following script:<br><br><b>$PATH/COMM_MAT/contours_extraction$ python get_cont_auto.py TEM_input/ contours_output/ 161 1 16 50 5000 5000<br></b><br>where $PATH is the location of the COMM_MAT folder<b><br><br>Figure2_GNP1-4_TEMimages and Figure2_GNP1-4_contours</b><br>SHAPE GROUP ASSIGNEMENT FROM EXTRACTED CONTOURS (generation of Graphs as in Figure 2): scatter plots of GNP1-4 shape groups for the first two and three principal components (PCs) clustering using center of gravity method; Normalized probability distribution of shape variability for each shape group.<br><br>The folder contains all the relevant contours file divided by subfolder for each GNP batch. The subfolders name will be the one of the legend in the plots. To generate the plots enter the folder and run the script<b> FT_analysis.py</b>, typing:<br><br>&gt; <i>python FT_analysis.py<br><br></i>The script r<b>eproduce_fig2_d.py</b> calculates the distribution of aspect ratios and compactness as described in Figure 2d. To run the code, type the following:<br><br>&gt; <i>python reproduce_fig2_d.py<br><br></i>The folders containing the contours for GNP1 and GNP2 must be named GNP1 and GNP2, respectively and must be in the same directory where the program is.<br><br><br><b>Figure3_GNP1_3a_3b_4_contours</b>CHARACTERIZATION OF GNPS USED IN THE BIOLOGICAL STUDY (generation of Graphs as in Figure 3b-d).<br>The folder contains all the relevant contours files divided by subfolder for each GNP batch. The subfolder name will be the one of the legend in the plots. To generate the plots enter the folder and run the script: <b>FT_immuno.py<br></b><b><br>Figure3_GNP1_3a_3b_4_DCS_ABS</b>CHARACTERIZATION OF GNPS USED IN THE BIOLOGICAL STUDY (generation of Graphs as in Figure 3e-f).<br><br>The folder contains data related to DCS and UV-Vis absorption spectra.<br><br><br><b>Figure3_GNP3b1_3b2_3b3_contours</b>CHARACTERIZATION OF GNPS REPRODUCIBILITY (generation of Graphs as in Figure 3g-j).<br><br>The folder contains all the relevant contours files divided by subfolder for each GNP batch. The subfolder name will be the one of the legend in the plots. To generate the plots enter the folder and run the script: <b>FT_analysis.py<br></b><br><b>Figure3_GNP3b1_3b2_3b3_DCS_ABS<br></b>CHARACTERIZATION OF GNPS REPRODUCIBILITY (generation of Graphs as in Figure 3k-l).<br><br>The folder contains data related to DCS and UV-Vis absorption spectra.<br><br><b>Figure4</b>File <b>Fig4a. Heatmap analysis</b>: The heatmap was generated by Morpheus (https://software.broadinstitute.org/morpheus). The analysis is based on Hierarchical Clustering with linkage method of “Average”. The value for each row is transformed by subtract row mean divided by row standard deviation.<br><br>Principal Coordinates Analysis was performed by open-source R-project (https://www.r-project.org). The code used for the PCoA is:<br>&gt;<i> #calculate the distance between samples</i>&gt; <i>distance <br></i>&gt; <i># rank samples according to PCoA</i><br><i>&gt; pcoa <br></i><i>&gt; #check the result with a simple picture</i><br><i>&gt; ordiplot(scores(pcoa)[ ,c(1, 2)], type = 't') <br></i><br>Files <b>Fig4c. GNP1 DEG</b> and <b>Fig4d. GNP3b DEG</b> are referred to the Volcano plots on Fig4c-d.<br><br>The Gene ontology (GO) enrichment analysis was performed by Metascape (http://metascape.org/gp/index.html#/main/step1). The analysis was performed by the default settings of “Express Analysis”.<br><b>SF11-13</b>The folder contains data related to the LAL assay and DCS of GNP1, GNP3a, GNP3b and GNP4.