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. 2024 Dec 16;5:e65506. doi: 10.2196/65506

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©Jayaram Thimmapuram, Kamlesh D Patel, Deepti Bhatt, Ajay Chauhan, Divya Madhusudhan, Kashyap K Bhatt, Snehal Deshpande, Urvi Budhbhatti, Chaitanya Joshi. Originally published in JMIR Bioinformatics and Biotechnology (https://bioinform.jmir.org), 16.12.2024.

This is an open-access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work, first published in JMIR Bioinformatics and Biotechnology, is properly cited. The complete bibliographic information, a link to the original publication on https://bioinform.jmir.org/, as well as this copyright and license information must be included.

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(A) PCA plot of pre- and post-Heartfulness DESeq data, clustering 2 groups separately. The graphical representation of the transcriptome profile includes pre- (0-week; cyan) and post-Heartfulness (12-week; orange) participants. Principal component 1 (PC1) and principal component 2 (PC2) account for 73% (x-axis) and 3% (y-axis) of the variance respectively in expression data. (B) PCA plot of post-Heartfulness and postcontrol DESeq data clustering after removing the outliers. The graphical representation of transcriptome profile of 22 post-Heartfulness (cyan) and 23 postcontrol (12-week; orange) participants, respectively. PC1 and PC2 account for 26% (x-axis) and 11% (y-axis) of the variance respectively in expression data. PCA: principal component analysis.