Archive 4 checkpoints (1-4)

paper-releases/pbisc-diffusion/updates/20261012T104812Z-jh-introduction-edit/diffusion/dataset/server_1/manuscript/editorial/jh_edit_integration_20261012.json

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+    "Bulk RNA sequencing of blood and tissue remains the most widely used transcriptomic assay in clinical research, including large clinical trials, biobanks and disease cohorts. Each bulk profile, however, is a mixture: changes in cell-type abundance are confounded with changes in cell-state-specific expression, and disease signals carried by minority populations are diluted. Single-cell RNA sequencing resolves these components, but cost and sample logistics still limit it to tens or hundreds of donors per study.",
+    "Computational deconvolution has been the principal bridge between the two. Digital cytometry methods such as CIBERSORTx1, MuSiC2, Bisque3, DWLS4, Scaden5 and TAPE6 estimate cell-type proportions from bulk using single-cell-derived signatures, and BayesPrism7, BLADE8 and bMIND9 additionally infer cell-type-specific expression. Despite of the highly advanced calculation algorithms, the accuracy of each model’s output highly depends on data transformation, single cell profile reference choice and annotation granularity, degrades for closely related and rare cell types, and changes rank when the reference differs from the bulk cohort10,11,12,13. These methods also return proportions or cell-type averages, which cannot represent heterogeneity within a cell type, thus masking many disease-associated states that could be inferred by transcriptomics.",
+    "Advances in machine learning have enabled generative approaches to infer single-cell expression profiles from bulk transcriptomes. Bulk2Space14 and BulkTrajBlend15 use estimated cell-type composition to guide generation from single-cell reference data, whereas scSemiProfiler16 adapts profiles from selected cohort representatives using bulk-derived constraints on aggregate expression. However, these approaches remain dependent on selected references, whose coverage and representativeness can influence the inferred cellular landscape14,15,16. Methods that organize generation around cell types or clusters also depend on how these populations are defined14,15, motivating approaches that do not require cell identities or proportions to be specified in advance.",
+    "Diffusion-based methods, including scDiffusion17, have expanded the possibilities for generating cellular profiles. However, realistic single-cell generation still requires faithful reproduction of the sparse patterns of zero and nonzero expression observed in experimental data. Meanwhile, Sparse Data Diffusion introduced “sparsity bits” to represent these patterns explicitly alongside expression magnitudes, improving the fidelity of sparse data generation in biological and physical datasets18. This approach provides a basis for jointly modeling which genes are detected and their expression levels when reconstructing cellular heterogeneity.",
+    "A further obstacle for such bulk transcriptome deconvolution is the scarcity of experimentally paired bulk and single-cell measurements. Large single-cell datasets can provide abundant bulk-to-single-cell training sets by aggregating cells into pseudobulk profiles, but these simulated profiles do not fully reproduce the expression distribution of measured bulk RNA-seq due to various biological reasons, such as, sequencing depth difference and cell loss during dissociation. Systematic differences between pseudobulk and measured bulk can therefore limit the transfer of models to experimental data19. Learning broadly from available single-cell resources and subsequently adapting to real bulk measurements with a small amount of paired data offers a practical strategy for addressing this limitation.",
+    "Here we introduce PBMC Bulk Sample to Immune Single Cells (PBISC), a bulk-conditioned generative model pretrained on pseudobulk–single-cell examples constructed from over 33 million cells across 22 scRNA-seq datasets. This pretraining establishes a shared model of cellular expression across cohorts, reducing reliance on selecting a separate single-cell reference for each target. We incorporated the sparsity-bit concept from Sparse Data Diffusion18 to jointly model gene-detection patterns and expression levels. With PBISC, we were able to generate cells from independent random initializations under a shared bulk condition, without requiring cell-type annotations or predicted cell-type proportions during training or generation.",
+    "To adapt PBISC to measured bulk RNA-seq, we fine-tuned the pretrained model using experimentally paired bulk and single-cell profiles, with as few as five paired samples in the evaluated settings. During adaptation, measured bulk profiles served as conditioning inputs, while observed single-cell profiles from the corresponding biological samples provided the training targets. We then applied the adapted model to generate single-cell profiles for the remaining cohort using bulk measurements alone.",
+    "In various evaluation standards,  we were able to reproduced the distributions of paired experimental single-cell profiles, as assessed by precision20 and coverage21.  PBISC-generated deconvoluted dataset preserved disease-associated expression changes more accurately than comparator methods in most evaluated settings. Furthermore, PBISC reproduced literature-defined rare cell populations22,23 observed in the single-cell data of the evaluated cohorts, without specifying these populations as separate generation categories. Together, these results support extending analyses of cellular heterogeneity and disease-associated transcriptional programs to large cohorts primarily profiled by bulk RNA-seq."
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+    "Bulk RNA sequencing of blood and tissue remains the most widely used transcriptomic assay in clinical research, including large clinical trials, biobanks and disease cohorts. Each bulk profile, however, is a mixture: changes in cell-type abundance are confounded with changes in cell-state-specific expression, and disease signals carried by minority populations are diluted. Single-cell RNA sequencing resolves these components, but cost and sample logistics still limit it to tens or hundreds of donors per study.",
+    "Computational deconvolution has been the principal bridge between the two. Digital cytometry methods such as CIBERSORTx, MuSiC, Bisque, DWLS, Scaden and TAPE estimate cell-type proportions from bulk using single-cell-derived signatures, and BayesPrism, BLADE and bMIND additionally infer cell-type-specific expression. Despite of the highly advanced calculation algorithms, the accuracy of each model’s output highly depends on data transformation, single cell profile reference choice and annotation granularity, degrades for closely related and rare cell types, and changes rank when the reference differs from the bulk cohort. These methods also return proportions or cell-type averages, which cannot represent heterogeneity within a cell type, thus masking many disease-associated states that could be inferred by transcriptomics.",
+    "Advances in machine learning have enabled generative approaches to infer single-cell expression profiles from bulk transcriptomes. Bulk2Space and BulkTrajBlend use estimated cell-type composition to guide generation from single-cell reference data, whereas scSemiProfiler adapts profiles from selected cohort representatives using bulk-derived constraints on aggregate expression. However, these approaches remain dependent on selected references, whose coverage and representativeness can influence the inferred cellular landscape. Methods that organize generation around cell types or clusters also depend on how these populations are defined, motivating approaches that do not require cell identities or proportions to be specified in advance.",
+    "Diffusion-based methods, including scDiffusion, have expanded the possibilities for generating cellular profiles. However, realistic single-cell generation still requires faithful reproduction of the sparse patterns of zero and nonzero expression observed in experimental data. Meanwhile, Sparse Data Diffusion introduced “sparsity bits” to represent these patterns explicitly alongside expression magnitudes, improving the fidelity of sparse data generation in biological and physical datasets. This approach provides a basis for jointly modeling which genes are detected and their expression levels when reconstructing cellular heterogeneity.",
+    "A further obstacle for such bulk transcriptome deconvolution is the scarcity of experimentally paired bulk and single-cell measurements. Large single-cell datasets can provide abundant bulk-to-single-cell training sets by aggregating cells into pseudobulk profiles, but these simulated profiles do not fully reproduce the expression distribution of measured bulk RNA-seq due to various biological reasons, such as, sequencing depth difference and cell loss during dissociation. Systematic differences between pseudobulk and measured bulk can therefore limit the transfer of models to experimental data. Learning broadly from available single-cell resources and subsequently adapting to real bulk measurements with a small amount of paired data offers a practical strategy for addressing this limitation.",
+    "Here we introduce PBMC Bulk Sample to Immune Single Cells (PBISC), a bulk-conditioned generative model pretrained on pseudobulk–single-cell examples constructed from over 33 million cells across 22 scRNA-seq datasets. This pretraining establishes a shared model of cellular expression across cohorts, reducing reliance on selecting a separate single-cell reference for each target. We incorporated the sparsity-bit concept from Sparse Data Diffusion to jointly model gene-detection patterns and expression levels. With PBISC, we were able to generate cells from independent random initializations under a shared bulk condition, without requiring cell-type annotations or predicted cell-type proportions during training or generation.",
+    "To adapt PBISC to measured bulk RNA-seq, we fine-tuned the pretrained model using experimentally paired bulk and single-cell profiles, with as few as five paired samples in the evaluated settings. During adaptation, measured bulk profiles served as conditioning inputs, while observed single-cell profiles from the corresponding biological samples provided the training targets. We then applied the adapted model to generate single-cell profiles for the remaining cohort using bulk measurements alone.",
+    "In various evaluation standards,  we were able to reproduced the distributions of paired experimental single-cell profiles, as assessed by precision and coverage.  PBISC-generated deconvoluted dataset preserved disease-associated expression changes more accurately than comparator methods in most evaluated settings. Furthermore, PBISC reproduced literature-defined rare cell populations observed in the single-cell data of the evaluated cohorts, without specifying these populations as separate generation categories. Together, these results support extending analyses of cellular heterogeneity and disease-associated transcriptional programs to large cohorts primarily profiled by bulk RNA-seq."
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paper-releases/pbisc-diffusion/updates/20261012T104812Z-jh-introduction-edit/diffusion/dataset/server_1/manuscript/editorial/reference_metadata/denisenko2020systematic.jh_20261012.crossref.json

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+      "abstract": "<jats:title>Abstract</jats:title>\n                  <jats:sec>\n                    <jats:title>Background</jats:title>\n                    <jats:p>Single-cell RNA sequencing has been widely adopted to estimate the cellular composition of heterogeneous tissues and obtain transcriptional profiles of individual cells. Multiple approaches for optimal sample dissociation and storage of single cells have been proposed as have single-nuclei profiling methods. What has been lacking is a systematic comparison of their relative biases and benefits.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Results</jats:title>\n                    <jats:p>Here, we compare gene expression and cellular composition of single-cell suspensions prepared from adult mouse kidney using two tissue dissociation protocols. For each sample, we also compare fresh cells to cryopreserved and methanol-fixed cells. Lastly, we compare this single-cell data to that generated using three single-nucleus RNA sequencing workflows. Our data confirms prior reports that digestion on ice avoids the stress response observed with 37 °C dissociation. It also reveals cell types more abundant either in the cold or warm dissociations that may represent populations that require gentler or harsher conditions to be released intact. For cell storage, cryopreservation of dissociated cells results in a major loss of epithelial cell types; in contrast, methanol fixation maintains the cellular composition but suffers from ambient RNA leakage. Finally, cell type composition differences are observed between single-cell and single-nucleus RNA sequencing libraries. In particular, we note an underrepresentation of T, B, and NK lymphocytes in the single-nucleus libraries.</jats:p>\n                  </jats:sec>\n                  <jats:sec>\n                    <jats:title>Conclusions</jats:title>\n                    <jats:p>Systematic comparison of recovered cell types and their transcriptional profiles across the workflows has highlighted protocol-specific biases and thus enables researchers starting single-cell experiments to make an informed choice.</jats:p>\n                  </jats:sec>",
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+          "unstructured": "Lun ATL, Riesenfeld S, Andrews T, Dao TP, Gomes T, Marioni JC. EmptyDrops: distinguishing cells from empty droplets in droplet-based single-cell RNA sequencing data. Genome Biol. 2019;20:63.",
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+          "unstructured": "McCarthy DJ, Campbell KR, Lun AT, Wills QF. Scater: pre-processing, quality control, normalization and visualization of single-cell RNA-seq data in R. Bioinformatics. 2017;33:1179–86.",
+          "journal-title": "Bioinformatics."
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+          "unstructured": "North BV, Curtis D, Sham PC. A note on the calculation of empirical P values from Monte Carlo procedures. Am J Hum Genet. 2002;71:439–41.",
+          "journal-title": "Am J Hum Genet"
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+        {
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+          "DOI": "10.1111/j.2517-6161.1995.tb02031.x",
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+          "unstructured": "Benjamini Y, Hochberg Y. Controlling the false discovery rate: a practical and powerful approach to multiple testing. J R Stat Soc Ser B Methodol. 1995;57:289–300.",
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+          "year": "2003",
+          "unstructured": "Rohrwasser A, Ishigami T, Gociman B, Lantelme P, Morgan T, Cheng T, et al. Renin and kallikrein in connecting tubule of mouse. Kidney Int. 2003;64:2155–62.",
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+      "issued": {
+        "date-parts": [
+          [
+            2020,
+            6,
+            2
+          ]
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+      },
+      "references-count": 57,
+      "journal-issue": {
+        "issue": "1",
+        "published-print": {
+          "date-parts": [
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+            ]
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+      ],
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+          "value": "1474-760X",
+          "type": "electronic"
+        }
+      ],
+      "subject": [],
+      "published": {
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+          [
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+            "label": "Article History"
+          }
+        },
+        {
+          "value": "Acknowledging the principles of 3Rs (Replacement, Reduction and Refinement), all kidneys used in this study were from mice that were euthanized by cervical dislocation as parts of other ongoing ethically approved experiments.",
+          "order": 1,
+          "name": "Ethics",
+          "group": {
+            "name": "EthicsHeading",
+            "label": "Ethics approval and consent to participate"
+          }
+        },
+        {
+          "value": "Not applicable.",
+          "order": 2,
+          "name": "Ethics",
+          "group": {
+            "name": "EthicsHeading",
+            "label": "Consent for publication"
+          }
+        },
+        {
+          "value": "The authors declare that they have no competing interests.",
+          "order": 3,
+          "name": "Ethics",
+          "group": {
+            "name": "EthicsHeading",
+            "label": "Competing interests"
+          }
+        }
+      ],
+      "article-number": "130"
+    }
+  }
+}

paper-releases/pbisc-diffusion/updates/20261012T104812Z-jh-introduction-edit/diffusion/dataset/server_1/manuscript/references.bib

@@ -0,0 +1,911 @@
+% references.bib for the PBISC-Diffusion manuscript (rewrite of 2026-10-10; Introduction updated 2026-10-11).
+% Includes references added for the supplied Introduction. Entries kept from the previous verified file are marked "Kept"
+% and were re-checked against Crossref; all other entries are new and were checked against Crossref, Europe PMC, the
+% PDFs in reference-papers/, or the official proceedings pages (PMLR, JMLR, NeurIPS, OpenReview via mlanthology.org).
+% Conventions (sn-nature.bst):
+%  - Conference papers (NeurIPS, ICML, ICLR) are misc entries with the venue in howpublished, as in the previous
+%    verified file. The inproceedings type of sn-nature.bst never prints the booktitle and makes BibTeX report
+%    "You can't pop an empty literal stack" (tested with BibTeX 0.99d on 2026-10-10). booktitle, volume, pages and
+%    year are kept as fields, so these entries can become inproceedings if the style file is fixed.
+%  - arXiv-only works are misc entries with eprint and archivePrefix.
+%  - Journal articles print their url (sn-nature.bst does not print the doi field); every url is https://doi.org/.
+%  - Non-ASCII letters are written as LaTeX accents; the file is plain ASCII.
+
+% New. Verified: Crossref; Europe PMC (PMID 32788292, also the PubMed ID of GEO GSE152418 and GSE155673); PDF
+%  paired-cohorts/Arunachalam2020_Science_systems_immunity_mild_vs_severe_COVID19.pdf. Author 10 is written as in the
+%  article byline (Owen Tak-Yin Tsang); Crossref and PubMed split it as Tak-Yin Tsang, O.
+@article{arunachalam2020systems,
+  author = {Arunachalam, Prabhu S. and Wimmers, Florian and Mok, Chris Ka Pun and Perera, Ranawaka A. P. M. and Scott, Madeleine and Hagan, Thomas and Sigal, Natalia and Feng, Yupeng and Bristow, Laurel and Tsang, Owen Tak-Yin and Wagh, Dhananjay and Coller, John and Pellegrini, Kathryn L. and Kazmin, Dmitri and Alaaeddine, Ghina and Leung, Wai Shing and Chan, Jacky Man Chun and Chik, Thomas Shiu Hong and Choi, Chris Yau Chung and Huerta, Christopher and Paine McCullough, Michele and Lv, Huibin and Anderson, Evan and Edupuganti, Srilatha and Upadhyay, Amit A. and Bosinger, Steve E. and Maecker, Holden Terry and Khatri, Purvesh and Rouphael, Nadine and Peiris, Malik and Pulendran, Bali},
+  title = {{Systems biological assessment of immunity to mild versus severe COVID-19 infection in humans}},
+  journal = {Science},
+  volume = {369},
+  number = {6508},
+  pages = {1210--1220},
+  year = {2020},
+  doi = {10.1126/science.abc6261},
+  url = {https://doi.org/10.1126/science.abc6261}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{avilacobos2020benchmarking,
+  author = {Avila Cobos, Francisco and Alquicira-Hernandez, Jos{\'e} and Powell, Joseph E. and Mestdagh, Pieter and De Preter, Katleen},
+  title = {{Benchmarking of cell type deconvolution pipelines for transcriptomics data}},
+  journal = {Nature Communications},
+  volume = {11},
+  number = {1},
+  pages = {5650},
+  year = {2020},
+  doi = {10.1038/s41467-020-19015-1},
+  url = {https://doi.org/10.1038/s41467-020-19015-1}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{babdor2026immicro,
+  author = {Babdor, Joel and Patel, Ravi K. and Davidson, Brittany and Koser, Kelvin and Noecker, Cecilia and Rahim, Maha K. and Bisanz, Jordan E. and Tenvooren, Iliana and Marquez, Diana and Calvo, Maria and Johri, Vrinda and McCarthy, Elizabeth E. and Shaheed, Avneet and Ekstrand, Christina and Weakley, Allison M. and Yu, Feiqiao B. and Krip, Kristen and Shaikh, Kashif A. and Amatullah, Hajera and Fiehn, Oliver and Turnbaugh, Peter J. and Combes, Alexis J. and Fragiadakis, Gabriela K. and Spitzer, Matthew H.},
+  title = {{Immune-microbiome coordination defines interferon setpoints in healthy humans}},
+  journal = {Cell},
+  volume = {189},
+  number = {10},
+  pages = {3144--3163.e20},
+  year = {2026},
+  doi = {10.1016/j.cell.2026.02.003},
+  url = {https://doi.org/10.1016/j.cell.2026.02.003}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{benjamini1995fdr,
+  author = {Benjamini, Yoav and Hochberg, Yosef},
+  title = {{Controlling the False Discovery Rate: A Practical and Powerful Approach to Multiple Testing}},
+  journal = {Journal of the Royal Statistical Society Series B: Statistical Methodology},
+  volume = {57},
+  number = {1},
+  pages = {289--300},
+  year = {1995},
+  doi = {10.1111/j.2517-6161.1995.tb02031.x},
+  url = {https://doi.org/10.1111/j.2517-6161.1995.tb02031.x}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 12642603); PDF
+%  rare-disease-states/Bennett2003_Interferon_granulopoiesis_signatures_SLE_PBMC.pdf.
+@article{bennett2003interferon,
+  author = {Bennett, Lynda and Palucka, A. Karolina and Arce, Edsel and Cantrell, Victoria and Borvak, Josef and Banchereau, Jacques and Pascual, Virginia},
+  title = {{Interferon and Granulopoiesis Signatures in Systemic Lupus Erythematosus Blood}},
+  journal = {The Journal of Experimental Medicine},
+  volume = {197},
+  number = {6},
+  pages = {711--723},
+  year = {2003},
+  doi = {10.1084/jem.20021553},
+  url = {https://doi.org/10.1084/jem.20021553}
+}
+
+% New. Verified: arXiv 2208.04202 (comment "ICLR'23") and mlanthology.org/iclr/2023 (OpenReview forum 3itjR9QxFw).
+@misc{chen2023analog,
+  howpublished = {In \emph{International Conference on Learning Representations}},
+  author = {Chen, Ting and Zhang, Ruixiang and Hinton, Geoffrey},
+  title = {{Analog Bits: Generating Discrete Data using Diffusion Models with Self-Conditioning}},
+  booktitle = {International Conference on Learning Representations},
+  year = {2023},
+  url = {https://openreview.net/forum?id=3itjR9QxFw}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{chu2022bayesprism,
+  author = {Chu, Tinyi and Wang, Zhong and Pe'er, Dana and Danko, Charles G.},
+  title = {{Cell type and gene expression deconvolution with BayesPrism enables Bayesian integrative analysis across bulk and single-cell RNA sequencing in oncology}},
+  journal = {Nature Cancer},
+  volume = {3},
+  number = {4},
+  pages = {505--517},
+  year = {2022},
+  doi = {10.1038/s43018-022-00356-3},
+  url = {https://doi.org/10.1038/s43018-022-00356-3}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{cui2024scgpt,
+  author = {Cui, Haotian and Wang, Chloe and Maan, Hassaan and Pang, Kuan and Luo, Fengning and Duan, Nan and Wang, Bo},
+  title = {{scGPT: toward building a foundation model for single-cell multi-omics using generative AI}},
+  journal = {Nature Methods},
+  volume = {21},
+  number = {8},
+  pages = {1470--1480},
+  year = {2024},
+  doi = {10.1038/s41592-024-02201-0},
+  url = {https://doi.org/10.1038/s41592-024-02201-0}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 35810186, the PubMed ID of GEO GSE206263, GSE206283 and SuperSeries
+%  GSE206284). No PDF in reference-papers.
+@article{giroux2022differential,
+  author = {Giroux, Nicholas S. and Ding, Shengli and McClain, Micah T. and Burke, Thomas W. and Petzold, Elizabeth and Chung, Hong A. and Rivera, Grecia O. and Wang, Ergang and Xi, Rui and Bose, Shree and Rotstein, Tomer and Nicholson, Bradly P. and Chen, Tianyi and Henao, Ricardo and Sempowski, Gregory D. and Denny, Thomas N. and De Ussel, Maria Iglesias and Satterwhite, Lisa L. and Ko, Emily R. and Ginsburg, Geoffrey S. and Kraft, Bryan D. and Tsalik, Ephraim L. and Shen, Xiling and Woods, Christopher W.},
+  title = {{Differential chromatin accessibility in peripheral blood mononuclear cells underlies COVID-19 disease severity prior to seroconversion}},
+  journal = {Scientific Reports},
+  volume = {12},
+  number = {1},
+  pages = {11714},
+  year = {2022},
+  doi = {10.1038/s41598-022-15668-8},
+  url = {https://doi.org/10.1038/s41598-022-15668-8}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 36522333); PDF
+%  paired-cohorts/Gisby2022_NatCommun_multiomics_LRRC15_COVID19_haemodialysis.pdf.
+@article{gisby2022multiomics,
+  author = {Gisby, Jack S. and Buang, Norzawani B. and Papadaki, Artemis and Clarke, Candice L. and Malik, Talat H. and Medjeral-Thomas, Nicholas and Pinheiro, Damiola and Mortimer, Paige M. and Lewis, Shanice and Sandhu, Eleanor and McAdoo, Stephen P. and Prendecki, Maria F. and Willicombe, Michelle and Pickering, Matthew C. and Botto, Marina and Thomas, David C. and Peters, James E.},
+  title = {{Multi-omics identify falling LRRC15 as a COVID-19 severity marker and persistent pro-thrombotic signals in convalescence}},
+  journal = {Nature Communications},
+  volume = {13},
+  number = {1},
+  pages = {7775},
+  year = {2022},
+  doi = {10.1038/s41467-022-35454-4},
+  url = {https://doi.org/10.1038/s41467-022-35454-4}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 41162704); PDF
+%  rare-disease-states/Gong2025_Multiomic_profiling_age_related_immune_dynamics_healthy_adults.pdf.
+@article{gong2025multiomic,
+  author = {Gong, Qiuyu and Sharma, Mehul and Glass, Marla C. and Kuan, Emma L. and Chander, Aishwarya and Singh, Mansi and Graybuck, Lucas T. and Thomson, Zachary J. and LaFrance, Christian M. and Rachid Zaim, Samir and Peng, Tao and Okada, Lauren Y. and Genge, Palak C. and Henderson, Katherine E. and Dornisch, Elisabeth M. and Layton, Erik D. and Wittig, Peter J. and Heubeck, Alexander T. and Mukuka, Nelson M. and Reading, Julian and Strawn, Garrett and Titus-Adewunmi, Teminijesu and Abadie, Kathleen and Roll, Charles R. and Hernandez, Veronica and Parthasarathy, Vaishnavi and Stuckey, Tyanna J. and Musgrove, Blessing and Swanson, Elliott and Lord, Cara and Weiss, Morgan D. A. and Phalen, Cole G. and Mettey, Regina R. and Lee, Kevin J. and Johanneson, John B. and Kawelo, Erin K. and Garber, Jessica and Krishnan, Upaasana and Smithmyer, Megan and Wherry, E. John and Vella, Laura A. and Henrickson, Sarah E. and Kopp, Mackenzie S. and Savage, Adam K. and Becker, Lynne A. and Meijer, Paul and Coffey, Ernest M. and Goronzy, Jorg J. and Sigvardsson, Mikael and Speake, Cate and Bumol, Thomas F. and Goldrath, Ananda W. and Torgerson, Troy R. and Li, Xiao-jun and Skene, Peter J. and Buckner, Jane H. and Gustafson, Claire E.},
+  title = {{Multi-omic profiling reveals age-related immune dynamics in healthy adults}},
+  journal = {Nature},
+  volume = {648},
+  number = {8094},
+  pages = {696--706},
+  year = {2025},
+  doi = {10.1038/s41586-025-09686-5},
+  url = {https://doi.org/10.1038/s41586-025-09686-5}
+}
+
+% New. Verified: PDF (JMLR 13 (2012) 723-773) and jmlr.org/papers/v13/gretton12a.html (issue 25). JMLR assigns no DOI.
+@article{gretton2012kernel,
+  author = {Gretton, Arthur and Borgwardt, Karsten M. and Rasch, Malte J. and Sch{\"o}lkopf, Bernhard and Smola, Alexander},
+  title = {{A Kernel Two-Sample Test}},
+  journal = {Journal of Machine Learning Research},
+  volume = {13},
+  number = {25},
+  pages = {723--773},
+  year = {2012},
+  url = {https://jmlr.org/papers/v13/gretton12a.html}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 31719197); PDF
+%  rare-disease-states/Hashimoto2019_CD4_CTL_supercentenarians.pdf.
+@article{hashimoto2019single,
+  author = {Hashimoto, Kosuke and Kouno, Tsukasa and Ikawa, Tomokatsu and Hayatsu, Norihito and Miyajima, Yurina and Yabukami, Haruka and Terooatea, Tommy and Sasaki, Takashi and Suzuki, Takahiro and Valentine, Matthew and Pascarella, Giovanni and Okazaki, Yasushi and Suzuki, Harukazu and Shin, Jay W. and Minoda, Aki and Taniuchi, Ichiro and Okano, Hideyuki and Arai, Yasumichi and Hirose, Nobuyoshi and Carninci, Piero},
+  title = {{Single-cell transcriptomics reveals expansion of cytotoxic CD4 T cells in supercentenarians}},
+  journal = {Proceedings of the National Academy of Sciences},
+  volume = {116},
+  number = {48},
+  pages = {24242--24251},
+  year = {2019},
+  doi = {10.1073/pnas.1907883116},
+  url = {https://doi.org/10.1073/pnas.1907883116}
+}
+
+% Kept from the previous verified references.bib (official NeurIPS 2020 BibTeX; NeurIPS 33, 6840-6851).
+@misc{ho2020ddpm,
+  howpublished = {In \emph{Advances in Neural Information Processing Systems} \textbf{33}, 6840--6851 (Curran Associates, Inc.)},
+  author = {Ho, Jonathan and Jain, Ajay and Abbeel, Pieter},
+  title = {{Denoising Diffusion Probabilistic Models}},
+  booktitle = {Advances in Neural Information Processing Systems},
+  volume = {33},
+  pages = {6840--6851},
+  year = {2020},
+  url = {https://proceedings.neurips.cc/paper_files/paper/2020/file/4c5bcfec8584af0d967f1ab10179ca4b-Paper.pdf}
+}
+
+% New. Verified: arXiv 2106.09685 (title, authors) and mlanthology.org/iclr/2022 (ICLR 2022, OpenReview forum
+%  nZeVKeeFYf9).
+@misc{hu2022lora,
+  howpublished = {In \emph{International Conference on Learning Representations}},
+  author = {Hu, Edward J. and Shen, Yelong and Wallis, Phillip and Allen-Zhu, Zeyuan and Li, Yuanzhi and Wang, Shean and Wang, Lu and Chen, Weizhu},
+  title = {{LoRA: Low-Rank Adaptation of Large Language Models}},
+  booktitle = {International Conference on Learning Representations},
+  year = {2022},
+  url = {https://openreview.net/forum?id=nZeVKeeFYf9}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 15846361); PDF
+%  evaluation-metrics/Irizarry2005_Multiple_laboratory_comparison_microarray_platforms.pdf.
+@article{irizarry2005multiple,
+  author = {Irizarry, Rafael A. and Warren, Daniel and Spencer, Forrest and Kim, Irene F. and Biswal, Shyam and Frank, Bryan C. and Gabrielson, Edward and Garcia, Joe G. N. and Geoghegan, Joel and Germino, Gregory and Griffin, Constance and Hilmer, Sara C. and Hoffman, Eric and Jedlicka, Anne E. and Kawasaki, Ernest and Mart{\'\i}nez-Murillo, Francisco and Morsberger, Laura and Lee, Hannah and Petersen, David and Quackenbush, John and Scott, Alan and Wilson, Michael and Yang, Yanqin and Ye, Shui Qing and Yu, Wayne},
+  title = {{Multiple-laboratory comparison of microarray platforms}},
+  journal = {Nature Methods},
+  volume = {2},
+  number = {5},
+  pages = {345--350},
+  year = {2005},
+  doi = {10.1038/nmeth756},
+  url = {https://doi.org/10.1038/nmeth756}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 30314758). No PDF in reference-papers (INDEX lists it as not downloaded).
+@article{jenks2018distinct,
+  author = {Jenks, Scott A. and Cashman, Kevin S. and Zumaquero, Esther and Marigorta, Urko M. and Patel, Aakash V. and Wang, Xiaoqian and Tomar, Deepak and Woodruff, Matthew C. and Simon, Zoe and Bugrovsky, Regina and Blalock, Emily L. and Scharer, Christopher D. and Tipton, Christopher M. and Wei, Chungwen and Lim, S. Sam and Petri, Michelle and Niewold, Timothy B. and Anolik, Jennifer H. and Gibson, Greg and Lee, F. Eun-Hyung and Boss, Jeremy M. and Lund, Frances E. and Sanz, Ignacio},
+  title = {{Distinct Effector B Cells Induced by Unregulated Toll-like Receptor 7 Contribute to Pathogenic Responses in Systemic Lupus Erythematosus}},
+  journal = {Immunity},
+  volume = {49},
+  number = {4},
+  pages = {725--739.e6},
+  year = {2018},
+  doi = {10.1016/j.immuni.2018.08.015},
+  url = {https://doi.org/10.1016/j.immuni.2018.08.015}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 40317333); PDF
+%  paired-cohorts/Ji2025_CancerImmunolImmunother_checkpoint_inhibitor_liver_injury.pdf (same title as GEO GSE287540
+%  and GSE287541).
+@article{ji2025chili,
+  author = {Ji, Changhua and Kumpf, Steven and Qian, Jessie and Federspiel, Joel D. and Sheehan, Mark and Capunitan, Darien and Atallah, Edmond and Astbury, Stuart and Arat, Seda and Oziolor, Elias and Ocana, Mireia Fernandez and Ramaiah, Shashi K. and Grove, Jane and Aithal, Guruprasad P. and Lanz, Thomas A.},
+  title = {{Transcriptomic and proteomic characterization of cell and protein biomarkers of checkpoint inhibitor-induced liver injury}},
+  journal = {Cancer Immunology, Immunotherapy},
+  volume = {74},
+  number = {6},
+  pages = {190},
+  year = {2025},
+  doi = {10.1007/s00262-025-04033-z},
+  url = {https://doi.org/10.1007/s00262-025-04033-z}
+}
+
+% New. Verified: PDF (PMLR 202, 2023) and proceedings.mlr.press/v202/khayatkhoei23a.html (pages 16326-16343).
+% Second author spelled as in the paper byline (AbdAlmageed); the PMLR BibTeX writes Abdalmageed.
+@misc{khayatkhoei2023emergent,
+  howpublished = {In \emph{Proceedings of the 40th International Conference on Machine Learning}, Proceedings of Machine Learning Research \textbf{202}, 16326--16343 (PMLR)},
+  author = {Khayatkhoei, Mahyar and AbdAlmageed, Wael},
+  title = {{Emergent Asymmetry of Precision and Recall for Measuring Fidelity and Diversity of Generative Models in High Dimensions}},
+  booktitle = {Proceedings of the 40th International Conference on Machine Learning},
+  series = {Proceedings of Machine Learning Research},
+  volume = {202},
+  pages = {16326--16343},
+  publisher = {PMLR},
+  year = {2023},
+  url = {https://proceedings.mlr.press/v202/khayatkhoei23a.html}
+}
+
+% New. Verified: PDF (arXiv 1904.06991v3, "NeurIPS 2019") and the official proceedings.neurips.cc BibTeX (volume 32).
+% UNVERIFIED: kynkaanniemi2019improved page range (the official NeurIPS 2019 BibTeX has an empty pages field; none
+%  given)
+@misc{kynkaanniemi2019improved,
+  howpublished = {In \emph{Advances in Neural Information Processing Systems} \textbf{32} (Curran Associates, Inc.)},
+  author = {Kynk{\"a}{\"a}nniemi, Tuomas and Karras, Tero and Laine, Samuli and Lehtinen, Jaakko and Aila, Timo},
+  title = {{Improved Precision and Recall Metric for Assessing Generative Models}},
+  booktitle = {Advances in Neural Information Processing Systems},
+  volume = {32},
+  publisher = {Curran Associates, Inc.},
+  year = {2019},
+  url = {https://proceedings.neurips.cc/paper_files/paper/2019/file/0234c510bc6d908b28c70ff313743079-Paper.pdf}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 24485249). No PDF in reference-papers.
+@article{law2014voom,
+  author = {Law, Charity W. and Chen, Yunshun and Shi, Wei and Smyth, Gordon K.},
+  title = {{voom: precision weights unlock linear model analysis tools for RNA-seq read counts}},
+  journal = {Genome Biology},
+  volume = {15},
+  number = {2},
+  pages = {R29},
+  year = {2014},
+  doi = {10.1186/gb-2014-15-2-r29},
+  url = {https://doi.org/10.1186/gb-2014-15-2-r29}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 42715312); PDF
+%  evaluation-metrics/Li2026_Systematic_comparison_sc_perturbation_response_prediction_models.pdf.
+@article{li2026systematic,
+  author = {Li, Lanxiang and You, Yue and Fu, Yunlin and Liao, Wenyu and Fan, Xueying and Lu, Shihong and Cao, Ye and Li, Bo and Ren, Wenle and Kong, Jiaming and Zheng, Shuangjia and Chen, Jizheng and Liu, Xiaodong and Tian, Luyi},
+  title = {{A systematic comparison of single-cell perturbation response prediction models}},
+  journal = {Science Advances},
+  volume = {12},
+  number = {37},
+  pages = {eaed3414},
+  year = {2026},
+  doi = {10.1126/sciadv.aed3414},
+  url = {https://doi.org/10.1126/sciadv.aed3414}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 33713619, the PubMed ID of GEO GSE161918). Byline as in Europe PMC, with
+%  the two collective authors; Crossref also lists ten consortium members. No PDF in reference-papers.
+@article{liu2021timeresolved,
+  author = {Liu, Can and Martins, Andrew J. and Lau, William W. and Rachmaninoff, Nicholas and Chen, Jinguo and Imberti, Luisa and Mostaghimi, Darius and Fink, Danielle L. and Burbelo, Peter D. and Dobbs, Kerry and Delmonte, Ottavia M. and Bansal, Neha and Failla, Laura and Sottini, Alessandra and Quiros-Roldan, Eugenia and Han, Kyu Lee and Sellers, Brian A. and Cheung, Foo and Sparks, Rachel and Chun, Tae-Wook and Moir, Susan and Lionakis, Michail S. and {NIAID COVID Consortium} and {COVID Clinicians} and Rossi, Camillo and Su, Helen C. and Kuhns, Douglas B. and Cohen, Jeffrey I. and Notarangelo, Luigi D. and Tsang, John S.},
+  title = {{Time-resolved systems immunology reveals a late juncture linked to fatal COVID-19}},
+  journal = {Cell},
+  volume = {184},
+  number = {7},
+  pages = {1836--1857.e22},
+  year = {2021},
+  doi = {10.1016/j.cell.2021.02.018},
+  url = {https://doi.org/10.1016/j.cell.2021.02.018}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{lopez2018scvi,
+  author = {Lopez, Romain and Regier, Jeffrey and Cole, Michael B. and Jordan, Michael I. and Yosef, Nir},
+  title = {{Deep generative modeling for single-cell transcriptomics}},
+  journal = {Nature Methods},
+  volume = {15},
+  number = {12},
+  pages = {1053--1058},
+  year = {2018},
+  doi = {10.1038/s41592-018-0229-2},
+  url = {https://doi.org/10.1038/s41592-018-0229-2}
+}
+
+% New. Verified: PDF ("Published as a conference paper at ICLR 2017") and mlanthology.org/iclr/2017 (OpenReview forum
+%  SJkXfE5xx).
+@misc{lopezpaz2017revisiting,
+  howpublished = {In \emph{International Conference on Learning Representations}},
+  author = {Lopez-Paz, David and Oquab, Maxime},
+  title = {{Revisiting Classifier Two-Sample Tests}},
+  booktitle = {International Conference on Learning Representations},
+  year = {2017},
+  url = {https://openreview.net/forum?id=SJkXfE5xx}
+}
+
+% New. Verified: arXiv 1711.05101 (comment "Published as a conference paper at ICLR 2019") and
+%  mlanthology.org/iclr/2019 (OpenReview forum Bkg6RiCqY7).
+@misc{loshchilov2019decoupled,
+  howpublished = {In \emph{International Conference on Learning Representations}},
+  author = {Loshchilov, Ilya and Hutter, Frank},
+  title = {{Decoupled Weight Decay Regularization}},
+  booktitle = {International Conference on Learning Representations},
+  year = {2019},
+  url = {https://openreview.net/forum?id=Bkg6RiCqY7}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{luo2024scdiffusion,
+  author = {Luo, Erpai and Hao, Minsheng and Wei, Lei and Zhang, Xuegong},
+  title = {{scDiffusion: conditional generation of high-quality single-cell data using diffusion model}},
+  journal = {Bioinformatics},
+  volume = {40},
+  number = {9},
+  pages = {btae518},
+  year = {2024},
+  doi = {10.1093/bioinformatics/btae518},
+  url = {https://doi.org/10.1093/bioinformatics/btae518}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 32669297); PDF
+%  rare-disease-states/Mathew2020_COVID_immunotypes_CD38_HLADR_T_cells.pdf. Byline as printed (the UPenn COVID
+%  Processing Unit after author 36); Crossref also lists the members of the unit.
+@article{mathew2020deep,
+  author = {Mathew, Divij and Giles, Josephine R. and Baxter, Amy E. and Oldridge, Derek A. and Greenplate, Allison R. and Wu, Jennifer E. and Alanio, C{\'e}cile and Kuri-Cervantes, Leticia and Pampena, M. Betina and D'Andrea, Kurt and Manne, Sasikanth and Chen, Zeyu and Huang, Yinghui Jane and Reilly, John P. and Weisman, Ariel R. and Ittner, Caroline A. G. and Kuthuru, Oliva and Dougherty, Jeanette and Nzingha, Kito and Han, Nicholas and Kim, Justin and Pattekar, Ajinkya and Goodwin, Eileen C. and Anderson, Elizabeth M. and Weirick, Madison E. and Gouma, Sigrid and Arevalo, Claudia P. and Bolton, Marcus J. and Chen, Fang and Lacey, Simon F. and Ramage, Holly and Cherry, Sara and Hensley, Scott E. and Apostolidis, Sokratis A. and Huang, Alexander C. and Vella, Laura A. and {UPenn COVID Processing Unit} and Betts, Michael R. and Meyer, Nuala J. and Wherry, E. John},
+  title = {{Deep immune profiling of COVID-19 patients reveals distinct immunotypes with therapeutic implications}},
+  journal = {Science},
+  volume = {369},
+  number = {6508},
+  pages = {eabc8511},
+  year = {2020},
+  doi = {10.1126/science.abc8511},
+  url = {https://doi.org/10.1126/science.abc8511}
+}
+
+% Kept from the previous verified references.bib (DataCite record); eprint and archivePrefix added, and the arXiv
+% identifier is now printed once, through eprint, instead of in both url and note. Checked against the arXiv API.
+@misc{mcinnes2018umap,
+  author = {McInnes, Leland and Healy, John and Melville, James},
+  title = {{UMAP: Uniform Manifold Approximation and Projection for Dimension Reduction}},
+  howpublished = {Preprint},
+  year = {2018},
+  doi = {10.48550/ARXIV.1802.03426},
+  eprint = {1802.03426},
+  archivePrefix = {arXiv}
+}
+
+% New. Verified: PDF (arXiv 2002.09797v2, "PMLR 119, 2020") and proceedings.mlr.press/v119/naeem20a.html (pages
+%  7176-7185).
+@misc{naeem2020reliable,
+  howpublished = {In \emph{Proceedings of the 37th International Conference on Machine Learning}, Proceedings of Machine Learning Research \textbf{119}, 7176--7185 (PMLR)},
+  author = {Naeem, Muhammad Ferjad and Oh, Seong Joon and Uh, Youngjung and Choi, Yunjey and Yoo, Jaejun},
+  title = {{Reliable Fidelity and Diversity Metrics for Generative Models}},
+  booktitle = {Proceedings of the 37th International Conference on Machine Learning},
+  series = {Proceedings of Machine Learning Research},
+  volume = {119},
+  pages = {7176--7185},
+  publisher = {PMLR},
+  year = {2020},
+  url = {https://proceedings.mlr.press/v119/naeem20a.html}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{nehar2020mapping,
+  author = {Nehar-Belaid, Djamel and Hong, Seunghee and Marches, Radu and Chen, Guo and Bolisetty, Mohan and Baisch, Jeanine and Walters, Lynnette and Punaro, Marilynn and Rossi, Robert J. and Chung, Cheng-Han and Huynh, Richie P. and Singh, Prashant and Flynn, William F. and Tabanor-Gayle, Joy-Ann and Kuchipudi, Navya and Mejias, Asuncion and Collet, Magalie A. and Lucido, Anna Lisa and Palucka, Karolina and Robson, Paul and Lakshminarayanan, Santhanam and Ramilo, Octavio and Wright, Tracey and Pascual, Virginia and Banchereau, Jacques F.},
+  title = {{Mapping systemic lupus erythematosus heterogeneity at the single-cell level}},
+  journal = {Nature Immunology},
+  volume = {21},
+  number = {9},
+  pages = {1094--1106},
+  year = {2020},
+  doi = {10.1038/s41590-020-0743-0},
+  url = {https://doi.org/10.1038/s41590-020-0743-0}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{newman2019cibersortx,
+  author = {Newman, Aaron M. and Steen, Chlo{\'e} B. and Liu, Chih Long and Gentles, Andrew J. and Chaudhuri, Aadel A. and Scherer, Florian and Khodadoust, Michael S. and Esfahani, Mohammad S. and Luca, Bogdan A. and Steiner, David and Diehn, Maximilian and Alizadeh, Ash A.},
+  title = {{Determining cell type abundance and expression from bulk tissues with digital cytometry}},
+  journal = {Nature Biotechnology},
+  volume = {37},
+  number = {7},
+  pages = {773--782},
+  year = {2019},
+  doi = {10.1038/s41587-019-0114-2},
+  url = {https://doi.org/10.1038/s41587-019-0114-2}
+}
+
+% New. Verified: proceedings.mlr.press/v139/nichol21a.html (PMLR 139, pages 8162-8171) and arXiv 2102.09672.
+@misc{nichol2021improved,
+  howpublished = {In \emph{Proceedings of the 38th International Conference on Machine Learning}, Proceedings of Machine Learning Research \textbf{139}, 8162--8171 (PMLR)},
+  author = {Nichol, Alexander Quinn and Dhariwal, Prafulla},
+  title = {{Improved Denoising Diffusion Probabilistic Models}},
+  booktitle = {Proceedings of the 38th International Conference on Machine Learning},
+  series = {Proceedings of Machine Learning Research},
+  volume = {139},
+  pages = {8162--8171},
+  publisher = {PMLR},
+  year = {2021},
+  url = {https://proceedings.mlr.press/v139/nichol21a.html}
+}
+
+% Kept from the previous verified references.bib (DataCite record; title and eight authors of version 3, revised
+% 22 January 2026; first publication year 2025). eprint and archivePrefix added; checked against the arXiv API.
+@misc{ostheimer2025sparse,
+  author = {Ostheimer, Phil and Nagda, Mayank and Balinskyy, Andriy and Radig, Jean and Herrmann, Carl and Mandt, Stephan and Kloft, Marius and Fellenz, Sophie},
+  title = {{Sparse Data Diffusion for Scientific Simulations in Biology and Physics}},
+  howpublished = {Preprint},
+  year = {2025},
+  doi = {10.48550/ARXIV.2502.02448},
+  eprint = {2502.02448},
+  archivePrefix = {arXiv},
+  note = {Version 3, 22 January 2026}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{randolph2021genetic,
+  author = {Randolph, Haley E. and Fiege, Jessica K. and Thielen, Beth K. and Mickelson, Clayton K. and Shiratori, Mari and Barroso-Batista, Jo{\~a}o and Langlois, Ryan A. and Barreiro, Luis B.},
+  title = {{Genetic ancestry effects on the response to viral infection are pervasive but cell type specific}},
+  journal = {Science},
+  volume = {374},
+  number = {6571},
+  pages = {1127--1133},
+  year = {2021},
+  doi = {10.1126/science.abg0928},
+  url = {https://doi.org/10.1126/science.abg0928}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 32066974); PDF
+%  rare-disease-states/Reyes2020_Immune_cell_signature_bacterial_sepsis.pdf (author manuscript).
+@article{reyes2020immune,
+  author = {Reyes, Miguel and Filbin, Michael R. and Bhattacharyya, Roby P. and Billman, Kianna and Eisenhaure, Thomas and Hung, Deborah T. and Levy, Bruce D. and Baron, Rebecca M. and Blainey, Paul C. and Goldberg, Marcia B. and Hacohen, Nir},
+  title = {{An immune-cell signature of bacterial sepsis}},
+  journal = {Nature Medicine},
+  volume = {26},
+  number = {3},
+  pages = {333--340},
+  year = {2020},
+  doi = {10.1038/s41591-020-0752-4},
+  url = {https://doi.org/10.1038/s41591-020-0752-4}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{robinson2010edger,
+  author = {Robinson, Mark D. and McCarthy, Davis J. and Smyth, Gordon K.},
+  title = {{edgeR: a Bioconductor package for differential expression analysis of digital gene expression data}},
+  journal = {Bioinformatics},
+  volume = {26},
+  number = {1},
+  pages = {139--140},
+  year = {2010},
+  doi = {10.1093/bioinformatics/btp616},
+  url = {https://doi.org/10.1093/bioinformatics/btp616}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 37592036); PDF
+%  sc-generation/Roohani2024_GEARS_multigene_perturbation_outcomes.pdf.
+@article{roohani2024gears,
+  author = {Roohani, Yusuf and Huang, Kexin and Leskovec, Jure},
+  title = {{Predicting transcriptional outcomes of novel multigene perturbations with GEARS}},
+  journal = {Nature Biotechnology},
+  volume = {42},
+  number = {6},
+  pages = {927--935},
+  year = {2024},
+  doi = {10.1038/s41587-023-01905-6},
+  url = {https://doi.org/10.1038/s41587-023-01905-6}
+}
+
+% Kept from the previous verified references.bib (ICLR 2021; OpenReview forum St1giarCHLP, also listed by
+%  mlanthology.org).
+@misc{song2021ddim,
+  howpublished = {In \emph{International Conference on Learning Representations}},
+  author = {Song, Jiaming and Meng, Chenlin and Ermon, Stefano},
+  title = {{Denoising Diffusion Implicit Models}},
+  booktitle = {International Conference on Learning Representations},
+  year = {2021},
+  url = {https://openreview.net/forum?id=St1giarCHLP}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 34584091); PDF
+%  methods-sources/Squair2021_Confronting_false_discoveries_scDE.pdf.
+@article{squair2021confronting,
+  author = {Squair, Jordan W. and Gautier, Matthieu and Kathe, Claudia and Anderson, Mark A. and James, Nicholas D. and Hutson, Thomas H. and Hudelle, R{\'e}mi and Qaiser, Taha and Matson, Kaya J. E. and Barraud, Quentin and Levine, Ariel J. and La Manno, Gioele and Skinnider, Michael A. and Courtine, Gr{\'e}goire},
+  title = {{Confronting false discoveries in single-cell differential expression}},
+  journal = {Nature Communications},
+  volume = {12},
+  number = {1},
+  pages = {5692},
+  year = {2021},
+  doi = {10.1038/s41467-021-25960-2},
+  url = {https://doi.org/10.1038/s41467-021-25960-2}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 33879890); PDF
+%  rare-disease-states/Stephenson2021_Single_cell_multiomics_COVID19.pdf. The CITIID-NIHR collaboration is placed as
+%  in the byline (after author 54); Crossref lists it first.
+@article{stephenson2021single,
+  author = {Stephenson, Emily and Reynolds, Gary and Botting, Rachel A. and Calero-Nieto, Fernando J. and Morgan, Michael D. and Tuong, Zewen Kelvin and Bach, Karsten and Sungnak, Waradon and Worlock, Kaylee B. and Yoshida, Masahiro and Kumasaka, Natsuhiko and Kania, Katarzyna and Engelbert, Justin and Olabi, Bayanne and Spegarova, Jarmila Stremenova and Wilson, Nicola K. and Mende, Nicole and Jardine, Laura and Gardner, Louis C. S. and Goh, Issac and Horsfall, Dave and McGrath, Jim and Webb, Simone and Mather, Michael W. and Lindeboom, Rik G. H. and Dann, Emma and Huang, Ni and Polanski, Krzysztof and Prigmore, Elena and Gothe, Florian and Scott, Jonathan and Payne, Rebecca P. and Baker, Kenneth F. and Hanrath, Aidan T. and {Schim van der Loeff}, Ina C. D. and Barr, Andrew S. and Sanchez-Gonzalez, Amada and Bergamaschi, Laura and Mescia, Federica and Barnes, Josephine L. and Kilich, Eliz and de Wilton, Angus and Saigal, Anita and Saleh, Aarash and Janes, Sam M. and Smith, Claire M. and Gopee, Nusayhah and Wilson, Caroline and Coupland, Paul and Coxhead, Jonathan M. and Kiselev, Vladimir Yu and van Dongen, Stijn and Bacardit, Jaume and King, Hamish W. and {Cambridge Institute of Therapeutic Immunology and Infectious Disease-National Institute of Health Research (CITIID-NIHR) COVID-19 BioResource Collaboration} and Rostron, Anthony J. and Simpson, A. John and Hambleton, Sophie and Laurenti, Elisa and Lyons, Paul A. and Meyer, Kerstin B. and Nikoli{\'c}, Marko Z. and Duncan, Christopher J. A. and Smith, Kenneth G. C. and Teichmann, Sarah A. and Clatworthy, Menna R. and Marioni, John C. and G{\"o}ttgens, Berthold and Haniffa, Muzlifah},
+  title = {{Single-cell multi-omics analysis of the immune response in COVID-19}},
+  journal = {Nature Medicine},
+  volume = {27},
+  number = {5},
+  pages = {904--916},
+  year = {2021},
+  doi = {10.1038/s41591-021-01329-2},
+  url = {https://doi.org/10.1038/s41591-021-01329-2}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 40532694); PDF
+%  paired-cohorts/Stephenson2025_CellGenom_temporal_multiomics_COVID19_ESKD.pdf.
+@article{stephenson2025temporal,
+  author = {Stephenson, Emily and Macdonald-Dunlop, Erin and Dratva, Lisa M. and Lindeboom, Rik G. H. and Tuong, Zewen Kelvin and Tun, Win Min and Kretschmer, Lorenz and Buang, Norzawani B. and Ballereau, Stephane and Cabantaus, Mia and Pe{\~n}alver, Ana and Prigmore, Elena and Ferdinand, John R. and Stewart, Benjamin J. and Gisby, Jack and Malik, Talat H. and Clarke, Candice L. and Medjeral-Thomas, Nicholas and Prendecki, Maria and McAdoo, Stephen and Portet, Anais and Willicombe, Michelle and Sandhu, Eleanor and Pickering, Matthew C. and Botto, Marina and Teichmann, Sarah A. and Haniffa, Muzlifah and Clatworthy, Menna R. and Thomas, David C. and Peters, James E.},
+  title = {{Temporal multi-omics analysis of COVID-19 in end-stage kidney disease}},
+  journal = {Cell Genomics},
+  volume = {5},
+  number = {8},
+  pages = {100918},
+  year = {2025},
+  doi = {10.1016/j.xgen.2025.100918},
+  url = {https://doi.org/10.1016/j.xgen.2025.100918}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{theodoris2023geneformer,
+  author = {Theodoris, Christina V. and Xiao, Ling and Chopra, Anant and Chaffin, Mark D. and Al Sayed, Zeina R. and Hill, Matthew C. and Mantineo, Helene and Brydon, Elizabeth M. and Zeng, Zexian and Liu, X. Shirley and Ellinor, Patrick T.},
+  title = {{Transfer learning enables predictions in network biology}},
+  journal = {Nature},
+  volume = {618},
+  number = {7965},
+  pages = {616--624},
+  year = {2023},
+  doi = {10.1038/s41586-023-06139-9},
+  url = {https://doi.org/10.1038/s41586-023-06139-9}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 29022598); PDF methods-sources/Tukiainen2017_Nature_XCI_landscape.pdf.
+%  GTEx Consortium placed as in the byline; its sub-groups (listed by Europe PMC) are omitted.
+@article{tukiainen2017landscape,
+  author = {Tukiainen, Taru and Villani, Alexandra-Chlo{\'e} and Yen, Angela and Rivas, Manuel A. and Marshall, Jamie L. and Satija, Rahul and Aguirre, Matt and Gauthier, Laura and Fleharty, Mark and Kirby, Andrew and Cummings, Beryl B. and Castel, Stephane E. and Karczewski, Konrad J. and Aguet, Fran{\c{c}}ois and Byrnes, Andrea and {GTEx Consortium} and Lappalainen, Tuuli and Regev, Aviv and Ardlie, Kristin G. and Hacohen, Nir and MacArthur, Daniel G.},
+  title = {{Landscape of X chromosome inactivation across human tissues}},
+  journal = {Nature},
+  volume = {550},
+  number = {7675},
+  pages = {244--248},
+  year = {2017},
+  doi = {10.1038/nature24265},
+  url = {https://doi.org/10.1038/nature24265}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 40854979); PDF
+%  evaluation-metrics/VinasTorne2026_Systema_perturbation_prediction_beyond_systematic_variation.pdf. Issue 44(6),
+%  2026; online 25 August 2025.
+@article{vinastorne2026systema,
+  author = {Vi{\~n}as Torn{\'e}, Ramon and Wiatrak, Maciej and Piran, Zoe and Fan, Shuyang and Jiang, Liangze and Teichmann, Sarah A. and Nitzan, Mor and Brbi{\'c}, Maria},
+  title = {{Systema: a framework for evaluating genetic perturbation response prediction beyond systematic variation}},
+  journal = {Nature Biotechnology},
+  volume = {44},
+  number = {6},
+  pages = {1050--1059},
+  year = {2026},
+  doi = {10.1038/s41587-025-02777-8},
+  url = {https://doi.org/10.1038/s41587-025-02777-8}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 34521850); PDF
+%  rare-disease-states/Wang2021_Kawasaki_PBMC_scRNAseq_GSE168732.pdf.
+@article{wang2021kawasaki,
+  author = {Wang, Zhen and Xie, Lijian and Ding, Guohui and Song, Sirui and Chen, Liqin and Li, Guang and Xia, Min and Han, Dingding and Zheng, Yue and Liu, Jia and Xiao, Tingting and Zhang, Hong and Huang, Yujuan and Li, Yixue and Huang, Min},
+  title = {{Single-cell RNA sequencing of peripheral blood mononuclear cells from acute Kawasaki disease patients}},
+  journal = {Nature Communications},
+  volume = {12},
+  number = {1},
+  pages = {5444},
+  year = {2021},
+  doi = {10.1038/s41467-021-25771-5},
+  url = {https://doi.org/10.1038/s41467-021-25771-5}
+}
+
+% Kept from the previous verified references.bib; re-checked against Crossref on 2026-10-10.
+@article{wang2024scsemiprofiler,
+  author = {Wang, Jingtao and Fonseca, Gregory J. and Ding, Jun},
+  title = {{scSemiProfiler: Advancing large-scale single-cell studies through semi-profiling with deep generative models and active learning}},
+  journal = {Nature Communications},
+  volume = {15},
+  number = {1},
+  pages = {5989},
+  year = {2024},
+  doi = {10.1038/s41467-024-50150-1},
+  url = {https://doi.org/10.1038/s41467-024-50150-1}
+}
+
+% New. Verified: Crossref; PDF evaluation-metrics/Webber2010_Rank_biased_overlap_similarity_indefinite_rankings.pdf
+%  (author version); article number 20 (pages 20:1-20:38) from Semantic Scholar/DBLP; Crossref gives pages 1-38.
+@article{webber2010similarity,
+  author = {Webber, William and Moffat, Alistair and Zobel, Justin},
+  title = {{A similarity measure for indefinite rankings}},
+  journal = {ACM Transactions on Information Systems},
+  volume = {28},
+  number = {4},
+  pages = {20},
+  year = {2010},
+  doi = {10.1145/1852102.1852106},
+  url = {https://doi.org/10.1145/1852102.1852106}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 33491336; article e9620, which Crossref records as MSB20209620). No PDF in
+%  reference-papers.
+@article{xu2021scanvi,
+  author = {Xu, Chenling and Lopez, Romain and Mehlman, Edouard and Regier, Jeffrey and Jordan, Michael I. and Yosef, Nir},
+  title = {{Probabilistic harmonization and annotation of single-cell transcriptomics data with deep generative models}},
+  journal = {Molecular Systems Biology},
+  volume = {17},
+  number = {1},
+  pages = {e9620},
+  year = {2021},
+  doi = {10.15252/msb.20209620},
+  url = {https://doi.org/10.15252/msb.20209620}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 41339491); PDF paired-cohorts/Single-cell multi-omic landscape reveals
+%  anatomical-specific immune features in adult and pediatric sepsis.pdf.
+@article{ye2026sepsis,
+  author = {Ye, Qian and Lai, Xiaofei and Liu, Yuliang and Zhang, Zhengtao and Fu, Yueqiang and Luo, Jie and Liu, Chengjun and Duan, Jun and Ding, Hao and Liu, Yuhan and Ao, Zhi and Tao, Yang and Ai, Shanmu and Huang, Wei and Jiang, Lei and Liu, Yi and Xu, Fang and Cao, Ju},
+  title = {{Single-cell multi-omic landscape reveals anatomical-specific immune features in adult and pediatric sepsis}},
+  journal = {Nature Immunology},
+  volume = {27},
+  number = {1},
+  pages = {150--165},
+  year = {2026},
+  doi = {10.1038/s41590-025-02345-x},
+  url = {https://doi.org/10.1038/s41590-025-02345-x}
+}
+
+% New. Verified: Crossref; Europe PMC (PMID 35727985, the PubMed ID of GEO GSE198533 and GSE198616); PDF
+%  paired-cohorts/bechet_c1q.pdf. Replaces the earlier incomplete entry ("and others").
+@article{zheng2022c1qbehcet,
+  author = {Zheng, Wenjie and Wang, Xiaoman and Liu, Jinjing and Yu, Xin and Li, Lu and Wang, Heping and Yu, Jijun and Pei, Xiaoya and Li, Chaoran and Wang, Zhimian and Zhang, Menghao and Zeng, Xiaofeng and Zhang, Fengchun and Wang, Chenfei and Chen, Hua and Chen, Hou-Zao},
+  title = {{Single-cell analyses highlight the proinflammatory contribution of C1q-high monocytes to Beh{\c{c}}et's disease}},
+  journal = {Proceedings of the National Academy of Sciences},
+  volume = {119},
+  number = {26},
+  pages = {e2204289119},
+  year = {2022},
+  doi = {10.1073/pnas.2204289119},
+  url = {https://doi.org/10.1073/pnas.2204289119}
+}
+
+% Added by the coordinator on 2026-10-10 (introduction, bulk-to-cell models). Titles, authors, journal, volume and
+% article number checked against the PDFs in reference-papers/bulk-to-sc/ (first page and footer).
+@article{liao2022bulk2space,
+  author = {Liao, Jie and Qian, Jingyang and Fang, Yin and Chen, Zhuo and Zhuang, Xiang and Zhang, Ningyu and Shao, Xin and Hu, Yining and Yang, Penghui and Cheng, Junyun and Hu, Yang and Yu, Lingqi and Yang, Haihong and Zhang, Jinlu and Lu, Xiaoyan and Shao, Li and Wu, Dan and Gao, Yue and Chen, Huajun and Fan, Xiaohui},
+  title = {{De novo analysis of bulk RNA-seq data at spatially resolved single-cell resolution}},
+  journal = {Nature Communications},
+  volume = {13},
+  pages = {6498},
+  year = {2022},
+  doi = {10.1038/s41467-022-34271-z},
+  url = {https://doi.org/10.1038/s41467-022-34271-z}
+}
+
+@article{zeng2024omicverse,
+  author = {Zeng, Zehua and Ma, Yuqing and Hu, Lei and Tan, Bowen and Liu, Peng and Wang, Yixuan and Xing, Cencan and Xiong, Yuanyan and Du, Hongwu},
+  title = {{OmicVerse: a framework for bridging and deepening insights across bulk and single-cell sequencing}},
+  journal = {Nature Communications},
+  volume = {15},
+  pages = {5983},
+  year = {2024},
+  doi = {10.1038/s41467-024-50194-3},
+  url = {https://doi.org/10.1038/s41467-024-50194-3}
+}
+
+@misc{cho2024bulk2sc,
+  author = {Cho, Hyun Jae and Xie, Eric and Zhang, Aidong and Bekiranov, Stefan},
+  title = {{Generating synthetic single cell data from bulk RNA-seq using a pretrained variational autoencoder}},
+  howpublished = {Preprint at bioRxiv},
+  year = {2024},
+  doi = {10.1101/2024.05.18.594837},
+  url = {https://doi.org/10.1101/2024.05.18.594837}
+}
+
+% Added by the coordinator on 2026-10-10 (limma lmFit/eBayes; metadata as confirmed on Crossref by the references agent).
+@article{ritchie2015limma,
+  author = {Ritchie, Matthew E. and Phipson, Belinda and Wu, Di and Hu, Yifang and Law, Charity W. and Shi, Wei and Smyth, Gordon K.},
+  title = {{limma powers differential expression analyses for RNA-sequencing and microarray studies}},
+  journal = {Nucleic Acids Research},
+  volume = {43},
+  number = {7},
+  pages = {e47},
+  year = {2015},
+  doi = {10.1093/nar/gkv007},
+  url = {https://doi.org/10.1093/nar/gkv007}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{wang2019music,
+  author = {Wang, Xuran and Park, Jihwan and Susztak, Katalin and Zhang, Nancy R. and Li, Mingyao},
+  title = {{Bulk tissue cell type deconvolution with multi-subject single-cell expression reference}},
+  journal = {Nature Communications},
+  volume = {10},
+  number = {1},
+  pages = {380},
+  year = {2019},
+  doi = {10.1038/s41467-018-08023-x},
+  url = {https://doi.org/10.1038/s41467-018-08023-x}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{jew2020bisque,
+  author = {Jew, Brandon and Alvarez, Marcus and Rahmani, Elior and Miao, Zong and Ko, Arthur and Garske, Kristina M. and Sul, Jae Hoon and Pietil{\"a}inen, Kirsi H. and Pajukanta, P{\"a}ivi and Halperin, Eran},
+  title = {{Accurate estimation of cell composition in bulk expression through robust integration of single-cell information}},
+  journal = {Nature Communications},
+  volume = {11},
+  number = {1},
+  pages = {1971},
+  year = {2020},
+  doi = {10.1038/s41467-020-15816-6},
+  url = {https://doi.org/10.1038/s41467-020-15816-6}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{tsoucas2019dwls,
+  author = {Tsoucas, Daphne and Dong, Rui and Chen, Haide and Zhu, Qian and Guo, Guoji and Yuan, Guo-Cheng},
+  title = {{Accurate estimation of cell-type composition from gene expression data}},
+  journal = {Nature Communications},
+  volume = {10},
+  number = {1},
+  pages = {2975},
+  year = {2019},
+  doi = {10.1038/s41467-019-10802-z},
+  url = {https://doi.org/10.1038/s41467-019-10802-z}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{menden2020scaden,
+  author = {Menden, Kevin and Marouf, Matteo and Oller, Sergio and Dalmia, Anupriya and Magruder, Daniel Sumner and Kloiber, Karin and Heutink, Peter and Bonn, Stefan},
+  title = {{Deep learning--based cell composition analysis from tissue expression profiles}},
+  journal = {Science Advances},
+  volume = {6},
+  number = {30},
+  pages = {eaba2619},
+  year = {2020},
+  doi = {10.1126/sciadv.aba2619},
+  url = {https://doi.org/10.1126/sciadv.aba2619}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{chen2022tape,
+  author = {Chen, Yanshuo and Wang, Yixuan and Chen, Yuelong and Cheng, Yuqi and Wei, Yumeng and Li, Yunxiang and Wang, Jiuming and Wei, Yingying and Chan, Ting-Fung and Li, Yu},
+  title = {{Deep autoencoder for interpretable tissue-adaptive deconvolution and cell-type-specific gene analysis}},
+  journal = {Nature Communications},
+  volume = {13},
+  number = {1},
+  pages = {6735},
+  year = {2022},
+  doi = {10.1038/s41467-022-34550-9},
+  url = {https://doi.org/10.1038/s41467-022-34550-9}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{barbosa2021blade,
+  author = {Andrade Barbosa, B{\'a}rbara and van Asten, Saskia D. and Oh, Ji Won and Farina-Sarasqueta, Arantza and Verheij, Joanne and Dijk, Frederike and van Laarhoven, Hanneke W. M. and Ylstra, Bauke and Garcia Vallejo, Juan J. and van de Wiel, Mark A. and Kim, Yongsoo},
+  title = {{Bayesian log-normal deconvolution for enhanced in silico microdissection of bulk gene expression data}},
+  journal = {Nature Communications},
+  volume = {12},
+  number = {1},
+  pages = {6106},
+  year = {2021},
+  doi = {10.1038/s41467-021-26328-2},
+  url = {https://doi.org/10.1038/s41467-021-26328-2}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{wang2021bmind,
+  author = {Wang, Jiebiao and Roeder, Kathryn and Devlin, Bernie},
+  title = {{Bayesian estimation of cell type--specific gene expression with prior derived from single-cell data}},
+  journal = {Genome Research},
+  volume = {31},
+  number = {10},
+  pages = {1807--1818},
+  year = {2021},
+  doi = {10.1101/gr.268722.120},
+  url = {https://doi.org/10.1101/gr.268722.120}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{sturm2019evaluation,
+  author = {Sturm, Gregor and Finotello, Francesca and Petitprez, Florent and Zhang, Jitao David and Baumbach, Jan and Fridman, Wolf H and List, Markus and Aneichyk, Tatsiana},
+  title = {{Comprehensive evaluation of transcriptome-based cell-type quantification methods for immuno-oncology}},
+  journal = {Bioinformatics},
+  volume = {35},
+  number = {14},
+  pages = {i436--i445},
+  year = {2019},
+  doi = {10.1093/bioinformatics/btz363},
+  url = {https://doi.org/10.1093/bioinformatics/btz363}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{dietrich2026omnideconv,
+  author = {Dietrich, Alexander and Merotto, Lorenzo and Pelz, Konstantin and Eder, Bernhard and Zackl, Constantin and Reinisch, Katharina and Edenhofer, Frank and Marini, Federico and Sturm, Gregor and List, Markus and Finotello, Francesca},
+  title = {{omnideconv: a unifying framework for using and benchmarking single-cell-informed deconvolution of bulk RNA-seq data}},
+  journal = {Genome Biology},
+  volume = {27},
+  number = {1},
+  pages = {6},
+  year = {2026},
+  doi = {10.1186/s13059-026-03955-w},
+  url = {https://doi.org/10.1186/s13059-026-03955-w}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+% Author order follows the publisher byline; consortium members are represented by their group author.
+@article{community2024assessment,
+  author = {White, Brian S. and de Reyni{\`e}s, Aur{\'e}lien and Newman, Aaron M. and Waterfall, Joshua J. and Lamb, Andrew and Petitprez, Florent and Lin, Yating and Yu, Rongshan and Guerrero-Gimenez, Martin E. and Domanskyi, Sergii and Monaco, Gianni and Chung, Verena and Banerjee, Jineta and Derrick, Daniel and Valdeolivas, Alberto and Li, Haojun and Xiao, Xu and Wang, Shun and Zheng, Frank and Yang, Wenxian and Catania, Carlos A. and Lang, Benjamin J. and Bertus, Thomas J. and Piermarocchi, Carlo and Caruso, Francesca P. and Ceccarelli, Michele and Yu, Thomas and Guo, Xindi and Bletz, Julie and Coller, John and Maecker, Holden and Duault, Caroline and Shokoohi, Vida and Patel, Shailja and Liliental, Joanna E. and Simon, Stockard and {Tumor Deconvolution DREAM Challenge consortium} and Saez-Rodriguez, Julio and Heiser, Laura M. and Guinney, Justin and Gentles, Andrew J.},
+  title = {{Community assessment of methods to deconvolve cellular composition from bulk gene expression}},
+  journal = {Nature Communications},
+  volume = {15},
+  number = {1},
+  pages = {7362},
+  year = {2024},
+  doi = {10.1038/s41467-024-50618-0},
+  url = {https://doi.org/10.1038/s41467-024-50618-0}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{scdiffusionx2026,
+  author = {Luo, Erpai and Wei, Lei and Hao, Minsheng and Zhang, Xuegong and Liu, Qiao},
+  title = {{A multi-modal diffusion model with dual-cross-attention for multi-omics data generation and translation}},
+  journal = {Nature Communications},
+  volume = {17},
+  number = {1},
+  pages = {5196},
+  year = {2026},
+  doi = {10.1038/s41467-026-71744-x},
+  url = {https://doi.org/10.1038/s41467-026-71744-x}
+}
+
+% Added for the Introduction supplied on 2026-10-11; verified against Crossref DOI metadata.
+@article{delafuente2025sweetwater,
+  author = {de la Fuente, Jes{\'u}s and Legarra-Marcos, Naroa and Diaz-Mazkiaran, Aintzane and Serrano, Guillermo and Mar{\'i}n-Go{\~n}i, Irene and Sendin, Markel Benito and Osta, Ana Garc{\'i}a and Kalari, Krishna R and Fernandez-Granda, Carlos and Ochoa, Idoia and Hernaez, Mikel},
+  title = {{An interpretable and adaptive autoencoder for efficient tissue deconvolution}},
+  journal = {Nucleic Acids Research},
+  volume = {53},
+  number = {16},
+  pages = {gkaf821},
+  year = {2025},
+  doi = {10.1093/nar/gkaf821},
+  url = {https://doi.org/10.1093/nar/gkaf821}
+}
+
+% Added for the JH Introduction revision of 2026-10-12; verified against Crossref DOI metadata.
+@article{denisenko2020systematic,
+  author = {Denisenko, Elena and Guo, Belinda B. and Jones, Matthew and Hou, Rui and de Kock, Leanne and Lassmann, Timo and Poppe, Daniel and Cl{\'e}ment, Olivier and Simmons, Rebecca K. and Lister, Ryan and Forrest, Alistair R. R.},
+  title = {{Systematic assessment of tissue dissociation and storage biases in single-cell and single-nucleus RNA-seq workflows}},
+  journal = {Genome Biology},
+  volume = {21},
+  number = {1},
+  pages = {130},
+  year = {2020},
+  doi = {10.1186/s13059-020-02048-6},
+  url = {https://doi.org/10.1186/s13059-020-02048-6}
+}

paper-releases/pbisc-diffusion/updates/20261012T104812Z-jh-introduction-edit/diffusion/dataset/server_1/manuscript/sections/introduction.tex

@@ -0,0 +1,19 @@
+% Introduction updated from JH Word edits on 2026-10-12; tracked insertions accepted and deletions omitted.
+% Minimal grammar and mechanism wording corrections plus citations are recorded in editorial/jh_edit_integration_20261012.json.
+\section*{Introduction}
+
+Bulk RNA sequencing of blood and tissue remains the most widely used transcriptomic assay in clinical research, including large clinical trials, biobanks and disease cohorts. Each bulk profile, however, is a mixture: changes in cell-type abundance are confounded with changes in cell-state-specific expression, and disease signals carried by minority populations are diluted. Single-cell RNA sequencing resolves these components, but cost and sample logistics still limit it to tens or hundreds of donors per study.
+
+Computational deconvolution has been the principal bridge between the two. Digital cytometry methods such as CIBERSORTx\cite{newman2019cibersortx}, MuSiC\cite{wang2019music}, Bisque\cite{jew2020bisque}, DWLS\cite{tsoucas2019dwls}, Scaden\cite{menden2020scaden} and TAPE\cite{chen2022tape} estimate cell-type proportions from bulk using single-cell-derived signatures, and BayesPrism\cite{chu2022bayesprism}, BLADE\cite{barbosa2021blade} and bMIND\cite{wang2021bmind} additionally infer cell-type-specific expression. Despite of the highly advanced calculation algorithms, the accuracy of each model's output highly depends on data transformation, single cell profile reference choice and annotation granularity, degrades for closely related and rare cell types, and changes rank when the reference differs from the bulk cohort\cite{avilacobos2020benchmarking,sturm2019evaluation,dietrich2026omnideconv,community2024assessment}. These methods also return proportions or cell-type averages, which cannot represent heterogeneity within a cell type, thus masking many disease-associated states that could be inferred by transcriptomics.
+
+Advances in machine learning have enabled generative approaches to infer single-cell expression profiles from bulk transcriptomes. Bulk2Space\cite{liao2022bulk2space} and BulkTrajBlend\cite{zeng2024omicverse} use estimated cell-type composition to guide generation from single-cell reference data, whereas scSemiProfiler\cite{wang2024scsemiprofiler} adapts profiles from selected cohort representatives using bulk-derived constraints on aggregate expression. However, these approaches remain dependent on selected references, whose coverage and representativeness can influence the inferred cellular landscape\cite{liao2022bulk2space,zeng2024omicverse,wang2024scsemiprofiler}. Methods that organize generation around cell types or clusters also depend on how these populations are defined\cite{liao2022bulk2space,zeng2024omicverse}, motivating approaches that do not require cell identities or proportions to be specified in advance.
+
+Diffusion-based methods, including scDiffusion\cite{luo2024scdiffusion}, have expanded the possibilities for generating cellular profiles. However, realistic single-cell generation still requires faithful reproduction of the sparse patterns of zero and nonzero expression observed in experimental data. Meanwhile, Sparse Data Diffusion introduced ``sparsity bits'' to represent these patterns explicitly alongside expression magnitudes, improving the fidelity of sparse data generation in biological and physical datasets\cite{ostheimer2025sparse}. This approach provides a basis for jointly modeling which genes are detected and their expression levels when reconstructing cellular heterogeneity.
+
+A further obstacle for such bulk transcriptome deconvolution is the scarcity of experimentally paired bulk and single-cell measurements. Large single-cell datasets can provide abundant bulk-to-single-cell training sets by aggregating cells into pseudobulk profiles, but these simulated profiles do not fully reproduce the expression distribution of measured bulk RNA-seq due to technical and sample-preparation differences, such as differences in sequencing depth\cite{wang2024scsemiprofiler} and cell loss during dissociation of solid tissues\cite{denisenko2020systematic}. Systematic differences between pseudobulk and measured bulk can therefore limit the transfer of models to experimental data\cite{delafuente2025sweetwater}. Learning broadly from available single-cell resources and subsequently adapting to real bulk measurements with a small amount of paired data offers a practical strategy for addressing this limitation.
+
+Here we introduce PBMC Bulk Sample to Immune Single Cells (PBISC), a bulk-conditioned generative model pretrained on pseudobulk--single-cell examples constructed from over 33 million cells across 22 scRNA-seq datasets. This pretraining establishes a shared model of cellular expression across cohorts, reducing reliance on selecting a separate single-cell reference for each target. We incorporated the sparsity-bit concept from Sparse Data Diffusion\cite{ostheimer2025sparse} to jointly model gene-detection patterns and expression levels. With PBISC, we were able to generate cells from independent random initializations under a shared bulk condition, without requiring cell-type annotations or predicted cell-type proportions during training or generation.
+
+To adapt PBISC to measured bulk RNA-seq, we fine-tuned the pretrained model using experimentally paired bulk and single-cell profiles, with as few as five paired samples in the evaluated settings. During adaptation, measured bulk profiles served as conditioning inputs, while observed single-cell profiles from the corresponding biological samples provided the training targets. We then applied the adapted model to generate single-cell profiles for the remaining cohort using bulk measurements alone.
+
+In various evaluation standards, we were able to reproduce the distributions of paired experimental single-cell profiles, as assessed by precision\cite{kynkaanniemi2019improved} and coverage\cite{naeem2020reliable}. PBISC-generated deconvoluted dataset preserved disease-associated expression changes more accurately than comparator methods in most evaluated settings. Furthermore, PBISC reproduced literature-defined rare cell populations\cite{reyes2020immune,nehar2020mapping} observed in the single-cell data of the evaluated cohorts, without specifying these populations as separate generation categories. Together, these results support extending analyses of cellular heterogeneity and disease-associated transcriptional programs to large cohorts primarily profiled by bulk RNA-seq.