Exploring the host response in infected lung organoids by processing gene expression data

Abstract

A method includes receiving statistical results corresponding to an organ tissue equivalent transition across conditions and time, and that describe significance of gene expression differences; generating adjusted statistical results using an adjustment procedure; and generating and displaying magnitude-altitude scores. A computer system includes a processor, and a memory storing computer-executable instructions that, when executed by the processor, cause the computer system to: receive statistical results corresponding an organ tissue equivalent transition across conditions and time, and that describe significance of gene expression differences; generate adjusted statistical results using an adjustment procedure; and generate and display magnitude¬ altitude scores. A non-transitory computer-readable medium includes instructions that, when executed by a processor, cause a computer to: receive statistical results corresponding to an organ tissue equivalent transition across conditions and time, and that describe significance of gene expression differences; generate adjusted statistical results using an adjustment procedure; and generate and display magnitude-altitude scores.

Description

Attorney Docket No.32103 / 59347A / PC EXPLORING THE HOST RESPONSE IN INFECTED LUNG ORGANOIDS BY PROCESSING GENE EXPRESSION DATA CROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of U.S. Application No.63 / 534,675, filed August 25, 2023, and entitled " EXPLORING THE HOST RESPONSE IN INFECTED LUNG ORGANOIDS BY PROCESSING GENE EXPRESSION DATA", which is incorporated herein by reference in its entirety. STATEMENT OF GOVERNMENT SUPPORT

[0002] This invention was made with U.S. Government support under Base Agreement No. W15QKN-16-9-1002, awarded by the ACC-NJ to the MCDC. The Government has certain rights in the invention. FIELD OF THE DISCLOSURE

[0003] The present disclosure is generally directed to methods and systems for exploring the host response in infected lung organoids (e.g., using NanoString technology) to process gene expression data, and more particularly for computing magnitude-altitude scores corresponding to genes expressing in various ways during one or more time periods during which at least one organ tissue equivalent transits from one or more conditions. BACKGROUND

[0004] Human microphysiological systems are three-dimensional, bioengineered constructs containing multiple cell types that are organized into tissue-like structures. The improved morphology, functional maturation, and physiological fidelity of these constructs renders them a valuable tool for studying organ function under healthy and diseased conditions.

[0005] Organoids are three-dimensional ex vivo cultures that reproduce the biological structures, interactions and arrangements found in living tissues. Organoids may be produced using tissue-specific adult stem cells sourced from biopsy samples or cadavers, or from pluripotent stem cell populations, such embryonic stem cells or induced pluripotent stem cells. While many organoid protocols rely on the expansion, differentiation and self-assembly of pluripotent stem cells into emergent structures,Attorney Docket No.32103 / 59347A / PC recent work has shown that combining primary adult stem cells with extracellular matrices can create micro-engineered organoids which recreate the architecture of a specific tissue. Indeed, scaffold-based “organ-tissue equivalents” (OTEs) exhibit several advantages over scaffold-free organoids or two-dimensional cell cultures, including accelerated functional maturation; defined phenotypic composition; microscale tissue architecture; and recapitulation of functionally important, organ-specific cell-cell, cell-extracellular matrix (ECM) and mechanical interactions.

[0006] Biomedical professionals often use the PCR method for mRNA gene expression quantification. However, many challenges exist involving PCR because it requires the reverse transcription and amplification of sample RNA. Thus, biomedical researchers began to look for new gene expression quantification methods. The recent growth in commercial sources offering primary human cells and technical advances in tissue-specific growth media have made it possible to create and evaluate OTEs for a large variety of human organs.

[0007] One emerging technology is the NanoString nCounter which does not require reverse transcription and amplification like PCR. Some studies have shown that the NanoString nCounter had far better gene expression quantification results compared to PCR, as discussed for example in Reis, Patricia P et al. “mRNA transcript quantification in archival samples using multiplexed, color-coded probes.” BMC biotechnology vol.11 46.9 May.2011, doi:10.1186 / 1472-6750-11-46, incorporated by reference herein in its entirety for all purposes.

[0008] As noted, organoids accurately replicate internal processes and reactions. These complex structures are increasingly being used to study human physiology and disease. One method to compare the effects of perturbations on organoid gene expression is by direct quantitation of mRNA molecules using target-specific, color- coded probe pairs. This method has the advantage of being quick and relatively low- cost compared to full transcriptomic sequencing. For example, the NanoString platform allows for highly multiplexed quantitation of mRNA targets using labeled probes, enabling the simultaneous processing of gene expression for hundreds of transcripts. By comparing the gene expression patterns of a focused panel of genes between different categories of organoids, researchers can infer functional differences in theAttorney Docket No.32103 / 59347A / PC responsiveness of OTEs to perturbation. The utilization of NanoString technology in organoid research highlights the power of modern molecular biology techniques to advance knowledge of complex biological systems.

[0009] One specific area utilizing organoids is the gastrointestinal field, as discussed in Dieterich, Walburga et al. “Intestinal ex vivo organoid culture reveals altered programmed crypt stem cells in patients with celiac disease.” Scientific reports vol.10,1 3535.26 Feb.2020, doi:10.1038 / s41598-020-60521-5, incorporated by reference herein in its entirety for all purposes. This study determined that celiac disease patients and healthy controls had vastly different gene expression patterns. Numerous ECM proteins, such as COL1A2, COL3A1, FN1, and TIMP3, showed limited expression patterns in CD patients compared to healthy patients.

[0010] Another study utilized NanoString technology to highlight potential immunotherapeutic antigen targets to treat melanoma, as discussed in Beard, Rachel E et al. “Gene expression profiling using nanostring digital RNA counting to identify potential target antigens for melanoma immunotherapy.” Clinical cancer research : an official journal of the American Association for Cancer Research vol.19,18 (2013): 4941-50. doi:10.1158 / 1078-0432.CCR-13-1253, incorporated by reference herein in its entirety for all purposes. Using NanoString technology, the authors identified seven potential genes that could help treat metastatic cancer. These genes were overexpressed in the melanoma cell lines and metastatic melanoma tumors and underexpressed in normal tissues. They found that normal tissues and tumor tissues have different gene expression profiles, and that a number of genes were differentially expressed in normal and tumor tissues. From those target genes, the authors identified seven genes as potential immunotherapeutic approaches to treat melanoma: CSAG2, MAGEA3, MAGEC2, IL13RA2, PRAME, CSPG4, and SOX10.

[0011] Further, cerebral organoids and NanoString technology were used to compare gene expression levels in wild-type and DISC1-disrupted organoids, as discussed in Daniel K. Crawford, Jasper Mullenders, Johanna Pott, Sylvia F. Boj, Shira Landskroner- Eiger, Matthew M. Goddeeris, Targeting G542X CFTR nonsense alleles with ELX-02 restores CFTR function in human-derived intestinal organoids, Journal of Cystic Fibrosis, Volume 20, Issue 3, 2021,Pages 436-442, ISSN 1569-1993,Attorney Docket No.32103 / 59347A / PC https: / / doi.org / 10.1016 / j.jcf.2021.01.009, incorporated by reference herein in its entirety for all purposes. Among other things, this study showed that many of the gene expression changes found in DISC1 mutation could be replicated by WNT agonism, and that BRN2, CALB2, and EAAT2 decreased expression, and OLFM1, CALB1, FEZF2, and NRG1 increased expression in DISC1-mutated organoids compared to the wild- type organoids.

[0012] Finally, another study involving perfused human kidney organoids transplanted into rats investigated the therapeutic action of GFB-887, a new kidney drug, in phase 2 clinical trials, as discussed in Westerling-Bui, Amy D et al. “Transplanted organoids empower human preclinical assessment of drug candidate for the clinic.” Science advances vol.8,27 (2022): eabj5633. doi:10.1126 / sciadv.abj5633, incorporated by reference herein in its entirety for all purposes.

[0013] Molecular classification of tumors is an effective approach for determining the suitable treatment for bladder cancer patients. However, the technology needed for molecular classification is expensive and rarely available in the medical field. Thus, biomedical professionals are searching for new techniques that easily categorize the molecular subtypes of bladder cancer tumors, as discussed in Lopez-Beltran, Antonio et al. “Molecular Classification of Bladder Urothelial Carcinoma Using NanoString-Based Gene Expression Analysis.” Cancers vol.13,215500.1 Nov.2021, doi:10.3390 / cancers13215500, incorporated by reference herein in its entirety for all purposes. This research shows that NanoString technology has the potential to be a cheap, easy, and largely reproducible technique to distinguish the different molecular subtypes of bladder cancer.

[0014] In sum, conventional lung organoid analysis techniques are lacking techniques for quantifying the expression activity of genes in various pathogenic conditions and across time. Thus, the conventional techniques are lacking in approaches (e.g., diagnostic tools) for quick and accurate identification of infections that are suitable for clinical diagnostics (e.g., tracking severity of respiratory viral infections).

[0015] Therefore, there is an opportunity for improved tests, tools and techniques for assessing the health status of individuals, identifying diseases / conditions, monitoring disease progression and determining severity of illness by assessing infection impact onAttorney Docket No.32103 / 59347A / PC patient respiratory system and health. This information is critical for guiding appropriate medical treatment and intervention decisions. BRIEF SUMMARY

[0016] In one aspect, a computer-implemented method for computing magnitude- altitude scores corresponding to genes expressing in various ways during one or more time periods during which at least one organ tissue equivalent transits from one or more conditions includes (1) receiving, via one or more processors, statistical results including one or more p-values that (a) correspond to a transition of the organ tissue equivalent from an initial condition to a subsequent condition over a period of time, and (b) describe significance of gene expression differences from the initial condition to the subsequent condition; (2) generating, via one or more processors, adjusted statistical results by applying an adjustment procedure to the statistical results including the one or more p-values; (3) generating, via one or more processors, a respective magnitude- altitude score for one or more genes identified as significant in the statistical results, by computing ^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, for ^ = 1, 2, … , ^, where ^ is the number ofand wherein M and A are tunable hyper-parameters; and (4) displaying, via one or more processors, the respective magnitude-altitude score for the one or more genes on a display.

[0017] In another aspect, a computer system for computing magnitude-altitude scores corresponding to genes expressing in various ways during one or more time periods during which at least one organ tissue equivalent transits from one or more conditions includes one or more processors, and one or more memories having stored thereon computer-executable instructions. The instructions, when executed by the one or more processors, may cause the computer system to: (1) receive statistical results including one or more p-values that (a) correspond to a transition of the organ tissue equivalent from an initial condition to a subsequent condition over a period of time, and (b) describe significance of gene expression differences from the initial condition to the subsequent condition; (2) generate adjusted statistical results by applying an adjustment procedure to the statistical results including the the one or more p-values; (3) generate a respective magnitude-altitude score for one or more genes identified as significant in the statistical results, by computing ^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, forAttorney Docket No.32103 / 59347A / PC ^ = 1, 2, … , ^, where ^ is the number of rejected null hypotheses rejected based on the adjustment procedure, and wherein M and A are tunable hyper-parameters; and display the respective magnitude-altitude score for the one or more genes on a display.

[0018] In yet another aspect, a non-transitory computer-readable medium may include instructions that, when executed by one or more processors, cause a computer to: (1) receive statistical results including one or more p-values that (a) correspond to a transition of the organ tissue equivalent from an initial condition to a subsequent condition over a period of time, and (b) describe significance of gene expression differences from the initial condition to the subsequent condition; (2) generate adjusted statistical results by applying an adjustment procedure to the statistical results including the one or more p-values; (3) generate a respective magnitude-altitude score for one or more genes identified as significant in the statistical results, by computing ^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, for ^ = 1, 2, … , ^, where ^ is the number of rejected nullthe adjustment procedure, and wherein M and A are tunable hyper-parameters; and (4) display the respective magnitude-altitude score for the one or more genes on a display. BRIEF DESCRIPTION OF THE DRAWINGS

[0019] FIG.1 depicts a block diagram of an exemplary computing environment for implementing the present techniques, according to some aspects;

[0020] FIG.2A depicts a block diagram of an exemplary computer-implemented method for computing statistically significant genes for viruses as organ-tissue equivalents move between conditions, according to some aspects;

[0021] FIG.2B depicts a block diagram of an exemplary computer-implemented method for applying a Benjamini-Hochberg adjustment procedure to the method of FIG. 2A, according to some aspects;

[0022] FIG.2C depicts a block diagram of an exemplary computer-implemented method for computing magnitude-altitude scores corresponding to genes expressing in various ways during one or more time periods during which at least one organ tissue equivalent transits from one or more conditions, according to some aspects;Attorney Docket No.32103 / 59347A / PC

[0023] FIG.3A depicts an exemplary three-dimensional visualization of OTEs over time, according to some aspects;

[0024] FIG.3B depicts an exemplary three-dimensional visualization of OTEs over time, according to some aspects;

[0025] FIG.3C depicts average points of all replicates pinned on the same 3-D PCs coordinate system in which replicates are projected, according to some aspects;

[0026] FIG.4A depicts an exemplary line chart illustrating a BH-cutoff line and a number of BH-based significant genes of the first two pair conditions for IAV, across a 24-hour period;

[0027] FIG.4B depicts an exemplary line chart illustrating a BH-cutoff line and a number of BH-based significant genes of the first two pair conditions for IAV, across a 72-hour period;

[0028] FIG.5 depicts an exemplary bar chart illustrating positive false discovery rates of the BH adjustment procedure for a period of time, according to some aspects;

[0029] FIG.6A depicts an exemplary volcano plot of hyperparameter tuning when (M,A) is set to (0,1), according to some aspects;

[0030] FIG.6B depicts an exemplary volcano plot of hyperparameter tuning when (M,A) is set to (1,0), according to some aspects;

[0031] FIG.6C depicts an exemplary volcano plot of hyperparameter tuning when (M,A) is set to (1,1), according to some aspects;

[0032] FIG.7A depicts an exemplary bar chart illustrating IAV affected genes as OTEs move between negative and experimental control groups, according to some aspects;

[0033] FIG.7B depicts an exemplary bar chart illustrating IAV affected genes as OTEs move between negative and experimental control groups, according to some aspects;

[0034] FIG.7C depicts an exemplary bar chart illustrating IAV affected genes as OTEs move between negative and experimental control groups, according to some aspects;Attorney Docket No.32103 / 59347A / PC

[0035] FIG.7D depicts an exemplary bar chart illustrating PIV3 affected genes as OTEs move between negative and experimental control groups over time, according to some aspects;

[0036] FIG.7E depicts an exemplary bar chart illustrating PIV3 affected genes as OTEs move between negative and experimental control groups over time, according to some aspects;

[0037] FIG.7F depicts an exemplary bar chart illustrating PIV3 affected genes as OTEs move between negative and experimental control groups over time, according to some aspects;

[0038] FIG.7G depicts an exemplary bar chart illustrating MPV affected genes as OTEs move between negative and experimental control groups over time, according to some aspects;

[0039] FIG.8A depicts an exemplary plot of common BH-significant genes among all viruses as OTEs transit UV-treated conditions over time, according to some aspects;

[0040] FIG.8B depicts an exemplary plot of common BH-significant genes among all viruses as OTEs transit UV-treated conditions over time, according to some aspects;

[0041]

[0042] FIG.8C depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV-treated conditions over time, according to some aspects;

[0043] FIG.8D depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV-treated conditions over time, according to some aspects;

[0044] FIG.8E depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV-treated conditions over time, according to some aspects;

[0045] FIG.8F depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV-treated conditions over time, according to some aspects; and

[0046] FIG.8G depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV-treated conditions over time, according to some aspects. DETAILED DESCRIPTIONAttorney Docket No.32103 / 59347A / PC

[0047] The present techniques provide methods and systems for, inter alia, exploring the host response in infected lung organoids (e.g., using NanoString technology) to analyze gene expression data, and more particularly for identifying patterns of gene expression enabling the development of diagnostic tools for quick and accurate identification of viral infections and track the severity of respiratory viral infections.

[0048] The present techniques may include quantifying changes in cellular gene expression of upper airway organ tissue equivalents (OTEs) infected by Influenza A virus (IAV), Human metapneumovirus (MPV), and / or Parainfluenza virus type 3 (PIV3).

[0049] For example, in some aspects, organoids such as lung organoids and / or OTEs may be constructed using techniques described in U.S. Patent No.11,001,811, “Multi-Layer Airway Organoids and Methods of Making and Using the Same,” to Murphy et al, filed May 11, 2021, and hereby incorporated by reference in its entirety for all purposes.

[0050] In particular, the present techniques include methods and systems for assessing influence of IAV, MPV and / or PIV3 on gene expression. In some aspects, the present techniques may utilize a Benjamini-Hochberg (BH) adjustment procedure as part of the assessment. For example, the present techniques may include measuring changes in gene expression IAV, MPV, and PIV3, at different times after infection. The present techniques may include identifying genes whose levels were significantly changed by infection with IAV and PIV3 at specific times after infection, potentially serving as fingerprints for infection by these viruses. The present techniques may further include identifying common patterns of gene expression or fingerprints that may identify infections caused by IAV, MPV, and PIV3, as well as fingerprints that inform on the progression of the viral infection and possibly viral disease. Advantageously, these findings may be used to develop diagnostic tools for quick and accurate identification of viral infections and track the severity of respiratory viral infections, ultimately reducing the impact of these infections on human health.

[0051] As noted, the present techniques may include techniques for analyzing gene expression patterns of respiratory viruses (e.g., IAV, MPV, and PIV3) in infected lung- tissue-equivalents using a statistical techniques. And as noted, in some aspects, the present statistical techniques may be based or utilize the Benjamini-Hochberg adjustment procedure. The present techniques may use principal component analysisAttorney Docket No.32103 / 59347A / PC (PCA) visualization and a significance gene score to quantify a magnitude and a significance of gene expression changes.

[0052] Empirical results illustrate that IAV had the most significant impact on gene expression compared to the other two viruses. PIV3 significantly affected genes between 24-72 hours post-infection, while MPV was the least intense virus during the first 72 hours of infection. Advantageously, the empirical findings enabled by the present techniques and included in this disclosure provide insights into the mechanisms of viral infections and may aid in the development of effective treatments for respiratory diseases caused by these viruses. The present techniques may be applicable to other virus / disease pathogenesis, and may advantageously aid in development of effective treatments for those processes and mechanisms, and specific clinical symptoms or disease manifestations.

[0053] As discussed herein, the present techniques have been used to identify several genes that are significantly affected by infections IAV, MPV, and PIV3 during different post-infection time frames. These results show that IFNL1 and CCL20 are the most affected genes during the first 24 hours of IAV post-infection. For MPV, IFIT1, IFIT2, MX1, OAS2, and IFI44 are the most affected genes during the first 24 hours of post-infection. Finally, for PIV3, IFIT1, MX1, OAS2, and OAS1 are the most affected genes during the first 24 hours after infection. These results also demonstrate that several genes such as CCL5, CXCL10, CXCL11, and TNFSF13B show significant changes during post-infection time points 24- and 72-hours for IAV. Similarly, IL33, CCL20, CXCL6, TNFSF18 and CXCL8 show significant changes for MPV during post- infection time points 24- and 72-hours. For PIV3 the genes IFIT3, IFIT2, IFIT1, HERC5, IFNL1, OAS1, OAS3 and ZBP1 significantly change within the post-infection timeframe between 24- and 72-hours. These findings advantageously contribute to a better understanding of the host-virus interaction and aid in the development of antiviral therapies.

[0054] The present techniques have been used to identify several unique genes that are significantly activated or changed by IAV and PIV3 infections during specific time points. These genes can serve as potential biomarkers or fingerprints for detecting and characterizing these infections. Specifically, IFNB1, OASL, IFNL2 / 3 and ISG15 areAttorney Docket No.32103 / 59347A / PC uniquely and significantly activated by IAV during the first 24 hours of infection, while TNF, PECAM1, and VWF are unique genes that change significantly between 24- and 72-hours after IAV infection. Additionally, IL31 is a unique gene that changes significantly during the first 24 hours of PIV3 post-infection, and IRF9 is a gene that significantly changes between PIV3 post-infection time points 24- and 72-hours. These findings provide advantageous and valuable insights into the molecular mechanisms of viral infections and may lead to the development of more effective diagnostic and therapeutic approaches.

[0055] The present techniques were used to identify five genes, namely OAS1, EIF2AK2, IFIT1, OAS2, and MX1 as common fingerprints for identifying respiratory viral infections caused by IAV, MPV and PIV3. Further, three genes (CCL20, IL6, and IL33) were identified as biomarkers for monitoring the progression of the disease between post-infection time points 24- and 72-hours. Advantageously, these findings may help develop diagnostic tools for quick and accurate identification of viral infections and track the severity of respiratory viral infections to provide timely and appropriate treatment. The present techniques may have significant implications for reducing the impact of viral infections on human health.

[0056] In some aspects, the present techniques may be applied to less active viruses with a lower multiplicity of infection, which may improve accuracy and generalizability. Furthermore, collecting data at more frequent post-infection time points may help reduce bias and improve the validity of the results. Moreover, the use of additional modalities may help address potential confounding factors and enhance robustness. Exemplary Computing Environment

[0057] FIG.1 depicts a block diagram of an exemplary computing environment 100 for implementing the present techniques, according to some aspects. The environment 100 may include a server computing device 102 and a client computing device communicatively coupled via an electronic network 106. Some aspects may include a plurality of server computing devices 102 and / or a plurality of client computing devices 104. In some aspects, some or all of the components of the environment 100 may be implemented using public / private / hybrid cloud computing resources (e.g., virtualized computing instances, virtualized databases, etc.).Attorney Docket No.32103 / 59347A / PC

[0058] The server computing device 102 includes a processor 110 and a network interface controller (NIC) 112. The server computing device may include an electronic database 108. The server 102 may include a library of client bindings for accessing the database 108. In some embodiments, the database 108 is located remote from the server 102. The database 108 may be a structured query language (SQL) database (e.g., a MySQL database, an Oracle database, etc.) or another type of database (e.g., a not only SQL (NoSQL) database). The database 108 may include data sets processed by the present techniques, as discussed herein.

[0059] The processor 110 may include any suitable number of processors and / or processor types, such as CPUs and one or more graphics processing units (GPUs). Generally, the processor 110 is configured to execute software instructions stored in a memory 114. The memory 114 may include one or more persistent memories (e.g., a hard drive / solid state memory) and stores one or more set of computer executable instructions / modules 150, including a statistical results module 152, a pair conditions module 154, an adjustment module 156, a magnitude-altitude module 158, a hyperparameter tuning module 160, a visualization module 162 and a diagnostics module 164. Each of the modules 160 implements specific functionality related to the present techniques. The modules 160 may execute concurrently / in parallel (e.g., on the CPUs 110) as respective computing processes, in some embodiments.

[0060] The client computing device 104 may be an individual server, a group (e.g., cluster) of multiple servers, or another suitable type of computing device or system (e.g., a collection of computing resources). For example, the client computing device 104 may be any suitable computing device (e.g., a server, a mobile computing device, a smart phone, a tablet, a laptop, a wearable device, etc.). Generally, a user (e.g., a researcher, a scientist, a diagnostician, an administrator, etc.) may access the data, processing software / modules and results generated by the server computing device 102 via the client computing device 102.

[0061] The network 106 may be a single communication network, or may include multiple communication networks of one or more types (e.g., one or more wired and / or wireless local area networks (LANs), and / or one or more wired and / or wireless wide area networks (WANs) such as the Internet). The network 106 may enable bidirectionalAttorney Docket No.32103 / 59347A / PC communication between the server computing device 102 and the client computing device 104, and / or between multiple server computing devices 102, for example.

[0062] The NIC 112 may include any suitable network interface controller(s), such as wired / wireless controllers (e.g., Ethernet controllers), and facilitate bidirectional / multiplexed networking over the network 106 between the server computing device 102, the client computing device 104 and other components of the environment 100 (e.g., another client computing device 104, the electronic database 108, etc.).

[0063] The input device 120 may include one or more systems and / or devices for generating data for processing. For example, the input device 120 may include cell culture equipment (e.g., a CO2 incubator, a microscope, etc.), RNA extraction / purification equipment, quantification and quality assessment equipment, reverse transcription and / or PCR equipment, sequencing equipment (including next- generation sequencing equipment / platforms) such as high-throughput RNA equipment (e.g., RNA-seq) and / or data processing software. In some aspects, the input device 140 may include a suitable device or devices for receiving input, such as one or more microphones, one or more cameras, a hardware keyboard, a hardware mouse, a capacitive touch screen, etc.

[0064] The output device 130 may include any suitable device for conveying output, such as a hardware speaker, a computer monitor, a touch screen, etc. In some cases, the input device 120 and the output device 130 may be integrated into a single device, such as a touch screen device that accepts user input and displays output. For example, the output device 130 may display one or more visualizations. In some aspects, the output device may be a virtual device.

[0065] Each of the modules 160 implements specific respective functionality related to the present techniques. For example, in some aspects, the statistical results module 152 includes instructions for receiving / retrieving statistical results from an experimental data sources (e.g., via the network 106 and / or the database 108). For example, the statistical results module 152 may receive / retrieve raw data summaries of gene expression data (e.g., measures such as mean, median, variance, range, etc.), NanoString data, other data distributions (e.g., histograms), quality control results,Attorney Docket No.32103 / 59347A / PC normalized / transformed data, etc. The statistical results module 152 may store the received statistical results data in the database 108, for example.

[0066] The pair conditions module 154 may load the data retrieved / received by the statistical data module 152 and generate pair conditions for one or more pathogens, as described herein, for example with respect to FIG.2A. For example, the pair conditions module 154 may include instructions for generating combination tables using one or more viruses, one or more treatment conditions, one or more control groups, and / or one or more time periods, as discussed further herein. The pair conditions module 154 may make the generated combination data available for further processing by other modules in the modules 150 and / or store the combined information in the database 108, for example, or the memory 114.

[0067] The adjustment module 156 may include instructions for performing one or more data processing steps, such as computing of t-value, p-value and other statistical measures as discussed herein, for example, with respect to FIG.2B. The adjustment module 156 may include instructions for performing one or more adjustment procedures, such as a Benjamini-Hochberg algorithm, a Bonferroni correction algorithm, and / or other suitable techniques. The adjustment module 156 may correct p-values and other values, and store the adjusted values in a memory or database for further processing, and / or make such values available to other modules for further processing. The adjustment module 156 may include further instructions for sorting values prior to performing the adjustment procedures, in some aspects. The adjust module 156 may include instructions for computing a positive false discovery rate.

[0068] The magnitude-altitude module 158 may include instructions for computing scoring operations on the data adjusted by the adjustment module 156, as discussed in greater detail below, for example with respect to FIG.2C. For example, the magnitude- altitude module 158 may include instructions for computing magnitude-altitude scores based on the data generated by the pair conditions module 154 and the adjustment module 156. The magnitude-altitude module 158 may include instructions for sorting genes in terms of the computed scoring operations, and ranking genes according to min-max scores. The magnitude-altitude module 158 may store the scores, gene rankings, etc. and / or provide that information to other of the modules 150.Attorney Docket No.32103 / 59347A / PC

[0069] The hyperparameter tuning module 160 may enable hyperparameter values used for computations (e.g., by the magnitude-altitude module 158) to be adjusted. For example, the hyperparameter tuning module 160 may enable a user to view results of analysis performed using different hyperparameters, and to adjust hyperparameters (e.g., via the client computing device 104).

[0070] The visualization module 162 may include sets of computer-executable instructions for generating charts, graphs, plots, and other visual representations of the data processing of modules 152-160, in some aspects. For example, visualizations that may be shown to users (e.g., via the output device 130 and / or the client computing device 104) may include those of FIGs.3A-3B, 4A-4B, 5, 6A-6C, 7A-7G and 8A-8G. It should be appreciated that as discussed below, visualizations may enable researchers and other users to quickly spot patterns in data, and as such, many other visualization techniques are envisioned, beyond those examples discussed herein such as heatmaps and volcano plots. For example, other visualization types that may be used include pathology images, flow cytometry, biomedical signal analysis, clinical data, etc.

[0071] The diagnostics module 164 may include instructions enabling diagnostic uses of the data processed by the modules 150. For example, the diagnostic module 164 may include a software application to facilitate review of the data generated by the modules 150. Examples of diagnostic usage are discussed in further detail below.

[0072] The interaction and operation of the modules 150 will now be described with respect to computer-implemented methods. Exemplary Computer-Implemented OTE Processing

[0073] The present techniques include methods and systems for comparing how upper airway lung OTEs respond to infection by respiratory viruses, including IAV, MPV and / or PIV3. Changes in cellular gene expression may be measured using the nCounter Host Response Panel of 785 cellular genes (or NanoString data). The present techniques may include evaluating changes in gene expression by analyzing patterns of gene expression after infection with IAV, MPV and / or PIV3, over different time schedules after infection, to identify statistically significant changes most affected by each virus by comparing infected OTEs to control conditions (e.g., mock-infected orAttorney Docket No.32103 / 59347A / PC infected with UV-inactivated virus), as discussed below. For example, the NanoString data may be sourced from the input device 120 and / or the database 108 of FIG.1.

[0074] The present techniques may include identifying genes that are uniquely affected by each virus (IAV, MPV, and PIV3) as OTEs transition from an experimental group (e.g., infected with active virus) to a negative experimental group (e.g., infected with UV-inactivated virus). This processing may be performed (e.g., by the statistical results module 152 and pair conditions module 154) from two times after infection (e.g., 24 and 72 hours) to gain a better understanding of the temporal trajectory of gene expression in the infected OTEs, as discussed below.

[0075] The present techniques may include identifying genes affected by all three viruses (IAV, MPV, and PIV3) as OTEs move from the experimental condition (e.g., infected with active virus) to the negative control condition (e.g., infected with UV- inactivated virus). The processing may be repeated for data obtained 24 and 72 hours after infection to further evaluate changes in the pattern of gene expression over time. This processing may include identifying statistically significant genes whose expression varied in in all three virus infections, as discussed below.

[0076] In an aspect, a NanoString data set may include quantified transcript levels for a number (e.g., 773) of host response genes associated with viral infections. Target genes may be compared in a number (e.g., 16) of different infection conditions, where each infection condition involves a number (e.g., six) of OTE replicates. For example, 16 different infection conditions classified based on virus, treat, and post-infection time may be as follows:

[0077] In the present techniques, OTEs may be infected by a number of viruses (e.g., IAV, MPV, and PIV3). In some aspects, more or fewer viruses, and / or different viruses (or different pathogens) may be selected for study. There may be two or more treatment types (e.g., UV and Non-UV (i.e., active)) and one or more post-infection time points (e.g., 24-hours and 72-hours). In this context, the term “treat” refers to how the viruses were handled before being introduced to the organoids. When a virus is exposed to inactivating doses of ultraviolet (UV) light, it is no longer able to productively infect cells. Thus, the level of UV exposure may be adjusted to ensure that the virus isAttorney Docket No.32103 / 59347A / PC inactivated without causing any other effects. The Non-UV treatment refers to active viruses that can infect the organoids. The use of UV-treated infected OTEs as a negative control group enables the present techniques to compare changes that occur in Non-UV infected OTEs with the changes that would be expected if there were no virus present at all.

[0078] To track changes in UV or Non-UV infected OTEs over time, additional groups of OTEs (e.g., naïve and Mock) may be included as control groups. Naïve OTEs may be untreated samples, meaning that no virus or other treatment is applied to them, and samples are collected from these OTEs at specified time points. Mock-infected samples may be those that are treated in the same way as the virus-infected samples, except that no actual virus is introduced to the tissue. These samples may serve as a control to ensure that any changes observed in the virus-infected samples are indeed due to the presence of the virus and not some other factor in the experimental setup. These may be referred to collectively as “mock-infected” samples. In this example, the Mock-infected OTEs may be used as a control group to compare the changes in gene expression between the UV-inactivated-treated and Non-UV infected OTEs. Additionally, Naïve OTEs may be used as a control group for the Mock-infected OTEs to ensure that any changes in gene expression observed in the Mock-infected OTEs are not due to the process of the infection itself but rather are specifically related to the presence or absence of the virus. The pair conditions module 154 may include instructions for creating the control groups and experimental groups.

[0079] Table 1 depicts the distribution of data points between post-infection time points, experimental groups, negative control groups, and control groups. CONDITI VIRUS TRE POST- CONDITI VIRUS TRE POST- TI E S S S SAttorney Docket No.32103 / 59347A / PC PIV3-UV- PIV3 UV 24- PIV3-UV- PIV3 UV 72- 24 HOURS 72 HOURS S S S ).

[0080] Table 2 depicts a distribution of replicates between post-infection timepoints, experimental groups, negative and regular control groups: AT POST- 6 REPLICATES FOR EACH OF THE FOLLOWINGS: INFECTION EXPERIMENTAL GROUP: NON-UV TREATED IAV, NON-UV

[0081] The present techniques may include comparing a number of pair conditions for the pathogens under study, as shown for example in the following table: Condition A Condition B Condition A Condition B

[0082] Table 3, above, depicts 12 different pair condition comparisons for viruses analyzed to determine the most statistically significant genes. To find the most affected genes as OTE infection conditions change for each virus, the present techniques may consider each of the 12 different pairs of comparisons. In particular, the presentAttorney Docket No.32103 / 59347A / PC techniques may identify genes that significantly increase or decrease as six replicates of OTEs move from a condition A to a condition B, wherein this transition represents the infection condition for six replicates. Mechanically, this identification involves performing a two-sample independent t-test for genes, and computation of p-values with a given significance level (e.g., α=0.05), as illustrated in FIG.2. Exemplary Computer-Implemented Methods

[0083] FIG.2A depicts a block diagram of an exemplary computer-implemented method 200 for computing statistically significant genes for viruses as OTEs move between conditions, according to some aspects. In general, the method 200 may be performed by one or more modules of the computing modules 150 of FIG.1 (e.g., the statistical results module 152, the pair conditions module 154, the adjustment module 156, the magnitude-altitude module 158, the hyperparameters module 160, the visualization module 162, and / or the diagnostics module 164). The method 200 may include performing t-test and computing p-values for all genes as OTEs move from a condition A to a condition B, where r is the number of OTE replicates in each condition (e.g., r=6) and G is the number of genes (e.g., G=773). In some aspects, condition A may be referred to as an “initial condition” and condition B may be referred to as a “subsequent condition.”

[0084] A proportion of significant results in any statistical test includes false positives. To control the false discovery rate, the present techniques may include applying a Benjamini-Hochberg (BH) procedure that adjusts the significance level first and then computes the BH-adjusted p-values, as discussed in Benjamini, Yoav and Hochberg, Yosef, "Controlling the false discovery rate: a practical and powerful approach to multiple testing," Journal of the Royal statistical society: series B (Methodological), vol. 57, no.1, pp.289-300, 1995; and in Benjamini, Yoav and Heller, Ruth and Yekutieli, Daniel, "Selective inference in complex research," Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, vol.367, pp.4255- 4271, 2009; both of which are incorporated by reference in their entireties for all purposes.

[0085] FIG.2B depicts applying the BH procedure to the method 200 of FIG.2A, according to some aspects. The method 200 may further include computing BH-Attorney Docket No.32103 / 59347A / PC adjusted p-values for multiple p-values obtained from t-tests (block 220). The method 200 may further include sorting all p-values of m hypotheses in ascending order, ^(^), ^(^), ... , ^(^), to enable BH to control the false discovery rate by rejecting all hypotheses whose ordered p-values are ^(^)≤^^ , where !, ", and are the rank of gene based on sorted p-values, the of hypotheses, and significance level(block 222). To measure how effective BH procedure is, the present techniques may utilize a positive false discovery rate (pFDR), defined as the expected number of false positives out of all significant tests, as discussed in J. D. Storey, "A direct approach to false discovery rates," Journal of the Royal Statistical Society: Series B (Statistical Methodology), vol.64, no.3, pp.479-498, 2002., incorporated by reference herein in its entirety for all purposes. Exemplary Magnitude-Altitude Score (MAS) Aspects

[0086] In some aspects, the present techniques may be used to determine one or more most affected genes that are deemed significant by the BH adjustment procedure. Specifically, FIG.3 depicts a block diagram of an exemplary computer-implemented method 200 for computing magnitude-altitude score (MAS) for all BH-based significant genes, according to some aspects. The method 200 may advantageously enable identification of genes that are not only BH-based significant, but also genes whose expression increases or decreases considerably as infection conditions change.

[0087] For example, a number (e.g., six) of OTE replicates may transition from condition A to condition B, like discussed above. The above-described BH adjusted method 200 may be applied to find all BH-significant genes, also like discussed above (block 242). In that case, the method 200 may include computing Let ^^^^(FC)^= ^^^^(^#$%&^$ (^)^^#$%&^$ (')), ^ for ^ = 1, 2, … , ^, where ^ is the number of rejected null hypotheses rejected based BH adjusted^#$%&^$(^)^and ^#$%&^$(')^are average of gene ^ of replicates in condition A and B, respectively (block 244). Note that if ^^^^(FC)^< 0, then it indicates that the average value of gene l decreases as OTEs move from condition A to B.Attorney Docket No.32103 / 59347A / PC Rejected null hypotheses are those for which adjusted p-values are considered significant. This means that the data provides enough evidence to conclude that the null hypothesis is unlikely to be true, and there is a significant effect. Thus, if log2FC^> 0, then it indicates that the average value of gene ^ increases as OTEs move from condition A to B. If ^#$%&^$ (^)^is + times as large as ^#$%&^$ (')^, i.e., ^#$%&^$ (^)^= +(^#$%&^$ (')^), then ì^^^^(FC)^ < −1, 120 < + < 0.5,ï−1 ≤ ^^^^(FC)^ < 0, 120.5 ≤ + < 1,12 + = 1,1, 121 < + ≤ 2,1, 12 + > 2.The method 200 may then identify genes having a maximum value of | ^^^^(FC)^| and |^^^^^(^^^^)|, simultaneously, by computing the following MAS algorithm:^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, for ^ = 1, 2, … , ^, where ^rejected based BH adjustedM and A are hyper-parameters (block 246).

[0088] The present techniques may interpret | ^^^^(FC)^| and |^^^^^(^^^^)| as magnitude and altitude control variables based on visualization of volcano plots, according to some aspects.

[0089] The method 200 may control the balance between magnitude and altitude variables by two hyper-parameters ^ and ^, where ^, ^ ≥ 0. Without loss of generality, suppose | ^^^^(FC)^56| >>| ^^^^(FC)^78|, where | ^^^^(FC)^56| and | ^^^^(FC)^78| are respectively absolute values of log2FC corresponding to two genes with maximum rate of increase and decrease as OTEs move from condition A to B. In that case, hyper- parameters ^ and ^ must be selected in a way that a gene with R=1 (MAS-based rank) is the one with minimum distance from the point (| ^^^^(FC)^56|, max^{|^^^^^(^^^^)|}). On the other hand, if | ^^^^(FC)^56| <<| ^^^^(FC)^78|, then hyper-parameters ^ and ^ must be selected in a way that a gene with R=1 (MAS-based rank) is the one with minimum distance from the point (| ^^^^(>?)^78|, max^{|^^^^^(^^^^)|}). It should beAttorney Docket No.32103 / 59347A / PC appreciated that MAS-based rank is a symmetric metric that assigns rank to genes identically whether OTEs move from condition A to B or vice versa.

[0090] The method 200 may include sorting the genes in terms of ^^^^in a descending order and ranking the gene(s) with the largest MAS as rank 1 (R=1), and the gene(s) with the smallest MAS as rank s (R=s) (block 248). The method 200 may include returning the genes’ respective ranks (block 250). Determining Unique Statistically Significant Genes Affected by Respective Viruses During OTE Condition Transitions

[0091] The unique statistically significant genes that are largely affected by each respective virus as OTEs move between conditions is of interest. The method 200 may be extended to compute this information. For example, in some aspects, the method 200 may include finding all BH-based significant genes that are only affected by a particular virus. Continuing the example of having three viruses IAV, MPV and PIV3, this means that n=3. Thus, the method 200 may include computing a unique gene selection for a particular virus by computing the following: Input: Given infection conditions A and B. Step 1: for 1 = 1,2, … , @ do: • By means of method 200 (as depicted in FIGs.2A-2C), as OTEs infected by virus i transmit between conditions A and B, determine BH-based significant genes and their MAS-based ranks and store them into a set, namely BH-MAS- virus-i. End (for) Step 2: for A = 1,2, … , @ do: • Construct a set Unique-BH-MAS-virus-j whose elements are differentially expressed genes of BH-MAS-virus-j obtained in Step 1 that do not belong to BH-MAS-virus-k for ! = 1,2, … , @ and ! ≠ A . End (for) Output: Unique-BH-MAS-virus-j for A = 1, 2, ... , @.

[0092] Further, the present techniques may include visualizing the unique statistically significant genes that are affected by the respective viruses, as discussed below with respect to FIGs.7A-7G. Determining Statistically Significant Genes Affected by All Viruses During OTE Condition TransitionsAttorney Docket No.32103 / 59347A / PC

[0093] Furthermore, the intersection set of BH-based significant genes among all viruses as the OTE replicates move between conditions is also of interest. The method 200 may be extended to compute this information, alternatively or in addition to finding unique statistically significant genes: Input: Given infection conditions A and B. Step 1: for 1 = 1,2, … , @ do: • By means of method 200 (as depicted in FIGs.2A-2C), as OTEs infected by virus i transmit between conditions A and B, determine BH-based significant genes and their MAS-based ranks and store them into a set namely BH-MAS- virus-i. End (for) Step 2: for A = 1,2, … , @ do: • Construct a set Common-BH-MAS whose elements are differentially expressed genes of BH-MAS-virus-j obtained in Step 1 that do belong to all BH-MAS-virus-k for ! = 1,2, … , @. End (for) Output: Common-BH-MAS

[0094] Further, the present techniques may include visualizing the common statistically significant genes during OTE condition transitions, as discussed below with respect to FIGs.8A-8B. Exemplary Computer-Implemented Visualization Aspects

[0095] FIG.3A depicts an exemplary three-dimensional visualization 300 of OTEs over time, according to some aspects. The visualization 300 visualizes all OTEs by means of PCA, for example, as discussed in Abdi, Hervé, and Lynne J. Williams, "Principal component analysis," Wiley interdisciplinary reviews: computational statistics, vol.2, no.4, pp.433-459, 2010, herein incorporated by reference in its entirety for all purposes. Since the NanoString data contains different genes of different scales, for the sake of accurate visualization, the present techniques may include standardizing the data by converting the scales into a common scale (through a min-max normalization strategy, for example, as discussed in Patro, S and Sahu, Kishore Kumar, "Normalization: A preprocessing stage," arXiv preprint arXiv:1503.06462, 2015, herein incorporated by reference in its entirety for all purposes). In particular, the visualization 300 depicts the projection of all OTEs (e.g., 96 in all infection conditions) onto a three- dimensional subspace that is generated by the first three principal components.Attorney Docket No.32103 / 59347A / PC

[0096] The present visualization techniques are advantageous as they enable patterns to be immediately identified as infected OTEs move between conditions (e.g., UV versus Non-UV) and over time (e.g., 24-hour versus 72-hour conditions).

[0097] For example, FIG.3B depicts an example of identified patterns based on the PCA analysis of FIG.3A, according to some aspects. FIG.3B enables a viewer to single out the identified pattern for IAV-infected OTEs as they move between conditions and over time (e.g., UV versus Non-UV and 24-hours versus 72-hours), and identified patterns for MPV and PIV3 infected OTEs. In some aspects, an average of OTE replicates is used for visualization. For example, the present techniques may take an average of projected replicates and pin them on the same three-dimensional coordinate system in which replicates are projected, as shown in FIG.3C. By viewing FIG.3B, the viewer can immediately see the relationship of UV and non-UV treated samples, as well as the pattern of progression of those samples over different time scales. It should be appreciated that the present techniques use UV / non-UV conditions for exemplary purposes, and that many other conditions may be added, in some aspects. Further, it should be appreciated that other time scales may be added, which may be shorter or longer. In some aspects, the relevant time scales may be much shorter (e.g., one second or less) or much longer (e.g., one week or longer).

[0098] FIG.4A depicts an exemplary line chart 402 illustrating a BH-cutoff line and a number of BH-based significant genes of the first two pair conditions for IAV, across a 24-hour period. FIG.4B depicts an exemplary line chart 404 illustrating a BH-cutoff line and a number of BH-based significant genes of the first two pair conditions for IAV, across a 72-hour period. The chart 402 and the chart 404 reflect the status of the BH and p-value products after execution of the block 222 of the method 200 in FIG.2B.

[0099] FIG.5 depicts an exemplary bar chart 500 illustrating positive false discovery rates of the BH adjustment procedure for a period of time, according to some aspects. Exemplary Computer-Implemented Hyperparameter Tuning

[0100] The present techniques may include tuning one or more hyperparameter values (e.g., the values of (Magnitude, Altitude)). For example, the hyperparameters may be chosen as follows: (0,1), (1,0), and (1,1). Assuming OTEs move from infection condition IAV-None-24 to IAV-UV-24, by means of method 200, blocks 242-250, theAttorney Docket No.32103 / 59347A / PC present techniques may compute MAS-based rankings of genes according to the changed (M, A) values. FIGs.6A-6C depict the result plotting these rankings using volcano plots. Specifically, FIG.6A depicts an exemplary volcano plot of hyperparameter tuning when (M,A) is set to (0,1), according to some aspects. FIG.6B depicts an exemplary volcano plot of hyperparameter tuning when (M,A) is set to (1,0), according to some aspects. FIG.6C depicts an exemplary volcano plot of hyperparameter tuning when (M,A) is set to (1,1), according to some aspects.

[0101] FIGs.6A-6C display the respective volcano plots and MAS-based ranking of genes corresponding to cases when (M, A) is equal to (0,1), (1,0), and (1,1), respectively. These plots indicate that the balance between the magnitude and altitude variables occurs if M= A. And further, that if (M, A) = (1,0) and (M, A) = (0,1), then CXCL10 and OAS2 are selected as MAS-based rank #1 (R=1), respectively, but neither of them satisfies the optimal balance between magnitude and altitude. FIGs.6A-6C further indicate that when the values of (M, A) are set to (1, 0), the method’s ranking weights shift towards larger magnitudes, suggesting a bias towards higher values. Conversely, when (M, A) is set to (0, 1), the ranking weights shift towards larger altitudes, indicating a bias towards higher altitude values. However, if (M, A) are set to (1, 1), there is a balance between the magnitude and altitude variables. In this case, the gene with the lowest distance from the point (| ^^^^(FC)^56|, max^{|(^^^^^(^^^^)}) and an appropriate balance between theas MAS-based rank #1 (R=1), which is IFNL1.

[0102] Additional volcano plots may be generated to depict MAS-based ranking of genes corresponding as OTEs move between different infection conditions when M=A=1. For example, such plots may display how top-20 MAS-based selected genes change within OTE replicates, illustrate the averages of top-20 genes for identical infection condition change, demonstrate 3-D visualizations of OTE replicates using the top-3 MAS-based selected genes, and provide the values of | ^^^^(FC)^| and |^^^^^(^^^^)| for the top-20 selected genes after all MAS scores are normalized (throughnormalization strategy

[0020] ) as OTEs move between 36 infection conditions i.e.12 different pair infection conditions comparisons for each virus, as shown in Table 3, supra.Attorney Docket No.32103 / 59347A / PC

[0103] Table 4, Table 5 and Table 6 (infra) may display the top-10 MAS-based selected genes as infected OTEs by IAV, MPV, and PIV3, respectively, move between 12 infection pair conditions. Moreover, to provide a better understanding of selected genes, Table 7 (infra) may display the MAS-based selected genes as OTEs move between control / negative-control group conditions. As shown, MAS is an appropriate global (within all pair infection conditions) metric to compare the intensity of infection conditions. Genes with larger MAS scores are more affected as OTEs move between two infection conditions. Condition A Condition The top-10 MAS-based selected genes B / 1 C , / ,Attorney Docket No.32103 / 59347A / PC IAV-UV-72 ↔ Naïve-72 RSAD2, OAS1, MX1, IFI44, IFIT1, OAS2, IFIT3, CXCL3, IL36RN, CXCL6Condition A Condition B The top-10 MAS-based selected genes MPV-None- ↔ MPV-UV-24 IFIT1, IFIT2, IFIT3, RSAD2, MX1, OAS1, OAS2, , , , , tbetween 12 pair conditions Condition A Condition B The top-10 MAS-based selected genes 3, , , ,Attorney Docket No.32103 / 59347A / PC PIV3-None- ↔ Naïve-24 SLC2A3, BNIP3, PLAUR, VEGFA, IL1RN, CD68, 24 PRDM1, TGFB1, PFKFB3, ULK1 teween par con ons Condition A Condition B The top-10 MAS-based selected genes Mock-24 ↔ Naïve-24 BNIP3 CXCL10 PLAUR VEGFA IL1RN CD68, 6, , olgroup conditions Visualizing Unique Statistically Significant Genes Affected by Each Virus During OTE Condition Transitions

[0104] As discussed above, the present techniques may include quantifying unique statistically significant genes that are largely affected by each virus as OTEs move between conditions. After computing the unique gene selection for the particular virus, the present techniques may generate one or more visualizations depicting p-values and other computed values. For example, bar charts may be plotted after computing p- values corresponding to pair infection conditions to find the unique genes corresponding to each virus.

[0105] FIG.7A depicts an exemplary bar chart illustrating IAV affected genes as OTEs move between negative and experimental control groups, according to some aspects.Attorney Docket No.32103 / 59347A / PC

[0106] FIG.7B depicts an exemplary bar chart illustrating IAV affected genes as OTEs move between negative and experimental control groups, according to some aspects.

[0107] FIG.7C depicts an exemplary bar chart illustrating IAV affected genes as OTEs move between negative and experimental control groups, according to some aspects.

[0108] FIG.7D depicts an exemplary bar chart illustrating PIV3 affected genes as OTEs move between negative and experimental control groups over time, according to some aspects.

[0109] FIG.7E depicts an exemplary bar chart illustrating PIV3 affected genes as OTEs move between negative and experimental control groups over time, according to some aspects.

[0110] FIG.7F depicts an exemplary bar chart illustrating PIV3 affected genes as OTEs move between negative and experimental control groups over time, according to some aspects.

[0111] FIG.7G depicts an exemplary bar chart illustrating MPV affected genes as OTEs move between negative and experimental control groups over time, according to some aspects. Visualizing Common Statistically Significant Genes Largely Affected by All Viruses During OTE Condition Transitions

[0112] As discussed above, the present techniques may include quantifying common statistically significant genes during OTE condition transitions. In particular, the present techniques may include generating one or more visualizations that depict common statistically significant genes that are largely affected to all viruses as OTEs move between two conditions. The visualizations may be generated after constructing the Common-BH-MAS set, as described above. There may be five BH-significant genes that are common among all viruses at post-infection timepoint 24-hours, in some aspects (e.g., OAS1, EIF2AK2, IFIT1, OAS2 and MX1).

[0113] For example, FIG.8A depicts a log2-transformed heatmap of averages of these five genes, according to some aspects. FIG.8B depicts a log2-transformed heatmap of five BH-significant genes at time 72-hours, according to some aspects.Attorney Docket No.32103 / 59347A / PC Advantageously, by visually comparing FIG.8A and FIG.8B, the viewer is able to quickly and intuitively compare how a gene (e.g., IFIT1) is affected as OTEs move between UV and Non-UV infection conditions.

[0114] FIG.8C depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV-treated conditions over time, according to some aspects. FIG.8D depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV-treated conditions over time, according to some aspects. In particular, FIG.8C and 8D depict that IFIT1 can separate all OTE replicates in UV- treated conditions from Non-UV infected conditions at time 24-hours. FIG.8E depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV-treated conditions over time, according to some aspects. FIG.8F depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV- treated conditions over time, according to some aspects. FIG.8E and 8F depict that IFIT1 can separate all replicates in UV-treated conditions from Non-UV infected conditions at time 72-hours.

[0115] FIG.8G depicts an exemplary heatmap plot showing separation of replicates of particular genes transit UV-treated conditions over time, according to some aspects. In particular, the method 200 may be applied as OTEs move from 24 hours to 72 hours for all conditions and all viruses to determine that three common genes separate OTEs during the transition from 24 hour from 72 hour conditions. In particular, FIG.8G depicts that genes CCL20, IL6 and IL33 separate the OTEs in 24 hour to 72 hour conditions. Advantages and Improvements

[0116] The present MAS scoring and visualization capabilities represent significant improvements over conventional techniques. Specifically, the ability to rapidly compare how organ tissue equivalent genes are affected as they move between disease conditions has profound implications for disease understanding, diagnosis, treatment, and research. This empowers scientists, clinicians, and healthcare providers with actionable insights that may lead to improved patient care, enhanced treatment strategies, and accelerated advancements in the field of biomedicine, biomedical research and healthcare.Attorney Docket No.32103 / 59347A / PC

[0117] Further specific improvements flowing from the present techniques include early disease detection and diagnosis, wherein rapid comparison of gene expression patterns across different disease conditions can aid in the early detection and diagnosis of diseases. Identifying unique gene signatures associated with specific diseases allows for more accurate and timely diagnosis, leading to early intervention and improved patient outcomes.

[0118] The present techniques also improve precision medicine capabilities, by helping clinicians and researchers to understand how OTE genes respond differently to various disease conditions. This may enable the development of personalized treatment approaches. By tailoring treatments based on an individual's genetic makeup, healthcare providers may increase treatment efficacy and reduce potential side effects.

[0119] The present techniques may improve drug discovery and development methods and systems. For example, rapid gene expression comparisons between disease conditions may contribute to drug discovery and development by helping researchers to identify potential drug targets and pathways that are specific to certain diseases, facilitating the design of targeted therapies.

[0120] Still further, comparing gene expression profiles may improve biomarker identification (specific genes or gene patterns that are indicative of certain diseases), an essential task for disease prognosis, monitoring of treatment response, and evaluation of disease progression.

[0121] Furthermore, many additional advantages are envisioned. For example, examining how OTE genes behave across different disease conditions may provide advantageous insights into the underlying mechanisms of diseases that is fundamental for unraveling disease pathogenesis and developing effective interventions. The ability to quickly compare gene expressions using the present techniques may also streamline research processes. For example, researchers may rapidly explore large datasets and identify significant gene changes associated with different disease states, accelerating the pace of scientific discovery. The ability to swiftly compare gene expression in different disease conditions may generate data-driven insights that can guide research hypotheses, inform experimental designs, and direct further investigations. MonitoringAttorney Docket No.32103 / 59347A / PC changes in gene expression during the course of treatment may advantageously help assess treatment effectiveness. It enables clinicians to make informed decisions about treatment adjustments based on real-time molecular responses. Rapid gene expression comparisons may contribute to understanding the spread and impact of diseases at a population level. Identifying gene expression patterns associated with disease outbreaks can aid in epidemiological studies and public health interventions. Bridging the gap between basic research and clinical applications is facilitated by quick gene expression comparisons, such that insights gained from laboratory studies can be more efficiently translated into clinical practice. Healthcare providers can use gene expression information to develop individualized treatment plans, monitor disease progression, and optimize patient management strategies.

[0122] A concrete example of developing learnings to further diagnostic techniques follows. As discussed, the present techniques may be used to analyze gene expression patterns respiratory viruses (e.g., IAV, MPV, and PIV3) in infected lung-tissue- equivalents using a statistical techniques. PCA visualization and a scoring were used to quantify the magnitude and significance of gene expression changes. In empirical testing, IAV had the most significant impact on gene expression compared to the other two viruses. PIV3 had significantly affected genes between 24-72 hours post-infection, while MPV was the least intense virus during the first 72 hours of infection. The findings provide insights into the mechanisms of viral infections and could aid in the development of effective treatments for respiratory diseases caused by these viruses.

[0123] In this example, several genes that are significantly affected by infections of IAV, MPV and PIV3 may be identified during different post-infection time frames. Analyzing these results may show that IFNL1 and CCL20 are the most affected genes during the first 24 hours of IAV post-infection. For MPV, IFIT1, IFIT2, MX1, OAS2, and IFI44 are the most affected genes during the first 24 hours of post-infection. Finally, for PIV3, IFIT1, MX1, OAS2, and OAS1 are the most affected genes during the first 24 hours after infection.

[0124] Using the present techniques, empirical testing also found that that several genes such as CCL5, CXCL10, CXCL11, and TNFSF13B show significant changes during post-infection time points 24- and 72-hours for IAV. Similarly, IL33, CCL20,Attorney Docket No.32103 / 59347A / PC CXCL6, TNFSF18, and CXCL8 show significant changes for MPV during post-infection time points 24- and 72-hours. For PIV3, IFIT3, IFIT2, IFIT1, HERC5, IFNL1, OAS1, OAS3, and ZBP1 are genes that significantly change within the post-infection timeframe between 24- and 72-hours. These findings may advantageously contribute to a better understanding of the host-virus interaction and aid in the development of antiviral therapies.

[0125] Further, processing that identified several unique genes that are significantly activated or changed by IAV and PIV3 infections during specific time points may serve as potential biomarkers or fingerprints for detecting and characterizing these infections. Specifically, IFNB1, OASL, IFNL2 / 3, and ISG15 are uniquely and significantly activated by IAV during the first 24 hours of infection, while TNF, PECAM1, and VWF are unique genes that change significantly between 24- and 72-hours after IAV infection. Additionally, IL31 is a unique gene that changes significantly during the first 24 hours of PIV3 post-infection, and IRF9 is a gene that significantly changes between PIV3 post- infection time points 24- and 72-hours. These findings provide valuable insights into the molecular mechanisms of viral infections and can potentially lead to the development of more effective diagnostic and therapeutic approaches.

[0126] Using the present techniques, five genes were identified, namely OAS1, EIF2AK2, IFIT1, OAS2, and MX1, as common fingerprints for identifying respiratory viral infections caused by IAV, MPV, and PIV3. Additionally, three genes, CCL20, IL6, and IL33, were identified as biomarkers for monitoring the progression of the disease between post-infection time points 24- and 72-hours. These findings advantageously may help develop diagnostic tools for quick and accurate identification of viral infections and track the severity of respiratory viral infections to provide timely and appropriate treatment. These specific findings may have significant implications for reducing the impact of viral infections on human health.

[0127] The various embodiments described above can be combined to provide further embodiments. All U.S. patents, U.S. patent application publications, U.S. patent application, foreign patents, foreign patent application and non-patent publications referred to in this specification and / or listed in the Application Data Sheet are incorporated herein by reference, in their entirety. Aspects of the embodiments can beAttorney Docket No.32103 / 59347A / PC modified if necessary to employ concepts of the various patents, applications, and publications to provide yet further embodiments.

[0128] These and other changes can be made to the embodiments in light of the above-detailed description. In general, in the following claims, the terms used should not be construed to limit the claims to the specific embodiments disclosed in the specification and the claims but should be construed to include all possible embodiments along with the full scope of equivalents to which such claims are entitled. Accordingly, the claims are not limited by the disclosure.

[0129] Aspects of the techniques described in the present disclosure may include any of the following aspects, either alone or in combination:

[0130] 1. A computer-implemented method for computing magnitude-altitude scores corresponding to genes expressing in various ways during one or more time periods during which at least one organ tissue equivalent transits from one or more conditions, the method comprising: (1) receiving, via one or more processors, statistical results including one or more p- values that (a) correspond to a transition of the organ tissue equivalent from an initial condition to a subsequent condition over a period of time, and (b) describe significance of gene expression differences from the initial condition to the subsequent condition; (2) generating, via one or more processors, adjusted statistical results by applying an adjustment procedure to the statistical results including one or more p-values; (3) generating, via one or more processors, a respective magnitude altitude score for one or more genes identified as significant in the statistical results, by computing ^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, for ^ = 1, 2, … , ^, where ^ is the number of rejected null hypotheses based on the adjustment procedure,wherein M and A are tunable hyper-parameters; and BH is Benjamini-Hochberg (BH) procedure; and FC is Fold Change, and, ^^^^is the BH adjusted p-value (4) displaying, via one or more processors, the respective magnitude-altitude score for the one or more genes on a display.

[0131] 2. The computer-implemented method of aspect 1, wherein the organ tissue equivalent is a lung organoid.

[0132] 3. The computer-implemented method of any of aspects 1-2, wherein the initial condition and / or the subsequent condition corresponds to infected, ultraviolet treated, non- ultraviolet treated, mock, or naive.

[0133] 4. The computer-implemented method of any of aspects 1-3, wherein the period of time corresponds to 24 hours or 72 hours.Attorney Docket No.32103 / 59347A / PC

[0134] 5. The computer-implemented method of any of aspects 1-4, wherein the organ tissue equivalent is infected by one or more pathogens.

[0135] 6. The computer-implemented method of aspect 5, wherein the one or more pathogens are respective viruses.

[0136] 7. The computer-implemented method of aspect 6, wherein the respective viruses include at least one of influenza A virus, human metapneumovirus, or parainfluenza virus type 3.

[0137] 8. The computer-implemented method of any of aspects 1-7, wherein the adjustment procedure is a Benjamini-Hochberg procedure.

[0138] 9. The computer-implemented method of any of aspects 1-8, further comprising: determining, via one or more processors, unique statistically significant genes affected by respective viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of aspect 1 and step 3 of aspect 1 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each of the respective viruses, generating a set of unique significant genes including respective differentially expressed genes.

[0139] 10. The computer-implemented method of any of aspects 1-9, further comprising: generating, via one or more processors, a visualization of the set of unique significant genes including respective differentially expressed genes.

[0140] 11. The computer-implemented method of any of aspects 1-8, further comprising: determining, via one or more processors, common statistically significant genes affected by all viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of aspect 1 and step 3 of aspect 1 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each respective virus, generating a set of common significant genes including respective differentially expressed genes.

[0141] 12. The computer-implemented method of aspect 11, further comprising: generating, via one or more processors, a visualization of the set of common significant genes including respective differentially expressed genes.

[0142] 13. The computer-implemented method of any of aspects 1-12, further comprising: generating, via one or more processors, a visualization of the adjusted statistical results.Attorney Docket No.32103 / 59347A / PC

[0143] 14. The computer-implemented method of any of aspects 1-14, further comprising: generating, via one or more processors, a visualization of the respective magnitude-altitude score for the one or more genes; and based on the visualization, tuning the hyperparameters M and A.

[0144] 15. A computer system for computing magnitude-altitude scores corresponding to genes expressing in various ways during one or more time periods during which at least one organ tissue equivalent transits from one or more conditions, comprising: one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computer system to: (1) receive statistical results including one or more p-values that (a) correspond to a transition of the organ tissue equivalent from an initial condition to a subsequent condition over a period of time, and (b) describe significance of gene expression differences from the initial condition to the subsequent condition; (2) generate adjusted statistical results by applying an adjustment procedure to the statistical results including the one or more p-values; (3) generate a respective magnitude-altitude score for one or more genes identified as significant in the statistical results, by computing ^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, for ^ = 1, 2, … , ^, where ^ is the number of rejected null hypotheses rejected based on the adjustmentand wherein M and A are tunable hyper-parameters; and BH is Benjamini-Hochberg (BH) procedure; and FC is Fold Change, and, ^^^^is the BH adjusted p-value (4) display the respective magnitude-altitude score for the or more genes on a display.

[0145] 16. The computer-system of aspect 15, wherein the organ tissue equivalent is infected by one or more viruses; and the memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computer system to: determine, via one or more processors, unique statistically significant genes affected by respective viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of aspect 15 and step 3 of aspect 15 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each of the respective viruses, generating a set of unique significant genes including respective differentially expressed genes.

[0146] 17. The computer-system of any of aspects 15-16, wherein the organ tissue equivalent is infected by one or more viruses; and the memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computer system to: determine, via one or more processors, common statistically significant genes affected by all viruses during organ tissue equivalent condition transitions, by: for each ofAttorney Docket No.32103 / 59347A / PC the respective viruses, repeating step 2 of aspect 15 and step 3 of aspect 15 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each respective virus, generating a set of common significant genes including respective differentially expressed genes.

[0147] 18. A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause a computer to: (1) receive statistical results including one or more p-values that (a) correspond to a transition of the organ tissue equivalent from an initial condition to a subsequent condition over a period of time, and (b) describe significance of gene expression differences from the initial condition to the subsequent condition; (2) generate adjusted statistical results by applying an adjustment procedure to the statistical results including the one or more p-values; (3) generate a respective magnitude- altitude score for one or more genes identified as significant in the statistical results, by computing ^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, for ^ = 1, 2, … , ^, where ^ is the number of rejected null procedure, and wherein M and A aretunable hyper-parameters; magnitude-altitude score for the one or more genes on a display.

[0148] 19. The non-transitory computer-readable medium of aspect 18, wherein the organ tissue equivalent is infected by one or more viruses, and having stored thereon instructions that, when executed by the one or more processors, cause a computer to: determine, via one or more processors, unique statistically significant genes affected by respective viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of aspect 15 and step 3 of aspect 15 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each of the respective viruses, generating a set of unique significant genes including respective differentially expressed genes.

[0149] 20. The non-transitory computer-readable medium of any of aspects 18-19, wherein the organ tissue equivalent is infected by one or more viruses, and having stored thereon instructions that, when executed by the one or more processors, cause a computer to: determine, via one or more processors, common statistically significant genes affected by all viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of aspect 15 and step 3 of aspect 15 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each respective virus, generating a set of common significant genes including respective differentially expressed genes.Attorney Docket No.32103 / 59347A / PC Additional Considerations

[0150] The following considerations also apply to the foregoing discussion. Each of the references cited herein is incorporated by reference herein in its entirety, or in relevant passage, as would be apparent from the context of its citation.

[0151] From the disclosure herein it will be appreciated that, although specific embodiments of the disclosure have been described herein for purposes of illustration, various modifications may be made without deviating from the spirit and scope of the disclosure.

[0152] Throughout this specification, plural instances may implement operations or structures described as a single instance. Although individual operations of one or more methods are illustrated and described as separate operations, one or more of the individual operations may be performed concurrently, and nothing requires that the operations be performed in the order illustrated. These and other variations, modifications, additions, and improvements fall within the scope of the subject matter herein.

[0153] It should also be understood that, unless a term is expressly defined in this patent using the sentence "As used herein, the term " " is hereby defined to mean ... " or a similar sentence, there is no intent to limit the meaning of that term, either expressly or by implication, beyond its plain or ordinary meaning, and such term should not be interpreted to be limited in scope based on any statement made in any section of this patent (other than the language of the claims). To the extent that any term recited in the claims at the end of this patent is referred to in this patent in a manner consistent with a single meaning, that is done for sake of clarity only so as to not confuse the reader, and it is not intended that such claim term be limited, by implication or otherwise, to that single meaning. Finally, unless a claim element is defined by reciting the word "means" and a function without the recital of any structure, it is not intended that the scope of any claim element be interpreted based on the application of 35 U.S.C. § 112(f).

[0154] Unless specifically stated otherwise, discussions herein using words such as "processing," "computing," "calculating," "determining," "presenting," "displaying," or the like may refer to actions or processes of a machine (e.g., a computer) that manipulatesAttorney Docket No.32103 / 59347A / PC or transforms data represented as physical (e.g., electronic, magnetic, or optical) quantities within one or more memories (e.g., volatile memory, non-volatile memory, or a combination thereof), registers, or other machine components that receive, store, transmit, or display information.

[0155] As used herein any reference to "one aspect" or "an aspect" means that a particular element, feature, structure, or characteristic described in connection with the aspect is included in at least one aspect. The appearances of the phrase "in one aspect" in various places in the specification are not necessarily all referring to the same aspect.

[0156] As used herein, the terms "comprises," "comprising," "includes," "including," "has," "having" or any other variation thereof, are intended to cover a non-exclusive inclusion. For example, a process, method, article, or apparatus that comprises a list of elements is not necessarily limited to only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Further, unless expressly stated to the contrary, "or" refers to an inclusive or and not to an exclusive or. For example, a condition A or B is satisfied by any one of the following: A is true (or present) and B is false (or not present), A is false (or not present) and B is true (or present), and both A and B are true (or present).

[0157] In addition, use of "a" or "an" is employed to describe elements and components of the aspects herein. This is done merely for convenience and to give a general sense of the invention. This description should be read to include one or at least one and the singular also includes the plural unless it is obvious that it is meant otherwise.

[0158] Upon reading this disclosure, those of skill in the art will appreciate still additional alternative structural and functional designs for implementing the concepts disclosed herein, through the principles disclosed herein. Thus, while particular aspects and applications have been illustrated and described, it is to be understood that the disclosed aspects are not limited to the precise construction and components disclosed herein. Various modifications, changes and variations, which will be apparent to those skilled in the art, may be made in the arrangement, operation and details of the methodAttorney Docket No.32103 / 59347A / PC and apparatus disclosed herein without departing from the spirit and scope defined in the appended claims.

Claims

Attorney Docket No.32103 / 59347A / PC WHAT IS CLAIMED:

1. A computer-implemented method for computing magnitude-altitude scores corresponding to genes expressing in various ways during one or more time periods during which at least one organ tissue equivalent transits from one or more conditions, the method comprising: (1) receiving, via one or more processors, statistical results including one or more p-values that (a) correspond to a transition of the organ tissue equivalent from an initial condition to a subsequent condition over a period of time, and (b) describe significance of gene expression differences from the initial condition to the subsequent condition; (2) generating, via one or more processors, adjusted statistical results by applying an adjustment procedure to the statistical results including one or more p- values; (3) generating, via one or more processors, a respective magnitude-altitude score for one or more genes identified as significant in the statistical results, by computing ^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, for ^ = 1, 2,null hypotheses based on the adjustmentand wherein M and A are tunable hyper-parameters; and BH is Benjamini-Hochberg (BH) procedure; and FC is Fold Change, and, ^^^^is the BH adjusted p-value (4) displaying, via one or more processors, the respective magnitude-altitude score for the one or more genes on a display.

2. The computer-implemented method of claim 1, wherein the organ tissue equivalent is a lung organoid.Attorney Docket No.32103 / 59347A / PC 3. The computer-implemented method of any of claims 1-2, wherein the initial condition and / or the subsequent condition corresponds to infected, ultraviolet treated, non-ultraviolet treated, mock, or naive.

4. The computer-implemented method of any of claims 1-3, wherein the period of time corresponds to 24 hours or 72 hours.

5. The computer-implemented method of any of claims 1-4, wherein the organ tissue equivalent is infected by one or more pathogens.

6. The computer-implemented method of claim 5, wherein the one or more pathogens are respective viruses.

7. The computer-implemented method of claim 6, wherein the respective viruses include at least one of influenza A virus, human metapneumovirus, or parainfluenza virus type 3.

8. The computer-implemented method of any of claims 1-7, wherein the adjustment procedure is a Benjamini-Hochberg procedure.

9. The computer-implemented method of any of claims 1-8, further comprising: determining, via one or more processors, unique statistically significant genes affected by respective viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of claim 1 and step 3 of claim 1 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; andAttorney Docket No.32103 / 59347A / PC for each of the respective viruses, generating a set of unique significant genes including respective differentially expressed genes.

10. The computer-implemented method of any of claims 1-9, further comprising: generating, via one or more processors, a visualization of the set of unique significant genes including respective differentially expressed genes.

11. The computer-implemented method of any of claims 1-8, further comprising: determining, via one or more processors, common statistically significant genes affected by all viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of claim 1 and step 3 of claim 1 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each respective virus, generating a set of common significant genes including respective differentially expressed genes.

12. The computer-implemented method of claim 11, further comprising: generating, via one or more processors, a visualization of the set of common significant genes including respective differentially expressed genes.

13. The computer-implemented method of any of claims 1-12, further comprising: generating, via one or more processors, a visualization of the adjusted statistical results.Attorney Docket No.32103 / 59347A / PC 14. The computer-implemented method of any of claims 1-14, further comprising: generating, via one or more processors, a visualization of the respective magnitude-altitude score for the one or more genes; and based on the visualization, tuning the hyperparameters M and A.

15. A computer system for computing magnitude-altitude scores corresponding to genes expressing in various ways during one or more time periods during which at least one organ tissue equivalent transits from one or more conditions, comprising: one or more processors, and one or more memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computer system to: (1) receive statistical results including one or more p-values that (a) correspond to a transition of the organ tissue equivalent from an initial condition to a subsequent condition over a period of time, and (b) describe significance of gene expression differences from the initial condition to the subsequent condition; (2) generate adjusted statistical results by applying an adjustment procedure to the statistical results including the one or more p-values; (3) generate a respective magnitude-altitude score for one or more genes identified as significant in the statistical results, by computing ^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, for ^ = 1, 2, …, null hypotheses rejected based onadjustment procedure, and wherein M and A are tunable hyper-parameters; and BH is Benjamini-Hochberg (BH) procedure; and FC is Fold Change, and, ^^^^is the BH adjusted p-valueAttorney Docket No.32103 / 59347A / PC (4) display the respective magnitude-altitude score for the one or more genes on a display.

16. The computer-system of claim 15, wherein the organ tissue equivalent is infected by one or more viruses; and the memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computer system to: determine, via one or more processors, unique statistically significant genes affected by respective viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of claim 15 and step 3 of claim 15 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each of the respective viruses, generating a set of unique significant genes including respective differentially expressed genes.

17. The computer-system of any of claims 15-16, wherein the organ tissue equivalent is infected by one or more viruses; and the memories having stored thereon computer-executable instructions that, when executed by the one or more processors, cause the computer system to: determine, via one or more processors, common statistically significant genes affected by all viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of claim 15 and step 3 of claim 15 to generate a significance score set that includesAttorney Docket No.32103 / 59347A / PC respective significant genes and respective ranked magnitude-altitude scores; and for each respective virus, generating a set of common significant genes including respective differentially expressed genes.

18. A non-transitory computer-readable medium having stored thereon instructions that, when executed by one or more processors, cause a computer to: (1) receive statistical results including one or more p-values that (a) correspond to a transition of the organ tissue equivalent from an initial condition to a subsequent condition over a period of time, and (b) describe significance of gene expression differences from the initial condition to the subsequent condition; (2) generate adjusted statistical results by applying an adjustment procedure to the statistical results including the one or more p-values; (3) generate a respective magnitude-altitude score for one or more genes identified as significant in the statistical results, by computing ^^^^= | ^^^^(FC)^|^|^^^^^(^^^^)|^, for ^ = 1, 2, … , ^, where ^ is the number of rejected null hypotheses rejected based onadjustment procedure, and wherein M and A are tunable hyper-parameters; and (4) display the respective magnitude-altitude score for the one or more genes on a display.

19. The non-transitory computer-readable medium of claim 18, wherein the organ tissue equivalent is infected by one or more viruses, and having stored thereon instructions that, when executed by the one or more processors, cause a computer to: determine, via one or more processors, unique statistically significant genes affected by respective viruses during organ tissue equivalent condition transitions, by:Attorney Docket No.32103 / 59347A / PC for each of the respective viruses, repeating step 2 of claim 15 and step 3 of claim 15 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each of the respective viruses, generating a set of unique significant genes including respective differentially expressed genes.

20. The non-transitory computer-readable medium of any of claims 18-19, wherein the organ tissue equivalent is infected by one or more viruses, and having stored thereon instructions that, when executed by the one or more processors, cause a computer to: determine, via one or more processors, common statistically significant genes affected by all viruses during organ tissue equivalent condition transitions, by: for each of the respective viruses, repeating step 2 of claim 15 and step 3 of claim 15 to generate a significance score set that includes respective significant genes and respective ranked magnitude-altitude scores; and for each respective virus, generating a set of common significant genes including respective differentially expressed genes.

Images