five

i4 -- raw regression output with regards to flight

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Figshare2023-08-18 更新2026-04-28 收录
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https://figshare.com/articles/dataset/i4_--_raw_regression_output_with_regards_to_flight/23992698
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Microbiome associations with spaceflight across classifiers, sequencing modalities, and microbiome features. Raw outputs are stored as .rds files. CSV files are the "categorized" (i.e., parsed) regression based on each feature's trajectory relative to flight (transiently increased, transiently decreased, persistently increased, persistently decreased, etc). Brief methods are as follows:We grouped microbial features associated with flight into six different categories. These were determined due to the fact that our model contained a categorical variable encoding a sample’s timing relative to flight: whether it was taken before, during, or afterwards. Since the modeling reference group was “MID-FLIGHT,” meaning that the interpretation of any coefficients would be directionally oriented relative to mid-flight microbial feature abundances. As a result, we were able to categorize features based on the jointly considered direction of association and significance for the “PRE-FLIGHT” and “POST-FLIGHT” levels of this variable. The below listed categories are all included in the association summaries provided in Supplementary Table 3.Transient increase in-flight – negative coefficient on the PRE-FLIGHT variable level, negative coefficient on the POST-FLIGHT variable, statistically significant for both Transient increase in-flight (low priority) – negative coefficient on the PRE-FLIGHT variable level, negative coefficient on the POST-FLIGHT variable, statistically significant for at least one of the twoTransient decrease in-flight – positive coefficient on the PRE-FLIGHT variable level, positive coefficient on the POST-FLIGHT variable level, statistically significant for both Transient decrease in-flight (low priority) – positive coefficient on the PRE-FLIGHT variable level, positive coefficient on the POST-FLIGHT variable level, statistically significant for at least one of the twoPotential persistent increase – negative coefficient on the PRE-FLIGHT variable level, positive coefficient on the POST-FLIGHT variable level, statistically significant for at least one of the twoPotential persistent decrease – positive coefficient on the PRE-FLIGHT variable level, negative coefficient on the POST-FLIGHT variable level, statistically significant for at least one of the two
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2023-08-18
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