اشترك بالحزمة الذهبية واحصل على وصول غير محدود شمرا أكاديميا
تسجيل مستخدم جديدتجريب للتفاوت الكمي في بيانات العدد التركيبي، مع تطبيقات لدراسات الميكروبيوم
Testing for differential abundance in compositional counts data, with application to microbiome studies
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تحديد الضرائب التي ترتبط بالخلايا البكتيرية الخاصة بنا مع الصفات المطلوبة هو مهم لتقدم العلوم والصحة. ومع ذلك، التحديد من المشكلة لأن الاتجاه المقاس لعدد الضرائب (بواسطة ترديد الأمبليكون) هو التركيبي، لذلك تحدث تغيير في كثافة ضريبة واحدة في البكتيريا تحديثا في عدد الترديد المسجل عبر جميع الضرائب. وتكون البيانات عادة ما تكون قليلة التوازن، مع عدد الصفرات الموجودة إما بسبب الاختلاف البيولوجي أو عمق الترديد الفني (الصفرات الفنية). للضرائب الكثافة المنخفضة، فإن الفرصة للصفرات الفنية غير محذوفة. نظرا لأن الطرق الموجودة المصممة لتحديد الكثافة المختلفة للبيانات التركيبية قد تتضمن عددا مرتفعا من الأشياء الخاطئة بسبب التعامل غير الصحيح مع الصفرات. نقدم طريقة جديدة غير باراميترية والتي توفر التأكيد الصحيح حتى عندما يكون نسبة الصفرات كبيرة. يستخدم طريقتنا مجموعة من الضرائب المرجعية التي لا تختلف في الكثافة، والتي يمكن تقديرها من البيانات أو من المعلومات الخارجية. نظرا لأننا نظهر أن الطريقة الجديدة هي مفيدة عبر المحاكاة، فضلا عن على ثلاث مجموعات بيانات مختلفة: دراسة مرض كرون، مشروع البكتيريا البشرية، وتجربة مع البكتيريا المحشوة.
Identifying which taxa in our microbiota are associated with traits of interest is important for advancing science and health. However, the identification is challenging because the measured vector of taxa counts (by amplicon sequencing) is compositional, so a change in the abundance of one taxon in the microbiota induces a change in the number of sequenced counts across all taxa. The data is typically sparse, with zero counts present either due to biological variance or limited sequencing depth (technical zeros). For low abundance taxa, the chance for technical zeros is non-negligible. We show that existing methods designed to identify differential abundance for compositional data may have an inflated number of false positives due to improper handling of the zero counts. We introduce a novel non-parametric approach which provides valid inference even when the fraction of zero counts is substantial. Our approach uses a set of reference taxa that are non-differentially abundant, which can be estimated from the data or from outside information. We show the usefulness of our approach via simulations, as well as on three different data sets: a Crohns disease study, the Human Microbiome Project, and an experiment with spiked-in bacteria.
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