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title: Ð²ÑÐ¿ÑÑÐºÐ½Ð°Ñ ÐºÐ²Ð°Ð»Ð¸ÑÐ¸ÐºÐ°ÑÐ¸Ð¾Ð½Ð½Ð°Ñ ÑÐ°Ð±Ð¾ÑÐ° Ð±Ð°ÐºÐ°Ð»Ð°Ð²ÑÐ°
titleType: Subtitle
title: Development of a method for determining gene expression from images of the brain of a fruit fly
Creators: name: Ð¢ÐµÐ¼Ð¸ÑÐ³Ð°Ð»Ð¸ÐµÐ², Ð Ð°ÑÐ°ÑÐ»Ñ
Publisher: name: Ð¡Ð°Ð½ÐºÑ-ÐÐµÑÐµÑÐ±ÑÑÐ³ÑÐºÐ¸Ð¹ Ð¿Ð¾Ð»Ð¸ÑÐµÑÐ½Ð¸ÑÐµÑÐºÐ¸Ð¹ ÑÐ½Ð¸Ð²ÐµÑÑÐ¸ÑÐµÑ ÐÐµÑÑÐ° ÐÐµÐ»Ð¸ÐºÐ¾Ð³Ð¾
Year: 2021
Subjects: Ð¾Ð±ÑÐ°Ð±Ð¾ÑÐºÐ° Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ð¹
ÑÐ´Ð°Ð»ÐµÐ½Ð¸Ðµ Ð°Ð²ÑÐ¾ÑÐ»ÑÐ¾ÑÐµÑÑÐµÐ½ÑÐ¸Ð¸
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ÑÑÑÑ Ð¼ÐµÑÐ½ÑÐµ Ð¸Ð·Ð¾Ð±ÑÐ°Ð¶ÐµÐ½Ð¸Ñ Ð¼Ð¾Ð·Ð³Ð°
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ÐºÐ»Ð°ÑÑÐµÑÐ¸Ð·Ð°ÑÐ¸Ñ ÑÐ¾ÑÐµÐº
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ÐºÐ»Ð°ÑÑÐµÑÑ ÑÐºÑÐ¿ÑÐµÑÑÐ¸ Ð³ÐµÐ½Ð¾Ð²
ÑÐ¸Ð»ÑÑÑÐ°ÑÐ¸Ñ Ð´Ð°Ð½Ð½ÑÑ
iimage processing
autofluorescence removal
glow point detection
3D brain images
border detection algorithm
drosophila fly
point clustering
ProStack package
gene expression clusters
data filtering

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description: This work is devoted to the development of an algorithm that makes it possible to isolate complexes of RNA molecules in experimental images and to use them for the analysis of gene expression patterns in the brain of a fruit fly. Gene expression clusters obtained from the isolated complexes from brain images are associated with the propensity for mating in flies of different sexes. Experimental images have channel overlaps that interfere with point detection. A modified algorithm for the detection and removal of autofluorescent objects was studied and applied with parameter adjustment. The algorithm has two main parts: removal of autofluorescence and areas of expansion. The first part is based on clustering the characteristics of kurtosis, standard deviation and Pearson's interchannel correlation coefficient for connected regions. The cluster with the highest average correlation coefficient is considered the cluster of autofluorescent objects. The second part expands the areas selected on the first by tracing the boundaries of the areas until the intensity value starts to grow. After detecting complexes of RNA molecules on images of the brain, quantitative data were obtained. Based on these data, using the clustering algorithm, expression clusters were constructed for individual breeds of the Drosophila fly. A comparative analysis of the clustering of quantitative expression data without and with removal of autofluorescence has been carried out. Comparisons were also made for each species of fly.