Method and device for preparing data for identifying analytes
A method for preparing data for identifying analytes by coloring one or more analytes with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting multiple markers using a camera, which for each coloring round generates at least one image that includes multiple pixels and that may contain color information of one or more markers, and storing the images of the particular coloring rounds stored for evaluating the color information, wherein the color values determined in the individual coloring rounds are clustered, according to their intensity values, in local or global clusters with similar intensity values, and only the clustered data are stored.
1 . A method for preparing data for identifying analytes by coloring one or more analytes with markers in multiple coloring rounds, the markers in each case being specific for a certain set of analytes, detecting multiple markers using a camera, which for each coloring round generates at least one image that includes multiple pixels and that may contain color information of one or more markers, and storing the images of the particular coloring rounds for evaluating the color information,
wherein
the color values determined in the individual coloring rounds are clustered, according to their intensity values, in local or global clusters with similar intensity values, and only the clustered data are stored.
2 . The method according to claim 1 ,
wherein
after each coloring round, the intensity values are reclustered as an additional feature dimension, using the newly obtained color information.
3 . The method according to claim 1 ,
wherein
for every pixel, a cluster ID is stored that describes to which cluster the particular pixel belongs.
4 . The method according to claim 1 ,
wherein
the intensity value of each coloring round is stored for each cluster.
5 . The method according to claim 1 ,
wherein
a sliding statistical value, in particular an average value and/or a variance and/or a median and/or a central color value, is stored for each cluster.
6 . The method according to claim 1 ,
wherein
each image of a coloring round is clustered separately.
7 . The method according to claim 1 ,
wherein
the clustering is carried out using a partitioning, hierarchical, graph-theoretical, or optimizing cluster method.
8 . The method according to claim 1 ,
wherein
the clustering is carried out using a supervised or unsupervised cluster method.
9 . The method according to claim 1 ,
wherein
intensity values which deviate by a predetermined threshold value from a central intensity value of the particular cluster are stored separately in order to generate a new cluster as needed.
10 . The method according to claim 1 ,
wherein
local clusters are generated, one of the image features for the clustering being intensity values, and a further image feature for the clustering being the position of the particular pixels in the image.
11 . The method according to claim 1 ,
wherein
an image encompasses a two-dimensional depiction including multiple pixels as image points, or a three-dimensional depiction including multiple voxels as image points, at least one pixel of each image being assignable to each measuring point of a sample, wherein the images may include time information as an additional dimension.
12 . The method according to claim 1 ,
wherein
certain clusters are unambiguously assigned to a certain analyte, so that the analytes may be identified by reading out the clusters in question.
13 . The method according to claim 1 ,
wherein
for identifying the analytes, the series of intensity values that are stored for the individual clusters and quantized by the clustering are compared to the series of target intensity values that encode the particular analytes, the target intensity values preferably being quantized beforehand to the same value range as the clusters.
14 . The method according to claim 1 ,
wherein
the identification of the analytes based on the clusters is carried out using a processing model, this processing model preferably being a classification model.
15 . The method according to claim 1 ,
wherein
the clustering is carried out using a processing model.
16 . The method according to claim 15 ,
wherein
the processing model for the clustering is a segmentation model and in particular is a semantic segmentation model.
17 . The method according to claim 15 ,
wherein
additional context information that describes further properties of the sample and/or of the experiment and/or of the expected analytes is supplied as input data to the processing model, and in particular may include parameters for coloring the sample and/or the expected number of analytes, or also the expected ratio of the analytes contained in the sample, the quantization of the clustering being set based on this context information.
18 . The method according to claim 1 ,
wherein
the analytes are one of the following: proteins, polypeptides, or nucleic acid molecules, and the markers couple to the analytes via analyte-specific probes and include a dye molecule that is coupled to the marker.
19 . A method for training a machine learning system, using a processing model for carrying out a method according to claim 1 , comprising:
providing an annotated data set, and
optimizing an objective function by adapting the model parameters of the processing model, the objective function detecting a difference between a result output that is output by the processing model and a target output, wherein
the annotated data set includes at least one target signal series of a candidate data point as well as a target signal series of a background data point, and the processing model processes a partial signal series of the target signal series of the annotated data set as input, and based on an output of the processing model, a data point corresponding to the particular target signal series is assessed as a background data point or a candidate data point.
20 . An evaluation unit for evaluating images of multiple coloring rounds, and which in particular is designed as a machine learning system, including the means for carrying out the method according to claim 1 .
21 . An image processing system, including an evaluation unit according to preceding claim 20 , in particular including an image generation unit such as a microscope.
22 . A non-transitory computer program product that includes commands which, when the program is executed by a computer, prompt the computer to carry out the method according to claim 1 , the non-transitory computer program product being in particular a non-transitory computer-readable memory medium.
23 . A machine learning system that includes an evaluation unit, the evaluation unit including a processing model that has been trained according to the method according to claim 19 , in particular including an image generation unit such as a microscope.