Pardon me if this doubt sounds too silly. It's just that I am having a hard time getting my head around the problem. These are the things I gathered from all the discussions:
\nFrom what I understand, the Green channel contains information about the Protein of interest. The Red channel contains information about the microtubule (i.e. label 10), the yellow channel contains information about the endoplasmic reticulum (label 6) and the blue channel contains information about the nucleus (not sure what labels it represents because labels 0-5 all are about nuclear organelles). And the overlap of the green channel with the other channels can give the exact organelle to which protein localizes to.
\nThe labels that do not correlate to Nucleus (blue), Microtubules (red), or Endoplasmic reticulum (yellow) do not have specific channels that indicate the particular organelle. Instead, you use the information from the red, blue, and yellow channels to find out where the protein (green) localizes to.
\nThis means that for example the label cytosol is recognizable from the fact that it overlaps really well with the boundaries of the cell but there is no specific channel that you can look at that overlaps with it.
\nWhen annotating manually, we use our knowledge of the structure of the cell and the blue, red, and yellow reference channels to tell where the protein is. Our hope is that the machine learning models will be able to learn how to use the green channel in conjunction with the reference channels to do this kind of annotation automatically.
", "rawMarkdown": "The labels that do not correlate to Nucleus (blue), Microtubules (red), or Endoplasmic reticulum (yellow) do not have specific channels that indicate the particular organelle. Instead, you use the information from the red, blue, and yellow channels to find out where the protein (green) localizes to.\n\nThis means that for example the label cytosol is recognizable from the fact that it overlaps really well with the boundaries of the cell but there is no specific channel that you can look at that overlaps with it.\n\nWhen annotating manually, we use our knowledge of the structure of the cell and the blue, red, and yellow reference channels to tell where the protein is. Our hope is that the machine learning models will be able to learn how to use the green channel in conjunction with the reference channels to do this kind of annotation automatically.", "votes": 1, "replies": [ { "id": 1231205, "postDate": "2021-03-08T18:39:54.837Z", "content": "So do you mean that the overlap organelle of the yellow and green channel in an image does not mean that the localized organelle is ER?
", "rawMarkdown": "So do you mean that the overlap organelle of the yellow and green channel in an image does not mean that the localized organelle is ER?" } ] }, { "id": 1229945, "postDate": "2021-03-07T17:51:35.297Z", "content": "Pardon me if this doubt sounds too silly. It's just that I am having a hard time getting my head around the problem. These are the things I gathered from all the discussions:
\nFrom what I understand, the Green channel contains information about the Protein of interest. The Red channel contains information about the microtubule (i.e. label 10), the yellow channel contains information about the endoplasmic reticulum (label 6) and the blue channel contains information about the nucleus (not sure what labels it represents because labels 0-5 all are about nuclear organelles). And the overlap of the green channel with the other channels can give the exact organelle to which protein localizes to.
\nHi Izzy,
\nI think your understanding is partially correct. Each image in the training and testing datasets has four channels, and some of those channels encode information relevant to some of the labels being predicted. For example, the red channel contains data about microtubules, the same subcellular organelle that label 10 signifies, as you observed. This may make it easier for the model to predict label 10: if there is a clear and consistent overlap of an image's green channel with its red channel, that would indicate that the protein is localized with the microtubules and hence the predicted labels for the cell ought to include 10 (and perhaps some other labels as well if the protein also overlaps other cellular regions distinct from the microtubules).
\nThe precise manner in which the RBY channels combine to provide information about the other labels is the challenge we're left with. There's no channel specifying where the cells' golgi apparatuses are, and yet our models need to combine information about the nuclei, the microtubules and the endoplasmic reticulum in order to predict label 7. Despite not having the locations of the golgi apparatuses (GA) explicitly, experts are nevertheless able to look at the same images we have and recognize that the protein's fluorescence patterns indicate they are localized with the GA…
\nIf you haven't yet seen it, you may find this notebook by @lnhtrang highly informative about the different organelles. Quoting from there:
\n\n\nThe Golgi apparatus is a rather large organelle that is located next to the nucleus, close to the centrosome, from which the microtubules in the red channel originate. It has a folded ribbon-like appearance, but the shape and size can vary between cell types, and in response to cellular various processes.
\n
With that in mind, if we were to see a ribbon pattern in the green channel that was located nearby and exterior to the nucleus (blue channel), and also noticed that the microtubules (red channel) have a terminus nearby, we might be able to infer that the protein is located in the GA. Further complicating things is the fact that the size and shape of the GA varies between cell types and may change dynamically even within a single cell over time--building an algorithm that can robustly detect if the protein is located with the GA will have to cope with this.
\nI hope that brings some clarity about the challenge!
", "rawMarkdown": "Hi Izzy,\n\nI think your understanding is partially correct. Each image in the training and testing datasets has four channels, and some of those channels encode information relevant to some of the labels being predicted. For example, the red channel contains data about microtubules, the same subcellular organelle that label 10 signifies, as you observed. This may make it easier for the model to predict label 10: if there is a clear and consistent overlap of an image's green channel with its red channel, that would indicate that the protein is localized with the microtubules and hence the predicted labels for the cell ought to include 10 (and perhaps some other labels as well if the protein also overlaps other cellular regions distinct from the microtubules).\n\nThe precise manner in which the RBY channels combine to provide information about the other labels is the challenge we're left with. There's no channel specifying where the cells' **golgi apparatuses** are, and yet our models need to combine information about the nuclei, the microtubules and the endoplasmic reticulum in order to predict **label 7**. Despite not having the locations of the golgi apparatuses (GA) explicitly, experts are nevertheless able to look at the same images we have and recognize that the protein's fluorescence patterns indicate they are localized with the GA...\n\nIf you haven't yet seen it, you may find [this notebook](https://www.kaggle.com/lnhtrang/single-cell-patterns/notebook) by @lnhtrang highly informative about the different organelles. Quoting from there:\n\n> The Golgi apparatus is a rather large organelle that is located next to the nucleus, close to the centrosome, from which the microtubules in the red channel originate. It has a folded ribbon-like appearance, but the shape and size can vary between cell types, and in response to cellular various processes.\n\nWith that in mind, if we were to see a ribbon pattern in the green channel that was located nearby and exterior to the nucleus (blue channel), and also noticed that the microtubules (red channel) have a terminus nearby, we might be able to infer that the protein is located in the GA. Further complicating things is the fact that the size and shape of the GA varies between cell types and may change dynamically even within a single cell over time--building an algorithm that can robustly detect if the protein is located with the GA will have to cope with this.\n\nI hope that brings some clarity about the challenge!", "votes": 2, "replies": [ { "id": 1231189, "postDate": "2021-03-08T18:24:20.270Z", "content": "Thanks, @milotoor, that was a pretty neat explanation. This definitely cleared a lot of my doubts!!
", "rawMarkdown": "Thanks, @milotoor, that was a pretty neat explanation. This definitely cleared a lot of my doubts!!" } ] }, { "id": 1231183, "postDate": "2021-03-08T18:21:39.027Z", "content": "Hello Izzy! I hope everything is going well with you.
\nFrom my point of view and from what I have read through the competition guidelines, every sample (image) is composed of 4 files, each one belonging to a specific filter on the subcellular protein patterns. The microtubules are represented by the red channel, nuclei by the blue channel, Endoplasmic Reticulum (ER) by the yellow channel, and the protein itself by the green channel.
\nThe aim of the competition is to predict the protein organelle localization for each cell in the image. Therefore, an image, which contains many cells, must be segmented and each individual cell classified according to the following labels:
\nNucleoplasm, Nuclear membrane, Nucleoli, Nucleoli fibrillar center, Nuclear speckles, Nuclear bodies, Endoplasmic reticulum, Golgi apparatus, Intermediate filaments, Actin filaments, Microtubules, Mitotic spindle, Centrosome, Plasma membrane, Mitochondria, Aggresome, Cytosol, Vesicles and punctate cytosolic patterns, Negative
\nFor example, let's take the sample 5c27f04c-bb99-11e8-b2b9-ac1f6b6435d0 into account. It is represented by three labels: 0, 5 and 8. That means the image contains cells whose protein organelles belongs to Nucleoplasm, Nuclear bodies and Intermediate filaments. Essentially, the green channel is what contains the organelle correct localization (you still have to predict which one is it), while the other channels serve as references and additional data that can assist you in correctly segmenting the cells within the image, as well as aiding in localizing the organelle.
The labels that do not correlate to Nucleus (blue), Microtubules (red), or Endoplasmic reticulum (yellow) do not have specific channels that indicate the particular organelle. Instead, you use the information from the red, blue, and yellow channels to find out where the protein (green) localizes to.
\nThis means that for example the label cytosol is recognizable from the fact that it overlaps really well with the boundaries of the cell but there is no specific channel that you can look at that overlaps with it.
\nWhen annotating manually, we use our knowledge of the structure of the cell and the blue, red, and yellow reference channels to tell where the protein is. Our hope is that the machine learning models will be able to learn how to use the green channel in conjunction with the reference channels to do this kind of annotation automatically.
", "votes": 1, "replies": [ { "id": 1231205, "author_name": "Izzy Adesanya", "author_url": "", "post_date": "2021-03-08T18:39:54.837000", "content": "So do you mean that the overlap organelle of the yellow and green channel in an image does not mean that the localized organelle is ER?
", "votes": 0, "replies": [] } ] }, { "id": 1230434, "author_name": "Milo Toor", "author_url": "", "post_date": "2021-03-08T06:11:11.600000", "content": "Hi Izzy,
\nI think your understanding is partially correct. Each image in the training and testing datasets has four channels, and some of those channels encode information relevant to some of the labels being predicted. For example, the red channel contains data about microtubules, the same subcellular organelle that label 10 signifies, as you observed. This may make it easier for the model to predict label 10: if there is a clear and consistent overlap of an image's green channel with its red channel, that would indicate that the protein is localized with the microtubules and hence the predicted labels for the cell ought to include 10 (and perhaps some other labels as well if the protein also overlaps other cellular regions distinct from the microtubules).
\nThe precise manner in which the RBY channels combine to provide information about the other labels is the challenge we're left with. There's no channel specifying where the cells' golgi apparatuses are, and yet our models need to combine information about the nuclei, the microtubules and the endoplasmic reticulum in order to predict label 7. Despite not having the locations of the golgi apparatuses (GA) explicitly, experts are nevertheless able to look at the same images we have and recognize that the protein's fluorescence patterns indicate they are localized with the GA…
\nIf you haven't yet seen it, you may find this notebook by @lnhtrang highly informative about the different organelles. Quoting from there:
\n\n\nThe Golgi apparatus is a rather large organelle that is located next to the nucleus, close to the centrosome, from which the microtubules in the red channel originate. It has a folded ribbon-like appearance, but the shape and size can vary between cell types, and in response to cellular various processes.
\n
With that in mind, if we were to see a ribbon pattern in the green channel that was located nearby and exterior to the nucleus (blue channel), and also noticed that the microtubules (red channel) have a terminus nearby, we might be able to infer that the protein is located in the GA. Further complicating things is the fact that the size and shape of the GA varies between cell types and may change dynamically even within a single cell over time--building an algorithm that can robustly detect if the protein is located with the GA will have to cope with this.
\nI hope that brings some clarity about the challenge!
", "votes": 2, "replies": [ { "id": 1231189, "author_name": "Izzy Adesanya", "author_url": "", "post_date": "2021-03-08T18:24:20.270000", "content": "Thanks, @milotoor, that was a pretty neat explanation. This definitely cleared a lot of my doubts!!
", "votes": 0, "replies": [] } ] }, { "id": 1231183, "author_name": "", "author_url": "", "post_date": "2021-03-08T18:21:39.027000", "content": "Hello Izzy! I hope everything is going well with you.
\nFrom my point of view and from what I have read through the competition guidelines, every sample (image) is composed of 4 files, each one belonging to a specific filter on the subcellular protein patterns. The microtubules are represented by the red channel, nuclei by the blue channel, Endoplasmic Reticulum (ER) by the yellow channel, and the protein itself by the green channel.
\nThe aim of the competition is to predict the protein organelle localization for each cell in the image. Therefore, an image, which contains many cells, must be segmented and each individual cell classified according to the following labels:
\nNucleoplasm, Nuclear membrane, Nucleoli, Nucleoli fibrillar center, Nuclear speckles, Nuclear bodies, Endoplasmic reticulum, Golgi apparatus, Intermediate filaments, Actin filaments, Microtubules, Mitotic spindle, Centrosome, Plasma membrane, Mitochondria, Aggresome, Cytosol, Vesicles and punctate cytosolic patterns, Negative
\nFor example, let's take the sample 5c27f04c-bb99-11e8-b2b9-ac1f6b6435d0 into account. It is represented by three labels: 0, 5 and 8. That means the image contains cells whose protein organelles belongs to Nucleoplasm, Nuclear bodies and Intermediate filaments. Essentially, the green channel is what contains the organelle correct localization (you still have to predict which one is it), while the other channels serve as references and additional data that can assist you in correctly segmenting the cells within the image, as well as aiding in localizing the organelle.