Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning
Deep learning for inferring gene relationships from single-cell expression data
Spatially resolved single-cell genomics and transcriptomics by imaging
Paper from Last HPA Competition: Analysis of the Human Protein Atlas Image Classification competition
Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning
Deep learning for inferring gene relationships from single-cell expression data
Spatially resolved single-cell genomics and transcriptomics by imaging
Paper from Last HPA Competition: Analysis of the Human Protein Atlas Image Classification competition
I created a few Kaggle notebooks covering the standard pipeline for scRNA-seq data analysis and did my best to describe the stuff going on.
\nI covered the following stuff:
I hope you will find this interesting.
\n", "rawMarkdown": "I created a few Kaggle notebooks covering the standard pipeline for scRNA-seq data analysis and did my best to describe the stuff going on. \nI covered the following stuff:\n- [Theory - Introduction to single-cell RNA-seq](https://www.kaggle.com/aayush9753/theory-introduction-to-single-cell-rna-seq?scriptVersionId=74733387)\n- [AnnData and Preprocessing spike-ins](https://www.kaggle.com/aayush9753/1-anndata-and-preprocessing-spike-ins)\n- [Quality Control in Single cell RNA-seq data](https://www.kaggle.com/aayush9753/2-quality-control-in-single-cell-rna-seq-data) \n- [Normalization & PCA](https://www.kaggle.com/aayush9753/3-normalization-pca-in-single-cell-rna-seq-data#Normalizing-gene-expression)\n- [Dimensionality reduction and Clustering](https://www.kaggle.com/aayush9753/4-dimensionality-reduction-and-clustering )\n- [Differential expression](https://www.kaggle.com/aayush9753/5-differential-expression-in-single-cell-rna-seq)\n\nI hope you will find this interesting.\n\n[My Github](https://github.com/aayush9753/Single-cell-RNA-seq-analysis-of-Tabula-Muris-data)", "votes": 1 }, { "id": 1173620, "postDate": "2021-01-28T02:30:56.417Z", "content": "Thank you for this. Upvoted : )
", "rawMarkdown": "Thank you for this. Upvoted : )" } ], "comments": [ { "id": 1535736, "author_name": "Aayush Sharma", "author_url": "", "post_date": "2021-10-06T06:39:38.073000", "content": "I created a few Kaggle notebooks covering the standard pipeline for scRNA-seq data analysis and did my best to describe the stuff going on.
\nI covered the following stuff:
I hope you will find this interesting.
\n", "votes": 1, "replies": [] }, { "id": 1173620, "author_name": "Rajeev Sharma", "author_url": "", "post_date": "2021-01-28T02:30:56.417000", "content": "Thank you for this. Upvoted : )
", "votes": 0, "replies": [] } ], "raw_markdown_by_id": { "1173178": "# Research Papers\n- [Predicting single-cell gene expression profiles of imaging flow cytometry data with machine learning](https://academic.oup.com/nar/article/48/20/11335/5943188)\n- [Deep learning for inferring gene relationships from single-cell expression data](https://www.pnas.org/content/116/52/27151)\n- [Spatially resolved single-cell genomics and transcriptomics by imaging](https://www.nature.com/articles/s41592-020-01037-8)\n\n- Paper from Last HPA Competition: [Analysis of the Human Protein Atlas Image Classification competition](https://www.nature.com/articles/s41592-019-0658-6)\n# Articles\n- [Deep Learning for Single Cell Biology](https://towardsdatascience.com/deep-learning-for-single-cell-biology-935d45064438)\n\n# Videos\n- [NeurIPS 2019 | Machine Learning Meets Single Cell Biology (Invited Talk)](https://www.youtube.com/watch?v=G_Rhp9LWDUM)\n\n# Tools\n- [SCANPY: large-scale single-cell gene expression data analysis](https://genomebiology.biomedcentral.com/articles/10.1186/s13059-017-1382-0?utm_source=linkresearcher&utm_medium=display&utm_content=article_highlight&utm_campaign=BSCN_1_JG02_CN_GBIO_20Years_AH_paid_display_LINKR)", "1535736": "I created a few Kaggle notebooks covering the standard pipeline for scRNA-seq data analysis and did my best to describe the stuff going on. \nI covered the following stuff:\n- [Theory - Introduction to single-cell RNA-seq](https://www.kaggle.com/aayush9753/theory-introduction-to-single-cell-rna-seq?scriptVersionId=74733387)\n- [AnnData and Preprocessing spike-ins](https://www.kaggle.com/aayush9753/1-anndata-and-preprocessing-spike-ins)\n- [Quality Control in Single cell RNA-seq data](https://www.kaggle.com/aayush9753/2-quality-control-in-single-cell-rna-seq-data) \n- [Normalization & PCA](https://www.kaggle.com/aayush9753/3-normalization-pca-in-single-cell-rna-seq-data#Normalizing-gene-expression)\n- [Dimensionality reduction and Clustering](https://www.kaggle.com/aayush9753/4-dimensionality-reduction-and-clustering )\n- [Differential expression](https://www.kaggle.com/aayush9753/5-differential-expression-in-single-cell-rna-seq)\n\nI hope you will find this interesting.\n\n[My Github](https://github.com/aayush9753/Single-cell-RNA-seq-analysis-of-Tabula-Muris-data)", "1173620": "Thank you for this. Upvoted : )" } }