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Hyunmin Kim edited this page Sep 14, 2021 · 49 revisions

Never met success? Maybe it hasn't come yet.

"because works produced during hot streaks garner substantially more impact, the uncovered hot streaks fundamentally drive the collective impact of an individual, and ignoring this leads us to systematically overestimate or underestimate the future impact of a career" https://www.nature.com/articles/s41586-018-0315-8

"a particular sequence of exploration followed by exploitation, where the transition from exploration to exploitation closely traces the onset of a hot streak." https://arxiv.org/abs/2103.01256

Don't forget once you collect it

Collected keywords and unrelated sentences can make topics and concepts. Topics are made of words, or sentences of your own opinion or cited by references. Out of topics you may begin to write projects. Project names start with pj- prefix. The contents of a project include your proposals in a formal structure. For example, the following structure is also acceptable:

  • introduction & background study
  • problems and solutions, methods
  • results, (negative) findings
  • conclusion Please make it clean and well organized for readers. The Markup language provides useful citation methods: Example "Harmony, LIGER, and Seurat 3 are the recommended methods for batch integration. Due to its significantly shorter runtime, Harmony is recommended as the first method to try, with the other methods as viable alternatives" 31948481.

Literatures not yet reviewed. Please move this to some topics

Literatures

hmgene will reallocate the following literatures to where it should be soon.

keyword description links
sc-basic nice perspective review https://www.nature.com/articles/s41592-019-0698-y
sc-clustering metacell algorithm PMC6790056
single-cell technologies, immunotherapy Applying high-dimensional single-cell technologies to the analysis of cancer immunotherapy PMID: 33277626
T-cell definition reviewed T-cell markers PMID: 30515169
RCC immunotheraphy review RCC-specific immune cycles, TIM-3+ exhausted CD8+T (T-0), partially exhausted CD8+T (T-1) and PD-1+CD4+T (T-18),CD39–CD73–adenosine (detect extracelullar ATP), cGAS/STING (detect cytosolic DNA) natureRev2020
cell-identification benchmark Classification performance depends on dataset complexity. Prior knowledge is limitedly improve the performance. This benchmark provides R and snakemake workflow for 22 classification methods. SVM_rejection is the winner, but I like scPred because it corrects batch using harmony. Zheng 68K dataset contains 11 immune cell populations which are harder to differentiate, particularly the T cell compartment (6 out of 11 cell populations article, github
clustering They compared the proposed method with five other existing methods: RaceID, SNN-Cliq, SINCERA, SEURAT, and SC3. The results show that the proposed method performs better than the existing methods. Avoid this approach because better performance depends on your questions DBSCAN
cell-identification immune T-cell subtypes of Tumor vs viral-infection. Pseudo-bulk links celltypes and clusters article
cell-identification immune extract cell markers article

Interesting topics

issues description link
deconvolution single-cell benchmarking review, short time and memory MuSiC natcomm2020
sc-micro, immunesuppressive inosine pathway single-cell microbiome and immunotherapy, inosine pathway science2020
celltype identification CellAlign link
diff trajectory tradeSeq codes
trajectory slingshot ggplot_example test_example, run_example
single-cell analysis workshop scone, a cell filter link
trajectory comparison codes and examples link
celltype identification https://github.com/rnabioco/clustifyR, pubmed
celltype identification SCCAF
celltype identification celltype trainer/predictor scPred
celltype identification TCR analyzer scirpy
T-cell clonal expansion Peripheral T cell expansion predicts tumour infiltration and clinical response link
clustering benchmark link
celltype db CellMarker db CellMarker
interactive clustering visualization Tune Seurat Cluster resolution overlaying with known markers link

Useless links

  1. immune cell type introduction :https://www.immunology.org/public-information/bitesized-immunology
  2. logical fallacies : cell heterogeneity complicates identification https://www.logicalfallacies.org/?fbclid=IwAR25EwirAf4imoPV7nYrVopaClzEKuuOv4BoaEjq4QSuoUY0WeS6Wp_7nFY
  3. code obfuscation : https://skyul.tistory.com/333?fbclid=IwAR2q-ox0uR51Q58700UddoOpf-uDTHqi_FGje7YgqxvGeMuvM2Ayn3dNNrQ
  4. need a single cell chat bot : https://zdnet.co.kr/view/?no=20210106171117&fbclid=IwAR1wZoJV286iaJxnUKjtp-bewmXFAz42PzmTDdDHde7_YrqycFYXWyQnVFI
  5. word2vec : https://jef.works/blog/2018/02/06/fun-with-word2vec/
  6. data model google plot like local panda : https://pypi.org/project/pandasgui/
  7. read what is github project https://github.com/features/project-management/
  8. immune cells in tomor vs viral https://www.sciencedirect.com/science/article/abs/pii/S1074761319304959
  9. this is organized in https://github.com/arc85
  10. joke an origin of C++ https://www-users.cs.york.ac.uk/susan/joke/cpp.htm?fbclid=IwAR1YrNHPKD1be5KBn95wEkpKqlktu7ELGh84W0dBU1gRhw5qQnvnSc4c5kw
  11. pair-programming with AI: https://www.youtube.com/watch?v=xnCgoEyz31M&ab_channel=NokiaBellLabs&fbclid=IwAR1kgMQuYkgt3byZQzBjBrVJ8A3OCXhzIZHBOLj8KijZaVOxmB_KGbFcBQE
  12. We collect pipelines in src directory directory related project in https://github.com/hmgene/mudcookies/projects/1
  13. How to share programs https://docs.github.com/en/free-pro-team@latest/github/managing-your-work-on-github/about-project-boards
  14. Seurat: https://github.com/satijalab/seurat
  15. Batch-correction bantchmarking : https://genomebiology.biomedcentral.com/articles/10.1186/s13059-019-1850-9 PMID 31948481
  16. Eleven grand challenges in single-cell data science: https://genomebiology.biomedcentral.com/articles/10.1186/s13059-020-1926-6
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