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      "body": "@@ -420,16 +420,18 @@\n lse posi\n+ti\n ve%EB%8A%94 5%25 %EB%AF%B8\n@@ -520,13 +520,8 @@\n %EC%9E%85%EB%8B%88%EB%8B%A4.\n- #%EC%9E%AC%ED%98%84%EC%84%B1\n %0A%0A%EC%9D%B4 \n",
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      "body": "대학원에 입학해서 많은 경우 선배나 포닥이 하는 실험 중 일부를 받아다가 그게 뭐하는 건지도 모르고 실험 방법만을 배워서 진행하곤 한다. 하지만 그러면 랩미팅 시간에 대뜸 '그래서 그 실험은 뭣땜에 and/or 뭘 보려고 한건데?'라는 질문에 대답 못하는 경우가 생긴다.\n\n가장 우선적으로, '내가 이렇게 실험을 많이 했다' 하는 것을 보여주기 이전에, 그 많은 실험들을 왜 하는가에 대한 고찰이 필요하리라고 생각한다. 예를 들어서 다음과 같은 사고 과정을 단계 단계별로 진행해 보면 될 것이다. 다만 이러한 과정이 절대적인 것은 아니며, 나름의 과학적 방법론에 입각해서 체계적으로 설득력 있는 가설-검증 과정을 수행할 수 있을 경우 다른 방법을 따르는 것도 좋다.\n\n**introduction**\n(1) 내가 어떠한 현상에 관심을 갖고 있으며, (what is my interest)\n(2) 그 중 알려진 부분은 어떤 것이고, (what is known)\n(3) 아직 알려져 있지 않은 (내가 궁금한) 점은 어떤 것인지, (what is unknown & where do I focus)\n\n**strategy**\n(4) 알려진 부분으로 미루어 보아 그 궁금한 부분은 무엇 때문일 것이다 하는 나의 가설 (setting up my hypothesis)\n(5) 그 궁금한 점을 해결하기 위해서는 어떠한 실험들이 필요한지 (experiments needed)\n[부가적] (5-1) 실험이 예상대로 나올 경우의 해석 (my interpretation with expected results)\n[부가적] (5-2) 실험이 예상대로 나오지 않을 경우의 대안적 해석 (my (alternative) interpretation with unexpected results)\n\n**results**\n(6) (비슷한 얘기지만) 지금 보여주는 특정 실험은 무엇을 알아보기 위해 하는 것인지, (What do I want to see with this experiment)\n(6-1) (부가적으로) 실험 방법에 대한 간단한 소개 (short introduction for the method)\n(7) 결과는 어떻게 나왔는지, (what's the result)\n(8) for results if you have more then goto (6) else goto (9)\n\n**discussion**\n(9) 이 실험들이 내 가설의 전체 맥락에서 어느 부분을 설명하는지,(what do these experiments explains about my hypothesis) 그리고 내가 알아보고자 하는 점(가설)을 이 실험 결과가 충분히 설명했는지 여부 (if this experiment is enough to explain my hypothesis)\n(10) 추가&반복실험 필요 여부 및 진행상황 (further studies (if needed) & current progress)",
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      "title": "연구 주제에 따른 연구 방법 설계하기"
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      "body": "다만 현실적으로는 생물학에서 대규모 스크리닝 등의 *비싼* 실험을 할 경우는 대개 2-3회 반복으로 그치는 경우가 많습니다.",
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      "body": "실험을 할 때 biological replication, 즉 \"새로 sample prep 해서 실험한 다음 결과 측정하기\" 전과정을 몇 번 반복해야 실제로 통계적 유의성을 보여주는 결과를 얻을 수 있을까요? 특히 RNA-sequencing에 대한 실험을 수행할 경우, 최소 몇 번을 반복해야 reproducibility issue에 걸리지 않고서 과학적으로 의미있는 결과를 얻는 것이 가능할까요?\n\n이에 대한 실험을 영국의 University of Dundee 에서 총 48회의 RNA-sequencing 반복 실험과 총 11가지의 분석 방법을 통해 확인해 보았는데요, 결론부터 말씀드리자면 최소한 20회 이상의 biological replication을 해야 최소 85% 이상의 모집단 일치율을 보여줄 수 있다고 합니다. 그리고 모든 경우에서 false posive는 5% 미만으로 나왔습니다.\n\n그리고 통상적으로 많이들 하는 3회 반복 실험의 경우 일치율이 20-40%에 불과하며, 따라서 과학적으로 믿을 수 없는 데이터입니다. #재현성\n\n이 논문에서 권장하기로는, \n\n> (1) 모든 실험에서 조건당 최소 6회 반복, \n(2) Significant Differential Expression을 보이는 gene들이 대부분 중요한 gene들일 경우는 조건당 최소 12회 반복, \n(3) 반복 횟수 12회 미만일 경우에는 edgeR(exact) 또는 DESeq2 분석툴 권장, \n(4) 12회 이상 반복할 경우 DESeq 분석툴 권장, \n(5) True positive와 False negative를 구분하기 위한 fold-change threshold는 0.1≤T≤0.5 사이\n\n...를 권장합니다.\n\n**참고:**\n[Schurch, N.J., Schofield, P., Gierliński, M., Cole, C., Sherstnev, A., Singh, V., Wrobel, N., Gharbi, K., Simpson, G.G., Owen-Hughes, T., et al. (2016). How many biological replicates are needed in an RNA-seq experiment and which differential expression tool should you use? RNA 22, 839–851.](https://www.ncbi.nlm.nih.gov/pmc/articles/pmid/27022035/)",
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      "body": "원문: https://byuldbyul.blogspot.kr/2018/04/n.html\n<center>\n![](https://steemitimages.com/DQmWmHEoL2kxtByxz3v3bdcWjXQ6GBHxKhEWnVjth6fkCJL/image.png)\n(그림출처: xkcd)\n</center>\n엊그제 학교 전산실에서 \"통계 표본크기 산출\" 주제의 세미나를 듣고 왔다.\n\n```\n???: \"네이처 개XX들아! high IF journal에 \n논문 섭미션하면 리뷰오고 리뷰오면 p-value만 들이대고,\n그러면 통계로 까이고 리젝먹고. \n그러면서도 리뷰어에게 허벌나게 치욕적 리뷰받고 \n낮은 IF 저널에 냅니다. 온코타겟을 살\"\n```\n(...)\n\n결국 관건은, 통계 검정력을 위한 표본 숫자를 정확하게 계산해서 제시해야 논문 피어 리뷰어도 납득할거라는 얘기인듯 하다. 최근 들어 의학 및 심리학 논문들에서 재현성 위기 (https://en.wikipedia.org/wiki/Replication_crisis) 때문에 엄청 난리였는데, 잘못된 통계 검증 때문에 연구 자체의 유의성이 잘못 설정되어 실제로 그 연구들을 다시 수행해 보았을 경우 재현성이 나오지 않는 경우가 엄청 많았다고 한다. 특히 생물학의 경우 조사 대상의 약 80% 가까이 다른 논문의 방법을 따라했을 경우 결과 재현에 실패한 경험이 있고, 약 60%의 연구자는 심지어 본인의 실험 재현에도 실패한 적이 있다고 하더라(...).\n\n\n그 이유로 생각해볼 만한 것이, 통계적 연관성이 인과성을 대표하지 않기 때문에 외부 조건에 따라 다른 결과가 나올 경우도 있겠지만, 많은 경우 통계적 계산을 잘못 적용하거나 너무 약한 통계검정을 사용하여 잘못된 통계치를 근거로 유의성을 주장했기 때문인 것으로 보인다.\n\n\n그래서 요즘같은 경우에는 p-value (귀무가설이 참일 경우에도 이를 기각하는 결과가 나타날 확률) 만으로는 부족하고, 통계적 검정력 및 그에 입각한 표본 갯수 설정 과정도 요구하는 저널이 많아지고 있다. 앞으로 이에 대해 많이 공부해야 할듯 하다.\n\n![](https://steemitimages.com/DQmNnppXNBnWPCmHn4gqtHnoNrayMR5HNsRxRtNFbQLyJCb/image.png)\n<center>(요즘 네이처 저널에서는 이런거 써서 내라고 한다. ㄷㄷㄷ)</center>\n\n```\n[Sample size 결정을 위한 effect size 결정]\nCohen's _d_ : 표준화된 효과크기=실제 효과크기/표준편차\n＜0.4: 작은 효과크기\n0.4-0.7: 중간 효과크기\n＞0.7: 큰 효과크기\n```\n\n\n여기서 G-Power 라는 프로그램이 실험군당 표본 수 결정을 계산하는 데 도움이 된다고 한다. (https://en.wikipedia.org/wiki/G*Power)\n\n\n참고:\nhttps://cs.kaist.ac.kr/colloquium/files/20161031.pdf",
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