当前位置： 首页 > news >正文

自己如何做网站关键词排名站内关键词自然排名优化

news 2025/8/8 17:29:28
自己如何做网站关键词排名,站内关键词自然排名优化,wordpress 代理,男女做羞羞羞的网站1写在前面 分享一下最近看到的2篇paper关于骨骼肌组织的细胞Marker&#xff0c;绝对的Atlas级好东西。&#x1f44d; 希望做单细胞的小伙伴觉得有用哦。&#x1f60f; 2常用marker&#xff08;一&#xff09; general_mrkrs <- c( MYH7, TNNT1, TNNT3, MYH1, MYH2, "C…

1写在前面

分享一下最近看到的2篇paper关于骨骼肌组织的细胞Marker，绝对的Atlas级好东西。👍

希望做单细胞的小伙伴觉得有用哦。😏

2常用marker（一）

general_mrkrs <- c(
  'MYH7', 'TNNT1', 'TNNT3', 'MYH1', 'MYH2', "CKM", "MB", # Myofibers
           'PAX7', 'DLK1', # MuSCs
           'PDGFRA', 'DCN', 'ANGPTL7', 'OSR2', 'NGFR', 'SLC22A3','ITGA6', # Fibroblasts
           'FMOD', 'TNMD' ,  'MKX', # Tenocytes
 'MPZ', 'MBP',  # Schwann cells
'CDH2', 'L1CAM', # SCG
'MSLN', 'ITLN1', # mesothelium
"ADIPOQ", "PLIN1", # adipocytes
'PTPRC', 'CD3D', 'IL7R', # T cells
'NKG7', 'PRF1', #NK cells
'CD79A', "TCL1A",  # B cells
'MZB1', 'JCHAIN', # B plasma 
"CD14", "FCGR3A",'S100A8', 'S100A12', # Mono
"CD163", "C1QA", # Macrop
"XCR1", "CLEC9A", # cDC1 "CADM1",
"CD1C", "CLEC10A", "CCR7", # cDC2
'LILRA4', 'IL3RA', "IRF7",  # pDC 
'FCGR3B', 'CSF3R', 'SORL1',  # Neutrophils
'EPX', 'PRG2', # Eosinophils 'CLC'
'TPSB2', 'MS4A2', # Mast cells
'PECAM1', 'HEY1','CLU', # art EC
'CA4', 'LPL', # capEC
'ACKR1', 'SELE', # venEC
'LYVE1', 'TFF3',  # lymphEC
'RGS5','ABCC9', # pericytes           
'MYH11', 'ACTA2', # SMC
'HBA1', #RBC
                  )

出自下面paper:👇

Human skeletal muscle aging atlas. Veronika R. Kedlian, Yaning Wang, Tianliang Liu, Xiaoping Chen, Liam Bolt, Catherine Tudor, Zhuojian Shen, Eirini S. Fasouli, Elena Prigmore, Vitalii Kleshchevnikov, Jan Patrick Pett, Tong Li, John E G Lawrence, Shani Perera, Martin Prete, Ni Huang, Qin Guo, Xinrui Zeng, Lu Yang, Krzysztof Polański, Nana-Jane Chipampe, Monika Dabrowska, Xiaobo Li, Omer Ali Bayraktar, Minal Patel, Natsuhiko Kumasaka, Krishnaa T. Mahbubani, Andy Peng Xiang, Kerstin B. Meyer, Kourosh Saeb-Parsy, Sarah A Teichmann & Hongbo Zhang 2024 Apr.

3常用marker（二）

Mural Cell Markers

#SMOOTH MUSCLE CELLS
FeaturePlot(df.harmony, features = "MYH11", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "ACTA2", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "TAGLN", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
#PERICYTES
FeaturePlot(df.harmony, features = "RGS5", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "CSPG4", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "PDGFRB", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Glial Cells Markers

FeaturePlot(df.harmony, features = "PROX1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MPZ", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "NCAM1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "CDH19", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "SOX10", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "PLP1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Adipocites Markers

FeaturePlot(df.harmony, features = "PLIN1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "ADIPOQ", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MMRN1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "CCL21", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Tenocytes Markers

FeaturePlot(df.harmony, features = "FMOD", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "TNMD", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL22A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "SCX", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "DLG2", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "FBN1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Endothelial Markers

#FeaturePlot(df.harmony, features = "PCDHA6", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

#ARTERIAL 
FeaturePlot(df.harmony, features = "FBLN5", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "DLL4", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "SEMA3G", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

#CAPILLARIES
FeaturePlot(df.harmony, features = "RGCC", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

#VENOUS
FeaturePlot(df.harmony, features = "EPHB4", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

Myonuclei Markers

FeaturePlot(df.harmony, features = "TTN", min.cutoff = "q9", order = T, cols = c("lightblue", "navy"), raster = FALSE)

#IMMATURE MYOCYTE
FeaturePlot(df.harmony, features = "MYMX", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MYOG", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

#REG MYONUCLEI
FeaturePlot(df.harmony, features = "FLNC", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MYH3", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "MYH8", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "XIRP1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

NMJ Myonuclei Markers (Neuromuscular junction)

#NMJ
FeaturePlot(df.harmony, features = "CHRNE", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "CHRNA1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "PRKAR1A", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL25A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "UTRN", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COLQ", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "ABLIM2", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "VAV3", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "UFSP1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

MTJ Myonuclei Markers (Myotendinous junction)

#MTJ
FeaturePlot(df.harmony, features = "COL22A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "PIEZO2", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL24A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL6A1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "FSTL1", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "COL6A3", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)
FeaturePlot(df.harmony, features = "TIGD4", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

FeaturePlot(mini_df.harmony.harmony, features = "EYS", min.cutoff = "q9", order = TRUE, cols = c("lightblue", "navy"), raster = FALSE)

出自下面paper:👇

Lai, Y., Ramírez-Pardo, I., Isern, J. et al. Multimodal cell atlas of the ageing human skeletal muscle. Nature (2024).

alt
最后祝大家早日不卷!~

点个在看吧各位~ ✐.ɴɪᴄᴇ ᴅᴀʏ 〰

📍 往期精彩

📍 🤩 LASSO | 不来看看怎么美化你的LASSO结果吗！？
📍 🤣 chatPDF | 别再自己读文献了！让chatGPT来帮你读吧！~
📍 🤩 WGCNA | 值得你深入学习的生信分析方法！~
📍 🤩 ComplexHeatmap | 颜狗写的高颜值热图代码！
📍 🤥 ComplexHeatmap | 你的热图注释还挤在一起看不清吗！？
📍 🤨 Google | 谷歌翻译崩了我们怎么办！？（附完美解决方案）
📍 🤩 scRNA-seq | 吐血整理的单细胞入门教程
📍 🤣 NetworkD3 | 让我们一起画个动态的桑基图吧~
📍 🤩 RColorBrewer | 再多的配色也能轻松搞定！~
📍 🧐 rms | 批量完成你的线性回归
📍 🤩 CMplot | 完美复刻Nature上的曼哈顿图
📍 🤠 Network | 高颜值动态网络可视化工具
📍 🤗 boxjitter | 完美复刻Nature上的高颜值统计图
📍 🤫 linkET | 完美解决ggcor安装失败方案（附教程）
📍 ......

alt
alt
alt
alt

本文由 mdnice 多平台发布

http://www.ds6.com.cn/news/93245.html

相关文章：

  • 一家专门做灯的网站企业网站设计规范
  • 做网站需要的语言seo链接优化建议
  • 贪玩传奇手游官方网站网络营销专业是做什么的
  • 自己给网站做logo品牌seo培训咨询
  • 牡丹江建设银行网站百度网站禁止访问怎么解除
  • 郑州有学网站制作郑州新闻发布
  • 网站 如何 备案竞价推广的基本流程
  • 考研培训机构排名前五的机构seo文章生成器
  • 花钱做网站注意些什么360竞价推广怎么做
  • 安阳网约车准入条件搜外seo视频 网络营销免费视频课程
  • 企业模板网站怎么做武汉seo排名扣费
  • 制作网站软件叫什么线上营销推广
  • 网站建设运营公司排行杭州优化外包哪里好
  • 做网站做网站希爱力副作用太强了
  • 门户网站建设与管理沈阳seo关键字优化
  • 如何做网站预览免费的外贸网站推广方法
  • 自己免费做网站(三)平台推广引流
  • seo关键词优化怎么操作北京seo服务行者
  • 女生做网站开发网站建设优化推广系统
  • 宁波建网站哪家好站长工具权重
  • 对于网站开发有什么要求个人如何在百度上做广告
  • 做平台网站怎么做的b2b采购平台
  • 城口网站建设实事新闻热点
  • 做黄色网站判刑几年2021年近期舆情热点话题
  • 网站建设系统海南网站设计
  • h5网站程序厦门推广平台较好的
  • 西安知名网站建设公司排名网络销售渠道有哪些
  • windows10网站建设软文平台发布
  • 河北省水利建设市场网站百度账号管家
  • 工作室网站网站营销策略有哪些