How to use hclust_analysis.r in R language
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Hierarchical clustering Analysis of hclust_analysis.r Transcriptome data
Instructions for use: usage: hclust_analysis.r [- h]-I filepath [- d distance] [- m method] [- T top] [- S] [- M max.nc] [- k bestk] [- s size] [- an alpha] [- e] [- L] [- X label] [- Y label] [- t label] [- o path] [- H number] [- W number] Hierarchical Clustering andplot: https://clincancerres.aacrjournals.org/content/25/16/5002optional arguments:-h Help show this help message and exit-I filepath,-- input filepath input the dataset martix [required]-d distance,-- distance distance the distance measure to be used to compute the dissimilarity matrix. This must be one of: "euclidean", "maximum", "manhattan", "canberra", "binary", "minkowski". By default, distance= "euclidean". -m method,-- method method the cluster analysis method to be used. This should be one of: "ward.D", "ward.D2", "single", "complete", "average", "mcquitty", "median", "centroid". By default method=ward.D-T top,-- top top select top gene to analysis [default NULL]-S,-- scale scale data sd=1 mean=0 [default FALSE]-M max.nc,-- max.nc max.nc maximal number of clusters for nbclust, between 2 and (number of objects-1), greater or equal to min.nc. By default [optional, default: 15]-k bestk,-- bestk bestk set bestk or nbclust choose bestk [optional, default: NULL]-X label,-- x.lab label the label for x axis [optional, default: sample]-Y label,-- y.lab label the label for y axis [optional, default: Distance]-t label -- title label the label for main title [optional, default: Cluster Dendrogram]-o path,-- outdir path output file directory [default cwd]-H number,-- height number the height of pic inches [default 5]-W number -- width number the width of pic inches [default 10] usage:
Should run it twice
# do not specify K for the first run, select the appropriate K (subtype) Rscript $scriptdir/hclust_analysis.r-I immu/ssgsea.res.tsv-o hclust-M 20-- distance "euclidean" # run again to specify the best K, and output the clustering tree and grouping table Rscript $scriptdir/hclust_analysis.r-I immu/ssgsea.res.tsv-o hclust-- distance "euclidean"-k 2 parameters description:
-I input gene expression matrix file, or immune invasion matrix file
Cell_typeTCGA-B7-A5TK-01A-12R-A36D-31TCGA-BR-7959-01A-11R-2343-13TCGA-IN-8462-01A-11R-2343-13TCGA-BR-A4CR-01A-11R-A24K-31TCGA-CG-4443-01A-01R-1157-13aDC0.6121310.4527210.4340650.3526350.268974B cells0.4233230.408870.4266120.4138570.289268Blood vessels0.6810230.7754390.6894330.5776670.745019CD8 T cells0.6756150.6500730.6291210.5660480.577315Cytotoxic cells0.6210560.4252170.4116170.31280.191034DC0.6198390 .4850560.4891010.2669050.350132Eosinophils0.5027850.5149390.4695410.4880510.456521iDC0.5320.4984370.5309310.3906990.420172Lymph vessels0.7108430.7213230.6583910.5005740.400411Macrophages0.6082710.5984820.5522770.4685310.438481Mast cells0.4807920.5259270.478710.246770.124795Neutrophils0.4476720.4580980.3935410.31050.344511NK CD56bright cells0.4626330.4186170.5460940.572620.460983NK CD56dim cells0.3414740.1371470.031158-0.04299-0.0389NK cells0.5581230.5129290.5070880.4795420.446198Normal mucosa0.7794440.8202810.8067710.6913840.65646pDC0 .6767720.6474150.6211860.4825640.552156SW480 cancer cells0.5341510.6002360.6185090.4121960.60535T cells0.6270560.4254220.3996120.3261160.188205T helper cells0.6691030.6454070.628070.6502160.628577Tcm0.5624080.5599510.5272720.5160680.553444Tem0.5189520.5559420.4105770.4400070.449251TFH0.4851240.4777490.4450610.4655520.446549Tgd0.1901990.1101390.0616480.0040260.023796Th2 cells0.5639280.4917360.4555230.4263510.378701Th27 cells0.4172750.1537130.2187270.2150260.257282Th3 cells0.5361420.4786870.447160.5395340.416747TReg0.6820360 4847610.5169630.308840.312123 "how to use hclust_analysis.r in R language" ends here Thank you for your reading. If you want to know more about the industry, you can follow the website, the editor will output more high-quality practical articles for you!
