test.use ="wilcoxon", require_DE_all_normals ="any", hspike_aggregate_normals =FALSE, no_plot =FALSE, no_prelim_plot =FALSE, output_format ="png", useRaster =TRUE, up_to_step =100) 多到让人头皮发麻! 其中文献运行infercnv::run的时候,下面两个参数,都是默认值: HMM参数 when set to ...
infercnv.dend %>% set("labels",rep("", nobs(infercnv.dend)) ) %>% plot(main="inferCNV dendrogram") %>% colored_bars(colors = as.data.frame(the_bars), dend = infercnv.dend, sort_by_labels_order =FALSE, add =T, y_scale=100, y_shift =0) 如下所示: 可以很清楚的看到,最大的...
TRUE,diagnostics=FALSE,remove_genes_at_chr_ends=FALSE,prune_outliers=FALSE,mask_nonDE_genes=FALSE,mask_nonDE_pval=0.05,test.use="wilcoxon",require_DE_all_normals="any",hspike_aggregate_normals=FALSE,no_plot=FALSE,no_prelim_plot=FALSE,output_format="png",useRaster=TRUE,up_to_step=100) ...
Inferring CNV from Single-Cell RNA-Seq. Contribute to broadinstitute/infercnv development by creating an account on GitHub.
Dear Christophe, I have a very large h5ad file that consists of ~950 patients and >1 million cells. I want to subset at least 100k cells to run inferCNV but unfortunately this takes up too much RAM. Do you have any solution to this probl...
hspike_aggregate_normals =FALSE, no_plot =FALSE, no_prelim_plot =FALSE, output_format ="png", useRaster =TRUE, up_to_step =100) 多到让人头皮发麻! 其中文献运行infercnv::run的时候,下面两个参数,都是默认值: HMM参数 whensetto True, runs HMM to predict CNV level (default: FALSE) ...
TRUE,diagnostics=FALSE,remove_genes_at_chr_ends=FALSE,prune_outliers=FALSE,mask_nonDE_genes=FALSE,mask_nonDE_pval=0.05,test.use="wilcoxon",require_DE_all_normals="any",hspike_aggregate_normals=FALSE,no_plot=FALSE,no_prelim_plot=FALSE,output_format="png",useRaster=TRUE,up_to_step=100)...
no_prelim_plot=TRUE, png_res=60, num_threads=2, BayesMaxPNormal=0 ) ``` Basic ouput from running inferCNV. ```{r, echo=FALSE} knitr::include_graphics("../example_output/infercnv.png") ``` HMM preditions ```{r, echo=FALSE} knitr::include_graphics("../example_output/infercnv.13...
infercnv.dend %>% set("labels",rep("", nobs(infercnv.dend)) ) %>% plot(main="inferCNV dendrogram") %>% colored_bars(colors = as.data.frame(the_bars), dend = infercnv.dend, sort_by_labels_order =FALSE, add =T, y_scale=100, y_shift =0) ...