![]() ![]() Step 2: Clustering variantsĬlustering the variants based on their cellular prevalence across samples is a critical step. However, be prepared that in highly heterogeneous patients/tumors, your data may yield models that underestimate the true model. This does not mean that we cannot study clonal evolution in cancer without such an ideal dataset. Multi-region samples (due to intra-tumor heterogeneity).Large number of variants (exome sequencing is okay, but whole genome sequencing provides much better coverage of passenger somatic mutations).The depth of sequencing, the quantity and quality of samples, and the quantity and quality of somatic variants can have a profound impact on the resulting clonal evolution models. Step 1: Preparing variants for clonal evolution inference Steps 1-2 should be done prior to running ClonEvol, using other tools (briefly described below). clonal frequency), given the $CCF$ of the variants and their clusters, via the following equation (referred to as the sum rule). ClonEvol can deal with statistical uncertainty and error in sequencing and data analysis that may distort the cellular prevalence estimate of individual variants.ĬlonEvol uses a bootstrap resampling approach to estimate the cancer cell fraction ($CCF$) of the clones (ie. It uses the clustering of heterozygous variants identified using other tools as input to infer consensus clonal evolution trees and estimate the cancer cell fraction (also called clonal frequency) of the clones in individual samples. In hdng/clonevol: Clonal ordering and visualizationĬlonEvol is a package for clonal ordering and clonal evolution visualization. : Plot the mean/median of the clusters of variants across.: Visualize clonal evolution models using various plots.: Plot a tumor as a cloud of cells reflecting the cellular..as.branch: Plot all branch-based consensus clonal evolution trees.: Merge variants and mapped events onto the same data frame.merge.samples: Merge multi region samples into a meta sample.: Merge clonnal evolution trees from multiple samples into a..trees: Recreate merged trees for matched models, given output of.: Check if two clonal structures are compatible (one evolves to.aph: Construct igraph object from clonal structures of a sample. ![]() : Create a data frame to hold clonal structure of a single.is.ancestor: Check if clone a is ancestor of clone b in the clonal.: Infer clonal structures and evolution models for multiple. ![]() : Get all set of subclones for all clone across samples..ccf.prob: Get probability of a model for a sample as the product of..value: Get the highest p.value of the test of Ho:ccf: Get the hex string of the preset colors optimized for.: Get cellular fraction confidence interval.generateFishplotInputs: Generate fishplot ready data from clonevol clonal evolution.: Generate fill points for bell/polygon plots..cells: Generate a cloud of circles to represent cell population.: Generate and calculate bootstrap means for all clusters.generate.boot: Generate and calculate bootstrap means for all clusters.: Find matched models between samples infer clonal evolution.: Produce a data frame of events mapped to clone and associated.: Estimate VAFs of clones/clusters from clonality analysis.enumerate.clones: Enumerate all possible clonal structures for a single sample.: Draw all enumerated clonal models for a single sample.: Draw clonal structures/evolution of a single sample.draw.clone: Draw a bell/polygon representing a clone evolution, annotated.draw.branch: Draw tree branch using polygon that allows for choosing.determine.subclone: Determine which clones are subclone in a single sample, also.cutBigValue: Replace values bigger than a cutoff by a value.: Apply cross rule to all clones in all matched models using.createFishPlotObjects: Create a list of fishplot objects that can then be called..to.branch: Create trees for all ees in clonevol output..branch: Create a tree from the ee ame in clonevol..leaves: Compare two merged clonal evolution trees.: Test if two clones have different VAFs Deprecated!.: Calculate CI for a cluster in a sample. ![]()
0 Comments
Leave a Reply. |
AuthorWrite something about yourself. No need to be fancy, just an overview. ArchivesCategories |