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This article introduces the relevant knowledge of "what is the role of maftools". In the operation of actual cases, many people will encounter such a dilemma. Then let the editor lead you to learn how to deal with these situations. I hope you can read it carefully and be able to achieve something!

Maftools is an R package, published on bioconductor, specifically for visualization of information in MAF files

Read the MAF file first, with the following code

> library (maftools)

> maf laml = read.maf (maf = maf) 1. Summary

Statistics on the number of different mutation types at sample level and gene level, the code is as follows

# sample level

> getSampleSummary (laml)

# Gene level

> getGeneSummary (laml)

# output summary results

> write.mafSummary (maf = laml, basename = 'laml')

The results of summary can be visualized with the following code

> plotmafSummary (maf = laml)

The visualization results are as follows

The first subgraph is a histogram of frequency distribution of different mutation types, which is divided into seven types such as Missense_Mutation according to their effects on protein coding, the second is a histogram of frequency distribution of different variation categories, which is divided into three categories SNP and INS,DEl, and the third is a histogram of frequency distribution of SNV mutation types, which is divided into six categories considering the combination and positive and negative chains of mutations. The fourth subgraph is the stacking histogram of different variation types in each sample, corresponding to the seven types of figure 1; the fifth subgraph is the box chart of the distribution of different variation types in each sample, corresponding to the seven types in figure 1, and the sixth map is the stacking histogram of different mutation types on the top10 genes with the highest mutation frequency, corresponding to the seven types of figure 1.

2. Oncoplot

Oncoplot is a common visualization method in tumor research, which can be realized by the following code

> oncoplot (maf = laml)

The visualization results are as follows

The whole map is divided into three parts, the first part is the middle heat map, each row represents a gene, each column represents a sample, showing the distribution of different mutation types in each sample, the second part is the stacked bar chart on the right, which represents the frequency distribution of different mutation types on each gene, and the third part is the stacked bar chart above, which represents the frequency distribution of different mutation types in each sample.

3. Oncostrip

Similar to oncoplot, only the heat map is shown. The code is as follows

> oncostrip (maf = laml)

The visualization results are as follows

4. Reversal and conversion

Count and visualize the number of transversions and transformations in all SNV, as follows

> laml.titv = titv (maf = laml, plot = FALSE, useSyn = TRUE)

> plotTiTv (res = laml.titv)

The visualization results are as follows

The top figure shows the box chart of each mutation type and the transversion and transformation sites in all samples, and the bottom figure shows the stacking bar chart of the percentage of different mutation types in each sample.

5. Lollipop plot

Lollipop plot is used to show the effect of mutation on protein, the code is as follows

> lollipopPlot (maf = laml, gene = 'DNMT3A', AACol =' Protein_Change', showMutationRate = TRUE)

The results of visualization are as follows

By default, taking the longest transcript as a reference, dots are used to represent the positions of different types of variation sites on the transcript, and the height of the vertical bar is used to represent the corresponding frequency of the variation sites.

6. TCGA compare

Used to compare the distribution of mutation frequencies among different tumor cohorts in TCGA, the code is as follows

> laml.mutload = tcgaCompare (maf = laml, cohortName = 'LAML')

The visualization results are as follows

The Abscissa is the tumor cohort, corresponding to different tumors, the ordinate is the number of somatic mutations of each sample in the tumor cohort, and the red line represents the median.

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