Description Usage Arguments See Also Examples
This core function applies filtering, TMM normalization, voom transformation and LM to the given raw count expression values, respectively. It takes four arguments: (i) raw gene counts, (ii) raw TE counts, (iii) a data frame containing user-defined covariates (e.g. tissue type, disease state), and (iv) the output of get_overlaps() function. This function returns three outputs: (i) a tsv file containing the p-value of each model, significance level of covariates and associated adjusted R squared values, (ii) another tsv file containing log2(CPM) values of genes and TEs included in LM, and (iii) a group of diagnostic plots for each significant model (p < 0.05).
1 | apply_lm(gene.annotation, gene.counts, repeat.counts, covariates, prefix)
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gene.annotation |
It is a data frame containing the output of get_intervals() function. |
gene.counts |
It is a data frame containing the raw read counts of genes. |
repeat.counts |
It is a data frame containing the output of summarize_repeat_counts() function. |
covariates |
It is a data frame containing the user-defined covariates (e.g. tissue type, disease state). If the TE expression as the single predictor, this parameter takes NULL value. |
prefix |
It is a string given by the user. This prefix is added to output of apply_lm() function. |
1 2 3 4 5 | #Create a data frame containing user-defined covariates.
df.cov<-data.frame( tissue_type=c(rep("Normal", 22), rep("Tumor", 22)), patient=c(c(1:22), c(1:22)) )
#Apply multiple linear regression models using the given list of covariates and TE counts.
TEffectR::apply_lm<-function(gene.annotation = annotation, count.matrix = count.matrix, repeat.counts = repeat.counts, covariates = df.cov, prefix="LTR-5kb-")
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