library(data.table)
library(tidyverse)
genes_merged <- read.csv("../../data/merged_genes_full.csv",row.names = 1, header= TRUE)
gene_list = genes_merged[,"gene_names"]
genes_merged <- column_to_rownames(genes_merged,var="gene_names")```r
ref_gene <- as.numeric(genes_merged[\acnA\,])
# gene_count =as.data.frame(rowSums(genes_merged))
# gene_count <- tibble::rownames_to_column(gene_count, \gene_name\)
# Calculate Jaccard Similarity
gene_matches <- colSums(t(genes_merged)+ref_gene==2)
gene_totalhits <- colSums(t(genes_merged)+ref_gene>0)
fave_genes <- sort(gene_matches/gene_totalhits, decreasing = TRUE)
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Generates a confusion matrix of the top 10 best hits
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```r
```r
# Check the confusion matrix of the top 10 best matched genes
test <- lapply(names(head(fave_genes, 10)), function(gene_name){
table(as.numeric(genes_merged[\acnA\,]),
as.numeric(genes_merged[gene_name,]))
})
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```r
```r
# plot the ordered data based on similarity score
x_steps = seq(1, length(fave_genes)-1, 1)
y_steps = fave_genes[-1]
df = data.frame(x=x_steps,y=y_steps)
ggplot(data=df,mapping = aes(x, y))+geom_point()
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<img 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" />
<!-- rnb-plot-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxueGxhYihcXFJhbmsgT3JkZXJcXClcbmBgYFxuYGBgIn0= -->
```r
```r
xlab(\Rank Order\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiJHhcblsxXSBcXFJhbmsgT3JkZXJcXFxuXG5hdHRyKCxcXGNsYXNzXFwpXG5bMV0gXFxsYWJlbHNcXFxuIn0= -->
$x [1] Order
attr(,) [1]
<!-- rnb-output-end -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxuYGBgclxueWxhYihcXFNpbWlsYXJpdHkgU2NvcmVcXClcbmBgYFxuYGBgIn0= -->
```r
```r
ylab(\Similarity Score\)
<!-- rnb-source-end -->
<!-- rnb-output-begin eyJkYXRhIjoiJHlcblsxXSBcXFNpbWlsYXJpdHkgU2NvcmVcXFxuXG5hdHRyKCxcXGNsYXNzXFwpXG5bMV0gXFxsYWJlbHNcXFxuIn0= -->
$y [1] Score
attr(,) [1]
<!-- rnb-output-end -->
<!-- rnb-chunk-end -->
<!-- rnb-text-begin -->
nifH is kinda interesting - lots of metal binding genes associated amidst the other nif genes
<!-- rnb-text-end -->
<!-- rnb-chunk-begin -->
<!-- rnb-source-begin eyJkYXRhIjoiYGBgclxucm93Lm5hbWVzKGphY2NhcmRfbWF0cml4KVxuXG5gYGAifQ== -->
```r
row.names(jaccard_matrix)
Error in row.names(jaccard_matrix) : object 'jaccard_matrix' not found