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// save to windows-user directory | |
linters: with_defaults(object_name_linter = NULL, | |
object_length_linter(50), | |
commented_code_linter = NULL, | |
object_usage_linter = NULL, | |
line_length_linter(120), | |
cyclocomp_linter = cyclocomp_linter(50)) |
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# example of bar plot with individual subject points for Anya -04/24/2019 | |
# added error bars - 04/25/2019 | |
library(tidyverse) #will need to install this first (run: install.packages("tidyverse")) | |
## fake data | |
data = tibble( # creating a dataframe (aka "tibble") called data | |
subject = rep(1:10,times = 2 ), # making a column/vector of subject numbers (1-10) x2 | |
condition = rep(c("hard", "easy"), each = 10), # making a column/vector of condition names |
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# Thanks for the suggestion from Joseph Elsherbini. | |
library(tidyverse) | |
library(ggforce) | |
n_pages(p) # use this to get the number of pages to print | |
# then run a for loop to loop over pages | |
pdf("test.pdf") | |
for (i in 1:5) { |
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# 0. Prep ----------------------------------------------------------------- | |
library(tidyverse) | |
library(here) | |
# 1. Load and clean EggNOG output ----------------------------------------- | |
## The output was downloaded from the eggnog-mapper online run. | |
eggnog_go <- read_tsv(here("data", "misc", "out.emapper.annotations.gz"), | |
skip = 4) %>% |
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# Download meta data using ffq. | |
# Need input `SraRunTable.txt` | |
# Author: Ji Huang | |
# Date: 2022-09-21 | |
import pandas as pd | |
import subprocess | |
import os |
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# Test on GENIE3 whether | |
# (Q1) if using more genes as regulator, for the same regulator-target edge | |
# do I get the same order? No. The edge order will be different. | |
# (Q2) if using more genes as targets, for the same regulator-target edges, | |
# do I get the same order? Yes. The same edge will have the exact same weight. | |
# Author: Ji Huang | |
# Date: 2021-01-06 |
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library(ggplot2) | |
theme_jh <- function (base_size = 11, base_family = "Arial", | |
base_line_size = base_size/22, | |
base_rect_size = base_size/22) { | |
theme_grey(base_size = base_size, base_family = base_family, | |
base_line_size = base_line_size, | |
base_rect_size = base_rect_size) + | |
theme(panel.background = element_rect(fill = "white", colour = NA), | |
panel.border = element_rect(fill = NA, colour = "grey20"), | |
panel.grid = element_line(colour = "grey92"), |
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# An R function to save pheatmap figure into pdf | |
# This was copied from Stackflow: https://stackoverflow.com/questions/43051525/how-to-draw-pheatmap-plot-to-screen-and-also-save-to-file | |
save_pheatmap_pdf <- function(x, filename, width=7, height=7) { | |
stopifnot(!missing(x)) | |
stopifnot(!missing(filename)) | |
pdf(filename, width=width, height=height) | |
grid::grid.newpage() | |
grid::grid.draw(x$gtable) |
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# | |
# If all files excluded and you will include only specific sub-directories | |
# the parent path must matched before. | |
# | |
/** | |
!/.gitignore | |
############################### | |
# Un-ignore the affected subdirectory |
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I used the precision curve only to decide the cutoff value. I chose a precision cutoff 0.2, then the correspondent normalized ranking is *~0.01*. The meaning of the normalized ranking is explained [here](https://github.com/takayasaito/precrec/issues/12). | |
Therefore, to calculate the **weight** value for precision 0.2, we got the following calculation: (rank-1)/(n-1) = 0.01. In this case, `n=nrow(dfg_ortho_label)`, so the rank is *649*. We went back to the *649* row of the `dfg_ortho_label`, the `weight` is 1.57. Therefore, we kept the edge that has weight higher than 1.57. | |
```{r, fig.width=5, fig.height=5, fig.align="center"} | |
scurve_os_b <- evalmod(scores = dfg_ortho_label$weight, labels = dfg_ortho_label$label, | |
mode = "basic") | |
sos_df_b <- fortify(scurve_os_b) | |
p2 <- ggplot(subset(sos_df_b, curvetype == "precision"), aes(x = x, y = y))+ | |
geom_point(color = "blue", size = 0.4)+ ylim(0:1) |
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