Tu Hu tuhulab

0 · About the User and Your Role

You are assisting Tu.
Tu is an senior bioinformatics engineer, proficient in Python and R and their ecosystems.
Tu values "Slow is Fast," focusing on: reasoning quality, scientific rigour, and long-term maintainability—not short-term speed.
Your core objectives:
- Act as a strong reasoning, strong planning coding assistant, delivering high-quality solutions and implementations with minimal back-and-forth;
- Prioritize getting it right the first time; avoid superficial answers and unnecessary clarifications.

	Baby Sleep Assistant Skill

	Role

	You are the dedicated sleep assistant for my 10-month-old baby.

	Your role is not to enforce generic sleep training rules, but to continuously learn my baby’s unique sleep patterns through longitudinal observation.

	Treat this as an ongoing research project rather than a one-time consultation.

	library(ComplexHeatmap)

	count_matrix # if your data is called count_matrix

	# Read data
	se <- readRDS("data/se.rds")
	count_matrix <- assay(se)

	# Data transformation
	count_matrix_log2 <- log2(count_matrix + 1)

	counttable_merge_library_fun <- function(counttable_data = ...,
	lib_to_merge_vector = ...){
	lib_id <- counttable_data %>% colnames() %>% str_extract("lib\\d{1,}")
	merged_counttable <- sapply(lib_to_merge_vector, function(one_lib_id_to_merge){
	merged_counts <- counttable_data %>% select((lib_id == one_lib_id_to_merge) %>% which()) %>% rowSums()
	merged_counts_df <- tibble(one_lib_id_to_merge = merged_counts)
	return(merged_counts_df)
	}) # The function to merge libs for counttable -----------------
	# list tidy
	MergedLib <- do.call(rbind.data.frame, merged_counttable) %>% t() %>% as.data.frame() %>% tibble()

	# Cluster analysis in R
	# inspired by Dima Gorenshteyn, DataCamp

	## standardize data
	df_st <- scale(df)

	## Hierachical clustering
	d <- dist(df)
	hc <- hclust(d, "method") # method %in% c("complete", "average", "single")
	c <- cutree(hc, h = the_height) # h: the height to cut the tree # assign cluster

	# First install R (https://www.r-project.org/) and RStudio(https://rstudio.com/)

	# Install enrichR
	install.packages("enrichR")

	# Load enrichR
	library(enrichR)

	# Get your gene list, e.g. type by hand
	Inflammatory_markers <- c("IL13","MMP12","IL22","NTRK1", "CCL17", "IL36A", "ICOS", "CCL18", "ALOX15", "CCL1", "CCR5", "IL13RA2", "IL19", "CCR7","CCL20", "CCR4","CCR2","CCL11","CCL22","CCR8","CCL19","CCL26","CCL3")

	library(dplyr)
	a_vector_of_genes <- c("AP005212.4", "Z98257.1", "U62317.4", "CLIC4P3", "PGLYRP2", "NEK4P1")
	a_vector_of_cleaned_genes <- data.frame(a_vector_of_genes) %>% filter(!a_vector_of_genes %>% stringr::str_detect("\\d{1,}P$\|\\d{1,}P\\d{1,}$\|\\.\|-AS\\d{1}\|-DT")) %>% pull(a_vector_of_genes)

	pos_n_max <- pos_data %>% pull(feature) %>% stringr::str_match("\\d{1,}") %>% max()
	neg_feature_n <- neg_data %>% pull(feature) %>% stringr::str_match("\\d{1,}")
	neg_data %>% mutate(feature = paste0("F", neg_feature_n + pos_n_max))