From 1ac8727c42f647fe26d70e8ac38db51a1da3ac6a Mon Sep 17 00:00:00 2001 From: m-jahn Date: Mon, 27 Apr 2026 09:47:17 +0200 Subject: [PATCH 1/5] fix: update envs and readme --- .gitignore | 6 + README.md | 33 +- pixi.lock | 4142 ++++++++++++++++++++++++++++++++++++++++++++++++++-- pixi.toml | 3 + 4 files changed, 4042 insertions(+), 142 deletions(-) diff --git a/.gitignore b/.gitignore index fcc3e19..0989ced 100644 --- a/.gitignore +++ b/.gitignore @@ -1,7 +1,13 @@ +# generic ignores +*.Rhistory +*.RData +*.Rproj.user +.ipynb_checkpoints .vscode exercises/* templates/*.html templates/*_files + # pixi environments .pixi/* !.pixi/config.toml diff --git a/README.md b/README.md index ab86315..b47490e 100644 --- a/README.md +++ b/README.md @@ -12,13 +12,30 @@ For comments, criticism, and general feedback please contact the [authors](#auth In order to work with the course materials, you need to have [Python](https://www.python.org/downloads/), [R](https://cran.r-project.org/mirrors.html), and optionally [Quarto](https://quarto.org/docs/get-started/) (a notebook framework) installed. +With [`pixi`](https://pixi.prefix.dev) as package manager (recommended), you can create an environment with the required dependencies by running: + +```bash +pixi init +pixi add python pandas matplotlib jupyter r-tidyverse r-irkernel +``` + +Or simply use the provided `pixi.toml` file to activate the environment: + +```bash +pixi shell +``` + With conda/mamba, you can create an environment with all dependencies by running: ```bash -mamba create -p /python-course python pandas matplotlib jupyter quarto r-recommended r-tidyverse r-reticulate +mamba create -p /python-course python pandas matplotlib jupyter r-tidyverse r-irkernel mamba activate python-course ``` +When using Jupyter Notebooks (in VSCode), make sure to select the correct kernel (Python or R) for each notebook. + +The R kernel for Jupyter is installed with the `r-irkernel` package, a detailed setup for VSCode can be found [here](https://davidzhao1015.github.io/blog/2024/R-kernel-jupyter/). + When running a Quarto notebook, make sure that the R package `reticulate` is installed and configured to use the correct Python environment. You can specify the path to the Python binary by running the following command in the terminal: ```bash @@ -35,17 +52,19 @@ export RETICULATE_PYTHON="/path/to/your/env/bin/python" - `data/`: Contains datasets used in the course - `lessons/`: Contains the lesson notebooks, e.g. in Jupyter format (`.ipynb`) +- `solutions/`: Contains solutions to the exercises provided in the lessons - `templates`: Contains templates for creating new lessons or exercises - `README.md`: Instructions and information about the course - `.gitignore`: Specifies files and directories to be ignored by Git ## Data origins / citations: -- gfmt_sleep*: Beattie, L., Walsh, D., McLaren, J., Biello, S.M. and White, D., 2016. Perceptual impairment in face identification with poor sleep. Royal Society Open Science, 3(10). -- iris.data: Anderson, E., 1935. The irises of the Gaspe Peninsula. Bulletin of American Iris Society, 59, pp.2-5. -- ls_orchids.*: Cox, A.V., Pridgeon, A.M., Albert, V.A. and Chase, M.W., 1997. Phylogenetics of the slipper orchids (Cypripedioideae, Orchidaceae): nuclear rDNA ITS sequences. Plant Systematics and Evolution, 208(3), pp.197-223. -- NC_005816.gb: Zhou, D., Tong, Z., Song, Y., Han, Y., Pei, D., Pang, X., Zhai, J., Li, M., Cui, B., Qi, Z. and Jin, L., 2004. Genetics of metabolic variations between Yersinia pestis biovars and the proposal of a new biovar, microtus. Journal of bacteriology, 186(15), pp.5147-5152. -- reeves_gradient_width_various_methods.cvs: Reeves, G.T., Trisnadi, N., Truong, T.V., Nahmad, M., Katz, S. and Stathopoulos, A., 2012. Dorsal-ventral gene expression in the Drosophila embryo reflects the dynamics and precision of the dorsal nuclear gradient. Developmental cell, 22(3), pp.544-557. -- towels.txt: Douglas Adams, The Hitch Hiker's Guide to the Galaxy, ISBN: 0345391802 + +- `gfmt_sleep\*`: Beattie, L., Walsh, D., McLaren, J., Biello, S.M. and White, D., 2016. Perceptual impairment in face identification with poor sleep. Royal Society Open Science, 3(10). +- `iris.data`: Anderson, E., 1935. The irises of the Gaspe Peninsula. Bulletin of American Iris Society, 59, pp.2-5. +- `ls_orchids.\*`: Cox, A.V., Pridgeon, A.M., Albert, V.A. and Chase, M.W., 1997. Phylogenetics of the slipper orchids (Cypripedioideae, Orchidaceae): nuclear rDNA ITS sequences. Plant Systematics and Evolution, 208(3), pp.197-223. +- `NC_005816.gb`: Zhou, D., Tong, Z., Song, Y., Han, Y., Pei, D., Pang, X., Zhai, J., Li, M., Cui, B., Qi, Z. and Jin, L., 2004. Genetics of metabolic variations between Yersinia pestis biovars and the proposal of a new biovar, microtus. Journal of bacteriology, 186(15), pp.5147-5152. +- `reeves_gradient_width_various_methods.cvs`: Reeves, G.T., Trisnadi, N., Truong, T.V., Nahmad, M., Katz, S. and Stathopoulos, A., 2012. Dorsal-ventral gene expression in the Drosophila embryo reflects the dynamics and precision of the dorsal nuclear gradient. Developmental cell, 22(3), pp.544-557. +- `towels.txt`: Douglas Adams, The Hitch Hiker's Guide to the Galaxy, ISBN: 0345391802 ## Adding and changing contents diff --git a/pixi.lock b/pixi.lock index 899310d..1a312d7 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,56 +9,337 @@ environments: packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2 + - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda + - conda: https://conda.anaconda.org/conda-forge/linux-64/argon2-cffi-bindings-25.1.0-py313h07c4f96_2.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/arrow-1.4.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/asttokens-3.0.1-pyhd8ed1ab_0.conda + - 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libgcc >=14 + - __glibc >=2.17,<3.0.a0 + - libstdcxx >=14 + - krb5 >=1.22.2,<1.23.0a0 + - libsodium >=1.0.21,<1.0.22.0a0 + license: MPL-2.0 + license_family: MOZILLA + size: 311150 + timestamp: 1772476812121 - conda: https://conda.anaconda.org/conda-forge/noarch/zipp-3.23.1-pyhcf101f3_0.conda sha256: 523616c0530d305d2216c2b4a8dfd3872628b60083255b89c5e0d8c42e738cca md5: e1c36c6121a7c9c76f2f148f1e83b983 diff --git a/pixi.toml b/pixi.toml index dd6afc9..be18195 100644 --- a/pixi.toml +++ b/pixi.toml @@ -12,3 +12,6 @@ check-nbs = { cmd = ["bash", "-lc", "find . -name '*.ipynb' -exec nbqa black --c [dependencies] nbqa = ">=1.9.0,<2" black = ">=26.3.1,<27" +r-irkernel = ">=1.3.2,<2" +r-tidyverse = ">=2.0.0,<3" +jupyter = ">=1.1.1,<2" From 859e37fe6296045dddcc8af29f8422ac232b8f83 Mon Sep 17 00:00:00 2001 From: m-jahn Date: Mon, 27 Apr 2026 09:47:57 +0200 Subject: [PATCH 2/5] fix: removed extra code chunk on top --- solutions/solutions_08.ipynb | 18 ------------------ 1 file changed, 18 deletions(-) diff --git a/solutions/solutions_08.ipynb b/solutions/solutions_08.ipynb index 9c2cf25..2fbbcbb 100644 --- a/solutions/solutions_08.ipynb +++ b/solutions/solutions_08.ipynb @@ -1,23 +1,5 @@ { "cells": [ - { - "cell_type": "code", - "execution_count": null, - "id": "f6b812a0", - "metadata": {}, - "outputs": [], - "source": [ - "(\n", - " ggplot(df_mean, aes(\"group\", \"mean_y\", fill=\"group\"))\n", - " + geom_col(width=0.7)\n", - " + coord_flip()\n", - " + scale_fill_brewer(type=\"qual\", palette=\"Set2\")\n", - " + labs(title=\"Mean y by group (horizontal)\", x=\"group\", y=\"mean(y)\")\n", - " + theme_minimal()\n", - " + theme(legend_position=\"none\")\n", - ")" - ] - }, { "cell_type": "markdown", "id": "ecee966f", From 0452dac5788cafad05fd0bbb9b609c4d791d0576 Mon Sep 17 00:00:00 2001 From: m-jahn Date: Mon, 27 Apr 2026 09:48:15 +0200 Subject: [PATCH 3/5] feat: added lesson 9 --- lessons/lesson_09.ipynb | 634 +++++++++++++++++++++++++++++++++++ solutions/solutions_09.ipynb | 149 ++++++++ 2 files changed, 783 insertions(+) create mode 100644 lessons/lesson_09.ipynb create mode 100644 solutions/solutions_09.ipynb diff --git a/lessons/lesson_09.ipynb b/lessons/lesson_09.ipynb new file mode 100644 index 0000000..6567414 --- /dev/null +++ b/lessons/lesson_09.ipynb @@ -0,0 +1,634 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a60ff099", + "metadata": {}, + "source": [ + "# Lesson 09\n", + "\n", + "### In this lesson, we will learn about the following topics:\n", + "\n", + "- _very brief_ introduction to the **R language**\n", + "- basic objects and functions in R\n", + "- importing, manipulating and exporting tabular data in R\n", + "- all major concepts are similar to Python, we focus on the **differences** in syntax and behavior\n", + "\n", + "### Recap of previous lesson\n", + "\n", + "- biopython\n", + "- sequence objects and manipulation\n", + "- querying sequence databases such as NCBI\n" + ] + }, + { + "cell_type": "markdown", + "id": "09216b2a", + "metadata": {}, + "source": [ + "## Introduction to R\n", + "\n", + "- the following introduction is based on two major resources\n", + " - the [R for Data Science](https://r4ds.had.co.nz/) book by Hadley Wickham and Garrett Grolemund, which is an excellent resource for learning R\n", + " - the official [Introduction to R](https://cran.r-project.org/doc/manuals/r-release/R-intro.pdf) document on the R project website\n", + "\n", + "### What is R?\n", + "\n", + "- R is a programming language and software environment for statistical computing and graphics\n", + "- it was created by Ross Ihaka and Robert Gentleman in the early 1990s at the University of Auckland, New Zealand\n", + "- R is **widely used among statisticians**, data scientists, computational biologists, etc.\n", + "- its main strengths are **native support for data** manipulation, statistical analysis, and visualization\n", + "- R packages are distributed through the Comprehensive R Archive Network [CRAN](https://cran.r-project.org/) and [Bioconductor](https://bioconductor.org/)" + ] + }, + { + "cell_type": "markdown", + "id": "e1a481d1", + "metadata": {}, + "source": [ + "## R basics for Python learners\n", + "\n", + "If you already know the essentials of Python, most ideas in R will feel familiar:\n", + "\n", + "- **variables** store values\n", + "- values have **types** (numeric, character, logical, ...)\n", + "- you can use **loops and functions**\n", + "\n", + "One of the big differences to Python is that **R is vector-first**. Many operations are naturally applied to whole vectors at once.\n", + "\n", + "**Let's look at the basic data types in R**\n", + "\n", + "1. Core atomic data types (\"single\" values)\n", + "2. Vectors and type coercion\n", + "3. Lists and data frames" + ] + }, + { + "cell_type": "markdown", + "id": "f05ec619", + "metadata": {}, + "source": [ + "### Atomic data types\n", + "\n", + "- `double`, `integer`, `character`, `logical` correspond to:\n", + "- `float`, `int`, `str`, `bool` in Python\n", + "- variables are assigned with `<-` or, less commonly `=`\n", + "- everything can be `print()`ed, and many things can be inspected with `summary()`\n", + "- note the `c()` (concatenate) function which is the universal generator for vectors (non-nested lists)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "25803319", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "x_num <- 3.14 # double (numeric floating point)\n", + "x_int <- 7L # integer (L suffix)\n", + "x_str <- \"hello\" # character\n", + "x_bool <- TRUE # logical (TRUE/FALSE, uppercase!)\n", + "\n", + "print(c(x_num, x_int, x_str, x_bool))\n" + ] + }, + { + "cell_type": "markdown", + "id": "2f8dc93b", + "metadata": {}, + "source": [ + "- inspecting types" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "dfbb36d2", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "typeof(x_num)\n", + "typeof(x_int)\n", + "typeof(x_str)\n", + "typeof(x_bool)" + ] + }, + { + "cell_type": "markdown", + "id": "e0070cbd", + "metadata": {}, + "source": [ + "- some things, like working with numbers, are similar to Python\n", + "- other things, like combining strings with `a + b`, `a * 3`, do not work" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "280c18cc", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "3 + 4\n", + "10 - 2\n", + "5 * 6\n", + "\n", + "# this does not work in R, unlike Python\n", + "try(expr = {\n", + " \"banana\" + \"split\" \n", + "})\n", + "\n" + ] + }, + { + "cell_type": "markdown", + "id": "a8fba684", + "metadata": {}, + "source": [ + "- **type conversions** are similar to Python and less strict\n", + "- for example, if numeric and character values are combined in a vector, the numeric values will be coerced to character" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "6866eae0", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "a = 3\n", + "a = as.character(a) # convert to string / character\n", + "print(c(a, typeof(a)))\n", + "\n", + "b = \"5\"\n", + "b = as.numeric(b) # convert to numeric\n", + "print(c(b, typeof(b)))" + ] + }, + { + "cell_type": "markdown", + "id": "69143b16", + "metadata": {}, + "source": [ + "### Vectors (non-nested lists)\n", + "\n", + "- very important data structure in R, stores values of same type\n", + "- R has no `dict` type, but vectors can be named to access values\n", + "- R works natively with vectors: applies functions to all elements automatically" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "b22e95f5", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# numeric vector\n", + "v <- c(10, 20, 30, 40)\n", + "\n", + "# note how operations are applied to all elements, unlike python\n", + "v + 5\n", + "v * 2\n", + "v > 25" + ] + }, + { + "cell_type": "markdown", + "id": "b5d78146", + "metadata": {}, + "source": [ + "- indexing is easier in R than in Python (starts with `1` not `0`)\n", + "- almost all elements can be accessed by their position (index)\n", + "- arbitrary sequences of elements can be accessed by ranges (``:``)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "590a1566", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "\n", + "v[1] # first element\n", + "v[2:3] # slice: elements 2 to 3\n", + "\n", + "names(v) = c(\"ten\", \"twenty\", \"thirty\", \"forty\")\n", + "v[\"twenty\"]\n" + ] + }, + { + "cell_type": "markdown", + "id": "144d36e4", + "metadata": {}, + "source": [ + "- missing values are handled natively and explicitly\n", + "- the universal missing value is `NA` (Not Available)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "80cf953a", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "v_missing <- c(1, 2, NA, 4)\n", + "v_missing\n", + "mean(v_missing) # NA by default\n", + "mean(v_missing, na.rm = TRUE) # ignore NA\n" + ] + }, + { + "cell_type": "markdown", + "id": "96d76bfa", + "metadata": {}, + "source": [ + "- mixed-type vectors are always coerced to the \"lowest\" possible type" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "3f201f9a", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "mixed <- c(1, TRUE, 0)\n", + "mixed\n", + "typeof(mixed) # the logical is coerced to numeric (0 or 1)\n", + "\n", + "mixed2 <- c(1, TRUE, \"23\")\n", + "mixed2\n", + "typeof(mixed2) # logicals and numbers coerced to string/character\n" + ] + }, + { + "cell_type": "markdown", + "id": "b469bf1f", + "metadata": {}, + "source": [ + "### Lists\n", + "\n", + "- lists can store arbitrary objects of mixed types\n", + "- list elements can be named, unlike python, making them a mixture of list and dict\n", + "- to pick a single element of a list use double squared brackets `[[ ]]`, or the dollar shorthand `my_list$my_element`\n", + "- single brackets `[ ]` will return a list again\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "9957502c", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "my_list <- list(\n", + " species = \"Streptococcus pyogenes\",\n", + " n_genes = 1800,\n", + " is_model = TRUE,\n", + " strains = c(\"SF370\", \"M1T1\")\n", + ")\n", + "\n", + "my_list\n", + "my_list$species\n", + "my_list[[\"is_model\"]]\n", + "\n", + "my_list[[2]] # returns a number\n", + "my_list[2] # returns a length-1 list\n" + ] + }, + { + "cell_type": "markdown", + "id": "41f7322e", + "metadata": {}, + "source": [ + "### Data frames\n", + "\n", + "- Data frames store tabular data (like a `pandas` DataFrame)\n", + "- are natively supported and arguably the **most important data type in R**\n", + "- columns can store any type of object, but usually atomic (non-nestd) elements\n", + "- single columns must store **elements of the same type**\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "bcf26428", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "\n", + "df <- data.frame(\n", + " sample_id = c(\"S1\", \"S2\", \"S3\", \"S4\"),\n", + " condition = c(\"control\", \"control\", \"treated\", \"treated\"),\n", + " expression = c(8.2, 7.9, 10.5, 11.0),\n", + " stringsAsFactors = FALSE\n", + ")\n", + "\n", + "df\n", + "summary(df)\n", + "colnames(df)\n", + "rownames(df)\n" + ] + }, + { + "cell_type": "markdown", + "id": "3e872a62", + "metadata": {}, + "source": [ + "- data frames can be easily accessed and changed using column names or numeric indexing\n", + "- arbitrary subsets of data frames are selected using `df[row, col]` syntax" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "398ad07c", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "# get one column (returns vector of elements)\n", + "df$expression\n", + "\n", + "# filter data frame\n", + "df[df$expression > 9, ]\n", + "\n", + "# filter and sub-select columns using df[row, col]\n", + "df[df$condition == \"treated\", c(\"sample_id\", \"expression\")]" + ] + }, + { + "cell_type": "markdown", + "id": "8058298e", + "metadata": {}, + "source": [ + "## Control flow in R\n", + "\n", + "- R has the same control-flow ideas as Python, with slightly different syntax:\n", + " - `if (...) { ... } else { ... }`\n", + " - `for (x in vector) { ... }`\n", + " - `while (...) { ... }`\n", + "\n", + "- conditions/iterators are wrapped in round, and function bodies in curly braces" + ] + }, + { + "cell_type": "markdown", + "id": "39b387e3", + "metadata": {}, + "source": [ + "**if-else** condition" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "e0b0088d", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "promoter <- \"AGCTTGACAGACGAAGATTATAATGCAGCG\"\n", + "\n", + "if (nchar(promoter) > 30) {\n", + " print(\"long promoter\")\n", + "} else {\n", + " print(\"short promoter\")\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "4c522c4d", + "metadata": {}, + "source": [ + "**for-loop** and **while-loop**" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "4b188af3", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "\n", + "sigma_factors <- c(\"sigma72\", \"sigma54\", \"sigma28\")\n", + "for (s in sigma_factors) {\n", + " print(paste(\"Sigma factor:\", gsub(\"sigma\", \"\", s)))\n", + "}\n", + "\n", + "count <- 1\n", + "while (count <= 3) {\n", + " print(count)\n", + " count <- count + 1\n", + "}" + ] + }, + { + "cell_type": "markdown", + "id": "a2080ac1", + "metadata": {}, + "source": [ + "## Functions in R\n", + "\n", + "- functions are defined with `fname <- function(...) { ... }` and can return values with `return(...)`\n", + "- in the example, we calculate the **z-score** which indicates by how many standard deviations a value deviates from the population mean" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f0672dce", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "z_score <- function(x) {\n", + " mu = mean(x)\n", + " sigma = sd(x)\n", + " return((x - mu) / sigma)\n", + "}\n", + "\n", + "z_score(rnorm(10)) # input: 10 random numbers from normal distribution\n" + ] + }, + { + "cell_type": "markdown", + "id": "886e1da5", + "metadata": {}, + "source": [ + "- function with mandatory and optional argument (can be omitted in call)\n" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c6b5bc37", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "\n", + "rescale_rpm <- function(x, total_reads, scaling = 1e6) {\n", + " round((x / total_reads) * scaling, 1)\n", + "}\n", + "\n", + "reads <- c(2534, 5788, 13221, 784, 6298)\n", + "total_reads <- 7.5e7\n", + "rescale_rpm(reads, total_reads)" + ] + }, + { + "cell_type": "markdown", + "id": "cb3953f2", + "metadata": {}, + "source": [ + "## Quick recap\n", + "\n", + "Key points to remember:\n", + "\n", + "- R uses `TRUE`/`FALSE`, not `True`/`False`\n", + "- R indexing starts at 1\n", + "- vectors and data frames are important data types, and operations are often vectorized\n", + "- `NA` represents missing data\n", + "- functions and loops do not use indentation, but curly braces `{}` for their bodies\n" + ] + }, + { + "cell_type": "markdown", + "id": "71ed706c", + "metadata": {}, + "source": [ + "\n", + "## Exercises\n", + "\n", + "\n", + "- compute mean and standard deviation of the following numeric vector\n", + "- note: use `mean()` and `sd()` functions, and handle missing values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "f3ddcbac", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "my_vec = c(3, 6, 12, 34, 2, 15, 26, NA)\n" + ] + }, + { + "cell_type": "markdown", + "id": "82880baa", + "metadata": {}, + "source": [ + "- you get the following data frame with gene expression values for 3 genes across 4 samples\n", + "- calculate the mean expression of treatment and control for each gene separately\n", + "- use a function and a for-loop\n", + "- NOTE 1: this data frame is already in the convenient **long format** where all observations are spread on individual rows, not columns\n", + "- NOTE 2: there are *much more efficient* ways to achieve this in R that we will learn about next lesson" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "c9191d86", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [], + "source": [ + "df <- data.frame(\n", + " gene = rep(c(\"tetR\", \"tetA\", \"tetB\"), each = 4),\n", + " condition = rep(c(\"treatment\", \"control\"), times = 6),\n", + " replicate = rep(c(1, 1, 2, 2), times = 3),\n", + " expression = c(10, 20, 30, 15, 25, NA, 12, 22, 32, 14, 24, 34)\n", + ")\n" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/solutions/solutions_09.ipynb b/solutions/solutions_09.ipynb new file mode 100644 index 0000000..e7d409a --- /dev/null +++ b/solutions/solutions_09.ipynb @@ -0,0 +1,149 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "a60ff099", + "metadata": {}, + "source": [ + "# Lesson 09" + ] + }, + { + "cell_type": "markdown", + "id": "71ed706c", + "metadata": {}, + "source": [ + "\n", + "## Exercises\n", + "\n", + "\n", + "- compute mean and standard deviation of the following numeric vector\n", + "- note: use `mean()` and `sd()` functions, and handle missing values" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "id": "f3ddcbac", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "data": { + "text/html": [ + "14" + ], + "text/latex": [ + "14" + ], + "text/markdown": [ + "14" + ], + "text/plain": [ + "[1] 14" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/html": [ + "12.0968315410827" + ], + "text/latex": [ + "12.0968315410827" + ], + "text/markdown": [ + "12.0968315410827" + ], + "text/plain": [ + "[1] 12.09683" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "my_vec = c(3, 6, 12, 34, 2, 15, 26, NA)\n", + "\n", + "mean(my_vec,na.rm = TRUE)\n", + "sd(my_vec,na.rm = TRUE)\n" + ] + }, + { + "cell_type": "markdown", + "id": "82880baa", + "metadata": {}, + "source": [ + "- you get the following data frame with gene expression values for 3 genes across 4 samples\n", + "- calculate the mean expression of treatment and control for each gene separately\n", + "- use a function and a for-loop\n", + "- NOTE 1: this data frame is already in the convenient **long format** where all observations are spread on individual rows, not columns\n", + "- NOTE 2: there are *much more efficient* ways to achieve this in R that we will learn about next lesson" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "id": "c9191d86", + "metadata": { + "vscode": { + "languageId": "r" + } + }, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "[1] \"tetR , treatment: 20 , control: 17.5\"\n", + "[1] \"tetA , treatment: 18.5 , control: 22\"\n", + "[1] \"tetB , treatment: 28 , control: 24\"\n" + ] + } + ], + "source": [ + "df <- data.frame(\n", + " gene = rep(c(\"tetR\", \"tetA\", \"tetB\"), each = 4),\n", + " condition = rep(c(\"treatment\", \"control\"), times = 6),\n", + " replicate = rep(c(1, 1, 2, 2), times = 3),\n", + " expression = c(10, 20, 30, 15, 25, NA, 12, 22, 32, 14, 24, 34)\n", + ")\n", + "\n", + "robust_mean <- function(x) {\n", + " mean(x, na.rm = TRUE)\n", + "}\n", + "\n", + "for (g in unique(df$gene)) {\n", + " sub_df <- df[df$gene == g, ]\n", + " mean_treatment = robust_mean(sub_df[sub_df$condition == \"treatment\", \"expression\"])\n", + " mean_control = robust_mean(sub_df[sub_df$condition == \"control\", \"expression\"])\n", + " print(paste(g, \", treatment:\", mean_treatment, \", control:\", mean_control))\n", + "}\n" + ] + } + ], + "metadata": { + "celltoolbar": "Slideshow", + "kernelspec": { + "display_name": "R", + "language": "R", + "name": "ir" + }, + "language_info": { + "codemirror_mode": "r", + "file_extension": ".r", + "mimetype": "text/x-r-source", + "name": "R", + "pygments_lexer": "r", + "version": "4.5.3" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} From 5a51e42aed179dd4f333a9a5e9ba71a65fbd11de Mon Sep 17 00:00:00 2001 From: m-jahn Date: Mon, 27 Apr 2026 14:58:00 +0200 Subject: [PATCH 4/5] feat: updated all data sources, removed some example sets --- LICENSE | 21 ++ README.md | 29 +- data/Jahn_eLife_2021.csv | 21 ++ .../reeves_gradient_width_various_methods.csv | 164 --------- data/towels.txt | 33 -- lessons/lesson_01.ipynb | 5 +- lessons/lesson_07.ipynb | 58 ++- pixi.lock | 342 ++++++++++-------- pixi.toml | 2 + 9 files changed, 296 insertions(+), 379 deletions(-) create mode 100644 LICENSE create mode 100644 data/Jahn_eLife_2021.csv delete mode 100644 data/reeves_gradient_width_various_methods.csv delete mode 100644 data/towels.txt diff --git a/LICENSE b/LICENSE new file mode 100644 index 0000000..968dd63 --- /dev/null +++ b/LICENSE @@ -0,0 +1,21 @@ +MIT License + +Copyright (c) 2026 Max Planck Unit for the Science of Pathogens + +Permission is hereby granted, free of charge, to any person obtaining a copy +of this software and associated documentation files (the "Software"), to deal +in the Software without restriction, including without limitation the rights +to use, copy, modify, merge, publish, distribute, sublicense, and/or sell +copies of the Software, and to permit persons to whom the Software is +furnished to do so, subject to the following conditions: + +The above copyright notice and this permission notice shall be included in all +copies or substantial portions of the Software. + +THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR +IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY, +FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE +AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER +LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM, +OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE +SOFTWARE. diff --git a/README.md b/README.md index b47490e..f114a80 100644 --- a/README.md +++ b/README.md @@ -6,7 +6,16 @@ This Github repository contains all course materials. Issues can be reported [he For comments, criticism, and general feedback please contact the [authors](#authors) at bioinformatics@mpusp.mpg.de -## Course materials and structure +## Course materials + +### Sources and license + +The course materials are a blend of own works and code examples from Justin Bois' [Introduction to Programming in the Biological Sciences Bootcamp](https://justinbois.github.io/bootcamp/2024/). The code examples from the Bootcamp course are released with the [MIT License](https://opensource.org/license/mit). + +All other code examples and documentation added by the [authors](#authors) is also released under the [MIT License](https://opensource.org/license/mit), except where explicitly noted. + +All example datasets are taken from published sources which are referenced in [Data sources](#data-sources). +These datasets are **not covered by the MIT License**, but are licensed under their respective terms. ### Dependencies @@ -57,18 +66,18 @@ export RETICULATE_PYTHON="/path/to/your/env/bin/python" - `README.md`: Instructions and information about the course - `.gitignore`: Specifies files and directories to be ignored by Git -## Data origins / citations: +## Data sources -- `gfmt_sleep\*`: Beattie, L., Walsh, D., McLaren, J., Biello, S.M. and White, D., 2016. Perceptual impairment in face identification with poor sleep. Royal Society Open Science, 3(10). -- `iris.data`: Anderson, E., 1935. The irises of the Gaspe Peninsula. Bulletin of American Iris Society, 59, pp.2-5. -- `ls_orchids.\*`: Cox, A.V., Pridgeon, A.M., Albert, V.A. and Chase, M.W., 1997. Phylogenetics of the slipper orchids (Cypripedioideae, Orchidaceae): nuclear rDNA ITS sequences. Plant Systematics and Evolution, 208(3), pp.197-223. -- `NC_005816.gb`: Zhou, D., Tong, Z., Song, Y., Han, Y., Pei, D., Pang, X., Zhai, J., Li, M., Cui, B., Qi, Z. and Jin, L., 2004. Genetics of metabolic variations between Yersinia pestis biovars and the proposal of a new biovar, microtus. Journal of bacteriology, 186(15), pp.5147-5152. -- `reeves_gradient_width_various_methods.cvs`: Reeves, G.T., Trisnadi, N., Truong, T.V., Nahmad, M., Katz, S. and Stathopoulos, A., 2012. Dorsal-ventral gene expression in the Drosophila embryo reflects the dynamics and precision of the dorsal nuclear gradient. Developmental cell, 22(3), pp.544-557. -- `towels.txt`: Douglas Adams, The Hitch Hiker's Guide to the Galaxy, ISBN: 0345391802 +- `gfmt_sleep\*`: Beattie, L., Walsh, D., McLaren, J., Biello, S.M. and White, D., 2016. Perceptual impairment in face identification with poor sleep. Royal Society Open Science, 3(10). Released to [Public domain, CC0](https://datadryad.org/dataset/doi:10.5061/dryad.r620r). +- `iris.data`: Anderson, E., 1935. The irises of the Gaspe Peninsula. Bulletin of American Iris Society, 59, pp.2-5. [CC-BY-4.0 License](https://doi.org/10.1111/j.1469-1809.1936.tb02137.x). +- `ls_orchids.\*`: Cox, A.V., Pridgeon, A.M., Albert, V.A. and Chase, M.W., 1997. Phylogenetics of the slipper orchids (Cypripedioideae, Orchidaceae): nuclear rDNA ITS sequences. Plant Systematics and Evolution, 208(3), pp.197-223. [Freely available on NCBI](https://www.ncbi.nlm.nih.gov/nuccore/?term=Phylogenetics%20of%20the%20slipper%20orchids). +- `NC_005816.gb`: Zhou, D., Tong, Z., Song, Y., Han, Y., Pei, D., Pang, X., Zhai, J., Li, M., Cui, B., Qi, Z. and Jin, L., 2004. Genetics of metabolic variations between Yersinia pestis biovars and the proposal of a new biovar, microtus. Journal of bacteriology, 186(15), pp.5147-5152. [Freely available on NCBI](https://www.ncbi.nlm.nih.gov/datasets/genome/GCF_000007885.1/). +- `Jahn_eLife_2021.csv`: Jahn, M., Crang, N., Janasch, M., Hober, A., Forsström, B., Kimler, K., Mattausch, A., Chen, Q., Asplund-Samuelsson, J., & Hudson, E. P. (2021). Protein allocation and utilization in the versatile chemolithoautotroph Cupriavidus necator. eLife, 10(e69019), 1–26. [Freely available on Github, GPL v3](https://github.com/m-jahn/R-notebook-ralstonia-proteome). -## Adding and changing contents +## Contributions -To be added. +**Contributions to the course materials are very welcome!** +If you have suggestions for improvements, or want to report issues, please feel free to [open an issue](https://github.com/MPUSP/coding-for-biologists/issues) or submit a pull request on GitHub. ## Authors diff --git a/data/Jahn_eLife_2021.csv b/data/Jahn_eLife_2021.csv new file mode 100644 index 0000000..263a995 --- /dev/null +++ b/data/Jahn_eLife_2021.csv @@ -0,0 +1,21 @@ +uniprot,substrate,growthrate,R1,R2,R3,R4 +Q0K1H0,formate,0.05,900578172,441270170,3122542832,680060600 +Q0K1H0,formate,0.1,614552208,2398450248,366775320,1015804992 +Q0K1H0,formate,0.15,705491384,367461526,803938584,782118008 +Q0K1H0,formate,0.2,438389228,580611700,938426916,464743138 +Q0K1H0,formate,0.25,522893276,1364078846,1373663968,942002356 +Q0K1H0,fructose,0.05,305158214,332735058,1400748272,877117100 +Q0K1H0,fructose,0.1,78090540,NA,396061392,778370782 +Q0K1H0,fructose,0.15,15727490,464742468,398025584,260101530 +Q0K1H0,fructose,0.2,79098500,590114016,133964096,296108304 +Q0K1H0,fructose,0.25,32359600,257136500,201190480,89447700 +Q0K1H0,ammonium,0.05,72514300,378991192,1095938008,641173112 +Q0K1H0,ammonium,0.1,8652209052,1020382916,682807584,388322042 +Q0K1H0,ammonium,0.15,292831192,728197400,691936776,243449700 +Q0K1H0,ammonium,0.2,181858100,260700392,5612858216,6030139888 +Q0K1H0,ammonium,0.25,7295757960,284956160,658380400,259553392 +Q0K1H0,succinate,0.05,607908460,418218000,463238600,692957484 +Q0K1H0,succinate,0.1,460987046,385657000,469888616,310468790 +Q0K1H0,succinate,0.15,450655732,408609784,1087130084,333188100 +Q0K1H0,succinate,0.2,299384900,656712030,591076116,691310308 +Q0K1H0,succinate,0.25,427697608,289053748,562829796,211568150 diff --git a/data/reeves_gradient_width_various_methods.csv b/data/reeves_gradient_width_various_methods.csv deleted file mode 100644 index dbcf613..0000000 --- a/data/reeves_gradient_width_various_methods.csv +++ /dev/null @@ -1,164 +0,0 @@ -# Raw data from Reeves, et al., Dev. Cell, 22, 544, 2012. -# -# These data were used to create figure 1F. -# -# The first row of column headings contains the genotypes. -# The second row contains the staining/visualization method. -# The data give the computed gradient width as parametrized by -# sigma (see the original paper). -# -# Kindly provided by Greg Reeves (NC State) and Nathanie Trisnadi and Angela Stathopoulos (Caltech) -wt,wt,dl1/+; dl-venus/+,dl1/+; dl-venus/+,dl1/+; dl-venus/+,dl1/+; dl-gfp/+,dl1/+; dl-gfp/+,dl1/+; dl-gfp/+ -wholemounts,cross-sections,anti-Dorsal,anti-Venus,Venus (live),anti-Dorsal,anti-GFP,GFP (live) -0.1288,0.1327,0.1482,0.1632,0.1666,0.2248,0.2389,0.2412 -0.1554,0.1457,0.1503,0.1671,0.1753,0.1891,0.2035,0.1942 -0.1306,0.1447,0.1577,0.1704,0.1705,0.1705,0.1943,0.2186 -0.1413,0.1282,0.1711,0.1779,,0.1735,0.2,0.2104 -0.1557,0.1487,0.1342,0.1483,,0.2135,0.256,0.2463 -0.1689,0.1203,0.1773,0.1831,,0.2129,0.2309,0.202 -0.1417,0.1315,0.1705,0.1783,,0.1782,0.1949, -0.1315,0.1463,0.1596,0.1667,,0.2011,0.2216, -0.1664,0.1458,0.1601,0.178,,0.1931,0.23, -0.156,0.1402,0.1553,0.1653,,0.2146,0.2682, -0.1479,0.133,0.1675,0.1708,,0.2398,0.2633, -0.1475,0.1375,0.1692,0.178,,0.2193,0.2495, -0.1347,0.1576,0.1584,0.174,,0.2112,0.2353, -0.1129,0.1498,0.1546,0.1739,,0.2516,0.2642, -0.125,0.1532,0.1605,0.1812,,0.1759,0.2238, -0.1648,0.1352,0.1754,0.1902,,0.1787,0.2115, -0.1555,0.1346,0.161,0.178,,0.164,0.1934, -0.1403,0.1364,0.1491,0.1624,,0.1756,0.2041, -0.1096,0.143,0.1442,0.1553,,0.1977,0.2286, -0.1779,0.144,0.1728,0.1768,,0.1809,0.2104, -0.1244,0.1805,0.1534,0.1701,,,, -0.1708,0.1688,0.1576,0.1724,,,, -0.1691,0.1532,0.1701,0.1738,,,, -0.13,0.1681,0.1436,0.1703,,,, -,0.1442,0.1746,0.1842,,,, -,0.1362,0.1698,0.172,,,, -,0.1391,0.171,0.1807,,,, -,0.1606,0.1555,0.1796,,,, -,0.1742,0.1616,0.1759,,,, -,0.1335,,,,,, -,0.1347,,,,,, -,0.1404,,,,,, -,0.1524,,,,,, -,0.1469,,,,,, -,0.1365,,,,,, -,0.1433,,,,,, -,0.1358,,,,,, -,0.1643,,,,,, -,0.1371,,,,,, -,0.1141,,,,,, -,0.1436,,,,,, -,0.1223,,,,,, -,0.1459,,,,,, -,0.1368,,,,,, -,0.1376,,,,,, -,0.1601,,,,,, -,0.1309,,,,,, -,0.1305,,,,,, -,0.1562,,,,,, -,0.1577,,,,,, -,0.154,,,,,, -,0.1443,,,,,, -,0.1364,,,,,, -,0.1344,,,,,, -,0.1315,,,,,, -,0.1572,,,,,, -,0.1441,,,,,, -,0.1399,,,,,, -,0.1497,,,,,, -,0.1647,,,,,, -,0.1629,,,,,, -,0.1194,,,,,, -,0.1296,,,,,, -,0.1574,,,,,, -,0.1365,,,,,, -,0.1759,,,,,, -,0.1522,,,,,, -,0.1535,,,,,, -,0.1398,,,,,, -,0.1453,,,,,, -,0.1569,,,,,, -,0.1582,,,,,, -,0.174,,,,,, -,0.1781,,,,,, -,0.1488,,,,,, -,0.1663,,,,,, -,0.1699,,,,,, -,0.173,,,,,, -,0.1655,,,,,, -,0.1341,,,,,, -,0.1014,,,,,, -,0.162,,,,,, -,0.1704,,,,,, -,0.1499,,,,,, -,0.1648,,,,,, -,0.1397,,,,,, -,0.1745,,,,,, -,0.1684,,,,,, -,0.1399,,,,,, -,0.1512,,,,,, -,0.1793,,,,,, -,0.1581,,,,,, -,0.1515,,,,,, -,0.1465,,,,,, -,0.162,,,,,, -,0.1581,,,,,, -,0.1559,,,,,, -,0.147,,,,,, -,0.1445,,,,,, -,0.1673,,,,,, -,0.1703,,,,,, -,0.154,,,,,, -,0.16,,,,,, -,0.1147,,,,,, -,0.1317,,,,,, -,0.1685,,,,,, -,0.1415,,,,,, -,0.1575,,,,,, -,0.1524,,,,,, -,0.1374,,,,,, -,0.1596,,,,,, -,0.1159,,,,,, -,0.1104,,,,,, -,0.1368,,,,,, -,0.1621,,,,,, -,0.1117,,,,,, -,0.1563,,,,,, -,0.158,,,,,, -,0.146,,,,,, -,0.1174,,,,,, -,0.155,,,,,, -,0.1327,,,,,, -,0.1443,,,,,, -,0.1329,,,,,, -,0.1567,,,,,, -,0.1388,,,,,, -,0.1449,,,,,, -,0.1548,,,,,, -,0.1727,,,,,, -,0.142,,,,,, -,0.1735,,,,,, -,0.1641,,,,,, -,0.1756,,,,,, -,0.1691,,,,,, -,0.1599,,,,,, -,0.1271,,,,,, -,0.1468,,,,,, -,0.149,,,,,, -,0.1706,,,,,, -,0.1283,,,,,, -,0.1325,,,,,, -,0.1663,,,,,, -,0.1325,,,,,, -,0.1529,,,,,, -,0.1682,,,,,, -,0.1557,,,,,, -,0.1577,,,,,, -,0.1466,,,,,, -,0.1671,,,,,, -,0.1265,,,,,, -,0.1448,,,,,, -,0.174,,,,,, \ No newline at end of file diff --git a/data/towels.txt b/data/towels.txt deleted file mode 100644 index f29427f..0000000 --- a/data/towels.txt +++ /dev/null @@ -1,33 +0,0 @@ -The Hitch Hiker's Guide to the Galaxy has a few things to -say on the subject of towels. -A towel, it says, is about the most massively useful thing -an interstellar hitchhiker can have. Partly it has great -practical value - you can wrap it around you for warmth as -you bound across the cold moons of Jaglan Beta; you can lie -on it on the brilliant marble-sanded beaches of Santraginus -V, inhaling the heady sea vapours; you can sleep under it -beneath the stars which shine so redly on the desert world -of Kakrafoon; use it to sail a mini raft down the slow heavy -river Moth; wet it for use in hand-to- hand-combat; wrap it -round your head to ward off noxious fumes or to avoid the -gaze of the Ravenous Bugblatter Beast of Traal (a -mindboggingly stupid animal, it assumes that if you can't -see it, it can't see you - daft as a bush, but very -ravenous); you can wave your towel in emergencies as a -distress signal, and of course dry yourself off with it if -it still seems to be clean enough. More importantly, a -towel has immense psychological value. For some reason, if -a strag (strag: non-hitch hiker) discovers that a hitch -hiker has his towel with him, he will automatically assume -that he is also in possession of a toothbrush, face flannel, -soap, tin of biscuits, flask, compass, map, ball of string, -gnat spray, wet weather gear, space suit etc., etc. -Furthermore, the strag will then happily lend the hitch -hiker any of these or a dozen other items that the hitch -hiker might accidentally have "lost". What the strag will -think is that any man who can hitch the length and breadth -of the galaxy, rough it, slum it, struggle against terrible -odds, win through, and still knows where his towel is is -clearly a man to be reckoned with. - -Douglas Adams: "The Hitchhiker's Guide to the Galaxy" diff --git a/lessons/lesson_01.ipynb b/lessons/lesson_01.ipynb index fda478e..9b5923e 100644 --- a/lessons/lesson_01.ipynb +++ b/lessons/lesson_01.ipynb @@ -36,9 +36,10 @@ "## Course materials and structure\n", "\n", "- all course participants can be contacted via the mailing list `python_2026@mpusp.mpg.de`\n", - "- all materials are available at our Github repository: https://github.com/MPUSP/python_course/ (currently private)\n", + "- all materials are available at our Github repository: https://github.com/MPUSP/python_course/\n", "- each lesson is provided as a Jupyter notebook (`.ipynb`), or alternatively a Quarto (`.qmd`) or R markdown (`.Rmd`) notebook\n", - "- the course material is a blend of own works examples from Justin Bois from Caltech (http://bois.caltech.edu/)\n", + "- the course materials are a blend of own works and code examples from Justin Bois' [Introduction to Programming in the Biological Sciences Bootcamp](https://justinbois.github.io/bootcamp/2024/). The code examples from the Bootcamp course are released with the [MIT License](https://opensource.org/license/mit).\n", + "- all other content that was added is also released under the [MIT License](https://opensource.org/license/mit), except where explicitly noted\n", "- each lesson is supposed to be worked through in about 90 minutes\n", "- the course is **interactive (!)**: we will mix small introductory lectures with hands-on exercises\n", "- participants are expected to actively work through the notebooks, run code cells, and try to understand/solve the given tasks\n", diff --git a/lessons/lesson_07.ipynb b/lessons/lesson_07.ipynb index 5f174ba..02e9a14 100644 --- a/lessons/lesson_07.ipynb +++ b/lessons/lesson_07.ipynb @@ -1005,12 +1005,22 @@ "metadata": {}, "source": [ "## Tidying up a dataset\n", - "- as outlined before, humanly readable data is rarely easy to handle, so called `wide format`\n", + "\n", + "- as outlined before, data often comes in the so called `wide format`, which is impractical for analysis\n", "- the following code will provide an example on how to:\n", " - read in a raw data file\n", " - re-assemble a table with one parameter per column. We will use the function `melt()` for that. This is called `long format`\n", - " - calculate general statistics\n", - "- the data comes from Reeves et al. 2012 in Developmental Cell https://doi.org/10.1016/j.devcel.2011.12.007" + " - calculate general statistics\n" + ] + }, + { + "cell_type": "markdown", + "id": "fe931314", + "metadata": {}, + "source": [ + "- we import a data set from [Jahn et al. eLife 2018](https://doi.org/10.7554/eLife.69019) that is not really tidy yet\n", + "- it contains MS measurements of a protein in 4 different conditions and 5 growth rates\n", + "- replicate measurements are stored in columns R1 to R4, which we want to have in long format" ] }, { @@ -1020,10 +1030,7 @@ "metadata": {}, "outputs": [], "source": [ - "df = pd.read_csv(\n", - " \"../data/reeves_gradient_width_various_methods.csv\", comment=\"#\", header=[0, 1]\n", - ")\n", - "\n", + "df = pd.read_csv(\"../data/Jahn_eLife_2021.csv\")\n", "df.head()" ] }, @@ -1034,17 +1041,24 @@ "metadata": {}, "outputs": [], "source": [ - "# The first row of column headings contains the genotypes.\n", - "# The second row contains the staining/visualization method.\n", - "# The data give the computed gradient width as parametrized by\n", - "# sigma (see the original paper).\n", - "\n", - "df.columns.names = [\"genotype\", \"method\"]\n", - "df = pd.melt(df, value_name=\"gradient width\")\n", + "df = pd.melt(\n", + " df,\n", + " id_vars=[\"uniprot\", \"substrate\", \"growthrate\"],\n", + " var_name=\"replicate\",\n", + " value_name=\"abundance\",\n", + ")\n", "\n", "df.head()" ] }, + { + "cell_type": "markdown", + "id": "e716f135", + "metadata": {}, + "source": [ + "- optionally remove n/a values" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1052,10 +1066,18 @@ "metadata": {}, "outputs": [], "source": [ - "# removing n/a values\n", + "\n", "df = df.dropna()" ] }, + { + "cell_type": "markdown", + "id": "8833505e", + "metadata": {}, + "source": [ + "- group by variables of choice and calculate summary statistics, i.e. mean, standard deviation" + ] + }, { "cell_type": "code", "execution_count": null, @@ -1063,7 +1085,7 @@ "metadata": {}, "outputs": [], "source": [ - "df.groupby([\"genotype\", \"method\"]).describe()" + "df.groupby([\"uniprot\", \"substrate\"]).describe()" ] }, { @@ -1337,7 +1359,7 @@ "metadata": { "celltoolbar": "Slideshow", "kernelspec": { - "display_name": ".venv (3.14.3)", + "display_name": "default", "language": "python", "name": "python3" }, @@ -1351,7 +1373,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.14.3" + "version": "3.14.4" } }, "nbformat": 4, diff --git a/pixi.lock b/pixi.lock index 1a312d7..cbb2dff 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,24 +9,23 @@ environments: packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda - - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2 - conda: https://conda.anaconda.org/conda-forge/noarch/anyio-4.13.0-pyhcf101f3_0.conda - conda: https://conda.anaconda.org/conda-forge/noarch/argon2-cffi-25.1.0-pyhd8ed1ab_0.conda - 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"args = parser.parse_args()\n", + "# args = parser.parse_args()\n", "\n", "# print(args.info)\n", "# print(args.unit_test)" @@ -272,16 +272,16 @@ "source": [ "import re\n", "\n", - "# reading file\n", - "with open(\"../data/towels.txt\", \"r\") as infile:\n", - " story = infile.read()\n", + "# # reading file\n", + "# with open(\"../data/towels.txt\", \"r\") as infile:\n", + "# story = infile.read()\n", "\n", - "# create pattern\n", - "pattern = \".*[H|h]itch *[H|h]iker.*\"\n", - "regex = re.compile(pattern)\n", + "# # create pattern\n", + "# pattern = \".*[H|h]itch *[H|h]iker.*\"\n", + "# regex = re.compile(pattern)\n", "\n", - "# search for pattern and print each line with the pattern in it\n", - "print(regex.findall(story))" + "# # search for pattern and print each line with the pattern in it\n", + "# print(regex.findall(story))" ] }, { @@ -663,7 +663,7 @@ "outputs": [], "source": [ "# showing first 10 lines of the file, missing data indicated by *\n", - "print(\"\\n\".join(open(\"data/gfmt_sleep.csv\").read().split(\"\\n\")[:10]))" + "print(\"\\n\".join(open(\"../data/gfmt_sleep.csv\").read().split(\"\\n\")[:10]))" ] }, { @@ -941,7 +941,7 @@ "metadata": {}, "outputs": [], "source": [ - "df.to_csv(\"data/gfmt_sleep_with_insomnia.csv\", index=False)" + "df.to_csv(\"../data/gfmt_sleep_with_insomnia.csv\", index=False)" ] }, { diff --git a/lessons/lesson_09.ipynb b/lessons/lesson_09.ipynb index 6567414..27f508e 100644 --- a/lessons/lesson_09.ipynb +++ b/lessons/lesson_09.ipynb @@ -604,6 +604,7 @@ }, "outputs": [], "source": [ + "# we use repeat function `rep(x, ...)` to fill the data frame with values\n", "df <- data.frame(\n", " gene = rep(c(\"tetR\", \"tetA\", \"tetB\"), each = 4),\n", " condition = rep(c(\"treatment\", \"control\"), times = 6),\n", diff --git a/pixi.lock b/pixi.lock index cbb2dff..b83b1d9 100644 --- a/pixi.lock +++ b/pixi.lock @@ -9,23 +9,24 @@ environments: packages: linux-64: - conda: https://conda.anaconda.org/conda-forge/linux-64/_openmp_mutex-4.5-20_gnu.conda + - conda: https://conda.anaconda.org/conda-forge/noarch/_python_abi3_support-1.0-hd8ed1ab_2.conda - conda: https://conda.anaconda.org/conda-forge/noarch/_r-mutex-1.0.1-anacondar_1.tar.bz2 - 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