In bioinfocz/scdrake: A pipeline for droplet-based single-cell RNA-seq data secondary analysis implemented in the drake Make-like toolkit for R language

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Introduction

{scdrake} configs are stored in YAML files (in the config/ directory by default), which are parsed in R to lists and used to build {drake} plans. YAML (quick reference, cheatsheets here and here) is a human-readable format and uses indentation (spaces) for nesting of parameters.

{scdrake} is using a concept of default and local configs. Default configs are bundled with the package and copied during project initialization, update, and pipeline run (e.g. in run_single_sample_r()). Local configs are, as their name suggests, purposed to make modifications to default configs (see the section below for how it is actually done). Default and local configs are named *.default.yaml and *.yaml, respectively.

Config files are separated according to general parameters and each pipeline's main stage (quality control, normalization, etc.), but for consistency are always read all at once (by default if you run e.g. run_single_sample_r()).

All paths in configs must be relative to project root, or absolute (not recommended), unless otherwise stated.

Updating (merging) configs

It may happen that new parameters are added to default configs. In that case, those new parameters need to be appended to local configs, while preserving the current local parameters. Calling this procedure "update" is a little bit misleading, as we actually overwrite default config by local one and append new parameters from default config to it. "Merging" should be a better word, as we merge default config with local one (and the order does matter).

Configs are merged recursively by parameter names. It is necessary to realize that YAML format are nested dictionaries (or named lists in the context of R). That is, this YAML

# Default config.
FOO: 1
BAR: "baz"
FOO_LIST: [1, "hello"]
FOO_NAMED_LIST:
  FOO_2: 2
  BAR_2: "zab"

is in R parsed to the list:

list(FOO = 1L, BAR = "baz", FOO_LIST = list(1L, "hello"), FOO_NAMED_LIST = list(FOO_2 = 2L, BAR_2 = "zab"))

So, for example, given that the YAML above is our default config, we want to merge it with the local config

# Local config.
BAR: "zab"
FOO_LIST: [4, 5, 6]
FOO_NAMED_LIST:
  FOO_2: 3
  BAR_3: 5

resulting in

# Updated local config.
FOO: 1
BAR: "zab"
FOO_LIST: [4, 5, 6]
FOO_NAMED_LIST:
  FOO_2: 3
  BAR_2: "zab"
  BAR_3: 5

• FOO is not updated.
• BAR and FOO_LIST are overwritten by the local config.
• FOO_NAMED_LIST: FOO_2 is updated, BAR_3 is added, but BAR_2 is still present, although it is not defined in the local config!

Merging of nested named lists

To overcome the problem with FOO_NAMED_LIST, parameters with such structure are specified as a named list wrapped by an unnamed list:

FOO_NAMED_LIST:
  - FOO_2: 2
    BAR_2: "zab"

Note the beginning - creating the unnamed list and indentation of values of the named list. This YAML is parsed in R to

list(FOO_NAMED_LIST = list(list(FOO_2 = 2L, BAR_2 = "zab")))

If we modify the example of local config above to

FOO_NAMED_LIST:
  - FOO_2: 3
    BAR_3: 5

then the whole FOO_NAMED_LIST parameter will be replaced during a config merge, and that is the desired behaviour.

Using structure of a default config

Also, by default, a structure (parameter positions, comments) of local config is preserved during update. However, it is possible to use the structure of a default config - see ?update_config for more details.

Using R code in parameters

A special type of parameter starting with !code can be used to evaluate a value as R code:

EXAMPLE: !code 1:3

First, non-code parameters are loaded to a separate environment and then code parameters are evaluated in the context of this environment. This means you can use other config parameters as R variables inside code parameters:

FOO: 1
BAR: !code FOO + 1

In addition, in stage configs (e.g. 02_norm_clustering) you can also use parameters (variables) from pipeline.yaml and 00_main.yaml configs:

EXAMPLE: !code glue("{PROJECT_NAME}_{INSTITUTE}")

See ?load_config for more details.

The yq tool

Internally, the yq tool (version 3) is used for merging of YAML files. It is a command line utility whose binary needs to be downloaded. This is done automatically during initialization of a new project, or you can do it manually through download_yq().

On {scdrake} load or attach, SCDRAKE_YQ_BINARY environment variable is read - if not set, a value from Sys.which("yq") is used (this function searches in PATH environment variable). Then scdrake_yq_binary option is set, and is used as default value to config-updating functions (see ?update_config).

You can also look at ?check_yq for details on how PATH variable is treated in terminal vs. RStudio.

scdrake_list

The base R's extracting operators ($, [, [[) are very benevolent for non-existing elements in list() for which return NULL. This is not a desired behaviour for config variables that must have a value or explicit NULL. Returning NULL when the value was actually not loaded from a config file can lead to unpredictable results.

Thus, for storing config variables, {scdrake} is using a modified list() called scdrake_list() which is using strict extracting operators. It behaves like a normal list:

cfg <- scdrake::scdrake_list(list(var_1 = 1, var_2 = 2))
cfg$var_1
cfg[["var_2"]]
cfg["var_1"]
cfg[c("var_1", "var_2")]

But extracting non-existing elements throws error:

cfg$var_3
cfg[["var_3"]]
cfg["var_3"]

For [ and [[, this control can be turned off by check = FALSE parameter:

cfg[["var_3", check = FALSE]]
cfg["var_3", check = FALSE]

Also, in this case [ is more consistent: it returns a valid named list, unlike the normal list, which sets NA_character_ as names of the non-existing elements.

bioinfocz/scdrake documentation built on Sept. 19, 2024, 4:43 p.m.