flowchart TB
v0(Channel.fromList)
v2(filter)
v10(filter)
v18(clr)
v25(cross)
v35(cross)
v41(filter)
v164(concat)
v53(branch)
v80(concat)
v65(cross)
v75(cross)
v84(branch)
v111(concat)
v96(cross)
v106(cross)
v112(filter)
v142(concat)
v127(cross)
v137(cross)
v149(cross)
v159(cross)
v171(cross)
v178(cross)
v190(cross)
v197(cross)
v201(Output)
subgraph group_prot_qc [prot_qc]
v58(grep_mitochondrial_genes)
v89(grep_ribosomal_genes)
v120(calculate_qc_metrics)
end
v53-->v80
v84-->v111
v111-->v112
v0-->v2
v2-->v10
v10-->v18
v18-->v25
v10-->v25
v10-->v35
v53-->v58
v58-->v65
v53-->v65
v53-->v75
v75-->v80
v84-->v89
v89-->v96
v84-->v96
v84-->v106
v106-->v111
v112-->v120
v120-->v127
v112-->v127
v112-->v137
v137-->v142
v142-->v149
v41-->v149
v41-->v159
v159-->v164
v164-->v171
v2-->v171
v171-->v178
v2-->v178
v2-->v190
v190-->v197
v2-->v197
v197-->v201
v35-->v41
v18-->v35
v41-->v53
v58-->v75
v80-->v84
v89-->v106
v120-->v137
v142-->v159
v164-->v190
style group_prot_qc fill:#F0F0F0,stroke:#969696;
style v0 fill:#e3dcea,stroke:#7a4baa;
style v2 fill:#e3dcea,stroke:#7a4baa;
style v10 fill:#e3dcea,stroke:#7a4baa;
style v18 fill:#e3dcea,stroke:#7a4baa;
style v25 fill:#e3dcea,stroke:#7a4baa;
style v35 fill:#e3dcea,stroke:#7a4baa;
style v41 fill:#e3dcea,stroke:#7a4baa;
style v164 fill:#e3dcea,stroke:#7a4baa;
style v53 fill:#e3dcea,stroke:#7a4baa;
style v80 fill:#e3dcea,stroke:#7a4baa;
style v58 fill:#e3dcea,stroke:#7a4baa;
style v65 fill:#e3dcea,stroke:#7a4baa;
style v75 fill:#e3dcea,stroke:#7a4baa;
style v84 fill:#e3dcea,stroke:#7a4baa;
style v111 fill:#e3dcea,stroke:#7a4baa;
style v89 fill:#e3dcea,stroke:#7a4baa;
style v96 fill:#e3dcea,stroke:#7a4baa;
style v106 fill:#e3dcea,stroke:#7a4baa;
style v112 fill:#e3dcea,stroke:#7a4baa;
style v142 fill:#e3dcea,stroke:#7a4baa;
style v120 fill:#e3dcea,stroke:#7a4baa;
style v127 fill:#e3dcea,stroke:#7a4baa;
style v137 fill:#e3dcea,stroke:#7a4baa;
style v149 fill:#e3dcea,stroke:#7a4baa;
style v159 fill:#e3dcea,stroke:#7a4baa;
style v171 fill:#e3dcea,stroke:#7a4baa;
style v178 fill:#e3dcea,stroke:#7a4baa;
style v190 fill:#e3dcea,stroke:#7a4baa;
style v197 fill:#e3dcea,stroke:#7a4baa;
style v201 fill:#e3dcea,stroke:#7a4baa;
Prot multisample
Processing unimodal multi-sample ADT data.
Info
ID: prot_multisample
Namespace: workflows/prot
Links
Example commands
You can run the pipeline using nextflow run.
View help
You can use --help as a parameter to get an overview of the possible parameters.
nextflow run openpipelines-bio/openpipeline \
-r 2.1.1 -latest \
-main-script target/nextflow/workflows/prot/prot_multisample/main.nf \
--helpRun command
Example of params.yaml
# Inputs
id: # please fill in - example: "concatenated"
input: # please fill in - example: "dataset.h5mu"
# layer: "foo"
# Outputs
# output: "$id.$key.output.h5mu"
# QC metrics calculation options
# var_qc_metrics: ["ercc,highly_variable"]
top_n_vars: [50, 100, 200, 500]
output_obs_num_nonzero_vars: "num_nonzero_vars"
output_obs_total_counts_vars: "total_counts"
output_var_num_nonzero_obs: "num_nonzero_obs"
output_var_total_counts_obs: "total_counts"
output_var_obs_mean: "obs_mean"
output_var_pct_dropout: "pct_dropout"
# CLR arguments
clr_axis: 0
# Nextflow input-output arguments
publish_dir: # please fill in - example: "output/"
# param_list: "my_params.yaml"
# Argumentsnextflow run openpipelines-bio/openpipeline \
-r 2.1.1 -latest \
-profile docker \
-main-script target/nextflow/workflows/prot/prot_multisample/main.nf \
-params-file params.yaml
Note
Replace -profile docker with -profile podman or -profile singularity depending on the desired backend.
Argument groups
Inputs
| Name | Description | Attributes |
|---|---|---|
--id |
ID of the concatenated file | string, required, example: "concatenated" |
--input |
Path to the samples. | file, required, example: "dataset.h5mu" |
--layer |
Input layer to use. If not specified, .X is used. | string |
Outputs
| Name | Description | Attributes |
|---|---|---|
--output |
Destination path to the output. | file, required, example: "output.h5mu" |
QC metrics calculation options
| Name | Description | Attributes |
|---|---|---|
--var_qc_metrics |
Keys to select a boolean (containing only True or False) column from .var. For each cell, calculate the proportion of total values for genes which are labeled ‘True’, compared to the total sum of the values for all genes. Defaults to the value from –var_name_mitochondrial_genes. | List of string, example: "ercc,highly_variable", multiple_sep: "," |
--top_n_vars |
Number of top vars to be used to calculate cumulative proportions. If not specified, proportions are not calculated. --top_n_vars 20,50 finds cumulative proportion to the 20th and 50th most expressed vars. |
List of integer, default: 50, 100, 200, 500, multiple_sep: "," |
--output_obs_num_nonzero_vars |
Name of column in .obs describing, for each observation, the number of stored values (including explicit zeroes). In other words, the name of the column that counts for each row the number of columns that contain data. | string, default: "num_nonzero_vars" |
--output_obs_total_counts_vars |
Name of the column for .obs describing, for each observation (row), the sum of the stored values in the columns. | string, default: "total_counts" |
--output_var_num_nonzero_obs |
Name of column describing, for each feature, the number of stored values (including explicit zeroes). In other words, the name of the column that counts for each column the number of rows that contain data. | string, default: "num_nonzero_obs" |
--output_var_total_counts_obs |
Name of the column in .var describing, for each feature (column), the sum of the stored values in the rows. | string, default: "total_counts" |
--output_var_obs_mean |
Name of the column in .obs providing the mean of the values in each row. | string, default: "obs_mean" |
--output_var_pct_dropout |
Name of the column in .obs providing for each feature the percentage of observations the feature does not appear on (i.e. is missing). Same as --output_var_num_nonzero_obs but percentage based. |
string, default: "pct_dropout" |
CLR arguments
| Name | Description | Attributes |
|---|---|---|
--clr_axis |
Axis across which CLR is performed. | integer, default: 0 |