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main.nf
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main.nf
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import static groovy.json.JsonOutput.*
nextflow.enable.dsl=2
include {
INIT;
} from './src/utils/workflows/utils' params(params)
INIT(params)
include {
SC__FILE_CONVERTER;
} from './src/utils/processes/utils' params(params)
include {
getDataChannel;
} from './src/channels/channels' params(params)
// run multi-sample with bbknn, output a scope loom file
workflow bbknn {
include {
bbknn as BBKNN;
} from './workflows/bbknn' params(params)
include {
PUBLISH as PUBLISH_SCOPE;
PUBLISH as PUBLISH_SCANPY;
} from "./src/utils/workflows/utils" params(params)
getDataChannel | BBKNN
if(params.utils?.publish) {
PUBLISH_SCOPE(
BBKNN.out.scopeloom,
"BBKNN",
"loom",
null,
false
)
PUBLISH_SCANPY(
BBKNN.out.scanpyh5ad,
"BBKNN",
"h5ad",
null,
false
)
}
}
// run multi-sample with mnncorrect, output a scope loom file
workflow mnncorrect {
include {
mnncorrect as MNNCORRECT;
} from './workflows/mnncorrect' params(params)
include {
PUBLISH as PUBLISH_SCOPE;
PUBLISH as PUBLISH_SCANPY;
} from "./src/utils/workflows/utils" params(params)
getDataChannel | MNNCORRECT
if(params.utils?.publish) {
PUBLISH_SCOPE(
MNNCORRECT.out.scopeloom,
"MNNCORRECT",
"loom",
null,
false
)
PUBLISH_SCANPY(
MNNCORRECT.out.scanpyh5ad,
"MNNCORRECT",
"h5ad",
null,
false
)
}
}
def getHarmonyBatchVariables = { params ->
batchVariables = params.sc.harmony.varsUse
if(batchVariables.size() > 1) {
throw new Exception("Currently it is not supported to run with multiple batch variables.")
}
return batchVariables
}
// run multi-sample with bbknn, output a scope loom file
workflow harmony {
include {
harmony as HARMONY;
} from './workflows/harmony' params(params)
include {
PUBLISH as PUBLISH_SCOPE;
PUBLISH as PUBLISH_SCANPY;
} from "./src/utils/workflows/utils" params(params)
batchVariables = getHarmonyBatchVariables(params)
outputSuffix = params.utils?.publish?.annotateWithBatchVariableName ? "HARMONY" + "_BY_" + batchVariables[0].toUpperCase() : "HARMONY"
getDataChannel | HARMONY
if(params.utils?.publish) {
PUBLISH_SCOPE(
HARMONY.out.scopeloom,
outputSuffix,
"loom",
null,
false
)
PUBLISH_SCANPY(
HARMONY.out.scanpyh5ad,
outputSuffix,
"h5ad",
null,
false
)
}
}
workflow harmony_only {
include {
BEC_HARMONY as HARMONY;
} from './src/harmony/workflows/harmony_only' params(params)
include {
PUBLISH as PUBLISH_HARMONY;
} from "./src/utils/workflows/utils" params(params)
batchVariables = getHarmonyBatchVariables(params)
outputSuffix = params.utils?.publish?.annotateWithBatchVariableName ? "HARMONY" + "_BY_" + batchVariables[0].toUpperCase() : "HARMONY"
getDataChannel | HARMONY
if(params.utils?.publish) {
PUBLISH_HARMONY(
HARMONY.out.scanpyh5ad,
params.utils?.publish?.annotateWithBatchVariableName ? "HARMONY" + "_BY_" + batchVariables[0].toUpperCase() : "HARMONY",
"h5ad",
null,
false
)
}
}
// run multi-sample with bbknn, then scenic from the filtered output:
workflow bbknn_scenic {
include {
bbknn as BBKNN;
} from './workflows/bbknn' params(params)
include {
scenic_append as SCENIC_APPEND;
} from './src/scenic/main' params(params)
include {
PUBLISH as PUBLISH_BBKNN;
PUBLISH as PUBLISH_BBKNN_SCENIC;
} from "./src/utils/workflows/utils" params(params)
getDataChannel | BBKNN
if(params.utils?.publish) {
PUBLISH_BBKNN(
BBKNN.out.scanpyh5ad,
"BBKNN",
"h5ad",
null,
false
)
}
SCENIC_APPEND(
BBKNN.out.filteredloom,
BBKNN.out.scopeloom
)
if(params.utils?.publish) {
PUBLISH_BBKNN_SCENIC(
SCENIC_APPEND.out,
"BBKNN_SCENIC",
"loom",
null,
false
)
}
}
// run multi-sample with harmony, then scenic from the filtered output:
workflow harmony_scenic {
include {
harmony as HARMONY;
} from './workflows/harmony' params(params)
include {
scenic_append as SCENIC_APPEND;
} from './src/scenic/main' params(params)
include {
PUBLISH as PUBLISH_HARMONY;
PUBLISH as PUBLISH_HARMONY_SCENIC;
} from "./src/utils/workflows/utils" params(params)
batchVariables = getHarmonyBatchVariables(params)
outputSuffix = params.utils?.publish?.annotateWithBatchVariableName ? "HARMONY" + "_BY_" + batchVariables[0].toUpperCase() : "HARMONY"
getDataChannel | HARMONY
if(params.utils?.publish) {
PUBLISH_HARMONY(
HARMONY.out.scanpyh5ad,
outputSuffix,
"h5ad",
null,
false
)
}
SCENIC_APPEND(
HARMONY.out.filteredloom,
HARMONY.out.scopeloom
)
if(params.utils?.publish) {
PUBLISH_HARMONY_SCENIC(
SCENIC_APPEND.out,
outputSuffix + "_SCENIC",
"loom",
null,
false
)
}
}
// run single_sample, output a scope loom file
workflow single_sample {
include {
single_sample as SINGLE_SAMPLE;
} from './workflows/single_sample' params(params)
include {
PUBLISH as PUBLISH_SINGLE_SAMPLE_SCOPE;
PUBLISH as PUBLISH_SINGLE_SAMPLE_SCANPY;
} from "./src/utils/workflows/utils" params(params)
getDataChannel | SINGLE_SAMPLE
if(params.utils?.publish) {
PUBLISH_SINGLE_SAMPLE_SCOPE(
SINGLE_SAMPLE.out.scopeloom,
"SINGLE_SAMPLE",
"loom",
null,
false
)
PUBLISH_SINGLE_SAMPLE_SCANPY(
SINGLE_SAMPLE.out.scanpyh5ad,
"SINGLE_SAMPLE",
"h5ad",
null,
false
)
}
}
// run single_sample QC
workflow single_sample_qc {
include {
single_sample_qc as SINGLE_SAMPLE_QC;
} from './main' params(params)
include {
PUBLISH as PUBLISH_SINGLE_SAMPLE_SCOPE;
} from "./src/utils/workflows/utils" params(params)
getDataChannel | SINGLE_SAMPLE_QC
if(params.utils?.publish) {
PUBLISH_SINGLE_SAMPLE_SCOPE(
SINGLE_SAMPLE_QC.out.filtered,
"SINGLE_SAMPLE_QC",
"h5ad",
null,
false
)
}
}
workflow multi_sample {
include {
multi_sample as MULTI_SAMPLE;
} from './workflows/multi_sample' params(params)
getDataChannel | MULTI_SAMPLE
include {
PUBLISH as PUBLISH_SCOPE;
PUBLISH as PUBLISH_SCANPY;
} from "./src/utils/workflows/utils" params(params)
if(params.utils?.publish) {
PUBLISH_SCOPE(
MULTI_SAMPLE.out.scopeloom,
"MULTI_SAMPLE",
"loom",
null,
false
)
PUBLISH_SCANPY(
MULTI_SAMPLE.out.scanpyh5ad,
"MULTI_SAMPLE",
"h5ad",
null,
false
)
}
}
// run single_sample, then scenic from the filtered output:
workflow single_sample_scenic {
include {
scenic_append as SCENIC_APPEND;
} from './src/scenic/main' params(params)
include {
single_sample as SINGLE_SAMPLE;
} from './workflows/single_sample' params(params)
include {
PUBLISH as PUBLISH_SCANPY;
PUBLISH as PUBLISH_SCOPE;
} from "./src/utils/workflows/utils" params(params)
getDataChannel | SINGLE_SAMPLE
if(params.utils?.publish) {
PUBLISH_SCANPY(
SINGLE_SAMPLE.out.scanpyh5ad,
"SINGLE_SAMPLE",
"h5ad",
null,
false
)
}
SCENIC_APPEND(
SINGLE_SAMPLE.out.filteredloom,
SINGLE_SAMPLE.out.scopeloom
)
if(params.utils?.publish) {
PUBLISH_SCOPE(
SCENIC_APPEND.out,
"SINGLE_SAMPLE_SCENIC",
"loom",
null,
false
)
}
}
workflow pcacv {
include {
PCACV__FIND_OPTIMAL_NPCS;
} from './src/pcacv/processes/runPCACV' params(params)
getDataChannel().map {
it -> tuple(it[0], it[1])
}
.set{ data }
PCACV__FIND_OPTIMAL_NPCS(data)
}
workflow single_sample_scrublet {
include {
SINGLE_SAMPLE as SCANPY__SINGLE_SAMPLE;
} from './src/scanpy/workflows/single_sample' params(params)
include {
DOUBLET_REMOVAL as SCRUBLET__DOUBLET_REMOVAL;
} from "./src/scrublet/workflows/doublet_removal" params(params)
include {
ANNOTATE_BY_CELL_METADATA;
} from './src/utils/workflows/annotateByCellMetadata.nf' params(params)
include {
PUBLISH as PUBLISH_SINGLE_SAMPLE_SCRUBLET;
} from './src/utils/workflows/utils.nf' params(params)
include {
SC__H5AD_TO_LOOM
} from './src/utils/processes/h5adToLoom.nf' params(params)
data = getDataChannel | SC__FILE_CONVERTER
SCANPY__SINGLE_SAMPLE( data )
SCRUBLET__DOUBLET_REMOVAL(
data.join( SCANPY__SINGLE_SAMPLE.out.dr_pca_data ),
SCANPY__SINGLE_SAMPLE.out.final_processed_data
)
// Annotate the final processed file with doublet information inferred from Scrublet
ANNOTATE_BY_CELL_METADATA(
SCANPY__SINGLE_SAMPLE.out.final_processed_data.map {
it -> tuple(it[0], it[1])
},
SCRUBLET__DOUBLET_REMOVAL.out.doublet_detection.map {
it -> tuple(it[0], it[1])
},
"scrublet"
)
SC__H5AD_TO_LOOM(
SCANPY__SINGLE_SAMPLE.out.filtered_data.map {
it -> tuple(it[0], it[1])
}.join(
ANNOTATE_BY_CELL_METADATA.out
)
)
if(params.utils?.publish) {
PUBLISH_SINGLE_SAMPLE_SCRUBLET(
SC__H5AD_TO_LOOM.out,
"SINGLE_SAMPLE_SCRUBLET",
"loom",
null,
false
)
}
}
workflow decontx {
include {
decontx as CELDA__DECONTX;
} from "./src/celda/main" params(params)
// Run DecontX on the data
CELDA__DECONTX()
}
workflow single_sample_decontx {
include {
SINGLE_SAMPLE as SCANPY__SINGLE_SAMPLE;
} from './src/scanpy/workflows/single_sample' params(params)
include {
decontx as CELDA__DECONTX;
} from "./src/celda/main" params(params)
include {
ANNOTATE_BY_CELL_METADATA_BY_PAIR;
} from './src/utils/workflows/annotateByCellMetadata.nf' params(params)
include {
PUBLISH;
} from './src/utils/workflows/utils.nf' params(params)
include {
SC__H5AD_TO_LOOM;
} from './src/utils/processes/h5adToLoom.nf' params(params)
data = getDataChannel \
| SC__FILE_CONVERTER
// Run Single-sample pipeline on the data
SCANPY__SINGLE_SAMPLE( data )
// Run DecontX on the data
CELDA__DECONTX()
// Annotate the final processed file with doublet information inferred from Scrublet
ANNOTATE_BY_CELL_METADATA_BY_PAIR(
SCANPY__SINGLE_SAMPLE.out.final_processed_data,
CELDA__DECONTX.out.outlier_table,
"celda.decontx"
)
SC__H5AD_TO_LOOM(
SCANPY__SINGLE_SAMPLE.out.filtered_data.map {
it -> tuple(it[0], it[1])
}.join(
ANNOTATE_BY_CELL_METADATA_BY_PAIR.out
)
)
if(params.utils?.publish) {
PUBLISH(
SC__H5AD_TO_LOOM.out,
"SINGLE_SAMPLE_CELDA_DECONTX_"+ params.sc.celda.decontx.strategy.toUpperCase(),
"loom",
null,
false
)
}
}
workflow single_sample_decontx_scrublet {
include {
SINGLE_SAMPLE as SCANPY__SINGLE_SAMPLE;
} from './src/scanpy/workflows/single_sample' params(params)
include {
decontx as CELDA__DECONTX;
} from "./src/celda/main" params(params)
include {
DOUBLET_REMOVAL as SCRUBLET__DOUBLET_REMOVAL;
} from "./src/scrublet/workflows/doublet_removal" params(params)
include {
ANNOTATE_BY_CELL_METADATA_BY_PAIR;
} from './src/utils/workflows/annotateByCellMetadata.nf' params(params)
include {
PUBLISH as PUBLISH_SINGLE_SAMPLE_ANNOTATED;
PUBLISH as PUBLISH_CELDA_DECONTX_SCRUBLET
} from './src/utils/workflows/utils.nf' params(params)
include {
SC__H5AD_TO_LOOM;
} from './src/utils/processes/h5adToLoom.nf' params(params)
data = getDataChannel \
| SC__FILE_CONVERTER
// Run Single-sample pipeline on the INPUT DATA
SCANPY__SINGLE_SAMPLE( data )
// Run DecontX on the INPUT DATA
CELDA__DECONTX()
// Run Scrublet on the DecontX FILTERED DATA
SCRUBLET__DOUBLET_REMOVAL(
CELDA__DECONTX.out.decontx_processed.join( SCANPY__SINGLE_SAMPLE.out.dr_pca_data ),
SCANPY__SINGLE_SAMPLE.out.final_processed_data
)
// Annotate the final processed file with doublet information inferred from Scrublet
ANNOTATE_BY_CELL_METADATA_BY_PAIR(
SCANPY__SINGLE_SAMPLE.out.final_processed_data,
SCRUBLET__DOUBLET_REMOVAL.out.doublet_detection.map {
it -> tuple(it[0], it[1])
}.mix(
CELDA__DECONTX.out.outlier_table
).groupTuple(),
"scrublet" // Works because decontx requires the same params in cell_annotate
)
SC__H5AD_TO_LOOM(
SCANPY__SINGLE_SAMPLE.out.filtered_data.map {
it -> tuple(it[0], it[1])
}.join(
ANNOTATE_BY_CELL_METADATA_BY_PAIR.out
)
)
if(params.utils?.publish) {
// Publish loom file where INPUT DATA:
// - processed by single_sample pipeline and ->
// - annotated by DecontX potential outliers
// - annotated by Scrublet potential doublets
PUBLISH_SINGLE_SAMPLE_ANNOTATED(
SC__H5AD_TO_LOOM.out,
"SINGLE_SAMPLE_ANNOTATED",
"loom",
null,
false
)
// Publish h5ad file where INPUT DATA:
// - processed by DecontX (either through filter or correct strategy) and ->
// - potential doublets removed by Scrublet
PUBLISH_CELDA_DECONTX_SCRUBLET(
SCRUBLET__DOUBLET_REMOVAL.out.data_doublets_removed,
"CELDA_DECONTX_"+ params.sc.celda.decontx.strategy.toUpperCase() +"_SCRUBLET",
"h5ad",
null,
false
)
}
}
workflow soupx {
include {
soupx as SOUPX__DECONTX;
} from "./src/soupx/main" params(params)
// Run DecontX on the data
SOUPX__DECONTX()
}
workflow single_sample_soupx {
include {
SINGLE_SAMPLE as SCANPY__SINGLE_SAMPLE;
} from './src/scanpy/workflows/single_sample' params(params)
include {
soupx as SOUPX;
} from "./src/soupx/main" params(params)
include {
ANNOTATE_BY_CELL_METADATA_BY_PAIR;
} from './src/utils/workflows/annotateByCellMetadata.nf' params(params)
include {
PUBLISH;
} from './src/utils/workflows/utils.nf' params(params)
include {
SC__H5AD_TO_LOOM;
} from './src/utils/processes/h5adToLoom.nf' params(params)
data = getDataChannel \
| SC__FILE_CONVERTER
// Run Single-sample pipeline on the INPUT DATA:
SCANPY__SINGLE_SAMPLE( data )
// Run SoupX on the INPUT DATA:
SOUPX()
SC__H5AD_TO_LOOM(
SCANPY__SINGLE_SAMPLE.out.filtered_data.map {
it -> tuple(it[0], it[1])
}
)
if(params.utils?.publish) {
PUBLISH(
SC__H5AD_TO_LOOM.out,
"SINGLE_SAMPLE",
"loom",
null,
false
)
}
}
workflow single_sample_soupx_scrublet {
include {
SINGLE_SAMPLE as SCANPY__SINGLE_SAMPLE;
} from './src/scanpy/workflows/single_sample' params(params)
include {
soupx as SOUPX;
} from "./src/soupx/main" params(params)
include {
DOUBLET_REMOVAL as SCRUBLET__DOUBLET_REMOVAL;
} from "./src/scrublet/workflows/doublet_removal" params(params)
include {
ANNOTATE_BY_CELL_METADATA_BY_PAIR;
} from './src/utils/workflows/annotateByCellMetadata.nf' params(params)
include {
PUBLISH as PUBLISH_SINGLE_SAMPLE_ANNOTATED;
PUBLISH as PUBLISH_SOUPX_SCRUBLET
} from './src/utils/workflows/utils.nf' params(params)
include {
SC__H5AD_TO_LOOM;
} from './src/utils/processes/h5adToLoom.nf' params(params)
data = getDataChannel \
| SC__FILE_CONVERTER
// Run Single-sample pipeline on the INPUT DATA
SCANPY__SINGLE_SAMPLE( data )
// Run Soupx on the INPUT DATA
SOUPX()
// Run Scrublet on the DecontX FILTERED DATA
SCRUBLET__DOUBLET_REMOVAL(
SOUPX.out.soupx_processed.join( SCANPY__SINGLE_SAMPLE.out.dr_pca_data ),
SCANPY__SINGLE_SAMPLE.out.final_processed_data
)
// Annotate the final processed file with doublet information inferred from Scrublet
ANNOTATE_BY_CELL_METADATA_BY_PAIR(
SCANPY__SINGLE_SAMPLE.out.final_processed_data,
SCRUBLET__DOUBLET_REMOVAL.out.doublet_detection.map {
it -> tuple(it[0], it[1])
},
"scrublet"
)
SC__H5AD_TO_LOOM(
SCANPY__SINGLE_SAMPLE.out.filtered_data.map {
it -> tuple(it[0], it[1])
}.join(
ANNOTATE_BY_CELL_METADATA_BY_PAIR.out
)
)
if(params.utils?.publish) {
// Publish loom file where INPUT DATA:
// - processed by single_sample pipeline and ->
// - annotated by Scrublet potential doublets
PUBLISH_SINGLE_SAMPLE_ANNOTATED(
SC__H5AD_TO_LOOM.out,
"SINGLE_SAMPLE_ANNOTATED",
"loom",
null,
false
)
// Publish h5ad file where INPUT DATA:
// - processed by SoupX and ->
// - potential doublets removed by Scrublet
PUBLISH_SOUPX_SCRUBLET(
SCRUBLET__DOUBLET_REMOVAL.out.data_doublets_removed,
"SOUPX_SCRUBLET",
"h5ad",
null,
false
)
}
}
// run single_sample, then scenic from the previous input (not standalone):
workflow pipe_single_sample_scenic {
take:
data
main:
include {
scenic_append as SCENIC_APPEND;
} from './src/scenic/main' params(params)
include {
single_sample as SINGLE_SAMPLE;
} from './workflows/single_sample' params(params)
include {
PUBLISH as PUBLISH_P_SINGLE_SAMPLE_SCENIC
} from "./src/utils/workflows/utils" params(params)
data | SINGLE_SAMPLE
SCENIC_APPEND(
SINGLE_SAMPLE.out.filteredloom,
SINGLE_SAMPLE.out.scopeloom
)
if(params.utils?.publish) {
PUBLISH_P_SINGLE_SAMPLE_SCENIC(
SCENIC_APPEND.out,
"P_SINGLE_SAMPLE_SCENIC",
"loom",
null,
false
)
}
}
// run scenic directly from an existing loom file:
workflow scenic {
include {
scenic as SCENIC;
} from './src/scenic/main' params(params)
include {
PUBLISH as PUBLISH_SCENIC;
} from "./src/utils/workflows/utils" params(params)
SCENIC(
Channel.of( tuple(params.global.project_name, file(params.sc.scenic.filteredLoom)))
)
if(params.utils?.publish) {
PUBLISH_SCENIC(
SCENIC.out,
"SCENIC",
"loom",
null,
false
)
}
}
// runs mkfastq, then CellRanger count:
workflow cellranger {
include {
CELLRANGER;
} from './src/cellranger/main' params(params)
CELLRANGER(
file(params.sc.cellranger.mkfastq.csv),
file(params.sc.cellranger.mkfastq.runFolder),
file(params.sc.cellranger.count.transcriptome)
)
emit:
CELLRANGER.out
}
workflow cellranger_libraries {
include {
CELLRANGER_LIBRARIES;
} from './src/cellranger/workflows/cellranger_libraries' params(params)
CELLRANGER_LIBRARIES(
file(params.sc.cellranger.mkfastq.csv),
file(params.sc.cellranger.mkfastq.runFolder),
file(params.sc.cellranger.count.transcriptome),
file(params.sc.cellranger.count.featureRef)
)
emit:
CELLRANGER_LIBRARIES.out
}
workflow cellranger_count_metadata {
include {
CELLRANGER_COUNT_WITH_METADATA;
} from './src/cellranger/workflows/cellRangerCountWithMetadata' params(params)
CELLRANGER_COUNT_WITH_METADATA(
file(params.sc.cellranger.count.transcriptome),
file(params.sc.cellranger.count.metadata)
)
emit:
CELLRANGER_COUNT_WITH_METADATA.out
}
workflow cellranger_count_metadata_single_sample_scenic {
cellranger_count_metadata | \
map {
it -> tuple(it[0], it[1], "10x_cellranger_mex", "h5ad")
} | \
pipe_single_sample_scenic
}
workflow cellranger_count_libraries {
include {
CELLRANGER_COUNT_WITH_LIBRARIES;
} from './src/cellranger/workflows/cellRangerCountWithLibraries' params(params)
CELLRANGER_COUNT_WITH_LIBRARIES(
file(params.sc.cellranger.count.transcriptome),
file(params.sc.cellranger.count.featureRef),
params.sc.cellranger.count.libraries
)
emit:
CELLRANGER_COUNT_WITH_LIBRARIES.out
}
workflow cellranger_count_demuxlet {
include {
cellranger_output_to_bam_barcodes;
DEMUXLET;
} from './src/popscle/workflows/demuxlet.nf' params(params)
include {
SC__CELLRANGER__COUNT as CELLRANGER_COUNT;
} from './src/cellranger/processes/count'
if (params.sc.cellranger.count.fastqs instanceof Map) {
// Remove default key
Channel.from(params.sc.cellranger.count.fastqs.findAll {
it.key != 'default'
}.collect { k, v ->
// Split possible multiple file paths
if(v.contains(',')) {
v = Arrays.asList(v.split(',')).collect { fqs -> file(fqs) }
} else {
v = file(v)
}
tuple(k, v)
})
// Group fastqs per sample
.groupTuple()
.map {
it -> tuple(it[0], *it[1])
}
.set { fastq_data }
}
data = CELLRANGER_COUNT(
params.sc.cellranger.count.transcriptome,
fastq_data
)
cellranger_output_to_bam_barcodes(data) |
DEMUXLET
}
workflow freemuxlet {
include {
cellranger_output_to_bam_barcodes;
FREEMUXLET;
} from './src/popscle/workflows/demuxlet.nf' params(params)
getDataChannel |
cellranger_output_to_bam_barcodes |
FREEMUXLET
}
workflow demuxlet {
include {
cellranger_output_to_bam_barcodes;
DEMUXLET;
} from './src/popscle/workflows/demuxlet.nf' params(params)
getDataChannel |
cellranger_output_to_bam_barcodes |
DEMUXLET
}
// runs mkfastq, CellRanger count, then single_sample:
workflow single_sample_cellranger {
include {
single_sample as SINGLE_SAMPLE;
} from './workflows/single_sample' params(params)
data = cellranger()
SINGLE_SAMPLE(
data.map {
tuple(it[0], it[1], "10x_cellranger_mex", "h5ad")
}
)
}
workflow cellranger_multi_sample {
include {
multi_sample as MULTI_SAMPLE;
} from './workflows/multi_sample' params(params)
data = cellranger()
MULTI_SAMPLE(
data.map {
tuple(it[0], it[1], "10x_cellranger_mex", "h5ad")
}
)
}
workflow cellranger_multi_sample_demuxlet {
include {
multi_sample as MULTI_SAMPLE;
} from './workflows/multi_sample' params(params)
include {
cellranger_output_to_bam_barcodes;
DEMUXLET;
} from './src/popscle/workflows/demuxlet.nf' params(params)
data = cellranger()
MULTI_SAMPLE(
data.map {
tuple(it[0], it[1], "10x_cellranger_mex", "h5ad")
}
)
cellranger_output_to_bam_barcodes(data) |
DEMUXLET
}
workflow cellranger_libraries_multi_sample {
include {
multi_sample as MULTI_SAMPLE;
} from './workflows/multi_sample' params(params)
data = cellranger_libraries()
MULTI_SAMPLE(
data.map {
tuple(it[0], it[1], "10x_cellranger_mex", "h5ad")
}
)
}
workflow cellranger_libraries_freemuxlet_multi_sample {
include {
multi_sample as MULTI_SAMPLE;
} from './workflows/multi_sample' params(params)
include {
cellranger_output_to_bam_barcodes;
FREEMUXLET;
} from './src/popscle/workflows/demuxlet.nf' params(params)
data = cellranger_libraries()
MULTI_SAMPLE(
data.map {
tuple(it[0], it[1], "10x_cellranger_mex", "h5ad")
}
)
cellranger_output_to_bam_barcodes(data) |
FREEMUXLET
}
workflow cellranger_libraries_demuxlet_multi_sample {
include {
multi_sample as MULTI_SAMPLE;
} from './workflows/multi_sample' params(params)
include {
cellranger_output_to_bam_barcodes;
DEMUXLET;
} from './src/popscle/workflows/demuxlet.nf' params(params)
data = cellranger_libraries()
MULTI_SAMPLE(
data.map {
tuple(it[0], it[1], "10x_cellranger_mex", "h5ad")
}
)
cellranger_output_to_bam_barcodes(data) |
DEMUXLET
}