Algorithms used to analyze ddPCR droplets | Step 1: Identify failed wells | Step 2: Identify outlier droplets | Step 3: Identify empty droplets | Step 4: Gate droplets | Step 4.1: Identify rain droplets | Step 4.2: Identify mutant versus wildtype droplets | Step 4.3: Classify wells as mutant versus wildtype | Step 5: Revisit gating of wildtype wells
Package: ddpcr 1.16.0
ddpcr: Analysis and Visualization of Droplet Digital PCR in R and on the Web
An interface to explore, analyze, and visualize droplet digital PCR (ddPCR) data in R. This is the first non-proprietary software for analyzing two-channel ddPCR data. An interactive tool was also created and is available online to facilitate this analysis for anyone who is not comfortable with using R.
Authors:
ddpcr_1.16.0.tar.gz
ddpcr_1.16.0.zip(r-4.7)ddpcr_1.16.0.zip(r-4.6)ddpcr_1.16.0.zip(r-4.5)
ddpcr_1.16.0.tgz(r-4.6-any)ddpcr_1.16.0.tgz(r-4.5-any)
ddpcr_1.16.0.tar.gz(r-4.7-any)ddpcr_1.16.0.tar.gz(r-4.6-any)
ddpcr_1.16.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
ddpcr/json (API)
| # Install 'ddpcr' in R: |
| install.packages('ddpcr', repos = c('https://daattali.r-universe.dev', 'https://cloud.r-project.org')) |
Bug tracker:https://github.com/daattali/ddpcr/issues
Last updated from:92acedb3eb. Checks:7 NOTE, 2 OK. Indexed: yes.
| Target | Result | Time | Files | Syslog |
|---|---|---|---|---|
| linux-devel-x86_64 | NOTE | 149 | ||
| source / vignettes | OK | 245 | ||
| linux-release-x86_64 | NOTE | 180 | ||
| macos-release-arm64 | NOTE | 107 | ||
| macos-oldrel-arm64 | NOTE | 97 | ||
| windows-devel | NOTE | 112 | ||
| windows-release | NOTE | 104 | ||
| windows-oldrel | NOTE | 116 | ||
| wasm-release | OK | 168 |
Exports:%>%%btwn%analysis_completeanalyzebind_df_endscalc_negative_freq_simplecalculate_concentrationcalculate_concentration_singlecalculate_neg_freq_singlecalculate_negative_freqscapitalizecat0check_stepclassify_dropletsclassify_droplets_singleclassify_thresholdsclustercluster_nameclusterscol_to_numdefine_clustersdefine_paramsdefine_stepserr_msgget_colget_empty_cutoffget_filled_borderget_filled_dropsget_outlier_cutoffget_rowget_single_wellget_wells_btwnhas_signif_negative_clusterhas_stepis_diris_dirtyis_empty_plateis_fileis_rangeis_well_successlaunchload_platelocal_maximalocal_minimalol_to_dfmark_clustersmerge_dfs_overwrite_colmeta_var_namemove_backmove_frontmsgnamename<-named_vec_to_dfnegative_namenegative_name<-new_platenext_stepnormalize_to_rdsnum_to_colnum_to_rowother_dimparamsparams<-parent_plate_typeplate_dataplate_data<-plate_metaplate_meta<-plate_typespoint2dpositive_dimpositive_dim_varpositive_dim<-positive_namepositive_name<-quietrange_list_to_vecrange_to_endpointsrange_to_seqreclassify_dropletsreclassify_droplets_singleremove_emptyremove_failuresremove_outliersresetrow_to_numsample_data_dirsample_data_filesample_platesample_results_filesave_plateset_default_paramsset_thresholdsstatusstatus<-stepstep_beginstep_endstep_namestepsthresholdsthresholds<-typeunanalyzed_clustersvariable_dimvariable_dim_varvariable_dim<-warn_msgWELL_ID_REGEXwell_infowells_failedwells_mutantwells_negativewells_positivewells_successwells_usedwells_wildtypex_thresholdx_threshold<-x_varx_var<-y_thresholdy_threshold<-y_vary_var<-
Dependencies:askpassbackportsbase64encbitbit64bslibcachemcheckmateclicliprcommonmarkcpp11crayoncrosstalkcurldata.tabledigestdplyrDTevaluatefarverfastmapfontawesomefsgenericsggplot2gluegtablehighrhmshtmltoolshtmlwidgetshttpuvhttrisobandjquerylibjsonlitekernlabknitrlabelinglaterlatticelazyevallifecyclemagrittrMASSMatrixmemoisemimemixtoolsnlmeopensslotelpillarpkgconfigplotlyplyrprettyunitsprogresspromisespurrrR6rappdirsRColorBrewerRcppreadrrlangrmarkdownS7sassscalessegmentedshinyshinydisconnectshinyjssourcetoolsstringistringrsurvivalsystibbletidyrtidyselecttinytextzdbutf8vctrsviridisLitevroomwithrxfunxtableyaml
Algorithms in ddpcr analysis
Last update: 2026-01-14
Started: 2015-08-28
Package ddpcr
ddpcr: Analysis and visualization of Droplet Digital PCR data in R and on the web | Table of contents
Last update: 2026-01-14
Started: 2015-08-26
Implementation technical details
A plate object is a list | Using S3 for plate objects to override base generic functions | Using S3 for plate objects to support inheritance | Analysis of different built-in plate types | How the analysis steps work
Last update: 2026-01-14
Started: 2016-05-08
Extending ddpcr by adding new plate types
Default plate type: ddpcr_plate | Built-in plate types | Functions to implement when adding a new plate type | parent_plate_type(): Define parent plate type | define_params(): Define plate type parameters | define_clusters(): Define droplet clusters | define_steps(): Define analysis steps | Changing the algorithm of an existing step | plot(): Plot a ddPCR plate | Example
Last update: 2016-07-29
Started: 2015-08-28
