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Getting started

  • Installation
  • Quickstart

Guides

  • General
    • Bring your own data
    • Quality control
    • Normalize, select, aggregate
    • Plate artifacts and corrections
    • Is the screen any good?
    • Choosing a readout
    • From a CellProfiler run
    • Published profiles and JUMP
    • Bringing your own embedding
    • Images and segmentations
    • Which measurements moved
    • Pitfalls
      • Pseudoreplication
  • Compound screens
    • Hits, effects and cell loss
    • Hit callers on well profiles
    • Mechanism of action
    • Feature-set enrichment
    • Concentration response
  • Genetic screens
    • Interpreting a screen end to end
    • Hit callers on cells and on guide profiles
  • Single cells
    • What the well median hides
    • Where cells sit in the cycle
    • How the cell-cycle model works
  • Case studies
    • Learned embeddings against CellProfiler
    • Reproducing across laboratories
    • Optical pooled screens
    • CRISPR knockouts
    • Calling hits against genes that cannot respond
    • Calling gene hits in a pooled screen
    • Correcting plate position for hit-calling and profiling

Reference

  • API
    • Datasets
    • Reading and writing
      • mantispy.io.read_profiles
      • mantispy.io.read_plate
      • mantispy.io.read_jump
      • mantispy.io.unexpressed_genes
      • mantispy.io.read
      • mantispy.io.write
      • mantispy.io.stamp
      • mantispy.io.from_dataframe
      • mantispy.io.from_embedding
      • mantispy.io.validate
    • Preprocessing
    • Tools
    • Plotting
    • Metrics
      • mantispy.metrics.known_relationships
      • mantispy.metrics.evaluate_integration
      • mantispy.metrics.diagnose_testing
      • mantispy.metrics.pc_regression
      • mantispy.metrics.batch_variance_explained
      • mantispy.metrics.variance_carried
    • Accessors
      • mantispy.get.features
      • mantispy.get.to_dataframe
      • mantispy.get.controls
      • mantispy.get.obs_df
      • mantispy.get.var_df
    • Settings
      • mantispy.settings
      • mantispy.settings.override
      • mantispy.settings.reset
  • Data model
  • The mantispy stamp (uns["mantispy"])
  • Schema specification
  • Known limitations
  • About mantispy

Project

  • Contributing guide
  • Citation
  • Bibliography
  • GitHub
  • Discourse
  • Single cells
  • GitHub
  • Discourse
  • scverse.org
  • Core
  • AnnData
  • decoupler
  • MuData
  • Muon
  • Pertpy
  • rapids-singlecell
  • Scanpy
  • Scirpy
  • scvi-tools
  • SnapATAC2
  • SpatialData
  • Squidpy
  • Ecosystem
  • AESTETIK
  • AlphaPeptTools
  • AmbiDose
  • annbatch
  • anndata for R
  • anndataR
  • annsel
  • AnnZarro
  • ATLAS
  • benGRN
  • bento-tools
  • biolord
  • cell2location
  • Cell_BLAST
  • CellAnnotator
  • CellCharter
  • CellMapper
  • CellOracle
  • CellphoneDB
  • CellRank
  • cellxgene
  • Cirrocumulus
  • clone2vec
  • cuspa
  • dandelion
  • delnx
  • DOTools_py
  • DoubletDetection
  • DRVI
  • dvp-io
  • dynamo-release
  • epiScanpy
  • eschr
  • fava
  • flashdeconv
  • flowsom
  • GATAC
  • GPTBioInsightor
  • grassp
  • GRnnData
  • gssnng
  • harpy
  • hotspot
  • illico
  • infercnvpy
  • kompot
  • LazySlide
  • liana
  • mantispy
  • Marsilea
  • maxspin
  • moscot
  • Mowgli
  • Multivelo
  • ncountr
  • NicheNetPy
  • nichepca
  • novae
  • omicverse
  • Palantir
  • Panpipes
  • ParTIpy
  • PathML
  • pcdl
  • PEAKQC
  • pegasus
  • PILOT
  • popV
  • pycea
  • pychromVAR
  • pyCrossTalkeR
  • PyDESeq2
  • pyLemur
  • pySCENIC
  • pytximport
  • pyUCell
  • Rectangle
  • SC2Spa
  • SCALEX
  • scanpro
  • scATrans
  • scCellFie
  • scDataLoader
  • scFair
  • scFates
  • scGen
  • schist
  • scib
  • scib-metrics
  • scib-rapids
  • scmcp
  • scPRINT
  • scPRINT-2
  • scTriangulate
  • scVelo
  • scviva-tools
  • scxmatch
  • scXpand
  • scyan
  • sift-sc
  • sincei
  • Sobolev Alignment
  • sopa
  • spatial-eggplant
  • spatialdataR
  • spatialproteomics
  • spatiomic
  • STMiner
  • Symphonypy
  • tangram
  • TAU Community Detection
  • TreeData
  • vitessce
  • wsidata
  • zellkonverter

Single cells#

  • What the well median hides
  • Where cells sit in the cycle
  • How the cell-cycle model works

previous

Hit callers on cells and on guide profiles

next

What the well median hides

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