Skip to main content

Feature extraction approach in single-cell gene expression profiling for cell-type marker identification.

Project description

MICTI- Marker gene Identification for Cell Type Identity

Recent advances in single-cell gene expression profiling technology have revolutionized the understanding of molecular processes underlying developmental cell and tissue differentiation, enabling the discovery of novel cell types and molecular markers that characterize developmental trajectories. Common approaches for identifying marker genes are based on pairwise statistical testing for differential gene expression between cell types in heterogeneous cell populations, which is challenging due to unequal sample sizes and variance between groups resulting in little statistical power and inflated type I errors.

Overview

We developed an alternative feature extraction method, Marker gene Identification for Cell Type Identity (MICTI), that encodes the cell-type specific expression information to each gene in every single cell. This approach identifies features (genes) that are cell-type specific for a given cell-type in heterogeneous cell population.

Installation

To install the current release:

pip install MICTI

How to use MICTI

Import MICTI:

from MICTI import MARKER

Creating MICTI object for known cell-type cluster label:

mictiObject=MARKER.MICTI(datamatrix, geneName, cellName, cluster_assignment=cell_type, k=None, th=0, ensembel=False, organisum="hsapiens")

2D visualisation with tSNE:

mictiObject.get_Visualization(dim=2, method="tsne")

Get MICTI marker genes:

    cluster_1_markers=mictiObject.get_markers_by_Pvalues_and_Zscore(1, threshold_pvalue=.01,threshold_z_score=0)

Markers heatmap plots:

mictiObject.heatMap()

Markers Radar plots:

mictiObject.get_Radar_plot()

Gene Ontology enrichment analysis for cell-type marker genes in each of cell-type clusters

enrechment_table=mictiObject.get_gene_list_over_representation_analysis(list(cluster_1_markers.index))
enrechment_table #gene-list enrichment analysis result for the cell-type marker genes for cluster-1

Licence

MICTI LICENCE

Project details


Download files

Download the file for your platform. If you're not sure which to choose, learn more about installing packages.

Source Distribution

MICTI-0.1.8.tar.gz (17.5 kB view details)

Uploaded Source

Built Distribution

If you're not sure about the file name format, learn more about wheel file names.

MICTI-0.1.8-py3-none-any.whl (21.4 kB view details)

Uploaded Python 3

File details

Details for the file MICTI-0.1.8.tar.gz.

File metadata

  • Download URL: MICTI-0.1.8.tar.gz
  • Upload date:
  • Size: 17.5 kB
  • Tags: Source
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/40.6.2 requests-toolbelt/0.8.0 tqdm/4.15.0 CPython/3.5.2

File hashes

Hashes for MICTI-0.1.8.tar.gz
Algorithm Hash digest
SHA256 2edc14a0c7254de739a184a42d5b93618e3e8880b6a822f0b129123c7b642827
MD5 ce1896dfa8bc032e2a0e1dd04957415d
BLAKE2b-256 71f8e300ab69dfa76b953f14f6be463d19c099d8b436da0b26fcfea464e12bea

See more details on using hashes here.

File details

Details for the file MICTI-0.1.8-py3-none-any.whl.

File metadata

  • Download URL: MICTI-0.1.8-py3-none-any.whl
  • Upload date:
  • Size: 21.4 kB
  • Tags: Python 3
  • Uploaded using Trusted Publishing? No
  • Uploaded via: twine/1.12.1 pkginfo/1.4.2 requests/2.18.4 setuptools/40.6.2 requests-toolbelt/0.8.0 tqdm/4.15.0 CPython/3.5.2

File hashes

Hashes for MICTI-0.1.8-py3-none-any.whl
Algorithm Hash digest
SHA256 c6b7d50a38b35a94d2b1cc3c20c834b8b943502f5f275336d70fced54428fdf0
MD5 2296619744e3766dce3c1e0712a904eb
BLAKE2b-256 db28ffba95c51bc7f1dba33a618c4f749e7252e54b390c50de65b2af6aab8297

See more details on using hashes here.

Supported by

AWS Cloud computing and Security Sponsor Datadog Monitoring Depot Continuous Integration Fastly CDN Google Download Analytics Pingdom Monitoring Sentry Error logging StatusPage Status page