We are hiring ! See our job offers.
##### https://github.com/AllenInstitute/patchseqtools
Tip revision: d1afcd4
calculatePatchSeqQCMetrics2.Rd
% Generated by roxygen2: do not edit by hand
% Please edit documentation in R/QC_wrappers.r
\name{calculatePatchSeqQCMetrics2}
\alias{calculatePatchSeqQCMetrics2}
\title{Calculate PatchSeq QC Metrics}
\usage{
calculatePatchSeqQCMetrics2(pat_df, facs_df, markers)
}
\arguments{
\item{pat_df}{a matrix (rows=samples x columns=genes + meta-data) of gene expression
data and meta-data for patch-seq data (e.g., the data data set for QCing)}

\item{facs_df}{an equivalent matrix of reference data}

\item{markers}{a list of marker genes (calculated using defineClassMarkers)}
}
\value{
a table containing all of the qc metrics:
sample_id: name of the samples.
major_type: cell type identities (provided by Cadwell2016)
contam_type: cell type identities (normalized to cell type names in markers)
marker_sum: Summed expression of 'On' cell type marker genes (with cell type
defined by contam_type)
marker_sum_norm: Normalized summed expression of 'on'-type marker genes,
normalized to median expression of same cell type in dissociated-cell
reference data
contam_sum: Contamination score, defined as the sum of normalized expression
across all 'off' cell types defined in compare_cell_types_inh
quality_score: Quality score, defined as the Spearman correlation of marker
expression between markers expressed in single cell with mean expression
of markers in dissociated cell reference data
This function also outputs normalized expression of each 'off'-cell type
(defined in compare_cell_types_inh) and we can use the function
plotContamHeatmap to show these (each column is one single cell)
}
\description{
This function identifies is the same as calculatePatchSeqQCMetrics from
patchSeqQC, except that it allows for any user-inputted comparison data set,
and fixes some other errors.  Importantly, it outputs the same quality score,
marker sum, and contamination score.
}