scautoqc plus logo

README.md

@@ -26,4 +26,6 @@ pip install --upgrade pip
 
 ## Usage and Documentation
 
-SCTK's documentation is available [here](https://teichlab.github.io/sctk/), and features a tutorial and API reference for the automated QC workflow.
+SCTK's documentation is available [here](https://teichlab.github.io/sctk/), and features a tutorial and API reference for scAutoQC (an automated QC workflow).
+
+<img src="https://github.com/Teichlab/sctk/tree/master/docs/notebooks/scautoqc_logo.png" alt="scautoqc_logo" width="200"/>

docs/api.rst

@@ -1,8 +1,8 @@
 API Reference
 =============
 
-QC pipeline
------------
+scAutoQC
+--------
 .. module:: sctk
 .. autosummary::
    :toctree: modules

docs/notebooks/automatic_qc.ipynb

@@ -2,16 +2,24 @@
  "cells": [
   {
    "cell_type": "markdown",
-   "id": "mexican-skill",
+   "id": "previous-province",
    "metadata": {},
    "source": [
-    "# Automatic QC workflow tutorial"
+    "# scAutoQC workflow tutorial"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "peripheral-feeling",
+   "metadata": {},
+   "source": [
+    "<img src=\"scautoqc_logo.png\" alt=\"scautoqc_logo\" width=\"200\"/>"
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 1,
-   "id": "hindu-treaty",
+   "id": "entitled-cargo",
    "metadata": {},
    "outputs": [],
    "source": [
@@ -23,10 +31,10 @@
   },
   {
    "cell_type": "markdown",
-   "id": "increasing-commercial",
+   "id": "foster-exhibit",
    "metadata": {},
    "source": [
-    "This notebook will show how to apply SCTK's automatic QC workflow. The process has four steps:\n",
+    "This notebook will show how to apply SCTK's automatic QC workflow, scAutoQC. The process has four steps:\n",
     "\n",
     " - `sctk.calculate_qc()` computes QC measures\n",
     " - `sctk.generate_qc_clusters()` creates QC-space clusters of cells\n",
@@ -39,7 +47,7 @@
   {
    "cell_type": "code",
    "execution_count": 2,
-   "id": "identified-costume",
+   "id": "economic-punishment",
    "metadata": {},
    "outputs": [
     {
@@ -61,7 +69,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "electrical-fighter",
+   "id": "streaming-facial",
    "metadata": {},
    "source": [
     "This object features raw counts for some PBMC data, and nothing else for now. We can use `sctk.calculate_qc()` to automatically flag some common technical features (the defaults are human mitochondrial, ribosomal and hemoglobin genes) and subsequently use `sc.pp.calculate_qc_metrics()` to compute their percentages in each cell, along with standard measures like the number of genes and counts."
@@ -70,7 +78,7 @@
   {
    "cell_type": "code",
    "execution_count": 3,
-   "id": "responsible-valentine",
+   "id": "hybrid-riverside",
    "metadata": {},
    "outputs": [
     {
@@ -93,7 +101,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "smoking-forum",
+   "id": "natural-rebound",
    "metadata": {},
    "source": [
     "You can see the `.obs` of the object populated with the QC measures we just generated.\n",
@@ -104,7 +112,7 @@
   {
    "cell_type": "code",
    "execution_count": 4,
-   "id": "internal-tissue",
+   "id": "trained-account",
    "metadata": {},
    "outputs": [
     {
@@ -138,7 +146,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "average-proceeding",
+   "id": "driven-consumption",
    "metadata": {},
    "source": [
     "There's now an `.obs` column called `qc_cluster`, and an embedding called `X_umap_qc`. Let's take a look!"
@@ -147,7 +155,7 @@
   {
    "cell_type": "code",
    "execution_count": 5,
-   "id": "separated-domain",
+   "id": "frozen-learning",
    "metadata": {},
    "outputs": [
     {
@@ -180,16 +188,16 @@
   },
   {
    "cell_type": "markdown",
-   "id": "alpine-friend",
+   "id": "educational-riding",
    "metadata": {},
    "source": [
-    "The next step of the workflow involves determining which of the cells pass QC individually."
+    "The next step of the scAutoQC workflow involves determining which of the cells pass QC individually."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 6,
-   "id": "cutting-pressure",
+   "id": "behind-adjustment",
    "metadata": {},
    "outputs": [
     {
@@ -227,7 +235,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "dominican-thousand",
+   "id": "endless-cooperative",
    "metadata": {},
    "source": [
     "There's now a `cell_passed_qc` column in `.obs` which features per-cell QC calls."
@@ -236,7 +244,7 @@
   {
    "cell_type": "code",
    "execution_count": 7,
-   "id": "capable-gauge",
+   "id": "ceramic-attention",
    "metadata": {},
    "outputs": [
     {
@@ -256,7 +264,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "ranging-knitting",
+   "id": "enhanced-server",
    "metadata": {},
    "source": [
     "Controlling the desired QC thresholds requires a bit of explanation.\n",
@@ -292,7 +300,7 @@
   {
    "cell_type": "code",
    "execution_count": 8,
-   "id": "incredible-parks",
+   "id": "mechanical-sympathy",
    "metadata": {},
    "outputs": [
     {
@@ -323,20 +331,20 @@
   },
   {
    "cell_type": "markdown",
-   "id": "basic-johnson",
+   "id": "square-technique",
    "metadata": {},
    "source": [
     "The gene count filtering is different now, as expected.\n",
     "\n",
-    "The final step of the workflow determines computes a fraction of passing cells for each cluster. If a cluster has a high enough fraction of passing cells (controlled via the `threshold` parameter, default 0.5), it's deemed to be a good QC cluster.\n",
+    "The final step of the scAutoQC workflow determines computes a fraction of passing cells for each cluster. If a cluster has a high enough fraction of passing cells (controlled via the `threshold` parameter, default 0.5), it's deemed to be a good QC cluster.\n",
     "\n",
     "You can use your own per-cell QC calls here rather than the ones provided by `sctk.cellwise_qc()` if desired, just specify which `.obs` column to take via the `cell_qc_key` argument."
    ]
   },
   {
    "cell_type": "code",
    "execution_count": 9,
-   "id": "lovely-webcam",
+   "id": "southern-highway",
    "metadata": {},
    "outputs": [
     {
@@ -361,7 +369,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "smoking-reaction",
+   "id": "rural-margin",
    "metadata": {},
    "source": [
     "There's now a `cluster_passed_qc` column. Let's take a look how it compares to the cell level QC."
@@ -370,7 +378,7 @@
   {
    "cell_type": "code",
    "execution_count": 10,
-   "id": "dedicated-hungarian",
+   "id": "norwegian-difficulty",
    "metadata": {},
    "outputs": [
     {
@@ -398,7 +406,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "eligible-episode",
+   "id": "economic-satisfaction",
    "metadata": {},
    "source": [
     "We seem to get a nontrivial number of cells individually flagged as poor quality colocating in the top left of the plot, but only a small subset of them remain after cluster membership is leveraged. Let's generate a new set of QC clusters by increasing the resolution parameter value, creating a higher number of smaller clusters as a result."
@@ -407,7 +415,7 @@
   {
    "cell_type": "code",
    "execution_count": 11,
-   "id": "optimum-cathedral",
+   "id": "assumed-westminster",
    "metadata": {},
    "outputs": [
     {
@@ -438,7 +446,7 @@
   },
   {
    "cell_type": "markdown",
-   "id": "pleased-superintendent",
+   "id": "expensive-check",
    "metadata": {},
    "source": [
     "The plots align better now.\n",