Kamil Slowikowski slowkow

## writeMMgz.R
#' @param x A sparse matrix from the Matrix package.
#' @param file A filename that ends in ".gz".
writeMMgz <- function(x, file) {
  mtype <- "real"
  if (is(x, "ngCMatrix")) {
    mtype <- "integer"
  }
  writeLines(
    c(
      sprintf("%%%%MatrixMarket matrix coordinate %s general", mtype),

## needleman-wunsch.py
#!/usr/bin/env python
"""
The Needleman-Wunsch Algorithm
==============================

This is a dynamic programming algorithm for finding the optimal alignment of
two strings.

Example
-------

## read_cellranger.R
# install.packages(c("Matrix", "rhdf5", "tidyverse"))
library(Matrix)
library(rhdf5)
library(tidyverse)
library(glue)

my_h5_files <- Sys.glob(
  "path/to/cellranger-per-channel/output/*/filtered_feature_bc_matrix.h5"
)

## install-units.md

      
              1 file
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                slowkow
                / install-units.md
            
            
              Last active Oct 1, 2019
            
              
                Install the 'units' R package on Partners
              
          
      View install-units.md
  
    
    Summary
I had a difficult time installing the units R package on the Partners ERIS servers.
I hope this post helps you to figure out how to work around the errors.
Instructions

  
## pubmed-publications.html
<!DOCTYPE html>
<body>

<!-- Copied directly from http://www.ephys.org/ by Damian J Williams -->

<input placeholder="Kamil Slowikowski" name="name"/>

<p id="demo"></p>

<script>

## README.md

      
              2 files
            
          
              0 forks
            
          
              0 comments
            
          
              1 star
            
          
                slowkow
                / README.md
            
            
              Last active May 15, 2019
            
              
                Generate a DESCRIPTION file with all of your installed packages
              
          
      View README.md
  
    
    Problem
We need to install a lot of R packages each time we upgrade to a new version of R.
Solution

Before installing the new R, run Rscript make_description.R to write a list of all installed packages.
Install the new version of R.
Use devtools to reinstall the packages.


## out.pdf

      
              2 files
            
          
              0 forks
            
          
              0 comments
            
          
              0 stars
            
          
                slowkow
                / out.pdf
            
            
              Last active Feb 5, 2019
            
              
                Make a PCA plot in Bash with the OpenCPU API
              
          
      View out.pdf
  
    
      Sorry, something went wrong. Reload?
      Sorry, we cannot display this file.
      Sorry, this file is invalid so it cannot be displayed.
      
          Viewer requires iframe.
      
    
## contentScript.js
/*
 * The purpose of this script is to automatically click the "Send SMS"
 * button as soon as the page is loaded, instead of clicking on it manually.
 *
 * This is the page with the button:
 *
 *    https://partnershealthcare.okta.com/signin/verify/okta/sms
 *
*/

## rsg2018.json
[
  {
    "id": "1",
    "title": "Methods for Identifying Tumor Heterogeneity and Rare Subclones in Single Cell DNA Sequence Data",
    "text": "Background With the advancements of single cell sequencing technologies it is now possible to interrogate thousands of cells in a single experiment. Single-cell RNA-Seq has been available for several years but high-throughput single-cell DNA analysis is in its infancy. Therefore, it is essential to develop new capabilities for assessing genetic variation present in rare cells and to better understand the role that these cells play in the evolution of tumor progression. To address these challenges and enable the characterization of genetic diversity in cancer cell populations, we developed a novel approach to identify mutation signatures which define subclones present in a tumor population. Methods Here we present a two-step clustering and subclone identification method using data generated on the Tapestri single-cell DNA platform which can generate upto 10000 cells

## flatten.R
flatten_points <- function(plot, width = 10, height = 5, dpi = 200) {
  is_geom_point <- sapply(plot$layers, function(layer) {
    "GeomPoint" %in% class(layer$geom)
  })

  plot_flat   <- plot + ggplot2::theme_void()
  plot_vector <- plot

  plot_flat$layers   <- plot$layers[is_geom_point]
  plot_vector$layers <- plot$layers[!is_geom_point]
	#' @param x A sparse matrix from the Matrix package.
	#' @param file A filename that ends in ".gz".
	writeMMgz <- function(x, file) {
	mtype <- "real"
	if (is(x, "ngCMatrix")) {
	mtype <- "integer"
	}
	writeLines(
	c(
	sprintf("%%%%MatrixMarket matrix coordinate %s general", mtype),
	#!/usr/bin/env python
	"""
	The Needleman-Wunsch Algorithm
	==============================

	This is a dynamic programming algorithm for finding the optimal alignment of
	two strings.

	Example
	-------
	# install.packages(c("Matrix", "rhdf5", "tidyverse"))
	library(Matrix)
	library(rhdf5)
	library(tidyverse)
	library(glue)

	my_h5_files <- Sys.glob(
	"path/to/cellranger-per-channel/output/*/filtered_feature_bc_matrix.h5"
	)
	<!DOCTYPE html>
	<body>

	<!-- Copied directly from http://www.ephys.org/ by Damian J Williams -->

	<input placeholder="Kamil Slowikowski" name="name"/>

	<p id="demo"></p>

	<script>
	/*
	* The purpose of this script is to automatically click the "Send SMS"
	* button as soon as the page is loaded, instead of clicking on it manually.
	*
	* This is the page with the button:
	*
	* https://partnershealthcare.okta.com/signin/verify/okta/sms
	*
	*/
	[
	{
	"id": "1",
	"title": "Methods for Identifying Tumor Heterogeneity and Rare Subclones in Single Cell DNA Sequence Data",
	"text": "Background With the advancements of single cell sequencing technologies it is now possible to interrogate thousands of cells in a single experiment. Single-cell RNA-Seq has been available for several years but high-throughput single-cell DNA analysis is in its infancy. Therefore, it is essential to develop new capabilities for assessing genetic variation present in rare cells and to better understand the role that these cells play in the evolution of tumor progression. To address these challenges and enable the characterization of genetic diversity in cancer cell populations, we developed a novel approach to identify mutation signatures which define subclones present in a tumor population. Methods Here we present a two-step clustering and subclone identification method using data generated on the Tapestri single-cell DNA platform which can generate upto 10000 cells
	flatten_points <- function(plot, width = 10, height = 5, dpi = 200) {
	is_geom_point <- sapply(plot$layers, function(layer) {
	"GeomPoint" %in% class(layer$geom)
	})

	plot_flat <- plot + ggplot2::theme_void()
	plot_vector <- plot

	plot_flat$layers <- plot$layers[is_geom_point]
	plot_vector$layers <- plot$layers[!is_geom_point]