update readme

Author: Zilong-Li

PCAone.pdf

@@ -1,5 +1,5 @@
 #+TITLE: Principal Component Analysis All in One
-#+subtitle: Version 0.4.9
+#+subtitle: Version 0.5.0
 #+author: Zilong Li 
 #+email: [email protected]
 #+options: toc:2 num:nil email:t -:nil ^:nil
@@ -21,20 +21,18 @@
 
 * Introduction
 
-PCAone is a fast and memory efficient PCA tool implemented in C++
-aiming at providing comprehensive features and algorithms for
-different scenarios. PCAone implements 3 fast PCA algorithms for
-finding the top eigenvectors of large datasets, which are [[https://en.wikipedia.org/wiki/Arnoldi_iteration][Implicitly
-Restarted Arnoldi Method]] (IRAM, --svd 0), [[https://www.ijcai.org/proceedings/2017/468][single pass Randomized SVD]]
-but with power iteration scheme (RSVD, --svd 1, Algorithm1 in paper)
-and *our own RSVD with window based power iteration scheme*
-(PCAone, --svd 2, Algorithm2 in paper). All have both in-core and
-out-of-core implementation. Addtionally, full SVD (--svd 3) is
-supported via in-core mode only. There is also an [[https://github.com/Zilong-Li/PCAoneR][R]] package
-here. PCAone supports multiple different input formats, such as [[https://www.cog-genomics.org/plink/1.9/formats#bed][PLINK]],
-[[https://www.well.ox.ac.uk/~gav/bgen_format][BGEN]], [[http://www.popgen.dk/angsd/index.php/Input#Beagle_format][Beagle]] genetic data formats and a general comma separated CSV
-format for other data, such as scRNAs and bulk RNAs. For genetics
-data, PCAone also implements [[https://github.com/Rosemeis/emu][EMU]] and [[https://github.com/Rosemeis/pcangsd][PCAngsd]] algorithm for data with
+PCAone is a fast and memory efficient PCA tool implemented in C++ aiming at
+providing comprehensive features and algorithms for different scenarios.
+PCAone implements 3 fast PCA algorithms for finding the top eigenvectors of
+large datasets, which are [[https://en.wikipedia.org/wiki/Arnoldi_iteration][Implicitly Restarted Arnoldi Method]] (IRAM, --svd 0),
+[[https://www.ijcai.org/proceedings/2017/468][single pass Randomized SVD]] with power iteration scheme (sSVD, --svd 1,
+Algorithm1 in paper) and *our proposed window based RSVD* (winSVD, --svd 2,
+Algorithm2 in paper). All have both in-core and out-of-core implementation.
+Addtionally, Full SVD (--svd 3) is supported via in-core mode only. Also,
+check out the [[https://github.com/Zilong-Li/PCAoneR][R]] package here. PCAone supports multiple different input
+formats, such as [[https://www.cog-genomics.org/plink/1.9/formats#bed][PLINK]], [[https://www.well.ox.ac.uk/~gav/bgen_format][BGEN]], [[http://www.popgen.dk/angsd/index.php/Input#Beagle_format][Beagle]] genetic data formats and a general comma
+separated CSV format for other data, such as scRNAs and bulk RNAs. For
+genetics data, PCAone also implements [[https://github.com/Rosemeis/emu][EMU]] and [[https://github.com/Rosemeis/pcangsd][PCAngsd]] algorithm for data with
 missingness and uncertainty. The PDF manual can be downloaded [[https://github.com/Zilong-Li/PCAone/blob/main/PCAone.pdf][here]].
 
 [[file:misc/architecture.png]]
@@ -131,21 +129,12 @@ There are 3 ways to install PCAone.
 ** Download compiled binary
 
 There are compiled binaries provided for both Linux and Mac platform. Check
-[[https://github.com/Zilong-Li/PCAone/releases][the releases page]] to download one. For most modern CPUs and Linux systems,
-download the one named with =avx2=.
+[[https://github.com/Zilong-Li/PCAone/releases][the releases page]] to download one.
 
 #+begin_src shell
-pkg=https://github.com/Zilong-Li/PCAone/releases/latest/download/PCAone-avx2-Linux.zip
-wget $pkg || curl -LO $pkg
-unzip -o PCAone-avx2-Linux.zip
-#+end_src
-
-If the server is too old to support =avx2= instruction, download the alternative version.
-
-#+begin_src shell
-pkg=https://github.com/Zilong-Li/PCAone/releases/latest/download/PCAone-x64-Linux.zip
+pkg=https://github.com/Zilong-Li/PCAone/releases/latest/download/PCAone-Linux.zip
 wget $pkg || curl -LO $pkg
-unzip -o PCAone-x64-Linux.zip
+unzip -o PCAone-Linux.zip
 #+end_src
 
 ** Via Conda
@@ -160,9 +149,7 @@ PCAone --help
 
 ** Build from source
 
-=PCAone= can be running on a normal computer/laptop with =x86-64= instruction
-set architecture. =PCAone= has been tested on both =Linux= and =MacOS= system. To
-build PCAone from the source code, the following dependencies are required:
+=PCAone= has been tested on both =Linux= and =MacOS= system. To build PCAone from the source code, the following dependencies are required:
 
 - GCC/Clang compiler with C++17 support
 - GNU make